A Smart Employment Compliance Risk Early Warning System and Its Working Method

Abstract

An intelligent employment compliance risk early warning system is characterized by comprising: an employment data collection module, a legal and rule database module, a risk identification and analysis module, a tiered early warning push module, a rectification closed-loop management module, and a data storage and encryption module. The beneficial effects of this invention include: significantly improved management efficiency: replacing the traditional manual review model, it achieves automated employment data collection and intelligent risk analysis, improving management efficiency by over 80%, significantly reducing the workload of HR and legal personnel, and lowering manual management costs; through a quantitative scoring model and a dynamic legal and rule database, this invention achieves accurate risk identification and scientific tiering, avoiding errors from human experience-based judgment, significantly improving early warning accuracy, and enabling early avoidance of various employment violation risks; the legal and rule database is automatically updated, ensuring that compliance verification standards always align with the latest laws and regulations, completely resolving compliance loopholes caused by outdated rules, and ensuring that enterprise employment management always meets regulatory requirements.

Description

Technical Field

[0001] This invention relates to the fields of enterprise human resource management, legal compliance control and computer data processing technology, specifically to an intelligent employment compliance risk early warning system and its working method, which is applicable to the whole process of labor employment compliance risk prevention and management for various large, medium and small enterprises, human resource outsourcing agencies and labor service companies. Background Technology

[0002] With the continuous improvement of labor laws and regulations and the increasing intensity of labor supervision, compliance management of enterprise employment has become a core aspect of operation and management. Once employment violations occur, they can easily lead to labor arbitration and litigation, causing enterprises to suffer losses such as double wages, economic compensation, and administrative penalties, while also affecting the enterprise's reputation.

[0003] Currently, traditional enterprise employment compliance management mainly relies on manual review and post-event investigation by human resource managers, which has many obvious shortcomings: First, manual review is inefficient. Faced with a large amount of data on employee contracts, attendance, salaries, social security, etc., omissions and misjudgments are easy to occur, making real-time monitoring impossible and risk detection delayed. Second, labor laws and local policies are frequently updated, and it is difficult for manual review to keep up with compliance standards in a timely manner, which can easily lead to compliance loopholes due to rule lag. Third, risk assessment relies entirely on the personal experience of managers, lacking unified quantitative standards, resulting in low accuracy of early warnings, vague classification, and inability to carry out targeted control. Fourth, the rectification process lacks closed-loop management, has no complete record and tracking mechanism, risks are prone to recurrence, and effective evidence cannot be provided when labor disputes occur.

[0004] In existing technologies, some human resource management systems only have basic functions such as employee information entry and attendance statistics, and do not achieve automatic matching of employment compliance rules and intelligent risk analysis; a few compliance management systems only cover a single employment scenario, fail to achieve full-cycle control, and lack functions such as automatic updating of regulations, closed-loop tracking of rectification, and data security encryption, thus failing to meet enterprises' needs for efficient, accurate, and full-process employment compliance risk management. Therefore, developing an automated, intelligent, and fully closed-loop employment compliance risk early warning system has become an urgent technical problem to be solved in the fields of business administration and human resource management. Summary of the Invention

[0005] This invention addresses the shortcomings of existing technologies by providing an intelligent employment compliance risk early warning system and its operating method, aiming to solve the following technical problems: Overcome the problems of low efficiency, easy omissions, and delayed risk identification in traditional manual employment compliance review, and realize the automated collection and real-time analysis of employment data throughout the entire life cycle; To address the issues of outdated labor laws and regulations and inconsistent compliance standards, and to achieve dynamic quantitative updates and precise verification of laws and regulations; To overcome the shortcomings of existing systems in risk assessment, such as lack of quantitative standards, inaccurate early warning, and unclear classification, and to achieve intelligent quantitative classification and targeted early warning of risks; To address the issues of lack of tracking, closed-loop management, and record-keeping in risk rectification, and to achieve full-process closed-loop management and traceability of employment compliance risks; Ensuring the security and confidentiality of employment data, meeting the compliance requirements for enterprises to provide evidence in labor disputes, and reducing enterprises' employment compliance costs and legal risks.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: A smart employment compliance risk early warning system, characterized by comprising: an employment data collection module, a legal rules database module, a risk identification and analysis module, a tiered early warning push module, a rectification closed-loop management module, and a data storage and encryption module; the employment data collection module collects enterprise employment data throughout its entire lifecycle in real time and performs standardized processing on the collected data; the legal rules database module integrates current national and local labor laws and regulations, judicial interpretations, industry employment standards, and typical precedents to construct an employment compliance verification rule database; the risk identification and analysis module matches and verifies the standardized employment data from the employment data collection module with the compliance verification rule database constructed by the legal rules database module, classifying... The risk level is assessed, and a risk list is generated. The tiered early warning push module, based on the risk level of the risk list generated by the risk identification and analysis module or the upgraded early warning from the rectification closed-loop management module, pushes early warning information to the corresponding management personnel. The rectification closed-loop management module tracks the rectification progress of the early warning risks, records the rectification measures, materials, and review results returned by the management personnel, and upgrades the early warning for risks that are not rectified on schedule, pushing the warning through the tiered early warning push module. The data storage and encryption module encrypts and stores the data and files generated by the employment data collection module, the legal rules database module, the risk identification and analysis module, the tiered early warning push module, and the rectification closed-loop management module.

[0007] The employment data collection module includes an interface connection unit, a data import unit, and a data cleaning unit. The interface connection unit connects with the enterprise OA system, attendance system, payroll system, and human resources management system via API to achieve real-time data synchronization. The data import unit supports batch upload of Excel files and manual data entry. The data cleaning unit performs standardized processing on the collected data, namely, automatically marking abnormal data and prompting managers to complete and correct it, cleaning and formatting the data, removing duplicate, missing, and formatted invalid data, standardizing data fields, marking abnormal data and prompting managers to complete it, and ensuring the accuracy of subsequent data analysis.

[0008] The data collected by the employment data collection module includes basic employee information, labor contract signing and renewal information, attendance and working hour records, overtime hours, salary calculation and payment records, social security and housing provident fund payment records, and onboarding and offboarding procedures.

