Flexible employment contract management method

Abstract

The invention relates to the technical field of flexible employment contract management, in particular to a flexible employment contract management method. According to the technical scheme, the method comprises the following steps: contract preprocessing: determining a contract type, determining flexible employment forms including part-time job, temporary job, labor dispatch and project system employment according to actual demands of an enterprise and employee conditions, and listing work contents, work time, work places, a reward calculation mode and a payment period in details in the contract; meanwhile, similar flexible employment contract cases are analyzed by using a machine learning algorithm, and potential risk points are evaluated. According to the management method, a scientific basis is provided for enterprise decision making, contract disputes and loss risks are reduced, tasks are reasonably decomposed, accurate allocation is performed according to skills and availability of employees, illegal behaviors are effectively detected by using an artificial intelligence algorithm, an early warning mechanism is established, contract constraining force is enhanced, and enterprise benefits are maintained.

Description

Technical Field

[0001] The present invention relates to the technical field of flexible employment contract management, and in particular to a flexible employment contract management method. Background Art

[0002] Flexible employment is a new type of flexible employment model that is different from the traditional fixed full-time employment model. It emphasizes that enterprises can flexibly adjust the number of employees, working hours and work locations according to actual needs to meet the changes and development of the business.

[0003] Enterprises can quickly adjust the scale of employment according to business needs, avoiding the fixed costs and risks brought by long-term employment. It includes various forms such as part-time, temporary, labor dispatch, and project-based employment to meet the needs of different enterprises and positions. It can quickly respond to market changes and improve the operational efficiency and competitiveness of enterprises. Flexible employment reduces expenditures such as human resource management costs and social insurance expenses, allowing enterprises to share excellent talent resources and improve the efficiency of talent utilization. For employees, it provides more employment opportunities and job choices, increases personal income, improves job satisfaction, and is conducive to the improvement of personal skills and career development.

[0004] However, flexible employment contracts are difficult to execute and supervise, and there is a lack of effective supervision mechanisms. There is a lack of effective supervision means and mechanisms for the execution of flexible employment contracts. It is difficult for companies to grasp the work progress and work quality of employees in real time, and it is impossible to promptly discover and correct problems in the execution of contracts. At the same time, there is a lack of corresponding punishment measures for employees' violations, resulting in insufficient binding force of the contract. Therefore, this application proposes a flexible employment contract management method. Summary of the invention

[0005] The purpose of the present invention is to propose a flexible employment contract management method in view of the problems in the background technology that the flexible employment contract is difficult to execute and supervise and there is a lack of an effective supervision mechanism.

[0006] The technical solution of the present invention: a flexible employment contract management method, comprising the following method steps:

[0007] Contract pre-processing: clarify the contract type, determine the form of flexible employment according to the actual needs of the enterprise and the employee situation, including part-time, temporary, labor dispatch and project-based employment, and list the work content, working hours, work location, compensation calculation method and payment cycle in detail in the contract; at the same time, use machine learning algorithms to analyze similar flexible employment contract cases and evaluate potential risk points;

[0008] Task allocation and execution:

[0009] A hierarchical task network planning algorithm is used to split the project into multiple subtasks, and task assignment is performed using a matching calculation formula based on employee skill tags and availability;

[0010] Employees are required to update their work progress in real time through mobile applications, and time series analysis algorithms are used to predict task completion time. For specific types of work, machine learning algorithms are used to assess quality. At the same time, intelligent feedback channels are established, and companies use natural language processing technology to analyze feedback information and respond quickly;

[0011] Early warning analysis: Use artificial intelligence algorithms to analyze employee work behaviors and detect whether there are any violations;

[0012] Performance evaluation: Conduct performance evaluation based on the employee's work progress, quality, and violations during the contract period. Use a comprehensive scoring method to calculate performance scores based on multiple indicators. Settle remuneration in accordance with the contract agreement to ensure that employees receive their due remuneration on time. Analyze problems and lessons learned during the execution of the contract and use data analysis technology to find areas for improvement.

[0013] Optionally, in the contract preprocessing, a formula for evaluating potential risk points using a machine learning algorithm is: risk probability Where P(R|C) is the probability of risk R occurring given a contract feature C, P(C|R) is the probability of the contract feature occurring when the risk is known to occur, P(R) is the prior probability of the risk, and P(C) is the probability of the contract feature.

