A method and system for intelligently updating employee information table of this month

Abstract

The present invention discloses a method and system for intelligently updating an employee information table for this month, relating to the field of artificial intelligence technology. The method comprises: converting multimodal data into text data with an enterprise logo; obtaining information acquisition requirements of the corresponding enterprise from a preset rule library based on the enterprise logo, calculating the information error rate of the text data according to the information acquisition requirements, generating prompt information if the information error rate is greater than a preset threshold, pushing the prompt information to an interactive interface, and reacquiring text data until the information error rate of the text data is less than the preset threshold; obtaining social security policy data, and constructing a policy change time series based on the social security policy data; and constructing an information table update model for outputting the final employee information table for this month, which helps to solve the problems of high information error rate, delayed response to social security policy changes, and low efficiency in existing employee information table update methods.

Description

Technical Field

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

[0002] In corporate human resources management, updating employee information tables involves multimodal data processing and policy adaptation, and existing technologies have significant shortcomings. Corporate data comes from a wide range of sources, including text, Excel, and images. Traditional methods rely on manual organization of data into standard formats before importing it into the system. This data integration is inefficient and error-prone, often resulting in information error rates exceeding 15%, which consumes a significant amount of HR time. Furthermore, social security policies change frequently, with significant regional variations and a complex population of applicants. Existing systems lack the ability to construct a time series of policy changes, making it impossible to link policy terms with employee information in real time. The average annual back pay costs incurred by companies due to policy adaptation errors account for 8%-12% of the total fees.

[0003] In addition, when updating employee information tables, traditional methods make it difficult to accurately identify the differences caused by changes in employee information and policy adjustments. Relying on manual comparison can easily lead to missing key information, resulting in delayed and inaccurate information updates.

[0004] Therefore, there is an urgent need for a method that can automatically parse multimodal data, dynamically respond to policy changes, accurately identify differences, and efficiently update employee information tables. 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 automatically parse multimodal data, dynamically respond to policy changes, accurately identify differences and efficiently update the employee information table.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A method for intelligently updating the employee information table of this month, including:

[0008] Acquiring multimodal data, parsing the multimodal data to obtain a corporate logo corresponding to the multimodal data, and converting the multimodal data into text data with the corporate logo;

[0009] Based on the enterprise identifier, the information acquisition requirements of the corresponding enterprise are obtained from a preset rule library. According to the information acquisition requirements, the information error rate of the text data is calculated. If the information error rate is greater than a preset threshold, a prompt message is generated and pushed to the interactive interface. The text data is acquired again until the information error rate of the text data is less than the preset threshold.

[0010] 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;

[0011] Constructing 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;

[0012] The input layer is used to receive text data with enterprise identification and the policy change time series;

[0013] 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 logo, 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 a first comprehensive difference value between the initial employee information table of this month and the employee information table of the previous month, calculate a 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 including information of new employees, information of resigned employees, information of employees with information changes, and employees whose social security is to be adjusted;

[0014] A conversion layer, configured to generate a final employee information table for this month based on the difference list, the initial employee information table for this month, and the employee information table for last month;

[0015] The output layer is used to output the final employee information table for this month.

[0016] On the basis of the above technical solution, the present invention can also be improved as follows:

[0017] Optionally, acquiring information acquisition requirements of a 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:

[0018] Obtain the set of fields that require format verification and the set of fields that require logic verification for enterprise e from the preset rule library;

[0019] Determine a set of fields with format errors and a set of fields with logical errors in the text data based on the set of fields that require format verification and the set of fields that require logical verification of the enterprise e;

[0020] The information error rate of the text information is calculated based on the field set that requires format verification of the enterprise e, the field set that requires logic verification of the enterprise e, the field set with format errors in the text data, and the field set with logic errors.

[0021] Optionally, obtaining social security policy data and constructing a policy change time series based on the social security policy data includes:

[0022] The policy change time series is constructed based on the time point t, the policy set that takes effect at time point t, the policy set that changes at time point t, and the set of time points T.

