CN119007723B - Response method and system based on artificial intelligence - Google Patents

Response method and system based on artificial intelligence Download PDF

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CN119007723B
CN119007723B CN202411106818.1A CN202411106818A CN119007723B CN 119007723 B CN119007723 B CN 119007723B CN 202411106818 A CN202411106818 A CN 202411106818A CN 119007723 B CN119007723 B CN 119007723B
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response
neural network
visitor
scene
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CN119007723A (en
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朱湘军
孟凯
汪壮雄
彭永坚
李利苹
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GUANGZHOU VIDEO-STAR ELECTRONICS CO LTD
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GUANGZHOU VIDEO-STAR ELECTRONICS CO LTD
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    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/22—Procedures used during a speech recognition process, e.g. man-machine dialogue
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F16/33—Querying
    • G06F16/332—Query formulation
    • G06F16/3329—Natural language query formulation
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F16/33—Querying
    • G06F16/3331—Query processing
    • G06F16/334—Query execution
    • G06F16/3343—Query execution using phonetics
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/82—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/16—Human faces, e.g. facial parts, sketches or expressions
    • G06V40/172—Classification, e.g. identification
    • G—PHYSICS
    • G07—CHECKING-DEVICES
    • G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
    • G07C9/00—Individual registration on entry or exit
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/08—Speech classification or search
    • G10L15/16—Speech classification or search using artificial neural networks
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/22—Procedures used during a speech recognition process, e.g. man-machine dialogue
    • G10L2015/225—Feedback of the input speech

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Abstract

The invention discloses an artificial intelligence-based response method and system, wherein the method comprises the steps of obtaining voice data of a current user and sensing data of a current environment through access control equipment, determining a visitor scene corresponding to the current user according to the sensing data of the current environment based on a neural network algorithm, screening out target prediction neural networks from a plurality of candidate response prediction neural networks according to the visitor scene and the voice data, inputting the voice data into the target prediction neural networks to generate response data, and displaying the response data to the current user through the access control equipment. Therefore, the invention can realize full-automatic and vivid visitor response, improve the intelligent degree of visitor response service and improve user experience.

Description

Response method and system based on artificial intelligence
Technical Field
The invention relates to the technical field of data processing, in particular to an artificial intelligence-based response method and system.
Background
Along with popularization and development of intelligent access control equipment, more and more users adopt an intelligent response mode to deal with visitor reception when residents are not at home, but most of the existing intelligent response technologies only adopt prerecorded user languages, and intelligent identification visitor scenes cannot be realized to perform more intelligent and natural response, so that the intelligent degree is not high, and the user experience is poor. It can be seen that the prior art has defects and needs to be solved.
Disclosure of Invention
The invention aims to solve the technical problem of providing an artificial intelligence-based response method and system, which can realize full-automatic and realistic visitor response, improve the intelligent degree of visitor response service and improve user experience.
To solve the above technical problems, the first aspect of the present invention discloses an artificial intelligence based response method, which includes:
acquiring voice data of a current user and sensing data of a current environment through access control equipment;
based on a neural network algorithm, determining a visitor scene corresponding to the current user according to the sensing data of the current environment;
screening out a target prediction neural network from a plurality of candidate response prediction neural networks according to the visitor scene and the voice data;
and inputting the voice data into the target prediction neural network to generate response data, and displaying the response data to the current user through access control equipment.
As an optional implementation manner, in the first aspect of the present invention, the response data is response text and/or response voice.
As an alternative embodiment, in the first aspect of the present invention, the sensing data includes temperature data, humidity data, image data, infrared ranging data, and light reflection three-dimensional modeling data.
As an optional implementation manner, in the first aspect of the present invention, the determining, based on the neural network algorithm, the guest scenario corresponding to the current user according to the sensing data of the current environment includes:
Inputting image data in the sensing data of the current environment into a face image recognition algorithm model to obtain a face recognition result;
Determining whether the current user is a homeowner user according to the face recognition result to obtain a first judgment result;
if the first judgment result is yes, determining that the visitor scene corresponding to the current user is a homeowner scene;
And if the second judgment result is negative, determining the visitor scene corresponding to the current user according to the sensing data of the current environment and a preset algorithm model.
