Method and system for intelligently updating current month employee information table

Abstract

The invention discloses a method and system for intelligently updating a current month employee information table, and relates to the technical field of artificial intelligence, and the method comprises the steps: converting multi-modal data into text data with an enterprise identifier; based on the enterprise identifier, acquiring an information acquisition requirement of the corresponding enterprise from a preset rule base, calculating an information error rate of the text data according to the information acquisition requirement, if the information error rate is greater than a preset threshold value, generating prompt information, pushing the prompt information to the interactive interface, and re-acquiring the text data, the information error rate of the text data is smaller than a preset threshold value; social security policy data are acquired, and a policy change time sequence is constructed based on the social security policy data; and an information table updating model is constructed for outputting the final employee information table of the current month, so that the problems of high information error rate, lagging social security policy change response and low efficiency in the existing employee information table updating method can be solved.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and particularly to a method and system for intelligently updating the employee information table of this month. Background Art

[0002] In enterprise human resource management, the update of the employee information table involves multi-modal data processing and policy adaptation, and there are significant deficiencies in the existing technologies. The enterprise data sources are extensive, covering various formats such as text, Excel, and images. Traditional methods rely on manual collation into a standard format and then import into the system. The data integration efficiency is low and error-prone, often resulting in an information error rate exceeding 15%, consuming a large amount of time of HR. At the same time, the social security policies change frequently, with large regional differences and complex applicable populations. The existing systems lack the ability to construct a time series of policy changes and cannot real-time associate policy terms with employee information. The enterprise generates an annual supplementary payment cost accounting for 8%-12% of the total charge due to policy adaptation errors.

[0003] In addition, when updating the employee information table, traditional methods are difficult to accurately identify the differences brought by changes in employee information and policy adjustments, and relying on manual comparison is prone to missing key information, resulting in lag and inaccuracy in information update.

[0004] Therefore, there is an urgent need for a method that can automatically analyze multi-modal data, dynamically respond to policy changes, accurately identify differences, and efficiently update the employee information table. Summary of the Invention

[0005] In view of this, the present invention proposes a method and system for intelligently updating the employee information table of this month, which can realize automatically analyzing multi-modal data, dynamically responding to policy changes, accurately identifying differences, and efficiently updating the employee information table.

[0006] To achieve the above object, the present invention provides the following technical solutions: A method for intelligently updating the employee information table of this month, comprising: Obtaining multi-modal data, analyzing the multi-modal data to obtain the enterprise identifier corresponding to the multi-modal data, and converting the multi-modal data into text data with the enterprise identifier; Based on the enterprise identifier, obtaining the information acquisition requirements of the corresponding enterprise from a preset rule library, calculating the information error rate of the text data according to the information acquisition requirements. If the information error rate is greater than a preset threshold, generating a prompt message and pushing the prompt message to an interaction interface, and re-obtaining the text data until the information error rate of the text data is less than the preset threshold; Obtaining social security policy data, constructing a policy change time series based on the social security policy data, and recording the effective time, regional scope, applicable population, and specific terms of each social security policy; Construct an information table update model, where the information table update model includes an input layer, a comparison layer, a conversion layer, and an output layer; The input layer is used to receive text data with an enterprise identifier and the policy change time series; The comparison layer is used to obtain the employee information table of the corresponding enterprise in the previous month based on the text data with the enterprise identifier, adjust the employee information table of the previous month based on the text data to obtain the initial employee information table of this month, calculate the first comprehensive difference value between the initial employee information table of this month and the employee information table of the previous month, calculate the second comprehensive difference value between the policy change time series and the initial employee information table of this month, and determine a difference list based on the first comprehensive difference value and the second comprehensive difference value. The difference list includes newly added employee information, departed employee information, information-changed employee information, and employees whose social insurance needs to be adjusted; The conversion layer is used to generate the final employee information table of this month based on the difference list, the initial employee information table of this month, and the employee information table of the previous month; The output layer is used to output the final employee information table of this month.