[0023] Optionally, calculating the first comprehensive difference value between the initial employee information table for this month and the employee information table for last month includes:

[0024] Obtain the employee quantity difference and information change difference between the initial employee information table of this month and the employee information table of last month;

[0025] A first comprehensive difference value between the initial employee information table of this month and the employee information table of last month is calculated 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 change, and the difference in information change.

[0026] Optionally, calculating a second comprehensive difference value between the policy change time series and the initial employee information table for this month includes:

[0027] The second comprehensive difference value is calculated based on the current set of 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 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.

[0028] Optionally, the calculating of a first comprehensive difference value between the initial employee information table of this month and the employee information table of last month, and the calculating of a second comprehensive difference value between the policy change time series and the initial employee information table of this month include:

[0029] When calculating the first comprehensive difference value and the second comprehensive difference value, a fuzzy matching algorithm is introduced to perform similarity calculation on semantically similar information to improve the accuracy of identifying the differences between the initial employee information table of this month and the employee information table of last month.

[0030] Optionally, the method for intelligently updating the employee information table for this month further includes:

[0031] The enterprise charge amount for this month is calculated based on the preset charge rule engine and the final employee information table for this month.

[0032] A system for intelligently updating the employee information table of this month, including:

[0033] A text data acquisition module, configured to acquire multimodal data, parse the multimodal data, obtain a corporate logo corresponding to the multimodal data, and convert the multimodal data into text data with the corporate logo;

[0034] a text data determination module, configured to obtain information acquisition requirements of the corresponding enterprise from a preset rule library based on the enterprise identifier, calculate an information error rate of the text data according to the information acquisition requirements, generate a prompt message if the information error rate is greater than a preset threshold, push the prompt message to the interactive interface, and reacquire text data until the information error rate of the text data is less than the preset threshold;

[0035] A social security policy acquisition module is used to obtain social security policy data, build 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;

[0036] A model building module, 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;

[0037] The input layer is used to receive text data with enterprise identification and the policy change time series;

[0038] 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 logo, 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 a first comprehensive difference value between the initial employee information table of this month and the employee information table of the previous month, calculate a 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 including information of new employees, information of resigned employees, information of employees with information changes, and employees whose social security is to be adjusted;

[0039] A conversion layer, configured to generate a final employee information table for this month based on the difference list, the initial employee information table for this month, and the employee information table for last month;

[0040] The output layer is used to output the final employee information table for this month.

[0041] An electronic device comprises a memory, a processor and a computer program stored in the memory and running on the processor, wherein the steps of the method are implemented when the processor executes the computer program.

[0042] A non-transitory computer-readable storage medium stores a computer program, which implements the steps of the method when executed by a processor.

[0043] The present invention has the following advantages:

[0044] The method for intelligently updating the employee information table of this month in the present invention, in terms of data processing, automatically converts multimodal data into a standard text format through multimodal data analysis and intelligent verification, greatly reducing manual sorting time, and the information error rate can be controlled within 5%. For social security policy management, a policy change time series is constructed to record policy changes in real time, helping companies to adapt to new policies in a timely manner, avoiding the annual average of 8%-12% supplementary payment costs caused by delayed policy understanding, and reducing compliance risks. In terms of employee information updates, the hierarchical difference calculation mechanism of the information table update model can accurately identify employee information changes (new additions, resignations, information changes) and the impact of policy adjustments, and ensure the timeliness and accuracy of employee information table updates through the initial employee information table of this month, double difference comparison and final employee information table of this month. Compared with traditional methods, the present invention improves the efficiency of employee information table updates by more than 60%, providing an efficient, accurate and compliant solution for corporate human resources management. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] For purposes of illustration and not limitation, the present invention will now be described with reference to embodiments thereof and the accompanying drawings, in which:

[0046] Figure 1 This is a flow chart of a method for intelligently updating the employee information table of this month in an embodiment of the present invention;

[0047] Figure 2 This is a 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;

[0048] Figure 3 This is a schematic diagram of the physical structure of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0049] 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 drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work should fall within the scope of protection of the present invention.