As an optional implementation manner, in the first aspect of the present invention, the determining, according to the sensing data of the current environment and a preset algorithm model, the guest scene corresponding to the current user includes:
inputting image data, infrared ranging data and light reflection three-dimensional modeling data in the sensing data of the current environment into a trained identity recognition neural network to obtain the visitor type of the current user;
The temperature data, the humidity data and the image data in the sensing data of the current environment are input into a trained weather identification neural network to obtain the weather type of the current environment;
and determining the visitor scene corresponding to the current user based on a preset scene corresponding relation according to the visitor type and the weather type.
As an optional implementation manner, in the first aspect of the present invention, the visitor scenario is a rain shelter scenario, a summer heat shelter scenario, a consumption scenario, a danger scenario, a home visit scenario, a promotion scenario or a relatives and friends visit scenario.
As an optional implementation manner, in the first aspect of the present invention, the screening the target prediction neural network from a plurality of candidate answer prediction neural networks according to the guest scenario and the voice data includes:
For each candidate response prediction neural network, obtaining a model training record and a model verification record of the response prediction neural network;
calculating scene similarity between scene labels of training data in the model training records and the visitor scene;
Calculating the verification success rate corresponding to similar voice data corresponding to the voice data in the model verification record;
Calculating the product of the scene similarity and the verification success rate to obtain a network priority parameter corresponding to the response prediction neural network;
and determining the response prediction neural network with the highest network priority parameter as a target prediction neural network.
As an optional implementation manner, in the first aspect of the present invention, the calculating a verification success rate corresponding to similar voice data corresponding to the voice data in the model verification record includes:
Inputting the voice data into a trained language gas recognition network and a trained language speed recognition network to obtain language gas type and language speed information of the voice data;
Calculating the voice parameters of voice data corresponding to each record in the model verification record and the parameter similarity between the voice parameters of the voice data, wherein the voice parameters comprise the voice type and the voice speed information;
Determining the voice data with the parameter similarity larger than a preset similarity threshold value as similar voice data;
And calculating the record proportion of the predicted success in the verification records corresponding to all the similar voice data in the model verification records, and obtaining the verification success rate.
A second aspect of the embodiments of the present invention discloses an artificial intelligence based response system, the system comprising:
The acquisition module is used for acquiring voice data of a current user and sensing data of a current environment through access control equipment;
The determining module is used for determining a visitor scene corresponding to the current user according to the sensing data of the current environment based on a neural network algorithm;
the screening module is used for screening out target prediction neural networks from a plurality of candidate response prediction neural networks according to the visitor scene and the voice data;
and the display module is used for inputting the voice data into the target prediction neural network to generate response data, and displaying the response data to the current user through access control equipment.
In a second aspect of the present invention, as an optional implementation manner, the response data is response text and/or response voice.
As an alternative embodiment, in the second aspect of the present invention, the sensing data includes temperature data, humidity data, image data, infrared ranging data, and light reflection three-dimensional modeling data.
As an optional implementation manner, in the second aspect of the present invention, the determining module determines, based on a neural network algorithm, a specific manner of the guest scene corresponding to the current user according to the sensing data of the current environment, where the specific manner includes:
Inputting image data in the sensing data of the current environment into a face image recognition algorithm model to obtain a face recognition result;
Determining whether the current user is a homeowner user according to the face recognition result to obtain a first judgment result;
if the first judgment result is yes, determining that the visitor scene corresponding to the current user is a homeowner scene;
And if the second judgment result is negative, determining the visitor scene corresponding to the current user according to the sensing data of the current environment and a preset algorithm model.