[0007] Based on the above technical solution, the present invention can also be improved as follows: Optionally, obtaining the information acquisition requirements of the corresponding enterprise from a preset rule library based on the enterprise identifier, and calculating the information error rate of the text data according to the information acquisition requirements includes: Obtaining the set of fields that need to be format-checked for enterprise e and the set of fields that need to be logically checked for enterprise e from the preset rule library; Determining the set of fields with format errors and the set of fields with logical errors in the text data based on the set of fields that need to be format-checked for enterprise e and the set of fields that need to be logically checked for enterprise e; Calculating the information error rate of the text information according to the set of fields that need to be format-checked for enterprise e, the set of fields that need to be logically checked for enterprise e, the set of fields with format errors in the text data, and the set of fields with logical errors in the text data.

[0008] Optionally, obtaining the social insurance policy data and constructing a policy change time series based on the social insurance policy data includes: Constructing a policy change time series according to time point t, the set of policies in effect at time point t, the set of policies that have changed at time point t, and the set of time points T.

[0009] Optionally, calculating the first comprehensive difference value between the initial employee information table of this month and the employee information table of the previous month includes: Obtaining the difference in the number of personnel and the difference in information changes between the initial employee information table of this month and the employee information table of the previous month; Calculate a first comprehensive difference value between the initial current-month employee information table and the previous-month employee information table based on the weight of the personnel quantity difference, the personnel quantity difference, the weight of the information change difference, and the information change difference.

[0010] Optionally, calculating a second comprehensive difference value between the policy change time series and the initial current-month employee information table includes: Calculate the second comprehensive difference value based on the set of currently effective social insurance policies, the business weight of policy p, the number of employees not covered by policy p, the total number of employees in the initial current-month employee information table, the number of non-compliant employees covered by policy p, the theoretically covered number of employees by policy p, the time decay factor of policy p, and the number of effective policies.

[0011] Optionally, calculating the first comprehensive difference value between the initial current-month employee information table and the previous-month employee information table, and calculating the second comprehensive difference value between the policy change time series and the initial current-month employee information table includes: When calculating the first and second comprehensive difference values, introduce a fuzzy matching algorithm to perform similarity calculations on semantically similar information to improve the accuracy of identifying the differences between the initial current-month employee information table and the previous-month employee information table.

[0012] Optionally, the method for intelligently updating the current-month employee information table further includes: Calculate the enterprise charge amount for this month based on a preset charging rule engine and the final current-month employee information table.

[0013] A system for intelligently updating the current-month employee information table includes: A text data acquisition module, configured to acquire multimodal data, parse the multimodal data to obtain the enterprise identifier corresponding to the multimodal data, and convert the multimodal data into text data with the enterprise identifier. A text data determination module, configured to obtain the information acquisition requirements for the corresponding enterprise from a preset rule library based on the enterprise identifier, calculate the information error rate of the text data according to the information acquisition requirements, and if the information error rate is greater than a preset threshold, generate a prompt message and push the prompt message to an interaction interface, and re-acquire the text data until the information error rate of the text data is less than the preset threshold. A social insurance policy acquisition module, configured to acquire social insurance policy data, construct a policy change time series based on the social insurance policy data, and record the effective time, regional scope, applicable population, and specific terms of each social insurance policy. A model construction module, configured to construct an information table update model, where the information table update model includes an input layer, a comparison layer, a conversion layer, and an output layer. The input layer is used to receive text data with enterprise identification and the time series of policy changes; The comparison layer is used to obtain the employee information table of the previous month of the corresponding enterprise based on the text data with enterprise identification, adjust the employee information table of the previous month based on the text data to obtain the initial employee information table of this month, calculate the first comprehensive difference value between the initial employee information table of this month and the employee information table of the previous month, calculate the second comprehensive difference value between the time series of policy changes and the initial employee information table of this month, and determine a difference list based on the first comprehensive difference value and the second comprehensive difference value. The difference list includes new employee information, separated employee information, information-changed employee information, and employees whose social security needs to be adjusted; The conversion layer is used to generate the final employee information table of this month based on the difference list, the initial employee information table of this month, and the employee information table of the previous month; The output layer is used to output the final employee information table of this month.