[0050] It should be noted that the terms "first," "second," and the like in the description of the present invention and the accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a particular order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate for the embodiments of the present invention described herein. In addition, the terms "including," "having," and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to these processes, methods, products, or apparatuses.

[0051] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features thereof can be combined with each other. The embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0052] Figure 1 FIG. 1 is a flow chart of a method for intelligently updating the employee information table of this month in an embodiment of the present invention. Figure 1 As shown, the method for intelligently updating the employee information table of this month provided by the embodiment of the present invention includes the following steps S101 to S104.

[0053] S101, acquiring multimodal data, parsing the multimodal data, obtaining a corporate logo corresponding to the multimodal data, and converting the multimodal data into text data with the corporate logo.

[0054] The multimodal data includes image data (such as screenshots, etc.), text data (business system forms), and audio data (recordings);

[0055] This step first preprocesses these multimodal data, such as enhancing image clarity and converting speech into text; then, through regular expressions, named entity recognition and other technologies, extract identification information such as the unified social credit code and company name; finally, fill the extracted information into the preset template to generate structured text data with corporate identification.

[0056] S102, based on the enterprise identification, obtains the information acquisition requirements of the corresponding enterprise from the preset rule library, calculates the information error rate of the text data according to the information acquisition requirements, and if the information error rate is greater than the preset threshold, generates a prompt message and pushes the prompt message to the interactive interface, and re-acquires the text data until the information error rate of the text data is less than the preset threshold.

[0057] Obtain the set of fields that require format verification and the set of fields that require logic verification for enterprise e from the preset rule library;

[0058] Determine a set of fields with format errors and a set of fields with logical errors in the text data based on the set of fields that require format verification and the set of fields that require logical verification of the enterprise e;

[0059] The information error rate of the text information is calculated based on the field set that requires format verification of the enterprise e, the field set that requires logic verification of the enterprise e, the field set with format errors in the text data, and the field set with logic errors.

[0060] The information error rate of text data is calculated by formula (1);

[0061] Formula (1);

[0062] Where, is the information error rate of text data, For corporate logo, A collection of fields with incorrect format in text data. A collection of fields with logical errors in text data. A set of fields that require format verification for enterprise e. A set of fields that require logical verification for enterprise e.

[0063] An example is:

[0064] Assume that the text data of enterprise e needs to verify social security information. In the preset rule library:

[0065] A collection of fields that require format validation :

[0066] Social Security Number (10 digits), Bank Account Number (19 digits), Date of Employment (YYYY - MM - DD);

[0067] A set of fields that require logical validation :

[0068] Salary (≥ local minimum wage of RMB 2,300), social security base (≥ salary × 60%), retirement status (the social security base for retired employees should be 0);

[0069] Fields were extracted from enterprise text data and verified:

[0070] 1. Format Error Field ;

[0071] Social Security Number: "A12345678" (contains letters and does not meet the 10-digit requirement) → 1 format error;

[0072] Joining date: "2023 / 13 / 01" (Month 13 is invalid and does not conform to YYYY - MM - DD) → 1 format error;

[0073] Bank account: "12345678901234567" (17 digits, does not meet the 19-digit requirement) → 1 format error → (total number of format error fields);

[0074] 2. Logical error field ;

[0075] Salary: 2,000 yuan (lower than the local minimum wage of 2,300 yuan) → 1 logical error;

[0076] Social security base: 2,500 yuan (2,000 yuan salary × 60% = 1,200 yuan, 2,500 yuan does not comply with the rules) → 1 logical error;

[0077] Retired employees: The social security base for retired employees is set to 3,000 yuan (should be 0) → 1 logical error → (total number of logical error fields);

[0078] Substituting into formula (1) we can get:

[0079] ;

[0080] The information error rate of the enterprise text data is 100%, which means:

[0081] All fields requiring format verification contain errors (social security number, bank account number, and employment date are all incorrect);

[0082] All fields requiring logical validation also contain errors (wage, social security base, and retirement status logical inconsistencies);

[0083] The "re-acquisition of text data" process needs to be triggered until the error rate is lower than the preset threshold.