In a second aspect of the present invention, the determining module determines, according to the sensing data of the current environment and a preset algorithm model, a specific manner of the guest scene corresponding to the current user, including:
inputting image data, infrared ranging data and light reflection three-dimensional modeling data in the sensing data of the current environment into a trained identity recognition neural network to obtain the visitor type of the current user;
The temperature data, the humidity data and the image data in the sensing data of the current environment are input into a trained weather identification neural network to obtain the weather type of the current environment;
and determining the visitor scene corresponding to the current user based on a preset scene corresponding relation according to the visitor type and the weather type.
As an optional implementation manner, in the second aspect of the present invention, the visitor scenario is a rain shelter scenario, a summer heat shelter scenario, a consumption scenario, a danger scenario, a home visit scenario, a promotion scenario or a relatives and friends visit scenario.
As an optional implementation manner, in the second aspect of the present invention, the specific manner in which the screening module screens the target prediction neural network from the plurality of candidate response prediction neural networks according to the guest scenario and the voice data includes:
For each candidate response prediction neural network, obtaining a model training record and a model verification record of the response prediction neural network;
calculating scene similarity between scene labels of training data in the model training records and the visitor scene;
Calculating the verification success rate corresponding to similar voice data corresponding to the voice data in the model verification record;
Calculating the product of the scene similarity and the verification success rate to obtain a network priority parameter corresponding to the response prediction neural network;
and determining the response prediction neural network with the highest network priority parameter as a target prediction neural network.
As an optional implementation manner, in the second aspect of the present invention, a specific manner of calculating, by the screening module, a verification success rate corresponding to similar voice data corresponding to the voice data in the model verification record includes:
Inputting the voice data into a trained language gas recognition network and a trained language speed recognition network to obtain language gas type and language speed information of the voice data;
Calculating the voice parameters of voice data corresponding to each record in the model verification record and the parameter similarity between the voice parameters of the voice data, wherein the voice parameters comprise the voice type and the voice speed information;
Determining the voice data with the parameter similarity larger than a preset similarity threshold value as similar voice data;
And calculating the record proportion of the predicted success in the verification records corresponding to all the similar voice data in the model verification records, and obtaining the verification success rate.
In a third aspect, the invention discloses another response system based on artificial intelligence, the system comprising:
A memory storing executable program code;
a processor coupled to the memory;
The processor invokes the executable program code stored in the memory to perform some or all of the steps in the artificial intelligence based response method disclosed in the first aspect of the invention.
A fourth aspect of the invention discloses a computer storage medium storing computer instructions which, when invoked, are adapted to perform part or all of the steps of the artificial intelligence based response method disclosed in the first aspect of the invention.
Compared with the prior art, the embodiment of the invention has the following beneficial effects:
According to the invention, the visitor scene corresponding to the current user can be determined according to the sensing data of the current environment according to the neural network algorithm, and the target prediction neural network is screened out from the plurality of candidate response prediction neural networks based on the visitor scene and the voice data, so that the prediction and the display of the response data of the voice data of the user are realized, the full-automatic and vivid visitor response can be realized, the intelligent degree of visitor response service is improved, and the user experience is improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of an artificial intelligence based response method according to an embodiment of the present invention.
FIG. 2 is a schematic diagram of an artificial intelligence based response system according to an embodiment of the present invention.
FIG. 3 is a schematic diagram of another response system based on artificial intelligence in accordance with an embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The terms first, second and the like in the description and in the claims and in the above-described figures are used for distinguishing between different objects and not necessarily for describing a sequential or chronological order. Furthermore, the terms "comprise" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, apparatus, article, or device that comprises a list of steps or elements is not limited to the list of steps or elements but may, in the alternative, include other steps or elements not expressly listed or inherent to such process, method, article, or device.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the invention. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.
The invention discloses an artificial intelligence based response method and system, which can determine a visitor scene corresponding to a current user according to sensing data of the current environment according to a neural network algorithm, and screen out a target prediction neural network from a plurality of candidate response prediction neural networks based on the visitor scene and voice data so as to realize prediction and display of response data of the voice data of the user, thereby realizing full-automatic and vivid visitor response, improving the intelligent degree of visitor response service and improving user experience. The following will describe in detail.
