CN116934283A

CN116934283A - Employee authority configuration method, device, equipment and storage medium thereof

Info

Publication number: CN116934283A
Application number: CN202311005485.9A
Authority: CN
Inventors: 李拾萱
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: 2023-08-09
Filing date: 2023-08-09
Publication date: 2023-10-24

Abstract

The embodiment of the application belongs to the technical field of financial science and technology, and relates to a staff authority configuration method, a device, equipment and a storage medium thereof, which are applied to a staff authority configuration scene of a financial company.

Description

Employee authority configuration method, device, equipment and storage medium thereof

Technical Field

The application relates to the technical field of financial science and technology, and is applied to a financial company employee permission configuration scene, in particular to an employee permission configuration method, an employee permission configuration device and a storage medium thereof.

Background

With the rapid development of the financial industry, more and more financial companies have more complicated business and huge staff, and interaction often exists among different business systems, so that when staff is configured with rights, the conventional manual and simple rights configuration mode is used, and obviously, the method is not suitable for the business requirements with more and less than all kinds.

In finance companies, sales, claim settlement and management processes are involved, and different dangerous types, personnel types and business scenes are covered, so that the background system supporting the business has a wide range of rights. Corporate employees may have the operating rights of multiple business systems at the same time. Currently, for such problems, authority authorization is mainly performed according to manual and individual leadership auditing. However, the service is often changed more, personnel change frequently, too much labor authorization cost is occupied, the intelligent performance is not enough, and the authority configuration efficiency is not high.

Disclosure of Invention

The embodiment of the application aims to provide a staff authority configuration method, device and equipment and a storage medium thereof, which are used for solving the problems that the prior art carries out authority authorization according to manual examination and examination of each leader, so that too much manual authorization cost is occupied, the intellectualization is not enough and the authority configuration efficiency is not high.

In order to solve the technical problems, the embodiment of the application provides an employee authority configuration method, which adopts the following technical scheme:

an employee authority configuration method comprises the following steps:

acquiring authority attribute information of all staff in each business system of a company through a preset authority management system, wherein the authority attribute information comprises a right authority item and an unauthorized authority item in each business system;

acquiring personal attribute information of all staff in each business system of a company through a preset staff management system, wherein the personal attribute information comprises department information and post information which belong to the company;

constructing a training data set based on department information and post information of each employee in the company;

constructing a learning knowledge base according to department information and post information of each employee in a company, and a right authority item and an unauthorized authority item in each business system;

inputting the training data set into a pre-constructed authority prediction model, and training according to the learning knowledge base to obtain a trained authority prediction model;

acquiring department information and post information of a target employee in a company, wherein the target employee comprises a new employee, an old employee for adjusting departments or posts;

Taking department information and post information of the target staff in the company as prediction input data, inputting the prediction input data into the trained authority prediction model, and obtaining a model output result;

obtaining corresponding right authority items and non-right authority items of target staff in each service system according to the output result of the model;

and configuring authority attribute information of the target staff based on the authority items and the non-authority items corresponding to the target staff in each service system respectively, and updating the authority attribute information corresponding to the target staff to the authority management system after the configuration is completed.

Further, the step of constructing a training data set based on the department information and the post information of each employee in the company specifically includes:

generating a training field by taking department information and post information of the current employee in the company as key value pairs;

sequentially taking all employees as current employees, and acquiring training fields respectively corresponding to all employees;

adding training fields corresponding to all staff respectively into a target set to complete the construction of the training data set;

the step of constructing a learning knowledge base according to the department information and the post information of each employee in the company and the right authority item and the no right authority item in each business system specifically comprises the following steps:

Acquiring authority matrixes corresponding to different posts in different departments in different business systems respectively according to department information and post information of each employee in a company and a right authority item and an unauthorized authority item in each business system;

screening out the strictest authority matrix and the widest authority matrix respectively contained in different service systems according to the authority matrixes respectively corresponding to different posts in different departments, wherein the strictest authority matrix refers to the authority matrix with the least authority items contained in the target service system, and the widest authority matrix refers to the authority matrix with the most authority items contained in the target service system;

and (3) sorting authority matrixes corresponding to different posts in different departments in different service systems respectively, acquiring all the authority matrixes as warehousing elements, and constructing the learning knowledge base.

