WO2023274402A1 - 紧急救治系统、紧急救治方法及电子设备 - Google Patents

紧急救治系统、紧急救治方法及电子设备 Download PDF

Info

Publication number
WO2023274402A1
WO2023274402A1 PCT/CN2022/103328 CN2022103328W WO2023274402A1 WO 2023274402 A1 WO2023274402 A1 WO 2023274402A1 CN 2022103328 W CN2022103328 W CN 2022103328W WO 2023274402 A1 WO2023274402 A1 WO 2023274402A1
Authority
WO
WIPO (PCT)
Prior art keywords
data
index
information
treated
medical
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/CN2022/103328
Other languages
English (en)
French (fr)
Inventor
张希颖
田福臣
王同波
崔璨
王洪亮
褚虓
张洪雷
吴新银
刘欣欣
佟晓彤
赵磊
胡延洋
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
BOE Technology Group Co Ltd
Original Assignee
BOE Technology Group Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by BOE Technology Group Co Ltd filed Critical BOE Technology Group Co Ltd
Priority to EP22832214.5A priority Critical patent/EP4345840A4/en
Priority to US18/574,007 priority patent/US20240363255A1/en
Publication of WO2023274402A1 publication Critical patent/WO2023274402A1/zh
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
    • G16H40/60—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
    • G16H40/67—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for remote operation
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/46—Multiprogramming arrangements
    • G06F9/54—Interprogram communication
    • G06F9/546—Message passing systems or structures, e.g. queues
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
    • G16H10/60—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
    • G16H40/20—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H80/00—ICT specially adapted for facilitating communication between medical practitioners or patients, e.g. for collaborative diagnosis, therapy or health monitoring
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2209/00—Indexing scheme relating to G06F9/00
    • G06F2209/54—Indexing scheme relating to G06F9/54
    • G06F2209/548—Queue