Example 1
Referring to fig. 1, fig. 1 is a schematic flow chart of an artificial intelligence-based response method according to an embodiment of the invention. The response method based on artificial intelligence described in fig. 1 can be applied to a data processing system/data processing device/data processing server (wherein the server comprises a local processing server or a cloud processing server). As shown in fig. 1, the artificial intelligence based response method may include the following operations:
101. and acquiring voice data of the current user and sensing data of the current environment through access control equipment.
102. Based on a neural network algorithm, determining a visitor scene corresponding to the current user according to the sensing data of the current environment.
103. And screening target prediction neural networks from the candidate response prediction neural networks according to the visitor scene and the voice data.
104. And inputting the voice data into a target prediction neural network to generate response data, and displaying the response data to the current user through the access control equipment.
Therefore, the embodiment of the invention can determine the visitor scene corresponding to the current user according to the sensing data of the current environment according to the neural network algorithm, and screen the target prediction neural network from the plurality of candidate response prediction neural networks based on the visitor scene and the voice data so as to realize the prediction and the display of the response data of the user voice data, thereby realizing full-automatic and vivid visitor response, improving the intelligent degree of visitor response service and improving the user experience.
As an alternative embodiment, in the step, the answer data is answer text and/or answer voice.
Therefore, through the optional embodiment, the types of the response data are limited, diversified response display options are conveniently provided for the user, full-automatic and vivid visitor response is assisted, the intelligent degree of visitor response service is improved, and the user experience is improved.
As an alternative embodiment, in the above steps, the sensing data includes temperature data, humidity data, image data, infrared ranging data, and light reflection three-dimensional modeling data.
Therefore, through the optional embodiment, the content of the sensing data is limited, the follow-up accurate prediction of the visitor scene is facilitated, full automation and vivid visitor response are assisted, the intelligent degree of visitor response service is improved, and the user experience is improved.
As an optional embodiment, in the step, based on the neural network algorithm, determining the guest scenario corresponding to the current user according to the sensing data of the current environment includes:
inputting image data in the sensing data of the current environment into a face image recognition algorithm model to obtain a face recognition result;
Determining whether the current user is a homeowner user according to the face recognition result to obtain a first judgment result;
If the first judgment result is yes, determining that the visitor scene corresponding to the current user is a homeowner scene;
If the second judgment result is negative, determining a visitor scene corresponding to the current user according to the sensing data of the current environment and a preset algorithm model.
Therefore, through the above-mentioned alternative embodiment, the face recognition result can be obtained based on the image data in the sensing data of the current environment and the face image recognition algorithm model, so as to determine whether the current user is a homeowner, and further make more complex scene prediction under the condition that the current user is not a homeowner, so as to realize low-cost and high-efficiency scene judgment, so that a proper screening target prediction neural network can be conveniently screened out later to realize response prediction, fully-automatic and vivid visitor response can be realized in an auxiliary manner, the intelligent degree of visitor response service can be improved, and the user experience can be improved.
As an optional embodiment, in the step, determining, according to the sensing data of the current environment and the preset algorithm model, a guest scene corresponding to the current user includes:
Inputting image data, infrared ranging data and light reflection three-dimensional modeling data in sensing data of a current environment into a trained identity recognition neural network to obtain a visitor type of a current user;
The temperature data, the humidity data and the image data in the sensing data of the current environment are input into a trained weather identification neural network to obtain the weather type of the current environment;
and determining the visitor scene corresponding to the current user based on the preset scene corresponding relation according to the visitor type and the weather type.
Therefore, through the above-mentioned optional embodiment, can confirm visitor type and weather type through neural network respectively based on the sensing data of present environment to synthesize accurate determination visitor scene, so that follow-up screening out suitable screening out target forecast neural network realizes the response prediction, and supplementary realization full automatization and lifelike visitor response improve visitor response service's intelligent degree, improve user experience.
