Employee efficiency dynamic evaluation method and system based on configuration rule engine

Abstract

The invention provides an employee efficiency dynamic evaluation method and system based on a configuration rule engine, and the method comprises the steps: integrating dispersed data through a multi-dimensional data collection engine, and completing the definition and recognition of post features through the configuration rule engine after feature engineering processing; and multi-level efficiency evaluation is carried out by combining a dynamic weight regulator, early warning is realized through a threshold detection module, and an intelligent recommendation and evaluation result is output after trend analysis. According to the invention, through modular cooperation and dynamic adaptation design, the accuracy and efficiency of evaluation are improved, the method is suitable for fine management of employee efficiency of various enterprises, and data support is provided for human resource decision making.

Description

Technical Field

[0001] This invention relates to the field of rule engines and data analysis, specifically to a method and system for dynamic evaluation of employee performance based on a configuration rule engine. Background Technology

[0002] As digital transformation deepens, enterprises have accumulated massive amounts of employee behavior data, including multi-dimensional information such as project hours, attendance records, code submissions, email communications, meeting participation, and document editing. However, existing employee performance evaluation methods have significant limitations: on the one hand, traditional methods analyze data based on only a single or limited data source, such as considering only work hours or attendance duration, failing to comprehensively reflect employees' true work status and contribution value; on the other hand, existing systems lack a deep understanding of the characteristics of different types of work, employing uniform evaluation standards and ignoring the differences in work patterns among different positions such as R&D, testing, and operations. More importantly, traditional methods cannot dynamically adapt to business changes and employee growth, resulting in evaluation results that are often lagging and lack predictability.

[0003] The specific problems with existing technologies are as follows: Firstly, the data dimension is too narrow, focusing only on time input while ignoring work quality and innovative contributions. This fails to accurately measure the true value output of knowledge workers, leading to a significant discrepancy between evaluation results and actual work efficiency. Secondly, there is a lack of differentiated evaluation models tailored to different job characteristics. Key indicators such as code quality for developers, defect detection rate for testers, and system stability contributions for operations personnel are not effectively incorporated into the evaluation system, and a one-size-fits-all approach fails to reflect the professionalism of each position. Thirdly, traditional static weight configurations cannot adapt to changes in project cycles and business priorities. Using the same evaluation standards in both urgent and regular development phases results in a lack of timeliness and accuracy in the evaluation results. Fourthly, existing systems lack the ability to learn from historical data and predictive functions, failing to identify trends in employee work patterns and providing forward-looking guidance for optimizing human resource allocation and individual career development. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of existing technologies by proposing a dynamic evaluation method and system for employee performance based on a configuration rule engine.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a method for dynamic evaluation of employee performance based on a configurable rule engine, comprising the following steps: S1: Multi-dimensional data acquisition and feature engineering construction: Collect multi-dimensional data on employee work behavior as raw data, specifically including development activity statistics (daily average number of code commits, average code lines change rate, code complexity level distribution, refactoring frequency, etc.), collaboration behavior statistics (email response time statistics, meeting participation duration statistics, cross-departmental collaboration frequency, and other anonymized indicators), knowledge contribution statistics (number of technical documents statistics, number of knowledge sharing sessions, internal training participation, etc.), and task completion statistics (task completion time distribution, bug fixing efficiency statistics, requirement response speed level, etc.).

[0006] All raw data collection has been anonymized to remove specific content and sensitive personal information, retaining only statistical features used for performance analysis.

[0007] The collected raw statistical data were standardized using a weighted feature fusion algorithm. The standardized feature value = (raw value - mean) / standard deviation; the comprehensive feature score = Σ(W i × standardized eigenvalues ​​i), where W i is the weight coefficient of the i-th feature.

[0008] Subsequently, principal component analysis (PCA) dimensionality reduction technology was used to compress the 200+ dimensional features into a key 50-dimensional core feature vector, which reduced computational complexity while retaining more than 95% of the information, thus constructing a comprehensive statistical profile of employee work behavior.

[0009] S2: Job Feature Recognition Based on Rule Engine: Design a configurable rules engine architecture that includes three core components: a job feature definer, a rules configuration library, and a rules execution engine.

[0010] The job feature definer defines key performance indicator weight matrices for different roles such as R&D, testing, operations and maintenance, and product, based on expert knowledge and business experience; the rule configuration library supports user-defined evaluation rules and threshold parameters, such as time utilization thresholds and task quality standards; the rule execution engine adopts a condition-action-based rule pattern, supports complex logical judgments and multi-level rule nesting, and has flexible rule update capabilities, supporting hot deployment and real-time adjustment.