Definitions

  • the present disclosure relates to the field of smart medical technology, and in particular to an emergency treatment system, an emergency treatment method and electronic equipment.
  • Emergency treatment refers to calling an emergency number or seeking emergency treatment in other ways when a patient is suddenly ill outside a hospital. It should be noted that the information disclosed in the above background section is only for enhancing the understanding of the background of the present disclosure, and therefore may include information that does not constitute the prior art known to those of ordinary skill in the art.
  • the disclosure provides an emergency treatment system, an emergency treatment method and electronic equipment.
  • an emergency treatment system including:
  • Information collection terminal deployed in ambulance vehicles, used to collect medical data of the objects to be treated and upload to the data lake cluster;
  • the data lake cluster is used for data access, data processing, data storage and data distribution of the medical data of the object to be treated;
  • the medical service system is configured to acquire the medical data of the subject to be treated from the data lake cluster.
  • the data lake cluster includes:
  • the data access module is used to determine the corresponding target access mode according to the type of information collection terminal and access the medical data of the object to be treated through the target access mode;
  • a data processing module configured to analyze the index information of the medical data of the subject to be treated, and establish a mapping relationship between each of the index information and standard indexes;
  • the data distribution module is used for distributing the medical data of the subject to be treated processed by the data processing module to the medical business system.
  • parsing the index information of the medical data of the subject to be treated in the data processing module includes:
  • the data parsing model is obtained by modeling based on the parsing protocol corresponding to the information collection terminal.
  • the index information of the medical data of the object to be treated includes n fields, wherein the ith field represents the binary value of the index of the medical data of the object to be rescued (i- 1) From the *m+1 bit to the i*m bit, m is a positive integer representing the number of digits represented by each field in the corresponding binary data; the data analysis model uses the following formula to analyze the medical data of the object to be treated
  • the indicator information for parsing is the following formula to analyze the medical data of the object to be treated.
  • f 1 (x) is used to convert the object into a binary value
  • f 2 (x) is used to convert the object into a decimal value
  • the standard index includes at least a standard coding identifier, and establishing a mapping relationship between each of the index information and the standard index in the data processing module includes:
  • a first message platform is configured between the data access module and the data processing module, and the first message platform is used for the data access module to transmit the accessed information to the Data processing module;
  • a second message platform is configured between the data distribution module and the medical service system, and the second message platform is used for the data distribution module to transmit the processed medical data of the subject to be treated to the The medical service system.
  • the data distribution module is further configured to send the medical data of the subject to be treated to a message queue, so that the second message platform can pull all Describe the medical data of the object to be treated.
  • the input indicators include hypopnea index x1, apnea index x2 and pressure parameter x3;
  • the output indicators include sleep apnea hypopnea index mean value y1;
  • the y1 and x1 , x2, and x3 have the following data conversion relationship:
  • y1 f1(x1, x2, x3); wherein, f1 represents a linear conversion relationship or a nonlinear conversion relationship.
  • the data conversion relationship is:
  • y1 w1*x1+w2*x2+w3*x3; wherein, w1, w2 and w3 are relationship parameters.
  • the value range of w1 is [1, 1.00000011]
  • the value range of w2 is [1, 1.00000011]
  • the value range of w3 is [7.85046229E -18, 7.85046229E-16].
  • the value of w1 is 1.00000011
  • the value of w2 is 1.00000011
  • the value of w3 is 7.85046229E-17.
  • the input index includes hypopnea index x1, apnea index x2, and pressure parameter x3;
  • the output index is the average value of the apnea index y2; the y2 and x1, x2,
  • y2 f2(x1, x2, x3); wherein, f2 represents a linear conversion relationship or a nonlinear conversion relationship.
  • the data conversion relationship is:
  • y2 w1*x1+w2*x2+w3*x3; wherein, w1, w2 and w3 are relationship parameters.
  • the value range of w1 is [5.55500801E-08, 0.50000006]
  • the value range of w2 is [0.50000006, 1.00000006]
  • the value range of w3 is [2.22044605E-17, 2.22044605E-15].
  • the value of w1 is 0.50000006, 0.500000056 or 5.55500801E-08
  • the value of w2 is 0.50000006 or 1.00000006
  • the value of w3 is 2.22044605E-16.
  • the input index includes hypopnea index x1, apnea index x2, and pressure parameter x3;
  • the output index is the mean value of hypopnea index y3; the y3 and x1, x2, The following data conversion relationship exists between x3:
  • y3 f3(x1, x2, x3); wherein, f3 represents a linear conversion relationship or a nonlinear conversion relationship.
  • the data conversion relationship is:
  • y3 w1*x1+w2*x2+w3*x3; wherein, w1, w2 and w3 are relationship parameters.
  • the value range of w1 is [0.500000056, 1.00000006]
  • the value range of w2 is [5.55500802E-08, 0.50000006]
  • the value range of w3 is [-7.85046229E-16, -7.85046229E-18].
  • the value of w1 is 0.50000006, 0.500000056 or 1.00000006, the value of w2 is 5.55500802E-08 or 0.50000006, and the value of w3 is -7.85046229E-17.
  • the data conversion relationship between the medical data of the subject to be treated accessed by the data access module and the processed medical data of the subject to be treated distributed by the data distribution module is obtained in the following manner :
  • the original mapping matrix R is an M ⁇ N matrix, and the element R(i,j) is used to represent the input index i and the sample in the sample medical data The relationship between the output indicators j in the business data;
  • mapping matrix determines the data conversion relationship between the output indicators in the business data and each of the input indicators
  • m and n are integers greater than 1, i, j, k are positive integers, and i ⁇ [1,m], j ⁇ [1,n], k ⁇ [1,m-1].
  • the data conversion relationship between the output indicators in the business data and each of the input indicators is determined .
  • the data conversion relationship is:
  • yj f(xi,i ⁇ [1,m]); wherein, yj is the value of the output index j, and the parameters of the function f are based on the column of the output index j in the original mapping matrix and at least one of the target mapping matrices The element value is determined, and xi is the value of the input index i.
  • the input index includes hypopnea index x1, apnea index x2 and pressure parameter x3;
  • the output index is sleep apnea hypopnea index mean value y1;
  • the sleep apnea The conversion relationship corresponding to the mean value of the hypopnea index is:
  • y1 f1(x1, x2, x3); wherein, the parameters of the function f1 are determined based on the original mapping matrix and at least one element value of the column where the mean value of the sleep apnea hypopnea index in the target mapping matrix is located.
  • the conversion relationship corresponding to the mean value of the sleep apnea hypopnea index is specifically:
  • y1 w1*x1+w2*x2+w3*x3; wherein, w1, w2 and w3 are determined based on the element values of the columns of the sleep apnea hypopnea index in the original mapping matrix and at least one of the target mapping matrices .
  • the input index includes hypopnea index x1, apnea index x2, and pressure parameter x3;
  • the output index is the mean value of the apnea index y2;
  • the mean value of the apnea index corresponds to The conversion relationship is:
  • y2 f2(x1, x2, x3); wherein, the parameters of the function f2 are determined based on the original mapping matrix and at least one element value of the column where the mean value of the apnea index in the target mapping matrix is located.
  • the conversion relationship corresponding to the mean value of the apnea index is specifically:
  • y2 w1*x1+w2*x2+w3*x3; wherein, w1, w2 and w3 are determined based on element values in the column of the mean value of the apnea index in the original mapping matrix and at least one target mapping matrix.
  • the input index includes hypopnea index x1, apnea index x2, and pressure parameter x3;
  • the output index is the mean value of the hypopnea index y3;
  • the mean value of the hypopnea index corresponds to The conversion relationship is:
  • y3 f3(x1, x2, x3); wherein, the parameters of the function f3 are determined based on the original mapping matrix and at least one element value of the column where the hypoventilation index average value is located in the target mapping matrix.
  • the conversion relationship corresponding to the mean value of the hypopnea index is specifically:
  • y3 w1*x1+w2*x2+w3*x3; wherein, w1, w2, and w3 are determined based on element values in the column where the mean value of the hypoventilation index is located in the original mapping matrix and at least one of the target mapping matrices.
  • a management terminal is also included:
  • the management terminal is used to record the device identification of the information collection terminal and the time when the device starts to be used;
  • the medical service system is further configured to receive the medical data associated with the user information of the object to be treated.
  • the management terminal is also used to store historical medical data of the object to be treated, and distribute the historical medical data and real-time medical data of the object to be treated to the medical service system.
  • the management terminal is also used to store the medical data of the relatives and friends of the object to be treated, and distribute the medical data of the relatives and friends of the object to be treated and the medical data of themselves to the medical service system.
  • the management terminal is further configured to receive a query request, and obtain relevant data from the data lake cluster according to the query conditions contained in the query request to generate a query result.
  • the management terminal further includes:
  • the data visualization module is configured to generate visualization content according to the data of the data lake cluster, so as to display the visualization content through a front-end page.
  • the management terminal further includes:
  • the equipment parameter monitoring module is used to record and analyze the behavior information of the information collection terminal, so as to confirm whether the information collection terminal is an abnormal terminal.
  • the emergency rescue system further includes:
  • the dispatch center terminal is used to receive alarm information and obtain the location of the object to be treated, and dispatch ambulance vehicles to the location of the object to be treated according to the preset dispatching algorithm.
  • the dispatch center terminal is further configured to dispatch internal rescue personnel of a medical institution to provide remote assistance according to the medical data related to the subject to be treated.
  • the emergency rescue system further includes:
  • the alarm terminal can communicate with the dispatch center terminal, and is used to send the alarm information to the dispatch center terminal.
  • the emergency rescue system further includes:
  • the rescue personnel terminal is communicatively connected with the dispatch center terminal, and is used for receiving the dispatch information of the dispatch center terminal, and for entering the medical record information of the object to be treated.
  • the emergency rescue system further includes:
  • the driver's terminal is communicatively connected with the terminal of the dispatch center, and is used to guide the driver to drive the ambulance to the position of the object to be treated.
  • the emergency rescue system further includes:
  • An information integration platform communicating with at least one of the management terminal, dispatch center terminal, alarm terminal, rescue personnel terminal, driver terminal and medical service system;
  • the information integration platform is used to retrieve personal health files and/or knowledge bases from third-party business systems, and send them to the management terminal, dispatch center terminal, alarm terminal, rescue personnel terminal, driver terminal and medical business system. at least one of the ; and/or,
  • the information integration platform is used to obtain the information of the object to be treated from at least one of the management terminal, the dispatch center terminal, the alarm terminal, the rescuer terminal, the driver terminal and the medical service system.
  • the information integration platform includes a decision evaluation module
  • the decision evaluation module evaluates the information of the object to be treated based on the knowledge base.
  • the information integration platform is used for:
  • the rescuer terminal is also used to record treatment information
  • the information integration platform is used to draw the treatment information record as a time axis according to the time sequence, and send it to the medical service system.
  • the information integration platform is configured to receive the resource information of the hospital sent from the medical business system, and send the information to the dispatch center terminal.
  • a method of emergency treatment comprising:
  • an electronic device characterized in that it includes:
  • the memory is configured to store one or more programs, and when the one or more programs are executed by the processor, the processor implements the method as provided by some aspects of the present disclosure.
  • a computer-readable storage medium on which a computer program is stored, and it is characterized in that, when the program is executed by a processor, the method as provided by some aspects of the present disclosure is implemented.
  • Fig. 1 shows a schematic diagram of an architecture of an emergency rescue system in an embodiment of the present disclosure.
  • Fig. 2 shows a schematic diagram of modules of a data lake cluster in an embodiment of the present disclosure.
  • Fig. 3 shows a schematic diagram of an architecture of an emergency rescue system in an embodiment of the present disclosure.
  • Fig. 4 shows a schematic diagram of a processing flow of a data processing module in an embodiment of the present disclosure.
  • Fig. 5 shows a schematic diagram of a processing flow of a data processing module in an embodiment of the present disclosure.
  • Fig. 6 shows a schematic diagram of a mapping relationship between indicator codes and identifiers in an embodiment of the present disclosure.
  • Fig. 7 shows a schematic diagram of modules of a data lake cluster in an embodiment of the present disclosure.
  • Fig. 8 shows a schematic diagram of a processing flow of a data query module in an embodiment of the present disclosure.
  • Fig. 9 shows a schematic diagram of a processing flow of a data interaction module in an embodiment of the present disclosure.
  • Fig. 10 shows a schematic diagram of a processing flow of a model acquisition module in an embodiment of the present disclosure.
  • Fig. 11 shows a schematic diagram of an architecture of an emergency rescue system in an embodiment of the present disclosure.
  • Fig. 12 shows a schematic diagram of some modules of the emergency rescue system in an embodiment of the present disclosure.
  • Fig. 13 shows a schematic flowchart of an emergency treatment method in an embodiment of the present disclosure.
  • FIG. 14 shows a schematic structural diagram of a computer system for realizing the electronic device of the embodiment of the present disclosure.
  • Example embodiments will now be described more fully with reference to the accompanying drawings.
  • Example embodiments may, however, be embodied in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete and will fully convey the concept of example embodiments to those skilled in the art.
  • the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
  • the scattered medical data of the objects to be treated are transmitted to the data lake cluster for integration, so that structured medical data, semi-structured medical data and unstructured medical data can be stored at any scale.
  • the emergency treatment system in the exemplary embodiments of the present disclosure can support medical institutions to provide more accurate treatment plans for treatment objects.
  • FIG. 1 shows a schematic diagram of an exemplary architecture of an emergency treatment system 100 to which embodiments of the present disclosure can be applied.
  • the emergency treatment system 100 may include an information collection terminal deployed in an ambulance vehicle 101 , a data lake cluster 102 , and a medical service system 103 deployed in a hospital. in:
  • vehicle-mounted emergency medical instruments may include: defibrillator monitors, ventilators, non-invasive multi-parameter detectors (MTX), Blood pressure testing equipment, body fat testing equipment, blood sugar testing equipment, sleep monitoring equipment, electric suction device, negative pressure system, oxygen supply device, sterilization device, lighting device, ventilation system, heating system, power supply line system, automatic boarding stretcher system etc.
  • MTX non-invasive multi-parameter detectors
  • Part of the on-board emergency medical equipment can collect medical data of the object to be treated, that is, as the following information collection terminal;
  • the information collection terminal can include, for example, the above-mentioned ventilator, non-invasive multi-parameter detector (MTX), blood pressure detection equipment, body fat detection equipment, Blood sugar testing equipment, sleep monitoring equipment, etc.
  • MTX non-invasive multi-parameter detector
  • blood pressure detection equipment blood pressure detection equipment
  • body fat detection equipment blood sugar testing equipment
  • sleep monitoring equipment sleep monitoring equipment
  • some electronic devices of some objects to be treated such as wearable devices with medical data collection functions or other mobile terminals, can also be regarded as information collection terminals described in this exemplary embodiment.
  • the information collection terminal collects the medical data of the object to be treated and uploads it to the data lake cluster 102 .
  • the data lake cluster 102 is used to perform data access, data processing, data storage and data distribution on the medical data of the subject to be treated.
  • the data lake cluster 102 can be composed of, for example, relational databases such as MariaDB and MySQL, document databases such as MongoDB and CouchDB, distributed file systems such as HDFS, PVFS, and PanFS, and graph databases such as Neo4j, Cayley, and rapgDB. Multiple types of databases constitute a data storage and management service system.
  • the data lake cluster 102 can adopt a distributed computing and storage architecture, integrate various computer stand-alone machines, servers, and computer clusters or server clusters with data storage and computing functions, and provide various solutions including data management and algorithm development. class functional components.
  • the data lake cluster 102 it is possible to store structured medical data, semi-structured medical data and unstructured medical data at any scale without structural processing of medical data, allowing each role in the emergency treatment system 100 to For example, medical staff, data developers, and business analysts access data through the analysis tools and frameworks of their choice, so as to collaboratively process and analyze data in different ways.
  • the medical service system 103 can acquire the medical data of the object to be treated from the data lake cluster 102 .
  • the medical service system 103 can be deployed in hospitals and other medical institutions for use by medical personnel.
  • the scattered medical data of the objects to be treated are transmitted to the data lake for integration, so that structured medical data, semi-structured medical data and unstructured medical data can be stored in any scale.
  • Medical data allows various roles in the emergency treatment system, such as medical staff, data developers, and business analysts, to access the data through their own choice of analysis tools and frameworks, to achieve collaborative processing and analysis of data in different ways, and then
  • the emergency treatment system in the exemplary embodiments of the present disclosure can support medical institutions to provide more accurate treatment plans for treatment objects.
  • the data lake cluster 102 includes a data access module 201 , a data processing module 202 and a data distribution module 204 .
  • a data storage module 203 may also be included. in:
  • the data access module 201 is used to determine the corresponding target access method according to the type of information collection terminal and access the medical data of the object to be treated through the target access method.
  • the type of each information collection terminal and the corresponding target access mode can be configured and stored in advance, and when a specific information collection terminal accesses, its corresponding target access mode is determined according to the pre-configured information.
  • the information collection terminal is a type of equipment such as a ventilator, a sleep monitoring device, or an oxygen generator, it can be determined according to the pre-configured information that its corresponding target access method is HTTP GET (based on hypertext transmission).
  • the protocol requests data from the specified resource) method, and then can use the HTTP GET method to access the medical data of the object to be treated; if the information collection terminal is a type of device such as a body fat scale, the corresponding target access can be determined according to the pre-configured information
  • the method is HTTP FORM (form transmission based on hypertext transfer protocol), and then HTTP FORM can be used to access the medical data of the object to be treated; if the information collection terminal is a blood pressure detection device, blood sugar detection device, etc.
  • the pre-configuration information determines that the corresponding target access method is TCP Socket (socket transmission based on transmission control protocol), and then the medical data of the object to be treated can be accessed by using the TCP Socket method; After the target medical data, the medical data of the target to be treated can also be pre-processed in a specified way.
  • TCP Socket ocket transmission based on transmission control protocol
  • the medical data of the target to be treated can also be pre-processed in a specified way.
  • other access methods such as HTTPS (Hypertext Transfer Security Protocol), UDP (User Datagram Protocol), etc. data, and is not limited thereto in this exemplary embodiment.
  • the data processing module 202 is configured to analyze the index information of the medical data of the subject to be treated, and establish a mapping relationship between each of the index information and standard indexes.
  • the indicator information is information related to the user's physiological indicators collected by the information collection terminal.
  • the indicator information may include indicator names and indicator values.
  • parsing the index information includes parsing the index value in the index information.
  • the indicator information collected by a wearable electronic device is hexadecimal data, such as FE121248012408, which needs to be converted into specific physiological indicators, such as diastolic blood pressure 120, systolic blood pressure 90, pulse Rate 88 etc.
  • the multiplexing part of the parsing method of some information collection terminals is also extracted for modeling.
  • the indicator information of the medical data of the subject to be treated uploaded by the information collection terminal is analyzed based on the data analysis model; wherein, the data analysis model is obtained by modeling based on the analysis protocol corresponding to the information collection terminal.
  • analyzing the index information of the medical data of the subject to be treated in the data processing module may include: obtaining analysis protocols corresponding to various information collection terminals, and based on the analysis protocols corresponding to each information collection terminal The protocol is modeled to obtain at least part of a common data analysis model; based on the data analysis model, the index information of the medical data of the subject to be treated uploaded by the applicable information collection terminal is analyzed.
  • the index information of the medical data of the object to be treated includes n fields, wherein the ith field represents the binary value of the index of the medical data of the object to be rescued (i- 1) *m+1 bit to i*m bit, wherein, m is a positive integer, and can be determined according to the system conversion relationship;
  • the data analysis model uses the following formula to analyze the index information of the medical data of the object to be treated To parse:
  • f 1 (x) is used to convert the object into a binary value
  • f 2 (x) is used to convert the object into a decimal value
  • the indicator information sent by a blood pressure detection device is the string 0x00, 0x78, 0x00, 0x50, 0x4B; in the data interface protocol provided by the blood pressure detection device manufacturer, the meaning of the characters in the string data is specified definition. Refer to Table 1 below:
  • SYS[15:8] indicates that the systolic blood pressure is eight digits higher
  • SYS[7:0] indicates that the systolic blood pressure is eight digits lower
  • DIA[15:8] indicates that the diastolic blood pressure is eight digits higher
  • DIA[7:0] indicates that the diastolic blood pressure is eight digits lower.
  • bit; PUL[7:0] indicates the lower eight bits of the pulse number.
  • the data analysis model is:
  • a mapping relationship between each index information and a standard index can be established.
  • the standard indicator includes at least a standard coded identifier.
  • the standard coded identifier and the index value in the index information of the medical data of the object to be treated are stored as key values;
  • a process of configuring standard indicators may also be included.
  • establishing the mapping relationship between each of the index information and the standard index in the data processing module may include the following steps S501 to S504. in:
  • a standard index is configured, and the standard index includes at least a standard code identifier.
  • standard indicator classifications may be created and associated with definitions of specific standard indicators.
  • the standard indicators of blood pressure and blood sugar are as follows:
  • step S502 obtain the non-standard coding identifier of the index information of the medical data of the subject to be treated uploaded by the information collection terminal, and establish a mapping relationship between the non-standard coding identifier and the standard coding identifier.