[0009] The regulatory rule base module includes a regulatory storage unit, an NLP semantic parsing unit, and an automatic update unit. The NLP semantic parsing unit transforms the legal provisions in the rule base into computer-executable quantitative verification rules, such as specific quantitative rules like "employees must sign a labor contract within 30 days of joining the company," "monthly overtime hours shall not exceed 36 hours," and "wages shall not be lower than the local minimum wage standard." The automatic update unit regularly captures the latest regulations and policy interpretations officially released by human resources and social security departments and judicial organs, updates the rule base content, and simultaneously adjusts the risk judgment standards of the risk identification and analysis module to ensure that the compliance basis always meets the current legal requirements.

[0010] The rule base contains current national and provincial labor laws and regulations, judicial interpretations, industry employment standards, and typical labor arbitration cases.

[0011] The NLP semantic parsing unit extracts four core entities from the legal provisions in the regulatory rule base module: responsible parties, time elements, numerical thresholds, and obligatory behaviors, through Named Entity Recognition (NER). Then, it clarifies the triggering conditions and constraint relationships between entities through a relation extraction model. Finally, it maps them into a four-tuple structured verification rule of "triggering condition - verification object - judgment threshold - violation consequences".

[0012] The risk identification and analysis module performs precise matching and verification between standardized employment data and the legal and regulatory rule database, identifying various employment risks such as no signed labor contract, expired and unrenewed contract, excessive overtime, wage arrears, social security arrears, and incomplete resignation procedures. It calculates risk scores and classifies risk levels through a built-in risk quantification scoring model. The risk list is a visualized risk report, which includes risk details, legal basis for violations, rectification suggestions, and processing time limits. Risk details include risk points, risk scores, and personnel involved.

[0013] The risk identification and analysis module matches and verifies employment data with verification rules, classifying the employment data into four risk types: minor violations, moderate violations, severe violations, and serious violations.

[0014] The risk identification and analysis module uses a risk quantification scoring model that employs a weighted scoring method, combining four indicators—risk type, frequency of violations, number of employees involved, and historical violation records—to calculate a risk score of 0-100. Among these, 0-30 points represent low risk, 31-60 points represent medium risk, 61-85 points represent high risk, and 86-100 points represent extremely high risk. If the calculated score exceeds 100 points, it is counted as 100 points.

[0015] The weighted scoring formula for the risk identification and analysis module is: Total Risk Score = R×0.7 + F×0.1 + N×0.12 + H×0.08; the rules for determining the values ​​of each indicator are as follows:

[0016] The scores and weights of the four indicators were trained and calibrated using historical risk data from 120 companies, and the risk level classification matched the actual arbitration loss rate by 94.6%.

[0017] The tiered early warning push module uses internal pop-ups, WeChat / DingTalk messages, SMS, and emails to push information. Extremely high risk levels trigger telephone reminders to ensure that responsible persons receive early warning information as soon as possible. At the same time, it exports a risk list to intuitively display information such as risk distribution and level percentage, which makes it easy for managers to quickly grasp the compliance status.

[0018] The tiered early warning push module is responsible for the accurate push of risk information, matching the corresponding push targets according to the risk level: low risk is pushed to HR specialists, medium risk to HR managers, high risk to corporate legal personnel, and extremely high risk to corporate leaders and legal leaders.

[0019] The rectification closed-loop management module includes a rectification submission unit, an automatic verification unit, and an early warning escalation unit. The automatic verification unit performs compliance verification on the submitted rectification materials, that is, it matches and verifies the rectified employment data with the verification rules. Risks that fail the review are returned for rectification again through the hierarchical early warning push module. Risks that are not rectified within the time limit are raised to the early warning level by the risk identification and analysis module and sent to the superior management personnel through the hierarchical early warning push module.

[0020] The rectification closed-loop management module enables full-process tracking and closed-loop control of risks. After receiving an alert, the responsible person can submit a rectification plan and upload supporting documentation through the system. The system uses an automatic verification unit to verify the compliance of the materials. If the verification is successful, the risk is marked as eliminated and recorded in the rectification log; if the verification fails, the rectification is returned for re-rectification. If the rectification is not completed within the specified time limit, the alert escalation unit automatically raises the risk level and simultaneously sends a copy to the superior management to urge rectification implementation and completely eliminate any remaining risks. After the materials are verified for compliance by the automatic verification unit, legal or HR management personnel can conduct a final review through the manual review unit.

[0021] The data storage and encryption module ensures system data security by using the AES-256 encryption algorithm to encrypt and store all employment data, compliance rules, risk records, and rectification logs. It also features a four-level access control system for administrators, HR, legal personnel, and department heads, allowing users with different permissions to view only their assigned data. All operations are fully traceable, and the data retention period complies with relevant legal requirements for labor dispute evidence, ensuring data is tamper-proof and traceable, effectively preventing data leakage risks and meeting the requirements for labor dispute evidence.

[0022] The intelligent employment compliance risk early warning system also includes a risk trend analysis module, which is used to collect historical risk data, analyze high-risk types, high-risk departments, and high-risk periods, generate monthly and quarterly employment compliance analysis reports, predict potential employment risks, and provide preventive management suggestions.

[0023] The risk trend analysis module performs big data analysis and mining based on historical risk data stored in the system. It statistically analyzes the types and frequency of high-risk events in various departments and time periods of the enterprise, analyzes the causes of risks, and generates monthly, quarterly, and annual employment compliance trend analysis reports. It predicts potential employment risks and provides data support for enterprises to optimize employment management processes and formulate preventive compliance measures, thereby reducing the risk incidence rate from the source.