[0014] An electronic device includes a memory, a processor, and a computer program stored on the memory and running on the processor. When the processor executes the computer program, the steps of the method are implemented.

[0015] A non-transitory computer-readable storage medium stores a computer program thereon. When the computer program is executed by a processor, the steps of the method are implemented.

[0016] The present invention has the following advantages: In the method for intelligently updating the employee information table of this month in the present invention, in terms of data processing, through multimodal data parsing and intelligent verification, multimodal data is automatically converted into a standard text format, greatly reducing the manual sorting time, and the information error rate can be controlled within 5%. For social security policy management, a time series of policy changes is constructed, which can record policy changes in real time, help enterprises adapt to new policies in a timely manner, avoid the annual supplementary payment cost of 8%-12% caused by lagging policy understanding, and reduce compliance risks. In terms of employee information update, the hierarchical difference calculation mechanism of the information table update model can accurately identify changes in employee information (new addition, separation, information change) and the impact of policy adjustments. Through the generation of the initial employee information table of this month, dual difference comparison, and the final employee information table of this month, the timeliness and accuracy of the employee information table update are ensured. Compared with traditional methods, the present invention improves the update efficiency of the employee information table by more than 60%, providing an efficient, accurate, and compliant solution for enterprise human resource management. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] For purposes of illustration and not limitation, the present invention is described in conjunction with the embodiments and drawings of the present invention, wherein: Figure 1Schematic flowchart of the method for intelligently updating the monthly employee information form in the embodiments of the present invention; Figure 2 Schematic diagram of the main components of the system for intelligently updating the monthly employee information form in the embodiments of the present invention; Figure 3 Schematic diagram of the physical structure of the electronic device provided by the present invention. Specific embodiments

[0018] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0019] It should be noted that the terms "first", "second", etc. in the specification of the present invention and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily need to describe a specific order or sequence. It should be understood that such data used in appropriate cases can be interchanged so as to implement the embodiments of the present invention described here. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device including a series of steps or units does not necessarily need to be limited to those clearly listed steps or units, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0020] It should be noted that, without conflict, the embodiments of the present invention and the features in the embodiments can be combined with each other. The embodiments of the present invention will be described in detail below in conjunction with the drawings.

[0021] Figure 1 Schematic flowchart of the method for intelligently updating the monthly employee information form in the embodiments of the present invention. As Figure 1 shown, the method for intelligently updating the monthly employee information form provided by the embodiments of the present invention includes the following steps S101 to S104.

[0022] S101. Obtain multimodal data, parse the multimodal data to obtain the enterprise identifier corresponding to the multimodal data, and convert the multimodal data into text data with the enterprise identifier.

[0023] The multimodal data includes image data (such as screenshots), text data (business system forms), and audio data (recordings); In this step, these multimodal data are first preprocessed, such as enhancing image clarity and converting speech to text. Then, through techniques such as regular expressions and named entity recognition, identification information such as unified social credit codes and enterprise names is extracted. Finally, the extracted information is filled into a preset template to generate structured text data with enterprise identification.

[0024] S102. Based on the enterprise identification, obtain the information acquisition requirements corresponding to the enterprise from the preset rule library. According to the information acquisition requirements, calculate the information error rate of the text data. If the information error rate is greater than the preset threshold, generate a prompt message and push the prompt message to the interaction interface, and re-obtain the text data until the information error rate of the text data is less than the preset threshold.

[0025] Obtain the set of fields that need to be format-checked for enterprise e and the set of fields that need to be logically checked for enterprise e from the preset rule library; Based on the set of fields that need to be format-checked for enterprise e and the set of fields that need to be logically checked for enterprise e, determine the set of fields with format errors and the set of fields with logical errors in the text data; Calculate the information error rate of the text information according to the set of fields that need to be format-checked for enterprise e, the set of fields that need to be logically checked for enterprise e, the set of fields with format errors in the text data, and the set of fields with logical errors in the text data.

[0026] Calculate the information error rate of the text data through formula (1); Formula (1); In the formula, is the information error rate of the text data, is the enterprise identification, is the set of fields with format errors in the text data, is the set of fields with logical errors in the text data, is the set of fields that need to be format-checked for enterprise e, is the set of fields that need to be logically checked for enterprise e.