[0011] A multi-level rule matching algorithm is used to calculate the job matching degree of the features; the formula for calculating the job matching degree is: The job matching degree of the i-th feature is Σ(Rj × feature similarity j), where Rj is the confidence weight of the j-th rule.

[0012] Determine employee job type using a job category judgment function: if the match is satisfactory. 研发 If the match score is >0.7, the candidate is identified as a research and development engineer; if the match score is >0.7, the candidate is identified as a research and development engineer. 测试If the match score is >0.6, the candidate is identified as a test engineer; if the match score is >0.6, the candidate is identified as a test engineer. 运维 If the score is >0.6, the candidate is identified as an operations and maintenance engineer; otherwise, the candidate is identified according to the preset job classification rules to achieve accurate job matching.

[0013] S3: Dynamic weight adjustment: The design incorporates a dynamic weight adjustment mechanism based on business scenarios, employing an adaptive weight adjustment algorithm that comprehensively considers four key factors: project urgency, team collaboration needs, impact on business value, and individual growth stage.

[0014] The urgency of the project is quantified using metrics such as release schedule, task priority, and bug severity. The evaluation function is as follows: Urgency = 0.4 × version release countdown weight + 0.3 × bug severity weight + 0.3 × task priority weight; Team collaboration needs are derived from task dependency and collaboration frequency analysis; business value impact is combined with business indicators such as user feedback ratings and revenue contribution statistics; personal growth stages are based on skill improvement statistics and learning curve analysis.

[0015] The formula for dynamic weight adjustment is: W i_new =W i_base ×(1+α×Urgency+β×Collaboration+γ×BusinessValue), where W i_base The basic weights for the i-th dimension are... α, β, and γ are configurable adjustment coefficients; Urgency is the project urgency index [0,1]; Collaboration is the collaboration requirement index [0,1]; and BusinessValue is the business value index [0,1]. This algorithm enables intelligent real-time adjustment of weights, ensuring the timeliness and accuracy of the evaluation results.

[0016] S4: Multi-level performance evaluation and threshold detection: Construct a hierarchical performance evaluation system to comprehensively measure employee work performance from four dimensions: basic level, capability level, collaboration level, and value level.

[0017] The efficiency of time investment in the basic layer is evaluated using the following formula: Score 基础 = 0.3 × work hour utilization rate + 0.2 × attendance rate + 0.2 × task completion rate + 0.3 × response time score; The competency level assesses professional skills and task quality, and the calculation formula is as follows: Score 能力=0.4 × Technical Ability Score + 0.3 × Innovation Contribution Score + 0.3 × Problem Solving Ability; The collaboration layer assesses team synergy and knowledge transfer using the following formula: Score 协作 =0.5 × Teamwork Score + 0.5 × Knowledge Sharing Score; The value layer assesses business impact and user satisfaction, calculated using the following formula: Score 价值 =0.6 × Business Impact Score + 0.4 × User Satisfaction; Overall Performance Score = W1 × Score 基础 +W2×Score 能力 + W3×Score 协作 + W4×Score 价值 ; Among them W 1、 W2, W3, and W4 are the weight coefficients for each level.

[0018] It also integrates a threshold detection module based on statistical analysis, where the state threshold is calculated as mean ± k × standard deviation (k is a configurable parameter of 2-3).

[0019] When the feature value is less than the lower threshold or greater than the upper threshold, an anomaly detection rule is triggered. The rule matching technology is used to analyze the cause of the anomaly and generate optimization suggestions to identify data anomalies, abnormal working patterns and other situations in a timely manner.

[0020] S5: Trend Analysis and Rule Recommendations An employee performance trend analysis module is constructed based on time series statistical analysis and rule matching algorithms. A sliding window trend analysis algorithm is used to calculate the trend slope as (average of the current period - average of the previous period) / time interval, and the volatility coefficient as standard deviation / average.

[0021] Identify potential efficiency fluctuation patterns by combining trend judgment rules: if the trend slope is >0.1 and the fluctuation coefficient is <0.3, then the trend = "stable upward". If the trend slope is less than -0.1 and the volatility coefficient is less than 0.3, then the trend is "stable downward"; if the volatility coefficient is greater than 0.5, then the trend is "unstable"; otherwise, the trend is "stable".

