Database management method and management system

Abstract

The invention discloses a database management method and management system, and relates to the technical field of general database management, and the system comprises a user information management module, a work management module, a salary settlement module, an evaluation feedback module, an analysis prediction module, a multi-terminal integration module and a safety guarantee module. In the invention, a role-based access control permission model is constructed in a permission control layer and a module definition, and a field-level data control mechanism is implemented for three roles of a platform administrator, a customer enterprise and an employee, for example, the employee can only check a specific public field in a salary list, and sensitive fields such as a salary calculation formula are dynamically shielded; through a permission inheritance mechanism, the system automatically adds a filtering condition that employee ID = current user ID for a query request of an employee role, data isolation is ensured, a multi-dimensional data relation graph of employee-post-customer-contract is constructed, and a dynamic association path between entities is mapped in real time through a visual view.

Description

Technical Field

[0001] The present invention relates to the technical field of general database management, and in particular to a database management method and management system. Background Art

[0002] General database management technology refers to the underlying principles, abstract models, standardized methods and core functionalities used to design, create, operate, maintain and protect structured data of various types and sizes. It provides a general framework that is independent of specific hardware, operating systems or application areas, enabling data to be efficiently organized, reliably stored, securely accessed and centrally managed. In other words, it defines how to effectively store, organize, retrieve, update and protect large amounts of complex and persistent data. It is built on key data model abstractions that define how data is structured and represented and how the relationships between data elements are described.

[0003] Published patent: A database management system and method for managing a database (publication number: CN114564466A). The system obtains a remote access request, which includes the IP address of the access terminal and the user ID; determines user access information based on the access request, generates first user information, and sets user access permission information; performs database management operations based on the user access permission information; the user enters the system's management page and selects a data object from the data object list as a pending object; the management page sends an HTTP request to the backend server and transmits the new data object resource configuration; the backend server verifies whether the resources used by the user exceed their resource limits and changes the data object computing resource configuration. By setting user access restrictions based on user access database information and executing backend object resource configuration on the database, efficient storage management of database objects suitable for user access is ensured.

[0004] As a general database management system, it has not been customized for labor dispatch business scenarios, and has deficiencies in data access granularity and security, making it impossible to achieve a closed business loop. There is a lack of detailed records of the entire process of data addition, deletion, modification, and query, and there is no built-in compliance rule engine for real-time compliance verification and early warning. There are gaps in data processing efficiency and intelligence levels. There is a lack of user experience optimization design for multi-terminal data synchronization and interface adaptation, which increases the complexity of deployment and maintenance and may affect the promotion and use of the system. The user experience is poor, and the loop detection mechanism may lead to excessive consumption of system resources, especially in high-concurrency scenarios. The process is lengthy, the response delay is high, and the reliance on a specific technology stack reduces the versatility of the system. Summary of the Invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a database management method and management system.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical scheme: a database management system, comprising a user information management module, a work management module, a salary settlement module, an evaluation feedback module, an analysis and prediction module, a multi-terminal integration module and a security assurance module, wherein the user information management module associates employee, position, customer enterprise and contract data through a data map view, the work management module outputs standardized working hour data to the salary settlement module, and outputs position performance records to the evaluation feedback module, the salary settlement module generates a payroll based on the working hour data, and provides the salary settlement details to the analysis and prediction module, the evaluation feedback module inputs two-way evaluation data and sentiment analysis results to the analysis and prediction module, the analysis and prediction module integrates the data of each module through a unified structured wide table, and outputs job demand trends, employee turnover probability and customer credit risk prediction, the multi-terminal integration module realizes data synchronization between the Web, mobile and PC clients, and exchanges data with external systems through RESTful API, and the security assurance module provides field-level encryption, behavior modeling monitoring and compliance rule engine for all modules.

[0007] As a further description of the above technical solution:

[0008] The user information management module supports three types of user roles: platform administrators, client companies and employees. The platform administrator is responsible for global configuration and monitoring, client companies can manage dispatch needs, and employees can access personal data. The system uses a role-based access control permission model to control the scope of user data access. It has a field-level data control mechanism and a dynamic permission inheritance mechanism. Based on the dynamic permission control model of the role, employees can only access personal data related to themselves, and client companies can only view the relevant information of their dispatched employees. The platform administrator has full authority operation capabilities, sets up multi-factor identity authentication technology, enhances the login security of the system, sets up an audit log, records all user operations, ensures the traceability of data access and modification behavior, and regularly generates compliance audit reports for platform administrators and regulatory authorities to review.

[0009] As a further description of the above technical solution:

[0010] The work management module builds a many-to-many dispatch relationship model, supports employees to be dispatched to multiple client companies or positions, and supports the arrangement of multiple employees to participate in the same position; collects working time data through clocking in, QR code scanning check-in and external attendance equipment, and sets up a three-level review mechanism to ensure the accuracy and compliance of working time data, supports data map views to visualize the multi-dimensional data relationship structure between employees and positions, client companies, and contracts, has multi-scenario working time collection methods, supports mobile terminal clocking in, QR code check-in and external attendance system data import, to ensure the accuracy and real-time nature of working time data.

[0011] As a further description of the above technical solution:

[0012] The salary settlement module has a flexible and configurable salary rule setting function, supports multiple salary items, and the incremental calculation engine recalculates only the changed parts of working hours, performance and subsidies, improves calculation efficiency, automatically performs compliance verification of wages and social security, ensures salary compliance, automatically identifies the changed fields of working hours, performance or subsidy rules, and recalculates the changed parts of the data, reduces calculation pressure, optimizes system response efficiency, and verifies the compliance of wages with social security and taxes. It automatically determines whether the salary structure complies with local regulations and triggers early warning prompts in case of violations to prevent payroll generation operations.

[0013] As a further description of the above technical solution:

[0014] The evaluation and feedback module supports customers' evaluation of employees and employees' feedback to customers, builds a two-way scoring system, and has built-in sentiment analysis and sensitive word recognition mechanisms. The system automatically generates comprehensive scores and provides an early warning mechanism based on sentiment analysis. It supports abnormal score processing and closed-loop rectification. Customer companies score employees' service quality, and employees provide feedback on customer companies' service quality. The system's two-way evaluations are set with structured evaluation indicators and text descriptions, and the evaluation data is standardized. The sentiment analysis engine analyzes the evaluation text in real time, identifies negative emotions or sensitive words, and generates early warning notifications based on score deviations to ensure the objectivity and fairness of service quality evaluations.

[0015] As a further description of the above technical solution:

[0016] The analysis and prediction module integrates the structured data from multiple modules to build a structured wide table, using position, time and user identity as key indexes. It integrates an AI analysis engine to perform data training and modeling, and outputs job demand trend forecasts, employee turnover probability assessments and customer credit risk analysis. It provides natural language intelligent query functions, and administrators can enter natural language questions. The system automatically parses and converts them into structured SQL statements, executes queries and returns results. When the prediction results exceed the normal threshold, the system automatically pushes risk alerts to the administrator terminal.

[0017] As a further description of the above technical solution:

[0018] The multi-terminal integration module supports data synchronization among three types of terminals: Web, mobile, and PC clients. Through unified interface specifications and authentication mechanisms, data access between terminals is consistent. The mobile terminal supports offline operations. After the network is restored, the system will automatically synchronize offline data to the cloud. The system manages the session status, operation behavior, and data cache between multiple terminals through centralized scheduling services, provides a standardized RESTful API interface, supports two-way data communication with external business platforms, and performs field-level legitimacy verification on all external data inputs.

[0019] As a further description of the above technical solution:

[0020] The security assurance module adopts field-level encryption and transport layer encryption protocols to encrypt and protect sensitive data, and uses symmetric encryption algorithms to encrypt sensitive fields. The data is stored independently in the database, and the keys are generated by the hardware security module and stored separately. The key rotation mechanism is automatic every 90 days, and the rotation process is recorded. The decryption process depends on user permissions. A temporary key is dynamically generated after the decryption request passes the permission verification. Through the behavior modeling mechanism, the user's operations in the system are learned and modeled in real time to identify potential abnormal behaviors. It has a built-in automatic compliance rule engine that supports multiple employment compliance judgments.

