Management system and method for enterprise management

Abstract

The invention relates to the field of enterprise management, in particular to a management system for enterprise management and a method thereof, which are used for solving the problems of lack of risk early warning capability, low emergency response efficiency, insufficient human capital toughness and stiffness of an assessment mechanism of the existing enterprise management system. The management system for enterprise management comprises the following modules: a data acquisition module, an AI risk early warning module, a human capital toughness module, an intelligent emergency response module, a dynamic assessment module and a data decision module which are in communication connection with one another and are in cooperative linkage. Full-process management and control including but not limited to employee sudden departure and emergency leave accidental events are realized; according to the method, managers can intervene in the risk germination stage, the probability of sudden departure is reduced, team stability is remarkably improved, and enterprises are prevented from being passive due to sudden abnormal movement of personnel.

Description

Technical Field

[0001] This invention relates to the field of enterprise management, and more specifically to a management system and method for enterprise management. Background Technology

[0002] With the deepening of enterprise digital transformation, various enterprise management systems have been widely applied in scenarios such as organizational personnel, project collaboration, and performance appraisal. Existing systems typically have basic functions such as attendance approval, workflow, and report statistics. Some systems have introduced machine learning algorithms to quantitatively evaluate employee performance, but they are mostly limited to single-dimensional data statistics and analysis, and have not formed a full-process risk prevention and emergency response system. In terms of human capital management, mainstream solutions mostly focus on static information recording throughout the employee's entire life cycle, and achieve basic talent inventory through job descriptions and skill tags. They lack a normalized talent redundancy reserve and dynamic scheduling mechanism. When dealing with personnel changes, they rely on manual triggering of handover processes and manual allocation of subsequent work tasks, and optimize management strategies through post-event statistical analysis of indicators such as turnover rate. This cannot achieve rapid response and proactive intervention in emergencies, and it is difficult to ensure the continuity of enterprise business.

[0003] However, existing technologies still have the following shortcomings. The existing system can only passively receive leave or resignation applications submitted by employees. It cannot proactively identify potential risks of sudden departures through multi-dimensional behavioral data. Furthermore, it has not built a standardized risk assessment model and cannot provide graded early warnings for employee turnover tendencies. This leaves companies in a passive position when key personnel suddenly leave their posts, making it difficult to ensure business continuity and easily causing losses such as project delays and loss of critical information.

[0004] In the face of sudden staff absences, existing systems rely on manual processes to complete tasks such as compiling handover lists, matching successors, and redistributing tasks. They lack intelligent handover list generation, optimal successor recommendation, and dynamic team load balancing mechanisms. The handover process is cumbersome and time-consuming, which can easily lead to the loss of critical information, project delays, and an inability to quickly fill staff shortages.

[0005] The existing system has not established a routine succession plan and skills redundancy mechanism for key positions, nor has it built a visualized skills matrix and dynamic talent pool. It is impossible to grasp the team's skills coverage and talent reserves in real time. When core personnel leave their posts, the company cannot quickly match replacements from the internal talent pool or external flexible staffing resources, resulting in business disruptions. Furthermore, it lacks a systematic training and tracking mechanism for successors.

[0006] The existing performance evaluation tools use a fixed indicator system, which cannot be dynamically adjusted for special circumstances such as sudden leave or force majeure. They also lack flexible rules for performance evaluation exemption and weighted adjustment, which can easily lead to a disconnect between the evaluation results and the actual contributions of employees, affecting employee satisfaction and team stability, and may even increase the risk of employees leaving voluntarily. Summary of the Invention

[0007] In order to overcome the above-mentioned technical problems, the purpose of this invention is to provide a management system and method for enterprise management: to solve the problems of existing enterprise management systems mentioned in the background art, such as lack of risk warning capability, low emergency response efficiency, insufficient human capital resilience and rigid assessment mechanism.

[0008] The objective of this invention can be achieved through the following technical solutions: A management system for enterprise management includes interconnected data acquisition modules, AI risk warning modules, human capital resilience modules, intelligent emergency response modules, dynamic assessment modules, and data decision-making modules. These modules work together to achieve full-process control over events including, but not limited to, sudden employee absences and emergency leave requests. The data acquisition module is used to integrate and standardize the processing of multi-dimensional employee behavior and survey data, and synchronize it to various related modules; The AI ​​risk warning module generates employee turnover risk profiles and provides tiered warnings through trained machine learning models, and captures high-risk behaviors to trigger immediate warnings. The human capital resilience module constructs succession plans for key positions, skills matrices, and flexible staffing pools to achieve talent redundancy management and flexible scheduling. When the intelligent emergency response module detects a sudden absence from duty, it automatically generates a handover list, recommends the best successor, dynamically assigns tasks, and fills personnel gaps. The dynamic assessment module is configured with force majeure adjustment rules to automatically adapt and adjust the assessment indicators of affected employees. The data decision module identifies high-frequency triggers for sudden absences from work, generates team health reports and management optimization suggestions, and provides data support for adjusting management strategies.