Further, the step of obtaining the authority matrixes corresponding to different posts in different departments in different business systems according to the department information and post information of each employee in the company and the authority items and the non-authority items in each business system specifically includes:

Adopting a double circulation mode to screen different current staff from all staff in turn and screen different current business systems from all business systems in turn;

identifying a right authority item and an unauthorized authority item of a current employee in a current business system;

constructing a right matrix corresponding to the current employee in the current business system according to the right and no right items of the current employee in the current business system, wherein a right value corresponding to the right item is set as a first right value in the right matrix, and a right value corresponding to the no right item is set as a second right value;

constructing corresponding authority matrixes of each employee in different service systems according to the screened different current employees and different current service systems;

based on the department information and the post information of each employee in the company and the authority matrixes corresponding to each employee in different service systems, the authority matrixes corresponding to different posts in different departments in different service systems are obtained.

Further, before the step of inputting the training data set into the pre-constructed authority prediction model, training according to the learning knowledge base, and obtaining the trained authority prediction model, the method further includes:

Taking all authority matrixes in the learning knowledge base as learning knowledge in advance, and importing the learning knowledge into the pre-constructed authority prediction model;

the step of inputting the training data set into a pre-constructed authority prediction model, training according to the learning knowledge base, and obtaining a trained authority prediction model specifically comprises the following steps:

analyzing each training field in the training data set respectively to obtain department information and post information corresponding to each employee respectively;

acquiring a right authority item and an unauthorized authority item of each employee in each business system output by a current authority prediction model according to the learning knowledge base and department information and post information respectively corresponding to each employee;

comparing the authority items and the non-authority items of each employee in each business system, which are output by the current prediction model, with the authority items and the non-authority items of each employee in each business system, which are acquired by the authority management system;

counting the number of staff with consistent comparison results, and calculating the proportion value of the number of staff with consistent comparison results in all staff according to a probability algorithm formula;

If the ratio value does not meet the preset ratio threshold, adjusting the super-parameters of the current authority prediction model, and inputting the training data set again to perform iterative training on the current authority prediction model;

and if the proportion value meets a preset proportion threshold value, acquiring the current authority prediction model as the trained authority prediction model, and stopping iterative training.

Further, the step of inputting the department information and the post information of the target employee in the company as prediction input data into the trained authority prediction model to obtain a model output result specifically includes:

taking the department information and the post information of the target staff in the company as key value pairs to generate a prediction field;

inputting the predicted field as the predicted input data into the trained authority prediction model;

analyzing the prediction field, and identifying department information and post information of the target employee in the company;

according to the department information and the post information of the target staff in the company, obtaining corresponding authority matrixes of the post information in the department information in different service systems from the trained authority prediction model;

Taking authority matrixes corresponding to the post information in the department information in different service systems as the model output result;

the step of obtaining the corresponding right authority items and the corresponding no right authority items of the target staff in each business system according to the output result of the model specifically comprises the following steps:

identifying corresponding authority matrixes of the target staff in different service systems respectively through corresponding authority matrixes of the post information in the department information in different service systems respectively;

according to a preset analysis rule, analyzing the authority matrixes corresponding to the target staff in different service systems respectively to obtain analysis results;

and according to the analysis result, obtaining the corresponding right authority items and the corresponding no right authority items of the target staff in each service system.

Further, the step of analyzing the authority matrixes corresponding to the target staff in different service systems according to a preset analysis rule to obtain an analysis result specifically includes:

sequentially taking authority matrixes corresponding to the target staff in different service systems as target analysis matrixes, and setting system identification information of the different service systems as distinguishing identification information of the corresponding target analysis matrixes;

Analyzing all right items corresponding to the first right value in each target analysis matrix;

analyzing all right items corresponding to the second right value in each target analysis matrix;

acquiring all right items corresponding to the first right value and all right items corresponding to the second right value in each target analysis matrix according to the distinguishing identification information, and taking the right items as analysis results;

the step of obtaining the corresponding right authority items and the corresponding no right authority items of the target staff in each service system according to the analysis result specifically comprises the following steps:

based on the distinguishing identification information, taking all right items corresponding to the first right values in different target analysis matrixes as right items corresponding to the target staff in the corresponding service system;

and according to the distinguishing identification information, taking all right items corresponding to the second right values in different target analysis matrixes as unauthorized right items corresponding to the target staff in the corresponding service system.