[0022] The system uses a predefined rule base and template matching, employing an intelligent recommendation algorithm to determine the matching degree as Σ(similarity i × success case weight i), and then assigns a recommendation level. If the match score is greater than 0.8, the recommendation level is "Strongly Recommended"; if the match score is greater than 0.6, the recommendation level is "Recommended"; otherwise, the recommendation level is "Optional".

[0023] It provides employees with suggestions on how to improve their skills, offers companies actionable suggestions on optimizing project allocation and improving team configuration, and provides decision support for human resource management.

[0024] S6: Data Security and Privacy Protection A distributed data storage and access control mechanism is adopted to achieve secure data sharing across departments and projects. Through data anonymization, desensitization, and role-based access control, effective performance evaluation and management analysis are ensured while protecting employee privacy. All sensitive data is encrypted during storage and transmission, guaranteeing both the accuracy of statistical analysis and the security of employee privacy.

[0025] This invention also provides a dynamic employee performance evaluation system based on a configurable rule engine. The system includes a security layer, a data layer, a rule engine layer, an analysis and processing layer, and an output layer. The security layer includes a data security coordinator. The data layer includes a multi-dimensional data acquisition engine. The rule engine layer includes a rule engine core module and a job feature definer. The analysis and processing layer includes a dynamic weight adjuster, a multi-level evaluator, and a threshold detection module. The data layer includes a rule recommendation engine and a report generation system.

[0026] This system is built around the core logic of "data flow - rule-driven - analysis and processing - output feedback - security fallback," with interconnected modules. The data security coordinator in the security layer operates across the entire domain, transmitting de-identification and encryption rules and access permissions to the multi-dimensional data acquisition engine in the data layer, while simultaneously receiving data operation logs from each module to achieve comprehensive security control. The multi-dimensional data acquisition engine sends the de-identified multi-dimensional feature data upwards to the rule engine core module in the rule engine layer and to various modules in the analysis and processing layer, while also receiving feature filtering instructions from the rule engine core module. The rule engine core module and the job feature definer work bidirectionally, generating a job-specific evaluation weight matrix based on user-defined rules, and then transmitting parameters such as job benchmark weights and differentiated indicator configurations to the analysis and processing layer. The system comprises a dynamic weight adjuster, a multi-level evaluator, and a threshold detection module. The dynamic weight adjuster transmits the calculated dynamic weight coefficients to the multi-level evaluator. The multi-level evaluator and the threshold detection module interact bidirectionally (the former provides hierarchical scores for the latter to compare with thresholds to identify anomalies, and the latter provides feedback on anomaly analysis to assist the former in correcting the scores). Together, these three components deliver comprehensive performance scores, anomaly reports, and other data to the rule recommendation engine in the output layer. The rule recommendation engine generates trend analysis results and optimization suggestions based on the input data, which are fed back to the core module of the rule engine to update the rules and weight matrix, forming a closed-loop linkage to ensure efficient adaptation to business scenarios and employee performance throughout the entire process.

[0027] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention adopts multi-dimensional feature fusion and PCA dimensionality reduction technology to integrate 200+ dimensions of employee behavior data and compress it into 50 core features. While retaining more than 95% of the information, it reduces the computational complexity by 80%, breaking through the limitations of traditional single data sources and achieving a comprehensive portrayal of employee work status. Secondly, the configurable rule engine technology enables adaptive identification of positions, customizing exclusive evaluation benchmarks for different positions such as R&D, testing, and operations. The rules are transparent and interpretable and support hot deployment and updates, with a response time of only 50 milliseconds, completely changing the "one-size-fits-all" evaluation mode and highlighting the professionalism of the positions. Furthermore, through a dynamic weight adjustment algorithm combined with business scenarios such as project urgency and collaboration needs, the evaluation accuracy is improved by 35% compared to the static weight method, effectively adapting to changes in business priorities and project cycle fluctuations.

[0028] Furthermore, at the application level, the four-tiered evaluation system considers time investment, professional skills, teamwork, and business value. The dynamic threshold detection algorithm reduces false alarm rates by 60%, accurately quantifying employee performance and promptly identifying work anomalies. The trend analysis function based on historical data provides a three-month warning period, and the intelligent recommendation module offers actionable suggestions for employee skill improvement, enterprise project allocation, and team configuration optimization, significantly improving the predictability and decision-making efficiency of human resource management. At the security level, through data anonymization, encrypted storage and transmission, and role-based access control, cross-departmental data security sharing is achieved while ensuring employee privacy. Practical verification shows that this invention improves evaluation accuracy by 45%, anomaly detection accuracy by 60%, system response speed by 75%, and maintenance costs by 70% compared to traditional methods, injecting strong momentum into enterprise digital transformation. Attached Figure Description

[0029] Figure 1 This is a schematic diagram of the core process of the employee performance evaluation method of the present invention; Figure 2 This is a system architecture block diagram of the employee performance evaluation method of the present invention. Detailed Implementation

[0030] To provide a further understanding of the purpose, structure, features, and functions of the present invention, detailed descriptions are provided below with reference to specific embodiments.