[0027] The example is as follows: Suppose the text data of enterprise e needs to verify social security information. In the preset rule library: The set of fields that need to be format-checked : Social security number (10 digits), bank account (19 digits), employment date (YYYY - MM - DD); The set of fields that need to be logically checked : Salary (≥ 2300 yuan, the local minimum wage), social security base (≥ 60% of the salary), retirement status (the social security base of retired employees should be 0); Extract fields from enterprise text data. After verification, it is found that: 1. Fields with format errors ; Social security number: "A12345678" (contains letters, does not meet the requirement of 10 digits) → 1 format error; Hire date: "2023 / 13 / 01" (month 13 is invalid, does not meet YYYY - MM - DD) → 1 format error; Bank account: "12345678901234567" (17 digits, does not meet the requirement of 19 digits) → 1 format error → (Total number of fields with format errors); 2. Fields with logical errors ; Salary: 2000 yuan (lower than the local minimum wage of 2300 yuan) → 1 logical error; Social security base: 2500 yuan (salary 2000 yuan × 60% = 1200 yuan, 2500 yuan does not meet the rule) → 1 logical error; Retired employees: The social security base of retired employees is filled as 3000 yuan (should be 0) → 1 logical error → (Total number of fields with logical errors); Substitute into formula (1) and calculate to get: ; The information error rate of this enterprise's text data is 100%, indicating that: All fields that require format verification have errors (social security number, bank account, hire date are all wrong); All fields that require logical verification also have errors (salary, social security base, retirement status have logical contradictions); It is necessary to trigger the "re - obtain text data" process until the error rate is lower than the preset threshold.

[0028] S103. Obtain social security policy data, construct a policy change time series based on the social security policy data, and record the effective time, regional scope, applicable population, and specific terms of each social security policy.

[0029] Construct a policy change time series according to the time point t, the set of policies in effect at time point t, the set of policies changed at time point t, and the set of time points T.

[0030] Calculate the policy change time series through formula (2); Formula (2); In the formula, is the policy change time series, is the time point, is the set of policies effective at time point t, is the set of policies that change at time point t, is the set of time points.

[0031] S104, construct an information table update model, which includes an input layer, a comparison layer, a conversion layer, a calculation layer, and an output layer.

[0032] The input layer is used to receive text data with enterprise identifiers and the policy change time series; The comparison layer is used to obtain the employee information table of the corresponding enterprise in the previous month based on the text data with enterprise identifiers, calculate the first comprehensive difference value between the text data and the employee information table of the previous month, calculate the second comprehensive difference value between the policy change time series and the employee information table of the previous month, and determine a difference list based on the first comprehensive difference value and the second comprehensive difference value. The difference list includes newly added employee information, departed employee information, employee information with changes, and employees whose social insurance needs to be adjusted; Obtain the difference in the number of personnel and the difference in information changes between the initial employee information table of this month and the employee information table of the previous month; Calculate the first comprehensive difference value between the initial employee information table of this month and the employee information table of the previous month according to the weight of the difference in the number of personnel, the difference in the number of personnel, the weight of the difference in information changes, and the difference in information changes; Calculate the first comprehensive difference value between the initial employee information table of this month and the employee information table of the previous month through formula (3); Formula (3); In the formula, is the difference value between the initial employee information table of this month and the employee information table of the previous month, is the weight of the difference in the number of personnel, is the difference in the number of personnel, is the weight of the difference in information changes, is the difference value of employee information changes.

[0033] Calculate the difference in the number of personnel; ; In the formula, is the difference in the number of personnel, is the set of on-the-job employees in the employee information table of the previous month, is the set of on-the-job employees in the initial employee information table of this month, is the set of departed employees in the employee information table of the previous month, is the set of departed employees in the initial employee information table of this month; Calculate the difference in information changes through formula (4); Formula (4); In the formula, For information change differences, is the number of employees who were employed both last month and this month. is the i-th employee who was employed both last month and this month, For the employees in the initial employee information table for this month A collection of information (such as wages, social security base, etc.), For employees in the employee information table of last month A collection of information, is the information difference indicator function.