[0024] A method for operating the aforementioned intelligent employment compliance risk early warning system, characterized by comprising the following steps: S1. The employment data collection module connects to the enterprise's OA, attendance, payroll, and human resources management system in real time through the API interface. It collects employment data for the entire life cycle of employees' onboarding, employment, and departure, calls data cleaning algorithms, removes duplicate data based on Bloom filters, completes format conversion and field standardization through regular expressions, and stores invalid data in an encrypted database. S2, the regulations and rules library module regularly crawls the latest regulations and policies officially released by human resources and social security departments and judicial organs, and calls the LaborBERT NLP semantic parsing model to convert legal provisions into a four-tuple quantitative verification rule of "triggering condition-verification object-judgment threshold-violation consequences", and dynamically updates the employment compliance rule library and corresponding risk thresholds; S3. The risk identification and analysis module will match and verify the standardized employment data collected by the employment data collection module with the compliance rule library one by one, call the risk quantification scoring model to calculate the risk score, divide it into four risk levels: low, medium, high and very high according to the score, identify specific risk points and generate a risk list. S4, the graded early warning push module will push early warning information, including risk details, legal basis for violations, rectification suggestions and processing time limits, to the management personnel at the corresponding level corresponding to different risk levels. The extremely high risk level will automatically trigger a third-party cloud call platform to complete the telephone reminder, and at the same time export a visual risk report. S5, the rectification closed-loop management module matches and verifies the rectified employment data with the verification rules. Risks that fail the review are returned for rectification again through the hierarchical early warning push module. Risks that are not rectified within the time limit are raised by the risk identification and analysis module and sent to the superior management personnel through the hierarchical early warning push module, forming a full-process control of "early warning-rectification-review-closed loop". S6. The data storage and encryption module uses the AES-256 encryption algorithm to encrypt and store all employment data, compliance rule data, risk analysis records, and rectification operation logs. Data access permissions are controlled based on a hierarchical permission system. All operation records are stored using blockchain technology to ensure they are tamper-proof. The data storage period is no less than 5 years to meet the requirements for evidence in labor disputes.

[0025] The risk trend analysis module of the intelligent employment compliance risk early warning system uses historical data stored in the data storage and encryption module to call the STL seasonal decomposition algorithm and LSTM prediction model to analyze the high-risk patterns, predict potential future employment risks, match them with the preventive management suggestion library, and generate monthly, quarterly, and annual employment compliance trend analysis reports to provide data support for enterprise management decisions.

[0026] The risk trend analysis module is implemented based on time series forecasting algorithms. The specific process is as follows: Multi-dimensional feature aggregation: Aggregate historical risk data from the past 12 months by five dimensions: department, position, employment type, risk type, and time, and construct a time series dataset containing four types of features: "frequency of risk occurrence, number of people involved, average rectification time, and cost of violation"; Trend analysis algorithm: The STL seasonal decomposition algorithm is used to split the historical risk data into trend items, seasonal items, and random items to identify monthly patterns and departmental distribution characteristics of high-risk events; Risk prediction and recommendation matching: Based on the LSTM time series prediction model, the probability of occurrence of various types of risks in the next 3 months is predicted. When the predicted probability of occurrence of a certain type of risk exceeds 60%, the corresponding preventive management recommendation library is automatically matched to generate targeted prevention and control plans. For example, when the risk of overtime is predicted to be high, it is recommended to "optimize production scheduling, increase the allocation of temporary staff, and calculate overtime pay in advance"; when the risk of resignation is predicted to be high, it is recommended to "conduct employee satisfaction surveys and compile a list of labor contracts expiring in advance".

[0027] The data cleaning unit performs standardization processing on the collected data as follows: Duplicate data removal: Based on the three-dimensional unique key of "employee ID + data type + business timestamp", the Bloom filter algorithm is used to quickly mark duplicate data, with the false positive rate controlled below 0.01%. The matched duplicate data is automatically moved to the recycle bin, retained for 7 days for future reference, and then automatically deleted. Missing data identification: Based on preset field non-empty validation rules, records with less than 10% of missing fields are automatically marked as needing to be completed and pushed to the corresponding data entry personnel for completion; records with more than or equal to 10% of missing fields are directly judged as invalid data and removed. Formatting anomaly conversion: Based on regular expression matching rules, date formats from different source systems are uniformly converted to the yyyy-MM-dd standard format; thousands separators and currency symbols are automatically removed from amount fields, and two decimal places are uniformly retained; ID number fields are automatically validated to be 18 digits compliant, and old 15-digit ID numbers are automatically upgraded to the 18-digit standard format. Unified field standards: Establish a field mapping dictionary for enterprise employment data, and strictly adhere to the 23 core field standards defined in the "Enterprise Employment Data Meta Specification" for all output fields, including heterogeneous fields from different systems.

[0028] The working method of the NLP semantic parsing unit is as follows: Pre-training corpus: A dedicated pre-training corpus is built based on labor-related laws and regulations, judicial interpretations, local policies, and publicly available labor arbitration / judgment documents. The BERT-base model is incrementally pre-trained to form the LaborBERT pre-trained model. Rule extraction algorithm: A three-level pipeline algorithm of "entity recognition + relation extraction + rule element annotation" is adopted: First, four types of core entities in the legal text are extracted through named entity recognition (NER): responsible subject, time element, numerical threshold and obligation behavior; then the triggering conditions and constraint relationships between entities are clarified through the relation extraction model; finally, it is mapped into a four-tuple structured verification rule of "triggering condition-verification object-judgment threshold-violation consequences".

[0029] The automatic verification unit of the rectification closed-loop management module adopts a triple verification logic of "rule matching + OCR verification + time sequence verification": Rule matching verification: The rectification materials submitted according to the rectification requirements corresponding to the risk points are matched and verified according to the rules. OCR verification: For uploaded image / PDF format rectification materials, call OCR to recognize core information and compare it with the employee employment data stored in the system. If the information matches, the verification is passed; if they do not match, suspicious points are automatically marked and the reviewers are prompted to check them carefully. Time sequence verification: Verify whether the rectification completion time is within the rectification deadline required by the warning. Rectification materials submitted after the deadline are automatically marked as "overdue rectification", the overdue period is recorded and included in the company's historical violation record.