Further, before executing the step of configuring the authority attribute information of the target employee based on the corresponding authorized authority item and unauthorized authority item of the target employee in each service system, the method further includes:

Step 501, obtaining corresponding right authority items of target staff in each business system respectively;

step 502, respectively obtaining the strictest authority matrix and the widest authority matrix corresponding to different service systems;

step 503, according to the first and second authority values, analyzing the authority items contained in the strictest authority matrixes corresponding to different service systems;

step 504, according to the first and second authority values, analyzing the authority items contained in the widest authority matrixes corresponding to different service systems;

step 505, obtaining the right authority items contained in the strictest authority matrix in the current business system, and comparing the number of the right authority items contained in the widest authority matrix in the current business system with the corresponding right authority items of the target staff in the current business system;

step 506, if the number of the corresponding right authority items of the target employee in the current service system is less than the number of the right authority items contained in the strictest authority matrix in the current service system, the corresponding right prediction result of the target employee in the current service system is false, and a right prediction abnormal prompt is sent to the target monitoring terminal;

Step 507, if the number of the corresponding rights items of the target employee in the current service system is greater than the number of the rights items contained in the broadest rights matrix in the current service system, the corresponding rights prediction result of the target employee in the current service system is false, and a rights prediction abnormal prompt is sent to the target monitoring terminal;

step 508, sequentially taking different service systems as current service systems, and comparing the number of the right authority items according to step 505;

the step of configuring authority attribute information of the target staff based on the corresponding authority items and non-authority items of the target staff in each service system specifically comprises the following steps:

step 509, if the number of the corresponding right terms in each service system of the target employee is not less than the number of the right terms contained in the strictest right matrix in the corresponding service system and is not greater than the number of the right terms contained in the widest right matrix in the corresponding service system, starting a preset right configuration component, and configuring right attribute information of the target employee based on the corresponding right terms and non-right terms of the target employee in each service system.

In order to solve the technical problems, the embodiment of the application also provides an employee authority configuration device, which adopts the following technical scheme:

an employee rights configuration apparatus comprising:

the authority attribute information acquisition module is used for acquiring the authority attribute information of all staff in each business system of a company through a preset authority management system, wherein the authority attribute information comprises a right authority item and a no right authority item in each business system;

the personal attribute information acquisition module is used for acquiring personal attribute information of all staff in each business system of the company through a preset staff management system, wherein the personal attribute information comprises department information and post information which belong to the company;

the training data set construction module is used for constructing a training data set based on department information and post information of each employee in the company;

the learning knowledge base construction module is used for constructing a learning knowledge base according to the department information and the post information of each employee in the company and the right authority items and the non-right authority items in each business system;

the authority prediction model training module is used for inputting the training data set into a pre-constructed authority prediction model, training according to the learning knowledge base to obtain a trained authority prediction model;

The system comprises a target employee information acquisition module, a target employee information processing module and a target employee information processing module, wherein the target employee information acquisition module is used for acquiring department information and post information of a target employee in a company, and the target employee comprises a new employee and an old employee for adjusting departments or posts;

the model prediction output module is used for taking department information and post information of the target staff in the company as prediction input data, inputting the prediction input data into the trained authority prediction model, and obtaining a model output result;

the corresponding authority item acquisition module is used for acquiring corresponding authority items and non-authority items of target staff in each service system according to the output result of the model;

and the authority attribute information configuration module is used for configuring the authority attribute information of the target staff based on the authority items and the non-authority items corresponding to the target staff in each service system respectively, and updating the authority attribute information corresponding to the target staff into the authority management system after the configuration is completed.

In order to solve the above technical problems, the embodiment of the present application further provides a computer device, which adopts the following technical schemes:

a computer device comprising a memory having stored therein computer readable instructions which when executed by a processor implement the steps of the employee entitlement configuration method described above.