[0034] Example: Assume that a company: Last month's employee information table: (Working), (Resigned).

[0035] Initial employee information form for this month: (Working), (Resigned).

[0036] Intersection of current employees Among them, the information of 10 people has changed (such as salary and position).

[0037] Weight setting , .

[0038] ; The combined difference between the company's employee information form last month and the initial employee information form this month is approximately 8.45%.

[0039] The larger it is, the more necessary it is for the company to adjust its fees (such as social security backpayment and wage arrears).

[0040] The second comprehensive difference value is calculated based on the current effective social security policy set, the business weight of policy p, the number of employees not covered by policy p, the total number of employees in the initial employee information table for this month, the number of employees covered but not compliant by policy p, the theoretical number of employees covered by policy p, the time decay factor of policy p and the number of effective policies.

[0041] The second comprehensive difference value is calculated by formula (5); Formula (5); In the formula, is the second comprehensive difference value, is the set of social security policies currently in effect, is the business weight of policy p, is the number of employees outside the coverage of policy p, is the total number of employees in the initial monthly employee information form, is the number of employees within the coverage of policy p but non - compliant, is the theoretical number of covered employees by policy p, is the time decay factor of policy p, is the number of effective policies.

[0042] Example: Effective policy P = {Base increase policy}, (single - policy scenario); Employees this month: = 200 people, theoretical policy coverage = 180 people (excluding 20 flexible employees); Compliance check: Among 180 people, 10 people's base does not reach the new policy lower limit; Policy effective time , current , (decay coefficient k = 0.1).

[0043] ; A conversion layer for converting the difference list into a specified format to generate the monthly employee information form; An output layer for outputting the final monthly employee information form.

[0044] The method for intelligently updating the monthly employee information form further includes: calculating the monthly enterprise charge amount based on a preset charging rule engine and the final monthly employee information form; Calculating the monthly enterprise charge amount based on a preset charging rule engine and the final monthly employee information form through formula (6); Formula (6); In the formula, is the total monthly enterprise charge amount, is the set of on - the - job employees in the final monthly employee information form, is the basic fee of employee e, is the set of information changes of employee e, is the fee change caused by difference type d, is the set of policies applicable to employee e, is the additional fee caused by policy p.

[0045] ; is a collection of expense types (such as social insurance, provident fund, and taxes). is the weight of expense type t (e.g. social security weight is 0.7), To calculate the expense of type t based on the current information of employee e.

[0046] Example: There are 100 employees on the job this month; Information change: 20 employees' wages increased from 7,500 yuan to 8,000 yuan, and the social security base was adjusted simultaneously; Policy adjustment: The lower limit of the social security base for all employees has been increased from 5,000 yuan to 6,000 yuan, and the difference from the previous month must be paid (16% for the enterprise); Personnel changes: 5 new employees (average salary 9,000 yuan), 3 employees resigned (average salary 7,000 yuan).

[0047] The calculation results are: .

[0048] Figure 2 Schematic diagram of the main components of the system for intelligently updating the employee information table of this month in an embodiment of the present invention. Figure 2 As shown, the system 1 for intelligently updating the employee information table of this month provided by the embodiment of the present invention includes a text data acquisition module 10 , a text data determination module 20 , a social security policy acquisition module 30 and a model construction module 40 .