[0084] S103, 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.

[0085] The policy change time series is constructed based on the time point t, the policy set that takes effect at time point t, the policy set that changes at time point t, and the set of time points T.

[0086] Calculate the policy change time series using formula (2);

[0087] Formula (2);

[0088] Where, is the policy change time series, For time point, is the set of policies effective at time t, is the set of policies that change at time t, A collection of time points.

[0089] S104, constructing an information table update model, which includes an input layer, a comparison layer, a conversion layer, a calculation layer, and an output layer.

[0090] The input layer is used to receive text data with enterprise identification and the policy change time series;

[0091] 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 logo, calculate a first comprehensive difference value between the text data and the employee information table of the previous month, calculate a 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, wherein the difference list includes information on newly added employees, information on resigned employees, information on employees with changed information, and information on employees whose social security is to be adjusted;

[0092] Obtain the employee quantity difference and information change difference between the initial employee information table of this month and the employee information table of last month;

[0093] Calculating a first comprehensive difference value between the initial employee information table of this month and the employee information table of last month based on the weight of the difference in the number of personnel, the difference in the number of personnel, the weight of the difference in information change, and the difference in information change;

[0094] Calculate the first comprehensive difference between the initial employee information table of this month and the employee information table of last month using formula (3);

[0095] Formula (3);

[0096] Where, The difference between the initial employee information table for this month and the employee information table for last month. is the weight of the difference in the number of personnel, Due to the difference in the number of personnel, is the weight of the information change difference, Change the variance value for employee information.

[0097] Calculate the difference in the number of personnel;

[0098] ;

[0099] Where, Due to the difference in the number of personnel, It is the set of employees in the employee information table of last month. The set of employees in service in the initial employee information table for this month. This is the set of employees who left the company in the employee information table last month. This is the collection of employees who left the company in the initial employee information table for this month;

[0100] The information change difference is calculated using formula (4);

[0101] Formula (4);

[0102] Where, For information change differences, is the number of employees who were employed in both the previous month and this month. is the i-th employee who worked in both last month and this month, For 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.

[0103] Example:

[0104] Assume that a company:

[0105] Last month's employee information table: (Employed), (Resigned).

[0106] Initial employee information form for this month: (Employed), (Resigned).

[0107] Intersection of current employees , among which 10 people’s information has changed (such as salary and position).

[0108] Weight setting , .

[0109] ;

[0110] The total difference between the company's employee information sheet last month and the initial employee information sheet this month is approximately 8.45%.

[0111] The larger it is, the more necessary it is for the company to adjust its charges (such as social security backpay and salary backpay).

[0112] The second comprehensive difference value is calculated based on the current set of 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 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.

[0113] The second comprehensive difference value is calculated by formula (5);

[0114] Formula (5);

[0115] Where, 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 not covered by policy p, The total number of employees in the initial employee information table for this month, is the number of employees covered by policy p but not in compliance, is the theoretical number of employees covered by policy p, is the time decay factor of policy p, is the number of effective policies.

[0116] Example:

[0117] Effective policy P = {base increase policy}, (single policy scenario);

[0118] Employee of the Month: = 200 people, policy theory coverage = 180 people (excluding 20 people in flexible employment);

[0119] Compliance check: 10 out of 180 people did not meet the minimum limit of the new policy;

[0120] Policy effective time ,current , (Attenuation coefficient k=0.1).

[0121] ;

[0122] A conversion layer, for converting the difference list into a specified format to generate an employee information table for this month;

[0123] The output layer is used to output the final employee information table for this month.