  • the device information of the information collection terminal can be obtained, for example, it can include basic information (such as product name, product type, product model, manufacturer name, product picture), configuration information (such as communication protocol, data address , data format, interface protocol, indicator rules, time rules), etc.
  • the type of information collection terminal can be determined according to information such as product type and product model; the data uploaded by the information collection terminal can be obtained according to information such as communication protocol and data address; non-standard The coded identifier, the non-standard coded identifier is, for example, the index name in the index information; furthermore, it can be used to establish a mapping relationship between the non-standard coded identifier and the standard coded identifier.
  • the non-standard codes Height and ShenGao correspond to the standard index H01; the non-standard codes Weight and TiZhong correspond to the standard index W01; the non-standard codes BMI and TZZS correspond to the standard index B01 corresponds to etc.
  • the user input may be monitored through the front-end control, and then the mapping relationship between the non-standard encoding identifier and the standard encoding identifier may be adjusted according to the user input.
  • step S503 after parsing the indicator information of the medical data of the subject to be treated uploaded by the information collection terminal, the non-standard code identifier is obtained and based on the difference between the non-standard code identifier and the standard code identifier, The mapping relationship is to store the standard encoding identifier and the index value of the medical data of the object to be treated as a key value.
  • the standard code mark corresponding to the non-standard code mark SYS is XY01, and the standard code mark corresponding to the non-standard code mark DIA is XY02;
  • Metric information is stored as [XY01:120] and [XY02:80] in key-value form.
  • step S504 the index information of the medical data of the subject to be treated stored in the key value is associated with the device identifier of the information collection terminal.
  • the device information of the information collection terminal is first matched with the device information collected in advance in the above step S502, so as to ensure that the index information of the medical data of the object to be treated is consistent with the Each information collection terminal has one-to-one correspondence.
  • step S503 and step S504 is not limited.
  • a first message platform 205 is also configured between the data access module 201 and the data processing module 202, and the first message platform 205 is used for
  • the data access module 201 transmits the access information to the data processing module 202 through message service.
  • the data accessed by the data access module 201 will first be written into message queues such as RabbitMQ, ActiveMQ, ZeroMQ, Kafka, MetaMQ, RocketMQ, etc.; the data processing module 202 can pull data from the message queue in a first-in-first-out manner and send to process.
  • the decoupling between the data access module and the data processing module is realized, and it is convenient for the data access module and the data
  • the asynchronous processing between processing modules can also realize data peak elimination and flow control through the first message platform, and buffer data to avoid network transmission congestion when the amount of data is large.
  • the data storage module 203 is used to store the medical data of the subject to be treated, and in some implementations, can also be used to store the standard indicators and corresponding indicator information.
  • the data storage module 203 can use relational databases such as MariaDB, MySQL, etc., document databases such as MongoDB, CouchDB, etc., distributed file systems HDFS, PVFS, PanFS, etc., and graph databases Neo4j, Cayley, rapgDB, etc.
  • relational databases such as MariaDB, MySQL, etc.
  • document databases such as MongoDB, CouchDB, etc.
  • Neo4j distributed file systems
  • Cayley, rapgDB etc.
  • some data can be written into distributed caches such as Redis, Memcached, or SSDB according to the calling frequency and data type of the data, thereby improving the reading speed.
  • the data distribution module 204 is used for distributing the medical data of the subject to be treated processed by the data processing module to the medical service system.
  • a second message platform 206 is also configured between the data distribution module 204 and the medical service system 103, and the second message platform 206 is used for the data distribution module 204 to provide message services
  • the medical data of the subject to be treated is transmitted to the medical service system 103 in a manner.
  • the data distribution module 204 writes the medical data of the object to be treated into message queues such as RabbitMQ, ActiveMQ, ZeroMQ, Kafka, MetaMQ, RocketMQ, etc.; the medical business system 103 can pull data from the message queue in a first-in-first-out manner and perform deal with.
  • the second message platform By configuring the second message platform between the data distribution module and the medical service system, the decoupling between the data distribution module and the medical service system is realized, and the communication between the data distribution module and the medical service system is facilitated. At the same time, the second message platform can also realize data peak elimination and flow control, and buffer data to avoid network transmission congestion when the amount of data is large.
  • the data distribution module can also be used to provide the medical data of the subject to be treated to the second Message platform 206 .
  • the data distribution module 204 writes the medical data of the subject to be treated into a message queue, so that the second message platform 206 can pull the medical data of the subject to be treated from the message queue.
  • first message platform and the second message platform there is no limit to the specific locations of the first message platform and the second message platform, for example, they can also be set between the data access module and the data processing module, or between the data distribution module and the medical service system other locations.
  • the first message platform and the second message platform may be implemented by the same message platform. There is no limit to the number of messaging platforms.
  • the emergency rescue system may further include a management terminal 701 .
  • the management terminal 701 includes a device parameter monitoring module 705 .
  • the device parameter monitoring module 705 is used to record and analyze the behavior information of the information collection terminal, so as to confirm whether the information collection terminal is an abnormal terminal.
  • the behavior information of the information collection terminal may include usage time, abnormal times, and the like.
  • the device parameter monitoring module 705 can record behavior information such as the usage time and abnormal times of normal information collection terminals, and perform statistical analysis based on this to determine fluctuations in behavior information such as normal information collection terminal usage time and abnormal times. Furthermore, when it is monitored that the usage time and abnormal times of an information collection terminal exceed the above fluctuation range, the information collection terminal can be considered as an abnormal terminal.
  • the sample data can also be obtained according to the historical behavior information of the information collection terminal, so that based on the sample data, such as random forest model, deep neural network model, support vector machine model, boosted tree model, general One or more of machine learning models such as linear model and progressive gradient regression tree model are trained to obtain an abnormal terminal judgment model.
  • the equipment parameter monitoring module 705 can input the behavior information of a certain information collection terminal into the abnormal terminal judgment model, so as to output the probability that the information collection terminal is an abnormal terminal through the abnormal terminal judgment model, when the information collection terminal is an abnormal terminal When the probability value of exceeds the threshold, the information collection terminal can be considered as an abnormal terminal.
  • the management terminal can also be used to record the device identification of the information collection terminal, the time when the device starts to be used, and in some implementations, the time to end the use can also be recorded;
  • the medical service system is also used to receive the medical data associated with the user information of the object to be treated.
  • the process of recording the device identifier of the information collection terminal, the start time of use and the end time of use of the device may be completed by the data processing module in the data lake cluster and sent to the management terminal, or the above recording process may be directly completed by the management terminal.
  • the information obtained after associating the medical data collected by the information collection terminal with the device identification, device start time and end use time is shown in Table 3 below:
  • the management terminal receives the medical data distributed from the data lake cluster, and the medical data at least includes device identification, device starting time and index information.
  • the user information includes the identity of the information collection terminal user, for example, may include at least one of a name, an ID number, and the like. Since the data stored in the data lake cluster does not include the user's user information, it can desensitize the user information. When sending the relevant medical data of the user to the medical service system, it is necessary to associate the user information with the medical data.
  • the user information when the user makes an alarm through the alarm terminal, and/or when entering the user's personal information through the doctor terminal on the ambulance, the user information can be obtained, and at the same time, the alarm and/or The time when the doctor terminal enters personal information.
  • the user's user information and recording time can be sent to the management terminal.
  • the device identification and the time when the device starts to be used the user whose personal information is obtained at the corresponding time is mapped, and then the medical data is associated with the user information of the object to be treated.
  • Send the medical data associated with the user information of the object to be treated that is, the medical data carrying the user information) to the medical service system.
  • the management terminal obtains: user ID UserID: 123321; device ID SN: 2820; device start time BindingTimeStart: 2021-03-10 05:22:01; device end time BindingTimeEnd: 2021-03-10 05 :36:01.
  • the management terminal After the management terminal obtains the information in Table 3 sent by the data lake cluster, it can match the time when the management terminal obtains user information according to the device identifier, device start time, and end use time in Table 3 to obtain the table The user identity of the information in Table 3, and bind the data in Table 3 with the user identity. Therefore, through the above method, the data lake cluster does not need to store user identity information, but only stores device information and medical data, thereby realizing the desensitization function of user identity information.
  • the management terminal can also be used to store the historical medical data of the object to be treated, and distribute the historical medical data and real-time medical data of the object to be treated to the medical business system.
  • the management terminal can obtain the user identity corresponding to the device identity of the information collection terminal; thus, the user identity can also be used to obtain the historical medical data of the subject to be treated corresponding to the user identity, and the The historical medical data and real-time medical data of the object to be treated are jointly distributed to the medical service system; furthermore, users of the medical service system (such as medical workers) can more comprehensively understand the information of the object to be treated.
  • the management terminal can also be used to store the medical data of the relatives and friends of the object to be treated, and distribute the medical data of the relatives and friends of the object to be treated and the medical data of themselves to the medical service system.
  • the management terminal stores the user identity corresponding to the device identifier of the information collection terminal.
  • the management terminal stores the user identity corresponding to the device identifier of the information collection terminal.
  • the medical data of the relatives and friends of the subject to be treated and the medical data of the subject are distributed to the medical service system together, so that the users of the medical service system (such as medical workers) can understand the genetic disease information or infectious disease information of the subject to be treated, etc. .
  • the management terminal 701 may further include a data query module 706 .
  • the data query module 706 may be configured to receive a query request, and obtain relevant data from the data lake cluster according to the query conditions included in the query request to generate a query result. For example, in this example embodiment, after receiving a query request from a medical institution, obtain the query fields included in the query request, and perform equal, greater than, less than, Operations such as greater than or equal to, less than or equal to, not equal to, etc., so as to realize precise query; also can realize fuzzy query by means of the LIKE mechanism of SQL statement or regular expression according to the query field; no special limitation is made on this in this exemplary embodiment .
  • the data query module 706 may also be configured to perform the following steps S801 to S803. in:
  • step S801 when it is determined that the query object is a target object according to the query condition, a tag identifier is generated for the target object, and the tag identifier is associated with the device identifier of the information collection terminal corresponding to the target object.
  • the data query module can judge whether the query object satisfies the above-mentioned query conditions, and if the above-mentioned query conditions are satisfied, then the query object can be used as the target object;
  • step S802 whether the medical data of the target object is received is monitored based on the device identifier of the information collection terminal associated with the tag identifier.
  • the data query module generates the monitoring rule "Device ID SN: A123456, Tag ID Generation Time Binding_Time: 2020-09-08-12:12:12, Mark mark Select: 1, blood diastolic blood pressure blood pressure: greater than 90mmHg, blood systolic blood pressure greater than 140mmHg", and judge whether the information collection terminal corresponding to the newly accessed medical data meets the monitoring rules; if it meets the monitoring rules, it is determined to receive the target Subject's medical data.
  • step S803 when it is monitored that the medical data of the target object is received, the medical data of the target object is distributed to the medical institution corresponding to the query request.
  • the management terminal 701 may further include a data visualization module 707 .
  • the data visualization module 707 can be used to generate visualization content according to the data of the data lake cluster, so as to display the visualization content through the front-end page.
  • the processes of data access, data processing, and data distribution in the data lake cluster can be displayed to the user through the data visualization module 707, so that the user can supervise each process.
  • the stored medical data can also be displayed.
  • the management terminal and/or the medical service system may further be configured with a data interaction module 901 .
  • the data interaction module 901 can obtain the user's personal information and the information of the user's corresponding information collection terminal and perform terminal binding; wherein, the user's personal information may include the user's location, age, gender, etc.; the information collection terminal information may include the manufacturer's information, device identification, device system information, index information, and parsing dictionary information, etc.
  • the data interaction module 901 can send a request to the data visualization module 707 of the management terminal, so that the data visualization module 707 can generate visualization content according to the data in the data lake cluster. Furthermore, after receiving the visualized content, the data interaction module 901 can display it to the user through the front-end page; in addition, in some exemplary embodiments, the data interaction module 901 can also actively push messages to the user, such as pushing device use reminder messages , health reminder messages, etc.
  • the medical service system 203 can acquire the medical data of the subject to be treated from the data lake cluster.
  • the input indicators include hypopnea index x1, apnea index x2, and pressure parameter x3;
  • the output indicators include sleep apnea hypopnea index mean y1;
  • y1 f1(x1, x2, x3); wherein, f1 represents a linear conversion relationship or a nonlinear conversion relationship.
  • the value range of w1 is [1, 1.00000011]
  • the value range of w2 is [1, 1.00000011]
  • the value range of w3 is [7.85046229E-18, 7.85046229E-16].
  • the value of w1 is 1.00000011
  • the value of w2 is 1.00000011
  • the value of w3 is 7.85046229E-17.
  • the input index includes hypopnea index x1, apnea index x2 and pressure parameter x3;
  • the output index is the mean value of the apnea index y2;
  • y2 f2(x1, x2, x3); wherein, f2 represents a linear conversion relationship or a nonlinear conversion relationship.
  • the value range of w1 is [5.55500801E-08, 0.50000006]
  • the value range of w2 is [0.50000006, 1.00000006]
  • the value range of w3 is [2.22044605E-17, 2.22044605E-15 ].
  • the value of w1 is 0.50000006, 0.500000056 or 5.55500801E-08
  • the value of w2 is 0.50000006 or 1.00000006
  • the value of w3 is 2.22044605E-16.
  • the input index includes hypopnea index x1, apnea index x2, and pressure parameter x3;
  • the output index is the mean value of hypopnea index y3;
  • y3 f3(x1, x2, x3); wherein, f3 represents a linear conversion relationship or a nonlinear conversion relationship.
  • the value range of w1 is [0.500000056, 1.00000006]
  • the value range of w2 is [5.55500802E-08, 0.50000006]
  • the value range of w3 is [-7.85046229E-16, -7.85046229E -18].
  • the value of w1 is 0.50000006, 0.500000056 or 1.00000006, the value of w2 is 5.55500802E-08 or 0.50000006, and the value of w3 is -7.85046229E-17.
  • the data conversion relationship between the medical data of the subject to be treated accessed by the data access module and the processed medical data of the subject to be treated distributed by the data distribution module is obtained in the following manner:
  • the machine learning model can be trained by using sample medical data and sample business data, so as to obtain the data conversion relationship.
  • the data conversion relationship can be obtained through a neural network model, such as a convolutional neural network model, a residual neural network model, a recurrent neural network model, a long-short-term memory neural network model, etc.; the data conversion relationship can also be Collaborative filtering model, hidden Markov model, conditional random field model and other types of models are obtained.
  • the process of obtaining the data conversion relationship may include the following steps S1001 to S1003. in:
  • step S1001 the original mapping matrix R between the sample medical data and the sample business data is obtained; wherein, the original mapping matrix R is an m ⁇ n matrix, and the element R(i, j) is used to represent the sample medical data The relationship between the input index i and the output index j in the sample business data.
  • m and n are integers greater than 1
  • i, j, k are positive integers, and i ⁇ [1,m], j ⁇ [1,n], k ⁇ [1,m-1].
  • the sample medical data uploaded by an information collection terminal to the data lake cluster contains 9 input indicators; the sample business data generated by the data lake cluster based on the medical data provided by an information collection terminal contains 10 output indicators.
  • the original mapping matrix R between sample medical data and sample business data is shown in Table 4 below:
  • the original mapping matrix R is a 9 ⁇ 10 matrix, and the element R(i, j) is used to represent the relationship between the input index i in the sample medical data and the output index j in the sample business data.
  • the value of the element R(1,1) is 1, indicating that there is a relationship between the input indicator IN-01 and the output indicator OUT-01; the value of the element R(1,2) is 0, indicating that it cannot be determined Whether there is a correlation between the input indicator IN-01 and the output indicator OUT-01.
  • the relationship between the mean value of the sleep apnea hypopnea index and the hypopnea index and apnea index is currently known Index-related, for example, the mean sleep apnea-hypopnea index is the sum of the hypopnea index and the apnea index, so R(1,1) and R(6,1) have a value of 1; however, sleep apnea Whether there is a relationship between the suspension of hypopnea and other input indicators (that is, there may be a relationship, or there may be no relationship). Therefore, the values of R(2,1), R(3,1), etc. are all 0.
  • k is a positive integer, and k ⁇ [1,m-1].
  • the first matrix and the second matrix can be obtained by mining hidden information based on the original mapping matrix, so that the influence degree of the input index on each output index can be represented by the first matrix; The degree of impact of the indicator. Furthermore, the third matrix obtained by multiplying the first matrix and the second matrix contains the correlation between each input index and each output index; Whether there is a relationship between the elements, through the third matrix, it can be determined whether there is a relationship between the input indicators and the output indicators.
  • the specific dimensions of the matrices P, S, and Q can be adjusted as needed, and are not specifically limited here.
  • the original mapping matrix R in the above table 4 as an example, in this example embodiment, can be firstly subjected to singular value decomposition, thereby obtaining the singular value ⁇ [1.73205081,1,1,1, 1,1,1,1,1]; at the same time, the first matrix P and the second matrix Q can be obtained, for example, the first matrix P of 9 ⁇ 9 and the second matrix Q of 10 ⁇ 10 can be obtained.
  • the first matrix P of 9 ⁇ 9 and the second matrix Q of 10 ⁇ 10 can be obtained.
  • the intermediate matrix S is constructed by the first 8 values of the singular value ⁇ .
  • the intermediate matrix S is a 9 ⁇ 10 matrix as shown in Table 5 below:
  • the original mapping matrix R can be decomposed (for example, using functions such as Python to call linalg.svd to decompose or decompose in other ways) to obtain the first matrix P and the second matrix Q.
  • the singular value ⁇ can be obtained.
  • the target mapping matrix R k is calculated according to the product of the first matrix P and the second matrix Q.
  • the target mapping matrix R k is calculated according to the product of the first matrix P, the second matrix Q and the intermediate matrix S.
  • the obtained target mapping matrix R k is shown in Table 6 below:
  • the intermediate matrix S is constructed by the first 5 values of the singular value ⁇ .
  • the intermediate matrix S is a 9 ⁇ 10 matrix as shown in Table 7 below:
  • the original mapping matrix R can be decomposed (for example, using functions such as Python to call linalg.svd to decompose or decompose in other ways) to obtain the first matrix P and the second matrix Q.
  • the singular value ⁇ can be obtained.
  • the target mapping matrix R k is calculated according to the product of the first matrix P and the second matrix Q.
  • the target mapping matrix R k is calculated according to the product of the first matrix P, the second matrix Q and the intermediate matrix S.
  • the obtained target mapping matrix R k is shown in Table 8 below:
  • the intermediate matrix S is constructed by the first value of the singular value ⁇ .
  • the intermediate matrix S is a 9 ⁇ 10 matrix as shown in Table 9 below:
  • the original mapping matrix R can be decomposed (for example, using functions such as Python to call linalg.svd to decompose or decompose in other ways) to obtain the first matrix P and the second matrix Q.
  • the singular value ⁇ can be obtained.
  • the target mapping matrix R k is calculated according to the product of the first matrix P and the second matrix Q.
  • the target mapping matrix R k is calculated according to the product of the first matrix P, the second matrix Q and the intermediate matrix S.
  • the obtained target mapping matrix R k is shown in Table 10 below:
  • the above-mentioned first matrix and second matrix may also be determined in other ways.
  • the above-mentioned first matrix and the second matrix may also be determined through a gradient descent algorithm combined with a preset loss function; in more exemplary embodiments of the present disclosure, further The foregoing first matrix and the second matrix may be determined through a neural network model; this is not specifically limited in this exemplary embodiment.
  • step S1003 according to the original mapping matrix and at least one of the target mapping matrices, the data conversion relationship between the output indicators in the business data and each of the input indicators is determined.
  • the data conversion relationship between the output indicators in the business data and each of the input indicators may be determined according to the original mapping matrix and the 1st to n-1 target mapping matrices.
  • the original mapping matrix in order to reduce the amount of computation, it may also be based on the original mapping matrix and a smaller number (for example, the 2nd to n-2th, odd number, even number, etc.)
  • the target mapping matrix determines the data conversion relationship, which is not specifically limited in this exemplary embodiment.
  • the data conversion relationship may be:
  • y j is the value of the output index j
  • the parameters of the function f are determined based on the original mapping matrix and at least one element value of the column of the output index j in the target mapping matrix
  • x i is the value of the input index i.
  • the output index y 1 (that is, OUT-01) in the above-mentioned Table 12
  • the input index x 1 that is, IN-01
  • the input index x 2 that is, IN-06
  • the data conversion relationship between the value of input index x 3 that is, IN-07
  • y 1 f(x 1 ,x 2 ,x 3 )
  • the relationship between other input indexes and output indexes can be determined relationships that exist between them.
  • the above-mentioned input index IN-01 is the hypopnea index x 1
  • the above-mentioned input index IN-06 is the apnea index x 2