As an optional embodiment, in the step, the visitor scenario is a rain shelter scenario, a summer heat shelter scenario, a consumption scenario, a danger scenario, a home visit scenario, a promotion scenario or a relatives and friends visit scenario.
Therefore, through the optional embodiment, the type of the visitor scene is limited, the scene characteristics are better represented, so that the appropriate screening target prediction neural network can be conveniently screened out in the follow-up process to realize response prediction, full automation and vivid visitor response can be realized in an auxiliary manner, the intelligent degree of visitor response service is improved, and the user experience is improved.
As an alternative embodiment, in the step, selecting the target prediction neural network from the plurality of candidate answer prediction neural networks according to the guest scene and the voice data includes:
For each candidate response prediction neural network, obtaining a model training record and a model verification record of the response prediction neural network;
calculating scene similarity between scene labels of training data in the model training records and visitor scenes;
calculating the verification success rate corresponding to similar voice data corresponding to the voice data in the model verification record;
calculating the product of the scene similarity and the verification success rate to obtain the network priority parameter corresponding to the response prediction neural network;
And determining the response prediction neural network with the highest network priority parameter as the target prediction neural network.
Therefore, through the optional embodiment, the more appropriate network can be screened through the scene similarity in the training of the model and the verification success rate of the more similar voice data in the verification record, so that full-automatic and vivid visitor response is realized, the intelligent degree of visitor response service is improved, and the user experience is improved.
As an optional embodiment, in the step, calculating the verification success rate corresponding to the similar voice data corresponding to the voice data in the model verification record includes:
Inputting the voice data into a trained language recognition network and a trained language speed recognition network to obtain language type and language speed information of the voice data;
Calculating the parameter similarity between the voice parameters of the voice data corresponding to each record in the model verification record and the voice parameters of the voice data, wherein the voice parameters comprise the language type and the language speed information;
determining the voice data with parameter similarity larger than a preset similarity threshold as similar voice data;
and calculating the record proportion of the predicted success in the verification records corresponding to all similar voice data in the model verification records, and obtaining the verification success rate.
Therefore, through the above-mentioned alternative embodiment, the similarity between the voice data in the verification record of the model and the current voice data in the language and the speech speed can be compared and calculated to screen more similar voice data, and the verification success rate of the more similar voice data is further calculated to screen more suitable networks, so that full-automatic and vivid visitor response is realized, the intelligent degree of visitor response service is improved, and the user experience is improved.
Example two
Referring to fig. 2, fig. 2 is a schematic diagram of an artificial intelligence-based response system according to an embodiment of the present invention. Wherein the artificial intelligence based response system depicted in fig. 2 may be applied in a data processing system/data processing device/data processing server (wherein the server comprises a local processing server or a cloud processing server). As shown in fig. 2, the artificial intelligence based response system may include:
The acquisition module 201 is configured to acquire, through the access control device, voice data of a current user and sensing data of a current environment where the current user is located.
The determining module 202 is configured to determine, based on a neural network algorithm, a guest scenario corresponding to the current user according to the sensing data of the current environment.
And the screening module 203 is configured to screen out target prediction neural networks from the plurality of candidate response prediction neural networks according to the guest scene and the voice data.
And the display module 204 is used for inputting the voice data into the target prediction neural network to generate response data, and displaying the response data to the current user through the access control equipment.
Therefore, the embodiment of the invention can determine the visitor scene corresponding to the current user according to the sensing data of the current environment according to the neural network algorithm, and screen the target prediction neural network from the plurality of candidate response prediction neural networks based on the visitor scene and the voice data so as to realize the prediction and the display of the response data of the user voice data, thereby realizing full-automatic and vivid visitor response, improving the intelligent degree of visitor response service and improving the user experience.
As an alternative embodiment, the answer data is answer text and/or answer speech.
Therefore, through the optional embodiment, the types of the response data are limited, diversified response display options are conveniently provided for the user, full-automatic and vivid visitor response is assisted, the intelligent degree of visitor response service is improved, and the user experience is improved.
