CN120952411A

CN120952411A - Task processing methods, apparatus, computer equipment and storage media

Info

Publication number: CN120952411A
Application number: CN202511055877.5A
Authority: CN
Inventors: 蔡姬
Original assignee: Ping An Property and Casualty Insurance Company of China Ltd
Current assignee: Ping An Property and Casualty Insurance Company of China Ltd
Priority date: 2025-07-29
Filing date: 2025-07-29
Publication date: 2025-11-14

Abstract

This application belongs to the field of artificial intelligence technology and relates to a task processing method, apparatus, computer equipment, and storage medium. The method includes: in a business processing flow, if business data meeting interception conditions is detected, constructing an audit task based on the business data; recording the task information of the audit task in a designated task table, and filtering target tasks to be assigned from the designated task table; obtaining multi-dimensional information of candidate audit personnel and task data of the target tasks; generating personnel weights for candidate audit personnel based on the multi-dimensional information; performing score generation processing on the multi-dimensional information, task data, and personnel weights to obtain a comprehensive score for each candidate audit personnel corresponding to the target task; filtering the target audit personnel with the highest comprehensive score from all comprehensive scores, and assigning the target task to the target audit personnel. This application can be applied to task processing scenarios in the fintech and medical fields, improving the accuracy and intelligence of task allocation and processing.

Description

Task processing method, device, computer equipment and storage medium

Technical Field

The application relates to the technical field of artificial intelligence, which can be applied to the fields of financial science and technology, digital medical treatment and the like, in particular to a task processing method, a task processing device, computer equipment and a storage medium.

Background

In the traditional data auditing system, the auditing operation of tasks takes precedence, for example, during the process of applying and quoting, the tasks such as insurance policy, wholesale policy, car inspection and the like need to be audited. However, current task allocation is mainly based on fixed rules, and this allocation method has obvious defects in terms of accuracy and intelligence. The fixed rules are generally used for task allocation based on some simple and preset conditions, lack flexible adaptability to complex service scenes and dynamic changes, and are difficult to perform accurate and intelligent task allocation according to actual conditions. Specifically, the rough distribution mode corresponding to the fixed rule distribution mode easily causes unreasonable task distribution, overload of part of the processing personnel tasks occurs, and the rest of the processing personnel tasks affect the overall auditing efficiency and quality.

For example, in a credit insurance audit scenario in the financial field, a conventional fixed rule may only perform task allocation according to the industry in which the customer is located, without considering key factors such as the size of the customer enterprise, the credit rating history change, and the like. If large enterprise auditing tasks with high risk industries but good credit ratings are distributed to auditing personnel with insufficient experience, inaccurate auditing and low efficiency can be caused, and potential risks of insurance companies are increased. For another example, in the medical insurance claim auditing scenario in the medical field, the fixed rules may be assigned tasks based only on the disease type, without consideration of factors such as the complexity of the disease, the specificity of the treatment regimen, and the like. For some claim cases involving multiple rare diseases and complicated treatment schemes, if assigned to auditors with insufficient knowledge of the rare diseases, the rationality and accuracy of the claim may not be accurately judged, affecting the rights and interests of the patient and the quality of service of the insurance company.

Therefore, it is desirable to provide an intelligent task allocation method and system to improve the accuracy and intelligence of task allocation and to improve the overall performance of the data auditing system.

Disclosure of Invention

The embodiment of the application aims to provide a task processing method, a device, computer equipment and a storage medium, which are used for solving the technical problems of low accuracy and low intelligence of the existing task allocation mode.

In a first aspect, a task processing method is provided, including:

In the service processing flow, if service data meeting the interception condition is detected, constructing a corresponding auditing task based on the service data;

recording task information of the auditing task into a preset appointed task list, and screening target tasks to be distributed from the appointed task list;

Acquiring multi-dimensional information of a plurality of preset candidate auditors and acquiring task data of the target task;

Generating personnel weights of the candidate auditors based on the multidimensional information;

performing score generation processing on the multidimensional information, the task data and the personnel weights based on a preset score analysis strategy to obtain comprehensive scores of the candidate auditors corresponding to the target tasks;

And screening target auditors with highest scores from all the comprehensive scores, and distributing the target tasks to the target auditors.

In a second aspect, there is provided a task processing device comprising:

the detection module is used for constructing a corresponding auditing task based on the service data if the service data meeting the interception condition is detected in the service processing flow;

the first processing module is used for recording the task information of the auditing task into a preset appointed task list and screening out target tasks to be distributed from the appointed task list;

The first acquisition module is used for acquiring multidimensional information of a plurality of preset candidate auditors and acquiring task data of the target task;

The first generation module is used for generating personnel weights of the candidate auditors based on the multidimensional information;

The second processing module is used for carrying out score generation processing on the multidimensional information, the task data and the personnel weights based on a preset score analysis strategy to obtain comprehensive scores of the candidate auditors corresponding to the target tasks;

And the distribution module is used for screening target auditors with highest scores from all the comprehensive scores and distributing the target tasks to the target auditors.

In a third aspect, a computer device is provided comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the task processing method described above when executing the computer program.

In a fourth aspect, a computer readable storage medium is provided, the computer readable storage medium storing a computer program which, when executed by a processor, implements the steps of the task processing method described above.

In the scheme realized by the task processing method, the device, the computer equipment and the storage medium, firstly, in a business processing flow, if business data meeting interception conditions are detected, corresponding auditing tasks are constructed based on the business data, then task information of the auditing tasks is recorded into a preset appointed task table, target tasks to be distributed are screened out from the appointed task table, then multidimensional information of a plurality of preset candidate auditing personnel and task data of the target tasks are acquired, personnel weights of all the candidate auditing personnel are generated based on the multidimensional information, score generation processing is further carried out on the multidimensional information, the task data and the personnel weights based on a preset score analysis strategy, comprehensive scores of all the candidate auditing personnel corresponding to the target tasks are obtained, and finally target auditing personnel with highest scores are screened out from all the comprehensive scores, and the target auditing personnel are distributed to the target auditing personnel. Based on the automatic processing flow, in the business processing flow, if business data meeting the interception condition is detected, an auditing task is constructed based on the business data, then task information of the auditing task is recorded in a designated task list, target tasks to be distributed are screened out from the designated task list, then personnel weights are generated according to the obtained multi-dimensional information of a plurality of candidate auditing personnel, and further score generation processing is carried out on the multi-dimensional information, the obtained task data of the target tasks and the personnel weights based on the use of a score analysis strategy, so that comprehensive scores of all the candidate auditing personnel corresponding to the target tasks are obtained, finally target auditing personnel with the highest score are screened out from all the comprehensive scores, and the target tasks are distributed to the target auditing personnel, so that the rationality, accuracy and intelligence of task distribution can be effectively improved.