[0030] The beneficial effects of this invention are as follows: 1. Significantly improved management efficiency: Replacing the traditional manual review model, it achieves automated collection of employment data and intelligent risk analysis, improving management efficiency by more than 80%, significantly reducing the workload of HR and legal personnel, and lowering manual management costs. This data is based on comparative test results from 120 pilot enterprises: Under the traditional manual model, it takes an average of 15 working days for a company with 1,000 employees to complete a full-cycle employment compliance review, requiring 3 HR personnel and 1 legal personnel; after using this system, the compliance review for companies of the same size only takes an average of 3 working days, requiring only 1 HR personnel to complete. Calculated at an average daily cost of 500 yuan per person, the cost of a single review decreases from 30,000 yuan to 1,500 yuan, management efficiency increases by (15-3) / 15×100% = 80%, and overall costs decrease by 95%. By the end of 2025, pilot enterprises have cumulatively identified 127,000 risks through the system, avoiding direct economic losses of over 320 million yuan. 2. Precise and Efficient Risk Identification: Through quantitative scoring models and a dynamic regulatory database, risks are accurately identified and scientifically classified, avoiding errors from human experience-based judgments. This significantly improves the accuracy of early warnings and allows for proactive avoidance of various employment violation risks. 3. Real-time Synchronization of Compliance Standards: The regulatory rule database is automatically updated, ensuring that compliance verification standards always align with the latest laws and regulations. This completely resolves compliance loopholes caused by outdated rules, guaranteeing that enterprise employment management always meets regulatory requirements. 4. Traceable Closed-Loop Rectification and Control: The entire process of risk rectification is tracked, with complete records of rectification and review processes, forming a closed-loop management system. This effectively prevents repeated risks, and operation logs can be directly used as evidence in labor disputes, reducing the enterprise's legal burden of proof. 5. Robust Data Security: Encrypted storage and tiered access control ensure the security and confidentiality of sensitive employment data, preventing data leakage and tampering, and meeting enterprise data management compliance requirements. 6. Widely Applicable Scenarios: Adaptable to various large, medium, and small enterprises and human resource service agencies. No modification to existing enterprise systems is required; integration is convenient, operation is simple, and it possesses strong practicality and scalability. Attached Figure Description

[0031] Figure 1 This is a block diagram of the overall architecture of the intelligent employment compliance risk early warning system of the present invention; Figure 2a , Figure 2b , Figure 2c The flowcharts of the intelligent employment compliance risk early warning method of the present invention are connected in sequence. Figure 3 This is a schematic diagram of the risk quantification and classification logic of the risk identification and analysis module of the present invention; Figure 4 This is a schematic diagram of the workflow of the rectification closed-loop management module of the present invention; Figure 5 This is a flowchart of the data cleaning and processing of the labor data acquisition module of the present invention; Figure 6 This is a flowchart of the NLP semantic parsing rule generation process of the regulatory rule base module of this invention; Figure 7 This is a flowchart of the prediction algorithm for the risk trend analysis module of the present invention. Detailed Implementation

[0032] The technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0033] Example: An intelligent employment compliance risk early warning system, characterized by comprising: an employment data collection module, a legal rules database module, a risk identification and analysis module, a tiered early warning push module, a rectification closed-loop management module, and a data storage and encryption module; the employment data collection module collects enterprise employment data throughout its entire lifecycle in real time and performs standardized processing on the collected data; the legal rules database module integrates current national and local labor laws and regulations, judicial interpretations, industry employment standards, and typical precedents to construct an employment compliance verification rule database; the risk identification and analysis module matches and verifies the standardized employment data from the employment data collection module with the compliance verification rule database constructed by the legal rules database module. The system categorizes risks into risk levels and generates a risk list. The tiered early warning push module, based on the risk level of the risk list generated by the risk identification and analysis module or the upgraded early warning from the rectification closed-loop management module, pushes early warning information to the corresponding management personnel. The rectification closed-loop management module tracks the rectification progress of the early warning risks, records the rectification measures, materials, and review results returned by the management personnel, and upgrades the early warning for risks that are not rectified on schedule, pushing the warning through the tiered early warning push module. The data storage and encryption module encrypts and stores the data and files generated by the employment data collection module, the legal rules database module, the risk identification and analysis module, the tiered early warning push module, and the rectification closed-loop management module.

[0034] The employment data collection module includes an interface connection unit, a data import unit, and a data cleaning unit. The interface connection unit connects with the enterprise OA system, attendance system, payroll system, and human resources management system via API to achieve real-time data synchronization. The data import unit supports batch upload of Excel files and manual data entry. The data cleaning unit performs standardized processing on the collected data, namely, automatically marking abnormal data and prompting managers to complete and correct it, cleaning and formatting the data, removing duplicate, missing, and formatted invalid data, standardizing data fields, marking abnormal data and prompting managers to complete it, and ensuring the accuracy of subsequent data analysis.

[0035] The data collected by the employment data collection module includes basic employee information, labor contract signing and renewal information, attendance and working hour records, overtime hours, salary calculation and payment records, social security and housing provident fund payment records, and onboarding and offboarding procedures.

[0036] This embodiment collects real-time data on attendance hours, salary payments, social security contributions, and labor contracts of 320 employees, automatically cleans up duplicate attendance records and missing social security contribution records, and prompts HR to complete the contract renewal information for 5 employees.

[0037] The regulatory rule base module includes a regulatory storage unit, an NLP semantic parsing unit, and an automatic update unit. The NLP semantic parsing unit transforms the legal provisions of the rule base into computer-executable quantitative verification rules, setting quantitative rules such as monthly overtime hours ≤ 36 hours, salary payment no later than the 15th of the following month, and signing a contract within 30 days of employment. The weekly automatic update unit regularly captures the latest regulations and policy interpretations officially released by human resources and social security departments and judicial organs, updates the rule base content, and simultaneously adjusts the risk judgment standards of the risk identification and analysis module to ensure that the compliance basis always meets the current legal requirements.

[0038] The rule base contains current national and provincial labor laws and regulations, judicial interpretations, industry employment standards, and typical labor arbitration cases.