[0009] As a further aspect of the present invention, the AI ​​risk warning module includes an abnormal behavior recognition unit, a model training unit, and a warning push unit. The abnormal behavior recognition unit is used to capture high-risk behaviors in real time, such as not clocking in for three consecutive days or more without approval records, batch downloading of key project documents, a sudden drop in work output of more than 30%, and a sudden drop in the frequency of collaborative behavior of more than 50%, and trigger an immediate warning. The model training unit adopts a random forest algorithm and is trained based on the historical behavior data and resignation data of more than 5,000 employees. The warning push unit pushes risk warning information to the corresponding person in charge through system messages and enterprise WeChat / DingTalk linkage, specifying the warning level, warning reason, and preliminary intervention suggestions.

[0010] As a further aspect of this invention, the key position succession plan in the human capital resilience module adopts a "1-2-1" backup mechanism, namely, one direct successor, two potential candidates, and one set of standardized handover document templates. The direct successor must have more than three years of experience in the same position and be able to quickly take over the core work of the position. The potential candidates must have one to two years of relevant work experience and will be systematically trained by the direct successor. The standardized handover document templates cover the core responsibilities of the position, work processes, to-do list, key document addresses, customer and partner information, etc. Department heads are required to update the succession plan and handover templates quarterly, and the system will automatically remind them to update.

[0011] As a further aspect of the present invention, the intelligent emergency response module includes a handover list generation unit, a successor matching unit, a load balancing unit, and a flexible staffing linkage unit. The handover list generation unit automatically extracts all work data associated with employees, sorts tasks by urgency and importance, and generates an editable, standardized handover list. The successor matching unit calculates a matching score based on skill matching degree (weight 0.6) and current load (weight 0.4), recommending the 1-2 successors with the highest scores. The load balancing unit calculates the workload saturation of each team member in real time; when the saturation exceeds 80%, it automatically stops task allocation for that member and assigns tasks to members with saturation below 60%. The flexible staffing linkage unit automatically pushes work requests to personnel in the flexible staffing pool who meet the job skill requirements and provides synchronous feedback on the response status.

[0012] As a further aspect of the present invention, the dynamic assessment module includes an assessment rule configuration unit, an indicator adjustment unit, and an assessment result verification unit. The assessment rule configuration unit allows managers to customize emergency event types (including emergency leave, sudden resignation, and force majeure events), assessment exemption conditions (including exemption of the corresponding periodic task completion rate indicator for emergency leave exceeding 7 days), and weighting coefficients (including a 5%-15% reduction in the weighting coefficient of the entire team's assessment indicators under force majeure events). The indicator adjustment unit receives event trigger signals from the intelligent emergency response module, automatically matches the corresponding adjustment rules, and completes the exemption or weighting adjustment of assessment indicators. The assessment result verification unit verifies the adjusted assessment results to ensure there are no logical errors and simultaneously generates an assessment adjustment explanation for subsequent traceability.

[0013] As a further embodiment of the present invention, the data decision-making module includes a root cause analysis unit, a team health assessment unit, and an optimization suggestion generation unit. The root cause analysis unit uses an association analysis algorithm to correlate historical risk data and behavioral data of employees who suddenly leave their posts with enterprise management data, identifying high-frequency triggering factors for sudden departures, including management style, salary competitiveness, work pressure, and career development opportunities. The team health assessment unit generates a team health score of 0-100 based on core indicators such as voluntary turnover rate, frequency of emergency leave, duration of vacancy in key positions, and talent redundancy. The optimization suggestion generation unit outputs targeted management optimization suggestions based on the root cause analysis results and the team health score, including specific measures for adjusting recruitment standards, optimizing training plans, improving incentive mechanisms, and optimizing succession plans.

[0014] A management method for enterprise management includes the following steps: S1. Through the data collection module, integrate employee attendance data, work output data, collaborative behavior data and turnover intention survey data, clean, deduplicatize and standardize the various types of data, remove invalid and abnormal data, ensure data accuracy, and synchronize the processed standardized data to the AI ​​risk warning module and data decision module. The S2 AI risk warning module uses a trained machine learning model to perform multi-dimensional analysis on the received standardized data, generate a profile of employee turnover risk, and issue warnings in three levels: low, medium, and high. At the same time, the abnormal behavior recognition unit captures high-risk behaviors of employees in real time, triggers immediate warnings, and pushes the warning information to the corresponding person in charge. Managers can then intervene based on the warning information. S3. Through the human capital resilience module, build a "1-2-1" succession plan for key positions, a visualized skills matrix, and a dynamic flexible staffing pool. Update employee skills information, successor training progress, and flexible staffing resources in real time. Review and update the succession plan for key positions every quarter to ensure sufficient talent reserves. S4. When an employee's sudden absence is detected, the intelligent emergency response module automatically extracts the employee's core work information, generates a standardized handover list, calculates and recommends the optimal handover person through the handover person matching unit, and links with the load balancing unit to complete the dynamic allocation of tasks. If there is a shortage of personnel, the elastic staffing linkage unit matches substitute personnel from the elastic staffing pool to ensure the normal progress of business. S5. The dynamic assessment module receives emergency information transmitted by the intelligent emergency response module, matches the corresponding assessment adjustment rules through the indicator adjustment unit, and automatically exempts or weights the assessment indicators of affected employees. After the assessment result verification unit verifies that there are no errors, it generates an assessment adjustment explanation to ensure the fairness of the assessment. S6. The data decision module receives operational data from each module, identifies high-frequency triggering factors for sudden absences through the root cause analysis unit, generates a team health report through the team health assessment unit, and outputs targeted management optimization suggestions by combining the two. The enterprise management adjusts management strategies based on the optimization suggestions, and at the same time feeds back the review data to the AI ​​risk warning module to optimize the model training effect and form a closed-loop iteration.