[0021] As a further description of the above technical solution:

[0022] The steps are as follows:

[0023] Step 1: Multi-role data permission control

[0024] 1.1 Establish an account system for three roles: platform administrators, client companies, and employees;

[0025] 1.2 Configure data access scope through a role-based access control permission model;

[0026] 1.3 Enable dynamic permission inheritance mechanism to automatically add the "employee ID = current user ID" condition to query requests;

[0027] 1.4 Implement field-level data control to shield sensitive fields such as salary calculation formulas and customer company profit sharing;

[0028] 1.5 Use multi-factor authentication technology to verify user identity;

[0029] Step 2: Dispatch Relationship and Working Hours Management

[0030] 2.1 Build a many-to-many dispatch relationship model to support cross-post dispatch of employees and multi-person participation in one post;

[0031] 2.2 Work time data is collected through three methods: mobile terminal clocking in automatically records the location and timestamp, QR code scanning check-in can verify location and time compliance, and external attendance device data is imported and stored after standardization and cleaning;

[0032] 2.3 Execute the three-level review process:

[0033] Employee entry: submit punch-in information and working time records;

[0034] Customer confirmation: The customer unit verifies the accuracy of the data;

[0035] Platform final review: Administrators archive and process disputed data;

[0036] 2.4 Detect and mark abnormal patterns in real time, including lateness, early departure, absence, daily working hours exceeding the limit, and location mismatch;

[0037] 2.5 Output the audited working hours data to the salary settlement module and evaluation feedback module;

[0038] Step 3: Intelligent salary calculation and payment

[0039] 3.1 Configure compensation rules, which are composed of basic salary, position allowance, performance bonus, and overtime pay;

[0040] 3.2 Execute salary calculations through the incremental calculation engine: Store historical snapshots of employee salary calculation parameters, monitor changes to work hours, performance scores, and subsidy rules, trigger cascade recalculation only for changed fields, and reuse cached results for unchanged data;

[0041] 3.3 Automatically verify the compliance of wages and social security: Compare the minimum wage standard, social security contribution ratio, and contract salary terms of the job location. If there is a violation, the payroll will be blocked and an alert will be issued.

[0042] 3.4 Generate structured payroll, including subsidy details, social security and provident fund withholding items and tax calculation instructions;

[0043] 3.5 Submit encrypted batch payroll instructions through the bank's API interface;

[0044] Step 4: Two-way evaluation and sentiment analysis

[0045] 4.1 Two-way rating by customers and employees:

[0046] Customer evaluation dimensions: attendance, task completion quality;

[0047] Employee evaluation dimensions: communication efficiency, timeliness of salary payment;

[0048] 4.2 Text evaluation sentiment analysis, including word segmentation, stop word removal, traditional Chinese to simplified Chinese standardization, matching predefined keyword libraries, and calculating sentiment weights using an LSTM model;

[0049] 4.3 Marking conflict scoring;

[0050] 4.4 Automatically generate rectification orders for negative reviews and track their status, including those pending, being rectified, and resolved;

[0051] Step 5: Data prediction and risk warning

[0052] 5.1 Build a unified structured wide table to integrate data from various modules;

[0053] 5.2 Output results through AI prediction model:

[0054] Job demand trends: Input job posting frequency and working hours saturation;

[0055] Employee turnover probability: input attendance stability and salary changes;

[0056] Customer credit risk: Enter salary payment on-time rate and contract fulfillment rate;

[0057] 5.3 Generate visual heat maps and high-risk lists;

[0058] Step 6: Multi-terminal collaboration and security monitoring

[0059] 6.1 Realize data synchronization between Web, mobile and PC clients through multi-terminal integration modules;

[0060] 6.2 The mobile terminal supports offline operation and automatically synchronizes to the cloud after the network is restored;

[0061] 6.3 Security Assurance Module Implementation:

[0062] Field-level encryption;

[0063] Behavioral modeling monitoring to identify high-frequency export of sensitive data and cross-enterprise access;

[0064] Compliance rules engine;

[0065] 6.4 Audit logs record all system operations;

[0066] Step 7: Closed-loop data linkage

[0067] 7.1 Abnormal working hours data from the work management module is pushed to the evaluation feedback module as the basis for performance scoring;

[0068] 7.2 The salary settlement details of the salary settlement module are input into the analysis and forecast module to drive salary cost forecast;

[0069] 7.3 When the sentiment analysis result is marked as "needing attention", an early warning notification from the security assurance module is triggered.

[0070] The present invention has the following beneficial effects:

[0071] 1. In the present invention, at the authority control level, the module definition constructs a role-based access control authority model, and implements a field-level data control mechanism for the three roles of platform administrators, customer enterprises, and employees. For example, employees can only view specific public fields in the payroll, while sensitive fields such as salary calculation formulas are dynamically shielded. Through the authority inheritance mechanism, the system automatically adds the filter condition of "employee ID = current user ID" to the query request of the employee role to ensure data isolation, and constructs a "employee-position-customer-contract" multi-dimensional data relationship map. Through the visual view, the dynamic association path between entities is mapped in real time. The administrator can intuitively grasp the personnel flow and business network. The dispatch relationship adopts a many-to-many model to support employee cross-position scheduling and multi-person collaboration. The work management module collects data through three types of working time collection methods and initiates a three-level review process to ensure data validity.

[0072] 2. In this invention, the incremental calculation engine monitors changes in working hours, performance ratings, or subsidy rules, triggering recalculation only for changed fields and reusing historical calculation results to improve efficiency. The compliance rule engine verifies the compliance of minimum wage standards, social security contribution ratios, contract salary terms, and other aspects in real time, preventing the generation of illegal payrolls. The evaluation and feedback module uses a two-way rating system to enable mutual evaluation between customers and employees. It combines a sentiment analysis model to identify negative keywords in text, automatically marks records as "needing attention," and generates a closed-loop tracking loop for rectification orders. The analysis and prediction module integrates data from the entire business chain through a unified data warehouse with wide tables. The AI ​​engine outputs three types of predictions: job demand trend forecasts based on working hour saturation and industry cycles, employee turnover probability assessments based on attendance and performance ratings, and customer credit risk analysis based on salary payment punctuality. Administrators can access analysis results by inputting commands using the natural language to SQL query function. The security module implements a field-level encryption mechanism with automatic key rotation every 90 days. Sensitive data is dynamically decrypted by role, with a memory residency of ≤60 seconds. Behavioral modeling technology identifies abnormal operations such as the frequent export of sensitive data in real time, and collaborates with the compliance rule engine to intercept illegal processes. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Figure 1 This is a system architecture diagram of the present invention. DETAILED DESCRIPTION

[0074] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 efforts are within the scope of protection of the present invention.

[0075] Reference Figure 1 , an embodiment provided by the present invention: a database management system, comprising a user information management module, a work management module, a salary settlement module, an evaluation feedback module, an analysis and prediction module, a multi-terminal integration module and a security assurance module, wherein the user information management module associates employee, position, client enterprise and contract data through a data map view, the work management module outputs standardized working hour data to the salary settlement module, and outputs position performance records to the evaluation feedback module, the salary settlement module generates a payroll based on the working hour data, and provides the salary settlement details to the analysis and prediction module, the evaluation feedback module inputs two-way evaluation data and sentiment analysis results to the analysis and prediction module, the analysis and prediction module integrates the data of each module through a unified structured wide table, and outputs job demand trends, employee turnover probability and customer credit risk prediction, the multi-terminal integration module realizes data synchronization between the Web, mobile and PC clients, and exchanges data with external systems through a RESTful API, and the security assurance module provides field-level encryption, behavior modeling monitoring and a compliance rule engine for all modules.