  • the above-mentioned input index IN-07 is the pressure parameter x 3
  • the above-mentioned output index OUT-01 is the The above-mentioned output index is the mean value of the sleep apnea-hypopnea index y1
  • the conversion mathematical relationship corresponding to the mean value of the sleep apnea-hypopnea index is:
  • the parameters of the function f1 are based on the original mapping matrix and at least one (here, take the 1st to 8th as an example) the column where the average value of the sleep apnea hypopnea index in the target mapping matrix is located (ie, the first column)
  • the element value of is determined.
  • the relationship between the mean value of the sleep apnea-hypopnea index and the hypopnea index, apnea index, and pressure parameter is a weighted sum; then the corresponding conversion relationship can be specifically:
  • w 1 , w 2 and w 3 are determined based on the original mapping matrix and at least one element value of the column in which the mean value of the sleep apnea hypopnea index is located in the target mapping matrix.
  • the element values of the first column of the row where the input indicator IN-01 is located are: 1.00000011, 1.00000011, 1.00000011, 1, 1.00000011, 1.00000011, 1.00000011; then the value range of w 1 can be is [1,1.00000011].
  • the value range of w 2 is [1, 1.00000011]
  • the value range of w 3 is [7.85046229E-18, 7.85046229E-16].
  • the value of w1 can be specifically 1.00000011
  • the value of w2 can be specifically 1.00000011
  • the value of w 3 may specifically be 7.85046229E-17.
  • the above-mentioned input index IN-01 is the hypopnea index x 1
  • the above-mentioned input index IN-06 is the apnea index x 2
  • the above-mentioned input index IN-07 is the pressure parameter x 3
  • the above-mentioned output index OUT-09 is The output index is the mean value of the apnea index y2 ; then the conversion mathematical relationship corresponding to the mean value of the apnea index is:
  • the parameters of the function f2 are based on the original mapping matrix and at least one (here, take the 1st to 8th as an example) element values of the column (i.e. column 1) where the mean value of the apnea index in the target mapping matrix is located Sure.
  • the relationship between the average value of the apnea index and the hypopnea index, apnea index and pressure parameters is a weighted sum; then the corresponding conversion mathematical relationship can be specifically:
  • w 1 , w 2 and w 3 are determined based on element values in the column where the mean value of the apnea index is located in the original mapping matrix and at least one of the target mapping matrices.
  • the element values of the first column of the row where the input index IN-01 is located are: 0.50000006, 0.500000056, 5.55500801E-08, 5.55500801E-08, 5.55500801E-08, 5.55500801E-08, 5.55500801E-08, 5.55500801E-08, 5.55500801E-08; then the value range of w 1 may be [5.55500801E-08, 0.50000006].
  • the value range of w 2 is [0.50000006, 1.00000006]
  • the value range of w 3 is [2.22044605E-17, 2.22044605E-15].
  • the value of w1 can be specifically 0.50000006 , 0.500000056 or 5.55500801E - 08, and the value of w2 can be Specifically, it is 0.50000006 or 1.00000006, and the value of w 3 may be specifically 2.22044605E-16.
  • the above-mentioned input index IN- 01 is the hypopnea index x1
  • the above-mentioned input index IN-06 is the apnea index x2
  • the above-mentioned input index IN-07 is the pressure parameter x3
  • the above-mentioned output index OUT-10 is The output index is the mean value of the hypoventilation index y 3 ; then the conversion mathematical relationship corresponding to the mean value of the hypoventilation index is:
  • the parameters of the function f3 are based on the original mapping matrix and at least one (here, take the 1st to 8th as an example) element values of the column (i.e. column 1) where the mean value of the hypoventilation index is located in the target mapping matrix Sure.
  • the relationship between the average value of the hypopnea index and the hypopnea index, apnea index and pressure parameters is a weighted sum; then the corresponding conversion mathematical relationship can be specifically:
  • w 1 , w 2 and w 3 are determined based on the original mapping matrix and at least one element value of the column where the mean value of the hypopnea index is located in the target mapping matrix.
  • the element values of the first column of the row where the input index IN-01 is located are: 0.50000006, 0.500000056, 1.00000006, 1.00000006, 1.00000006, 1.00000006, 1.00000006 ; then the value range of w1 can be is [0.500000056, 1.00000006].
  • the value range of w 2 is [5.55500802E-08, 0.50000006]
  • the value range of w 3 is [-7.85046229E-16, -7.85046229E-18].
  • the value of w 1 can be specifically 0.50000006, 0.500000056 or 1.00000006, and the value of w 2 can be specifically 5.55500802 E-08 or 0.50000006, the value of w 3 can be specifically -7.85046229E-17.
  • the data processing module is used to obtain the value ranges of the relationship parameters w1, w2 and w3 through training in the following manner:
  • the sample medical data includes 9 input indicators
  • the sample business data includes 10 output indicators
  • the input indicators include hypopnea index x1, apnea index x2 and pressure parameter x3
  • the output indicators include sleep apnea hypopnea index mean value y1, apnea index mean value y2 and hypopnea index mean value y3;
  • the first matrix represents the degree of influence of the input index on each output index
  • the second matrix represents the degree of influence of the output index by each input index
  • the step of obtaining the first matrix P and the second matrix Q based on the original mapping matrix R includes:
  • Singular value decomposition is performed on the original mapping matrix R to obtain the singular value ⁇ [1.73205081,1,1,1,1,1,1,1] of the original mapping matrix R and the first matrix P and the second matrix Q , wherein, the first matrix P is a 9*9-dimensional matrix, and the second matrix Q is a 10*10-dimensional matrix;
  • the product of the first matrix P, the second matrix Q and each intermediate matrix S determine a plurality of target mapping matrices Rk;
  • the step of determining the value ranges of the relationship parameters w1, w2, and w3 includes:
  • the above data conversion and model training processes can be completed by the data processing module of the data lake cluster.
  • the emergency treatment system may also include a dispatch center terminal 1101; in addition, it may also include one or more of an alarm terminal 1102, a rescuer terminal 1103, and a driver terminal 1104 . in:
  • the alarm terminal 1102 can communicate with the dispatch center terminal 1101 and is used for sending alarm information to the dispatch center terminal 1101 .
  • the alarm terminal 1102 is a device capable of collecting information such as a user's voice, image, video, or location.
  • the alarm terminal 1102 can be a mobile electronic device, such as a smart phone, a smart watch, or a smart bracelet; the alarm terminal 1102 can also be an Internet of Things device in a community, a park, or a factory, such as a monitoring device or other information collection equipment.
  • the alarm terminal 1102 can establish a communication connection with the dispatch center terminal 1101, and then collect the user's voice, image, video or location information and transmit it to the dispatch center terminal 1101.
  • the dispatch center terminal 1101 is used to receive alarm information and obtain the location of the object to be treated, and dispatch ambulance vehicles (such as rescue resources) to the location of the object to be treated according to a preset scheduling algorithm.
  • the dispatch center terminal 1101 can be deployed with a distributed agent system and a comprehensive display system (such as a large LCD splicing screen) so as to realize the dispatch, dispatch, and The deployment of emergency medical personnel in the hospital, the patient's condition and treatment arrangements, etc.;
  • the preset scheduling algorithm may include, for example, establishing a regional road network model and generating an optimal path according to the emergency plan and the regional road network model; Then, according to the regional road network model and the optimal route, the ambulance vehicles are dispatched.
  • the dispatch center terminal 1101 can also be used to intelligently identify the emergency scene according to the received alarm information; when receiving new alarm information, if it is judged that the new alarm information is located Recognize the emergency scene, then determine whether to configure new ambulance vehicles and rescue resources according to the ambulance vehicles and rescue resources configured for the identified emergency scene.
  • the terminal 1101 of the dispatch center can intelligently identify the emergency scene through audio and video semantic analysis, big data processing and other technologies based on the alarm information generated within a certain period of time (such as within 1 hour, etc.) in a similar location (such as within 1 kilometer, etc.) , and record it.
  • new alarm information When new alarm information is received, it can be judged whether it is located in an identified emergency scene according to the scene corresponding to the new alarm information. If it is judged that the new alarm information is located in the identified emergency scene, and the ambulance vehicles and rescue resources configured in the identified emergency scene can already meet the treatment plan corresponding to the new alarm information, there is no need to configure new ambulance vehicles and rescue resources; Otherwise, add new ambulance vehicles and rescue resources to meet the treatment plan corresponding to the new alarm information.
  • the terminal 1101 of the dispatching center can also synchronize the alarm information to the community near the alarm, the emergency station in the factory area, or other institutions that can provide emergency services according to the location of the alarm, so that it is convenient for volunteers on duty or other rescue personnel. Rescue can be carried out in a short time to realize the full utilization of emergency resources and avoid delaying the precious golden emergency time.
  • the rescue personnel terminal 1103 is connected in communication with the dispatch center terminal 1101 and the medical service system, and is used to receive the dispatch information of the dispatch center terminal 1101, for example, it can also receive medical data related to the rescue object, for example, The medical record information of the rescued object can be entered through the rescuer terminal 1103 .
  • the terminal 1103 for rescuers can be a dedicated communication terminal, or a smart phone with related application programs installed, so that rescuers can receive dispatch information from the terminal 1101 of the dispatch center conveniently, and at the same time, it is also convenient for rescuers. The personnel feed back the information to the terminal 1101 of the dispatch center.
  • the dispatch center terminal 1101 can also be used to dispatch rescue personnel in the medical institution to provide remote assistance according to the medical data related to the subject to be treated.
  • the medical institution can use the medical business system to obtain real-time and historical medical data of the object to be treated from the data lake cluster; therefore, the internal rescue personnel of the medical institution can provide remote Assistance, such as remote diagnosis, remote equipment operation, etc.; in addition, it can also facilitate medical institutions to carry out relevant preparations in advance, further reducing the waste of treatment time.
  • the dispatch center terminal 1101 can also obtain emergency information such as bed conditions and treatment resources of each hospital in real time to It is convenient for the dispatch center terminal 1101 to guide the ambulance vehicles to transport the object to be treated to the preferred medical institution; at the same time, the dispatch center terminal 1101 can also send the medical institution's treatment capacity and bed information to the rescue personnel terminal 1103, so that the rescue personnel can more accurately Judging whether it is necessary to transfer to another hospital, so as to reduce the impact caused by sudden changes in beds.
  • emergency information such as bed conditions and treatment resources of each hospital in real time to It is convenient for the dispatch center terminal 1101 to guide the ambulance vehicles to transport the object to be treated to the preferred medical institution; at the same time, the dispatch center terminal 1101 can also send the medical institution's treatment capacity and bed information to the rescue personnel terminal 1103, so that the rescue personnel can more accurately Judging whether it is necessary to transfer to another hospital, so as to reduce the impact caused by sudden changes in beds.
  • the driver terminal 1104 is connected in communication with the dispatch center terminal 1101, and is used to guide the driver to drive the ambulance vehicle to the location of the object to be treated.
  • the driver terminal 1104 may be a vehicle-mounted terminal, such as a vehicle-mounted central control screen, a vehicle-mounted navigator, etc.; meanwhile, the driver's terminal 1104 may also be a smart phone or other communication terminals installed with relevant applications.
  • the driver terminal 1104 can display the current driving progress in real time, for example, when arriving at the alarm location, the display status is "arrived at the alarm location". When rushing to the emergency hospital, the display status is "on the way”, when arriving at the emergency hospital, the display status is "arrived at the hospital", and so on.
  • the emergency treatment system may further include an information integration platform 1201, which is connected to the management terminal 701, the dispatch center terminal 1101, the alarm terminal 1102, the rescuer terminal 1103, and the driver terminal 1104. Communicate with at least one of the medical business systems 103 . in:
  • the information integration platform 1201 is used to retrieve personal health files and/or knowledge bases from the third-party business system 1202, and send them to the management terminal 701, dispatch center terminal 1101, alarm terminal 1102, rescue personnel terminal 1103, and drivers. At least one of the terminal 1104 and the medical service system 103; and/or,
  • the information integration platform 1201 is used to obtain information about the object to be treated from at least one of the management terminal 701 , dispatch center terminal 1101 , alarm terminal 1102 , rescuer terminal 1103 , driver terminal 1104 and medical service system 103 .
  • the personal health files in the third-party business system 1202 are integrated through the information integration platform 1201, such as historical medical information, medical history, inspection reports, etc., and sent to the management terminal 701, dispatch center terminal 1101, alarm terminal 1102, At least one of the rescuer terminal 1103, the driver terminal 1104, and the medical service system 103 fuses and compares the user information (including medical record information) stored in these terminals with the personal health files in the information integration platform 1201, It is convenient for a more comprehensive understanding of the patient's personal health status. For example, key information such as mental illness, infectious disease, and inspection records can also be extracted and displayed to the dispatch center terminal 1101, which is convenient for dispatchers to quickly predict the alarm situation.
  • the information integration platform 1201 such as historical medical information, medical history, inspection reports, etc.
  • the information integration platform 1201 integrates the knowledge base in the third-party business system 1202, and the knowledge base can be synchronized to the dispatch center terminal 1101, the rescue personnel terminal 1103, etc., to facilitate standardized treatment.
  • the dispatch center terminal 1101 can synchronize the dispatch information in the dispatcher dispatch process, such as preliminary diagnosis, graphic guidance, audio and video, etc. to the information integration platform 1201, and the rescue information can be synchronized to the Information integration platform 1201.
  • the information integration platform 1201 may include a decision evaluation module; the decision evaluation module evaluates the information of the object to be treated based on a knowledge base. Effective evaluation is performed uniformly by the decision-making evaluation module. According to the evaluation results, relevant personnel can carry out targeted reinforcement and update the knowledge base at the same time. For example, the decision evaluation module makes a prediction on the condition of the subject to be treated based on the knowledge base, or provides a standard treatment method based on the knowledge base.
  • the information integration platform 1201 can also be used to intelligently identify emergency scenarios according to the received alarm information; when receiving new alarm information, if it is judged that the new alarm information is located in the Identify the emergency scene, then determine whether to configure new ambulance vehicles and rescue resources according to the ambulance vehicles and rescue resources configured for the identified emergency scene
  • the dispatch center terminal 1101 can also be realized by the dispatch center terminal 1101 .
  • the information integration platform 1201 completes the intelligent recognition of the emergency scene, it sends the scene information to the dispatch center terminal 1101, and the dispatch center terminal 1101 dispatches ambulance vehicles and rescue resources according to specific conditions.
  • the emergency scene is identified as a collective public event based on the received alarm information, there may be multiple people calling the police at the place where the collective public event occurs.
  • the emergency scene can be identified based on the information provided by the caller, or the emergency scene can be identified through environmental information such as voice and video.
  • the information integration platform 1201 can assign a certain number of ambulances and configure corresponding rescue resources according to the analysis results. For example, when two people with minor injuries report to the police, it may be considered to assign only one ambulance to save ambulance resources.
  • the terminal 1101 of the dispatch center can intelligently identify the emergency scene through audio and video semantic analysis, big data processing and other technologies based on the alarm information generated within a certain period of time (such as within 1 hour, etc.) in a similar location (such as within 1 kilometer, etc.) , and record it.
  • new alarm information When new alarm information is received, it can be judged whether it is located in an identified emergency scene according to the scene corresponding to the new alarm information. If it is judged that the new alarm information is located in the identified emergency scene, and the ambulance vehicles and rescue resources configured in the identified emergency scene can already meet the treatment plan corresponding to the new alarm information, there is no need to configure new ambulance vehicles and rescue resources; Otherwise, add new ambulance vehicles and rescue resources to meet the treatment plan corresponding to the new alarm information.
  • the alarm terminal 1102 uploads the alarm information (location, audio and video) to the information integration platform 1201; the dispatch center terminal 1101 uploads dispatch information (preliminary diagnosis, vehicle assignment, operation records, audio and video) and other information to the emergency information integration platform.
  • the dispatcher can mark the emergency level (such as urgent, general, not urgent, etc.)
  • the information integration platform 1201 intelligently recognizes emergency scenarios based on the number of alarms generated in a similar location, semantic analysis of audio and video, etc., and synchronizes the emergency situation to the dispatch center terminal 1101. After a new alarm enters, it can intelligently Judgment describes the scene, whether the symptoms are the same, and whether the car has been assigned for prompting.
  • the information integration platform 1201 recognizes the emergency scene, it can summarize and display the existing alarm information and dispatch information on the 120 emergency command platform according to the geographical location in the dispatch information and the scene description in the semantic analysis of the audio and video, so as to facilitate timely emergency command and unified coordination.
  • the dispatch center terminal 1101 and/or the information integration platform 1201 can also synchronize the alarm information to the community near the alarm, the emergency station in the factory area, or other institutions that can provide emergency services according to the location of the alarm, so as to facilitate on-duty Volunteers or other rescue personnel start rescue at the first time, realize the full use of first aid resources, and avoid delaying the precious golden first aid time.
  • the rescuer terminal 1103 is also used to record the treatment information, and the information integration platform 1201 is used to draw the treatment information record as a time axis according to the time sequence, and send it to the medical service system 103 .
  • the rescue personnel terminal 1103 collect on-site video and identify patient identity information through the rescue personnel terminal 1103, and upload them to the information integration platform 1201 in a unified manner.
  • Doctors can take real-time photos and videos of physical and drug treatment operations carried out through the treatment personnel terminal 1103, or manually enter them into the real-time upload information integration platform 1201, and record the operation time when uploading.
  • the location information of the ambulance terminal is uploaded to the information integration platform 1201 in a unified manner.
  • the information integration platform 1201 summarizes and displays the timeline and location information of in-vehicle operations to the medical information system, and intelligently sorts the patients who are about to arrive at the hospital through treatment information, illness, distance, etc., so that emergency doctors can carry out treatment according to the time point and The location of the vehicle determines the time of arrival at the hospital, customizes the rescue plan, and coordinates resources in the hospital.
  • the information integration platform 1201 is used to receive the resource information of the hospital sent from the medical business system 103 and send it to the dispatch center terminal 1101 .
  • each hospital can timely update and synchronize the bed situation and treatment capacity of each hospital to the information integration platform 1201 through the medical information system, and the dispatcher can choose to dispatch according to the vacant bed situation at the dispatch center terminal 1101.
  • the doctor in the car can check the real-time bed situation sent to the hospital on the terminal 1103 of the rescuers, so as to confirm in time whether it is necessary to transfer to another hospital, and reduce the impact caused by sudden changes in bed numbers.
  • an emergency treatment method is also provided.
  • the emergency treatment method can be applied to an emergency treatment system.
  • the emergency treatment method may include the following steps S1301 to S1303. in:
  • step S1301 the medical data of the subject to be treated is collected through the information collection terminal deployed in the ambulance vehicle and uploaded to the data lake cluster;
  • step S1302 perform data access, data processing, data storage and data distribution on the medical data of the subject to be treated through the data lake cluster;
  • step S1303 obtain the medical data of the subject to be treated from the data lake cluster through the medical business system.
  • an electronic device including: a processor; a memory configured to store processor-executable instructions; described method.
  • FIG. 14 is a schematic structural diagram of a computer system for realizing the electronic device of the embodiment of the present disclosure. It should be noted that the computer system 1400 of the electronic device shown in FIG. 14 is only an example, and should not limit the functions and application scope of the embodiments of the present disclosure.
  • a computer system 1400 includes a central processing unit 1401, which can perform various appropriate actions and processes according to programs stored in a read-only memory 1402 or loaded from a storage section 1408 into a random access memory 1403 .
  • random access memory 1403 various programs and data necessary for system operation are also stored.
  • the CPU 1401 , the ROM 1402 and the RAM 1403 are connected to each other through a bus 1404 .
  • the input/output interface 1405 is also connected to the bus 1404 .
  • the following components are connected to the input/output interface 1405: an input section 1406 including a keyboard, a mouse, etc.; an output section 1407 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker; a storage section 1408 including a hard disk, etc. and a communication section 1409 including a network interface card such as a local area network (LAN) card, a modem, or the like.
  • the communication section 1409 performs communication processing via a network such as the Internet.
  • a driver 1410 is also connected to the input/output interface 1405 as needed.
  • a removable medium 1411 such as a magnetic disk, optical disk, magneto-optical disk, semiconductor memory, etc. is mounted on the drive 1410 as necessary so that a computer program read therefrom is installed into the storage section 1408 as necessary.
  • embodiments of the present disclosure include a computer program product, which includes a computer program carried on a computer-readable medium, where the computer program includes program codes for executing the methods shown in the flowcharts.
  • the computer program may be downloaded and installed from a network via communication portion 1409 and/or installed from removable media 1411 .
  • the central processing unit 1401 When the computer program is executed by the central processing unit 1401, various functions defined in the apparatus of the present application are performed.
  • a non-volatile computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a computer, the computer executes any one of the methods described above.
  • non-volatile computer-readable storage medium shown in the present disclosure may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any of the above combination. More specific examples of computer-readable storage media may include, but are not limited to, electrical connections with one or more conductors, portable computer diskettes, hard disks, random access memory, read-only memory, erasable programmable read-only memory (EPROM) or flash memory), fiber optics, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.
  • a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.
  • a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code therein. Such propagated data signals may take many forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing.
  • a computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.
  • Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wires, optical cables, radio frequency, etc., or any suitable combination of the above.