[0015] As a further aspect of the present invention, in the AI ​​risk assessment and early warning step, the machine learning model adopts the random forest algorithm to perform weighted calculations on behavioral features, wherein attendance abnormalities account for 15%, work output fluctuations account for 25%, collaboration activity accounts for 20%, turnover intention survey data accounts for 20%, and other behavioral features account for 20%. The model has an accuracy rate of no less than 92% in identifying high-risk turnover, with an AUC value of 0.89, demonstrating good generalization ability and adaptability to the needs of enterprises of different sizes and industries.

[0016] As a further aspect of the present invention, in the intelligent emergency response step, the formula for calculating the matching score of the successor is: Matching score = Skill matching degree × 0.6 + (1 - Current workload saturation) × 0.4, where the skill matching degree is calculated by the overlap between the employee's skill tags and the job requirement tags, and the value range is 0-1. The current workload saturation is calculated by the number of tasks to be done by the employee, the urgency of the tasks, and the estimated completion time, and the value range is 0-1. When the matching score is ≥ 0.8, the successor is recommended as the optimal successor.

[0017] As a further aspect of this invention, in the data review and iteration steps, a team health report is generated quarterly, including core indicators such as voluntary turnover rate, frequency of emergency leave, vacancy duration of key positions, talent redundancy, and early warning accuracy. At the same time, the AI ​​risk early warning model is retrained every six months, incorporating the latest employee behavior data and turnover data to continuously improve the model's early warning accuracy. The succession plan and skills matrix of the human capital resilience module are comprehensively optimized annually to adapt to the needs of enterprise business development.

[0018] The beneficial effects of this invention are: By constructing AI-driven employee risk behavior profiles and integrating multi-dimensional data such as attendance, work output, and collaboration activity, an optimized machine learning model is used to achieve early warning and tiered control of turnover intentions. This allows managers to intervene at the initial stage of risk, reducing the probability of sudden departures, significantly improving team stability, and preventing the company from being caught off guard by sudden personnel changes. By capturing high-risk employee behaviors in real time and triggering immediate alerts, the timeliness and effectiveness of risk prevention and control are further improved, reducing potential losses such as the leakage of critical information and business interruptions.

[0019] When an unexpected absence occurs, the system automatically extracts core information such as the employee's pending tasks, project documents, and customer contacts, generates a standardized handover list, and calculates the optimal successor based on skill matching and current workload, thus improving handover efficiency. At the same time, it connects with the flexible staffing pool to fill key positions within 24 hours. Combined with the team's dynamic load balancing mechanism, it automatically adjusts task allocation to effectively avoid business interruptions and project delays.

[0020] By mandating the implementation of the "1-2-1" key position succession plan and a visualized skills matrix, the talent redundancy of core positions in enterprises is improved, and the team's skill coverage and talent reserve status can be monitored in real time. Combined with dynamic talent pool management, internal talent and external flexible resources can be flexibly allocated, while the progress of successor development can be tracked, which greatly enhances the organization's ability to cope with personnel changes and avoids business disruptions.

[0021] By introducing a "force majeure adjustment mechanism," the system can automatically exempt or weight and adjust performance indicators based on events such as sudden absences or emergency leave. It also supports managers in customizing performance adjustment rules, making performance results more aligned with employees' actual contributions, thereby increasing employee satisfaction, reducing voluntary turnover, and enhancing team cohesion.

[0022] Through the model of tracking the impact of job departures and analyzing root causes, the system automatically generates team health reports and optimization suggestions, identifies high-frequency triggering factors for sudden job departures, promotes closed-loop iteration of human capital management system, risk warning model, and emergency response process, and continuously improves the accuracy and effectiveness of enterprise talent management strategies to meet the management needs of different stages of enterprise development. Attached Figure Description

[0023] The invention will now be further described with reference to the accompanying drawings.