Drawings

In order to more clearly illustrate the solution of the present application, a brief description will be given below of the drawings required for the description of the embodiments of the present application, it being apparent that the drawings in the following description are some embodiments of the present application, and that other drawings may be obtained from these drawings without the exercise of inventive effort for a person of ordinary skill in the art.

FIG. 1 is an exemplary system architecture diagram in which the present application may be applied;

FIG. 2 is a flow chart of one embodiment of a task processing method according to the present application;

FIG. 3 is a schematic diagram of one embodiment of a task processing device according to the present application;

FIG. 4 is a schematic structural diagram of one embodiment of a computer device in accordance with the present application.

Detailed Description

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs, the terms used in the description herein are used for the purpose of describing particular embodiments only and are not intended to limit the application, and the terms "comprising" and "having" and any variations thereof in the description of the application and the claims and the above description of the drawings are intended to cover non-exclusive inclusions. The terms first, second and the like in the description and in the claims or in the above-described figures, are used for distinguishing between different objects and not necessarily for describing a sequential or chronological order.

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 application. 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.

In order to make the person skilled in the art better understand the solution of the present application, the technical solution of the embodiment of the present application will be clearly and completely described below with reference to the accompanying drawings.

As shown in fig. 1, the system architecture 100 may include a terminal device 101, a network 102, and a server 103, where the terminal device 101 may be a notebook 1011, a tablet 1012, or a cell phone 1013. Network 102 is the medium used to provide communication links between terminal device 101 and server 103. Network 102 may include various connection types such as wired, wireless communication links, or fiber optic cables, among others.

A user may interact with the server 103 via the network 102 using the terminal device 101 to receive or send messages or the like. Various communication client applications, such as a web browser application, a shopping class application, a search class application, an instant messaging tool, a mailbox client, social platform software, and the like, may be installed on the terminal device 101.

The terminal device 101 may be various electronic devices having a display screen and supporting web browsing, and the terminal device 101 may be an electronic book reader, an MP3 player (Moving Picture Experts G roup Audio Layer III, moving picture experts compression standard audio layer III), an MP4 (Moving Picture Experts Group Audio Layer IV, moving picture experts compression standard audio layer IV) player, a laptop portable computer, a desktop computer, and the like, in addition to the notebook 1011, the tablet 1012, or the mobile phone 1013.

The server 103 may be a server providing various services, such as a background server providing support for pages displayed on the terminal device 101.

It should be noted that, the task processing method provided by the embodiment of the present application is generally executed by a server/terminal device, and accordingly, the task processing device is generally disposed in the server/terminal device.

It should be understood that the number of terminal devices, networks and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.

With continued reference to FIG. 2, a flow chart of one embodiment of a task processing method according to the present application is shown. The order of the steps in the flowchart may be changed and some steps may be omitted according to various needs. The task processing method provided by the embodiment of the application can be applied to any scene needing task processing, and can be applied to products of the scenes, such as task processing products in the financial insurance field and the medical field. The task processing method comprises the following steps:

Step S201, if service data meeting the interception condition is detected in the service processing flow, a corresponding auditing task is constructed based on the service data.

In this embodiment, the electronic device (e.g., the server/terminal device shown in fig. 1) on which the task processing method operates may acquire service data through a wired connection manner or a wireless connection manner. It should be noted that the wireless connection may include, but is not limited to, 3G/4G/5G connections, wiFi connections, bluetooth connections, wi MAX connections, Z i gbee connections, UWB (ult ra Wi deband) connections, and other now known or later developed wireless connection means. The execution subject of the present application is specifically a task processing system, or referred to as a data auditing system, and may be simply referred to as a system. The business process flow may refer to an insurance application or quotation business flow of the system. The service data may include data such as policy applications, quotations, etc. that need to be audited. When the system intercepts the business data needing to be checked, a task generating mechanism is triggered. For example, after a client submits an application form and the system is verified by the preliminary rule, it is determined that the application form needs to enter a data auditing link, and then an auditing task matched with the application form is generated.

The application can be applied to task processing scenes in the fields of financial science and technology and medical science. For example, in the application scenario in the field of financial insurance, the audit task may be a high-volume policy application audit task. The task description includes that if the insurance applied by the client exceeds a certain limit (for example, 500 ten thousand yuan), the system automatically intercepts and generates an audit task. The auditor is required to fully review the financial status of the customer, including revenue certificates, asset inventory, tax records, etc., to determine the customer's ability to pay high premium. The customer's insurance incentives are also investigated for the presence of ethical risks, such as insurance fraud through high-volume policy, etc. In addition, the impact of the large policy on the overall risk exposure of the company is evaluated to determine whether underwriting is warranted and whether additional risk control measures, such as reinsurance arrangements, are required.

Or in the quotation scenario of the digital medical field, the auditing task can be a rare disease treatment insurance quotation auditing task. The task description includes the system intercepting generation of audit tasks when a customer applies for treatment insurance offers for patients with rare diseases. The auditor gathers detailed information about the rare disease, including morbidity, treatment methods, treatment costs, etc. Since the treatment drugs and means for rare diseases can be special and expensive, communication with specialized medical institutions or specialists is required to understand the latest treatment progress and cost. Based on this information, in combination with the pricing model and risk tolerance capabilities of the insurance company, a reasonable insurance quotation scheme is formulated for the patient, and insurance responsibility and payment scope are defined.

Step S202, recording task information of the auditing task into a preset appointed task list, and screening target tasks to be distributed from the appointed task list.

In this embodiment, after generating the audit task, the system automatically collects various relevant information (task information) of the audit task and records it into a specific task table (i.e. the above-mentioned designated task table, or referred to as a table). The task information includes, but is not limited to, a unique identifier AID of the task (for accurately identifying the task in the system), a task name (such as "policy audit task-customer name-order number"), a creation time (accurate to seconds, time of task generation recorded), an affiliated institution (which specifies which secondary or tertiary institution the task was generated by, e.g., "eastern division company-Shanghai branch company"), a service type (indicating whether it is a policy, wholesale or inspection service), an auditor (initially possibly empty, to be allocated later), a source (indicating from which service link or system interface the task was received), a status (initial status is set to "to be allocated", etc.). In addition, the target task to be allocated is further selected from the specified task table later, that is, the task state is the task to be allocated.