[0124] The method for intelligently updating the employee information table for this month further includes: calculating the enterprise charge amount for this month based on a preset charging rule engine and the final employee information table for this month;

[0125] Calculate the enterprise charge amount for this month based on the preset charge rule engine and the final employee information table for this month using formula (6);

[0126] Formula (6);

[0127] Where, The total amount of enterprise charges this month, This is the final set of employees in the employee information table for this month. The basic cost for employee e, is the information change collection of employee e, Cost changes due to variance type d, A collection of policies that apply to employees. The additional costs incurred by policy p.

[0128] ;

[0129] 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.

[0130] Example:

[0131] There are 100 employees on the job this month;

[0132] Information change: 20 employees' wages increased from 7,500 yuan to 8,000 yuan, and the social security base was adjusted simultaneously;

[0133] Policy adjustment: The lower limit of the social security base for all employees will be increased from 5,000 yuan to 6,000 yuan, and the difference from the previous month must be paid (16% for the enterprise);

[0134] Personnel changes: 5 new employees (average salary 9,000 yuan), 3 employees resigned (average salary 7,000 yuan).

[0135] Calculation yields:

[0136] .

[0137] 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 .

[0138] A text data acquisition module 10 is used to acquire multimodal data, parse the multimodal data, obtain the enterprise logo corresponding to the multimodal data, and convert the multimodal data into text data with the enterprise logo;

[0139] a text data determination module 20 for obtaining information acquisition requirements of a corresponding enterprise from a preset rule library based on the enterprise identifier, calculating an information error rate of the text data according to the information acquisition requirements, and generating a prompt message if the information error rate is greater than a preset threshold, pushing the prompt message to the interactive interface, and reacquiring text data until the information error rate of the text data is less than the preset threshold;

[0140] 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;

[0141] A model building module 40 is 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;

[0142] The input layer is used to receive text data with enterprise identification and the policy change time series;

[0143] 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 logo, 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 a first comprehensive difference value between the initial employee information table of this month and the employee information table of the previous month, calculate a 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 including information of new employees, information of resigned employees, information of employees with information changes, and employees whose social security is to be adjusted;

[0144] A conversion layer, configured to generate a final employee information table for this month based on the difference list, the initial employee information table for this month, and the employee information table for last month;

[0145] The output layer is used to output the final employee information table for this month.

[0146] Figure 3 A schematic diagram of the physical structure of an electronic device provided in an embodiment of the present invention, such as Figure 3 As shown, the electronic device 50 includes: a processor 501 (processor), a memory 502 (memory) and a bus 503;

[0147] The processor 501 and the memory 502 communicate with each other via the bus 503.

[0148] 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 methods provided by the implementation methods of the present invention.

[0149] This embodiment provides a non-transitory computer-readable storage medium, which stores computer instructions. The computer instructions enable a computer to execute the method provided by the embodiment of the present invention.

[0150] Those skilled in the art will understand that all or part of the steps of implementing the above-mentioned method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above-mentioned method embodiment; and the aforementioned storage medium includes: ROM, RAM, disk or optical disk, etc. Various storage media that can store program codes.

[0151] The above specific embodiments do not limit the scope of protection of the present invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may occur depending on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention are intended to be included within the scope of protection of the present invention.