[0024] Figure 1 This is a schematic diagram of a management system for enterprise management according to the present invention. Detailed Implementation

[0025] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. 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 of ordinary skill in the art without creative effort are within the scope of protection of the present invention. Example

[0026] Please see Figure 1 As shown, this embodiment is a management system for enterprise management, including a data acquisition module, an AI risk warning module, a human capital resilience module, an intelligent emergency response module, a dynamic assessment module, and a data decision-making module that are interconnected. The modules work together to achieve full-process control of unexpected events such as sudden employee absences and emergency leave requests. The data acquisition module is used to integrate and standardize the processing of multi-dimensional employee behavior and survey data, and synchronize it to various related modules; The AI ​​risk warning module generates employee turnover risk profiles and provides tiered warnings through trained machine learning models, and captures high-risk behaviors to trigger immediate warnings. The human capital resilience module constructs succession plans for key positions, skills matrices, and flexible staffing pools to achieve talent redundancy management and flexible scheduling. When the intelligent emergency response module detects a sudden absence from duty, it automatically generates a handover list, recommends the best successor, dynamically assigns tasks, and fills personnel gaps. The dynamic assessment module is configured with force majeure adjustment rules to automatically adapt and adjust the assessment indicators of affected employees. The data decision module identifies high-frequency triggers for sudden absences from work, generates team health reports and management optimization suggestions, and provides data support for adjusting management strategies.

[0027] The AI ​​risk warning module includes an abnormal behavior recognition unit, a model training unit, and a warning push unit. The abnormal behavior recognition unit is used to capture high-risk behaviors in real time, such as not clocking in for three or more consecutive days without approval records, batch downloading of key project documents, a sudden drop in work output of more than 30%, and a sudden drop in the frequency of collaborative behavior of more than 50%, and triggers an immediate warning. The model training unit uses a random forest algorithm and is trained based on the historical behavior data and resignation data of more than 5,000 employees. The warning push unit pushes risk warning information to the corresponding person in charge through system messages and enterprise WeChat / DingTalk linkage, specifying the warning level, the reason for the warning, and preliminary intervention suggestions.

[0028] The succession plan for key positions in the Human Capital Resilience module adopts a "1-2-1" backup mechanism, namely one direct successor, two potential candidates, and one set of standardized handover document templates. The direct successor must have more than three years of experience in the same position and be able to quickly take over the core work of the position. The potential candidates must have 1-2 years of relevant work experience and will be systematically trained by the direct successor. The standardized handover document templates cover the core responsibilities of the position, work processes, to-do list, key document addresses, customer and partner information, etc. Department heads are required to update the succession plan and handover templates quarterly, and the system will automatically remind them to update.

[0029] The intelligent emergency response module includes a handover list generation unit, a successor matching unit, a load balancing unit, and a flexible staffing linkage unit. The handover list generation unit automatically extracts all work data associated with employees, prioritizes tasks by urgency and importance, and generates an editable, standardized handover list. The successor matching unit calculates a matching score based on skill matching (weight 0.6) and current workload (weight 0.4), recommending the 1-2 successors with the highest scores. The load balancing unit calculates the workload saturation of each team member in real time; when saturation exceeds 80%, it automatically stops task allocation for that member and assigns tasks to members with saturation below 60%. The flexible staffing linkage unit automatically pushes work requests to personnel in the flexible staffing pool who meet the job skill requirements and provides synchronous feedback on the response status.

[0030] The dynamic assessment module includes an assessment rule configuration unit, an indicator adjustment unit, and an assessment result verification unit. The assessment rule configuration unit allows managers to customize emergency event types (including emergency leave, sudden resignation, and force majeure events), assessment exemption conditions (including exemption of the corresponding period task completion rate indicator for emergency leave exceeding 7 days), and weighting coefficients (including a 5%-15% reduction in the weighting coefficient of the entire team's assessment indicators under force majeure events). The indicator adjustment unit receives event trigger signals from the intelligent emergency response module, automatically matches the corresponding adjustment rules, and completes the exemption or weighting adjustment of assessment indicators. The assessment result verification unit is used to verify the adjusted assessment results to ensure there are no logical errors and simultaneously generates assessment adjustment explanations for subsequent traceability.

[0031] The data-driven decision-making module includes a root cause analysis unit, a team health assessment unit, and an optimization suggestion generation unit. The root cause analysis unit uses correlation analysis algorithms to link historical risk data and behavioral data of employees experiencing sudden departures with enterprise management data, identifying high-frequency triggering factors for such departures, including management style, salary competitiveness, work pressure, and career development opportunities. The team health assessment unit generates a team health score from 0 to 100 based on core indicators such as voluntary turnover rate, frequency of emergency leave, duration of vacancy in key positions, and talent redundancy. The optimization suggestion generation unit outputs targeted management optimization suggestions based on the root cause analysis results and the team health score, including specific measures for adjusting recruitment standards, optimizing training plans, improving incentive mechanisms, and optimizing succession plans.