[0049] A text data acquisition module 10 is used to acquire multimodal data, parse the multimodal data, obtain the corporate logo corresponding to the multimodal data, and convert the multimodal data into text data with the corporate logo; The text data determination module 20 is used to obtain the information acquisition requirements of the corresponding enterprise from the preset rule library based on the enterprise identification, calculate the information error rate of the text data according to the information acquisition requirements, and if the information error rate is greater than a preset threshold, generate prompt information and push the prompt information to the interactive interface, and re-acquire text data until the information error rate of the text data is less than the preset threshold; A social security policy acquisition module 30 is used to acquire social security policy data, construct a policy change time series based on the social security policy data, and record the effective time, regional scope, applicable population and specific terms of each social security policy; A model building module 40, used to build an information table update model, wherein the information table update model includes an input layer, a comparison layer, a conversion layer and an output layer; The input layer is used to receive text data with enterprise logo and the policy change time series; The comparison layer is used to obtain the employee information table of the corresponding enterprise for the previous month based on the text data with enterprise identification, adjust the employee information table of the previous month based on the text data to obtain the initial employee information table for the current month, calculate the first comprehensive difference value between the initial employee information table for the current month and the employee information table of the previous month, calculate the second comprehensive difference value between the policy change time series and the initial employee information table for the current month, and determine a difference list based on the first comprehensive difference value and the second comprehensive difference value. The difference list includes newly added employee information, departed employee information, employee information with changes, and employees whose social insurance needs to be adjusted; A conversion layer, which is used to generate the final employee information table for the current month based on the difference list, the initial employee information table for the current month, and the employee information table of the previous month; An output layer, which is used to output the final employee information table for the current month.

[0050] Figure 3 The following is a schematic diagram of the physical structure of the electronic device provided by the embodiment of the present invention, as Figure 3 shown, the electronic device 50 includes: a processor 501 (processor), a memory 502 (memory), and a bus 503; Among them, the processor 501 and the memory 502 complete communication with each other through the bus 503; The processor 501 is used to call the program instructions in the memory 502 to execute the methods provided by the above-mentioned method embodiments, so as to execute the method provided by the embodiment of the present invention.

[0051] This embodiment provides a non-transitory computer-readable storage medium. The non-transitory computer-readable storage medium stores computer instructions, and the computer instructions enable the computer to execute the method provided by the embodiment of the present invention.

[0052] Those of ordinary skill in the art can understand that all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions. The foregoing program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps including the above method embodiments; and the foregoing storage medium includes: various storage media such as ROM, RAM, magnetic disk, or optical disc that can store program codes.

[0053] The above specific embodiments do not constitute a limitation on the protection scope of the present invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can occur depending on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for intelligently updating the employee information form of this month, characterized in that, Including: Obtain multimodal data, parse the multimodal data to obtain the enterprise identifier corresponding to the multimodal data, and convert the multimodal data into text data with the enterprise identifier; Based on the enterprise identifier, obtain the information acquisition requirements of the corresponding enterprise from the preset rule library. According to the information acquisition requirements, calculate the information error rate of the text data. If the information error rate is greater than the preset threshold, generate a prompt message and push the prompt message to the interaction interface, and re-obtain the text data until the information error rate of the text data is less than the preset threshold; Obtain social insurance policy data, construct a policy change time series based on the social insurance policy data, and record the effective time, regional scope, applicable population, and specific terms of each social insurance policy; Construct an information table update model, where the information table update model includes an input layer, a comparison layer, a conversion layer, and an output layer; The input layer is used to receive text data with the enterprise identifier and the policy change time series; The comparison layer is used to obtain the employee information table of the previous month of the corresponding enterprise based on the text data with the enterprise identifier, adjust the employee information table of the previous month based on the text data to obtain the initial employee information table of this month, calculate the first comprehensive difference value between the initial employee information table of this month and the employee information table of the previous month, calculate the second comprehensive difference value between the policy change time series and the initial employee information table of this month, and determine a difference list based on the first comprehensive difference value and the second comprehensive difference value. The difference list includes new employee information, separated employee information, information-changed employee information, and employees to be adjusted for social insurance; The conversion layer is used to generate the final employee information table of this month based on the difference list, the initial employee information table of this month, and the employee information table of the previous month; The output layer is used to output the final employee information table of this month.

2. The method for intelligently updating the monthly employee information form according to claim 1, wherein The obtaining the information acquisition requirements of the corresponding enterprise from the preset rule library according to the enterprise identifier and calculating the information error rate of the text data according to the information acquisition requirements includes: Obtain the set of fields that need format verification and the set of fields that need logical verification for enterprise e from the preset rule library; Based on the set of fields that need format verification for enterprise e and the set of fields that need logical verification for enterprise e, determine the set of fields with format errors and the set of fields with logical errors in the text data; Calculate the information error rate of the text information according to the set of fields that need format verification for enterprise e, the set of fields that need logical verification for enterprise e, the set of fields with format errors in the text data, and the set of fields with logical errors.