[0076] A database management system includes a user information management module, a work management module, a salary settlement module, an evaluation feedback module, an analysis and prediction module, a multi-terminal integration module and a security assurance module. The user information management module associates employee, position, client enterprise and contract data through a data map view. The work management module outputs standardized working hour data to the salary settlement module and outputs position performance records to the evaluation feedback module. The salary settlement module generates a payroll based on the working hour data and provides salary settlement details to the analysis and prediction module. The evaluation feedback module inputs two-way evaluation data and sentiment analysis results to the analysis and prediction module. The analysis and prediction module integrates data from each module through a unified structured wide table and outputs job demand trends, employee turnover probability and customer credit risk prediction. The multi-terminal integration module realizes data synchronization between the Web, mobile and PC clients and exchanges data with external systems through a RESTful API. The security assurance module provides field-level encryption, behavior modeling monitoring and a compliance rule engine for all modules.

[0077] The user information management module supports three types of user roles: platform administrators, client companies and employees. The platform administrator is responsible for global configuration and monitoring, client companies can manage dispatch needs, and employees can access personal data. The system uses a role-based access control permission model to control the scope of user data access. It has a field-level data control mechanism and a dynamic permission inheritance mechanism. Based on the dynamic permission control model of the role, employees can only access personal data related to themselves, and client companies can only view the relevant information of their dispatched employees. The platform administrator has full authority operation capabilities, sets up multi-factor identity authentication technology, enhances the login security of the system, sets up an audit log, records all user operations, ensures the traceability of data access and modification behavior, and regularly generates compliance audit reports for platform administrators and regulatory authorities to review.

[0078] The user information management module establishes a multi-role account management system, supporting three types of user roles: platform administrator, client enterprise, and employee. The platform administrator is responsible for global configuration and monitoring; client enterprises can manage their own dispatch needs; employees can access personal data, and the role-based access control permission model controls user permissions. Users configure the data access scope and operation permissions in the system. There is a field-level data control mechanism and a dynamic permission inheritance mechanism. The platform can divide permissions for sensitive fields through preset rules, so that different users can obtain different information content when viewing data according to their identities. Employees can only access information related to themselves, client enterprises can only manage dispatch needs related to them, and platform administrators have full access capabilities. Adopting multi-factor authentication technology, such as dynamic passwords and device fingerprints, users need to authenticate their identities when accessing to ensure login security and manage customer and employee information. Customer enterprise management includes storing enterprise qualifications, job requirements, contract texts and invoice information, supporting contract status tracking and qualification validity reminders, employee file information including maintaining employee basic files, labor contracts, social security payment records, job resumes and performance scores, and updating employee status in real time, such as employment, resignation, and transfer. The audit chain of the security module builds an audit log, and all user-related data additions, deletions, modifications, and inquiries are automatically recorded in the audit log by the system. The audit log includes the operator's identity, access time, operation type, operation object, operation content, and changes. The content and source terminals of the previous and subsequent data ensure that the operation is traceable, and regularly generate compliance audit reports for the platform administrator and regulatory authorities to review. The audit log is synchronized to the tracking chain of the multi-terminal integrated module to achieve unified management of the whole system operation traces. This mechanism ensures that in the event of a dispute or violation, the operation process can be quickly traced back and the responsibility can be defined. In terms of customer enterprise information management, it provides customer information collection and maintenance functions, supports the input of basic information of customer enterprises, qualification documents, contract content, job posting information and invoice information. The entered data will be used as the basis for the establishment of dispatched positions and matching with contracts, and is linked with the work management module. In terms of employee file information, it supports the unified entry and update of basic employee files, including core information identity proof, Contact information, labor contracts, social security payment status, job history and performance scores. These data will serve as the input basis for other modules. After logging in through their accounts, employees can view their payslips, clock-in records, labor contract details, social security payment status, job information, performance scores, customer evaluation feedback, and safety operation record information. The user information management module is linked to the work management module and the evaluation feedback module, allowing workers to report working hours, submit evaluations or make wage appeals, and construct a multi-dimensional data relationship map of "employee-job-customer-contract" to visualize the multi-dimensional data relationship structure between employees and jobs, customer companies, and contracts. The entity associations presented in a graphical manner make it easy for administrators to intuitively grasp the personnel flow paths and business relationship networks.

[0079] The platform administrator is responsible for configuring the core rules of the entire system, defining the scope of role permissions in the role-based access control permission model, such as the functional boundaries that the client enterprise can operate, the data fields that employees can access, setting field-level data control policies, such as limiting employees to only view specific fields of their own payrolls, configuring identity authentication rules for all platform users, such as dynamic password strength device fingerprint verification rules, maintaining the association logic of "employee-position-customer-contract" data, ensuring the accuracy of visual views, tracking the key operations of all users through audit logs, and identifying abnormal operations based on the behavioral modeling capabilities of the security assurance module, such as high-frequency export of sensitive data cross-enterprise access attempts, and automatically responding to violations. Automatically trigger account freezing or warning notifications, and detect the compliance of data on the entire platform in real time, such as the social security payment status during the validity period of the labor contract, automatic warning of abnormalities, security module functions, and supervision of the execution of multi-level review processes. For example, whether the client company handles the working time confirmation process in a timely manner. The platform administrator configures role-based access control permissions and multi-factor authentication rules and data association logic to achieve unified management of global system rules; at the same time, with the help of security behavior modeling and compliance detection mechanisms, the security and compliance status of user operation data on the entire platform are monitored in real time and risk intervention is carried out to ensure stable operation of the system and meet regulatory requirements. Client companies can create, modify or close the dispatch positions they need in the system. The system can also be used to monitor the performance of employees in the company, such as attendance rate, contract validity period, etc., to ensure that the demand and manpower are matched. When the job demand changes, the system will automatically trigger the update of the contract terms. If the employment period is extended, the contract attachment needs to be supplemented. If the demand is closed, the system needs to contact the company. In the dynamic work management module, the dispatch status of the associated employees will be terminated synchronously. Only the needs under the name of the company can be operated, and employees cannot access other customer data across companies. Key operations such as the termination of employee unbinding needs require a second review by the platform administrator and linkage with the security assurance module to ensure process compliance. Employees can only view information directly related to themselves through the system, including: Position information: the work content of the current dispatched position, the validity period of the position in the client company; Contract information: the content of the individual labor contract, the signing status, the start and end time, and key terms; Salary information: salary slip details and payment status; Social security payment records: social security type, payment base, and deposit month; Punch record: daily punch-in time and place, and working hours statistics;Performance rating: Customer ratings and comments on the quality of their service. The system defines the employee role as "only able to access their own data". The permission inheritance mechanism can only inherit. The permission inheritance mechanism automatically filters non-related data. The query request automatically adds the condition of "employee ID = current user ID". For example, when querying the payroll, only the personal data is returned. Employees cannot see any fields of other employees, and dispatched employees cannot obtain customer permissions. Sensitive fields, such as salary calculation formulas and customer company profit sharing, are blocked, and only employee-public fields are displayed, such as the final actual amount. The clauses involving commercial confidentiality in the contract text are not visible to employees, and only their rights and obligations are displayed. When employees view the "position-contract" information, the system automatically associates their own ID through the data map view, and only renders the positions and contract nodes related to them. If employees serve multiple customers at the same time, they can Switching to view independent data for different positions is possible, but cross-position access to other people's information is prohibited. All access behaviors are recorded in the audit log, such as the time and IP address of payroll queries. Abnormal operations, such as frequent attempts to access other people's data, trigger the violation freeze mechanism. When employees view their payroll, the system calls the incremental calculation engine to generate their personal salary details in real time. When employees access contract details, the field-level encryption mechanism decrypts sensitive terms and displays them partially. Under the constraints of the permission inheritance mechanism and field-level control, employees can only view information directly related to themselves through the employee portal. They can also connect to other modules through the portal to report work hours, submit evaluations, or file salary appeals. All operations are monitored by the audit log. This design not only protects employees' right to know, but also ensures data isolation through a role-based access control model and data graph filtering, meeting privacy protection and security compliance requirements.