As an alternative embodiment, the sensing data includes temperature data, humidity data, image data, infrared ranging data, and light reflection three-dimensional modeling data.
Therefore, through the optional embodiment, the content of the sensing data is limited, the follow-up accurate prediction of the visitor scene is facilitated, full automation and vivid visitor response are assisted, the intelligent degree of visitor response service is improved, and the user experience is improved.
As an optional embodiment, the determining module determines, based on a neural network algorithm, a specific manner of a guest scene corresponding to the current user according to sensing data of the current environment, including:
inputting image data in the sensing data of the current environment into a face image recognition algorithm model to obtain a face recognition result;
Determining whether the current user is a homeowner user according to the face recognition result to obtain a first judgment result;
If the first judgment result is yes, determining that the visitor scene corresponding to the current user is a homeowner scene;
If the second judgment result is negative, determining a visitor scene corresponding to the current user according to the sensing data of the current environment and a preset algorithm model.
Therefore, through the above-mentioned alternative embodiment, the face recognition result can be obtained based on the image data in the sensing data of the current environment and the face image recognition algorithm model, so as to determine whether the current user is a homeowner, and further make more complex scene prediction under the condition that the current user is not a homeowner, so as to realize low-cost and high-efficiency scene judgment, so that a proper screening target prediction neural network can be conveniently screened out later to realize response prediction, fully-automatic and vivid visitor response can be realized in an auxiliary manner, the intelligent degree of visitor response service can be improved, and the user experience can be improved.
As an optional embodiment, the determining module determines, according to the sensing data of the current environment and a preset algorithm model, a specific manner of the guest scene corresponding to the current user, including:
Inputting image data, infrared ranging data and light reflection three-dimensional modeling data in sensing data of a current environment into a trained identity recognition neural network to obtain a visitor type of a current user;
The temperature data, the humidity data and the image data in the sensing data of the current environment are input into a trained weather identification neural network to obtain the weather type of the current environment;
and determining the visitor scene corresponding to the current user based on the preset scene corresponding relation according to the visitor type and the weather type.
Therefore, through the above-mentioned optional embodiment, can confirm visitor type and weather type through neural network respectively based on the sensing data of present environment to synthesize accurate determination visitor scene, so that follow-up screening out suitable screening out target forecast neural network realizes the response prediction, and supplementary realization full automatization and lifelike visitor response improve visitor response service's intelligent degree, improve user experience.
As an alternative embodiment, the visitor scenario is a rain shelter scenario, a summer shelter scenario, a consumption scenario, a danger scenario, a home visit scenario, a promotion scenario, or a relatives and friends visit scenario.
Therefore, through the optional embodiment, the type of the visitor scene is limited, the scene characteristics are better represented, so that the appropriate screening target prediction neural network can be conveniently screened out in the follow-up process to realize response prediction, full automation and vivid visitor response can be realized in an auxiliary manner, the intelligent degree of visitor response service is improved, and the user experience is improved.
As an alternative embodiment, the specific manner of screening the target prediction neural network from the plurality of candidate response prediction neural networks according to the guest scene and the voice data by the screening module includes:
For each candidate response prediction neural network, obtaining a model training record and a model verification record of the response prediction neural network;
calculating scene similarity between scene labels of training data in the model training records and visitor scenes;
calculating the verification success rate corresponding to similar voice data corresponding to the voice data in the model verification record;
calculating the product of the scene similarity and the verification success rate to obtain the network priority parameter corresponding to the response prediction neural network;
And determining the response prediction neural network with the highest network priority parameter as the target prediction neural network.
Therefore, through the optional embodiment, the more appropriate network can be screened through the scene similarity in the training of the model and the verification success rate of the more similar voice data in the verification record, so that full-automatic and vivid visitor response is realized, the intelligent degree of visitor response service is improved, and the user experience is improved.
