Salary management system
By using a payroll management system that combines regular expressions and large model APIs to automatically extract key information and leverages blockchain technology to ensure data security, the system solves the problems of inefficiency and error-prone manual calculation in university payroll management, achieving efficient and secure payroll calculation and management.
Patent Information
- Application Number
- CN202511874583.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-12
- Publication Date
- 2026-01-23
AI Technical Summary
The salary management of universities suffers from inefficiency, error-prone manual calculations, and poor scalability. Existing automated tools are not accurate enough when processing unstructured text information, making it difficult to meet the complex salary management needs of universities, and there is a lack of systematic and integrated solutions.
The payroll management system, combined with regular expressions and large model APIs, automatically extracts key fields from unstructured personnel information and matches them with pay standards to generate pay calculation results. Blockchain technology is introduced for data storage, verification, and traceability to ensure the immutability and transparency of the data.
It has enabled the automation and precision of salary management in universities, improved computing efficiency, reduced human error, ensured data security and traceability, and met the Level 3 compliance requirements of information security protection.
Smart Images
Figure CN121391202A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of salary management, and more particularly to a salary management system. BACKGROUND
[0002] In the operation and management of higher education institutions (hereinafter referred to as "colleges and universities"), salary management is a crucial and highly complex task. The core challenge lies in processing massive, multi-source, heterogeneous information data. These data not only include structured attendance, salary standards, etc., but also contain a large amount of unstructured text information, such as detailed educational background of teaching staff, rich and varied work history, job title evaluation and appointment records in different periods, and various special allowances or job change explanations, etc.
[0003] Currently, the calculation of college and university salaries is highly dependent on manual operation processes. Staff need to manually identify and extract key information points (such as job title level, tenure, job category, etc.) from a large amount of unstructured text, and check and match them one by one with the established, usually complex salary standard system, and finally complete the calculation. This method has significant drawbacks: first, the efficiency is extremely low, and manual processing takes a lot of time in the face of a large number of teaching staff in colleges and universities; second, accuracy is difficult to guarantee, and manual identification and calculation are prone to errors due to negligence or misunderstanding of the rules, especially when dealing with rule intersections or special cases; third, scalability is poor, and personnel changes and salary policy adjustments will bring a huge amount of repetitive work, making it difficult to respond quickly to changes.
[0004] In recent years, the progress of natural language processing (NLP) technology provides a potential path to solve the problem of automatic extraction of text information. Related technologies can theoretically assist in understanding the semantics and key entities in unstructured text. However, existing automated tools often lack the precision and robustness of extracting key information when dealing with the unique, varied formats, and complex semantics of unstructured personnel information in colleges and universities, making it difficult to completely replace manual checks, especially when dealing with edge cases or non-standard expressions.
[0005] In addition, salary management is not an isolated link, and it is closely related to the dynamic maintenance of personnel information, the generation of various commonly used proof documents, and the auditability and traceability requirements of data processing. Current technology applications are often point-like, and lack a systematic and integrated solution to support the modernization, efficiency, and compliance of college and university salary management needs. SUMMARY
[0006] Therefore, in order to at least partially solve the above technical problems, the present application provides a salary management system, In order to achieve the above purpose, the present application adopts the following technical solutions: A salary management system, comprising a salary management module and a personnel management module; The salary management module comprises a salary data query unit, a salary data import unit, a salary standard query unit, a salary standard maintenance unit, and a salary calculation unit; wherein, The salary calculation unit is used to extract key fields in unstructured personnel information and match them with salary standards to generate salary calculation results; The personnel management module comprises a personnel information query unit, a personnel information import unit, and a related file generation unit.
[0007] In an optional embodiment, the query keywords supported by the salary data query unit include employee name, employee number, and department; and the query keywords supported by the salary standard query unit include post, title, and education.
[0008] In an optional embodiment, the salary calculation unit comprises: A preprocessing subunit for preprocessing unstructured personnel information; A text analysis engine for identifying key fields in the text through regular expressions and setting up a key information supplementary extraction mechanism based on semantic understanding; A salary comparison engine for matching key fields with salary standards to generate salary calculation results.