Landscapes

  • Engineering & Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • Medical Informatics (AREA)
  • Public Health (AREA)
  • Biomedical Technology (AREA)
  • Epidemiology (AREA)
  • General Health & Medical Sciences (AREA)
  • Primary Health Care (AREA)
  • Data Mining & Analysis (AREA)
  • Pathology (AREA)
  • General Business, Economics & Management (AREA)
  • Business, Economics & Management (AREA)
  • Databases & Information Systems (AREA)
  • Software Systems (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Medical Treatment And Welfare Office Work (AREA)
  • Measuring And Recording Apparatus For Diagnosis (AREA)

Abstract

提供一种紧急救治系统、紧急救治方法及电子设备。紧急救治系统包括：信息采集终端，部署于救护车辆，用于采集待救治对象医疗数据并上传至数据湖集群；数据湖集群，用于对待救治对象医疗数据进行数据接入、数据处理、数据存储以及数据分发；医疗业务系统，用于从数据湖集群获取待救治对象医疗数据。

Description

紧急救治系统、紧急救治方法及电子设备
交叉引用
本公开要求于2021年7月1日提交的申请号为202110745539.X名称为“紧急救治系统、紧急救治方法及电子设备”的中国专利申请的优先权，该中国专利申请的全部内容通过引用全部并入本文。
技术领域
本公开涉及智慧医疗技术领域，尤其涉及一种紧急救治系统、紧急救治方法及电子设备。
背景技术
紧急救治，是指在医院以外的地方，病人突发疾病情况下，拨打急救电话或者通过其他方式，寻求紧急救治帮助。需要说明的是，在上述背景技术部分公开的信息仅用于加强对本公开的背景的理解，因此可以包括不构成对本领域普通技术人员已知的现有技术的信息。
公开内容
本公开提供一种紧急救治系统、紧急救治方法及电子设备。
根据本公开的一个方面，提供一种紧急救治系统，包括：
信息采集终端，部署于救护车辆，用于采集待救治对象医疗数据并上传至数据湖集群；
数据湖集群，用于对所述待救治对象医疗数据进行数据接入、数据处理、数据存储以及数据分发；
医疗业务系统，用于从所述数据湖集群获取所述待救治对象医疗数据。
在本公开的一种示例性实施例中，所述数据湖集群包括：
数据接入模块，用于根据信息采集终端的类型确定对应的目标接入方式并通过目标接入方式接入待救治对象医疗数据；
数据处理模块，用于对所述待救治对象医疗数据的指标信息进行解析，并建立各所述指标信息与标准指标之间的映射关系；
数据分发模块，用于将经由所述数据处理模块处理后的待救治对象医疗数据分发至所述医疗业务系统。
在本公开的一种示例性实施例中，所述数据处理模块中对所述待救治对象医疗数据的指标信息进行解析包括：
基于数据解析模型对所述信息采集终端上传的所述待救治对象医疗数据的指标信息进行解析；其中，
所述数据解析模型为基于所述信息采集终端对应的解析协议进行建模得到。
在本公开的一种示例性实施例中，所述待救治对象医疗数据的指标信息包括n个字段，其中第i个字段表示所述待救治对象医疗数据的指标的二进制值的第(i-1)*m+1位至第i*m位，m为正整数，表征各字段在对应的二进制数据中表示的位数；所述数据解析模型通过下述公式对所述待救治对象医疗数据的指标信息进行解析：
Figure PCTCN2022103328-appb-000001
其中，函数f 1(x)用于将对象转换为二进制数值，f 2(x)用于将对象转换为十进制数值。
在本公开的一种示例性实施例中，所述标准指标至少包括标准编码标识，所述数据处理模块中建立各所述指标信息与标准指标之间的映射关系包括：
在解析所述信息采集终端上传的所述待救治对象医疗数据的指标信息后，获取其中的非标准编码标识；
基于所述非标准编码标识与所述标准编码标识之间的映射关系，将所述标准编码标识与所述待救治对象医疗数据的指标信息中的指标值进行键值存储；
将与所述待救治对象医疗数据对应的标准编码标识与所述信息采集终端的设备标识关联。
在本公开的一种示例性实施例中，其中：
所述数据接入模块与所述数据处理模块之间配置有第一消息平台，所述第一消息平台用于供所述数据接入模块通过消息服务的方式将接入的信息传输至所述数据处理模块；
所述数据分发模块与所述医疗业务系统之间配置有第二消息平台，所述第二消息平台用于供所述数据分发模块通过消息服务的方式将处理后的待救治对象医疗数据传输至所述医疗业务系统。
在本公开的一种示例性实施例中，所述数据分发模块还用于，将所述待救治对象医疗数据发送至消息队列，以便于所述第二消息平台从所述消息队列拉取所述待救治对象医疗数据。
在本公开的一种示例性实施例中，所述数据接入模块接入的待救治对象医疗数据与数据分发模块分发的处理后的待救治对象医疗数据之间存在数据转换关系。
在本公开的一种示例性实施例中，所述输入指标包括低通气指数x1、呼吸暂停指数x2以及压力参数x3；所述输出指标包括睡眠呼吸暂停低通气指数均值y1；所述y1和x1、x2、x3之间存在如下数据转换关系：
y1＝f1(x1,x2,x3)；其中，f1表示线性转换关系或非线性转换关系。
在本公开的一种示例性实施例中，所述数据转换关系为：
y1＝w1*x1+w2*x2+w3*x3；其中，w1、w2以及w3为关系参数。
在本公开的一种示例性实施例中，w1的取值范围为[1,1.00000011]，w2的取值范围为[1，1.00000011]，w3的取值范围为的取值范围为[7.85046229E-18，7.85046229E-16]。
在本公开的一种示例性实施例中，w1的取值为1.00000011，w2的取值为 1.00000011，w3的值为7.85046229E-17。
在本公开的一种示例性实施例中，所述输入指标包括低通气指数x1、呼吸暂停指数x2以及压力参数x3；所述输出指标为呼吸暂停指数均值y2；所述y2和x1、x2、x3之间存在如下数据转换关系：
y2＝f2(x1,x2,x3)；其中，f2表示线性转换关系或非线性转换关系。
在本公开的一种示例性实施例中，所述数据转换关系为：
y2＝w1*x1+w2*x2+w3*x3；其中，w1、w2以及w3为关系参数。
在本公开的一种示例性实施例中，w1的取值范围为[5.55500801E-08，0.50000006]，w2的取值范围为[0.50000006，1.00000006]，w3的取值范围为的取值范围为[2.22044605E-17，2.22044605E-15]。
在本公开的一种示例性实施例中，w1的取值为0.50000006、0.500000056或者5.55500801E-08，w2的取值为0.50000006或者1.00000006，w3的值为2.22044605E-16。
在本公开的一种示例性实施例中，所述输入指标包括低通气指数x1、呼吸暂停指数x2以及压力参数x3；所述输出指标为低通气指数均值y3；所述y3和x1、x2、x3之间存在如下数据转换关系：
y3＝f3(x1,x2,x3)；其中，f3表示线性转换关系或非线性转换关系。
在本公开的一种示例性实施例中，所述数据转换关系为：
y3＝w1*x1+w2*x2+w3*x3；其中，w1、w2以及w3为关系参数。
在本公开的一种示例性实施例中，w1的取值范围为[0.500000056，1.00000006]，w2的取值范围为[5.55500802E-08，0.50000006]，w3的取值范围为的取值范围为[-7.85046229E-16，-7.85046229E-18]。
在本公开的一种示例性实施例中，w1的取值为0.50000006、0.500000056或者1.00000006，w2的取值为5.55500802E-08或者0.50000006，w3的值为-7.85046229E-17。
在本公开的一种示例性实施例中，所述数据接入模块接入的待救治对象医疗数据与数据分发模块分发的处理后的待救治对象医疗数据之间的数据转换关系通过如下方式获取：
获取样本医疗数据和样本业务数据之间的原始映射矩阵R；其中，所述原始映射矩阵R为M×N的矩阵，元素R(i,j)用于表征样本医疗数据中输入指标i与样本业务数据中输出指标j之间的关系；
对于维度数k，以P×Q＝R为目标获取M×k的第一矩阵P以及k×N的第二矩阵Q，并基于第一矩阵P和第二矩阵Q计算目标映射矩阵Rk；
根据所述原始映射矩阵以及至少一个所述目标映射矩阵，确定业务数据中输出指标与各所述输入指标之间的数据转换关系；
其中，m、n为大于1的整数，i、j、k为正整数，且i∈[1,m]、j∈[1,n]、k∈[1,m-1]。
在本公开的一种示例性实施例中，根据所述原始映射矩阵以及第1至n-1个所述目标映射矩阵，确定业务数据中输出指标与各所述输入指标之间的数据转换关系。
在本公开的一种示例性实施例中，所述数据转换关系为：
yj＝f(xi,i∈[1,m])；其中，yj为输出指标j的值，函数f的参数基于所述原始映射矩阵以及至少一个所述目标映射矩阵中输出指标j所在列的元素值确定，xi为输入指标i的值。
在本公开的一种示例性实施例中，所述输入指标包括低通气指数x1、呼吸暂停指数x2以及压力参数x3；所述输出指标为睡眠呼吸暂停低通气指数均值y1；所述睡眠呼吸暂停低通气指数均值对应的转换关系为：
y1＝f1(x1,x2,x3)；其中，函数f1的参数基于所述原始映射矩阵以及至少一个所述目标映射矩阵中所述睡眠呼吸暂停低通气指数均值所在列的元素值确定。
在本公开的一种示例性实施例中，所述睡眠呼吸暂停低通气指数均值对应的转换关系具体为：
y1＝w1*x1+w2*x2+w3*x3；其中，w1、w2以及w3基于所述原始映射矩阵以及至少一个所述目标映射矩阵中所述睡眠呼吸暂停低通气指数所在列的元素值确定。
在本公开的一种示例性实施例中，所述输入指标包括低通气指数x1、呼吸暂停指数x2以及压力参数x3；所述输出指标为呼吸暂停指数均值y2；所述呼吸暂停指数均值对应的转换关系为：
y2＝f2(x1,x2,x3)；其中，函数f2的参数基于所述原始映射矩阵以及至少一个所述目标映射矩阵中所述呼吸暂停指数均值所在列的元素值确定。
在本公开的一种示例性实施例中，所述呼吸暂停指数均值对应的转换关系具体为：
y2＝w1*x1+w2*x2+w3*x3；其中，w1、w2以及w3基于所述原始映射矩阵以及至少一个所述目标映射矩阵中所述呼吸暂停指数均值所在列的元素值确定。
在本公开的一种示例性实施例中，所述输入指标包括低通气指数x1、呼吸暂停指数x2以及压力参数x3；所述输出指标为低通气指数均值y3；所述低通气指数均值对应的转换关系为：
y3＝f3(x1,x2,x3)；其中，函数f3的参数基于所述原始映射矩阵以及至少一个所述目标映射矩阵中所述低通气指数均值所在列的元素值确定。
在本公开的一种示例性实施例中，所述低通气指数均值对应的转换关系具体为：
y3＝w1*x1+w2*x2+w3*x3；其中，w1、w2以及w3基于所述原始映射矩阵以及至少一个所述目标映射矩阵中所述低通气指数均值所在列的元素值确定。
在本公开的一种示例性实施例中，还包括管理终端：
所述管理终端用于，记录所述信息采集终端的设备标识、设备开始使用时间；
接收从数据湖集群分发的医疗数据；
根据所述设备标识、设备开始使用时间将所述医疗数据与待救治对象的用户信息关联；
所述医疗业务系统还用于接收所述与待救治对象的用户信息关联后的医疗数据。
在本公开的一种示例性实施例中，所述管理终端还用于，存储待救治对象的历史医疗数据，将所述待救治对象的历史医疗数据和实时医疗数据共同分发至所述医疗业务系统。
在本公开的一种示例性实施例中，所述管理终端还用于，存储待救治对象的亲友医疗数据，将所述待救治对象的亲友医疗数据和自身医疗数据共同分发至所述医疗业务系统。
在本公开的一种示例性实施例中，
所述管理终端，还用于接收查询请求，并根据所述查询请求包含的查询条件在所述数据湖集群获取相关数据生成查询结果。
在本公开的一种示例性实施例中，所述管理终端还包括：
数据可视化模块，用于根据所述数据湖集群的数据生成可视化内容，以通过前端页面展示所述可视化内容。
在本公开的一种示例性实施例中，所述管理终端还包括：
设备参数监控模块，用于对所述信息采集终端的行为信息进行记录和分析，以确认所述信息采集终端是否为异常终端。
在本公开的一种示例性实施例中，所述紧急救治系统还包括：
调度中心终端，用于接收报警信息并获取待救治对象的位置，并根据预设调度算法调度救护车辆到达待救治对象的位置进行救治。
在本公开的一种示例性实施例中，所述调度中心端还用于，根据所述待救治对象相关的医疗数据调度医疗机构内部救治人员提供远程协助。
在本公开的一种示例性实施例中，所述紧急救治系统还包括：
报警终端，能够与所述调度中心终端通信连接，用于向所述调度中心终端发出所述报警信息。
在本公开的一种示例性实施例中，所述紧急救治系统还包括：
救治人员终端，与所述调度中心终端通信连接，用于接收所述调度中心终端的调度信息，以及，用于录入待救治对象的病历信息。
在本公开的一种示例性实施例中，所述紧急救治系统还包括：
驾驶人员终端，与所述调度中心终端通信连接，用于引导驾驶人员将所述救护车辆驾驶至所述待救治对象的位置。
在本公开的一种示例性实施例中，所述紧急救治系统还包括：
信息集成平台，与所述管理终端、调度中心终端、报警终端、救治人员终端、驾驶人员终端和医疗业务系统中的至少之一通信；
所述信息集成平台用于从第三方业务系统调取个人健康档案和/或知识库，并发送给所述管理终端、调度中心终端、报警终端、救治人员终端、驾驶人员终端和医疗业务系统中的至少之一；和/或，
所述信息集成平台用于从所述管理终端、调度中心终端、报警终端、救治人员终端、驾驶人员终端和医疗业务系统中的至少之一获取待救治对象信息。
在本公开的一种示例性实施例中，所述信息集成平台包括决策评估模块；
所述决策评估模块基于所述知识库对所述待救治对象信息进行评估。
在本公开的一种示例性实施例中，所述信息集成平台用于：
根据已接收到的待救治对象信息识别应急场景；
在接收到新的报警信息时，如果判断所述新的报警信息位于已识别应急场景，则根据为所述已识别应急场景配置的救护车辆确定是否配置新的救护车辆。
在本公开的一种示例性实施例中，所述救治人员终端还用于记录治疗信息，
所述信息集成平台用于，根据时间顺序将所述治疗信息记录绘制为时间轴，并发送给所述医疗业务系统。
在本公开的一种示例性实施例中，所述信息集成平台用于，接收从所述医疗业务系统发送的医院的资源信息，并发送给调度中心终端。
根据本公开的一个方面，提供一种紧急救治方法，包括：
通过部署于救护车辆的信息采集终端采集待救治对象医疗数据并上传至数据湖集群；
通过数据湖集群对所述待救治对象医疗数据进行数据接入、数据处理、数据存储以及数据分发；
通过医疗业务系统从所述数据湖集群获取所述待救治对象医疗数据。
根据本公开的一个方面，提供一种电子设备，其特征在于，包括：
处理器；以及
存储器，用于存储一个或多个程序，当所述一个或多个程序被所述处理器执行时，使得所述处理器实现如本公开一些方面提供的所述的方法。
根据本公开的一个方面，提供一种计算机可读存储介质，其上存储有计算机程序，其特征在于，该程序被处理器执行时，实现如本公开一些方面提供的所述的方法。
应当理解的是，以上的一般描述和后文的细节描述仅是示例性和解释性的，并不能限制本公开。
附图说明
此处的附图被并入说明书中并构成本说明书的一部分，示出了符合本公开的实施例，并与说明书一起用于解释本公开的原理。显而易见地，下面描述中的附图仅仅是本公开的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。
图1示出了本公开实施例中紧急救治系统的一种架构示意图。
图2示出了本公开实施例中数据湖集群的一种模块示意图。
图3示出了本公开实施例中紧急救治系统的一种架构示意图。
图4示出了本公开实施例中数据处理模块的一种处理流程示意图。
图5示出了本公开实施例中数据处理模块的一种处理流程示意图。
图6示出了本公开实施例中一种指标编码标识映射关系示意图。
图7示出了本公开实施例中数据湖集群的一种模块示意图。
图8示出了本公开实施例中数据查询模块的一种处理流程示意图。
图9示出了本公开实施例中数据交互模块的一种处理流程示意图。
图10示出了本公开实施例中模型获取模块的一种处理流程示意图。
图11示出了本公开实施例中紧急救治系统的一种架构示意图。
图12示出了本公开实施例中紧急救治系统的部分模块示意图。
图13示出了本公开实施例中紧急救治方法的一种流程示意图。
图14示出了用于实现本公开实施例的电子设备的计算机系统的结构示意图。
具体实施方式
现在将参考附图更全面地描述示例实施方式。然而，示例实施方式能够以多种形式实施，且不应被理解为限于在此阐述的范例；相反，提供这些实施方式使得本公开将更加全面和完整，并将示例实施方式的构思全面地传达给本领域的技术人员。所描述的特征、结构或特性可以以任何合适的方式结合在一个或更多实施方式中。
此外，附图仅为本公开的示意性图解，并非一定是按比例绘制。图中相同的附图标记表示相同或类似的部分，因而将省略对它们的重复描述。附图中所示的一些方框图是功能实体，不一定必须与物理或逻辑上独立的实体相对应。可以采用软件形式来实现这些功能实体，或在一个或多个硬件模块或集成电路中实现这些功能实体，或在不同网络和/或处理器装置和/或微控制器装置中实现这些功能实体。
需要说明的是，本公开中，用语“包括”、“配置有”、“设置于”用以表示开放式的包括在内的意思，并且是指除了列出的要素/组成部分/等之外还可存在另外的要素/组成部分/等。
传统的紧急救治中，医院与救护车辆之间存在信息隔离。在一些情况下，部分救护车辆配备的急救设备可以提供医疗数据传输功能，但是仍然缺乏对于医疗数据的统一管理能力。
本公开示例性实施例中的紧急救治系统中，将待救治对象分散的医疗数据均传输至数据湖集群进行整合，从而能够实现以任意规模存储结构化医疗数据、半结构化医疗数据和非结构化医疗数据；同时，允许紧急救治系统中的各个角色，例如，医护人员、数据开发人员和业务分析师等通过各自选择的分析工具和框架来访问数据，达到以不同方式协同处理和分析数据，进而本公开示例性实施例中的紧急救治系统能够支持医疗机构对待救治对象提供更加精准的救治方案。
图1示出了可以应用本公开实施例的紧急救治系统100的示例性架构的示意图。如图1所示，紧急救治系统100可以包括部署于救护车辆101的信息采集终端、数据湖集群102以及部署于医院的医疗业务系统103。其中：
救护车辆101除了用于运载医护人员以及待救治对象，通常还用于部署车载急救医疗仪器；例如，车载急救医疗仪器可以包括：除颤监护仪、呼吸机、无创多参数检测仪(MTX)、血压检测设备、体脂检测设备、血糖检测设备、睡眠监测设备、电动吸引器、负压系统、供氧装置、杀菌装置、照明装置、抽风系统、暖风系统、供电线路系统、自动上车担架系统等。部分车载急救医疗仪器能够采集待救治对象 医疗数据，即作为下述的信息采集终端；信息采集终端例如可以包括上述呼吸机、无创多参数检测仪(MTX)、血压检测设备、体脂检测设备、血糖检测设备、睡眠监测设备等。此外，部分待救治对象的部分电子设备，例如具有医疗数据采集功能的可穿戴设备或者其他移动终端，亦可视为本示例实施方式中所述的信息采集终端。
信息采集终端采集待救治对象医疗数据后上传至数据湖集群102。数据湖集群102用于对所述待救治对象医疗数据进行数据接入、数据处理、数据存储以及数据分发。本示例实施方式中，数据湖集群102例如可以是由关系型数据库MariaDB、MySQL等，文档型数据库MongoDB、CouchDB等，分布式文件系统HDFS、PVFS、PanFS等以及图数据库Neo4j、Cayley、rapgDB等，多类数据库构成数据存储和管理服务系统。本示例实施方式中，数据湖集群102可以采用分布式运算和存储架构，集成具有数据存储以及运算功能的各类计算机单机、服务器以及计算机集群或者服务器集群，并提供包括数据管理、算法开发的各类功能组件。通过所述数据湖集群102，能够无需对医疗数据进行结构化处理，实现以任意规模存储结构化医疗数据、半结构化医疗数据和非结构化医疗数据，允许紧急救治系统100中的各个角色，例如，医护人员、数据开发人员和业务分析师等通过各自选择的分析工具和框架来访问数据，达到以不同方式协同处理和分析数据。
本示例实施方式中，医疗业务系统103能够从所述数据湖集群102获取所述待救治对象医疗数据。医疗业务系统103例如可以部署在医院等医疗机构，供医护人员使用。
本公开示例性实施例中的紧急救治系统中，将待救治对象分散的医疗数据均传输至数据湖进行整合，从而能够实现以任意规模存储结构化医疗数据、半结构化医疗数据和非结构化医疗数据；同时，允许紧急救治系统中的各个角色，例如，医护人员、数据开发人员和业务分析师等通过各自选择的分析工具和框架来访问数据，达到以不同方式协同处理和分析数据，进而本公开示例性实施例中的紧急救治系统能够支持医疗机构对待救治对象提供更加精准的救治方案。
以下对本公开实施例的紧急救治系统进行更加详细的介绍。
参考图2所示，在本公开的一种示例性实施例中，所述数据湖集群102包括数据接入模块201、数据处理模块202以及数据分发模块204。在一些实施方式中，还可以包括数据存储模块203。其中：
数据接入模块201用于根据信息采集终端的类型确定对应的目标接入方式并通过目标接入方式接入待救治对象医疗数据。
本示例实施方式中，可以预先对各信息采集终端的类型和对应的目标接入方式进行配置并存储，当特定的信息采集终端接入时，根据预配置信息确定其对应的目标接入方式。例如，参考图3所示，如果信息采集终端为呼吸机、睡眠监测设备或者制氧设备等类型的设备，则可以根据预配置信息确定其对应的目标接入方式为HTTP GET(基于超文本传输协议从指定的资源请求数据)方式，进而可以采用HTTP  GET方式接入待救治对象医疗数据；如果信息采集终端为体脂秤等类型的设备，则可以根据预配置信息确定其对应的目标接入方式为HTTP FORM(基于超文本传输协议的表单传输)方式，进而可以采用HTTP FORM方式接入待救治对象医疗数据；如果信息采集终端为血压检测设备、血糖检测设备等类型的设备，则可以根据预配置信息确定其对应的目标接入方式为TCP Socket(基于传输控制协议的套接字传输)方式，进而可以采用TCP Socket方式接入待救治对象医疗数据；在采用TCP Socket方式接入待救治对象医疗数据之后，还可以对待救治对象医疗数据进行指定方式的预处理。当然，在本公开的其他示例性实施例中，对于上述信息采集终端，也可以采用如HTTPS(超文本传输安全协议)、UDP(用户数据报协议)等其他接入方式接入待救治对象医疗数据，且本示例性实施例中并不以此为限。
数据处理模块202用于对所述待救治对象医疗数据的指标信息进行解析，并建立各所述指标信息与标准指标之间的映射关系。
指标信息为信息采集终端采集到的用户生理指标的相关信息，在一些实施方式中，指标信息可以包括指标名称和指标值。
具体而言，由于不同类型的信息采集终端采集到的数据格式、通信协议、接口等可能不同，因此首先需要对其进行解析。在一些实施方式中，对指标信息进行解析包括对指标信息中的指标值进行解析。例如，参考图4所示，某可穿戴电子设备采集到的指标信息为16进制数据，例如FE121248012408，则需要将其转换为具体的生理指标，例如转换为舒张压120、收缩压90、脉率88等。
为了提升对所述待救治对象医疗数据的指标信息的解析效率以及减少解析方式的预配置工作，本示例实施方式中还提取了部分信息采集终端的解析方式的复用部分进行建模。在一些实施方式中，基于数据解析模型对信息采集终端上传的待救治对象医疗数据的指标信息进行解析；其中，数据解析模型为基于信息采集终端对应的解析协议进行建模得到。在一些实施方式中，所述数据处理模块中对所述待救治对象医疗数据的指标信息进行解析可以包括：获取多种信息采集终端对应的解析协议，并基于各所述信息采集终端对应的解析协议进行建模，以得到至少部分通用的数据解析模型；基于所述数据解析模型，对适用的所述信息采集终端上传的所述待救治对象医疗数据的指标信息进行解析。
在本公开的一种示例性实施例中，所述待救治对象医疗数据的指标信息包括n个字段，其中第i个字段表示所述待救治对象医疗数据的指标的二进制值的第(i-1)*m+1位至第i*m位，其中，m为正整数，且可根据进制转换关系确定；所述数据解析模型通过下述公式对所述待救治对象医疗数据的指标信息进行解析：
Figure PCTCN2022103328-appb-000002
其中，函数f 1(x)用于将对象转换为二进制数值，f 2(x)用于将对象转换为十进制数值。
例如：某血压检测设备发送的指标信息是字符串0x00,0x78,0x00,0x50,0x4B； 该血压检测设备生产商提供的数据接口协议中，对于该字符串数据中字符的所代表的含义进行了定义。参考下表1所示：
表1
Figure PCTCN2022103328-appb-000003
其中，SYS[15:8]表示收缩压高八位，SYS[7:0]表示收缩压低八位；DIA[15:8]表示舒张压高八位，DIA[7:0]表示舒张压低八位；PUL[7:0]表示脉搏数低八位。以收缩压为例，收缩压包括2个字段(即0x00,0x78)，其中第1个字段表示收缩压的二进制值的第1位至第8位；此时m＝8；则收缩压对应的数据解析模型为：
Figure PCTCN2022103328-appb-000004
进而，通过函数f 1(x)将SYS[7:0]，即0x78转换为二进制数值01111000；通过函数f 2(x)将01111000转换为十进制数值120；同样的，可以得到SYS[15:8]，即0x00对应的十进制数值为0。则收缩压y＝0*256 1+120*256 0＝120mmHg。类似的，可以得到舒张压为80mmHg；脉搏数为75次/分钟。当然，本领域技术人员容易理解的是，在本公开的其他示例性实施例中，也可以根据信息采集终端接口协议的不同得到其他的数据解析模型，这同样属于本公开的保护范围。
继续参考图4所示，在对所述待救治对象医疗数据的指标信息解析之后，则可以建立各所述指标信息与标准指标之间的映射关系。
在一些实施方式中，标准指标至少包括标准编码标识。
在解析所述信息采集终端上传的待救治对象医疗数据的指标信息后，获取其中的非标准编码标识；
基于非标准编码标识与标准编码标识之间的映射关系，将标准编码标识与待救治对象医疗数据的指标信息中的指标值进行键值存储；
将与待救治对象医疗数据对应的标准编码标识与信息采集终端的设备标识关联。
在一些实施方式中，还可以包括配置标准指标的过程。
参考图5所示，所述数据处理模块中建立各所述指标信息与标准指标之间的映射关系可以包括下述步骤S501至步骤S504。其中：