In order to solve the above technical problems, an embodiment of the present application further provides a computer readable storage medium, which adopts the following technical schemes:

a computer readable storage medium having stored thereon computer readable instructions which when executed by a processor implement the steps of an employee entitlement configuration method as described above.

Compared with the prior art, the embodiment of the application has the following main beneficial effects:

according to the employee authority configuration method, the authority prediction model is trained by acquiring the authority attribute information and the personal attribute information of all employees in each business system of a company in a machine learning and neural network mode, the authority attribute of the target employee is predicted, and the authority abnormality judgment is carried out before the authority attribute information of the target employee is configured, so that the abnormal authority content predicted by the authority prediction model is ensured to be found in time, the configuration of abnormal authorities is avoided to a certain extent, and the authority of the target employee is predicted by adopting the authority prediction model due to the fact that the financial company business system and the number of the employees are more, so that the intelligent and automatic configuration is realized, the complexity of manual configuration in the past is reduced, and the authority configuration efficiency can be improved compared with manual configuration.

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 employee rights configuration method in accordance with the application;

FIG. 3 is a flow chart of one embodiment of step 204 shown in FIG. 2;

FIG. 4 is a flow chart of one embodiment of step 208 of FIG. 2;

FIG. 5 is a flowchart of one embodiment of a method for determining authority abnormality prior to configuring authority attribute information of a target employee in an employee authority configuration method according to the present application;

FIG. 6 is a schematic diagram of an embodiment of an employee rights configuration apparatus in accordance with the application;

FIG. 7 is a schematic diagram of an embodiment of a computer device in accordance with the 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 terminology used in the description of the applications herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "comprising" and "having" and any variations thereof in the description of the application and the claims and the description of the drawings above are intended to cover a non-exclusive inclusion. 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, a system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 is used as a medium to provide communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.

The user may interact with the server 105 via the network 104 using the terminal devices 101, 102, 103 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, etc., may be installed on the terminal devices 101, 102, 103.

The terminal devices 101, 102, 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smartphones, tablet computers, electronic book readers, MP3 players (Moving Picture ExpertsGroup Audio Layer III, dynamic video expert compression standard audio plane 3), MP4 (Moving PictureExperts Group Audio Layer IV, dynamic video expert compression standard audio plane 4) players, laptop and desktop computers, and the like.

The server 105 may be a server providing various services, such as a background server providing support for pages displayed on the terminal devices 101, 102, 103.

It should be noted that, the employee permission configuration method provided by the embodiment of the present application is generally executed by a server/terminal device, and accordingly, the employee permission configuration apparatus is generally set 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 employee rights configuration method in accordance with the application is shown. The employee authority configuration method comprises the following steps:

Step 201, obtaining authority attribute information of all staff in each business system of a company through a preset authority management system, wherein the authority attribute information comprises a right authority item and an unauthorized authority item in each business system.

Specifically, the preset authority management system is an authority management system of a target finance company, and correspondingly, each business system of the company refers to different business systems of the target finance company, such as an intelligent AI e-commerce sales system, an online car insurance claim settlement system, an online car insurance application system, a personal insurance business system, a health insurance business system, a stock investment business system, a credit card business system and the like in the finance company.

Step 202, obtaining personal attribute information of all staff in each business system of a company through a preset staff management system, wherein the personal attribute information comprises department information and post information which belong to the company.

Specifically, personal attribute information of all employees in each business system of the company, for example, seat staff of a sales department in an on-line car insurance claim system, the department information of the personnel is the sales department, and the post information is the seat staff, for example, the front end U I designer of a research and development department in the on-line car insurance claim system, the department information of the personnel is the research and development department, and the post information is the front end U I designer.

Step 203, a training data set is constructed based on the department information and the post information of each employee in the company.

In this embodiment, the step of constructing the training data set based on the department information and the post information of each employee in the company specifically includes: generating a training field by taking department information and post information of the current employee in the company as key value pairs; sequentially taking all employees as current employees, and acquiring training fields respectively corresponding to all employees; and adding training fields corresponding to all staff respectively into a target set to complete the construction of the training data set.