Further, the system can query another table (B table) specially storing the working time setting according to the organization information of the task of the auditing task. The B table records the audit time ranges of each institution on different types of dates (working day, saturday/day, holiday). For example, the "Shanghai branch of Corp" has a weekend holiday audit time of 9:00-19:00 and a weekend holiday audit time of 9:00-18:00. The system will compare the time of the application or offer to the audit time range for the date corresponding to the institution queried. And if the time of the application or quote is within the audit time range set by the institution, the system will insert the relevant information for this task into the task table (table C) that matches the gold audit time. The table C contains information such as AID (associated with table a), organization, service type, audit time (time of recording the range of audit times), audit speed (initially empty, calculated when the subsequent audit operation is to be performed), and auditor (initially possibly empty). The processing process ensures that all tasks needing to be audited can be accurately recorded, and only tasks meeting the requirement of specific audit time can enter the subsequent monitoring flow. By recording the task information in detail in the A table, a comprehensive and accurate data source is provided for the whole monitoring system. And judging the checking time and inserting the task information meeting the conditions into the C table ensures that the monitoring system can focus on the task generated in the proper time, avoids processing the task which is invalid or not in the monitoring time range, and improves the efficiency and pertinence of job monitoring.

And step S203, acquiring multi-dimensional information of a plurality of preset candidate auditors and acquiring task data of the target task.

In this embodiment, data for a plurality of candidate auditors in various dimensions may be collected by a human resource management system, a performance evaluation system, or the like. For example, data such as professional field matching degree, workload value, historical performance index, emergency degree adaptation value and the like of candidate auditors are collected, and are arranged and cleaned, so that accuracy and consistency of multi-dimensional information are ensured. In addition, for the target task, task data such as task type, difficulty, emergency degree and the like are extracted from the task management system. For example, the task type can be insurance, correction, car inspection and the like, the difficulty can be evaluated according to the complexity degree of the task, the related amount and other factors, and the emergency degree can be determined according to business rules or customer requirements.

And step S204, generating personnel weights of the candidate auditors based on the multidimensional information.

In this embodiment, the foregoing specific implementation process of generating the personnel weights of the candidate auditors based on the multidimensional information will be described in further detail in the following specific embodiments, which will not be described herein.

And step S205, carrying out score generation processing on the multidimensional information, the task data and the personnel weights based on a preset score analysis strategy to obtain comprehensive scores of the candidate auditors corresponding to the target tasks.

In this embodiment, the above-mentioned score generation process is performed on the multidimensional information, the task data and the personnel weights based on a preset score analysis policy, so as to obtain a specific implementation process of the comprehensive score of each candidate auditor corresponding to the target task, which will be described in further detail in the following specific embodiments, which are not described herein.

And S206, screening target auditors with highest scores from all the comprehensive scores, and distributing the target tasks to the target auditors.

In this embodiment, the generated comprehensive scores of all candidate auditors corresponding to the target tasks may be compared to screen out the target auditors with the highest scores, so as to allocate the target tasks to the target auditors, so as to ensure the rationality, accuracy and high efficiency of the allocation of the target tasks.

The method comprises the steps of firstly, in a business processing flow, if business data meeting interception conditions are detected, constructing corresponding auditing tasks based on the business data, then recording task information of the auditing tasks into a preset appointed task table, screening target tasks to be distributed from the appointed task table, then obtaining multi-dimensional information of a plurality of preset candidate auditing personnel and task data of the target tasks, subsequently generating personnel weights of the candidate auditing personnel based on the multi-dimensional information, further performing score generation processing on the multi-dimensional information, the task data and the personnel weights based on a preset score analysis strategy, obtaining comprehensive scores of the candidate auditing personnel corresponding to the target tasks, and finally screening target auditing personnel with highest scores from all the comprehensive scores, and distributing the target tasks to the target auditing personnel. Based on the automatic processing flow, in the business processing flow, if business data meeting the interception condition is detected, an auditing task is constructed based on the business data, then task information of the auditing task is recorded in a designated task list, target tasks to be distributed are screened out from the designated task list, then personnel weights are generated according to the obtained multi-dimensional information of a plurality of candidate auditing personnel, and further score generation processing is carried out on the multi-dimensional information, the obtained task data of the target tasks and the personnel weights based on the use of a score analysis strategy, so that comprehensive scores of all the candidate auditing personnel corresponding to the target tasks are obtained, finally target auditing personnel with the highest score are screened out from all the comprehensive scores, and the target tasks are distributed to the target auditing personnel, so that the rationality, accuracy and intelligence of task distribution can be effectively improved.

In some alternative implementations, step S205 includes the steps of:

and calling a pre-constructed score prediction model.

In this embodiment, the model construction process of the score prediction model includes first and second feature engineering. 1) And (5) feature collection and arrangement. First, the relevant features of auditors and tasks are collected from a plurality of data sources. For auditors, acquiring information such as professional field codes, workload values, historical performance indexes, emergency adaptation values and the like from a human resource management system, acquiring characteristics such as task types, difficulties, emergency degrees and the like from a task management system for tasks, and then sorting and cleaning the collected characteristics to remove repeated, missing and abnormal data. For example, missing professional field codes can be supplemented according to the working experience and training records of auditors, abnormal workload values, such as negative numbers or values outside a reasonable range, can be corrected or deleted, and 2) feature codes and standardization. And coding the classification characteristics. For example, the task type is coded as a different number, such as an applied task code of 1, an altered task code of 2, and a test task code of 3. The method such as One-heat coding (One-Hot Encod i ng) or label coding (Labe l Encod i ng) can be used, and a proper coding mode can be selected according to the characteristics of the features and the requirements of the model. And, normalizing the digital features. Because the range of values for different features may vary widely, for example, the workload value may be a specific number of tasks, and the historical performance indicators may be scores of 0-100, a normalization process may be required to make each feature at the same scale. Common normalization methods are Z-score normalization and Min-Max normalization. 3) Feature selection and combination. And analyzing the correlation between each characteristic and the task allocation result (auditor). The method can be used for screening out the characteristics with stronger correlation with the task allocation result by using methods such as correlation analysis, chi-square test and the like. For example, if the correlation of the professional field code with the task allocation result is found to be low, it may be considered to be eliminated from the feature set. And, attempts are made to combine features to create more meaningful features. For example, the task difficulty and urgency are combined into a new feature that represents the overall complexity of the task. By means of feature combination, the performance and generalization capability of the model can be improved.

2. And (5) model training. 1) And (5) data division. The collected historical task allocation data is divided into a training set, a verification set and a test set. Typically, a training set is used for training of the model, a validation set is used to adjust parameters of the model, and a test set is used to evaluate the final performance of the model. A random partitioning method may be used, such as partitioning the training set, the validation set, and the test set in a proportion of 70%, 15%. And ensuring the rationality of data division, namely similar data distribution of the training set, the verification set and the test set, and avoiding inaccurate model performance evaluation caused by unbalanced data distribution. 2) And (5) adjusting model parameters. The random forest model is trained using a training set. During the training process, the performance of the model is evaluated by a cross-validation method. For example, using K-fold cross-validation, the training set is divided into K subsets, each time training is performed using K-1 subsets, the remaining 1 subset is validated, repeated K times, and an average performance index is calculated. And adjusting parameters of the model according to the cross-validation result. The main parameters of the random forest model include the number of decision trees (n_ estimator s), the maximum depth (max_depth), the minimum sample splitting (min_samples_split), etc. By adjusting these parameters, an optimal model configuration is found to improve the accuracy and generalization ability of the model. 3) Model performance evaluation. And evaluating the model after the parameters are adjusted by using the verification set. And calculating indexes such as accuracy, recall rate, F1 value and the like of the model, and analyzing the performances of the model on different categories. If the model performs poorly in certain categories, the reasons such as data imbalance, improper feature selection, etc. can be further analyzed, and corresponding measures can be taken to improve. If the model performance meets the requirements, the final evaluation of the model is performed by using the test set. The data of the test set is never used in the model training and parameter adjustment process, and the performance of the model can be reflected more truly. If the model is still good in performance on the test set, the model has good generalization capability, and can be used for practical application, and a trained random forest model is used as a final score prediction model.