[0080] The work management module builds a many-to-many dispatch relationship model, supports employees to be dispatched to multiple client companies or positions, and supports the arrangement of multiple employees for the same position; collects working time data through clocking in, QR code scanning sign-in and external attendance equipment, and sets up a three-level review mechanism to ensure the accuracy and compliance of working time data, supports data map view to visualize the multi-dimensional data relationship structure between employees and positions, client companies and contracts, has multi-scenario working time collection methods, supports mobile terminal clocking in, QR code sign-in and external attendance system data import to ensure the accuracy and real-time of working time data, and builds a many-to-many dispatch relationship model, supports one employee to be dispatched to multiple client companies or different positions at the same time, and also supports Multiple employees can be assigned to the same position to meet the actual labor needs of rotation, replacement or temporary scheduling. The dispatch relationship is stored in the system as structured data and associated with the employee and customer data provided by the user information management module. The employee dispatch status is mapped in real time through the relationship network. Administrators can intuitively view the dynamic relationship path between employees, positions and customers through the data map view. After the dispatch relationship is established, the system will automatically start the working time collection process through various collection methods, including: employees clocking in / out on mobile terminal devices, the system automatically records the location and timestamp; employees scan the QR code set on site to check in, and the system verifies whether the check-in location and time are compliant;The platform can also connect to external attendance equipment or third-party attendance systems, import their original punch-in data, and then standardize and clean it before storing it in the warehouse. The above three methods can be flexibly combined to meet the employment record needs of multiple scenarios. In order to ensure the validity and traceability of working time data, a three-level review mechanism is set up, namely employee input, customer confirmation, and platform final review. The punch-in information and the working time record of the day submitted by the employee are first confirmed by their corresponding customer unit. The system will then be handed over to the platform administrator for final review and archiving. When disputed data occurs, the platform administrator will review and force corrections. The review process can set a deadline and automatic reminder function to prevent information retention. All review behaviors are recorded in the audit log for subsequent tracing. The system detects abnormal patterns in working time data in real time and marks them. It can identify lateness, early departure, absence, single-day working hours exceeding the limit, etc. according to preset rules. If the clock-in location doesn't match the job address, once an anomaly is identified, the system will issue a prompt simultaneously on both the employee and client management sides, and will store the anomaly for platform managers to decide and address. This anomaly data also serves as an important input for performance management and employee ratings, integrating with the evaluation and feedback module. The audited work hour data output by the work management module serves as the input for multiple other modules. The work management module communicates with the rest of the system via a unified interface, ensuring data consistency and integrity across multiple business processes. Through many-to-many dispatch relationship modeling, three types of work hour collection methods, a three-level audit process, and automatic anomaly warning technology, the system achieves full lifecycle management of dispatch relationships and work hour data. By outputting standardized work hour data to other modules, it drives subsequent payroll calculation and performance evaluation processes, ensuring a closed-loop data chain across the entire system.

[0081] The salary settlement module has a flexible and configurable salary rule setting function, supports multiple salary items, and the incremental calculation engine recalculates only the changed parts of working hours, performance and subsidies, improves calculation efficiency, automatically performs compliance verification of wages and social security, ensures salary compliance, automatically identifies the changed fields of working hours, performance or subsidy rules, and recalculates the changed parts of the data, reduces calculation pressure, optimizes system response efficiency, and verifies the compliance of wages with social security and taxes. It automatically determines whether the salary structure complies with local regulations and triggers early warning prompts in case of violations to prevent payroll generation operations.

[0082] The salary settlement module is used to automatically calculate, verify compliance and execute payments for employee salaries in labor dispatch scenarios. Its core functions include salary rule configuration, salary calculation, compliance verification and payroll generation. It is a key module in the system that directly faces employee remuneration and salary management. Through a flexible and configurable salary rule setting mechanism, it supports setting basic salary, position allowance, performance bonus and overtime pay according to the different attributes of dispatched positions. Platform administrators can use the configuration interface to establish a variety of salary structure models including hourly wage system, daily wage system and comprehensive working hour system. Each salary structure can be bound to the corresponding position and automatically matched with the employee's working hour data to ensure that the accounting results comply with actual business rules and the calculation efficiency is guaranteed by the incremental calculation engine. Rate and system response speed. When an employee's monthly working hours performance score or subsidy rules change, the engine automatically identifies the changed fields and recalculates the parts with data changes. If the employee's position, working hours or performance data have not changed, the system can directly reuse the last calculation result without repeating the calculation, thereby effectively reducing the calculation pressure and improving the system throughput. The incremental calculation engine monitors changes in working hours data, performance scores and subsidy rules. When an employee's monthly working hours, performance scores or subsidy rules change, the engine automatically identifies the changed fields. The system stores the last calculation snapshot for each employee's salary calculation parameters. When new data is generated, the engine automatically compares the difference between the current value and the snapshot value, marks the difference field as a changed item, and transmits it through the system's underlying event bus. , listen to the data update events of the related modules, the work management module submits new data, triggers the `working time field` change event, and the evaluation feedback module updates the performance. The engine's built-in wage calculation dependencies include: basic salary = working hours × hourly wage, performance bonus = basic salary × performance coefficient, overtime pay = overtime hours × overtime unit price, social security provident fund = wage base × payment ratio. When the dependency changes, recalculation is triggered. The calculation is only started for employee data with changes, and other employee data is skipped. The recalculation task is placed in the background queue to avoid blocking the main thread. By combining with job contract information and social security policy standards, the automatic verification function of wages and social security rules is realized. The system can calculate the minimum wage standard, social security payment ratio, and provident fund deposits in the area where the job is located. The system will use the salary ratio, industry-specific subsidy standards, contractually agreed salary terms, and upper and lower limit parameters of the social security payment base to determine whether the salary structure complies with local regulations. Once it is found that the salary calculation result is lower than the minimum standard or the social security base is insufficient, the system will issue an early warning and prevent the payroll generation operation to ensure that the platform is legal and compliant in terms of employment management. After the calculation is completed, based on the one-click payroll generation capability, the detailed salary structure of each employee will be summarized into a structured payroll. The payroll includes details of basic salary and various subsidies, and automatically attaches social security and provident fund withholding details and tax calculation instructions, and supports exporting to standard electronic file formats. The payroll can be pushed directly to the employee's personal port through the system for them to view. Employees can confirm online or initiate a salary objection application.This system forms a virtuous feedback loop and connects to the bank's API interface. After review and confirmation by the administrator or client company, the system can automatically generate batch payroll instructions that meet bank standards from batch payroll information and submit them to the bank through an encrypted channel, realizing batch fund transfers and significantly reducing the operational burden of manual payroll processing. Payrolls can be exported to a standard format for corporate financial archiving. The evaluation and feedback module can read wage performance payment information as an evaluation reference; the analysis and prediction module can predict salary cost trends and job attractiveness based on historical salary fluctuations; and the security module continuously monitors potential violations in the salary structure. The salary settlement module ensures the accuracy, compliance, and efficient payment of employee wages through a flexible salary rule configuration mechanism, an efficient incremental calculation engine, compliant salary rule verification capabilities, and a complete payroll generation and payroll process. This ensures the automation of the entire salary process and serves as one of the core components of data-driven and linked execution in the labor dispatch system. The incremental engine only recalculates changes to improve efficiency, compliance verification proactively avoids regulatory risks, and the payment process ensures execution accuracy. All operations rely on the work time data from the work management module and the contract rules input from the user information management module, forming a closed-loop salary management chain.

[0083] The salary settlement module has a flexible and configurable salary rule setting function, supports multiple salary items, and the incremental calculation engine recalculates only the changed parts of working hours, performance and subsidies, improves calculation efficiency, automatically performs compliance verification of wages and social security, ensures salary compliance, automatically identifies the changed fields of working hours, performance or subsidy rules, and recalculates the changed parts of the data, reduces calculation pressure, optimizes system response efficiency, and verifies the compliance of wages with social security and taxes. It automatically determines whether the salary structure complies with local regulations and triggers early warning prompts in case of violations to prevent payroll generation operations.