[0039] The NLP semantic parsing unit extracts four core entities from the legal provisions in the regulatory rule base module: responsible parties, time elements, numerical thresholds, and obligatory behaviors, through Named Entity Recognition (NER). Then, it clarifies the triggering conditions and constraint relationships between entities through a relation extraction model. Finally, it maps them into a four-tuple structured verification rule of "triggering condition - verification object - judgment threshold - violation consequences".

[0040] The risk identification and analysis module performs precise matching and verification between standardized employment data and the legal and regulatory rule database, identifying various employment risks such as no signed labor contract, expired and unrenewed contract, excessive overtime, wage arrears, social security arrears, and incomplete resignation procedures. It calculates risk scores and classifies risk levels through a built-in risk quantification scoring model. The risk list is a visualized risk report, which includes risk details, legal basis for violations, rectification suggestions, and processing time limits. Risk details include risk points, risk scores, and personnel involved.

[0041] The risk identification and analysis module matches and verifies employment data with verification rules, classifying the employment data into four risk types: minor violations, moderate violations, severe violations, and serious violations.

[0042] The risk identification and analysis module uses a risk quantification scoring model that employs a weighted scoring method, combining four indicators—risk type, frequency of violations, number of employees involved, and historical violation records—to calculate a risk score of 0-100. Among these, 0-30 points represent low risk, 31-60 points represent medium risk, 61-85 points represent high risk, and 86-100 points represent extremely high risk. If the calculated score exceeds 100 points, it is counted as 100 points.

[0043] The weighted scoring formula for the risk identification and analysis module is: Total Risk Score = R×0.7 + F×0.1 + N×0.12 + H×0.08; the rules for determining the values ​​of each indicator are as follows:

[0044] The risk identification and analysis module matched and verified the collected data, finding that 12 employees in the production department had a cumulative monthly overtime of 42 hours. Calculated using the risk quantification scoring model, the risk type was classified as serious violation (R=80), 3 or more violations (F=20), involving 3.75% of employees (N=20), and with no previous record of losing a lawsuit (H=20). The final risk score was 80×0.7 + 20×0.1 + 20×0.12 + 20×0.08 = 62 points, classifying it as high risk. Simultaneously, it was found that two employees had not signed labor contracts within 40 days of joining the company. The risk type was serious violation (R=100), first offense (F=20), involving 0.625% of employees (N=20), and with no previous record of losing a lawsuit (H=20). The risk score was 100×0.7 + 20×0.1 + 20×0.12 + 20 × 0.08 = 76 points, judged as high risk; In addition, 3 employees with unpaid social security contributions and 5 employees with contracts expiring within 15 days were identified as risk points. After scoring, the social security contribution unpaid risk type is serious violation (R=80), first occurrence (F=20), number of people involved <5% (N=20), no history (H=20), risk score = 80 × 0.7 + 20 × 0.1 + 20 × 0.12 + 20 × 0.08 = 62 points, which is high risk; the contract expiring risk type is moderate violation (R=50), first occurrence (F=20), number of people involved <5% (N=20), no history (H=20), risk score = 50 × 0.7 + 20 × 0.1 + 20 × 0.12 + 20 × 0.08 = 41 points, which is medium risk.

[0045] The tiered early warning push module uses internal pop-ups, WeChat / DingTalk messages, SMS, and emails to push information. Extremely high risk levels trigger telephone reminders to ensure that responsible persons receive early warning information as soon as possible. At the same time, it exports a risk list to intuitively display information such as risk distribution and level percentage, which makes it easy for managers to quickly grasp the compliance status.

[0046] The tiered early warning push module pushes extremely high risk warnings to the company's legal head and HR manager via DingTalk and SMS, along with the legal basis for the violation of the Labor Law, rectification suggestions, and a 3-working-day rectification deadline; high risk warnings are pushed to HR supervisors to urge them to sign labor contracts as soon as possible, push high risk warnings for social security arrears, and push risk warnings for contract expiration to HR specialists.

[0047] The rectification closed-loop management module includes a rectification submission unit, an automatic verification unit, and an early warning escalation unit. The automatic verification unit performs compliance verification on the submitted rectification materials, that is, it matches and verifies the rectified employment data with the verification rules. Risks that fail the review are returned for rectification again through the hierarchical early warning push module. Risks that are not rectified within the time limit are raised to the early warning level by the risk identification and analysis module and sent to the superior management personnel through the hierarchical early warning push module.

[0048] In the rectification closed-loop management module, the HR manager submits the adjusted work schedule and overtime pay vouchers. The system automatically verifies that the overtime hours have been reduced to within 36 hours and that the overtime pay records match the information of the employees involved. After verification, the legal staff reviews and approves the data, marking the risk as eliminated. The labor contracts of two employees who did not sign contracts are signed retroactively. After uploading the scanned copies of the contracts, the system OCR identifies the signing date as the 38th day after their employment. Although it exceeds 30 days, the retroactive signing has been completed, marking the rectification as completed. After review and approval, the process is closed-loop. The rectification progress is tracked, and the social security arrears are paid and the contracts are renewed. After review and approval, the entire process is closed-loop. All rectification records are automatically stored in the data storage and encryption module and encrypted for retention.

[0049] The rectification closed-loop management module enables full-process tracking and closed-loop control of risks. After receiving an alert, the responsible person can submit a rectification plan and upload supporting documentation through the system. The system uses an automatic verification unit to verify the compliance of the materials. If the verification is successful, the risk is marked as eliminated and recorded in the rectification log; if the verification fails, the rectification is returned for re-rectification. If the rectification is not completed within the specified time limit, the alert escalation unit automatically raises the risk level and simultaneously sends a copy to the superior management to urge rectification implementation and completely eliminate any remaining risks. After the materials are verified for compliance by the automatic verification unit, legal or HR management personnel can conduct a final review through the manual review unit.

[0050] The data storage and encryption module ensures system data security by using the AES-256 encryption algorithm to encrypt and store all employment data, compliance rules, risk records, and rectification logs. It also features a four-level access control system for administrators, HR, legal personnel, and department heads, allowing users with different permissions to view only their assigned data. All operations are fully traceable, and the data retention period complies with relevant legal requirements for labor dispute evidence, ensuring data is tamper-proof and traceable, effectively preventing data leakage risks and meeting the requirements for labor dispute evidence.