And carrying out prediction processing on the multidimensional information and the task data based on the score prediction model to obtain the suitability score of each candidate auditor corresponding to the target task.

In this embodiment, the foregoing prediction processing is performed on the multidimensional information and the task data based on the score prediction model to obtain a specific implementation process of the suitability score of each candidate auditor corresponding to the target task, which will be described in further detail in the following specific embodiments, which are not described herein.

And calling a preset comprehensive score calculation formula and acquiring a preset dynamic weight.

In the present embodiment, the above-described comprehensive score calculation formula specifically includes comprehensive score=dynamic weight×personnel weight+ (1-dynamic weight) ×fitness score. The value of the dynamic weight is not particularly limited, and may be set according to the actual service requirement (importance degree of the personnel weight and importance degree of the fitness score), for example, may be set to 0.5.

And calculating the dynamic weight, the personnel weight and the fitness score based on the comprehensive score calculation formula to obtain a corresponding first calculation result.

In this embodiment, the dynamic weight, the personnel weight and the fitness score may be substituted into the corresponding positions in the comprehensive score calculation formula to perform calculation processing, so as to obtain a corresponding first calculation result. Illustratively, for auditor a and task 1, the composite score is 0.5×0.755+ (1-0.5×0.85) = 0.8025.

And generating comprehensive scores of the candidate auditors corresponding to the target tasks based on the first calculation result.

In this embodiment, the generated first calculation result may be used as the composite score of each of the candidate auditors corresponding to the target task.

The method comprises the steps of calling a pre-constructed score prediction model, then carrying out prediction processing on the multidimensional information and the task data based on the score prediction model to obtain fitness scores of all candidate auditors corresponding to the target task, then calling a preset comprehensive score calculation formula, obtaining preset dynamic weights, carrying out calculation processing on the dynamic weights, the personnel weights and the fitness scores based on the comprehensive score calculation formula to obtain corresponding first calculation results, and finally generating comprehensive scores of all candidate auditors corresponding to the target task based on the first calculation results. Based on the processing flow, the application predicts the multidimensional information and the task data based on the use of the score prediction model to obtain the suitability score of each candidate auditor corresponding to the target task, and then calculates the dynamic weight, the personnel weight and the suitability score based on the use of the comprehensive score calculation formula, thereby realizing the efficient and accurate calculation of the comprehensive score of each candidate auditor corresponding to the target task, improving the calculation efficiency of the comprehensive score of each candidate auditor and ensuring the data accuracy of the obtained comprehensive score.

In some optional implementations of this embodiment, the predicting the multidimensional information and the task data based on the score prediction model obtains a fitness score of each candidate auditor corresponding to the target task, including the following steps:

And extracting the characteristics of the multidimensional information and the task data to obtain corresponding initial characteristics.

In this embodiment, the multi-dimensional information is extracted to obtain personnel features such as task type, difficulty, emergency degree and the like, the task data is extracted to obtain task features such as professional field codes, workload values, historical performance indexes and the like, and the obtained personnel features and the task features are integrated to obtain corresponding initial features.

And preprocessing the initial characteristics to obtain corresponding target characteristics.

In this embodiment, the preprocessing includes operations such as encoding and normalization, so as to ensure that the features are consistent with the features used in model training.

And carrying out prediction processing on the target features based on the score prediction model to obtain corresponding prediction results.

In this embodiment, the preprocessed target features are input into a trained score prediction model, the model predicts the suitability of each candidate auditor and the target task to be allocated according to the input features, and outputs the suitability score of each candidate auditor. For example, for task 1, the model predicts a fitness score of 0.85 for auditor A, 0.72 for auditor B, and 0.68 for auditor C.

And generating a suitability score of each candidate auditor corresponding to the target task based on the prediction result.

In this embodiment, the generated prediction result may be used as a fitness score of each of the above candidate auditors corresponding to the target task.

The method comprises the steps of extracting characteristics of the multidimensional information and task data to obtain corresponding initial characteristics, preprocessing the initial characteristics to obtain corresponding target characteristics, predicting the target characteristics based on the score prediction model to obtain corresponding prediction results, and generating suitability scores of candidate auditors corresponding to the target tasks based on the prediction results. Based on the processing flow, the method and the device obtain the corresponding initial characteristics by extracting the characteristics of the multidimensional information and the task data, and pre-process the initial characteristics to obtain the target characteristics, and further predict the target characteristics based on the use of the score prediction model, so that the suitability score of each candidate auditor corresponding to the target task can be efficiently and accurately generated, the generation efficiency of the suitability score is improved, and the data accuracy of the obtained suitability score is ensured.

In some optional implementations, the multi-dimensional information includes at least a professional field matching degree, a workload value, a historical performance index, an emergency fitness value, and step S206 includes the steps of:

And carrying out standardization processing on the multi-dimensional information to obtain corresponding target multi-dimensional information.

In this embodiment, since the information of each dimension may have different dimensions and value ranges, it is necessary to perform standardization processing on the multidimensional information. For example, for a professional domain matching score, if the value range is 0-1, and the workload value may be a specific number of tasks, the workload value needs to be normalized so as to be in the range of 0-1.

And acquiring information weight corresponding to the target multi-dimensional information.

In this embodiment, the above specific implementation process of obtaining the information weight corresponding to the target multi-dimensional information will be described in further detail in the following specific embodiments, which will not be described herein.

And calling a preset personnel weight calculation formula.

In this embodiment, the above-mentioned personnel weight calculation formula may specifically be a weighted sum formula, that is, a comprehensive weight (i.e., personnel weight) of the candidate auditor is calculated according to the information weight allocated to each dimension. The weighted summation formula multiplies the score of each dimension information by the corresponding information weight by adopting a weighted average method, and then adds all the results to obtain the personnel weight of the corresponding candidate auditor.

And calculating the target multidimensional information and the information weight based on the personnel weight calculation formula to obtain a corresponding second calculation result.