[0084] The evaluation and feedback module is used to realize the two-way evaluation and feedback process between employees and customers, and to build a credit evaluation system based on data drive. It is an important tool module for improving service quality and optimizing employment matching strategies. It supports customers' evaluation of employees and employees' evaluation of customers through a two-way scoring system. Both two-way evaluations are equipped with structured evaluation indicators, and each dimension supports score quantification. Customer companies can conduct a comprehensive evaluation of the work performance of dispatched employees after the completion of the dispatch task or in the periodic stage. The evaluation dimensions include but are not limited to attendance, task completion quality, work attitude, and communication skills. Customers can select the score level through the system front end and can attach a text description of the evaluation. In order to enhance the credibility of the data, the system will automatically associate the employee's objective Indicators include working hour data, task completion records, contract fulfillment status, safety operation records, historical scoring trends, and salary settlement status. As auxiliary reference content, employees can provide feedback to the client companies they serve after the dispatch task is completed. The evaluation content includes communication efficiency, on-site management, timeliness of salary payment, safety protection, comfort of working environment, adequacy of resource support, rigor of contract fulfillment, and efficiency of complaint response. The employee evaluation process is consistent with that of the customer, using a combination of structured scoring and text supplements, supporting anonymous submission, encouraging true expression, and structuring the evaluation records and storing them. All evaluation content is archived with user-position-dispatch task as the index, forming a traceable and searchable historical evaluation library. After the system standardizes the scoring data, it automatically generates comprehensive scores and rankings for employees and customers for subsequent job recommendations and personnel screening. In order to improve the intelligence of the system, a built-in evaluation text sentiment analysis function is used to process text evaluation content, and sentiment tendency analysis and keyword recognition are performed on the evaluation text content submitted by customers or employees. The keyword library is predefined and set up first. The keyword library includes negative words, risk combination words, industry compliance vocabulary, and labor law related terms. A general sentiment dictionary is used to supplement the vocabulary not covered by the industry, and historical evaluation texts of employees and customers are accumulated. High-frequency negative expressions that are not included are automatically identified through text clustering. In actual use, the evaluation text is first segmented, stop words are removed, and standardized, and then the LSTM model is used for Conduct contextual sentiment analysis, match keywords in the text, update the vocabulary priority according to the high-frequency problems in the labor dispatch scenario, introduce negative word detection, correct the sentiment polarity, and assign weights to the keywords in the text. For example, the combination of "serious" + "late" has a higher weight than the single "late". The overall sentiment tendency is calculated by weight. Once the evaluation contains negative words or emotional expressions, the system will automatically mark the record as "needs attention" and remind the platform administrator to intervene in the review through system messages, and issue an early warning for rating deviations. When an employee has a large difference in ratings in multiple customer reviews, or the same customer has abnormal differences in ratings for different employees, or the difference in ratings between customers and employees for the same service is larger, it will be automatically marked as "conflicting ratings".The system will automatically identify the trend of score fluctuations and generate abnormal prompts. When a single score deviates extremely from the historical mean, a rectification order will be automatically generated for the negative evaluation and pushed to the responsible party among customers or employees, requiring feedback within a time limit. The rectification process status includes pending / rectifying / resolved, and the rectification status is updated in real time. If it is not processed within the time limit, it will automatically escalate to the platform administrator for intervention, triggering the administrator's review notification, which can effectively prevent the distortion of the evaluation system due to subjective bias or non-objective factors. The generated evaluation results will be used as one of the modeling factors for employee turnover probability, job suitability and customer credit risk in the analysis and prediction module; the evaluation score will also be used for dynamic adjustment of personnel portraits and job matching strategies. The employee and customer rating data can be compared with salary settlement status, attendance records, contract fulfillment status, and safety operation record data. The system's overall closed-loop management capabilities are further enhanced through comparisons of different lines. The evaluation and feedback module, through a two-way evaluation system, sentiment recognition mechanism, rating deviation monitoring, and data archiving, builds a credible, quantifiable, and traceable labor service credit system. This not only improves the authenticity and timeliness of service feedback, but also provides a reliable data foundation for employment optimization and risk control. Through a two-way rating system, multi-dimensional evaluation items, sentiment analysis, rating deviation warnings, and closed-loop tracking of rectification orders, intelligent management of mutual evaluations between customers and employees is achieved. Sentiment analysis identifies text risks, deviation warnings proactively identify rating contradictions, and rectification processes ensure that problems are tracked to the end. All operations rely on performance data from the user information management module and job performance records from the work management module, forming a complete closed-loop evaluation-feedback-improvement cycle.

[0085] The analysis and prediction module integrates the structured data in multiple modules to build a structured wide table, with position, time and user identity as key indexes, integrates AI analysis engine, conducts data training and modeling, outputs job demand trend forecast, employee turnover probability assessment and customer credit risk analysis, and provides natural language intelligent query function. Administrators can enter natural language questions, and the system automatically parses and converts them into structured SQL statements, executes queries and returns results. When the prediction results exceed the normal threshold, the system will automatically push risk prompts to the administrator terminal. The analysis and prediction module is mainly used for multi-dimensional analysis and predictive modeling of various business data in the system, and realizes job trend analysis, employee turnover warning, and customer risk scoring functions. It is the core module for the system to achieve data-driven decision-making. This module first builds a unified data warehouse structure, integrates user information, employee file information, dispatch records, working hours data, salary settlement data, and performance scores from multiple modules, builds a unified structured wide table, and uses position, time, and user identity as key indexes to ensure the consistency, integrity, and traceability of the analysis data. It provides a flexible multi-dimensional visual analysis interface that can display key business indicators in real time. Platform administrators can cross-compare attendance rates, salary levels, and satisfaction index indicators based on position type, customer industry, employee role, and dispatch cycle dimensions, and display working hours distribution trends, position replenishment status, and key changes in employee participation in a graphical manner. Business situation, based on the historical job posting data, draw the monthly change curve of the number of new jobs, express the job demand trend, link the scoring data of the intelligent evaluation feedback module to generate a satisfaction heat map, which is used to analyze the distribution of employee satisfaction. Combined with the working time records of the work management module, a working time distribution heat map is generated to show the peak and trough periods of employment. The AI ​​analysis engine is integrated to train and model historical data, output job demand trend prediction results, employee turnover probability assessment and customer credit risk analysis, and job demand trend prediction is performed through a time series prediction model. The job demand trend prediction is based on the historical job posting frequency, working time saturation, customer industry cyclical data, and macroeconomic indicators to predict the peak employment period or idle week of a certain type of job in the future. period, and use visual heat maps to prompt administrators to allocate human resources, integrate classification models to evaluate the probability of employee turnover, and combine employee attendance stability, salary changes, performance scores, contract expiration and historical evaluation content factors to build an employee mobility risk scoring model, generate risk warnings for employees who may leave or are dissatisfied with the service, and assist the platform to intervene in advance or conduct personnel scheduling, and use regression models to analyze customer credit risk. Customer credit risk analysis uses customer data on salary payment punctuality, employee scores, and contract performance to calculate customer credit ratings, and mark high-risk customers through rating reports to assist in dispatch matching decisions and cooperation strategy optimization. It is equipped with a natural language intelligent query function, and administrators can input in the system interface,For example, for natural language questions such as "Which type of position has the highest turnover rate in the past three months?" and "Which client companies have an average overtime of more than 10 hours?", the system automatically parses the semantics of the user's natural language instructions, converts them into structured SQL statements, executes queries, and returns standardized analysis results. It uses a unified data interface to call the working time records of the work management module, accesses the salary settlement details of the salary settlement module, reads the scores and feedback records of the evaluation feedback module, and aggregates them into a complete analysis feature set, thereby improving the accuracy of model training and the interpretability of the results. The analysis and prediction module also has an early warning prompt function. If the prediction results exceed the normal threshold, such as a surge in job vacancies, an increased probability of employee turnover, and a rapid decline in customer scores, the system will automatically push risk prompts to the platform administrator terminal and recommend specific response strategies, such as publishing a job reserve plan, arranging performance Interview or suspend dispatch cooperation. The analysis and prediction module realizes trend analysis and early warning management of labor dispatch business by building a unified analysis data warehouse, embedding multiple types of prediction models, integrating natural language processing capabilities and multi-module data linkage mechanism, and improving the platform's intelligence level in labor dispatch, risk prevention and control, and resource allocation. By building a data warehouse with a wide table to integrate data from the entire business chain, using a large visual screen to display key indicators in real time, using the AI ​​engine to predict and output three types of prediction results: job demand, loss risk, and credit risk. The analysis threshold is lowered by converting natural language to SQL query. All operations rely on data input from other modules, forming a closed-loop support system of "analysis-prediction-decision-making", realizing trend analysis and early warning management of labor dispatch business, and improving the platform's intelligence level in labor dispatch, risk prevention and control, and resource allocation.