[0009] In an optional embodiment, preprocessing unstructured personnel information includes removing irrelevant characters and formatting errors in unstructured personnel information and unifying date formats.
[0010] In an optional embodiment of the text analysis engine, when the regular expression cannot identify the key fields in the text, the key information supplementary extraction mechanism based on semantic understanding is activated; The supplementary extraction mechanism calls a large model API to nest a prompt word outside the text, limits the extraction rules and returns the data format.
[0011] In an optional embodiment, the related file generation unit is used to generate on-the-job certificates, income certificates, salary transfer forms, retirement approval forms, salary change approval forms, and statistical reports.
[0012] In an optional embodiment, the statistical reports include salary statistical reports and performance statistical reports.
[0013] In an optional embodiment, it further comprises a data chaining module for chaining salary-related data, including a data storage unit, a data verification unit, and a data traceability unit; The data storage unit is used to calculate a hash value, call a blockchain smart contract to store the hash value, submitter information, and a timestamp in the blockchain and store the record; A data verification unit is configured to obtain a hash value, submitter information and a timestamp of the stored evidence, and verify whether the data is tampered by comparing with current information. A data traceability unit is configured to call a blockchain smart contract to obtain data operation history and generate a visual traceability report.
[0014] The salary management system disclosed in the application provides reliable technical support for digital transformation of human resources in colleges and universities; compared with the prior art, the salary management system effectively solves the problems of low efficiency and easy errors in manual accounting caused by difficulties in processing unstructured data in college salary management, and overcomes the deficiencies of traditional systems in data security and business process coverage.
[0015] Specifically, through the innovative text analysis and intelligent comparison architecture, the automatic and accurate extraction of complex personnel information and salary calculation are realized, the core business processing cycle is shortened, and the human error rate is significantly reduced; the integrated dynamic file generation mechanism covers the whole scene of personnel file requirements such as on-the-job certificate and salary change approval, so that the efficiency of high-frequency business handling is improved from hours to minutes; the distributed data storage system is built to ensure that the whole process of salary operation is traceable and tamper-proof, meet the third-level compliance requirements of the Cybersecurity Protection Law, and fundamentally solve the data trust crisis of traditional centralized storage. BRIEF DESCRIPTION OF DRAWINGS
[0016] In order to more clearly illustrate the technical solutions in the embodiments of the application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description are only a part of the embodiments of the application, and for those skilled in the art, other drawings can be obtained without creative labor on the basis of the provided drawings.
[0017] Figure 1 The figure is a schematic diagram of the salary management system architecture of the application; Figure 2 The figure is a schematic diagram of the salary calculation unit structure of the application; Figure 3 The figure is a workflow diagram of the salary calculation unit of the application; Figure 4 The figure is a schematic diagram of the data on-chain module structure of the application. DETAILED DESCRIPTION
[0018] The technical solutions in the embodiments of the application will be described clearly and completely in the following with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only a part of the embodiments of the application, not all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the application.
[0019] In the following description, a lot of specific details are set forth in order to provide a thorough understanding of the present application, however, the present application can be practiced in other manners different from those described herein, and those skilled in the art can make similar extensions without departing from the concept of the present application, therefore, the present application is not limited to the specific embodiments disclosed below.
[0020] The embodiments of the present application disclose a salary management system, which not only realizes automation and precision of functions such as salary calculation, personnel management, file generation and data storage through automatic and intelligent technology, but also introduces a blockchain technology, to ensure that the salary data is tamper-proof and transparent.
[0021] The present application can provide data storage, verification and traceability functions through data chaining, further enhancing the credibility and security of salary management; Through comprehensive management of personnel information, including personal information query and import, and automatic generation of common files such as employment certificates, income certificates and various statistical reports, the business process of university salary management can be fully optimized.
[0022] In one embodiment, as Figure 1 , Figure 1 is a schematic diagram of the salary management system architecture; In the present embodiment, the salary management system includes a salary management module and a personnel management module; The salary management module includes a salary data query unit, a salary data import unit, a salary standard query unit, a salary standard maintenance unit and a salary calculation unit; wherein, The salary calculation unit is used to extract key fields in unstructured personnel information and match them with salary standards to generate salary calculation results; The personnel management module includes a personnel information query unit, a personnel information import unit and a related file generation unit.