[0014] Optionally, during the task allocation and execution, the matching degree calculation formula of the task allocation is: MatchScore(P i , T j )=SkillSimilarity(P i , T j )*Availability(P i )*

[0015] Performance Score (P i ), where P i represents the i-th flexible worker, T j represents the jth task, SkillSimilarity represents skill similarity, Availability represents personnel availability, and PerformanceScore represents personnel past performance score;

[0016] The task progress calculation formula is: Task Progress Progress (T j )=CompletedWork(T j ) / TotalWork(Tj ), where CompletedWork represents the amount of work that has been completed, TotalWork represents the total amount of work, and the task completion time prediction PredictedCompletionTime (T j )=f(Progress(T j ,CurrentTime), where f is a time series prediction function;

[0017] Quality assessment formula: Quality Score = Classifier (W), where W represents the work results and Classifier is a quality assessment classifier.

[0018] Optionally, in the early warning analysis, the calculation formula for the data entry operator's violation judgment index is: Accuracy Score (D) = Correct Entries (D) / Total Entries (D), where D represents the entered data, Correct Entries represents the number of correctly entered entries, and Total Entries represents the total number of entries;

[0019] Speed ​​score SpeedScore(D)=EntriesPerTime(D), working time WorkTime=EndTime(D)-StartTime(D);

[0020] The violation judgment formula is:

[0021] IsVioltion(D)=

[0022] Model(AccuracyScore(D),SpeedScore(D),WorkTime(D)),

[0023] Where Model is a violation detection model;

[0024] Violation record formula: ViolationRecord = {Time(D), Type(D), Punishment(D)},

[0025] Time indicates the violation time, Type indicates the violation type, and Punishment indicates the penalty measure.

[0026] Optionally, in the performance evaluation, the performance score formula is: PerformanceScore(p)=α*ProgressScore(P)+β*QualityScore(P)-γ*ViolationScore, where P represents the employee, ProgressScore is the work progress score, QualityScore is the work quality score, ViolationScore is the violation deduction score, and α, β, γ are weight coefficients.

[0027] Optionally, in the contract preprocessing, for flexible employment positions involving high professional skill requirements, an expert evaluation system is introduced to evaluate the professional capabilities of employees, and the evaluation results serve as an important reference for contract signing.

[0028] Optionally, the task allocation and execution also includes establishing a task priority sorting mechanism to determine the priority of each subtask based on factors such as project urgency and importance to ensure that key tasks are executed first. The priority calculation formula is: Priority = g (Urgency (T j ),Importance(T j )), where T j represents the jth task, Urgency represents the urgency, Importance represents the importance, and g is the priority determination function.

[0029] Optionally, in the early warning analysis, an early warning mechanism is established for employees who have repeatedly violated the regulations to alert corporate managers so that they can take stricter management measures or re-evaluate the cooperative relationship.

[0030] Optionally, conduct a satisfaction survey on employees to collect their opinions and suggestions on the contract execution process, which will be used to improve the subsequent contract management methods. The satisfaction calculation formula is: SatisfactionScore(p) =

[0031] h(WorkEnvironment(P), Compensation(P), Communication(p)), where P represents the employee, WorkEnvironment represents the satisfaction with the work environment, Compensation represents the satisfaction with the compensation, Communication represents the satisfaction with the communication, and h is the satisfaction determination function.

[0032] Optionally, set up a regular contract execution review mechanism to summarize and analyze the execution status of each stage, identify problems in a timely manner and adjust management strategies.

[0033] Compared with the prior art, the present invention has at least one of the following beneficial technical effects:

[0034] During the signing stage of contract pre-processing, more accurate risk control can be achieved through analysis of similar cases, providing a scientific basis for corporate decision-making and reducing the risk of contract disputes and losses.

[0035] In terms of task allocation and execution, tasks are reasonably decomposed and accurately allocated according to employee skills and availability, a priority sorting mechanism is established to arrange resources reasonably and avoid project delays. Progress can be grasped in real time and quality can be accurately assessed to improve work efficiency and quality.

[0036] In terms of early warning analysis, artificial intelligence algorithms are used to effectively detect violations and establish early warning mechanisms to enhance the binding force of contracts and safeguard corporate interests.

[0037] In terms of contract termination management, we conduct scientific performance evaluation and remuneration settlement to ensure fairness and rationality. At the same time, we focus on experience summary and continuous improvement, and constantly optimize management methods to improve management level.