[0086] The multi-terminal integration module supports data synchronization between three types of terminals: Web, mobile, and PC clients. Through unified interface specifications and authentication mechanisms, data access between terminals is consistent. The mobile terminal supports offline operations. After the network is restored, the system will automatically synchronize offline data to the cloud. The system manages the session status, operation behavior, and data cache between multiple terminals through centralized scheduling services, provides standardized RESTful API interfaces, supports two-way data communication with external business platforms, and performs field-level legitimacy verification on all external data inputs. The multi-terminal integration module is used to achieve data synchronization and operation consistency between different terminals of the system, and is responsible for data docking and interaction between the system and external business systems, ensuring that the system has good openness, compatibility, and scalability. It is a key technical module for the platform to support multi-role and multi-scenario operations and external ecological collaboration. Through the multi-terminal data synchronization mechanism, it supports W Data from three types of terminals, namely, EB terminal, mobile terminal and PC client, are synchronized, and three types of users, namely platform administrators, customer enterprises and employees, can access system functions through their respective terminals according to their roles. The system uses a unified interface specification and authentication mechanism to achieve consistency in data access and flexibility in interface adaptation, ensuring consistency in the operating experience of employees, customers and administrators. All terminal access is based on a role-based access control permission model, only loading data content within the user's permission range, and maintaining consistent operating logic of the user interface. The mobile terminal supports offline operation for working time recording and mutual evaluation, and automatically synchronizes to the cloud after network recovery. The session status, operation behavior and data cache between multiple terminals are managed by a centralized scheduling service to ensure consistency and continuity of operations of the same user on multiple terminals. For example, after entering personal information on the PC, employees can continue to view their working time records and payroll on the mobile terminal.Client companies can publish job requirements on the Web and receive real-time audit result prompts on mobile phones, providing a standardized RESTful API interface. This module provides a standardized RESTful API interface and supports two-way data communication with external business platforms including human resource management systems, enterprise resource planning systems, social security reporting systems, tax systems, third-party attendance platforms, and bank payment systems. External systems can push key business data including job requirements, employee lists, social security payment bases, tax rules, contract texts, and job adjustment plan key business data through the interface. They can also read structured output results such as payrolls, contract information, evaluation results, performance data, and job configuration plans from the platform. In order to ensure the accuracy and security of data exchange, field-level legitimacy verification is implemented for all external data inputs, supporting field type verification, required item detection, format specification checking, and multi-value mapping standardization. For data that is redundant, missing, or does not conform to the platform structure, the system automatically performs data cleaning and formatting. It reorganizes the format, performs standardized conversion, and unifies the data unit format for subsequent tracing and operation and maintenance inspection. It has a data tracking chain mechanism, and records all data items and data processing operations exchanged through external interfaces through audit logs. At the collaborative level between multi-terminal and interface integration, it automatically determines the dimension to which the data belongs based on the access data source and routes it to the corresponding business module. For example, the employee file information pushed by the external HR system will be automatically updated to the user information management module, and the payment details provided by the social security system will complete the data binding with the salary settlement module to avoid manual transfer, improve system integration, and realize linkage with the security assurance module. It performs behavioral modeling on all external data traffic, interface call behavior and multi-terminal operation logs, and identifies security risk behaviors in real time, including abnormal access frequency, illegal field reading, and cross-terminal data synchronization failure. It automatically triggers alarms, blocks or rollback operations according to system policies to ensure the stability of the entire system operation and data integrity. This module uses a three-terminal synchronization mechanism and RESTfulAP I interface, external data standardization processing, and tracking chain management technology enable efficient integration of the system with multiple terminals and third-party platforms, ensuring data consistency and unified user experience across multiple platforms. It also enables efficient interoperability with external business systems, building an open, controllable, and traceable system ecosystem interaction capability, greatly enhancing the platform's ability to adapt to labor dispatch scenarios in multiple industries.

[0087] The security assurance module adopts field-level encryption and transport layer encryption protocols to encrypt and protect sensitive data, and uses symmetric encryption algorithms to encrypt sensitive fields. The data is stored independently in the database, and the keys are generated by the hardware security module and stored separately. The key rotation mechanism is automatic every 90 days, and the rotation process is recorded. The decryption process depends on user permissions. A temporary key is dynamically generated after the decryption request passes the permission verification. Through the behavior modeling mechanism, the user's operations in the system are learned and modeled in real time to identify potential abnormal behaviors. It has a built-in automatic compliance rule engine that supports multiple employment compliance judgments.

[0088] The security module is designed to provide data access control, security operation monitoring and compliance behavior assurance for the entire platform, ensuring that the system complies with various laws, regulations and industry standards while meeting the needs of multi-role use, and has high controllability, high traceability and high fault tolerance. It is a key module to ensure the stable operation and compliance of the platform. Through field-level encryption mechanism and transport layer encryption protocol, it comprehensively protects sensitive data including user privacy, labor contracts, salary calculations, social security payments, provident fund data, performance rating data, evaluation content and credit points, customer qualifications and invoice information, and audit logs. It uses symmetric encryption algorithms to encrypt sensitive fields, which are stored independently in the database, and plain text It is not directly exposed. Under the premise that the user has the permission, the system automatically decrypts and displays it in a local manner. Other users cannot obtain unauthorized information through technical means to ensure that unauthorized users cannot decrypt the original content. The decryption behavior is bound to the role-based access control permission. For example, the employee role has no right to access the salary calculation formula. This field is always encrypted. The key is generated by the hardware security module. The master key and the data key are stored separately to avoid the risk of single point leakage. The key access right is strictly limited and can only be called by the security service process. It is triggered by the HSM interface and automatically rotated every 90 days. The old key is retained for 30 days to decrypt historical data. The rotation is triggered when the following events occur: employee position change, Permissions need to be reallocated, key leakage risks, abnormal access logs, compliance audit requirements, GDPR key life cycle restrictions, after the new key is generated, historical data is asynchronously re-encrypted, this marks the old key as invalid, the audit log records the key rotation operation, the decryption request dynamically generates a temporary key after passing the permission check, the memory resides for no more than 60 seconds, the memory is immediately cleared after the field rendering is completed, the key management server records the destruction audit log, if the decryption timeout or process crashes, the key management server automatically recycles the undestroyed DEK, uses the SSL secure transmission protocol to encrypt all data transmission links to prevent network eavesdropping or tampering, and adopts a security policy control mechanism based on behavior modeling. , manage access behaviors, conduct real-time learning and modeling of user operation modes within the system. Once abnormal behaviors such as high-frequency export of sensitive data, cross-enterprise batch access, high-frequency access to other people's data, illegal data tampering attempts, and cross-role operation attempts are identified, and risk operations such as login during irregular time periods, abnormal changes in permissions, login from abnormal devices / IPs, changes beyond the scope of permissions, and data export to external addresses are detected, the system will immediately trigger an automatic response mechanism, issue a risk alert, and force the cancellation or freezing of accounts that perform high-risk operations. This mechanism can effectively prevent internal unauthorized operations and external attack penetration, and has a built-in automatic compliance rule engine that supports multiple employment compliance judgment logics.Including labor contract period verification, minimum wage standard verification, social security payment ratio verification, provident fund payment ratio verification, working hours compliance verification, special position subsidy compliance, employee qualification matching verification, contract key terms completeness verification, salary payment timeliness verification, rest and vacation compliance, the engine monitors in real time whether the employee contract validity period, job salary structure, and social security data meet the requirements of national and regional laws and regulations. Once an anomaly is found, the system will automatically issue a compliance warning and push the warning to the administrator to prevent the continued execution of related business processes in key scenarios, including preventing employees with expired contracts from being dispatched, prohibiting the generation of payrolls below the minimum wage line, and not ensuring the social security base is sufficient. The system supports one-click freezing of related accounts or business processes for confirmed illegal data or operations, and quickly restores the data status before the violation through the "snapshot rollback mechanism" to ensure the continuity and stability of system operation. Administrators can locate the source of risks based on the audit chain records and decide whether to restore accounts, repair data or notify supervision based on the event level. Compliance audit reports are automatically exported for regulatory filing or dispute tracing. In terms of joint management and control at the platform level, the security module keeps linkage with others and automatically obtains the information generated by them. Key information of the company, such as employee status, job history, salary calculation items, salary terms agreed in the contract, social security and provident fund payment parameters, actual working hours record, qualification matching status, as well as the minimum wage standard of the job location, industry special subsidy standard, and individual tax threshold, is used as the input basis for compliance verification to achieve cross-module rule integration and verification, and export structured compliance audit reports and regulatory reports, including contract validity statistics, working hours deviation details, salary legality analysis, data access behavior list, social security payment compliance statistics, authority change audit records, evaluation conflict analysis report, credit score change details, abnormal operation aggregation analysis, employee turnover risk distribution content, support PDF and Excel formats facilitate the platform's submission of relevant materials to human resources and social security departments and auditing agencies to meet industry regulatory requirements. The security module comprehensively establishes a system security boundary and operational specification system through data encryption protection, behavioral modeling and identification, operational audit traces, a compliance rule engine, and a rapid response mechanism. This provides a technical level of security protection and compliance assurance for the labor dispatch database management system, ensuring the platform's stable, secure, and compliant operation under complex business processes. All operations rely on the tracking chain input of the multi-terminal integrated module and are linked with other modules to form a "monitoring-verification-interception-traceability" security closed loop, ensuring that the system meets regulatory requirements and that risks are controllable.