[0023] In an exemplary embodiment, in the salary management module: The salary data query unit is used to provide a query function of employee salary data, and supports accurate or fuzzy query according to employee name, employee number, department and other conditions. The query result is displayed in table form, including detailed information such as post salary, salary level salary, performance salary and various subsidies. At the same time, it supports exporting query results in Excel, CSV and other formats, which is convenient for further analysis and use.
[0024] The salary data import unit supports batch import of salary data from Excel, CSV and other format files, the system automatically verifies the data format and integrity, and provides a template download function to ensure that the user prepares the data according to the correct format. Real-time feedback of progress and error information during the import process facilitates the user to correct the problem in time.
[0025] The salary standard query unit is configured to provide a query function of a high salary standard, and support query according to conditions such as a post, a title, and an educational background. The query result displays detailed information of the salary standard, including post salary, salary level salary, performance salary, and various subsidies. Meanwhile, the query result can be exported in a PDF format for printing and archiving.
[0026] The salary standard maintenance unit allows an administrator to add, modify, and delete the salary standard. A visual operation interface is provided to simplify the operation process of the administrator. In addition, a log record is recorded for each operation to facilitate tracing and auditing.
[0027] The salary calculation unit, in this embodiment, is configured to Figure 2 , include: 1. A preprocessing subunit configured to preprocess unstructured personnel information, including cleaning the input unstructured data to remove irrelevant characters and formatting errors, unifying the date format, for example, converting all dates to the YYYY.MM format, and extracting key information in the text, such as name, gender, and ID number; 2. A text analysis engine configured to identify key fields in the text through a regular expression and set a key information supplement extraction mechanism based on semantic understanding; In this embodiment, the regular expression is used to match and extract key information such as title, education, education time, and work time. The example regular expression is as follows: Title: lecturer | experimentalist | engineer | assistant researcher | associate professor | senior experimentalist | senior engineer | vice researcher | professor | senior experimentalist | senior engineer | researcher | second-class professor |... Education: junior college | bachelor's degree | master's degree | doctorate Date: (\d{4}.\d{2})-(\d{4}.\d{2}|\to date) If the regular expression fails to extract the complete resume time point, the large model API is called. The prompt word is nested outside the text to explain the extraction rule and the return data format, for example: Prompt word: please extract the title, education, education time, and work time from the following text, and return it in JSON format: {“title”: “experimentalist”, “education”: “master's degree”, “education time”: “2013.07”, “work time”: “2005.09”} Further, the information extracted by the regular expression and the large model API is integrated and standardized to ensure that it meets the format requirements of the salary standard table.
[0028] 3. A salary comparison engine for matching key fields with salary standards to generate salary calculation results. That is, according to the extracted key information, the corresponding post, salary level, performance, and various subsidies are searched in the salary standard table. Then according to the matching results, the various salaries of the employees are calculated according to the salary calculation formula of the university, and output in a clear format, including post salary, salary level salary, performance salary, and various subsidies.
[0029] It should be noted that in the present embodiment, the salary standard table supports dynamic updating and maintenance, ensuring consistency with the latest policies and regulations.
[0030] In a preferred embodiment, it further comprises: A starting salary table verification subunit for automatically checking whether the key information in the starting salary table meets the requirements of the salary standard table and providing a detailed verification report indicating possible problems and suggestions, and supporting manual intervention and correction to ensure the accuracy of the starting salary table. A salary adjustment table verification subunit for verifying the salary adjustment table to ensure that the salary adjustment information meets the regulations. This includes automatically checking whether the salary adjustment reason and salary adjustment range in the salary adjustment table meet the relevant policies and standards, and providing a detailed verification report indicating possible problems and suggestions, and supporting manual intervention and correction to ensure the accuracy of the salary adjustment table.