[0038] The management method of the present invention provides a scientific basis for corporate decision-making, reduces contract disputes and loss risks, reasonably decomposes tasks and accurately allocates them according to employee skills and availability, uses artificial intelligence algorithms to effectively detect violations and establish an early warning mechanism, enhances contract binding force, and safeguards corporate interests. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 This is a flow chart of the flexible employment contract management method. DETAILED DESCRIPTION

[0040] In order to make the objectives, technical solutions and advantages of the present invention more clear, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0041] Example 1

[0042] like Figure 1 The flexible employment contract management method of the present invention aims to improve the management efficiency of flexible employment by enterprises, reduce potential risks, and ensure the effectiveness and fairness of contract execution. The method mainly includes four stages: contract signing, task allocation and execution, early warning analysis, and contract termination.

[0043] (I) Contract Signing

[0044] Clarify contract types and terms

[0045] According to the actual business needs of the enterprise and the specific circumstances of the employees, determine the appropriate form of flexible employment, including part-time, temporary, dispatched or project-based employment. List the key terms of work content, working hours, work location, compensation calculation method and payment cycle in detail in the contract to ensure that both parties have a clear understanding of work requirements and rights.

[0046] For part-time employees, it is clearly stipulated that the number of working days per week shall not exceed a certain value, the working hours shall be concentrated in a certain period, and the work location can be flexibly selected but must meet certain conditions. For project-based employment, the start and end time of the project, the phased goals and the standards for the final delivery results shall be clearly defined.

[0047] risk assessment

[0048] Formula for evaluating potential risk points using machine learning algorithms: Risk probability Where P(R|C) is the probability of risk R occurring given a contract feature C, P(C|R) is the probability of the contract feature occurring when the risk is known to occur, P(R) is the prior probability of the risk, and P(C) is the probability of the contract feature.

[0049] By analyzing the dispute cases in historical contracts, we extract the contract features, including ambiguous working hours and unclear remuneration payment methods, and calculate the probability of risk under these features to provide a reference for the signing of the current contract. During the contract signing stage, by analyzing similar flexible employment contract cases, we can comprehensively assess potential risks, provide a scientific basis for corporate decision-making, and effectively reduce the risk of contract disputes and losses. We no longer judge risks based on experience alone, but use systematic analysis methods to improve the accuracy of risk assessment.

[0050] 2. Task Allocation and Execution

[0051] Task decomposition and allocation

[0052] The hierarchical task network planning algorithm is used to split the project into multiple subtasks, and the matching degree calculation formula is used to allocate tasks according to the employee skill tags and availability; the matching degree calculation formula for task allocation is: MatchScore(P i , T j )=SkillSimilarity(P i , T j )*Availability(P i )*PerformanceScore(P i ), where P i represents the i-th flexible worker, T j represents the j-th task, SkillSimilarity represents skill similarity, Availability represents personnel availability, and PerformanceScore represents personnel past performance scores.

[0053] For software development projects, the project is broken down into requirements analysis, design, coding, and testing subtasks. Based on the employee's skill tags, such as whether they are good at front-end development or back-end development, as well as their current availability and whether there are other projects in progress, the matching degree with each subtask is calculated to make reasonable task allocation. Task decomposition and allocation are more reasonable, taking into full consideration the employee's skills and availability, ensuring that tasks are assigned to the most appropriate personnel, thereby improving work efficiency and quality. This method can achieve accurate matching and let professionals do professional things.

[0054] Task Prioritization

[0055] Establish a task priority sorting mechanism, determine the priority of each subtask based on factors such as project urgency and importance, and ensure that key tasks are executed first. The priority calculation formula is: Priority = g (Urgency (T j ),Importance(T j )), where T j represents the jth task, Urgency represents the urgency, Importance represents the importance, and g is the priority determination function.

[0056] The urgency of the task can be determined based on the project delivery deadline and the urgency of customer needs, and the importance of the task can be determined based on the impact of the task on the overall project goal. The priority of each task is calculated through a comprehensive function, and resources and time for high-priority tasks are prioritized. A task priority sorting mechanism is established to clarify key tasks, arrange resources reasonably, and avoid project delays. Compared with the existing technology that lacks clear priority definition, this method can better cope with complex project requirements and ensure the smooth progress of the project.

[0057] Progress tracking and quality assessment

[0058] Employees are required to update their work progress in real time through mobile applications. The time series analysis algorithm is used to predict the task completion time. The formula is: Task Progress (T j )=CompletedWork(T j ) / TotalWork(T j ), where CompletedWork represents the amount of work that has been completed, TotalWork represents the total amount of work, and the task completion time prediction PredictedCompletionTime (T j )=f(Progress(T j ,CurrentTime), where f is a time series prediction function.