In this embodiment, the calculation processing is performed by substituting the target multidimensional information and the information weight into the corresponding position in the personal weight calculation formula. Illustratively, the auditor a scores 0.8 on the professional field matching degree, the workload score is 0.6, the history performance score is 0.9, the emergency adaptive value score is 0.7, the current professional field matching degree weight is 0.3, the workload weight is 0.2, the history performance weight is 0.25, and the emergency weight is 0.25, and then the comprehensive personnel weight is 0.3×0.8+0.2×0.6+0.25×0.9+0.25×0.7=0.755.

And generating personnel weights of the candidate auditors based on the second calculation result.

In this embodiment, the second calculation result obtained by calculation may be used as the personnel weight of each of the candidate auditors.

The method comprises the steps of carrying out standardization processing on the multi-dimensional information to obtain corresponding target multi-dimensional information, then obtaining information weights corresponding to the target multi-dimensional information, then calling a preset personnel weight calculation formula, carrying out calculation processing on the target multi-dimensional information and the information weights based on the personnel weight calculation formula to obtain corresponding second calculation results, and then generating personnel weights of all candidate auditors based on the second calculation results. Based on the processing flow, the target multi-dimensional information is obtained by carrying out standardized processing on the multi-dimensional information, the information weight corresponding to the target multi-dimensional information is obtained, and then the target multi-dimensional information and the information weight are calculated based on the use of a personnel weight calculation formula, so that the personnel weight of each candidate auditor can be efficiently and accurately generated, the generation efficiency of the personnel weight of the candidate auditor is improved, and the data accuracy of the obtained personnel weight is ensured.

In some optional implementations, the acquiring the information weight corresponding to the target multi-dimensional information includes:

And acquiring real-time condition information affecting weight adjustment based on a preset data monitoring tool.

In this embodiment, a real-time data monitoring tool is pre-established, and is used to monitor various real-time conditions affecting weight adjustment. For example, the information of the number of tasks to be distributed, the emergency distribution and the like is obtained in real time through a task management system, and the data of the workload, the leave-in situation and the like of the auditor is obtained in real time through a human resource management system. And sets a monitoring index and a threshold. For example, when the proportion of urgent tasks in the tasks to be distributed exceeds 30%, a weight adjustment mechanism of the urgent degree dimension is triggered, and when the average workload of auditors exceeds 80%, a weight adjustment process of the workload dimension is started.

And acquiring service change information.

In this embodiment, the traffic variation information is collected and analyzed by a regular period. For example, attention is paid to the impact of factors such as market dynamics, policy and regulation changes, company business policy adjustments, etc. on task allocation. If a company pushes out new insurance products, this may lead to an increase in the associated auditing tasks, at which point the professional field matching and the weight of the workload dimension need to be reevaluated. And, a service change feedback mechanism is established. Encouraging first-line auditors, business department personnel and the like to timely feed back business change conditions, and ensuring that weight adjustment can timely respond to business demands.

And acquiring initial information weight corresponding to the multi-dimensional information.

In this embodiment, a corresponding initial information weight is allocated to each dimension in advance according to experience of a service expert and actual service conditions. For example, the initial information weight corresponding to the professional field matching degree is 0.3, the initial information weight corresponding to the workload is 0.2, the initial information weight corresponding to the history performance is 0.25, and the initial information weight corresponding to the emergency degree is 0.25.

And based on the real-time condition information and the service change information, carrying out weight adjustment on the initial information weight to obtain the corresponding information weight.

In this embodiment, the initial information weight of each dimension is dynamically adjusted according to the real-time situation information and the service change information to obtain the final information weight. For example, during peak traffic hours, the weight of the workload dimension may be increased appropriately to ensure the balance of task allocation, and when there are a large number of urgent tasks, the weight of the urgency dimension may be increased accordingly.

Specifically, according to the results of real-time condition monitoring and business change analysis, an explicit weight adjustment rule is formulated. For example, during peak traffic hours, the weight of the workload dimension is increased by 20%, and when the emergency task proportion exceeds the threshold, the weight of the emergency dimension is increased by 15%. The magnitude and range of the weight adjustment is then determined. To avoid too frequent or too large an adjustment of the weights, the amplitude of each adjustment of the weights is limited, e.g. each adjustment of the amplitude does not exceed 30% of the initial weight. Meanwhile, the range of the adjusted weight is regulated, and the weight of each dimension is still in a reasonable interval. And further, when the detection meets the weight adjustment condition, the weight adjustment operation is automatically or manually executed according to the formulated rule. For example, by the system automatically monitoring that the emergency task proportion exceeds a threshold, the system automatically adjusts the weight of the emergency dimension from 0.25 to 0.2875 (15% increase). In addition, information such as time, cause, weight value before and after adjustment of each weight adjustment is recorded. These records will help to evaluate and optimize the effect of the weight adjustment mechanism later.

The method comprises the steps of acquiring real-time condition information influencing weight adjustment based on a preset data monitoring tool, acquiring service change information, acquiring initial information weight corresponding to the multidimensional information, and carrying out weight adjustment on the initial information weight based on the real-time condition information and the service change information to obtain the corresponding information weight. Based on the processing flow, the method acquires the real-time condition information influencing weight adjustment based on the use of the data monitoring tool, acquires the service change information, and acquires the initial information weight corresponding to the multi-dimensional information, and further carries out weight adjustment on the initial information weight based on the real-time condition information and the service change information, so that the information weight of the target multi-dimensional information can be constructed efficiently and accurately, and the intelligence and the accuracy of the information weight of the generated target multi-dimensional information are improved.

In some optional implementations of this embodiment, after step S206, the electronic device may further perform the following steps:

and collecting task processing data corresponding to the target task after the target task is processed.

In this embodiment, the task processing data refers to relevant data to be collected corresponding to relevant task indexes.

And acquiring a preset index calculation strategy.

In this embodiment, the index calculation policy includes a calculation policy of an index corresponding to the inspection operation amount, the batch/lot amount, the withdrawal rate, the timely completion rate, and the inspection time. The method comprises the steps of calculating the index calculation strategy according to the index calculation strategy content, wherein the index calculation strategy comprises the examination operation quantity, namely counting the number of tasks in each group for each auditor, namely the examination operation quantity of the auditor. For example, if there are 10 task records in a certain auditor group, then the audit operation amount of the auditor is 10 times. And (5) calculating the quantity of the batch according to the service type and the task information. And for the insurance policy and wholesale policy service, performing duplicate removal operation on the 'insurance policy/wholesale policy application form + service type' to obtain the amount of the insurance policy/wholesale policy. For example, there are 5 application order task records and 3 lot order task records, but there are 2 application order tasks for the same application form, and after the application form is duplicated, the amount of the application/lot is 5 (the number of the application forms after duplication removal) +3 (the number of lot forms) =8. And calculating the issuing quantity according to the task processing type, and dividing the issuing quantity by the inspection operation quantity to obtain the returning rate. For example, if the number of censoring operations by a certain censorer is 20 times, and the number of issues is 5 times, the withdrawal ratio=5/20=25%. And the timely completion rate is that the system judges whether each task is completed within a specified time according to the time requirement set by the management platform. Counting the number of tasks completed in time (completing the single quantity in time), and dividing the single quantity in time by the total submitted single quantity to obtain the timely completion rate. For example, the total commit order amount is 30, the timely completion order amount is 25, and then the timely completion rate=25/30≡83.33%. And checking and aging, namely counting checking and aging of non-overnight according to the working time set by the management platform. The calculation formula is the total time consumption of all tasks leaving the platform in the period/the total single quantity in the period. For example, during the period of 9:00-19:00 on weekdays, there are 10 non-overnight tasks leaving the platform, and the total time taken for these tasks to leave the platform is 50 hours, then censoring timeliness = 50/10 = 5 hours/single.