3. The method for intelligently updating the monthly employee information form according to claim 1, characterized in that, The obtaining the social insurance policy data and constructing the policy change time series based on the social insurance policy data includes: Construct a policy change time series according to time point t, the set of policies in effect at time point t, the set of policies changed at time point t, and the set of time points T.

4. The method for intelligently updating the monthly employee information table according to claim 1, wherein The calculating the first comprehensive difference value between the initial employee information table of this month and the employee information table of the previous month includes: Obtain the difference in the number of personnel and the difference in information changes between the initial employee information table of this month and the employee information table of the previous month; Calculate the first comprehensive difference value between the initial monthly employee information table and the previous month's employee information table based on the weight of the personnel quantity difference, the personnel quantity difference, the weight of the information change difference, and the information change difference.

5. The method for intelligently updating the monthly employee information table according to claim 1, wherein The calculation of the second comprehensive difference value between the policy change time series and the initial monthly employee information table includes: Calculate the second comprehensive difference value based on the currently effective social insurance policy set, the business weight of policy p, the number of employees outside the coverage of policy p, the total number of employees in the initial monthly employee information table, the number of non-compliant employees within the coverage of policy p, the theoretically covered number of employees of policy p, the time decay factor of policy p, and the number of effective policies.

6. The method for intelligently updating the monthly employee information table according to claim 1, wherein The calculation of the first comprehensive difference value between the initial monthly employee information table and the previous month's employee information table, and the calculation of the second comprehensive difference value between the policy change time series and the initial monthly employee information table include: When calculating the first comprehensive difference value and the second comprehensive difference value, introduce a fuzzy matching algorithm to calculate the similarity of semantically similar information, so as to improve the accuracy of identifying the differences between the initial monthly employee information table and the previous month's employee information table.

7. The method for intelligently updating the monthly employee information form according to claim 1, wherein The method for intelligently updating the monthly employee information table further includes: Calculate the enterprise charge amount for this month based on a preset charging rule engine and the final monthly employee information table.

8. A system for intelligently updating the monthly employee information form, characterized in that, Include: A text data acquisition module, configured to acquire multimodal data, parse the multimodal data to obtain the enterprise identifier corresponding to the multimodal data, and convert the multimodal data into text data with the enterprise identifier. A text data determination module, configured to obtain the information acquisition requirements of the corresponding enterprise from a preset rule library based on the enterprise identifier, calculate the information error rate of the text data according to the information acquisition requirements, and if the information error rate is greater than a preset threshold, generate a prompt message and push the prompt message to the interaction interface, and re-acquire the text data until the information error rate of the text data is less than the preset threshold. A social insurance policy acquisition module, configured to acquire social insurance policy data, construct a policy change time series based on the social insurance policy data, and record the effective time, regional scope, applicable population, and specific terms of each social insurance policy. A model construction module, configured to construct an information table update model, where the information table update model includes an input layer, a comparison layer, a conversion layer, and an output layer. The input layer is configured to receive text data with an enterprise identifier and the policy change time series. The comparison layer is configured to obtain the previous month's employee information table of the corresponding enterprise based on the text data with the enterprise identifier, adjust the previous month's employee information table based on the text data to obtain the initial monthly employee information table, calculate the first comprehensive difference value between the initial monthly employee information table and the previous month's employee information table, calculate the second comprehensive difference value between the policy change time series and the initial monthly employee information table, and determine a difference list based on the first comprehensive difference value and the second comprehensive difference value. The difference list includes new employee information, separated employee information, information change employee information, and employees to be adjusted for social insurance. A conversion layer for generating a final monthly employee information table based on the difference list, the initial monthly employee information table, and the previous month's employee information table; An output layer for outputting the final monthly employee information table.

9. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

10. A non-transitory computer-readable medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the method according to any one of claims 1 to 7.

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