Step 204, a learning knowledge base is constructed according to the department information and the post information of each employee in the company, and the right authority items and the no right authority items in each business system.

With continued reference to FIG. 3, FIG. 3 is a flow chart of one embodiment of step 204 shown in FIG. 2, comprising:

step 301, acquiring authority matrixes corresponding to different posts in different departments in different service systems respectively according to department information and post information of each employee in the company and the authority items and the non-authority items in each service system;

In this embodiment, the step of obtaining the authority matrix corresponding to different posts in different business systems in different departments according to the department information and post information of each employee in the company, and the authorized authority item and the unauthorized authority item in each business system specifically includes: adopting a double circulation mode to screen different current staff from all staff in turn and screen different current business systems from all business systems in turn; identifying a right authority item and an unauthorized authority item of a current employee in a current business system; constructing a right matrix corresponding to the current employee in the current business system according to the right and no right items of the current employee in the current business system, wherein a right value corresponding to the right item is set as a first right value in the right matrix, and a right value corresponding to the no right item is set as a second right value; constructing corresponding authority matrixes of each employee in different service systems according to the screened different current employees and different current service systems; based on the department information and the post information of each employee in the company and the authority matrixes corresponding to each employee in different service systems, the authority matrixes corresponding to different posts in different departments in different service systems are obtained.

Step 302, screening out the strictest authority matrix and the widest authority matrix respectively contained in different service systems according to the authority matrixes respectively corresponding to different posts in different departments, wherein the strictest authority matrix refers to the authority matrix with the least authority items contained in the target service system, and the widest authority matrix refers to the authority matrix with the most authority items contained in the target service system;

the strictest authority matrix and the widest authority matrix respectively contained in different business systems are selected through screening, and whether the authority prediction abnormality occurs in the authority matrix corresponding to the target employee can be judged after the authority prediction model outputs the authority matrix corresponding to the target employee.

And 303, sorting right matrixes corresponding to different posts in different departments in different service systems respectively, acquiring all right matrixes as warehousing elements, and constructing the learning knowledge base.

By constructing a learning knowledge base, the employee authority prediction model is conveniently trained by combining artificial intelligence and adopting a machine learning mode.

And 205, inputting the training data set into a pre-constructed authority prediction model, and training according to the learning knowledge base to obtain a trained authority prediction model.

In this embodiment, before the step of inputting the training data set into the pre-constructed permission prediction model and training according to the learning knowledge base to obtain the trained permission prediction model, the method further includes: and taking all authority matrixes in the learning knowledge base as learning knowledge in advance, and importing the learning knowledge into the pre-constructed authority prediction model.

In this embodiment, the step of inputting the training data set into a pre-constructed authority prediction model, and training according to the learning knowledge base to obtain a trained authority prediction model specifically includes: analyzing each training field in the training data set respectively to obtain department information and post information corresponding to each employee respectively; acquiring a right authority item and an unauthorized authority item of each employee in each business system output by a current authority prediction model according to the learning knowledge base and department information and post information respectively corresponding to each employee; comparing the authority items and the non-authority items of each employee in each business system, which are output by the current prediction model, with the authority items and the non-authority items of each employee in each business system, which are acquired by the authority management system; counting the number of staff with consistent comparison results, and calculating the proportion value of the number of staff with consistent comparison results in all staff according to a probability algorithm formula; if the ratio value does not meet the preset ratio threshold, adjusting the super-parameters of the current authority prediction model, and inputting the training data set again to perform iterative training on the current authority prediction model; and if the proportion value meets a preset proportion threshold value, acquiring the current authority prediction model as the trained authority prediction model, and stopping iterative training.

In this embodiment, the permission prediction model may be configured by adopting a neural learning network architecture, and when the ratio value does not meet a preset ratio threshold, a counter-propagation algorithm may be adopted to adjust the super-parameters of the current permission prediction model.

Step 206, obtaining department information and post information of a target employee in the company, wherein the target employee comprises a new employee and an old employee for adjusting departments or posts.