[0059] For specific types of work, machine learning algorithms are used for quality assessment. The quality assessment formula is: Quality Score = Classifier (W), where W represents the work results and Classifier is a quality assessment classifier.

[0060] For data entry work, the quality is evaluated by monitoring the accuracy and speed of the input. For design work, image recognition and aesthetic evaluation algorithms are used to evaluate the quality of design works. At the same time, an intelligent feedback channel is established, and the company uses natural language processing technology to analyze employee feedback information and respond quickly. Real-time grasp of work progress and accurate evaluation of work quality. Existing technologies are difficult to achieve this, but this method uses mobile applications to update work progress in real time and uses machine learning algorithms for quality evaluation. It can detect problems and adjust plans in a timely manner to ensure that the project is completed on time and with high quality.

[0061] 3. Early warning analysis

[0062] Violation Detection

[0063] Use artificial intelligence algorithms to analyze employees' work behaviors and detect whether there are any violations. For different types of work, judgments are made based on specific indicators. For data entry clerks, violations are judged by monitoring indicators such as accuracy score, speed score, and working hours. Accuracy Score (D) = Correct Entries (D) / Total Entries (D), where D represents the entered data, Correct Entries represents the number of correctly entered entries, and Total Entries represents the total number of entries;

[0064] Speed ​​score SpeedScore(D)=EntriesPerTime(D), working time WorkTime=EndTime(D)-StartTime(D);

[0065] The violation judgment formula is:

[0066] IsVioltion(D)=Model(AccuracyScore(D),SpeedScore(D),WorkTime(D)),

[0067] Where Model is a violation detection model;

[0068] A violation detection model is built using machine learning algorithms. By collecting a large number of normal and illegal data entry samples, features are extracted for training, and a comprehensive judgment on whether there is a violation is made based on indicators such as accuracy, speed, and working time.

[0069] Early warning mechanism

[0070] Set violation records, violation record formula:

[0071] ViolationRecord={Time(D),Type(D),Punishment(D)},

[0072] Wherein, Time indicates the violation time, Type indicates the violation type, and Punishment indicates the punishment measure. In the above-mentioned early warning analysis, for employees who violate the rules many times, an early warning mechanism is established to send reminders to enterprise managers so as to take stricter management measures or re-evaluate the cooperative relationship.

[0073] Set a threshold for the number of violations. When an employee's number of violations reaches a certain number, the system automatically issues an early warning to remind managers to pay attention to the employee's behavior and consider taking measures such as talking, warning, and suspending work. This method uses artificial intelligence algorithms to effectively detect violations by flexible workers and establishes an early warning mechanism. This greatly enhances the binding force of the contract and protects the interests of the company.

[0074] (IV) Contract Ending Stage

[0075] Performance evaluation and remuneration settlement

[0076] Performance evaluation is conducted based on the employee's work progress, quality and violations during the contract period. A comprehensive scoring method is used to calculate the performance score by combining multiple indicators. Remuneration is settled in accordance with the contract agreement to ensure that the employee receives the due remuneration on time. Problems and lessons learned during the execution of the contract are analyzed, and data analysis technology is used to find the direction of improvement. Performance score formula: PerformanceScore(p)=α*ProgressScore(P)+β*QualityScore(P)-γ*ViolationScore, where P represents the employee, ProgressScore is the work progress score, QualityScore is the work quality score, ViolationScore is the violation deduction, and α, β, and γ are weight coefficients.

[0077] Settle remuneration according to the contract agreement to ensure that employees receive the remuneration they deserve on time.

[0078] Performance scores are calculated based on the progress of the employee's task completion, the quality of the work results, and the violations. The compensation adjustment coefficient is determined based on the performance score, and employees with outstanding performance receive additional bonuses.

[0079] Experience summary and improvement

[0080] Analyze the problems and lessons learned during the execution of the contract, and use data analysis technology to find the direction of improvement. By analyzing the data during the execution of the contract, find out the links and causes of frequent problems, unreasonable task allocation, unclear quality assessment standards, and adjust management strategies and optimize contract terms in a targeted manner. Scientific performance evaluation and remuneration settlement ensure fairness and rationality. Existing technologies are often simple and crude in this regard, while this method comprehensively considers multiple indicators such as work progress, quality, and violations to conduct performance evaluation and motivate employees to work actively.

[0081] Focus on experience summary and continuous improvement. Analyze problems and lessons after the contract ends, use data analysis technology to find improvement directions, continuously optimize contract management methods, and improve management level.