[0089] The steps are as follows:

[0090] Step 1: Multi-role data permission control

[0091] 1.1 Establish an account system for three roles: platform administrators, client companies, and employees;

[0092] 1.2 Configure data access scope through a role-based access control permission model;

[0093] 1.3 Enable dynamic permission inheritance mechanism to automatically add the "employee ID = current user ID" condition to query requests;

[0094] 1.4 Implement field-level data control to shield sensitive fields such as salary calculation formulas and customer company profit sharing;

[0095] 1.5 Use multi-factor authentication technology to verify user identity;

[0096] Step 2: Dispatch Relationship and Working Hours Management

[0097] 2.1 Build a many-to-many dispatch relationship model to support cross-post dispatch of employees and multi-person participation in one post;

[0098] 2.2 Work time data is collected through three methods: mobile terminal clocking in automatically records the location and timestamp, QR code scanning check-in can verify location and time compliance, and external attendance device data is imported and stored after standardization and cleaning;

[0099] 2.3 Execute the three-level review process:

[0100] Employee entry: submit punch-in information and working time records;

[0101] Customer confirmation: The customer unit verifies the accuracy of the data;

[0102] Platform final review: Administrators archive and process disputed data;

[0103] 2.4 Detect and mark abnormal patterns in real time, including lateness, early departure, absence, daily working hours exceeding the limit, and location mismatch;

[0104] 2.5 Output the audited working hours data to the salary settlement module and evaluation feedback module;

[0105] Step 3: Intelligent salary calculation and payment

[0106] 3.1 Configure compensation rules, which are composed of basic salary, position allowance, performance bonus, and overtime pay;

[0107] 3.2 Execute salary calculations through the incremental calculation engine: Store historical snapshots of employee salary calculation parameters, monitor changes to work hours, performance scores, and subsidy rules, trigger cascade recalculation only for changed fields, and reuse cached results for unchanged data;

[0108] 3.3 Automatically verify the compliance of wages and social security: Compare the minimum wage standard, social security contribution ratio, and contract salary terms of the job location. If there is a violation, the payroll will be blocked and an alert will be issued.

[0109] 3.4 Generate structured payroll, including subsidy details, social security and provident fund withholding items and tax calculation instructions;

[0110] 3.5 Submit encrypted batch payroll instructions through the bank's API interface;

[0111] Step 4: Two-way evaluation and sentiment analysis

[0112] 4.1 Two-way rating by customers and employees:

[0113] Customer evaluation dimensions: attendance, task completion quality;

[0114] Employee evaluation dimensions: communication efficiency, timeliness of salary payment;

[0115] 4.2 Text evaluation sentiment analysis, including word segmentation, stop word removal, traditional Chinese to simplified Chinese standardization, matching predefined keyword libraries, and calculating sentiment weights using an LSTM model;

[0116] 4.3 Marking conflict scoring;

[0117] 4.4 Automatically generate rectification orders for negative reviews and track their status, including those pending, being rectified, and resolved;

[0118] Step 5: Data prediction and risk warning

[0119] 5.1 Build a unified structured wide table to integrate data from various modules;

[0120] 5.2 Output results through AI prediction model:

[0121] Job demand trends: Input job posting frequency and working hours saturation;

[0122] Employee turnover probability: input attendance stability and salary changes;

[0123] Customer credit risk: Enter salary payment on-time rate and contract fulfillment rate;

[0124] 5.3 Generate visual heat maps and high-risk lists;

[0125] Step 6: Multi-terminal collaboration and security monitoring

[0126] 6.1 Realize data synchronization between Web, mobile and PC clients through multi-terminal integration modules;

[0127] 6.2 The mobile terminal supports offline operation and automatically synchronizes to the cloud after the network is restored;

[0128] 6.3 Security Assurance Module Implementation:

[0129] Field-level encryption;

[0130] Behavioral modeling monitoring to identify high-frequency export of sensitive data and cross-enterprise access;

[0131] Compliance rules engine;

[0132] 6.4 Audit logs record all system operations;

[0133] Step 7: Closed-loop data linkage

[0134] 7.1 Abnormal working hours data from the work management module is pushed to the evaluation feedback module as the basis for performance scoring;

[0135] 7.2 The salary settlement details of the salary settlement module are input into the analysis and forecast module to drive salary cost forecast;

[0136] 7.3 When the sentiment analysis result is marked as "needing attention", an early warning notification from the security assurance module is triggered.

[0137] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A database management system, characterized in that: The system includes a user information management module, a work management module, a salary settlement module, an evaluation feedback module, an analysis and prediction module, a multi-terminal integration module and a security assurance module. The user information management module associates employee, position, customer enterprise and contract data through a data map view. The work management module outputs standardized working hour data to the salary settlement module and outputs job performance records to the evaluation feedback module. The salary settlement module generates a payroll based on the working hour data and provides the salary settlement details to the analysis and prediction module. The evaluation feedback module inputs two-way evaluation data and sentiment analysis results to the analysis and prediction module. The analysis and prediction module integrates the data of each module through a unified structured wide table, and outputs job demand trends, employee turnover probability and customer credit risk prediction. The multi-terminal integration module realizes data synchronization between the Web, mobile and PC clients, and exchanges data with external systems through RESTful API. The security assurance module provides field-level encryption, behavior modeling monitoring and compliance rule engine for all modules.