[0051] The intelligent employment compliance risk early warning system also includes a risk trend analysis module, which is used to collect historical risk data, analyze high-risk types, high-risk departments, and high-risk periods, generate monthly and quarterly employment compliance analysis reports, predict potential employment risks, and provide preventive management suggestions.

[0052] The risk trend analysis module performs big data analysis and mining based on historical risk data stored in the system. It statistically analyzes the types and frequency of high-risk events in various departments and time periods of the enterprise, analyzes the causes of risks, and generates monthly, quarterly, and annual employment compliance trend analysis reports. It predicts potential employment risks and provides data support for enterprises to optimize employment management processes and formulate preventive compliance measures, thereby reducing the risk incidence rate from the source.

[0053] The risk trend analysis module shows that the risk of excessive overtime work in the company has been high in the production department over the past three months, with 68% of the occurrences occurring at the end of each month. The probability of overtime work occurring at the end of next month is predicted to be 75%. A trend report is generated, recommending that the production scheduling process be optimized, a job rotation mechanism be added, and temporary workers be reserved in advance to reduce the risk of excessive overtime work from the source. The risk data analysis also found that the risk of social security payment is occasional, and it is recommended that HR regularly check social security payment data every month to prevent it in advance.

[0054] A method for operating the aforementioned intelligent employment compliance risk early warning system, characterized by comprising the following steps: S1. The employment data collection module connects to the enterprise's OA, attendance, payroll, and human resources management system in real time through the API interface. It collects employment data of 320 enterprise employees throughout their entire employment cycle, including employee onboarding information, labor contracts, attendance, payroll, and social security data. It calls data cleaning algorithms, uses Bloom filters to remove duplicate data, completes format conversion and field standardization through regular expressions, and stores invalid data in an encrypted database. S2, the regulations and rules library module regularly crawls the latest regulations and policies officially released by human resources and social security departments and judicial organs, and calls the LaborBERT NLP semantic parsing model to convert legal provisions into a four-tuple quantitative verification rule of "triggering condition-verification object-judgment threshold-violation consequences", and dynamically updates the employment compliance rule library and corresponding risk thresholds; S3. The risk identification and analysis module will match and verify the standardized employment data collected by the employment data collection module with the compliance rule library one by one, call the risk quantification scoring model to calculate the risk score, divide it into four risk levels: low, medium, high and very high according to the score, identify specific risk points and generate a risk list. S4, the graded early warning push module will push early warning information, including risk details, legal basis for violations, rectification suggestions and processing time limits, to the management personnel at the corresponding level corresponding to different risk levels. The extremely high risk level will automatically trigger a third-party cloud call platform to complete the telephone reminder, and at the same time export a visual risk report. S5, the rectification closed-loop management module matches and verifies the rectified employment data with the verification rules. Risks that fail the review are returned for rectification again through the hierarchical early warning push module. Risks that are not rectified within the time limit are raised by the risk identification and analysis module and sent to the superior management personnel through the hierarchical early warning push module, forming a full-process control of "early warning-rectification-review-closed loop". S6. The data storage and encryption module uses the AES-256 encryption algorithm to encrypt and store all employment data, compliance rule data, risk analysis records, and rectification operation logs. Data access permissions are controlled based on a hierarchical permission system. All operation records are stored using blockchain technology to ensure they are tamper-proof. The data storage period is no less than 5 years to meet the requirements for evidence in labor disputes.

[0055] The risk trend analysis module of the intelligent employment compliance risk early warning system uses historical data stored in the data storage and encryption module to call the STL seasonal decomposition algorithm and LSTM prediction model to analyze the high-risk patterns, predict potential future employment risks, match them with the preventive management suggestion library, and generate monthly, quarterly, and annual employment compliance trend analysis reports to provide data support for enterprise management decisions.

[0056] The risk trend analysis module is implemented based on time series forecasting algorithms. The specific process is as follows: Multi-dimensional feature aggregation: Aggregate historical risk data from the past 12 months by five dimensions: department, position, employment type, risk type, and time, and construct a time series dataset containing four types of features: "frequency of risk occurrence, number of people involved, average rectification time, and cost of violation"; Trend analysis algorithm: The STL seasonal decomposition algorithm is used to split the historical risk data into trend items, seasonal items, and random items to identify monthly patterns of high risk (such as high risk of resignation before the Spring Festival and high risk of attendance and overtime at the end of the quarter) and departmental distribution characteristics (such as high risk of overtime in the production department and high risk of commission payment in the sales department). Risk Prediction and Recommendation Matching: Based on the LSTM time series forecasting model, the probability of occurrence of various types of risks in the next 3 months is predicted. When the predicted probability of occurrence of a certain type of risk exceeds 60%, the corresponding preventive management recommendation library is automatically matched to generate targeted prevention and control plans. For example, when the risk of overtime is predicted to be high, it is recommended to "optimize production scheduling, increase the allocation of temporary staff, and calculate overtime pay in advance"; when the risk of resignation is predicted to be high, it is recommended to "conduct employee satisfaction surveys and compile a list of expiring labor contracts in advance". The prediction accuracy rate reaches 82.7%, which can effectively help companies carry out risk prevention and control 7-15 days in advance.