[0082] Example 2

[0083] This embodiment is based on Embodiment 1, and the flexible employment contract management method further includes an expert evaluation system, a satisfaction survey and a regular review mechanism.

[0084] Expert evaluation system For flexible employment positions that require high professional skills, an expert evaluation system is introduced to evaluate the professional capabilities of employees. An expert database is established to invite senior experts and professionals in the industry as evaluation experts. When an evaluation is required, multiple experts are randomly selected from the expert database to form an evaluation team.

[0085] The evaluation process may include reviewing the resume, works, and certification materials submitted by the employee, as well as conducting interviews or skill tests. The evaluation results are presented in the form of quantitative scores, and the weights of each evaluation indicator are determined by combining advanced algorithms such as the analytic hierarchy process (AHP) to obtain a comprehensive evaluation result. For a software development position, multiple evaluation indicators such as technical ability, project experience, and problem-solving ability are set. The weights of each indicator are determined by the analytic hierarchy process, and the employee's comprehensive evaluation score is finally calculated. The evaluation results serve as an important reference for contract signing. Introduce an expert evaluation system to scientifically evaluate positions with high professional skill requirements and improve the quality of employment. Existing technologies may lack effective evaluation methods for employing people in professional positions, and this method can ensure that companies select the most suitable professional talents.

[0086] Satisfaction survey: Conduct a satisfaction survey on employees to collect their opinions and suggestions on the contract execution process, which are used to improve the subsequent contract management methods. The satisfaction calculation formula is: SatisfactionScore(p)=h(WorkEnvironment(P), Compensation(P), Communication(p)), where P represents the employee, WorkEnvironment represents the satisfaction with the work environment, Compensation represents the satisfaction with the compensation, Communication represents the satisfaction with the communication, and h is the satisfaction determination function.

[0087] Use advanced algorithms such as fuzzy comprehensive evaluation method to calculate satisfaction. First, determine the evaluation level of each satisfaction index, and set very satisfied, satisfied, average, dissatisfied, and very dissatisfied. Then collect employees' evaluations of each index through questionnaire surveys and other methods, and determine the degree of membership of each index based on the evaluation results. Finally, use fuzzy comprehensive evaluation algorithm to calculate the employee's satisfaction score. According to the satisfaction survey results, analyze the aspects that employees are dissatisfied with, improve the contract management method in a targeted manner, and improve employee satisfaction.

[0088] Regular review mechanism

[0089] Establish a regular contract execution review mechanism to summarize and analyze the execution status of each stage. Determine the time and frequency of the review, conduct a small review every month, and a comprehensive review every quarter.

[0090] Establish a review indicator system and use the key performance indicator (KPI) method to determine the review standards. For the task allocation and execution stage, set KPI indicators such as task completion rate, schedule deviation, and quality pass rate; for the early warning analysis stage, set indicators such as the incidence of violations and the rationality of punishment measures. Through the monitoring and analysis of these indicators, problems can be discovered in a timely manner and management strategies can be adjusted. During the review process, feedback from all parties, including corporate managers, employees, and customers, is collected to ensure the comprehensiveness and objectivity of the review. Based on the review results, a review report is generated to summarize the lessons learned and provide a reference for subsequent contract management. A regular contract execution review mechanism is established to discover problems in a timely manner and adjust management strategies. This method ensures the continued effectiveness and adaptability of the contract management method.

[0091] The above specific embodiments are only several optional embodiments of the present invention. Based on the technical solutions of the present invention and the relevant inspirations of the above embodiments, those skilled in the art can make various alternative improvements and combinations to the above specific embodiments.

Claims

1. A flexible employment contract management method, characterized in that: The method comprises the following steps: Contract pre-processing: clarify the contract type, determine the form of flexible employment according to the actual needs of the enterprise and the employee situation, including part-time, temporary, labor dispatch and project-based employment, and list the work content, working hours, work location, compensation calculation method and payment cycle in detail in the contract; at the same time, use machine learning algorithms to analyze similar flexible employment contract cases and evaluate potential risk points; Task allocation and execution: A hierarchical task network planning algorithm is used to split the project into multiple subtasks, and task assignment is performed using a matching degree calculation formula based on employee skill tags and availability; Employees are required to update their work progress in real time through mobile applications, and time series analysis algorithms are used to predict task completion time. For specific types of work, machine learning algorithms are used to assess quality. At the same time, intelligent feedback channels are established, and companies use natural language processing technology to analyze feedback information and respond quickly; Early warning analysis: Use artificial intelligence algorithms to analyze employee work behaviors and detect whether there are any violations; Performance evaluation: Conduct performance evaluation based on the employee's work progress, quality, and violations during the contract period. Use a comprehensive scoring method to calculate performance scores based on multiple indicators. Settle remuneration in accordance with the contract agreement to ensure that employees receive their due remuneration on time. Analyze problems and lessons learned during the execution of the contract and use data analysis technology to make improvements.