[0032] A management method for enterprise management includes the following steps: S1. Through the data collection module, integrate employee attendance data, work output data, collaborative behavior data and turnover intention survey data, clean, deduplicatize and standardize the various types of data, remove invalid and abnormal data, ensure data accuracy, and synchronize the processed standardized data to the AI ​​risk warning module and data decision module. The S2 AI risk warning module uses a trained machine learning model to perform multi-dimensional analysis on the received standardized data, generate a profile of employee turnover risk, and issue warnings in three levels: low, medium, and high. At the same time, the abnormal behavior recognition unit captures high-risk behaviors of employees in real time, triggers immediate warnings, and pushes the warning information to the corresponding person in charge. Managers can then intervene based on the warning information. S3. Through the human capital resilience module, build a "1-2-1" succession plan for key positions, a visualized skills matrix, and a dynamic flexible staffing pool. Update employee skills information, successor training progress, and flexible staffing resources in real time. Review and update the succession plan for key positions every quarter to ensure sufficient talent reserves. S4. When an employee's sudden absence is detected, the intelligent emergency response module automatically extracts the employee's core work information, generates a standardized handover list, calculates and recommends the optimal handover person through the handover person matching unit, and links with the load balancing unit to complete the dynamic allocation of tasks. If there is a shortage of personnel, the elastic staffing linkage unit matches substitute personnel from the elastic staffing pool to ensure the normal progress of business. S5. The dynamic assessment module receives emergency information transmitted by the intelligent emergency response module, matches the corresponding assessment adjustment rules through the indicator adjustment unit, and automatically exempts or weights the assessment indicators of affected employees. After the assessment result verification unit verifies that there are no errors, it generates an assessment adjustment explanation to ensure the fairness of the assessment. S6. The data decision module receives operational data from each module, identifies high-frequency triggering factors for sudden absences through the root cause analysis unit, generates a team health report through the team health assessment unit, and outputs targeted management optimization suggestions by combining the two. The enterprise management adjusts management strategies based on the optimization suggestions, and at the same time feeds back the review data to the AI ​​risk warning module to optimize the model training effect and form a closed-loop iteration.

[0033] In the AI ​​risk assessment and early warning process, the machine learning model uses the random forest algorithm to perform weighted calculations on behavioral characteristics. Among these, attendance abnormalities account for 15%, work output fluctuations account for 25%, collaboration activity accounts for 20%, turnover intention survey data accounts for 20%, and other behavioral characteristics account for 20%. The model has an accuracy rate of no less than 92% in identifying high-risk turnover, with an AUC value of 0.89. It has good generalization ability and can adapt to the needs of companies of different sizes and industries.

[0034] In the intelligent emergency response process, the formula for calculating the matching score of the takeover person is: Matching score = Skill matching degree × 0.6 + (1 - Current workload saturation) × 0.4, where the skill matching degree is calculated by the overlap between the employee's skill tags and the job requirement tags, and the value range is 0-1. The current workload saturation is calculated by the number of tasks to be done by the employee, the urgency of the tasks, and the estimated completion time, and the value range is 0-1. When the matching score is ≥ 0.8, the best takeover person is recommended.

[0035] In the data review and iteration process, a team health report is generated every quarter, including core indicators such as voluntary turnover rate, frequency of emergency leave, vacancy duration of key positions, talent redundancy, and early warning accuracy. At the same time, the AI ​​risk early warning model is retrained every six months, incorporating the latest employee behavior data and turnover data to continuously improve the model's early warning accuracy. Every year, the succession plan and skills matrix of the human capital resilience module are comprehensively optimized to adapt to the needs of enterprise business development. Example

[0036] The present invention will now be further explained in conjunction with Embodiment 1: 1. Training of AI risk warning model The AI ​​risk warning module of this invention adopts the random forest algorithm. The training dataset contains historical behavioral data and turnover data of 5,000 employees, covering 18 features such as attendance anomalies, task delay rate, IM communication frequency, cross-departmental collaboration response speed, and turnover intention survey score. I. Attendance anomalies (accounting for 15%, totaling 3 items) Frequency of attendance discrepancies: Total number and percentage of late arrivals, early departures, and missed attendances per month; Unexcused absence duration: The cumulative number of days of absence without approval in a month and the longest duration of a single absence; Emergency Leave Frequency: The number of emergency leave requests per month without prior notice (less than 24 hours in advance) and the cumulative duration.

[0037] II. Work output fluctuation category (accounting for 25%, totaling 4 items) Task completion rate: The percentage of monthly planned tasks completed, compared with the fluctuation range of the average completion rate in previous periods; Task delay status: monthly percentage of delayed tasks, average delay duration, and fluctuations compared to previous delay data; Delivery quality score: The quality inspection score of monthly work deliverables (such as code bug rate, document qualification rate), and the difference between the team's average score and the score. Work efficiency fluctuation: The average time spent completing similar basic tasks, compared with the fluctuation range of one's own average time and the team's average time in the past.

[0038] III. Collaboration Activity Category (20% of the total, 4 items) IM communication frequency: The average number of messages proactively sent daily by the company's internal instant messaging tools (such as WeChat Work and DingTalk), compared with the company's own past frequency and the team's average frequency; Meeting participation: monthly attendance rate at required meetings, frequency of speaking at meetings, and timeliness of submitting meeting minutes; Cross-departmental collaboration response speed: The average response time after receiving a cross-departmental collaboration request, compared with the team's average response time; Collaborative task completion quality: In cross-departmental collaborative tasks, the evaluation scores and feedback satisfaction of the collaborating parties.