And 207, taking department information and post information of the target staff in the company as prediction input data, inputting the prediction input data into the trained authority prediction model, and obtaining a model output result.

In this embodiment, the step of inputting the department information and the post information of the target employee in the company as prediction input data into the trained authority prediction model to obtain a model output result specifically includes: taking the department information and the post information of the target staff in the company as key value pairs to generate a prediction field; inputting the predicted field as the predicted input data into the trained authority prediction model; analyzing the prediction field, and identifying department information and post information of the target employee in the company; acquiring corresponding authority matrixes of the position information in the department information in different service systems from the trained authority prediction model according to the department information and the position information of the target staff in the company; and taking authority matrixes corresponding to the post information in the department information in different service systems as the model output result.

And step 208, obtaining the corresponding right authority items and the corresponding no right authority items of the target staff in each service system according to the output result of the model.

With continued reference to FIG. 4, FIG. 4 is a flow chart of one embodiment of step 208 of FIG. 2, including:

step 401, identifying authority matrixes corresponding to the target staff in different service systems respectively through authority matrixes corresponding to the post information in the department information in different service systems respectively;

step 402, according to a preset analysis rule, analyzing the authority matrixes corresponding to the target staff in different service systems respectively to obtain an analysis result;

in this embodiment, the step of analyzing the authority matrices corresponding to the target staff in different service systems according to a preset analysis rule to obtain an analysis result specifically includes: sequentially taking authority matrixes corresponding to the target staff in different service systems as target analysis matrixes, and setting system identification information of the different service systems as distinguishing identification information of the corresponding target analysis matrixes; analyzing all right items corresponding to the first right value in each target analysis matrix; analyzing all right items corresponding to the second right value in each target analysis matrix; and acquiring all right items corresponding to the first right value and all right items corresponding to the second right value in each target analysis matrix according to the distinguishing identification information, and taking the obtained right items as analysis results.

Step 403, obtaining the corresponding right authority items and the corresponding no right authority items of the target staff in each service system according to the analysis result.

In this embodiment, the step of obtaining, according to the analysis result, the right authority item and the no right authority item corresponding to the target employee in each service system, includes: according to the distinguishing identification information, taking all authority items corresponding to the first authority values in different target analysis matrixes as the authority items corresponding to the target staff in the corresponding service system; and according to the distinguishing identification information, taking all right items corresponding to the second right values in different target analysis matrixes as unauthorized right items corresponding to the target staff in the corresponding service system.

Step 209, configuring authority attribute information of the target staff based on the authority items and the non-authority items corresponding to the target staff in each service system, and updating the authority attribute information corresponding to the target staff to the authority management system after the configuration is completed.

With continued reference to fig. 5, fig. 5 is a flowchart of a specific embodiment of a permission anomaly determination method before configuring permission attribute information of a target employee in the employee permission configuration method according to the present application, including:

step 508, sequentially taking different service systems as the current service system, and comparing the number of the right authority items according to step 505.

In this embodiment, the step of configuring authority attribute information of the target employee based on the corresponding authority item and non-authority item of the target employee in each service system specifically includes:

By performing authority abnormality judgment before configuring the authority attribute information of the target staff, abnormal authority content predicted by the authority prediction model is ensured to be found in time, and configuration of abnormal authorities is avoided to a certain extent.

The authority attribute information and the personal attribute information of all staff in each business system of a company are acquired, the authority prediction model is trained by adopting a machine learning and neural network mode, the authority attribute of the target staff is predicted, and the authority abnormality judgment is carried out before the authority attribute information of the target staff is configured, so that the abnormal authority content predicted by the authority prediction model is ensured to be found in time, the configuration of abnormal authorities is avoided to a certain extent, and the authority of the target staff is predicted by adopting the authority prediction model because of more business systems and staff numbers of the finance company, so that the complexity of the conventional manual configuration is reduced, and the authority configuration efficiency can be improved compared with the manual configuration.

The embodiment of the application can acquire and process the related data based on the artificial intelligence technology. Among these, artificial intelligence (Artificial Intelligence, AI) is the theory, method, technique and application system that uses a digital computer or a digital computer-controlled machine to simulate, extend and extend 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.