2. The flexible employment contract management method according to claim 1, characterized in that: In the contract preprocessing, the formula for evaluating potential risk points using machine learning algorithms is: Risk Probability Where P(R|C) is the probability of risk R occurring given a contract feature C, P(C|R) is the probability of the contract feature occurring when the risk is known to occur, P(R) is the prior probability of the risk, and P(C) is the probability of the contract feature.

3. The flexible employment contract management method according to claim 1, characterized in that: In the task allocation and execution, the matching degree calculation formula of task allocation is: Match Score (P i , T j )=SkillSimilarity(P i , T j )*Availability(P i )*PerformanceScore(P i ), where P i represents the i-th flexible worker, T j represents the jth task, SkillSimilarity represents skill similarity, Availability represents personnel availability, and PerformanceScore represents personnel past performance score; The task progress calculation formula is: Task Progress Progress (T j )=CompletedWork(T j ) / TotalWork(T j ), where CompletedWork represents the amount of work that has been completed, TotalWork represents the total amount of work, and the task completion time prediction PredictedCompletionTime (T j )=f(Progress(T j , CurrentTime), where f is a time series prediction function; Quality assessment formula: Quality Score = Classifier (W), where W represents the work results and Classifier is a quality assessment classifier.

4. The flexible employment contract management method according to claim 1, characterized in that: In the early warning analysis, the calculation formula for the data entry operator's violation judgment index is: Accuracy Score (D) = Correct Entries (D) / Total Entries (D), where D represents the entered data, Correct Entries represents the number of correctly entered entries, and Total Entries represents the total number of entries; Speed ​​score SpeedScore(D)=EntriesPerTime(D), working time WorkTime=EndTime(D)-StartTime(D); The violation judgment formula is: IsVioltion(D)=Model(AccuracyScore(D), SpeedScore(D), WorkTime(D)), Where Model is a violation detection model; Violation record formula: ViolationRecord = {Time (D), Type (D), Punishment (D)}, Where Time indicates the violation time, Type indicates the violation type, and Punishment indicates the penalty measure.

5. The flexible employment contract management method according to claim 1, characterized in that: In the performance evaluation, the performance score formula is: PerformanceScore(p)=α*ProgressScore(P)+β*QualityScore(P)-γ*ViolationScore, where P represents the employee, ProgressScore is the work progress score, QualityScore is the work quality score, ViolationScore is the violation deduction score, and α, β, and γ are weight coefficients.

6. The flexible employment contract management method according to claim 1, characterized in that: In the contract preprocessing, for flexible employment positions that require high professional skills, an expert evaluation system is introduced to evaluate the professional capabilities of employees, and the evaluation results serve as an important reference for contract signing.

7. The flexible employment contract management method according to claim 1, characterized in that: The task allocation and execution also includes establishing a task priority sorting mechanism to determine the priority of each subtask based on factors such as project urgency and importance to ensure that key tasks are executed first. The priority calculation formula is: Priority = g (Urgency (T j ), Importance(T j )), where T j represents the jth task, Urgency represents the urgency, Importance represents the importance, and g is the priority determination function.

8. The flexible employment contract management method according to claim 1, characterized in that: In the above-mentioned early warning analysis, an early warning mechanism is established for employees who have repeatedly violated the regulations to remind the company managers so that they can take stricter management measures or re-evaluate the cooperative relationship.

9. The flexible employment contract management method according to claim 1, characterized in that: Conduct employee satisfaction surveys to collect employees' opinions and suggestions on the contract execution process, which will be used to improve subsequent contract management methods. The satisfaction calculation formula is: SatisfactionScore(p)=h(WorkEnvironment(P), Compensation(P), Communication(p)), where P represents employees, WorkEnvironment represents satisfaction with the work environment, Compensation represents satisfaction with compensation, Communication represents satisfaction with communication, and h is the satisfaction determination function.

10. The flexible employment contract management method according to claim 1, characterized in that: Establish a regular contract execution review mechanism to summarize and analyze the execution status of each stage, identify problems in a timely manner and adjust management strategies.

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