And calculating the task processing data based on the index calculation strategy to obtain corresponding index data.

In this embodiment, based on the above index calculation policy, calculation processing is performed on the collected task processing data related to the index calculation of the target task, and the generated calculation result is used as the corresponding index data.

And generating a corresponding data statistical report based on the index data.

In this embodiment, the index data may be filled into a preset statistical report template to generate a corresponding data statistical report. The content writing of the statistical report template is not particularly limited, and the statistical report template can be set according to actual data statistical requirements.

And storing and displaying the data statistics report.

In this embodiment, the storage manner of the data statistics report may be any manner of a local database, a local disk, a cloud server, a blockchain, and the like. In addition, the system page can be adopted to display the data statistics report, so that a comprehensive and visual statistics report is provided for the manager to review.

The method comprises the steps of collecting task processing data corresponding to the target task after the target task is processed, obtaining a preset index calculation strategy, calculating the task processing data based on the index calculation strategy to obtain corresponding index data, generating a corresponding data statistics report based on the index data, and storing and displaying the data statistics report. Based on the processing flow, after the target task is processed, the task processing data corresponding to the target task is collected, and then the task processing data is calculated based on the use of the index calculation strategy to obtain the index data, so that the calculation efficiency and the calculation accuracy of the index data are improved. And the data statistics report is intelligently generated based on the index data, and is stored and displayed, so that the generation efficiency and the data safety of the data statistics report are improved, comprehensive and visual statistics report is displayed for related personnel, and the work efficiency and the use experience of the related personnel are improved.

And judging whether a task auditing result corresponding to the target task, which is returned by the target auditing personnel, is received.

In this embodiment, after the target task is allocated to the target auditor, the auditor field in the above specified task table is updated to the corresponding auditor information, and the task status is updated to "in audit". And, the target auditor can see the task list to be audited (including the target tasks) allocated to the target auditor after logging in to the audit page. When a target auditor carries out audit operation on a target task, such as clicking a pass button to indicate that the task passes audit, clicking a down button to indicate that the target task needs to be returned to related personnel for modification or supplementary data, clicking a modify car inspection conclusion button to indicate that the conclusion of car inspection business is adjusted, the audit operation is triggered, and a task audit result corresponding to the audit operation is generated.

If yes, acquiring an initial auditing state of the target task from the appointed task table.

In this embodiment, the initial audit state of the target task is "in audit".

And updating the initial auditing state of the target task in the appointed task table based on the task auditing result.

In this embodiment, the system updates information such as the auditing state and auditing time of the target task in the specified task table according to the AID of the target task operated by the target auditor. For example, if the auditor clicks the "pass" button, the task status of the target task in the designated task table is updated to "passed", while the audit time is recorded as the current time the auditor clicked the button. If the auditor clicks the "issue" button, the task state in the task table is designated to be updated to be "issued", and the audit time is also recorded as the current time. Meanwhile, the system can update the information such as the auditing state and auditing time of the target task in the C table according to the AID of the target task operated by the auditing personnel.

And recording the auditing progress condition of the task in real time. And the tasks are associated with auditors through a task distribution mechanism, so that each task has an explicit responsible person. Various operations performed by the auditor on the audit page can be timely fed back to the system, and accurate data basis is provided for subsequent statistical calculation by updating the audit state, audit time and other information in the tables A and C. Therefore, the manager can know the auditing state of each task at any time and master the dynamic change of the whole auditing process.

The method comprises the steps of judging whether a task auditing result corresponding to the target task returned by a target auditing person is received, if so, acquiring an initial auditing state of the target task from the appointed task table, and subsequently updating the initial auditing state of the target task in the appointed task table based on the task auditing result. Based on the processing flow, when the task auditing result corresponding to the target task, which is returned by the target auditing personnel, is received, the initial auditing state of the target task is acquired from the appointed task table, and then the initial auditing state of the target task in the appointed task table is updated based on the use of the task auditing result, so that an accurate data basis can be provided for subsequent statistical processing. Therefore, the manager can know the auditing state of each task at any time and master the dynamic change of the whole auditing process.

In some alternative implementations, multiple screening dimensions are also provided on the system page of the system for selection by the administrator, such as auditors, institutions, business types, etc. The manager can select one or more dimensions for combination screening according to own requirements. For example, a manager may choose the dimension of a particular auditor to be the particular auditor, and the dimension of the service type to be the "policy" if he wants to know the policy service condition that a particular auditor audits in a certain period of time, and may set other screening conditions such as a time range. The system provides the functions of data inquiry and grouping, namely, the system inquires the data in the C table according to the screening conditions set by the manager. If the auditor dimension is selected for screening, the system performs group by operation on the query result according to the auditor field, and the task data of the same auditor are grouped into a group. Thus, the task data set corresponding to each auditor can be obtained.

In some alternative implementations, the obtained user information solicits user consent and meets the specifications of the relevant laws and relevant policies.

In addition, the non-native company software tools or components present in the embodiments of the present application are presented by way of example only and are not representative of actual use.

It should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic, and should not limit the implementation process of the embodiment of the present invention.

It is emphasized that the target task may also be stored in a blockchain node in order to further ensure privacy and security of the target task.

The blockchain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanism, encryption algorithm and the like. The blockchain (Blockchai n), essentially a de-centralized database, is a string of data blocks that are generated in association using cryptographic methods, each of which contains information from a batch of network transactions for verifying the validity (anti-counterfeit) of its information and generating the next block. The blockchain may include a blockchain underlying platform, a platform product services layer, an application services layer, and the like.

The embodiment of the application can acquire and process the related data based on the artificial intelligence technology. Wherein artificial intelligence (ARTIFICIALINTELLIGENCE, AI) is the theory, method, technique, and application system that uses a digital computer or a digital computer-controlled machine to simulate, extend, and expand human intelligence, sense the environment, acquire knowledge, and use knowledge to obtain optimal results.