As an optional embodiment, the specific manner of calculating the verification success rate corresponding to the similar voice data corresponding to the voice data in the model verification record by the screening module includes:
Inputting the voice data into a trained language recognition network and a trained language speed recognition network to obtain language type and language speed information of the voice data;
Calculating the parameter similarity between the voice parameters of the voice data corresponding to each record in the model verification record and the voice parameters of the voice data, wherein the voice parameters comprise the language type and the language speed information;
determining the voice data with parameter similarity larger than a preset similarity threshold as similar voice data;
and calculating the record proportion of the predicted success in the verification records corresponding to all similar voice data in the model verification records, and obtaining the verification success rate.
Therefore, through the above-mentioned alternative embodiment, the similarity between the voice data in the verification record of the model and the current voice data in the language and the speech speed can be compared and calculated to screen more similar voice data, and the verification success rate of the more similar voice data is further calculated to screen more suitable networks, so that full-automatic and vivid visitor response is realized, the intelligent degree of visitor response service is improved, and the user experience is improved.
Example III
Referring to fig. 3, fig. 3 is a schematic diagram of yet another response system based on artificial intelligence in accordance with an embodiment of the present invention. The artificial intelligence based response system depicted in fig. 3 is applied in a data processing system/data processing device/data processing server (wherein the server comprises a local processing server or a cloud processing server). As shown in fig. 3, the artificial intelligence based response system may include:
A memory 301 storing executable program code;
A processor 302 coupled with the memory 301;
Wherein the processor 302 invokes executable program code stored in the memory 301 for performing the steps of the artificial intelligence based response method described in embodiment one.
Example IV
The embodiment of the invention discloses a computer-readable storage medium storing a computer program for electronic data exchange, wherein the computer program causes a computer to execute the steps of the artificial intelligence based response method described in the embodiment one.
Example five
The present invention discloses a computer program product comprising a non-transitory computer readable storage medium storing a computer program, and the computer program is operable to cause a computer to perform the steps of the artificial intelligence based response method described in the embodiment.
The foregoing describes certain embodiments of the present disclosure, other embodiments being within the scope of the following claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve desirable results. Furthermore, the processes depicted in the accompanying drawings do not necessarily have to be in the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
The system, apparatus, module or unit set forth in the above embodiments may be implemented in particular by a computer chip or entity, or by a product having a certain function. One typical implementation is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.
For convenience of description, the above devices are described as being functionally divided into various units, respectively. Of course, the functions of each element may be implemented in one or more software and/or hardware elements when implemented in the present specification.
It will be appreciated by those skilled in the art that the present description may be provided as a method, system, or computer program product. Accordingly, the present specification embodiments may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present description embodiments may take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.
The present description is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the specification. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of computer-readable media.
Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Disks (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises an element.
The description may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for system embodiments, since they are substantially similar to method embodiments, the description is relatively simple, as relevant to see a section of the description of method embodiments.
Finally, it should be noted that the disclosure of the response method and system based on the artificial intelligence in the embodiments of the present invention is only a preferred embodiment of the present invention, and is only for illustrating the technical solution of the present invention, but not for limiting the same, and although the present invention has been described in detail with reference to the foregoing embodiments, it should be understood by those skilled in the art that the technical solution described in the foregoing embodiments may be modified or some of the technical features thereof may be equivalently replaced, and these modifications or replacements do not make the essence of the corresponding technical solution deviate from the spirit and scope of the technical solution of the embodiments of the present invention.

Claims (8)

1. An artificial intelligence based response method, the method comprising:
acquiring voice data of a current user and sensing data of a current environment through access control equipment;
based on a neural network algorithm, determining a visitor scene corresponding to the current user according to the sensing data of the current environment;
Screening a target predictive neural network from a plurality of candidate answer predictive neural networks according to the guest scene and the voice data, including:
For each candidate response prediction neural network, obtaining a model training record and a model verification record of the response prediction neural network;
calculating scene similarity between scene labels of training data in the model training records and the visitor scene;
Inputting the voice data into a trained language gas recognition network and a trained language speed recognition network to obtain language gas type and language speed information of the voice data;
Calculating the voice parameters of voice data corresponding to each record in the model verification record and the parameter similarity between the voice parameters of the voice data, wherein the voice parameters comprise the voice type and the voice speed information;
Determining the voice data with the parameter similarity larger than a preset similarity threshold value as similar voice data;
Calculating the record proportion of the predicted success in the verification records corresponding to all the similar voice data in the model verification records to obtain the verification success rate;
Calculating the product of the scene similarity and the verification success rate to obtain a network priority parameter corresponding to the response prediction neural network;
Determining the response prediction neural network with the highest network priority parameter as a target prediction neural network;
and inputting the voice data into the target prediction neural network to generate response data, and displaying the response data to the current user through access control equipment.