在步骤S501中，配置标准指标，所述标准指标至少包括标准编码标识。
举例而言，本示例实施方式中可以创建标准指标分类，关联具体标准指标的定义。例如血压和血糖的标准指标如下：
表2
Figure PCTCN2022103328-appb-000005
Figure PCTCN2022103328-appb-000006
在步骤S502中，获取所述信息采集终端上传的待救治对象医疗数据的指标信息的非标准编码标识，并建立所述非标准编码标识与所述标准编码标识之间的映射关系。
举例而言，本示例实施方式中可以获取信息采集终端的设备信息，例如可以包括基本信息(如产品名称、产品类型、产品型号、厂商名称、产品图片)、配置信息(如通讯协议、数据地址、数据格式、接口协议、指标规则、时间规则)等。进而，根据产品类型以及产品型号等信息可以确定信息采集终端的类型；根据通讯协议和数据地址等信息可以获取信息采集终端上传的数据；根据数据格式等信息可以从上传的数据中解析出非标准编码标识，非标准编码标识例如为指标信息中的指标名称；进而可以用于建立所述非标准编码标识与所述标准编码标识之间的映射关系。例如参考图6所示，其中非标准编码标识Height和ShenGao均与标准指标标识H01对应；非标准编码标识Weight和TiZhong均与标准指标标识W01对应；非标准编码标识BMI和TZZS均与标准指标标识B01对应等。此外，在本公开的其他示例性实施例中，可以通过前端控件监控用户输入，进而根据用户输入对非标准编码标识与所述标准编码标识之间的映射关系进行调整。
在步骤S503中，在解析所述信息采集终端上传的所述待救治对象医疗数据的指标信息后，获取其中的非标准编码标识并基于所述非标准编码标识与所述标准编码标识之间的映射关系，将所述标准编码标识与所述待救治对象医疗数据的指标值进行键值存储。例如对于指标信息进行解析后得到收缩压SYS＝120mmHg，舒张压DIA＝80mmHg；而非标准编码标识SYS对应的标准编码标识为XY01，非标准编码标识DIA对应的标准编码标识为XY02；则可以将指标信息以键值形式存储为[XY01:120]以及[XY02:80]。
在步骤S504中，将键值存储的所述待救治对象医疗数据的指标信息与所述信息采集终端的设备标识关联。本示例实施方式中，在接收信息采集终端上传的数据时，首先将信息采集终端的设备信息与上述步骤S502中预先采集的设备信息进行匹配，从而确保所述待救治对象医疗数据的指标信息与每一个信息采集终端一一对应。
在一些实施方式中，上述步骤中的S501和S502中涉及标准指标的配置过程和映射关系预先构建过程可以省略。步骤S503和步骤S504的执行顺序不做限制。
此外，继续参考图3所示，本示例实施方式中，在所述数据接入模块201与所述数据处理模块202之间还配置有第一消息平台205，所述第一消息平台205用于供所述数据接入模块201通过消息服务的方式将接入的信息传输至所述数据处理模块202。例如，数据接入模块201接入的数据会首先写入如RabbitMQ、ActiveMQ、ZeroMQ、Kafka、MetaMQ、RocketMQ等消息队列；数据处理模块202则可以以先进先出的方式从消息队列拉取数据并进行处理。通过在所述数据接入模块与所述数据处理模块之间配置第一消息平台，实现了数据接入模块和所述数据处理模块之间 的解耦合，而且便于数据接入模块和所述数据处理模块之间的异步处理，同时还可以通过第一消息平台实现数据消峰和控流，以及对数据进行缓冲，避免数据量大时造成网络传输阻塞。
数据存储模块203用于存储待救治对象的医疗数据，在一些实施方式中，还可以用于存储所述标准指标以及对应的指标信息。
本示例实施方式中，数据存储模块203可以利用如关系型数据库MariaDB、MySQL等，文档型数据库MongoDB、CouchDB等，分布式文件系统HDFS、PVFS、PanFS等以及图数据库Neo4j、Cayley、rapgDB等。此外，可以根据数据的调用频次以及数据类型将部分数据写入分布式缓存如Redis、Memcached或者SSDB等，从而提高读取速度。
数据分发模块204用于将经由所述数据处理模块处理后的待救治对象医疗数据分发至所述医疗业务系统。
本示例实施方式中，在所述数据分发模块204与所述医疗业务系统103之间还配置有第二消息平台206，所述第二消息平台206用于供所述数据分发模块204通过消息服务的方式将待救治对象医疗数据传输至所述医疗业务系统103。进而，数据分发模块204将待救治对象医疗数据写入如RabbitMQ、ActiveMQ、ZeroMQ、Kafka、MetaMQ、RocketMQ等消息队列；医疗业务系统103则可以以先进先出的方式从消息队列拉取数据并进行处理。通过在所述数据分发模块与所述医疗业务系统之间配置第二消息平台，实现了数据分发模块和所述医疗业务系统之间的解耦合，而且便于数据分发模块和所述医疗业务系统之间的异步处理，同时还可以通过第二消息平台实现数据消峰和控流，以及对数据进行缓冲，避免数据量大时造成网络传输阻塞。
此外，在本公开的一些示例性实施例中，所述数据分发模块还可以用于，通过HTTP传输方式、RSA加密传输方式、消息队列等方式，将所述待救治对象医疗数据提供至第二消息平台206。例如，数据分发模块204将所述待救治对象医疗数据写入消息队列，以便于所述第二消息平台206从所述消息队列拉取所述待救治对象医疗数据。
在一些实施方式中，对第一消息平台和第二消息平台具体设置的位置不做限定，例如，也可以设置在数据接入模块与数据处理模块之间、数据分发模块与医疗业务系统之间之外的其它位置。
在一些实施方式中，第一消息平台和第二消息平台可以由同一消息平台实现。对消息平台的个数不做限制。
参考图7所示，在本公开的一些示例性实施例中，紧急救治系统还可以包括管理终端701。
在一些实施方式中，管理终端701包括设备参数监控模块705。其中，设备参数监控模块705用于对所述信息采集终端的行为信息进行记录和分析，以确认所述信息采集终端是否为异常终端。本示例实施方式中，信息采集终端的行为信息可以包括使用时间、异常次数等。举例而言，设备参数监控模块705可以对正常信息采集终端的使用时间、异常次数等行为信息进行记录，并据此进行统计分析从而确定 正常信息采集终端的使用时间、异常次数等行为信息的波动范围；进而，当监测到某一信息采集终端的使用时间、异常次数等行为信息超出上述波动范围时，即可认为该信息采集终端为异常终端。
在本公开的其他示例性实施例中，还可以根据信息采集终端的历史行为信息得到样本数据，从而基于样本数据对如随机森林模型、深度神经网络模型、支持向量机模型、提升树模型、一般线性模型以及渐进梯度回归树模型等机器学习模型中的一种或多种进行训练，得到异常终端判断模型。进而，设备参数监控模块705可以将某一信息采集终端的行为信息输入至异常终端判断模型，以藉由异常终端判断模型输出该信息采集终端为异常终端的概率，当该信息采集终端为异常终端的概率值超过阈值时，即可认为该信息采集终端为异常终端。
在本公开的一些示例性实施例中，管理终端还可以用于记录所述信息采集终端的设备标识、设备开始使用时间，在一些实施方式中，还可以记录结束使用时间；
接收从数据湖集群分发的待救治对象的医疗数据；
根据设备标识、设备开始使用时间将医疗数据与待救治对象的用户信息关联；
医疗业务系统还用于接收与待救治对象的用户信息关联后的医疗数据。
记录所述信息采集终端的设备标识、设备开始使用时间以及结束使用时间的过程可以由数据湖集群中的数据处理模块完成后发送给管理终端，也可以直接由管理终端完成以上记录过程。
举例而言，将信息采集终端采集的医疗数据与设备标识、设备开始使用时间和结束使用时间进行关联之后得到的信息如下表3所示：
表3
设备标识 标准指标编码 指标值 时间
2820 291 0.00 2021-03-10 05:36:01
2820 291 1.00 2021-03-10 05:35:01
2820 291 1.00 2021-03-10 05:34:01
2820 291 0.00 2021-03-10 05:33:01
2820 291 2.00 2021-03-10 05:32:01
2820 291 0.00 2021-03-10 05:31:01
2820 291 1.00 2021-03-10 05:30:01
2820 291 0.00 2021-03-10 05:29:01
2820 291 0.00 2021-03-10 05:28:01
2820 291 0.00 2021-03-10 05:27:01
2820 291 0.00 2021-03-10 05:26:01
2820 291 0.00 2021-03-10 05:25:01
2820 291 0.00 2021-03-10 05:24:01
2820 291 1.00 2021-03-10 05:23:01
2820 291 0.00 2021-03-10 05:22:01
管理终端接收从数据湖集群分发的医疗数据，医疗数据中至少包括设备标识、设备开始使用时间和指标信息。
在一些实施方式中，用户信息包括信息采集终端使用者的身份标识，例如可以 包括姓名、身份证号等其中至少之一。由于数据湖集群中存储的数据并不包括使用者的用户信息，因此可以起到用户信息脱敏的作用。而将使用者的相关医疗数据发送给医疗业务系统时，需要将用户信息与医疗数据进行关联。
在一些实施方式中，使用者在通过报警终端进行报警时，和/或，在救护车上通过医生终端录入使用者的个人信息时，均可获取用户信息，同时，还可以记录报警和/或医生终端录入个人信息的时间。使用者的用户信息和记录时间可以发送至管理终端。根据设备标识、设备开始使用时间将对应时间获取到个人信息的使用者对应，进而完成医疗数据与待救治对象的用户信息关联。将与待救治对象的用户信息关联后的医疗数据(即携带有用户信息的医疗数据)发送给医疗业务系统。
例如，管理终端获取到：使用者身份标识UserID:123321；设备标识SN:2820；设备开始使用时间BindingTimeStart:2021-03-10 05:22:01；设备结束使用时间BindingTimeEnd:2021-03-10 05:36:01。
进而，管理终端在获取到数据湖集群发送的如表3中的信息之后，可以根据表3的设备标识、设备开始使用时间和结束使用时间与管理终端获取到用户信息的时间进行匹配，得到表3中的信息的使用者身份标识，并将表3中的数据与使用者身份标识进行绑定。因此，通过上述方法，数据湖集群无需存储使用者身份信息，而仅存储设备信息以及医疗数据，进而能够实现使用者身份信息脱敏的功能。
在本公开的一些示例性实施例中，所述管理终端还可以用于存储待救治对象的历史医疗数据，并将所述待救治对象的历史医疗数据和实时医疗数据共同分发至所述医疗业务系统。具体而言，管理终端能够获取信息采集终端的设备标识对应的使用者身份标识；从而，也能够通过使用者身份标识获取与该使用者身份标识对应的待救治对象的历史医疗数据，并将所述待救治对象的历史医疗数据和实时医疗数据共同分发至所述医疗业务系统；进而，能够使得医疗业务系统的用户(如医务工作者)更加全面的了解待救治对象的信息。
在本公开的一些示例性实施例中，所述管理终端还可以用于存储待救治对象的亲友医疗数据，并将所述待救治对象的亲友医疗数据和自身医疗数据共同分发至所述医疗业务系统。具体而言，管理终端存储有信息采集终端的设备标识对应的使用者身份标识。从而，也能够通过当前信息采集终端使用者身份标识获取和其存在亲友关系的使用者的身份标识；继而通过亲友身份标识在所述管理终端中获取所述待救治对象的亲友医疗数据，并将所述待救治对象的亲友医疗数据和自身医疗数据共同分发至所述医疗业务系统，进而，能够使得医疗业务系统的用户(如医务工作者)了解待救治对象的遗传疾病信息或者传染疾病信息等。
继续参考图7所示，本示例实施方式中，所述管理终端701还可以包括数据查询模块706。数据查询模块706可以用于接收查询请求，并根据所述查询请求包含的查询条件在所述数据湖集群获取相关数据生成查询结果。举例而言，本示例实施方式中，在接收到医疗机构的查询请求之后，获取查询请求所包含的查询字段，并根据查询请求对查询字段在数据湖集群中对查询字段进行等于、大于小于、大于等 于、小于等于、不等于等操作，从而实现精确查询；也可以根据查询字段，利用SQL语句的LIKE机制或正则表达式等方式实现模糊查询；本示例性实施例中对此不做特殊限定。
参考图8所示，在本公开的一些示例性实施例中，所述数据查询模块706还可以用于执行下述步骤S801至步骤S803。其中：
在步骤S801中，在根据查询条件判断所述查询对象为目标对象时，为所述目标对象生成标记标识，并将所述标记标识与所述目标对象对应的信息采集终端的设备标识进行关联。
举例而言，如果某医疗机构需要获取满足如下查询条件的目标对象的医疗数据：位于指定地区、性别为女性、年龄大于50岁、血液舒张压大于90mmHg且血液收缩压大于140mmHg；则本示例实施方式中，所述数据查询模块可以判断查询对象是否满足上述查询条件，如果满足上述查询条件则将查询对象作为目标对象；继而，可以为目标对象对应的信息采集终端生成标记标识，例如，生成Select＝1的属性标识用于标记其为目标对象。相应的，如果不满足上述查询条件，则可以生成Select＝0的属性标识用于标记其不为目标对象。
在步骤S802中，基于与所述标记标识关联的信息采集终端的设备标识监测是否接收到所述目标对象的医疗数据。
举例而言，数据查询模块根据目标对象对应的设备标识、标记标识以及标记标识生成时间生成监测规则“设备标识SN:A123456，标记标识生成时间Binding_Time:2020-09-08-12:12:12，标记标识Select:1，血液舒张压blood pressure:大于90mmHg，血液收缩压大于140mmHg”，并判断新接入的医疗数据对应的信息采集终端是否满足监测规则；如果满足监测规则，则确定接收到目标对象的医疗数据。
在步骤S803中，在监测到接收到所述目标对象的医疗数据时，将所述目标对象的医疗数据分发至所述查询请求对应的医疗机构。
继续参考图7所示，在本公开的一些示例性实施例中，所述管理终端701还可以包括数据可视化模块707。数据可视化模块707可以用于根据所述数据湖集群的数据生成可视化内容，以通过前端页面展示所述可视化内容。
在一些实施方式中，可通过数据可视化模块707将数据湖集群中的数据接入、数据处理、数据分发等过程展示给用户，便于用户对各过程进行监管。同时，还可以对存储的医疗数据进行展示。
举例而言，参考图9所示，本示例实施方式中，所述管理终端和/或医疗业务系统还可以配置数据交互模块901。数据交互模块901能够获取用户的个人信息以及该用户对应的信息采集终端的信息并进行终端绑定；其中，用户的个人信息可以包括用户所在地区、年龄、性别等；信息采集终端信息可以包括厂商信息、设备标识、设备系统信息、指标信息和解析字典信息等。
在一些实施方式中，数据交互模块901可以向管理终端的数据可视化模块707发送请求，以便于所述数据可视化模块707根据所述数据湖集群中的数据生成可视化内容。进而，数据交互模块901在接收到所述可视化内容之后能够通过前端页面 向用户展示；此外，在一些示例性实施例中，数据交互模块901还可以主动向用户推送消息，例如推送设备使用提醒消息、健康提示消息等。
医疗业务系统203能够从所述数据湖集群获取所述待救治对象医疗数据。
在本公开的一些示例性实施例中，数据接入模块接入的待救治对象医疗数据与数据分发模块分发的处理后的待救治对象医疗数据之间存在数据转换关系。在一些实施方式中，输入指标包括低通气指数x1、呼吸暂停指数x2以及压力参数x3；输出指标包括睡眠呼吸暂停低通气指数均值y1；
y1和x1、x2、x3之间存在如下数据转换关系：
y1＝f1(x1,x2,x3)；其中，f1表示线性转换关系或非线性转换关系。
例如，数据转换关系为：y1＝w1*x1+w2*x2+w3*x3；其中，w1、w2以及w3为关系参数。
例如，w1的取值范围为[1,1.00000011]，w2的取值范围为[1，1.00000011]，w3的取值范围为的取值范围为[7.85046229E-18，7.85046229E-16]。
例如，w1的取值为1.00000011，w2的取值为1.00000011，w3的值为7.85046229E-17。
在一些实施方式中，输入指标包括低通气指数x1、呼吸暂停指数x2以及压力参数x3；输出指标为呼吸暂停指数均值y2；
y2和x1、x2、x3之间存在如下数据转换关系：
y2＝f2(x1,x2,x3)；其中，f2表示线性转换关系或非线性转换关系。
例如，数据转换关系为：y2＝w1*x1+w2*x2+w3*x3；其中，w1、w2以及w3为关系参数。
例如，w1的取值范围为[5.55500801E-08，0.50000006]，w2的取值范围为[0.50000006，1.00000006]，w3的取值范围为的取值范围为[2.22044605E-17，2.22044605E-15]。
例如，w1的取值为0.50000006、0.500000056或者5.55500801E-08，w2的取值为0.50000006或者1.00000006，w3的值为2.22044605E-16。
在一些实施方式中，输入指标包括低通气指数x1、呼吸暂停指数x2以及压力参数x3；输出指标为低通气指数均值y3；
y3和x1、x2、x3之间存在如下数据转换关系：
y3＝f3(x1,x2,x3)；其中，f3表示线性转换关系或非线性转换关系。
例如数据转换关系为：y3＝w1*x1+w2*x2+w3*x3；其中，w1、w2以及w3为关系参数。
例如，w1的取值范围为[0.500000056，1.00000006]，w2的取值范围为[5.55500802E-08，0.50000006]，w3的取值范围为的取值范围为[-7.85046229E-16，-7.85046229E-18]。
例如，w1的取值为0.50000006、0.500000056或者1.00000006，w2的取值为 5.55500802E-08或者0.50000006，w3的值为-7.85046229E-17。
在本公开的一些示例性实施例中，上述数据接入模块接入的待救治对象医疗数据与数据分发模块分发的处理后的待救治对象医疗数据之间的数据转换关系通过如下方式获取：
可以使用样本医疗数据和样本业务数据对机器学习模型进行训练，从而得到所述数据转换关系。举例而言，所述数据转换关系可以通过神经网络模型，例如卷积神经网络模型、残差神经网络模型、循环神经网络模型、长短期记忆神经网络模型等获得；所述数据转换关系还可以为协同过滤模型、隐马尔科夫模型、条件随机场模型等其他类型的模型获得。
参考图10所示，在一种示例性实施例中，获取所述数据转换关系的过程可以包括下述步骤S1001至步骤S1003。其中：
在步骤S1001中，获取样本医疗数据和样本业务数据之间的原始映射矩阵R；其中，所述原始映射矩阵R为m×n的矩阵，元素R(i,j)用于表征样本医疗数据中输入指标i与样本业务数据中输出指标j之间的关系。其中，m、n为大于1的整数，i、j、k为正整数，且i∈[1,m]、j∈[1,n]，k∈[1,m-1]。
举例而言，某信息采集终端上传至数据湖集群的样本医疗数据包含9个输入指标；数据湖集群根据某信息采集终端提供的医疗数据生成的样本业务数据中包含10个输出指标。样本医疗数据和样本业务数据之间的原始映射矩阵R例如如下表4所示：
表4
  OUT-01 OUT-02 OUT-03 OUT-04 OUT-05 OUT-06 OUT-07 OUT-08 OUT-09 OUT-10
IN-01 1 0 0 0 0 0 0 0 0 1
IN-02 0 0 1 0 0 0 0 0 0 0
IN-03 0 0 0 0 0 0 0 1 0 0
IN-04 0 0 0 0 1 0 0 0 0 0
IN-05 0 0 0 0 0 0 1 0 0 0
IN-06 1 0 0 0 0 0 0 0 1 0
IN-07 0 1 0 0 0 0 0 0 0 0
IN-08 0 0 0 0 0 1 0 0 0 0
IN-09 0 0 0 1 0 0 0 0 0 0
原始映射矩阵R为9×10的矩阵，元素R(i,j)用于表征样本医疗数据中输入指标i与样本业务数据中输出指标j之间的关系。例如，元素R(1,1)的取值为1，表示输入指标IN-01与输出指标OUT-01之间存在关联关系；元素R(1,2)的取值为0，则表示无法确定输入指标IN-01与输出指标OUT-01之间是否存在关联关系。以输出指标OUT-01是睡眠呼吸暂停低通气指数均值、IN-01是低通气指数、IN-06是呼吸暂停指数为例，目前已知睡眠呼吸暂停低通气指数均值与低通气指数和呼吸暂停指数相关，例如，睡眠呼吸暂停低通气指数均值为低通气指数和呼吸暂停指数之和，因此，R(1,1)和R(6,1)的取值为1；但目前无法确定睡眠呼吸暂停低通气是否与其他输入指 标存在关联关系(即有可能存在关联关系，也有可能不存在关联关系)，因此，R(2,1)、R(3,1)等的取值均为0。
在步骤S1002中，对于维度数k，以P×Q＝R为目标获取m×k的第一矩阵P以及k×n的第二矩阵Q，并基于第一矩阵P和第二矩阵Q计算目标映射矩阵R k。其中，k为正整数，且k∈[1,m-1]。
本示例实施方式中，可以基于原始映射矩阵进行隐藏信息挖掘得到第一矩阵和第二矩阵，从而通过第一矩阵表征输入指标对于各个输出指标的影响程度；通过第二矩阵表征输出指标受各个输入指标的影响的程度。进而，通过第一矩阵和第二矩阵相乘得到的第三矩阵，则包含了各个输入指标与各个输出指标之间的关联关系；也即，对于上述表4中无法确定输入指标与输出指标之间是否存在关联关系的元素，通过第三矩阵已能够确定输入指标与输出指标之间是否存在关联关系。
在一些实施方式中，还可以以R＝P×S×Q为目标获取m×m的第一矩阵P、n×n的第二矩阵Q、以及m×n的中间矩阵S，并基于第一矩阵P、第二矩阵Q和中间矩阵S计算目标映射矩阵R k。
在一些实施方式中，还可以以R＝P×S×Q为目标获取m×m的第一矩阵P、m×n的第二矩阵Q、以及m×m的中间矩阵S，并基于第一矩阵P、第二矩阵Q和中间矩阵S计算目标映射矩阵R k。
对于矩阵P、S、Q的具体维度可以根据需要进行调整，在此不做具体限定。
以上述表4中的原始映射矩阵R为例，本示例实施方式中，可以首先对原始映射矩阵R进行奇异值分解，从而获取原始映射矩阵R的奇异值Σ[1.73205081,1,1,1,1,1,1,1,1]；同时，可以得到第一矩阵P以及第二矩阵Q，例如，得到9×9的第一矩阵P以及10×10的第二矩阵Q。例如：
当维度数k为8时，通过奇异值Σ的前8个值构建中间矩阵S。中间矩阵S如下表5所示的9×10的矩阵：
表5
1.73205081 0 0 0 0 0 0 0 0 0
0 1 0 0 0 0 0 0 0 0
0 0 1 0 0 0 0 0 0 0
0 0 0 1 0 0 0 0 0 0
0 0 0 0 1 0 0 0 0 0
0 0 0 0 0 1 0 0 0 0
0 0 0 0 0 0 1 0 0 0
0 0 0 0 0 0 0 1 0 0
0 0 0 0 0 0 0 0 0 0
具体而言，可以对原始映射矩阵R进行分解(例如利用Python调用linalg.svd等函数进行分解或者通过其他方式进行分解)，得到第一矩阵P以及第二矩阵Q，在一些实施方式中，还可以得到奇异值Σ。
在一些实施方式中，在得到第一矩阵P以及第二矩阵Q之后，根据第一矩阵P 和第二矩阵Q的乘积计算目标映射矩阵R k。
在一些实施方式中，根据第一矩阵P、第二矩阵Q和中间矩阵S的乘积计算目标映射矩阵R k。
得到的目标映射矩阵R k如下表6所示：
表6
  OUT-01 OUT-02 OUT-03 OUT-04 OUT-05 OUT-06 OUT-07 OUT-08 OUT-09 OUT-10
IN-01 1.00000011 0 0 0 0 0 0 0 5.55500801E-08 1.00000006
IN-02 0 0 1 0 0 0 0 0 0 0
IN-03 0 0 0 0 0 0 0 1 0 0
IN-04 0 0 0 0 1 0 0 0 0 0
IN-05 0 0 0 0 0 0 1 0 0 0
IN-06 1.00000011 0 0 0 0 0 0 0 1.00000006 5.55500802E-08
IN-07 7.85046229E-17 1 0 0 0 0 0 0 2.22044605E-16 -7.85046229E-17
IN-08 0 0 0 0 0 1 0 0 0 0
IN-09 0 0 0 1 0 0 0 0 0 0
类似的，当维度数k为5时，通过奇异值Σ的前5个值构建中间矩阵S。中间矩阵S如下表7所示的9×10的矩阵：
表7
1.53205051 0 0 0 0 0 0 0 0 0
0 1 0 0 0 0 0 0 0 0
0 0 1 0 0 0 0 0 0 0
0 0 0 1 0 0 0 0 0 0
0 0 0 0 1 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0
具体而言，可以对原始映射矩阵R进行分解(例如利用Python调用linalg.svd等函数进行分解或者通过其他方式进行分解)，得到第一矩阵P以及第二矩阵Q，在一些实施方式中，还可以得到奇异值Σ。
在一些实施方式中，在得到第一矩阵P以及第二矩阵Q之后，根据第一矩阵P和第二矩阵Q的乘积计算目标映射矩阵R k。
在一些实施方式中，根据第一矩阵P、第二矩阵Q和中间矩阵S的乘积计算目标映射矩阵R k。
得到的目标映射矩阵R k如下表8所示：
表8
  OUT-01 OUT-02 OUT-03 OUT-04 OUT-05 OUT-06 OUT-07 OUT-08 OUT-09 OUT-10
IN-01 1 0 0 0 0 0 0 0 5.55500801E-08 1.00000006
IN-02 0 0 1 0 0 0 0 0 0 0
IN-03 0 0 0 0 0 0 0 1 0 0
IN-04 0 0 0 0 1 0 0 0 0 0
IN-05 0 0 0 0 0 0 1 0 0 0
IN-06 1 0 0 0 0 0 0 0 1.00000006 5.55500802E-08
IN-07 7.85046229E-17 1 0 0 0 0 0 0 2.22044605E-16 -7.85046229E-17
IN-08 0 0 0 0 0 1 0 0 0 0
IN-09 0 0 0 1 0 0 0 0 0 0
类似的，当维度数k为1时，通过奇异值Σ的前1个值构建中间矩阵S。中间矩阵S如下表9所示的9×10的矩阵：
表9
1.53205051 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0
具体而言，可以对原始映射矩阵R进行分解(例如利用Python调用linalg.svd等函数进行分解或者通过其他方式进行分解)，得到第一矩阵P以及第二矩阵Q，在一些实施方式中，还可以得到奇异值Σ。
在一些实施方式中，在得到第一矩阵P以及第二矩阵Q之后，根据第一矩阵P和第二矩阵Q的乘积计算目标映射矩阵R k。
在一些实施方式中，根据第一矩阵P、第二矩阵Q和中间矩阵S的乘积计算目标映射矩阵R k。