[0039] IV. Data related to turnover intention survey (accounting for 20%, 3 items in total) Job satisfaction rating: Employees' overall satisfaction with their own job in an anonymous survey (1-10 points). Company Belonging Rating: Employees' rating of their sense of belonging and recognition of the company in an anonymous survey (1-10 points). Turnover intention tendency: Employees' responses to "Do you have any plans to leave in the next 6 months?" in the anonymous survey (no, uncertain, have preliminary plans, have specific plans) are quantified.

[0040] V. Other behavioral characteristics (accounting for 20%, totaling 4 items) Key document operation behaviors: frequency of batch downloading, deleting, and exporting key project documents (such as core code and customer information) each month; Job skills update frequency: Number of job-related training sessions participated in annually, and update of skills certifications; Worktime focus: The percentage of time spent using non-work-related software (such as entertainment and shopping apps) monitored by the office system; Career development aspiration matching degree: The degree to which the employee's submitted career development plan matches the promotion and training resources provided by the company (quantitative score). The model included 1200 positive samples (former employees) and 3800 negative samples (current employees). During training, 5-fold cross-validation was used to optimize hyperparameters, with the number of decision trees limited to 100, a maximum depth of 15 layers, and a minimum number of splits of 5, to avoid overfitting and underfitting. After training, the model achieved a 92% accuracy rate in identifying high-risk departures (AUC 0.89), an 88% accuracy rate in identifying medium-risk departures, and an 85% accuracy rate in identifying low-risk departures, demonstrating good generalization ability and adaptability to the needs of companies in various industries such as internet, manufacturing, and services. After deployment, the model is retrained every six months using the latest employee behavior and departure data to continuously improve its warning accuracy. For example, after applying this model, a manufacturing company successfully warned of 15 potential departure risks for employees in core positions. Management intervened promptly, and ultimately 12 employees chose to remain, reducing the probability of sudden departures by 35%.

[0041] 2. Example of implementing a succession plan for key positions Taking a core R&D position (backend development engineer) at an internet company as an example, this position is responsible for the development and maintenance of the company's core business systems and is a key position. The system mandates that the department head designate one direct successor, two potential candidates, and a standardized handover document template for this position. The direct successor is a senior engineer with four years of backend development experience and familiarity with the company's core business systems, capable of quickly taking over all the core work of the position. The two potential candidates are engineers with 1-2 years of backend development experience and familiarity with the relevant technology stack. The direct successor will develop a personalized training plan, conduct monthly skills assessments, and conduct quarterly practical exercises. The standardized handover document template covers the core responsibilities of the position (core system development, bug fixing, version iteration), workflow (requirements coordination, development testing, deployment), a to-do list (clearly specifying task names, urgency, and deadlines), key document addresses (code repository address, API document address, test report address), and customer and partner information (contact person's name, contact information, and coordination requirements). The department head reviews and updates the succession plan and handover template quarterly to ensure the accuracy and effectiveness of the content. When an employee in this position takes an emergency leave of one month due to a sudden illness, the system automatically triggers the emergency response process, recommends a direct successor to take over the work, and simultaneously initiates an accelerated training program for potential candidates, arranging for the candidates to assist the direct successor in handling some basic tasks, ensuring the normal progress of the development and maintenance of the core business system, and preventing any project delays.

[0042] 3. Example of dynamic assessment adjustment rules Types of emergencies Assessment Adjustment Rules Applicable Scenarios Adjustment instructions Urgent leave request (>7 days) The task completion rate metric for the corresponding period is waived; other metrics are assessed normally, and the total assessment score must not be lower than 80% of the team's average score. Employees taking leave due to force majeure events such as serious illness or sudden illness of immediate family members If an employee has completed more than 50% of the tasks for the corresponding period before taking leave, their total performance evaluation score must be no less than 90% of the team's average score. Sudden resignation Performance responsibility for unfinished projects will automatically transfer to the person who takes over. Departing employees will not be included in the team rankings for the current period, and their performance evaluation will be rated as "qualified". Key employees suddenly left their posts without warning and without completing handover procedures. If a departing employee's work error resulted in losses, their performance evaluation will be rated "Unsatisfactory," and this rule will not apply. Force majeure The weighting coefficient for the team's overall performance indicators has been reduced by 10%, and the task completion rate indicator has been exempted from the 20% performance requirement. Regional outbreaks and natural disasters have led to everyone working from home, making it impossible to carry out work normally. If some employees are able to work remotely and complete their tasks normally, their performance indicators will not be lowered. 4. Flexible workforce pool linkage process When the system detects a shortage of personnel in key positions, it automatically matches qualified personnel with corresponding skills from the internal part-time resource pool and external outsourcing suppliers. The specific linkage process is as follows: First, the intelligent emergency response module identifies the job vacancy information, including the job title, core skill requirements, vacancy duration, and job content. Second, it links with the skills matrix of the human capital resilience module to match personnel with a skill matching degree ≥ 0.7 from the internal part-time resource pool and pushes the job requirements. If there are no suitable personnel internally, proceed to the third step. Third, it links with the external outsourcing supplier resource pool to push the requirements based on the job skill requirements, selects 3-5 suitable outsourcing personnel, and provides feedback to the department head. Fourth, after the department head reviews and confirms, the system automatically generates a temporary employment agreement and simultaneously pushes the job-related documents and work tasks to the replacement personnel, completing the personnel replacement. For example, if a production manager at a manufacturing company suddenly resigns, the system can identify two qualified candidates with production management experience from its internal skills matrix within two hours (skill matching scores of 0.82 and 0.78, respectively). The system then simultaneously sends the requirements to two partner outsourcing platforms, selecting three suitable outsourced personnel. After review by the department head, the system chooses an internal candidate to take over the work, while outsourced personnel are assigned to assist with basic management tasks. The replacement is completed within 12 hours, production is not interrupted, and the project schedule remains unaffected. In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0043] The above description is merely an example and illustration of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the invention or exceed the scope defined in the claims, all of which should fall within the protection scope of the present invention.