2. The response method based on artificial intelligence according to claim 1, wherein the response data is response text and/or response voice.
3. The artificial intelligence based response method of claim 1, wherein the sensory data includes temperature data, humidity data, image data, infrared ranging data, and light reflection three-dimensional modeling data.
4. The response method based on artificial intelligence according to claim 3, wherein the determining, based on the neural network algorithm, the guest scene corresponding to the current user according to the sensing data of the current environment includes:
Inputting image data in the sensing data of the current environment into a face image recognition algorithm model to obtain a face recognition result;
Determining whether the current user is a homeowner user according to the face recognition result to obtain a first judgment result;
if the first judgment result is yes, determining that the visitor scene corresponding to the current user is a homeowner scene;
And if the first judgment result is negative, determining the visitor scene corresponding to the current user according to the sensing data of the current environment and a preset algorithm model.
5. The response method based on artificial intelligence according to claim 4, wherein the determining the guest scene corresponding to the current user according to the sensing data of the current environment and a preset algorithm model includes:
inputting image data, infrared ranging data and light reflection three-dimensional modeling data in the sensing data of the current environment into a trained identity recognition neural network to obtain the visitor type of the current user;
The temperature data, the humidity data and the image data in the sensing data of the current environment are input into a trained weather identification neural network to obtain the weather type of the current environment;
and determining the visitor scene corresponding to the current user based on a preset scene corresponding relation according to the visitor type and the weather type.
6. The response method based on artificial intelligence according to claim 5, wherein the visitor scenario is a rain shelter scenario, a summer heat shelter scenario, a consumption scenario, a danger scenario, a home visit scenario, a sales promotion scenario or a relatives and friends visit scenario.
7. An artificial intelligence based response system, the system comprising:
The acquisition module is used for acquiring voice data of a current user and sensing data of a current environment through access control equipment;
The determining module is used for determining a visitor scene corresponding to the current user according to the sensing data of the current environment based on a neural network algorithm;
The screening module is configured to screen out target prediction neural networks from a plurality of candidate response prediction neural networks according to the visitor scene and the voice data, and includes:
For each candidate response prediction neural network, obtaining a model training record and a model verification record of the response prediction neural network;
calculating scene similarity between scene labels of training data in the model training records and the visitor scene;
Inputting the voice data into a trained language gas recognition network and a trained language speed recognition network to obtain language gas type and language speed information of the voice data;
Calculating the voice parameters of voice data corresponding to each record in the model verification record and the parameter similarity between the voice parameters of the voice data, wherein the voice parameters comprise the voice type and the voice speed information;
Determining the voice data with the parameter similarity larger than a preset similarity threshold value as similar voice data;
Calculating the record proportion of the predicted success in the verification records corresponding to all the similar voice data in the model verification records to obtain the verification success rate;
Calculating the product of the scene similarity and the verification success rate to obtain a network priority parameter corresponding to the response prediction neural network;
Determining the response prediction neural network with the highest network priority parameter as a target prediction neural network;
and the display module is used for inputting the voice data into the target prediction neural network to generate response data, and displaying the response data to the current user through access control equipment.
8. An artificial intelligence based response system, the system comprising:
A memory storing executable program code;
a processor coupled to the memory;
The processor invokes the executable program code stored in the memory to perform the artificial intelligence based response method of any one of claims 1-6.
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