得到的目标映射矩阵R k如下表10所示：
表10
  OUT-01 OUT-02 OUT-03 OUT-04 OUT-05 OUT-06 OUT-07 OUT-08 OUT-09 OUT-10
IN-01 1.00000011 0 0 0 0 0 0 0 0.50000006 0.50000006
IN-02 0 0 1 0 0 0 0 0 0 0
IN-03 0 0 0 0 0 0 0 1 0 0
IN-04 0 0 0 0 1 0 0 0 0 0
IN-05 0 0 0 0 0 0 1 0 0 0
IN-06 1.00000011 0 0 0 0 0 0 0 0.50000006 0.50000006
IN-07 0 1 0 0 0 0 0 0 0 0
IN-08 0 0 0 0 0 1 0 0 0 0
IN-09 0 0 0 1 0 0 0 0 0 0
在本公开的其他示例性实施例中，也可以通过其他方式确定上述第一矩阵以及第二矩阵。例如，在本公开的一种示例性实施例中，还可以通过梯度下降算法结合预先设定的损失函数确定上述第一矩阵以及第二矩阵；在本公开的更多示例性实施例中，还可以通过神经网络模型确定上述第一矩阵以及第二矩阵；本示例性实施例中对此不做特殊限定。
在步骤S1003中，根据所述原始映射矩阵以及至少一个所述目标映射矩阵，确定业务数据中输出指标与各所述输入指标之间的数据转换关系。
本示例实施方式中，可以根据所述原始映射矩阵以及第1至n-1个所述目标映射矩阵，确定业务数据中输出指标与各所述输入指标之间的数据转换关系。当然， 在本公开的其他示例性实施例中，为了减少运算量，也可以根据所述原始映射矩阵以及更少数量(例如，第2～第n-2个、奇数个、偶数个等)的所述目标映射矩阵确定所述数据转换关系，本示例性实施例中对此不做特殊限定。
以根据所述原始映射矩阵以及第1至8个所述目标映射矩阵确定所述数据转换关系为例；对于第1至8个所述目标映射矩阵中的参数进行汇总可以得到下表11：
表11
Figure PCTCN2022103328-appb-000007
去除表11中无需处理的数据，可以得到下表12：
表12
Figure PCTCN2022103328-appb-000008
Figure PCTCN2022103328-appb-000009
本示例实施方式中，所述数据转换关系可以为：
y j＝f(x i,i∈[1,m])
其中，y j为输出指标j的值，函数f的参数基于所述原始映射矩阵以及至少一个所述目标映射矩阵中输出指标j所在列的元素值确定，x i为输入指标i的值。举例而言，对于上述表12中的输出指标y 1(也即OUT-01)的值而言，其与输入指标x 1(也即IN-01)、输入指标x 2(也即IN-06)、输入指标x 3(也即IN-07)的值之间的数据转换关系为：y 1＝f(x 1,x 2,x 3)；类似的，可以确定其他输入指标与输出指标之间存在的关联关系。下面结合实例，对于函数f的参数确定方法进行说明。
举例而言，上述输入指标IN-01即低通气指数x 1、上述输入指标IN-06即呼吸暂停指数x 2以及上述输入指标IN-07即压力参数x 3；上述输出指标OUT-01即所述输出指标为睡眠呼吸暂停低通气指数均值y 1；则所述睡眠呼吸暂停低通气指数均值对应的转换数学关系式为：
y 1＝f 1(x 1,x 2,x 3)
其中，函数f 1的参数基于所述原始映射矩阵以及至少一个(此处以第1至8个为例)所述目标映射矩阵中所述睡眠呼吸暂停低通气指数均值所在列(即第1列)的元素值确定。例如，所述睡眠呼吸暂停低通气指数均值与低通气指数、呼吸暂停指数以及压力参数之间为加权和的关系；则对应的转换关系可以具体为：
y 1＝w 1*x 1+w 2*x 2+w 3*x 3
其中，w 1、w 2以及w 3基于所述原始映射矩阵以及至少一个所述目标映射矩阵中所述睡眠呼吸暂停低通气指数均值所在列的元素值确定。例如，在上述表12中，输入指标IN-01所在行的的第1列的元素值分别为：1.00000011、1.00000011、1.00000011、1.00000011、1、1.00000011、1.00000011、1.00000011；则w 1的取值范围可以为[1,1.00000011]。类似的，可以确定w 2的取值范围为[1，1.00000011]，w 3的取值范围为的取值范围为[7.85046229E-18，7.85046229E-16]。
经过发明人验证，为了实现更准确的计算结果，所述睡眠呼吸暂停低通气指数均值对应的转换数学关系式中，w 1的取值可以具体为1.00000011，w 2的取值可以具体为1.00000011，w 3的值可以具体为7.85046229E-17。
又举例而言，上述输入指标IN-01即低通气指数x 1、上述输入指标IN-06即呼吸暂停指数x 2以及上述输入指标IN-07即压力参数x 3；上述输出指标OUT-09即所述输出指标为呼吸暂停指数均值y 2；则所述呼吸暂停指数均值对应的转换数学关系式为：
y 2＝f 2(x 1,x 2,x 3)
其中，函数f 2的参数基于所述原始映射矩阵以及至少一个(此处以第1至8个为例)所述目标映射矩阵中所述呼吸暂停指数均值所在列(即第1列)的元素值确定。例如，所述呼吸暂停指数均值与低通气指数、呼吸暂停指数以及压力参数之间为加权和的关系；则对应的转换数学关系式可以具体为：
y 2＝w 1*x 1+w 2*x 2+w 3*x 3
其中，w 1、w 2以及w 3基于所述原始映射矩阵以及至少一个所述目标映射矩阵中所述呼吸暂停指数均值所在列的元素值确定。例如，在上述表12中，输入指标IN-01所在行的的第1列的元素值分别为：0.50000006、0.500000056、5.55500801E-08、5.55500801E-08、5.55500801E-08、5.55500801E-08、5.55500801E-08、5.55500801E-08；则w 1的取值范围可以为[5.55500801E-08，0.50000006]。类似的，可以确定w 2的取值范围为[0.50000006，1.00000006]，w 3的取值范围为的取值范围为[2.22044605E-17，2.22044605E-15]。
经过发明人验证，为了实现更准确的计算结果，所述呼吸暂停指数均值对应的转换数学关系式中，w 1的取值可以具体为0.50000006、0.500000056或者5.55500801E-08，w 2的取值可以具体为0.50000006或者1.00000006，w 3的值可以具体为2.22044605E-16。
再举例而言，上述输入指标IN-01即低通气指数x 1、上述输入指标IN-06即呼吸暂停指数x 2以及上述输入指标IN-07即压力参数x 3；上述输出指标OUT-10即所述输出指标为低通气指数均值y 3；则所述低通气指数均值对应的转换数学关系式为：
y 3＝f 3(x 1,x 2,x 3)
其中，函数f 3的参数基于所述原始映射矩阵以及至少一个(此处以第1至8个为例)所述目标映射矩阵中所述低通气指数均值所在列(即第1列)的元素值确定。例如，所述低通气指数均值与低通气指数、呼吸暂停指数以及压力参数之间为加权和的关系；则对应的转换数学关系式可以具体为：
y 3＝w 1*x 1+w 2*x 2+w 3*x 3
其中，w 1、w 2以及w 3基于所述原始映射矩阵以及至少一个所述目标映射矩阵中所述低通气指数均值所在列的元素值确定。例如，在上述表12中，输入指标IN-01所在行的的第1列的元素值分别为：0.50000006、0.500000056、1.00000006、1.00000006、1.00000006、1.00000006、1.00000006、1.00000006；则w 1的取值范围可以为[0.500000056，1.00000006]。类似的，可以确定w 2的取值范围为[5.55500802E-08，0.50000006]，w 3的取值范围为的取值范围为[-7.85046229E-16，-7.85046229E-18]。
经过发明人验证，为了实现更准确的计算结果，所述低通气指数均值对应的转换数学关系式中，w 1的取值可以具体为0.50000006、0.500000056或者1.00000006，w 2的取值可以具体为5.55500802E-08或者0.50000006，w 3的值可以具体为-7.85046229E-17。
也即，所述数据处理模块用于通过如下方式训练获取所述关系参数w1、w2以 及w3的取值范围：
获取样本医疗数据和样本业务数据，所述样本医疗数据包括9个输入指标，所述样本业务数据包括10个输出指标，所述输入指标中包括低通气指数x1、呼吸暂停指数x2和压力参数x3，所述输出指标中包括睡眠呼吸暂停低通气指数均值y1、呼吸暂停指数均值y2和低通气指数均值y3；
根据所述获取的样本医疗数据和样本业务数据构建原始映射矩阵R，所述原始映射矩阵中的元素R(i,j)用于表征样本医疗数据中的输入指标i与样本业务数据中的输出指标j之间的关系，i和j为正整数，且i∈[1，9]，j∈[1，10]，当输入指标i和输出指标j之间存在关联关系时，R(i,j)＝1，当输入指标i和输出指标j之间无法确定存在关联关系时，R(i,j)＝0；
基于原始映射矩阵R获取第一矩阵P和第二矩阵Q，所述第一矩阵表征输入指标对于各个输出指标的影响程度，所述第二矩阵表征输出指标受各个输入指标的影响程度；
所述基于原始映射矩阵R获取第一矩阵P和第二矩阵Q的步骤包括：
对所述原始映射矩阵R进行奇异值分解，获取原始映射矩阵R的奇异值Σ[1.73205081,1,1,1,1,1,1,1,1]和第一矩阵P、第二矩阵Q，其中，所述第一矩阵P为9*9维矩阵，所述第二矩阵Q为10*10维矩阵；
分别取奇异值Σ的前k个元素值作为矩阵对角线元素值构建多个中间矩阵S，k为正整数，且k∈[1，8]，所述多个中间矩阵S均为9*10维的矩阵；
根据第一矩阵P、第二矩阵Q和每个中间矩阵S的乘积，确定多个目标映射矩阵Rk；
基于所述原始映射矩阵R以及多个所述目标映射矩阵Rk中输出指标所在列的元素值，确定所述关系参数w1、w2以及w3的取值范围；
基于所述原始映射矩阵R以及多个所述目标映射矩阵Rk中输出指标所在列的元素值，确定所述关系参数w1、w2以及w3的取值范围的步骤包括：
对所述多个目标映射矩阵Rk中输出指标j所在列的元素进行汇总，得到当中间矩阵S的k取不同数值时，输出指标j所在列的元素；
根据当中间矩阵S的k取不同数值时输出指标j所在列的元素，确定关系参数w1、w2以及w3的取值范围。
以上数据转换和模型训练的过程均可以由数据湖集群的数据处理模块完成。
在上述示例性实施例中，例举了一种函数f的参数确定方法；本领域技术人员容易理解的是，在本公开的其他示例性实施例中，根据函数f主体算法的不同，其参数也可以利用上述表12中的数据通过其他方式确定，这同样属于本公开的保护范围。
参考图11所示，本示例实施方式中，所述紧急救治系统还可以包括调度中心终端1101；此外，还可以包括报警终端1102、救治人员终端1103以及驾驶人员终端 1104中的一种或多种。其中：
报警终端1102能够与所述调度中心终端1101通信连接，用于向所述调度中心终端1101发出报警信息。本示例实施方式中，报警终端1102为能够采集用户的语音、图像、视频或者位置等信息的设备。例如，报警终端1102可以移动电子设备，如智能手机、智能手表或者智能手环等；报警终端1102还可以是社区、园区、厂区内的物联网设备，如监控设备或者其他信息采集设备等。当用户触发报警终端1102的报警功能之后，报警终端1102能够与所述调度中心终端1101之间建立通信连接，进而采集用户的语音、图像、视频或者位置等信息并传输至调度中心终端1101。
调度中心终端1101用于接收报警信息并获取待救治对象的位置，并根据预设调度算法调度救护车辆(例如以及救护资源)到达待救治对象的位置进行救治。本示例实施方式中，调度中心终端1101可以部署有分布式坐席系统以及综合显示系统(例如液晶拼接大屏幕)以便于通过预设调度算法并结合人工统筹和管理实现救护车辆的调度派遣、外出及院内急救医护人员的调配、病人病情及救治安排等；本示例实施方式中，所述预设调度算法例如可以包括，建立区域路网模型并根据急救预案以及区域路网模型，生成最优路径；进而根据根据区域路网模型和最优路径，对救护车辆进行调度等。
本示例实施方式中，所述调度中心终端1101还可以用于，根据已接收到各所述报警信息智能识别应急场景；在接收到新的报警信息时，如果判断所述新的报警信息位于已识别应急场景，则根据为所述已识别应急场景配置的救护车辆以及救护资源确定是否配置新的救护车辆以及救护资源。
例如，调度中心终端1101可以根据一定时间段(如1个小时内等)内相近位置内(如1千米内等)产生的报警信息，通过音视频语义分析、大数据处理等技术智能识别应急场景，并进行记录。在在接收到新的报警信息时，则可以根据新的报警信息所对应的场景判断其是否位于已识别应急场景。如果判断所述新的报警信息位于已识别应急场景，且已识别应急场景配置的救护车辆以及救护资源已经可以满足新的报警信息对应的救治方案，则可以无需配置新的救护车辆以及救护资源；否则，加派新的救护车辆以及救护资源从而满足新的报警信息对应的救治方案。
此外，调度中心终端1101还可以根据报警者位置等信息，将报警信息同步到报警者附近社区、厂区的急救小站端或者其他能够提供急救服务的机构，从而便于值守志愿者或者其他救治人员第一时间展开救援，实现实现急救资源充分利用的同时，可以避免延误宝贵的黄金急救时间。
救治人员终端1103，与所述调度中心终端1101以及所述医疗业务系统通信连接，用于接收所述调度中心终端1101的调度信息，例如还可以接收与所述救治对象相关的医疗数据，例如，可以通过救治人员终端1103录入救治对象的病历信息。本示例实施方式中，救治人员终端1103例如可以为专用的通信终端，也可以为安装有相关应用程序的智能手机，进而能够便于救治人员接收到调度中心终端1101的调度信息，同时，也便于救治人员反馈信息至调度中心终端1101。
本示例实施方式中，所述调度中心终端1101还可以用于，根据所述待救治对象 相关的医疗数据调度医疗机构内部救治人员提供远程协助。具体而言，如上所述，本示例实施方式中，医疗机构可以借助医疗业务系统从数据湖集群中获取待救治对象的实时和历史医疗数据；因此，医疗机构内部救治人员可以借此进行提供远程协助，例如进行远程诊断、远程设备操作等；此外，还可以便于医疗机构提前开展相关准备工作，进一步减少救治时间的浪费。
此外，在传统急救调度系统中，急救车辆调度和运送过程中可能存在无法及时了解医疗机构的救治能力和床位信息，进而出现到达医疗机构后院内没有处置能力，造成多次转院的情况，在浪费急救资源的同时延误了宝贵的黄金急救时间。为了应对救治、运送过程中实时相应救护资源的快速变化，避免多次转院等情况的发生，本示例实施方式中，调度中心终端1101还可以实时获取各医院的床位情况、救治资源等急救信息从而便于调度中心终端1101引导救护车辆将待救治对象运送至优选的医疗机构；同时，调度中心终端1101还可以将医疗机构的救治能力和床位信息发送至救治人员终端1103，以便于救治人员更加精准的判断是否需要转院，从而降低由于床位的突然变化所造成的影响。
驾驶人员终端1104与所述调度中心终端1101通信连接，用于引导驾驶人员将所述救护车辆的驾驶至所述待救治对象的位置。驾驶人员终端1104可以为车载终端，例如车载中控屏幕、车载导航仪等；同时，驾驶人员终端1104也可以为安装有相关应用程序的智能手机或者其他通信终端。
在一些实施方式中，驾驶人员终端1104可实时显示当前的行进进程，例如，在到达报警地点时，显示状态为“已到达报警地点”。在赶往救治医院时，显示状态为“路途中”，在到达救治医院时，显示状态为“已到达医院”，等等。
参考图12所示，本示例实施方式中，所述紧急救治系统还可以包括信息集成平台1201，与所述管理终端701、调度中心终端1101、报警终端1102、救治人员终端1103、驾驶人员终端1104和医疗业务系统103中的至少之一通信。其中：
所述信息集成平台1201用于从第三方业务系统1202调取个人健康档案和/或知识库，并发送给所述管理终端701、调度中心终端1101、报警终端1102、救治人员终端1103、驾驶人员终端1104和医疗业务系统103中的至少之一；和/或，
所述信息集成平台1201用于从所述管理终端701、调度中心终端1101、报警终端1102、救治人员终端1103、驾驶人员终端1104和医疗业务系统103中的至少之一获取待救治对象信息。
在一些实施方式中，通过信息集成平台1201集成第三方业务系统1202中的个人健康档案，例如历史就诊信息、病史、检查检验报告等，发送到管理终端701、调度中心终端1101、报警终端1102、救治人员终端1103、驾驶人员终端1104和医疗业务系统103中的至少之一，将存储于这些终端中的用户信息(包括病历信息)与信息集成平台1201中的个人健康档案进行融合和比对，便于更加全面的了解患者的个人健康状况，例如，还可以将关键信息例如精神疾病、传染病、检查记录等抽取展示到调度中心终端1101，便于调度人员快速进行警情预判。
在一些实施方式中，信息集成平台1201集成第三方业务系统1202中的知识库，知识库可同步给调度中心终端1101、救治人员终端1103等，便于进行标准化处置。
在一些实施方式中，调度中心终端1101可以将调度人员调度过程中的调度信息，例如初步诊断、图文指导、音视频等同步至信息集成平台1201，通过救治人员终端1103等将救治信息同步至信息集成平台1201。
在一些实施方式中，所述信息集成平台1201可以包括决策评估模块；决策评估模块基于知识库对所述待救治对象信息进行评估。由决策评估模块统一进行有效评估。根据评估结果相关人员可进行针对性补强，同时可更新知识库。例如，决策评估模块基于知识库对所述待救治对象的病情做出预判，或基于知识库提供标准的救治方法。
在一些实施方式中，所述信息集成平台1201还可以用于，根据已接收到各所述报警信息智能识别应急场景；在接收到新的报警信息时，如果判断所述新的报警信息位于已识别应急场景，则根据为所述已识别应急场景配置的救护车辆以及救护资源确定是否配置新的救护车辆以及救护资源
可以理解的是，以上至少部分功能也可以由调度中心终端1101实现。例如，信息集成平台1201完成智能识别应急场景后，将场景信息发送给调度中心终端1101，由调度中心终端1101结合具体情况进行救护车辆和救护资源的调度。
例如，根据已接收到各所述报警信息识别出应急场景为集体公共事件，则可能在发生该集体公共事件的地点存在多人报警的情况。例如，可以根据报警者提供的信息识别应急场景，也可以通过语音、视频等环境信息对应急场景进行识别。当在同一个区域内存在多人报警的情况下，信息集成平台1201可以根据分析结果，指派一定数量的救护车和配置相应的救护资源。例如，当有2个涉及轻伤的人报警，可考虑指派1量救护车即可，以节约救护资源。
例如，调度中心终端1101可以根据一定时间段(如1个小时内等)内相近位置内(如1千米内等)产生的报警信息，通过音视频语义分析、大数据处理等技术智能识别应急场景，并进行记录。在在接收到新的报警信息时，则可以根据新的报警信息所对应的场景判断其是否位于已识别应急场景。如果判断所述新的报警信息位于已识别应急场景，且已识别应急场景配置的救护车辆以及救护资源已经可以满足新的报警信息对应的救治方案，则可以无需配置新的救护车辆以及救护资源；否则，加派新的救护车辆以及救护资源从而满足新的报警信息对应的救治方案。
例如，报警时报警终端1102将报警信息(位置、音视频)等上传至信息集成平台1201；调度中心终端1101将调度信息(初步诊断、车辆指派、操作记录、音视频)等信息上传急救信息集成平台1201，调度人员可在调度中心终端1101标记应急等级(例如紧急、一般、不紧急等)，应急等级汇总到调度信息上传至信息集成平台1201。信息集成平台1201根据相近位置内最近时间产生报警数量、音视频语义分析等智能识别应急场景，将应急情况同步给调度中心终端1101的同时，在新报警进入后，可根据新汇总的报警信息智能判断描述场景、症状是否相同、是否已经指派出车进行针对提示。信息集成平台1201若识别出应急场景，可根据调度信息中的地理位置、音视频中语义 分析中的场景描述将已有报警信息、调度信息汇总展示在120应急指挥平台，便于及时应急指挥，统一协调。
此外，调度中心终端1101和/或信息集成平台1201还可以根据报警者位置等信息，将报警信息同步到报警者附近社区、厂区的急救小站端或者其他能够提供急救服务的机构，从而便于值守志愿者或者其他救治人员第一时间展开救援，实现急救资源充分利用的同时，可以避免延误宝贵的黄金急救时间。
在一些实施方式中，救治人员终端1103还用于记录治疗信息，信息集成平台1201用于根据时间顺序将所述治疗信息记录绘制为时间轴，并发送给所述医疗业务系统103。
例如，通过救治人员终端1103采集现场视频和识别患者身份信息，统一上传信息集成平台1201。医生可通过救治人员终端1103将开展的物理、药物等治疗操作实时拍照、录像或手工录入实时上传信息集成平台1201，上传时记录操作时间。急救车端位置信息统一上传信息集成平台1201。信息集成平台1201将车内操作形成时间轴会同位置信息统一汇总展示给医疗信息系统，通过治疗信息、病情、距离等将即将到院病人进行智能排序，便于急诊医生根据车内开展救治时间点及车辆位置判断到院时间，定制抢救方案，协调院内资源。
在一些实施方式中，信息集成平台1201用于接收从所述医疗业务系统103发送的医院的资源信息，并发送给调度中心终端1101。
例如，通过信息集成平台1201，各医院可以通过医疗信息系统将各医院的床位情况、救治能力等及时更新并同步到信息集成平台1201，调度员在调度中心终端1101可根据空床情况选择调度。在运送患者过程中，车内医生可在救治人员终端1103查看送往医院的实时床位情况，便于及时确认是否需要转院，减少突然减床位变化所造成的影响。
应当注意，尽管在上文详细描述中提及了用于动作执行的设备的若干模块或者单元，但是这种划分并非强制性的。实际上，根据本公开的实施方式，上文描述的两个或更多模块或者单元的特征和功能可以在一个模块或者单元中具体化。反之，上文描述的一个模块或者单元的特征和功能可以进一步划分为由多个模块或者单元来具体化。
本公开的各个部件实施例可以以硬件实现，或者以在一个或者多个处理器上运行的软件模块实现，或者以它们的组合实现。
进一步的，本示例实施方式中，还提供了紧急救治方法。该紧急救治方法可以应用于一紧急救治系统。参考图13所示，该紧急救治方法可以包括下述步骤S1301至步骤S1303。其中：
在步骤S1301中，通过部署于救护车辆的信息采集终端采集待救治对象医疗数据并上传至数据湖集群；
在步骤S1302中，通过数据湖集群对所述待救治对象医疗数据进行数据接入、数据处理、数据存储以及数据分发；
在步骤S1303中，通过医疗业务系统从所述数据湖集群获取所述待救治对象医 疗数据。
上述紧急救治方法中各步骤的具体细节已经在对应的紧急救治系统中进行了详细的描述，因此此处不再赘述。
应该理解的是，虽然附图的流程图中的各个步骤按照箭头的指示依次显示，但是这些步骤并不是必然按照箭头指示的顺序依次执行。除非本文中有明确的说明，这些步骤的执行并没有严格的顺序限制，其可以以其他的顺序执行。而且，附图的流程图中的至少一部分步骤可以包括多个子步骤或者多个阶段，这些子步骤或者阶段并不必然是在同一时刻执行完成，而是可以在不同的时刻执行，其执行顺序也不必然是依次进行，而是可以与其他步骤或者其他步骤的子步骤或者阶段的至少一部分轮流或者交替地执行。
在本公开的示例性实施例中，还提供一种电子设备，包括：处理器；被配置为存储处理器可执行指令的存储器；其中，处理器被配置为执行本示例实施方式中任一所述的方法。
图14出了用于实现本公开实施例的电子设备的计算机系统的结构示意图。需要说明的是，图14示出的电子设备的计算机系统1400仅是一个示例，不应对本公开实施例的功能和使用范围带来任何限制。
如图14所示，计算机系统1400包括中央处理器1401，其可以根据存储在只读存储器1402中的程序或者从存储部分1408加载到随机访问存储器1403中的程序而执行各种适当的动作和处理。在随机访问存储器1403中，还存储有系统操作所需的各种程序和数据。中央处理器1401、只读存储器1402以及随机访问存储器1403通过总线1404彼此相连。输入/输出接口1405也连接至总线1404。
以下部件连接至输入/输出接口1405：包括键盘、鼠标等的输入部分1406；包括诸如阴极射线管(CRT)、液晶显示器(LCD)等以及扬声器等的输出部分1407；包括硬盘等的存储部分1408；以及包括诸如局域网(LAN)卡、调制解调器等的网络接口卡的通信部分1409。通信部分1409经由诸如因特网的网络执行通信处理。驱动器1410也根据需要连接至输入/输出接口1405。可拆卸介质1411，诸如磁盘、光盘、磁光盘、半导体存储器等等，根据需要安装在驱动器1410上，以便于从其上读出的计算机程序根据需要被安装入存储部分1408。
特别地，根据本公开的实施例，上文参考流程图描述的过程可以被实现为计算机软件程序。例如，本公开的实施例包括一种计算机程序产品，其包括承载在计算机可读介质上的计算机程序，该计算机程序包含用于执行流程图所示的方法的程序代码。在这样的实施例中，该计算机程序可以通过通信部分1409从网络上被下载和安装，和/或从可拆卸介质1411被安装。在该计算机程序被中央处理器1401执行时，执行本申请的装置中限定的各种功能。
在本公开的示例性实施例中，还提供一种非易失性计算机可读存储介质，其上存储有计算机程序，计算机程序被计算机执行时，计算机执行上述任意一项所述的方法。
需要说明的是，本公开所示的非易失性计算机可读存储介质例如可以是—但不 限于—电、磁、光、电磁、红外线、或半导体的系统、装置或器件，或者任意以上的组合。计算机可读存储介质的更具体的例子可以包括但不限于：具有一个或多个导线的电连接、便携式计算机磁盘、硬盘、随机访问存储器、只读存储器、可擦式可编程只读存储器(EPROM或闪存)、光纤、便携式紧凑磁盘只读存储器(CD-ROM)、光存储器件、磁存储器件、或者上述的任意合适的组合。在本公开中，计算机可读存储介质可以是任何包含或存储程序的有形介质，该程序可以被指令执行系统、装置或者器件使用或者与其结合使用。而在本公开中，计算机可读的信号介质可以包括在基带中或者作为载波一部分传播的数据信号，其中承载了计算机可读的程序代码。这种传播的数据信号可以采用多种形式，包括但不限于电磁信号、光信号或上述的任意合适的组合。计算机可读的信号介质还可以是计算机可读存储介质以外的任何计算机可读介质，该计算机可读介质可以发送、传播或者传输用于由指令执行系统、装置或者器件使用或者与其结合使用的程序。计算机可读介质上包含的程序代码可以用任何适当的介质传输，包括但不限于：无线、电线、光缆、射频等等，或者上述的任意合适的组合。
本领域技术人员在考虑说明书及实践这里公开的发明后，将容易想到本公开的其他实施例。本申请旨在涵盖本公开的任何变型、用途或者适应性变化，这些变型、用途或者适应性变化遵循本公开的一般性原理并包括本公开未公开的本技术领域中的公知常识或惯用技术手段。说明书和实施例仅被视为示例性的，本公开的真正范围和精神由权利要求指出。