Claims

1. A management system for enterprise management, characterized in that, It includes interconnected data acquisition modules, AI risk warning modules, human capital resilience modules, intelligent emergency response modules, dynamic assessment modules, and data decision-making modules. These modules work together to achieve full-process control of unexpected events such as sudden employee absences and emergency leave requests. The data acquisition module is used to integrate and standardize the processing of multi-dimensional employee behavior and survey data, and synchronize it to various related modules; The AI ​​risk warning module generates employee turnover risk profiles and provides tiered warnings through trained machine learning models, and captures high-risk behaviors to trigger immediate warnings. The human capital resilience module constructs succession plans for key positions, skills matrices, and flexible staffing pools to achieve talent redundancy management and flexible scheduling. When the intelligent emergency response module detects a sudden absence from duty, it automatically generates a handover list, recommends the best successor, dynamically assigns tasks, and fills personnel gaps. The dynamic assessment module is configured with force majeure adjustment rules to automatically adapt and adjust the assessment indicators of affected employees. The data decision module identifies high-frequency triggers for sudden absences from work, generates team health reports and management optimization suggestions, and provides data support for adjusting management strategies.

2. The management system for enterprise management according to claim 1, characterized in that, The AI ​​risk warning module includes an abnormal behavior recognition unit, a model training unit, and a warning push unit. The abnormal behavior recognition unit is used to capture high-risk behaviors in real time, such as not clocking in for three or more consecutive days without approval records, batch downloading of key project documents, a sudden drop in work output of more than 30%, and a sudden drop in the frequency of collaborative behavior of more than 50%, and trigger an immediate warning. The model training unit uses a random forest algorithm and is trained based on the historical behavior data and resignation data of more than 5,000 employees. The warning push unit pushes risk warning information to the corresponding person in charge through system messages and enterprise WeChat / DingTalk linkage, specifying the warning level, the reason for the warning, and preliminary intervention suggestions.

3. The management system for enterprise management according to claim 1, characterized in that, The key position succession plan in the human capital resilience module adopts a "1-2-1" backup mechanism, namely one direct successor, two potential candidates, and one set of standardized handover document templates. The direct successor must have more than three years of experience in the same position and be able to quickly take over the core work of the position. The potential candidates must have 1-2 years of relevant work experience and will be systematically trained by the direct successor. The standardized handover document templates cover the core responsibilities of the position, work processes, to-do list, key document addresses, customer and partner information, etc. Department heads are required to update the succession plan and handover templates quarterly, and the system will automatically remind them to update.

4. The management system for enterprise management according to claim 1, characterized in that, The intelligent emergency response module includes a handover list generation unit, a handover person matching unit, a load balancing unit, and a flexible staffing linkage unit. The handover list generation unit can automatically extract all work data associated with employees, classify and sort tasks according to urgency and importance, and generate an editable standardized handover list. The successor matching unit calculates a matching score based on skill matching degree (weight 0.6) and current workload (weight 0.4), and recommends the 1-2 successors with the highest scores; The load balancing unit calculates the workload saturation of each team member in real time. When the saturation exceeds 80%, it automatically stops task allocation for that member and assigns tasks to members with a saturation of less than 60%. The flexible staffing linkage unit can automatically push work requirements to personnel in the flexible staffing pool who meet the job skill requirements and provide synchronous feedback on the response status.

5. A management system for enterprise management according to claim 1, characterized in that, The dynamic assessment module includes an assessment rule configuration unit, an indicator adjustment unit, and an assessment result verification unit. The assessment rule configuration unit allows managers to customize emergency event types (including emergency leave, sudden resignation, and force majeure events), assessment exemption conditions (including exemption of the corresponding periodic task completion rate indicator for emergency leave exceeding 7 days), and weighting coefficients (including a 5%-15% reduction in the weighting coefficient of the entire team's assessment indicators under force majeure events). The indicator adjustment unit receives event trigger signals from the intelligent emergency response module, automatically matches the corresponding adjustment rules, and completes the exemption or weighting adjustment of assessment indicators. The assessment result verification unit verifies the adjusted assessment results to ensure there are no logical errors and simultaneously generates an assessment adjustment explanation for subsequent traceability.