Claims

1. A method for intelligently updating the employee information table of this month, characterized in that: include: Acquiring multimodal data, parsing the multimodal data to obtain a corporate logo corresponding to the multimodal data, and converting the multimodal data into text data with the corporate logo; Based on the enterprise identifier, the information acquisition requirements of the corresponding enterprise are obtained from a preset rule library. According to the information acquisition requirements, the information error rate of the text data is calculated. If the information error rate is greater than a preset threshold, a prompt message is generated and pushed to the interactive interface. The text data is acquired again until the information error rate of the text data is less than the preset threshold. 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; Constructing 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 identification 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 logo, and adjust the employee information table of the previous month based on the text data to obtain the initial employee information table of this month; Calculating a first comprehensive difference between the initial employee information table for this month and the employee information table for last month, including: Obtain the employee quantity difference and information change difference between the initial employee information table of this month and the employee information table of last month; Calculating a first comprehensive difference value between the initial employee information table of this month and the employee information table of last month based on the weight of the difference in the number of personnel, the difference in the number of personnel, the weight of the difference in information change, and the difference in information change; Calculating the second comprehensive difference value between the policy change time series and the initial employee information table for this month, including: Calculate a second comprehensive difference value based on the currently effective social insurance policy set and the social insurance policy set including policy p, 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; Determining a difference list based on the first comprehensive difference value and the second comprehensive difference value, the difference list including information on newly added employees, resigned employees, employees with changed information, and employees whose social security is to be adjusted; A conversion layer, configured to generate a final employee information table for this month based on the difference list, the initial employee information table for this month, and the employee information table for last month; The output layer is used to output the final employee information table for this month.

2. The method for intelligently updating the employee information table of this month according to claim 1, characterized in that: The step of obtaining information acquisition requirements of a corresponding enterprise from a preset rule library based on the enterprise identifier, and calculating an information error rate of the text data according to the information acquisition requirements, includes: Obtain the set of fields that require format verification and the set of fields that require logic verification for enterprise e from the preset rule library; Determine a set of fields with format errors and a set of fields with logical errors in the text data based on the set of fields that require format verification and the set of fields that require logical verification of the enterprise e; The information error rate of the text information is calculated based on the field set that requires format verification of the enterprise e, the field set that requires logic verification of the enterprise e, the field set with format errors in the text data, and the field set with logic errors.

3. The method for intelligently updating the employee information table of this month according to claim 1, characterized in that: The step of obtaining social security policy data and constructing a policy change time series based on the social security policy data includes: The policy change time series is constructed based on the time point t, the policy set that takes effect at time point t, the policy set that changes at time point t, and the set of time points T.

4. The method for intelligently updating the employee information table of this month according to claim 1, characterized in that: The method for intelligently updating the employee information table for this month also includes: The enterprise charge amount for this month is calculated based on the preset charge rule engine and the final employee information table for this month.

5. A system for intelligently updating the employee information table of this month, characterized in that: include: A text data acquisition module, configured to acquire multimodal data, parse the multimodal data, obtain a corporate logo corresponding to the multimodal data, and convert the multimodal data into text data with the corporate logo; a text data determination module, configured to obtain information acquisition requirements of the corresponding enterprise from a preset rule library based on the enterprise identifier, calculate an information error rate of the text data according to the information acquisition requirements, generate a prompt message if the information error rate is greater than a preset threshold, push the prompt message to the interactive interface, and reacquire text data until the information error rate of the text data is less than the preset threshold; A social security policy acquisition module is used to obtain social security policy data, build 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, 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 identification 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 logo, and adjust the employee information table of the previous month based on the text data to obtain the initial employee information table of this month; Calculating a first comprehensive difference between the initial employee information table for this month and the employee information table for last month, including: Obtain the employee quantity difference and information change difference between the initial employee information table of this month and the employee information table of last month; Calculating a first comprehensive difference value between the initial employee information table of this month and the employee information table of last month based on the weight of the difference in the number of personnel, the difference in the number of personnel, the weight of the difference in information change, and the difference in information change; Calculating the second comprehensive difference value between the policy change time series and the initial employee information table for this month, including: Calculate a second comprehensive difference value based on the currently effective social insurance policy set and the social insurance policy set including policy p, 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; Determining a difference list based on the first comprehensive difference value and the second comprehensive difference value, the difference list including information on newly added employees, resigned employees, employees with changed information, and employees whose social security is to be adjusted; A conversion layer, configured to generate a final employee information table for this month based on the difference list, the initial employee information table for this month, and the employee information table for last month; The output layer is used to output the final employee information table for this month.

6. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 4 are implemented.

7. A non-transitory computer-readable medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 4 are implemented.

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