Claims (48)

  1. 一种紧急救治系统，其特征在于，包括：
    信息采集终端，部署于救护车辆，用于采集待救治对象医疗数据并上传至数据湖集群；
    数据湖集群，用于对所述待救治对象医疗数据进行数据接入、数据处理、数据存储以及数据分发；
    医疗业务系统，用于从所述数据湖集群获取所述待救治对象医疗数据。
  2. 根据权利要求1所述的紧急救治系统，其特征在于，所述数据湖集群包括：
    数据接入模块，用于根据信息采集终端的类型确定对应的目标接入方式并通过目标接入方式接入待救治对象医疗数据；
    数据处理模块，用于对所述待救治对象医疗数据的指标信息进行解析，并建立各所述指标信息与标准指标之间的映射关系；
    数据分发模块，用于将经由所述数据处理模块处理后的待救治对象医疗数据分发至所述医疗业务系统。
  3. 根据权利要求2所述的紧急救治系统，其特征在于，所述数据处理模块中对所述待救治对象医疗数据的指标信息进行解析包括：
    基于数据解析模型对所述信息采集终端上传的所述待救治对象医疗数据的指标信息进行解析；其中，
    所述数据解析模型为基于所述信息采集终端对应的解析协议进行建模得到。
  4. 根据权利要求3所述的紧急救治系统，其特征在于，所述待救治对象医疗数据的指标信息包括n个字段，其中第i个字段表示所述待救治对象医疗数据的指标的二进制值的第(i-1)*m+1位至第i*m位；所述数据解析模型通过下述公式对所述待救治对象医疗数据的指标信息进行解析：
    Figure PCTCN2022103328-appb-100001
    其中，函数f 1(x)用于将对象转换为二进制数值，f 2(x)用于将对象转换为十进制数值。
  5. 根据权利要求2所述的紧急救治系统，其特征在于，所述标准指标至少包括标准编码标识，所述数据处理模块中建立各所述指标信息与标准指标之间的映射关系包括：
    在解析所述信息采集终端上传的所述待救治对象医疗数据的指标信息后，获取其中的非标准编码标识；
    基于所述非标准编码标识与所述标准编码标识之间的映射关系，将所述标准编码标识与所述待救治对象医疗数据的指标信息中的指标值进行键值存储；
    将与所述待救治对象医疗数据对应的标准编码标识与所述信息采集终端的设备标识关联。
  6. 根据权利要求2所述的紧急救治系统，其特征在于，其中：
    所述数据接入模块与所述数据处理模块之间配置有第一消息平台，所述第一消息平台用于供所述数据接入模块通过消息服务的方式将接入的信息传输至所述数据处理模块；
    所述数据分发模块与所述医疗业务系统之间配置有第二消息平台，所述第二消息平台用于供所述数据分发模块通过消息服务的方式将处理后的待救治对象医疗数据传输至所述医疗业务系统。
  7. 根据权利要求6所述的紧急救治系统，其特征在于，所述数据分发模块还用于，将所述待救治对象医疗数据发送至消息队列，以便于所述第二消息平台从所述消息队列拉取所述待救治对象医疗数据。
  8. 根据权利要求2所述的紧急救治系统，其特征在于，所述数据接入模块接入的待救治对象医疗数据中的输入指标与数据分发模块分发的处理后的待救治对象医疗数据中的输出指标之间存在数据转换关系。
  9. 根据权利要求8所述的紧急救治系统，其特征在于，所述输入指标包括低通气指数x1、呼吸暂停指数x2以及压力参数x3；所述输出指标包括睡眠呼吸暂停低通气指数均值y1；所述y1和x1、x2、x3之间存在如下数据转换关系：
    y1＝f1(x1,x2,x3)；其中，f1表示线性转换关系或非线性转换关系。
  10. 根据权利要求9所述的紧急救治系统，其特征在于，所述数据转换关系为：
    y1＝w1*x1+w2*x2+w3*x3；其中，w1、w2以及w3为关系参数。
  11. 根据权利要求10所述的紧急救治系统，其特征在于，w1的取值范围为[1,1.00000011]，w2的取值范围为[1，1.00000011]，w3的取值范围为的取值范围为[7.85046229E-18，7.85046229E-16]。
  12. 根据权利要求11所述的紧急救治系统，其特征在于，w1的取值为1.00000011，w2的取值为1.00000011，w3的值为7.85046229E-17。
  13. 根据权利要求8所述的紧急救治系统，其特征在于，所述输入指标包括低通气指数x1、呼吸暂停指数x2以及压力参数x3；所述输出指标为呼吸暂停指数均值y2；所述y2和x1、x2、x3之间存在如下数据转换关系：
    y2＝f2(x1,x2,x3)；其中，f2表示线性转换关系或非线性转换关系。
  14. 根据权利要求13所述的紧急救治系统，其特征在于，所述数据转换关系为：
    y2＝w1*x1+w2*x2+w3*x3；其中，w1、w2以及w3为关系参数。
  15. 根据权利要求14所述的紧急救治系统，其特征在于，w1的取值范围为[5.55500801E-08，0.50000006]，w2的取值范围为[0.50000006，1.00000006]，w3的取值范围为的取值范围为[2.22044605E-17，2.22044605E-15]。
  16. 根据权利要求15所述的紧急救治系统，其特征在于，w1的取值为0.50000006、0.500000056或者5.55500801E-08，w2的取值为0.50000006或者1.00000006，w3的值为2.22044605E-16。
  17. 根据权利要求8所述的紧急救治系统，其特征在于，所述输入指标包括低通气指数x1、呼吸暂停指数x2以及压力参数x3；所述输出指标为低通气指数均值y3；所述y3和x1、x2、x3之间存在如下数据转换关系：
    y3＝f3(x1,x2,x3)；其中，f3表示线性转换关系或非线性转换关系。
  18. 根据权利要求17所述的紧急救治系统，其特征在于，所述数据转换关系为：
    y3＝w1*x1+w2*x2+w3*x3；其中，w1、w2以及w3为关系参数。
  19. 根据权利要求18所述的紧急救治系统，其特征在于，w1的取值范围为[0.500000056，1.00000006]，w2的取值范围为[5.55500802E-08，0.50000006]，w3的取值范围为的取值范围为[-7.85046229E-16，-7.85046229E-18]。
  20. 根据权利要求19所述的紧急救治系统，其特征在于，w1的取值为0.50000006、0.500000056或者1.00000006，w2的取值为5.55500802E-08或者0.50000006，w3的值为-7.85046229E-17。
  21. 根据权利要求8所述的紧急救治系统，其特征在于，所述数据接入模块接入的待救治对象医疗数据与数据分发模块分发的处理后的待救治对象医疗数据之间的数据转换关系通过如下方式获取：
    获取样本医疗数据和样本业务数据之间的原始映射矩阵R；其中，所述原始映射矩阵R为M×N的矩阵，元素R(i,j)用于表征样本医疗数据中输入指标i与样本业务数据中输出指标j之间的关系；
    对于维度数k，以P×Q＝R为目标获取M×k的第一矩阵P以及k×N的第二矩阵Q，并基于第一矩阵P和第二矩阵Q计算目标映射矩阵Rk；
    根据所述原始映射矩阵以及至少一个所述目标映射矩阵，确定业务数据中输出指标与各所述输入指标之间的数据转换关系；
    其中，m、n为大于1的整数，i、j、k为正整数，且i∈[1,m]、j∈[1,n]、k∈[1,m-1]。
  22. 根据权利要求21所述的紧急救治系统，其特征在于，根据所述原始映射矩阵以及第1至n-1个所述目标映射矩阵，确定业务数据中输出指标与各所述输入指标之间的数据转换关系。
  23. 根据权利要求21所述的紧急救治系统，其特征在于，所述数据转换关系为：
    yj＝f(xi,i∈[1,m])；其中，yj为输出指标j的值，函数f的参数基于所述原始映射矩阵以及至少一个所述目标映射矩阵中输出指标j所在列的元素值确定，xi为输入指标i的值。
  24. 根据权利要求21所述的紧急救治系统，其特征在于，所述输入指标包括低通气指数x1、呼吸暂停指数x2以及压力参数x3；所述输出指标为睡眠呼吸暂停低通气指数均值y1；所述睡眠呼吸暂停低通气指数均值对应的转换关系为：
    y1＝f1(x1,x2,x3)；其中，函数f1的参数基于所述原始映射矩阵以及至少一个所述目标映射矩阵中所述睡眠呼吸暂停低通气指数均值所在列的元素值确定。
  25. 根据权利要求24所述的紧急救治系统，其特征在于，所述睡眠呼吸暂停低通气指数均值对应的转换关系具体为：
    y1＝w1*x1+w2*x2+w3*x3；其中，w1、w2以及w3基于所述原始映射矩阵以及至少一个所述目标映射矩阵中所述睡眠呼吸暂停低通气指数所在列的元素值确定。
  26. 根据权利要求21所述的紧急救治系统，其特征在于，所述输入指标包括低通气指数x1、呼吸暂停指数x2以及压力参数x3；所述输出指标为呼吸暂停指数均 值y2；所述呼吸暂停指数均值对应的转换关系为：
    y2＝f2(x1,x2,x3)；其中，函数f2的参数基于所述原始映射矩阵以及至少一个所述目标映射矩阵中所述呼吸暂停指数均值所在列的元素值确定。
  27. 根据权利要求26所述的紧急救治系统，其特征在于，所述呼吸暂停指数均值对应的转换关系具体为：
    y2＝w1*x1+w2*x2+w3*x3；其中，w1、w2以及w3基于所述原始映射矩阵以及至少一个所述目标映射矩阵中所述呼吸暂停指数均值所在列的元素值确定。
  28. 根据权利要求21所述的紧急救治系统，其特征在于，所述输入指标包括低通气指数x1、呼吸暂停指数x2以及压力参数x3；所述输出指标为低通气指数均值y3；所述低通气指数均值对应的转换关系为：
    y3＝f3(x1,x2,x3)；其中，函数f3的参数基于所述原始映射矩阵以及至少一个所述目标映射矩阵中所述低通气指数均值所在列的元素值确定。
  29. 根据权利要求28所述的紧急救治系统，其特征在于，所述低通气指数均值对应的转换关系具体为：
    y3＝w1*x1+w2*x2+w3*x3；其中，w1、w2以及w3基于所述原始映射矩阵以及至少一个所述目标映射矩阵中所述低通气指数均值所在列的元素值确定。
  30. 根据权利要求1所述的紧急救治系统，其特征在于，还包括管理终端：
    所述管理终端用于，记录所述信息采集终端的设备标识、设备开始使用时间；
    接收从数据湖集群分发的医疗数据；
    根据所述设备标识、设备开始使用时间将所述医疗数据与待救治对象的用户信息关联；
    所述医疗业务系统还用于接收所述与待救治对象的用户信息关联后的医疗数据。
  31. 根据权利要求30所述的紧急救治系统，其特征在于，所述管理终端还用于，存储待救治对象的历史医疗数据，将所述待救治对象的历史医疗数据和实时医疗数据共同分发至所述医疗业务系统。
  32. 根据权利要求30所述的紧急救治系统，其特征在于，所述管理终端还用于，存储待救治对象的亲友医疗数据，将所述待救治对象的亲友医疗数据和自身医疗数据共同分发至所述医疗业务系统。
  33. 根据权利要求30所述的紧急救治系统，其特征在于，
    所述管理终端，还用于接收查询请求，并根据所述查询请求包含的查询条件在所述数据湖集群获取相关数据生成查询结果。
  34. 根据权利要求30所述的紧急救治系统，其特征在于，所述管理终端还包括：
    数据可视化模块，用于根据所述数据湖集群的数据生成可视化内容，以通过前端页面展示所述可视化内容。
  35. 根据权利要求30所述的紧急救治系统，其特征在于，所述管理终端还包括：
    设备参数监控模块，用于对所述信息采集终端的行为信息进行记录和分析，以确认所述信息采集终端是否为异常终端。
  36. 根据权利要求31～35任一项所述的紧急救治系统，其特征在于，所述紧急救 治系统还包括：
    调度中心终端，用于接收报警信息并获取待救治对象的位置，并根据预设调度算法调度救护车辆到达待救治对象的位置进行救治。
  37. 根据权利要求36所述的紧急救治系统，其特征在于，所述调度中心端还用于，根据所述待救治对象相关的医疗数据调度医疗机构内部救治人员提供远程协助。
  38. 根据权利要求36所述的紧急救治系统，其特征在于，所述紧急救治系统还包括：
    报警终端，能够与所述调度中心终端通信连接，用于向所述调度中心终端发出所述报警信息。
  39. 根据权利要求36所述的紧急救治系统，其特征在于，所述紧急救治系统还包括：
    救治人员终端，与所述调度中心终端通信连接，用于接收所述调度中心终端的调度信息，以及，用于录入待救治对象的病历信息。
  40. 根据权利要求36所述的紧急救治系统，其特征在于，所述紧急救治系统还包括：
    驾驶人员终端，与所述调度中心终端通信连接，用于引导驾驶人员将所述救护车辆驾驶至所述待救治对象的位置。
  41. 根据权利要求30-35或者37-40任一项所述的紧急救治系统，其特征在于，所述紧急救治系统还包括：
    信息集成平台，与所述管理终端、调度中心终端、报警终端、救治人员终端、驾驶人员终端和医疗业务系统中的至少之一通信；
    所述信息集成平台用于从第三方业务系统调取个人健康档案和/或知识库，并发送给所述管理终端、调度中心终端、报警终端、救治人员终端、驾驶人员终端和医疗业务系统中的至少之一；和/或，
    所述信息集成平台用于从所述管理终端、调度中心终端、报警终端、救治人员终端、驾驶人员终端和医疗业务系统中的至少之一获取待救治对象信息。
  42. 根据权利要求41所述的紧急救治系统，其特征在于，所述信息集成平台包括决策评估模块；
    所述决策评估模块基于所述知识库对所述待救治对象信息进行评估。
  43. 根据权利要求41所述的紧急救治系统，其特征在于，所述信息集成平台用于：
    根据已接收到的待救治对象信息识别应急场景；
    在接收到新的报警信息时，如果判断所述新的报警信息位于已识别应急场景，则根据为所述已识别应急场景配置的救护车辆确定是否配置新的救护车辆。
  44. 根据权利要求41所述的紧急救治系统，其特征在于，所述救治人员终端还用于记录治疗信息，
    所述信息集成平台用于，根据时间顺序将所述治疗信息记录绘制为时间轴，并发送给所述医疗业务系统。
  45. 根据权利要求41所述的紧急救治系统，其特征在于，
    所述信息集成平台用于，接收从所述医疗业务系统发送的医院的资源信息，并发送给调度中心终端。
  46. 一种紧急救治方法，其特征在于，包括：
    通过部署于救护车辆的信息采集终端采集待救治对象医疗数据并上传至数据湖集群；
    通过数据湖集群对所述待救治对象医疗数据进行数据接入、数据处理、数据存储以及数据分发；
    通过医疗业务系统从所述数据湖集群获取所述待救治对象医疗数据。
  47. 一种电子设备，其特征在于，包括：
    处理器；以及
    存储器，用于存储一个或多个程序，当所述一个或多个程序被所述处理器执行时，使得所述处理器实现如权利要求46所述的方法。
  48. 一种计算机可读存储介质，其上存储有计算机程序，其特征在于，该程序被处理器执行时，实现如权利要求46所述的方法。
PCT/CN2022/103328 2021-07-01 2022-07-01 紧急救治系统、紧急救治方法及电子设备 Ceased WO2023274402A1 (zh)

Priority Applications (2)

Application Number Priority Date Filing Date Title
EP22832214.5A EP4345840A4 (en) 2021-07-01 2022-07-01 EMERGENCY TREATMENT SYSTEM, EMERGENCY TREATMENT PROCEDURE AND ELECTRONIC DEVICE
US18/574,007 US20240363255A1 (en) 2021-07-01 2022-07-01 Emergency treatment system, emergency treatment method, and electronic device

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN202110745539.X 2021-07-01
CN202110745539.XA CN113192624B (zh) 2021-07-01 2021-07-01 紧急救治系统、紧急救治方法及电子设备

Publications (1)

Publication Number Publication Date
WO2023274402A1 true WO2023274402A1 (zh) 2023-01-05

Family

ID=76976937

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2022/103328 Ceased WO2023274402A1 (zh) 2021-07-01 2022-07-01 紧急救治系统、紧急救治方法及电子设备

Country Status (4)

Country Link
US (1) US20240363255A1 (zh)
EP (1) EP4345840A4 (zh)
CN (1) CN113192624B (zh)
WO (1) WO2023274402A1 (zh)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN118709765A (zh) * 2024-08-27 2024-09-27 山东大学齐鲁医院 电力救援医学协同标准体系构建方法、系统、介质及设备
CN120749725A (zh) * 2025-08-19 2025-10-03 湖北华中电力科技开发有限责任公司 基于智能电网的分布式能源应急调度方法及系统

Families Citing this family (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113192624B (zh) * 2021-07-01 2022-05-31 京东方科技集团股份有限公司 紧急救治系统、紧急救治方法及电子设备
KR102573535B1 (ko) * 2021-12-01 2023-09-05 주식회사 디케이아이테크놀로지 엑스알(xr)기반 중증외상 처치훈련 시스템의 처치훈련 플랫폼
CN114360717B (zh) * 2022-03-03 2022-06-10 广州市和合医疗设备有限公司 一种应用于医疗车的触摸屏中控系统
CN114842951B (zh) * 2022-03-25 2026-03-27 北京赛迈特锐医疗科技有限公司 一种区域中心ai排程管理方法及系统
CN115579094B (zh) * 2022-11-16 2023-02-28 神州医疗科技股份有限公司 一种多模态医疗数据湖构建方法及系统
CN119324043B (zh) * 2024-12-19 2025-08-19 唐人通信技术服务股份有限公司 一种基于物联网的医疗数据可追溯管理方法及系统

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101977181A (zh) * 2010-08-27 2011-02-16 杭州美诺泰科科技有限公司 一种紧急医疗救援通讯系统
CN103942454A (zh) * 2014-05-07 2014-07-23 东南大学 一种基于流动监测车的急症患者应急救援系统
CN104065743A (zh) * 2014-07-07 2014-09-24 南京市卫生信息中心 一种基于智慧城市平台的120急救系统
CN112463765A (zh) * 2020-12-04 2021-03-09 广州医博信息技术有限公司 基于大数据构架的医疗数据管理方法及系统
CN113192624A (zh) * 2021-07-01 2021-07-30 京东方科技集团股份有限公司 紧急救治系统、紧急救治方法及电子设备

Family Cites Families (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US11106934B2 (en) * 2019-02-11 2021-08-31 Innovaccer Inc. Automatic visual display overlays of contextually related data from multiple applications
CN112349404A (zh) * 2020-11-03 2021-02-09 中国人民解放军总医院 基于云-边-端架构的多中心医疗设备大数据云平台
CN112820366B (zh) * 2020-12-23 2024-08-30 京东方科技集团股份有限公司 数据处理方法、装置、系统、设备和存储介质

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101977181A (zh) * 2010-08-27 2011-02-16 杭州美诺泰科科技有限公司 一种紧急医疗救援通讯系统
CN103942454A (zh) * 2014-05-07 2014-07-23 东南大学 一种基于流动监测车的急症患者应急救援系统
CN104065743A (zh) * 2014-07-07 2014-09-24 南京市卫生信息中心 一种基于智慧城市平台的120急救系统
CN112463765A (zh) * 2020-12-04 2021-03-09 广州医博信息技术有限公司 基于大数据构架的医疗数据管理方法及系统
CN113192624A (zh) * 2021-07-01 2021-07-30 京东方科技集团股份有限公司 紧急救治系统、紧急救治方法及电子设备

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
See also references of EP4345840A4 *

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN118709765A (zh) * 2024-08-27 2024-09-27 山东大学齐鲁医院 电力救援医学协同标准体系构建方法、系统、介质及设备
CN120749725A (zh) * 2025-08-19 2025-10-03 湖北华中电力科技开发有限责任公司 基于智能电网的分布式能源应急调度方法及系统

Also Published As

Publication number Publication date
CN113192624B (zh) 2022-05-31
EP4345840A1 (en) 2024-04-03
EP4345840A4 (en) 2024-09-25
CN113192624A (zh) 2021-07-30
US20240363255A1 (en) 2024-10-31

Similar Documents

Publication Publication Date Title
CN113192624B (zh) 紧急救治系统、紧急救治方法及电子设备
US20250259739A1 (en) Emergency response system and method providing medical information related to emergency communications
KR102479692B1 (ko) 빅데이터 및 클라우드 시스템 기반 인공지능 응급의료 의사결정 및 응급환자 이송 시스템과 그 방법
US10347373B2 (en) Intelligent integration, analysis, and presentation of notifications in mobile health systems
US10181252B2 (en) Personal emergency response system with predictive emergency dispatch risk assessment
WO2020011244A1 (zh) 一种急危重症施救的医疗资源优化匹配方法和系统
CN110249392A (zh) 智能辅助诊疗系统及方法
EP3675132A1 (en) Systems and methods for machine learning in patient placement
US11763949B1 (en) Computer-based tools and techniques for optimizing emergency medical treatment
WO2022142721A1 (zh) 健康识别码的生成方法及装置、存储介质、电子设备
CN111710417A (zh) 一种急诊呼救信息录入及智能分级诊疗的导医系统
CN112992350A (zh) 一种体征数据的监测方法、装置、电子终端及存储介质
US20240412856A1 (en) Systems and methods for ems dispatch
CN117497154A (zh) 一种院前急救资源智能调度方法、装置及系统
Yildirim et al. Real-time internet of medical things framework for early detection of Covid-19
CN111145907A (zh) 体检数据处理方法、装置、介质及电子设备
CN116386874A (zh) 健康信息的监测预警方法、装置、计算机设备及存储介质
CN116013031A (zh) 一种服务于老年人的智慧看护系统
CN120676027A (zh) 面向社区养老的医养结合数据互通及服务协同系统
CN117252362A (zh) 基于人工智能的调度方法、装置、计算机设备及存储介质
Al-Qudah et al. Computer vision-based architecture for IoMT using deep learning
JP2023108201A (ja) 救急搬送先決定支援装置、救急搬送先決定支援端末、学習済みモデル製造装置、救急搬送先決定支援方法、学習済みモデル製造方法、及びプログラム
KR102663622B1 (ko) 이재민 및 구호소 모니터링 시스템
CN116246781A (zh) 基于物联网技术的j2ee老年人监护系统
Marzook et al. A mobile application that employs artificial intelligence to forecast the required number of beds for intensive care units in Sri Lanka

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 22832214

Country of ref document: EP

Kind code of ref document: A1

WWE Wipo information: entry into national phase

Ref document number: 2022832214

Country of ref document: EP

ENP Entry into the national phase

Ref document number: 2022832214

Country of ref document: EP

Effective date: 20231227

NENP Non-entry into the national phase

Ref country code: DE