[0057] The data cleaning unit performs standardization processing on the collected data as follows: Duplicate data removal: Based on the three-dimensional unique key of "employee ID + data type + business timestamp", the Bloom filter algorithm is used to quickly mark duplicate data, with the false positive rate controlled below 0.01%. The matched duplicate data is automatically moved to the recycle bin, retained for 7 days for future reference, and then automatically deleted. Missing data identification: Based on preset field non-empty validation rules (such as employee ID, ID number, and salary amount being mandatory fields), records with less than 10% of missing fields are automatically marked as needing to be completed and pushed to the corresponding data entry personnel for completion; records with more than or equal to 10% of missing fields are directly judged as invalid data and removed. Formatting anomaly conversion: Based on regular expression matching rules, date formats from different source systems (such as yyyy / MM / dd, yyyy-MM-dd, MM / dd / yyyy) are uniformly converted to the yyyy-MM-dd standard format. For amount fields, thousands separators and currency symbols are automatically removed, and two decimal places are uniformly retained. For ID number fields, 18-digit compliance is automatically verified, and 15-digit old ID numbers are automatically upgraded to the 18-digit standard format. Unified Field Standards: Establish a field mapping dictionary for enterprise employment data, and uniformly map heterogeneous fields from different systems (such as "Employee ID" and "Employee Number" to "Employee ID", and "Gross Salary" and "Gross Wages" to "Gross Compensation"), and strictly follow the 23 core field standards defined in the "Enterprise Employment Data Meta Specification".

[0058] The system automates the cleaning of heterogeneous employment data from multiple sources, improving data accuracy to 99.2% and providing a reliable data foundation for subsequent risk analysis. The working method of the NLP semantic parsing unit is as follows: Pre-training corpus: A dedicated pre-training corpus was built based on all labor-related laws, regulations, judicial interpretations, local policies, and 120,000 publicly available labor arbitration / judgment documents from 1994 to the present. The BERT-base model was incrementally pre-trained to form the LaborBERT pre-trained model, which improved the semantic understanding accuracy in the labor domain by 42% compared with the general model. Rule extraction algorithm: A three-stage pipeline algorithm of "entity recognition + relation extraction + rule element annotation" is adopted. First, named entity recognition (NER) is used to extract four types of core entities from legal provisions: responsible parties (such as "employers" and "employees"), time elements (such as "30 days" and "15th of the following month"), numerical thresholds (such as "36 hours" and "minimum wage standard"), and obligatory behaviors (such as "signing a labor contract" and "paying social insurance"). Then, the relation extraction model is used to clarify the triggering conditions and constraints between entities. Finally, it is mapped to a four-tuple structured verification rule of "triggering condition - verification object - judgment threshold - violation consequences".

[0059] Example of a verification rule: Article 10 of the Labor Contract Law states, "A written labor contract shall be concluded when a labor relationship is established. If a labor relationship has been established but a written labor contract has not been concluded simultaneously, a written labor contract shall be concluded within one month from the date of employment." After parsing, the generated rule is as follows: The accuracy rate of manually annotated rules is 98.7%, and the accuracy rate of automatically parsed rules reaches 92.3%. All automatically generated rules must be manually reviewed by legal personnel before officially taking effect.

[0060] Triggering condition: The employee's employment status is "employed". Verification Target: The difference between the employee's employment contract signing date and their start date Judgment threshold: A difference greater than 30 days is considered a violation. Consequences of violation: The default risk level is high, and double the wage difference must be paid. The automatic verification unit of the rectification closed-loop management module adopts a triple verification logic of "rule matching + OCR verification + time sequence verification": Rule matching verification: The rectification materials submitted according to the rectification requirements corresponding to the risk points are matched and verified according to the rules. For example, for the risk of "no signed labor contract", the submitted materials are verified to see whether the labor contract is included, whether the contract signing date is later than 30 days after the start date, and whether the contract term complies with the legal requirements. OCR verification: For uploaded image / PDF format rectification materials, call OCR to recognize core information (such as employee name, ID number, signing date, and salary standard in the contract), and compare it with the employee employment data stored in the system. If the information matches, the verification is passed; if they do not match, suspicious points are automatically marked and the reviewer is prompted to check them carefully. Time sequence verification: Verify whether the rectification completion time is within the rectification deadline required by the warning. Rectification materials submitted after the deadline are automatically marked as "overdue rectification", the overdue period is recorded and included in the company's historical violation record; The automatic verification pass rate reaches 78%. Materials that fail the automatic verification are automatically transferred to the manual review process, effectively reducing the workload of manual review by more than 60%.

[0061] This invention combines labor compliance management methods in the field of business administration with computer data processing and artificial intelligence semantic analysis technology to form a complete technical solution. It solves the technical problems of automated, intelligent, and closed-loop control that cannot be achieved by traditional manual management, and produces clear technical effects. It does not fall under the rules of purely intellectual activities stipulated by patent law and has novelty, inventiveness, and practicality.

Claims

1. An intelligent employment compliance risk early warning system, characterized in that, include: The module includes: employment data collection module, legal and rule database module, risk identification and analysis module, hierarchical early warning push module, rectification closed-loop management module, and data storage and encryption module. The employment data collection module collects enterprise employment data throughout its entire lifecycle in real time and performs standardized processing on the collected data; the legal and regulatory rule base module integrates current national and local labor laws and regulations, judicial interpretations, industry employment standards and typical cases to construct an employment compliance verification rule base; the risk identification and analysis module matches and verifies the employment data after standardization by the employment data collection module with the compliance verification rule base constructed by the legal and regulatory rule base module, classifies risk levels, and generates a risk list. The tiered early warning push module pushes early warning information to the management personnel at the corresponding level based on the risk level of the risk list generated by the risk identification and analysis module or the early warning from the rectification closed-loop management module upgrade. The rectification closed-loop management module tracks the rectification progress of early warning risks, records the rectification measures, rectification materials, and review results returned by the management personnel, and upgrades the early warning for risks that are not completed on schedule through the hierarchical early warning push module; the data storage and encryption module encrypts and stores the data and files generated by the employment data collection module, the legal rules database module, the risk identification and analysis module, the hierarchical early warning push module, and the rectification closed-loop management module.

2. The intelligent employment compliance risk early warning system according to claim 1, characterized in that, The employment data collection module includes an interface connection unit, a data import unit, and a data cleaning unit. The interface connection unit connects with the enterprise OA system, attendance system, payroll system, and human resources management system to achieve real-time data synchronization via API. The data import unit supports batch upload of Excel files and manual data entry. The data cleaning unit performs standardized processing on the collected data, namely, automatically marking abnormal data and prompting managers to complete and correct it, cleaning and converting the data format, removing duplicate, missing, and abnormally formatted invalid data, and standardizing data fields.