2. A database management system according to claim 1, characterized in that: The user information management module supports three types of user roles: platform administrators, client companies and employees. The platform administrator is responsible for global configuration and monitoring, client companies can manage dispatch needs, and employees can access personal data. The system uses a role-based access control permission model to control the scope of user data access. It has a field-level data control mechanism and a dynamic permission inheritance mechanism. Based on the dynamic permission control model of the role, employees can only access personal data related to themselves, and client companies can only view the relevant information of their dispatched employees. The platform administrator has full authority operation capabilities, sets up multi-factor identity authentication technology, enhances the login security of the system, sets up an audit log, records all user operations, ensures the traceability of data access and modification behavior, and regularly generates compliance audit reports for platform administrators and regulatory authorities to review.

3. A database management system according to claim 1, characterized in that: The work management module builds a many-to-many dispatch relationship model, supports employees to be dispatched to multiple client companies or positions, and supports the arrangement of multiple employees to participate in the same position; collects working time data through clocking in, QR code scanning check-in and external attendance equipment, and sets up a three-level review mechanism to ensure the accuracy and compliance of working time data, supports data map views to visualize the multi-dimensional data relationship structure between employees and positions, client companies, and contracts, has multi-scenario working time collection methods, supports mobile terminal clocking in, QR code check-in and external attendance system data import, to ensure the accuracy and real-time nature of working time data.

4. A database management system according to claim 1, characterized in that: The salary settlement module has a flexible and configurable salary rule setting function, supports multiple salary items, and the incremental calculation engine recalculates only the changed parts of working hours, performance and subsidies, improves calculation efficiency, automatically performs compliance verification of wages and social security, ensures salary compliance, automatically identifies the changed fields of working hours, performance or subsidy rules, and recalculates the changed parts of the data, reduces calculation pressure, optimizes system response efficiency, and verifies the compliance of wages with social security and taxes. It automatically determines whether the salary structure complies with local regulations and triggers early warning prompts in case of violations to prevent payroll generation operations.

5. The database management system according to claim 1, wherein: The evaluation and feedback module supports customers' evaluation of employees and employees' feedback to customers, builds a two-way scoring system, and has built-in sentiment analysis and sensitive word recognition mechanisms. The system automatically generates comprehensive scores and provides an early warning mechanism based on sentiment analysis. It supports abnormal score processing and closed-loop rectification. Customer companies score employees' service quality, and employees provide feedback on customer companies' service quality. The system's two-way evaluations are set with structured evaluation indicators and text descriptions, and the evaluation data is standardized. The sentiment analysis engine analyzes the evaluation text in real time, identifies negative emotions or sensitive words, and generates early warning notifications based on score deviations to ensure the objectivity and fairness of service quality evaluations.

6. A database management system according to claim 1, characterized in that: The analysis and prediction module integrates the structured data from multiple modules to build a structured wide table, using position, time and user identity as key indexes. It integrates an AI analysis engine to perform data training and modeling, and outputs job demand trend forecasts, employee turnover probability assessments and customer credit risk analysis. It provides natural language intelligent query functions, and administrators can enter natural language questions. The system automatically parses and converts them into structured SQL statements, executes queries and returns results. When the prediction results exceed the normal threshold, the system automatically pushes risk alerts to the administrator terminal.

7. A database management system according to claim 1, characterized in that: The multi-terminal integration module supports data synchronization among three types of terminals: Web, mobile, and PC clients. Through unified interface specifications and authentication mechanisms, data access between terminals is consistent. The mobile terminal supports offline operations. After the network is restored, the system will automatically synchronize offline data to the cloud. The system manages the session status, operation behavior, and data cache between multiple terminals through centralized scheduling services, provides a standardized RESTful API interface, supports two-way data communication with external business platforms, and performs field-level legitimacy verification on all external data inputs.

8. A database management system according to claim 1, characterized in that: The security assurance module adopts field-level encryption and transport layer encryption protocols to encrypt and protect sensitive data, and uses symmetric encryption algorithms to encrypt sensitive fields. The data is stored independently in the database, and the keys are generated by the hardware security module and stored separately. The key rotation mechanism is automatic every 90 days, and the rotation process is recorded. The decryption process depends on user permissions. A temporary key is dynamically generated after the decryption request passes the permission verification. Through the behavior modeling mechanism, the user's operations in the system are learned and modeled in real time to identify potential abnormal behaviors. It has a built-in automatic compliance rule engine that supports multiple employment compliance judgments.

9. A method for managing a database according to any one of claims 1 to 8, characterized in that: The steps are as follows: Step 1: Multi-role data permission control 1.1 Establish an account system for three roles: platform administrators, client companies, and employees; 1.2 Configure data access scope through a role-based access control permission model; 1.3 Enable the dynamic permission inheritance mechanism to automatically add the "employee ID = current user ID" condition to query requests; 1.4 Implement field-level data control to shield sensitive fields such as salary calculation formulas and customer company profit sharing; 1.5 Use multi-factor authentication technology to verify user identity; Step 2: Dispatch Relationship and Working Hours Management 2.1 Build a many-to-many dispatch relationship model to support cross-post dispatch of employees and multi-person participation in one post; 2.2 Work time data is collected through three methods: mobile terminal clocking in automatically records the location and timestamp, QR code scanning check-in can verify location and time compliance, and external attendance device data is imported and stored after standardization and cleaning; 2.3 Execute the three-level review process: Employee entry: submit punch-in information and working time records; Customer confirmation: The customer unit verifies the accuracy of the data; Platform final review: Administrators archive and process disputed data; 2.4 Detect and mark abnormal patterns in real time, including lateness, early departure, absence, daily working hours exceeding the limit, and location mismatch; 2.5 Output the audited working hours data to the salary settlement module and evaluation feedback module; Step 3: Intelligent salary calculation and payment 3.1 Configure compensation rules, which are composed of basic salary, position allowance, performance bonus, and overtime pay; 3.2 Execute salary calculations through the incremental calculation engine: Store historical snapshots of employee salary calculation parameters, monitor changes to work hours, performance scores, and subsidy rules, trigger cascade recalculation only for changed fields, and reuse cached results for unchanged data; 3.3 Automatically verify the compliance of wages and social security: Compare the minimum wage standard, social security contribution ratio, and contract salary terms of the job location, and block the generation of payrolls and issue an alert if there is a violation; 3.4 Generate structured payroll, including subsidy details, social security and provident fund withholding items and tax calculation instructions; 3.5 Submit encrypted batch payroll instructions through the bank's API interface; Step 4: Two-way evaluation and sentiment analysis 4.1 Two-way rating by customers and employees: Customer evaluation dimensions: attendance, task completion quality; Employee evaluation dimensions: communication efficiency, timeliness of salary payment; 4.2 Text evaluation sentiment analysis, including word segmentation, stop word removal, traditional Chinese to simplified Chinese standardization, matching predefined keyword libraries, and calculating sentiment weights using an LSTM model; 4.3 Marking conflict scoring; 4.4 Automatically generate rectification orders for negative reviews and track their status, including those pending, being rectified, and resolved; Step 5: Data prediction and risk warning 5.1 Build a unified structured wide table to integrate data from various modules; 5.2 Output results through AI prediction model: Job demand trends: Input job posting frequency and working hours saturation; Employee turnover probability: input attendance stability and salary changes; Customer credit risk: Enter salary payment on-time rate and contract fulfillment rate; 5.3 Generate visual heat maps and high-risk lists; Step 6: Multi-terminal collaboration and security monitoring 6.1 Realize data synchronization between Web, mobile and PC clients through multi-terminal integration modules; 6.2 The mobile terminal supports offline operation and automatically synchronizes to the cloud after the network is restored; 6.3 Security Assurance Module Implementation: Field-level encryption; Behavioral modeling monitoring to identify high-frequency export of sensitive data and cross-enterprise access; Compliance rules engine; 6.4 Audit logs record all system operations; Step 7: Closed-loop data linkage 7.1 Abnormal working hours data from the work management module is pushed to the evaluation feedback module as the basis for performance scoring; 7.2 The salary settlement details of the salary settlement module are input into the analysis and forecast module to drive salary cost forecast; 7.3 When the sentiment analysis result is marked as "needs attention", an early warning notification from the security module is triggered.

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