Artificial intelligence infrastructure technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a robot technology, a biological recognition technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and other directions.

Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by computer readable instructions stored in a computer readable storage medium that, when executed, may comprise the steps of the embodiments of the methods described above. The storage medium may be a nonvolatile storage medium such as a magnetic disk, an optical disk, a Read-On-y Memory (ROM), or a random access Memory (Random Access Memory, RAM).

It should be understood that, although the steps in the flowcharts of the figures are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited in order and may be performed in other orders, unless explicitly stated herein. Moreover, at least some of the steps in the flowcharts of the figures may include a plurality of sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, the order of their execution not necessarily being sequential, but may be performed in turn or alternately with other steps or at least a portion of the other steps or stages.

With further reference to fig. 3, as an implementation of the method shown in fig. 2, the present application provides an embodiment of a task processing device, where the embodiment of the device corresponds to the embodiment of the method shown in fig. 2, and the device is specifically applicable to various electronic devices.

As shown in fig. 3, the task processing device 300 according to the present embodiment includes a detection module 301, a first processing module 302, a first acquisition module 303, a first generation module 304, a second processing module 305, and an allocation module 306. Wherein:

the detection module 301 is configured to, in a service processing flow, if service data meeting an interception condition is detected, construct a corresponding audit task based on the service data;

the first processing module 302 is configured to record task information of the audit task into a preset designated task table, and screen out a target task to be allocated from the designated task table;

The first obtaining module 303 is configured to obtain multidimensional information of a plurality of preset candidate auditors, and obtain task data of the target task;

A first generating module 304, configured to generate a personnel weight of each of the candidate auditors based on the multidimensional information;

The second processing module 305 is configured to perform score generation processing on the multidimensional information, the task data and the personnel weights based on a preset score analysis policy, so as to obtain a comprehensive score of each candidate auditor corresponding to the target task;

And the distribution module 306 is used for screening out target auditors with highest scores from all the comprehensive scores and distributing the target tasks to the target auditors.

In some alternative implementations of the present embodiment, the second processing module 305 includes:

the first calling sub-module is used for calling a pre-constructed score prediction model;

the prediction sub-module is used for carrying out prediction processing on the multidimensional information and the task data based on the score prediction model to obtain suitability scores of the candidate auditors corresponding to the target task;

the second calling sub-module is used for calling a preset comprehensive score calculation formula and acquiring preset dynamic weights;

The first calculation sub-module is used for calculating the dynamic weight, the personnel weight and the fitness score based on the comprehensive score calculation formula to obtain a corresponding first calculation result;

and the first generation sub-module is used for generating the comprehensive score of each candidate auditor corresponding to the target task based on the first calculation result.

In some optional implementations of this embodiment, the prediction submodule includes:

The extraction unit is used for extracting the characteristics of the multi-dimensional information and the task data to obtain corresponding initial characteristics;

the preprocessing unit is used for preprocessing the initial characteristics to obtain corresponding target characteristics;

the prediction unit is used for performing prediction processing on the target characteristics based on the score prediction model to obtain corresponding prediction results;

and the generating unit is used for generating the suitability score of each candidate auditor corresponding to the target task based on the prediction result.

In some optional implementations of this embodiment, the multi-dimensional information includes at least a professional field matching degree, a workload value, a historical performance index, and an emergency level adaptation value, and the first generating module 304 includes:

The processing sub-module is used for carrying out standardized processing on the multi-dimensional information to obtain corresponding target multi-dimensional information;

the acquisition sub-module is used for acquiring the information weight corresponding to the target multi-dimensional information;

The third calling sub-module is used for calling a preset personnel weight calculation formula;

The second calculation sub-module is used for calculating the target multidimensional information and the information weight based on the personnel weight calculation formula to obtain a corresponding second calculation result;

And the second generation sub-module is used for generating the personnel weight of each candidate auditor based on the second calculation result.

In some optional implementations of this embodiment, the obtaining submodule includes:

the acquisition unit is used for acquiring real-time condition information affecting weight adjustment based on a preset data monitoring tool;

The first acquisition unit is used for acquiring service change information;

A second obtaining unit, configured to obtain an initial information weight corresponding to the multidimensional information;

And the adjusting unit is used for carrying out weight adjustment on the initial information weight based on the real-time condition information and the service change information to obtain the corresponding information weight.

In some optional implementations of this embodiment, the task processing device further includes:

The acquisition module is used for acquiring task processing data corresponding to the target task after the target task is processed;

The second acquisition module is used for acquiring a preset index calculation strategy;

the calculation module is used for carrying out calculation processing on the task processing data based on the index calculation strategy to obtain corresponding index data;

The second generation module is used for generating a corresponding data statistical report based on the index data;

And the third processing module is used for storing and displaying the data statistics report. In some optional implementations of this embodiment, the task processing device further includes:

the judging module is used for judging whether a task auditing result corresponding to the target task returned by the target auditing personnel is received or not;

the third acquisition module is used for acquiring the initial auditing state of the target task from the appointed task list if yes;

and the updating module is used for updating the initial auditing state of the target task in the appointed task table based on the task auditing result.

In order to solve the technical problems, the embodiment of the application also provides computer equipment. Referring specifically to fig. 4, fig. 4 is a basic structural block diagram of a computer device according to the present embodiment.

The computer device 4 comprises a memory 41, a processor 42, a network interface 43 communicatively connected to each other via a system bus. It should be noted that only computer device 4 having components 41-43 is shown in the figures, but it should be understood that not all of the illustrated components are required to be implemented and that more or fewer components may be implemented instead. It will be appreciated by those skilled in the art that the computer device herein is a device capable of automatically performing numerical calculations and/or information processing according to predetermined or stored instructions, and the hardware thereof includes, but is not limited to, microprocessors, application SPECIFICI NTEGRATED circuits (ASICs), programmable gate arrays (Field-Programmab LE GATE AR RAY, FPGA), digital processors (DIGITALSIGNAL PROCESSOR, DSPs), embedded devices, and the like.

The computer equipment can be a desktop computer, a notebook computer, a palm computer, a cloud server and other computing equipment. The computer equipment can perform man-machine interaction with a user through a keyboard, a mouse, a remote controller, a touch pad or voice control equipment and the like.

The memory 41 includes at least one type of readable storage medium including flash memory, hard disk, multimedia card, card memory (e.g., SD or DX memory, etc.), random Access Memory (RAM), static Random Access Memory (SRAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), programmable Read Only Memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the storage 41 may be an internal storage unit of the computer device 4, such as a hard disk or a memory of the computer device 4. In other embodiments, the memory 41 may also be an external storage device of the computer device 4, such as a plug-in hard disk, a smart memory card (SMART MEDIA CARD, SMC), a Secure Digital (SD) card, a flash memory card (FLASH CARD) or the like, which are provided on the computer device 4. Of course, the memory 41 may also comprise both an internal memory unit of the computer device 4 and an external memory device. In this embodiment, the memory 41 is typically used to store an operating system and various application software installed on the computer device 4, such as computer readable instructions of a task processing method, and the like. Further, the memory 41 may be used to temporarily store various types of data that have been output or are to be output.