[0031] The salary calculation process of the salary calculation unit of the present application is shown in Figure 3 , which includes: S1. Unstructured work history and education information; S2. Data preprocessing, text cleaning, and date format unification; S3. Determine if the regular expression extracts complete salary parameters. If not, call the large model API. Then perform step S4, if yes, directly perform step S4; S4. Salary parameter information integration; S5. Match the standard table and calculate the salary items; S6. Save the salary slip.
[0032] The present application realizes automatic extraction of key information from unstructured input data by combining regular expressions and large model APIs, and quickly and accurately calculates the various salaries of employees by comparing with the salary standard table. Not only improves the efficiency of salary calculation, but also reduces manual input errors, and ensures the accuracy of salary calculation.
[0033] In another exemplary embodiment, the personnel management module comprises: The personnel information query unit is configured to provide a query function of personal information of employees, support accurate or fuzzy query according to conditions such as name, work number, department, and the like. The query result is displayed in a table form, and contains detailed information such as name, gender, ID number, education, professional title, work experience, and the like. Meanwhile, the query result can be exported in formats such as Excel and CSV, which is convenient for further analysis and use.
[0034] The personnel information import unit is configured to support batch import of personal information of employees from files in formats such as Excel and CSV, and automatically verify data format and integrity. Meanwhile, the unit is also configured to provide a download function of import templates, so as to ensure that the user prepares data according to the correct format. In addition, progress and error information can be fed back in real time during the import process, so as to facilitate the user to correct problems in time.
[0035] The related file generation unit is configured to generate on-duty certificate, income certificate, salary transfer sheet, retirement approval form, salary change approval form, and statistical report according to needs. In a specific embodiment, The on-duty certificate is configured to automatically generate an on-duty certificate file of an employee, contain information such as name, work number, department, and time of entering the job of the employee, and support customizing a template and format of the on-duty certificate, so as to meet the needs of different scenarios. The generated on-duty certificate file supports being exported in a PDF format, which is convenient for printing and archiving.
[0036] The income certificate is configured to automatically generate an income certificate file of an employee, contain information such as name, work number, department, and income amount of the employee. The income certificate supports customizing a template and format of the income certificate, so as to meet the needs of different scenarios. The generated income certificate file supports being exported in a PDF format, which is convenient for printing and archiving.
[0037] The salary transfer sheet is configured to automatically generate a salary transfer sheet, contain information such as name, work number, department, and transfer amount of an employee, support customizing a template and format of the salary transfer sheet, so as to meet the needs of different scenarios. The generated salary transfer sheet supports being exported in a PDF format, which is convenient for printing and archiving.
[0038] The retirement approval form is configured to automatically generate a retirement approval form, contain information such as name, work number, department, and retirement time of an employee. The retirement approval form supports customizing a template and format of the retirement approval form, so as to meet the needs of different scenarios. The generated retirement approval form supports being exported in a PDF format, which is convenient for printing and archiving.
[0039] The salary change approval form is configured to automatically generate a salary change approval form, contain information such as name, work number, department, change reason, and change amount of an employee. The salary change approval form supports customizing a template and format of the salary change approval form, so as to meet the needs of different scenarios. The generated salary change approval form supports being exported in a PDF format, which is convenient for printing and archiving.
[0040] Statistical report: In this embodiment, the statistical report includes commonly used reports such as salary statistical report, performance statistical report, etc. At the same time, statistics can be performed according to time period, department, etc. The report is displayed in table form, containing the summary information of various salary data. And support to export the report to Excel, CSV, etc. Format, convenient for further analysis and use.
[0041] To further optimize the above technical solutions, in an embodiment, the system is also provided with a data chaining module for chaining salary-related data, realizing the storage, verification and traceability of salary data through blockchain technology, and ensuring the data tamper resistance.