3. The intelligent employment compliance risk early warning system according to claim 1, characterized in that, The legal rules base module includes a legal storage unit, an NLP semantic parsing unit, and an automatic update unit. The NLP semantic parsing unit transforms legal provisions into computer-executable quantitative verification rules. The automatic update unit regularly captures the latest legal regulations and policy interpretations officially released by human resources and social security departments and judicial organs, updates the rule base content, and simultaneously adjusts the risk judgment standards of the risk identification and analysis module.

4. The intelligent employment compliance risk early warning system according to claim 3, characterized in that, The NLP semantic parsing unit extracts four core entities from the legal provisions in the legal rule base module through named entity recognition: responsible parties, time elements, numerical thresholds, and obligatory behaviors. Then, it clarifies the triggering conditions and constraint relationships between entities through a relation extraction model. Finally, it maps them into a four-tuple structured verification rule of "triggering condition - verification object - judgment threshold - violation consequences".

5. The intelligent employment compliance risk early warning system according to claim 1, characterized in that, The risk identification and analysis module calculates risk scores using a built-in risk quantification scoring model, classifies risk levels, and identifies compliance risks in the employment process. The risk list is a visualized risk report, including risk details, legal basis for violations, rectification suggestions, and processing timelines. The risk identification and analysis module matches and verifies employment data with verification rules, classifying the employment data into four risk types: minor violations, moderate violations, severe violations, and serious violations. The risk identification and analysis module uses a risk quantification scoring model that employs a weighted scoring method, combining four indicators—risk type, frequency of violations, number of employees involved, and historical violation records—to calculate a risk score from 0 to 100. Specifically, 0-30 points represent low risk, 31-60 points represent medium risk, 61-85 points represent high risk, and 86-100 points represent extremely high risk. Scores exceeding 100 are recorded as 100. The weighted scoring formula for the risk identification and analysis module is: Total Risk Score = R×0.7 + F×0.1 + N×0.12 + H×0.08; R is the risk type score: minor violation: 20 points, moderate violation: 50 points, severe violation: 80 points, serious violation: 100 points, weight 70%; F is the violation frequency coefficient: first occurrence: 20, second occurrence: 50, third or more occurrences: 80, weight 10%; N is the number of people involved coefficient: percentage <5%: 20, 5% ≤ percentage <20%: 50, percentage ≥20%: 80, weight 12%; H is the historical violation coefficient: no historical record: 20, historical record but won: 50, historical record and lost: 80, weight 8%.

6. The intelligent employment compliance risk early warning system according to claim 1, characterized in that, The tiered early warning push module uses internal pop-ups, WeChat / DingTalk messages, SMS, and emails to send alerts. Extremely high risk levels trigger phone reminders and export a risk list.

7. The intelligent employment compliance risk early warning system according to claim 1, characterized in that, The rectification closed-loop management module includes a rectification submission unit, an automatic verification unit, and an early warning escalation unit. The automatic verification unit performs compliance verification on the submitted rectification materials, that is, it matches and verifies the rectified employment data with the verification rules. Risks that fail the review are returned for rectification again through the hierarchical early warning push module. Risks that are not rectified within the time limit are raised to the early warning level by the risk identification and analysis module and sent to the superior management personnel through the hierarchical early warning push module.

8. The intelligent employment compliance risk early warning system according to claim 1, characterized in that, The intelligent employment compliance risk early warning system also includes a risk trend analysis module, which is used to collect historical risk data, analyze high-risk types, high-risk departments, and high-risk periods, generate monthly and quarterly employment compliance analysis reports, predict potential employment risks, and provide preventive management suggestions.

9. A method for operating the intelligent employment compliance risk early warning system according to any one of claims 1 to 8, characterized in that, Includes the following steps: S1. The employment data collection module connects to the enterprise's OA, attendance, payroll, and human resources management system in real time through the API interface. It collects employment data for the entire life cycle of employees' onboarding, employment, and departure, calls data cleaning algorithms, removes duplicate data based on Bloom filters, completes format conversion and field standardization through regular expressions, and stores invalid data in an encrypted database. S2, the regulations and rules library module regularly captures the latest regulations and policies officially released by human resources and social security departments and judicial organs, and calls the LaborBERT NLP semantic parsing model to convert legal provisions into "triggering conditions-verification objects-judgment thresholds-violation consequences" four-tuple quantitative verification rules, and dynamically updates the employment compliance rule library and corresponding risk thresholds; S3. The risk identification and analysis module will match and verify the standardized employment data collected by the employment data collection module with the compliance rule library one by one, call the risk quantification scoring model to calculate the risk score, divide it into four risk levels: low, medium, high and very high according to the score, identify specific risk points and generate a risk list. S4, the graded early warning push module will push early warning information, including risk details, legal basis for violations, rectification suggestions and processing time limits, to the management personnel at the corresponding level corresponding to different risk levels. The extremely high risk level will automatically trigger a third-party cloud call platform to complete the telephone reminder, and at the same time export a visual risk report. S5, the rectification closed-loop management module matches and verifies the rectified employment data with the verification rules. Risks that fail the review are returned for rectification again through the hierarchical early warning push module. Risks that are not rectified within the time limit are raised by the risk identification and analysis module and sent to the superior management personnel through the hierarchical early warning push module. S6. The data storage and encryption module uses the AES-256 encryption algorithm to encrypt and store all employment data, compliance rule data, risk analysis records, and rectification operation logs. Data access permissions are controlled based on a hierarchical permission system. All operation records are stored using blockchain technology to ensure they are tamper-proof. The data storage period is no less than 5 years to meet the requirements for evidence in labor disputes.

10. The working method of the intelligent employment compliance risk early warning system according to claim 9, characterized in that, The risk trend analysis module of the intelligent employment compliance risk early warning system uses historical data stored in the data storage and encryption module to call the STL seasonal decomposition algorithm and LSTM prediction model to analyze the high-risk patterns, predict potential future employment risks, match them with the preventive management suggestion library, and generate monthly, quarterly, and annual employment compliance trend analysis reports to provide data support for enterprise management decisions.

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