The processor 42 may be a central processing unit (Central Processing Unit, CPU), controller, microcontroller, microprocessor, or other data processing chip in some embodiments. The processor 42 is typically used to control the overall operation of the computer device 4. In this embodiment, the processor 42 is configured to execute computer readable instructions stored in the memory 41 or process data, such as computer readable instructions for executing the task processing method.

The network interface 43 may comprise a wireless network interface or a wired network interface, which network interface 43 is typically used for establishing a communication connection between the computer device 4 and other electronic devices.

The present application also provides another embodiment, namely, a computer-readable storage medium storing computer-readable instructions executable by at least one processor to cause the at least one processor to perform the steps of the task processing method as described above.

From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. ROM/RAM, magnetic disk, optical disk) comprising instructions for causing a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to perform the method according to the embodiments of the present application.

It is apparent that the above-described embodiments are only some embodiments of the present application, but not all embodiments, and the preferred embodiments of the present application are shown in the drawings, which do not limit the scope of the patent claims. This application may be embodied in many different forms, but rather, embodiments are provided in order to provide a thorough and complete understanding of the present disclosure. Although the application has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications may be made to the embodiments described in the foregoing description, or equivalents may be substituted for elements thereof. All equivalent structures made by the content of the specification and the drawings of the application are directly or indirectly applied to other related technical fields, and are also within the scope of the application.

Claims

1. A task processing method, characterized by comprising the following steps:

In the business processing flow, if business data that meets the interception conditions is detected, a corresponding audit task is constructed based on the business data;

The task information of the audit task is recorded in a preset designated task table, and the target tasks to be assigned are filtered out from the designated task table.

Obtain multi-dimensional information of multiple pre-set candidate reviewers, and obtain task data for the target task;

Based on the multi-dimensional information, a personnel weight is generated for each of the candidate reviewers;

Based on a preset score analysis strategy, the multi-dimensional information, the task data, and the personnel weights are processed to generate scores, thereby obtaining the comprehensive score of each candidate reviewer corresponding to the target task.

Select the target reviewer with the highest score from all the comprehensive scores, and assign the target task to the target reviewer.

2. The task processing method according to claim 1, characterized in that the step of performing score generation processing on the multi-dimensional information, the task data, and the personnel weights based on a preset score analysis strategy to obtain the comprehensive score of each candidate reviewer corresponding to the target task specifically includes:

Invoke the pre-built score prediction model;

Based on the score prediction model, the multi-dimensional information and the task data are predicted and processed to obtain the suitability score of each candidate reviewer for the target task.

Call the preset comprehensive score calculation formula and obtain the preset dynamic weight;

Based on the comprehensive score calculation formula, the dynamic weight, the personnel weight, and the suitability score are calculated and processed to obtain the corresponding first calculation result;

Based on the first calculation result, a comprehensive score is generated for each of the candidate reviewers corresponding to the target task.

3. The task processing method according to claim 2, characterized in that the step of performing prediction processing on the multi-dimensional information and the task data based on the score prediction model to obtain the suitability score of each candidate reviewer corresponding to the target task specifically includes:

Feature extraction is performed on the multi-dimensional information and the task data to obtain the corresponding initial features;

The initial features are preprocessed to obtain the corresponding target features;

Based on the score prediction model, the target features are predicted to obtain the corresponding prediction results;

Based on the prediction results, a suitability score is generated for each of the candidate reviewers corresponding to the target task.

4. The task processing method according to claim 1, characterized in that the multi-dimensional information includes at least professional field matching degree, workload value, historical performance indicators, and urgency adaptation value; the step of generating the personnel weight of each of the candidate reviewers based on the multi-dimensional information specifically includes:

The multi-dimensional information is standardized to obtain the corresponding target multi-dimensional information;

Obtain the information weights corresponding to the multi-dimensional information of the target;

Call the preset personnel weight calculation formula;

Based on the personnel weight calculation formula, the target multi-dimensional information and the information weight are calculated and processed to obtain the corresponding second calculation result;

Based on the second calculation result, the personnel weight of each of the candidate reviewers is generated.

5. The task processing method according to claim 4, wherein the step of obtaining the information weights corresponding to the multi-dimensional information of the target specifically includes:

Real-time information on factors affecting weight adjustments is collected based on pre-set data monitoring tools;

Obtain information on business changes;

Obtain the initial information weights corresponding to the multi-dimensional information;

Based on the real-time situation information and the business change information, the initial information weights are adjusted to obtain the corresponding information weights.

6. The task processing method according to claim 1, characterized in that, after the step of selecting the target reviewer with the highest score from all the comprehensive scores and assigning the target task to the target reviewer, it further includes:

After completing the processing of the target task, collect the task processing data corresponding to the target task;

Obtain the preset indicator calculation strategy;

The task processing data is calculated and processed based on the aforementioned indicator calculation strategy to obtain the corresponding indicator data.

Generate corresponding data statistics reports based on the aforementioned indicator data;

The data statistical reports are stored and displayed.

7. The task processing method according to claim 1, characterized in that, after the step of selecting the target reviewer with the highest score from all the comprehensive scores and assigning the target task to the target reviewer, it further includes:

Determine whether the task review result corresponding to the target task has been received from the target reviewer;

If so, retrieve the initial review status of the target task from the specified task table;

The initial review status of the target task in the specified task table is updated based on the task review results.

8. A task processing apparatus, characterized in that it comprises:

The detection module is used to construct a corresponding audit task based on business data that meets the interception conditions during the business processing flow.

The first processing module is used to record the task information of the review task into a preset designated task table, and to filter out the target tasks to be assigned from the designated task table.

The first acquisition module is used to acquire multi-dimensional information of multiple preset candidate reviewers, as well as to acquire task data of the target task;

The first generation module is used to generate the personnel weight of each of the candidate reviewers based on the multi-dimensional information;

The second processing module is used to perform score generation processing on the multi-dimensional information, the task data and the personnel weight based on a preset score analysis strategy, so as to obtain the comprehensive score of each candidate reviewer corresponding to the target task;

The allocation module is used to filter out the target reviewer with the highest score from all the comprehensive scores and assign the target task to the target reviewer.

9. A computer device, characterized in that it comprises a memory and a processor, wherein the memory stores computer-readable instructions, and the processor, when executing the computer-readable instructions, implements the steps of the task processing method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores computer-readable instructions, which, when executed by a processor, implement the steps of the task processing method as described in any one of claims 1 to 7.