[0042] The module includes a data storage unit, a data verification unit and a data traceability unit. Figure 4 The data storage unit includes: Hash calculation: the system automatically calculates the hash value of the salary data and related files, ensuring the uniqueness and tamper resistance of the data; Smart contract call: call the blockchain smart contract to store the hash value, submitter information and timestamp to the blockchain; Storage record: the storage record contains hash value, submitter address, timestamp, etc. Information, ensuring the traceability of data; The data verification unit includes: Hash calculation: the system automatically calculates the hash value of the current salary data and related files; Smart contract call: call the blockchain smart contract to get the stored hash value, submitter information and timestamp; Hash comparison: compare the current hash value with the stored hash value to verify whether the data has been tampered with; The data traceability unit includes: Operation record: the system records the timestamp, operator and operation type of each operation, ensuring the integrity and transparency of the data; Smart contract call: call the blockchain smart contract to get the operation history of the data; Traceability report: provide a visual traceability report to show the generation, modification and storage process of the data.
[0043] The application has the following advantages: Automation and intelligence: Automatically extract key information through regular expressions and large model API, reduce manual input errors, and improve work efficiency; automatically calculate salary according to salary standard table to ensure the accuracy of the calculation result.
[0044] Data security and tamper resistance: The salary data is stored, verified and traced through blockchain technology, ensuring data security and non-tamperability; detailed verification reports and traceability reports are provided to enhance data credibility.
[0045] Flexibility and scalability: The system supports dynamic updating of salary standard table, ensuring consistency with the latest policies and regulations; supports import and export of multiple file formats to meet the needs of different scenarios.
[0046] User experience: Provide a visual operation interface to simplify the user operation process; support batch operation and single operation to meet the needs of different users.
[0047] Through the university salary management system and the AI starting salary method of the present application, the automation and precision of salary calculation can be realized, the work efficiency is improved, the manual error is reduced, and the security and non-tamperability of the data are ensured, which provides a comprehensive, efficient and safe solution for university salary management.
[0048] The various embodiments in the specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be mutually referred to. For the device disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method part.
[0049] The above description of the disclosed embodiments enables a person skilled in the art to implement or use the present application. Various modifications to the embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.
Claims
1. A compensation management system, characterized by, The system comprises a salary management module and a personnel management module; The salary management module comprises a salary data query unit, a salary data import unit, a salary standard query unit, a salary standard maintenance unit, and a salary calculation unit; wherein, The salary calculation unit is configured to extract key fields from unstructured personnel information and match the key fields with salary standards to generate a salary calculation result; The personnel management module comprises a personnel information query unit, a personnel information import unit, and a related file generation unit.
2. The compensation management system of claim 1, wherein, The salary data query unit supports query keywords including employee name, employee ID, and department; and the salary standard query unit supports query keywords including post, title, and education.
3. The compensation management system of claim 1, wherein, The salary calculation unit comprises: a preprocessing subunit configured to preprocess unstructured personnel information; a text analysis engine configured to identify key fields in the text through a regular expression and set a key information supplementary extraction mechanism based on semantic understanding; a salary comparison engine configured to match the key fields with salary standards to generate a salary calculation result.
4. The compensation management system of claim 3, wherein, The preprocessing of the unstructured personnel information comprises removing irrelevant characters and formatting errors in the unstructured personnel information and unifying date formats.
5. The compensation management system of claim 3, wherein, In the text analysis engine, when the regular expression cannot identify the key fields in the text, the key information supplementary extraction mechanism based on semantic understanding is activated; The supplementary extraction mechanism nests a prompt word outside the text by calling a large model API, limits the extraction rules, and returns the data format.
6. The compensation management system of claim 1, wherein, The related file generation unit is configured to generate an on-duty certificate, an income certificate, a salary transfer form, a retirement approval form, a salary change approval form, and statistical reports.
7. The compensation management system of claim 6, wherein, The statistical reports include salary statistical reports and performance statistical reports.
8. The compensation management system of claim 1, wherein, The system further comprises a data chaining module configured to chain salary-related data, including a data storage unit, a data verification unit, and a data traceability unit; The data storage unit is configured to calculate a hash value, call a blockchain smart contract, store the hash value, submitter information, and a timestamp in the blockchain, and store the records; The data verification unit is configured to obtain the stored hash value, submitter information, and timestamp, and verify whether the data has been tampered with by comparing with the current information; The data traceability unit is configured to call a blockchain smart contract to obtain data operation history and generate a visual traceability report.