In the embodiment of the application, the authority of the target staff is predicted by adopting the authority prediction model, so that the method is more intelligent and automatic, reduces the complexity of manual configuration in the past, and can improve the efficiency of authority configuration compared with manual configuration.

With further reference to fig. 6, as an implementation of the method shown in fig. 2, the present application provides an embodiment of an employee rights configuration apparatus, which corresponds to the method embodiment shown in fig. 2, and which is particularly applicable to various electronic devices.

As shown in fig. 6, the employee right configuration apparatus 600 according to the present embodiment includes: the authority attribute information acquisition module 601, the personal attribute information acquisition module 602, the training data set construction module 603, the learning knowledge base construction module 604, the authority prediction model training module 605, the target employee information acquisition module 606, the model prediction output module 607, the corresponding authority item acquisition module 608 and the authority attribute information configuration module 609. Wherein:

the authority attribute information obtaining module 601 is configured to obtain authority attribute information of all employees in each business system of a company through a preset authority management system, where the authority attribute information includes a right authority item and an unauthorized authority item in each business system;

the personal attribute information obtaining module 602 is configured to obtain personal attribute information of all employees in each business system of a company through a preset employee management system, where the personal attribute information includes department information and post information that belong to the company;

a training data set construction module 603, configured to construct a training data set based on department information and post information that each employee belongs to in a company;

the learning knowledge base construction module 604 is configured to construct a learning knowledge base according to department information and post information of each employee in the company, and a right authority item and a no right authority item in each business system;

The permission prediction model training module 605 is configured to input the training data set into a pre-constructed permission prediction model, and perform training according to the learning knowledge base to obtain a trained permission prediction model;

a target employee information obtaining module 606, configured to obtain department information and post information that a target employee belongs to in a company, where the target employee includes a new employee, an old employee performing department or post adjustment;

the model prediction output module 607 is configured to input, as prediction input data, department information and post information of the target employee in the company into the trained authority prediction model, and obtain a model output result;

the corresponding authority item obtaining module 608 is configured to obtain, according to the output result of the model, a corresponding authority item and an unauthorized authority item of the target employee in each service system respectively;

the authority attribute information configuration module 609 is configured to configure authority attribute information of a target employee based on a corresponding authority item and an unauthorized authority item of the target employee in each service system, and update the authority attribute information corresponding to the target employee to the authority management system after the configuration is completed.

Those skilled in the art will appreciate that implementing all or part of the above described embodiment methods may be accomplished by computer readable instructions, stored on a computer readable storage medium, that the program when executed may comprise the steps of 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-Only 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.

In order to solve the technical problems, the embodiment of the application also provides computer equipment. Referring specifically to fig. 7, fig. 7 is a basic structural block diagram of a computer device according to the present embodiment.

The computer device 7 comprises a memory 7a, a processor 7b, a network interface 7c communicatively connected to each other via a system bus. It should be noted that only a computer device 7 having components 7a-7c is shown in the figures, but it should be understood that not all of the illustrated components need be implemented, and that more or fewer components may alternatively be implemented. 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 in accordance with predetermined or stored instructions, the hardware of which includes, but is not limited to, microprocessors, application specific integrated circuits (Application Specific Integrated Circuit, ASICs), programmable gate arrays (fields-Programmable Gate Array, FPGAs), digital processors (Digital Signal Processor, DSPs), embedded devices, etc.

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 7a 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 7a may be an internal storage unit of the computer device 7, such as a hard disk or a memory of the computer device 7. In other embodiments, the memory 7a may also be an external storage device of the computer device 7, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash Card (Flash Card) or the like, which are provided on the computer device 7. Of course, the memory 7a may also comprise both an internal memory unit of the computer device 7 and an external memory device. In this embodiment, the memory 7a is typically used to store an operating system and various application software installed on the computer device 7, such as computer readable instructions of an employee authority configuration method. Further, the memory 7a may be used to temporarily store various types of data that have been output or are to be output.

The processor 7b may be a central processing unit (Central Processing Unit, CPU), controller, microcontroller, microprocessor, or other data processing chip in some embodiments. The processor 7b is typically used to control the overall operation of the computer device 7. In this embodiment, the processor 7b is configured to execute computer readable instructions stored in the memory 7a or process data, such as computer readable instructions for executing the employee authority configuration method.