6. A management system for enterprise management according to claim 1, characterized in that, The data decision-making module includes a root cause analysis unit, a team health assessment unit, and an optimization suggestion generation unit. The root cause analysis unit uses an association analysis algorithm to link the historical risk data and behavioral data of employees who suddenly leave their posts with the enterprise management data to identify high-frequency triggering factors for sudden departures, including management style, salary competitiveness, work pressure, and career development space. The team health assessment unit generates a team health score of 0-100 based on core indicators such as voluntary turnover rate, frequency of emergency leave, vacancy duration of key positions, and talent redundancy. The optimization suggestion generation unit outputs targeted management optimization suggestions based on the root cause analysis results and the team health score, including specific measures for adjusting recruitment standards, optimizing training plans, improving incentive mechanisms, and optimizing succession plans.

7. A management method for enterprise management, characterized in that, The implementation of the management system for enterprise management based on any one of claims 1-6 includes the following steps: S1. Through the data collection module, integrate employee attendance data, work output data, collaborative behavior data and turnover intention survey data, clean, deduplicatize and standardize the various types of data, remove invalid and abnormal data, ensure data accuracy, and synchronize the processed standardized data to the AI ​​risk warning module and data decision module. The S2 AI risk warning module uses a trained machine learning model to perform multi-dimensional analysis on the received standardized data, generate a profile of employee turnover risk, and issue warnings in three levels: low, medium, and high. At the same time, the abnormal behavior recognition unit captures high-risk behaviors of employees in real time, triggers immediate warnings, and pushes the warning information to the corresponding person in charge. Managers can then intervene based on the warning information. S3. Through the human capital resilience module, build a "1-2-1" succession plan for key positions, a visualized skills matrix, and a dynamic flexible staffing pool. Update employee skills information, successor training progress, and flexible staffing resources in real time. Review and update the succession plan for key positions every quarter to ensure sufficient talent reserves. S4. When an employee's sudden absence is detected, the intelligent emergency response module automatically extracts the employee's core work information, generates a standardized handover list, calculates and recommends the optimal handover person through the handover person matching unit, and links with the load balancing unit to complete the dynamic allocation of tasks. If there is a shortage of personnel, the elastic staffing linkage unit matches substitute personnel from the elastic staffing pool to ensure the normal progress of business. S5. The dynamic assessment module receives emergency information transmitted by the intelligent emergency response module, matches the corresponding assessment adjustment rules through the indicator adjustment unit, and automatically exempts or weights the assessment indicators of affected employees. After the assessment result verification unit verifies that there are no errors, it generates an assessment adjustment explanation to ensure the fairness of the assessment. S6. The data decision module receives operational data from each module, identifies high-frequency triggering factors for sudden absences through the root cause analysis unit, generates a team health report through the team health assessment unit, and outputs targeted management optimization suggestions by combining the two. The enterprise management adjusts management strategies based on the optimization suggestions, and at the same time feeds back the review data to the AI ​​risk warning module to optimize the model training effect and form a closed-loop iteration.

8. A management method for enterprise management according to claim 7, characterized in that, In the AI ​​risk assessment and early warning step, the machine learning model uses the random forest algorithm to perform weighted calculations on behavioral features, including 15% for attendance abnormalities, 25% for work output fluctuations, 20% for collaboration activity, 20% for turnover intention survey data, and 20% for other behavioral features. The model has an accuracy rate of no less than 92% in identifying high-risk turnover, with an AUC value of 0.89, demonstrating good generalization ability and adaptability to the needs of companies of different sizes and industries.

9. A management method for enterprise management according to claim 7, characterized in that, In the intelligent emergency response steps, the formula for calculating the matching score of the takeover person is: Matching score = Skill matching degree × 0.6 + (1 - Current workload saturation) × 0.4, where the skill matching degree is calculated by the overlap between the employee's skill tags and the job requirement tags, and the value range is 0-1. The current workload saturation is calculated by the number of tasks to be done by the employee, the urgency of the tasks, and the estimated completion time, and the value range is 0-1. When the matching score is ≥ 0.8, the best takeover person is recommended.

10. A management method for enterprise management according to claim 7, characterized in that, In the data review and iteration steps, a team health report is generated quarterly, including core indicators such as voluntary turnover rate, frequency of emergency leave, vacancy duration of key positions, talent redundancy, and early warning accuracy. At the same time, the AI ​​risk early warning model is retrained every six months, incorporating the latest employee behavior data and turnover data to continuously improve the model's early warning accuracy. The succession plan and skills matrix of the human capital resilience module are comprehensively optimized annually to adapt to the needs of enterprise business development.

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