The network interface 7c may comprise a wireless network interface or a wired network interface, which network interface 7c is typically used for establishing a communication connection between the computer device 7 and other electronic devices.

The computer equipment provided by the embodiment belongs to the technical field of financial science and technology, and is applied to a staff authority configuration scene of a financial company. The authority attribute information and the personal attribute information of all staff in each business system of a company are acquired, the authority prediction model is trained by adopting a machine learning and neural network mode, the authority attribute of the target staff is predicted, and the authority abnormality judgment is carried out before the authority attribute information of the target staff is configured, so that the abnormal authority content predicted by the authority prediction model is ensured to be found in time, the configuration of abnormal authorities is avoided to a certain extent, and the authority of the target staff is predicted by adopting the authority prediction model because of more business systems and staff numbers of the finance company, so that the complexity of the conventional manual configuration is reduced, and the authority configuration efficiency can be improved compared with the manual configuration.

The present application also provides another embodiment, namely, a computer readable storage medium, where computer readable instructions are stored, where the computer readable instructions are executable by a processor, to cause the processor to perform steps of an employee authority configuration method as described above.

The computer readable storage medium provided by the embodiment belongs to the technical field of financial science and technology, and is applied to a staff authority configuration scene of a financial company. The authority attribute information and the personal attribute information of all staff in each business system of a company are acquired, the authority prediction model is trained by adopting a machine learning and neural network mode, the authority attribute of the target staff is predicted, and the authority abnormality judgment is carried out before the authority attribute information of the target staff is configured, so that the abnormal authority content predicted by the authority prediction model is ensured to be found in time, the configuration of abnormal authorities is avoided to a certain extent, and the authority of the target staff is predicted by adopting the authority prediction model because of more business systems and staff numbers of the finance company, so that the complexity of the conventional manual configuration is reduced, and the authority configuration efficiency can be improved compared with the manual configuration.

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. The employee authority configuration method is characterized by comprising the following steps of:

2. The employee permission configuration method according to claim 1, wherein the step of constructing the training data set based on department information and post information to which each employee belongs in the company, specifically comprises:

3. The employee authority configuration method according to claim 2, wherein the step of obtaining authority matrices corresponding to different posts in different departments in different service systems according to department information and post information of each employee in the company, and the authority items and the non-authority items in the respective service systems, specifically comprises:

4. An employee entitlement configuration method according to claim 2, wherein prior to performing said step of inputting said training dataset into a pre-constructed entitlement prediction model, training in accordance with said learning knowledge base to obtain a trained entitlement prediction model, said method further comprises:

5. The employee authority configuration method according to claim 3, wherein the step of inputting the department information and the post information of the target employee in the company as prediction input data into the trained authority prediction model to obtain a model output result specifically comprises:

acquiring corresponding authority matrixes of the position information in the department information in different service systems from the trained authority prediction model according to the department information and the position information of the target staff in the company;

6. The employee authority configuration method according to claim 5, wherein the step of analyzing the authority matrix corresponding to the target employee in different service systems according to a preset analysis rule to obtain an analysis result specifically includes:

according to the distinguishing identification information, taking all authority items corresponding to the first authority values in different target analysis matrixes as the authority items corresponding to the target staff in the corresponding service system;

7. An employee rights configuration method as claimed in claim 3, wherein prior to performing the step of configuring rights attribute information of a target employee based on the respective rights items and non-rights items of the target employee in respective business systems, the method further comprises:

8. An employee rights configuration apparatus, comprising:

the authority prediction model training module is used for inputting the training data set into a pre-constructed authority prediction model, and training according to the learning knowledge base to obtain a trained authority prediction model;

9. A computer device comprising a memory having stored therein computer readable instructions which when executed by a processor implement the steps of the employee entitlement configuration method of any of claims 1 to 7.

10. A computer readable storage medium having stored thereon computer readable instructions which when executed by a processor implement the steps of the employee entitlement configuration method of any of claims 1 to 7.