[0031] Please refer to the reference. Figure 1 as well as Figure 2 This invention provides a method and system for dynamic evaluation of employee performance based on a configuration rule engine.

[0032] Example 1: Employee Performance Evaluation of a Certain XXX R&D Team: 1. Multi-dimensional data acquisition and feature engineering construction: Data on development activities (daily average number of code commits, average line count change rate, code complexity level distribution, refactoring frequency), collaboration behavior (email response time statistics, meeting participation duration statistics, cross-departmental collaboration frequency), knowledge contribution (number of technical documents, number of knowledge sharing sessions, internal training participation) and task completion (task completion time distribution, bug fixing efficiency, requirement response speed level) of R&D team employees were collected through enterprise internal system interfaces. After all data was anonymized, more than 200 statistical features were extracted.

[0033] The original data is standardized using a weighted feature fusion algorithm to calculate the standardized feature value and comprehensive feature score of each feature. Then, PCA dimensionality reduction technology is used to compress the 200+ dimensional features into a 50-dimensional core feature vector, retaining 95.6% of the information content, and constructing a statistical profile of employee work behavior.

[0034] The method of using Principal Component Analysis (PCA) to effectively compress high-dimensional features and retain information, ultimately constructing an accurate statistical profile of employee work behavior, is as follows: 1) Preprocessing: Constructing a standardized feature matrix. First, based on the 200+ dimension original feature data of employees collected and weighted by S1, an m×n original feature matrix is ​​constructed (m is the number of employees in the R&D team, and n is the 200+ dimension of features, such as the average number of code submissions per day, email response time, number of technical documents, etc.).

[0035] The matrix was then standardized by calculating each feature dimension according to the formula “Standardized eigenvalue = (Original value - Mean) / Standard deviation”. This eliminated the influence of differences in the units of different feature dimensions (such as the difference between the number of lines of code and the frequency of collaboration), resulting in a standardized feature matrix with a mean of 0 and a variance of 1, laying the data foundation for subsequent PCA analysis.

[0036] 2) Core Calculation: Solving for Principal Components and Variance Contribution Rates. First, calculate the covariance matrix of the standardized feature matrix to reflect the degree of linear correlation between each feature dimension. Second, solve for the eigenvalues ​​and corresponding eigenvectors of the covariance matrix through eigenvalue decomposition or singular value decomposition. The magnitude of the eigenvalue represents the amount of original data information carried by the corresponding eigenvector. Third, sort all eigenvalues ​​from largest to smallest, calculate the variance contribution rate (single eigenvalue / sum of all eigenvalues) and cumulative variance contribution rate for each eigenvalue, and sequentially select the top k eigenvectors with a cumulative variance contribution rate reaching 95.6%. Finally, determine k=50, meaning these 50 eigenvectors can carry 95.6% of the core information from the original 200+ dimensional features.

[0037] 3) Dimensional Compression: Generate 50-dimensional core feature vectors. Perform matrix multiplication on the standardized feature matrix and the 50 selected feature vectors (forming an n×50 projection matrix) to complete the projection transformation of the feature space. This transforms the original m×n standardized feature matrix into an m×50 low-dimensional feature matrix. Each row in this matrix is ​​the corresponding 50-dimensional core feature vector of the employee. This process not only eliminates redundant information in the original features and reduces computational complexity by 80%, but also fully preserves the key features of the employee's work behavior.

[0038] 4) Profile Construction: Mapping Employee Work Behavior Characteristics. Supported by 50-dimensional core feature vectors, each dimension's features are precisely mapped to the type of employee work behavior (e.g., code submission features correspond to development capabilities, collaboration frequency corresponds to team collaboration capabilities, document quantity corresponds to knowledge contribution capabilities, etc.). The numerical value of the feature vectors quantifies the employee's performance in each work dimension, ultimately forming a statistical profile of employee work behavior covering four major categories of behaviors: development, collaboration, knowledge contribution, and task completion. This clearly presents the employee's work patterns and core competency characteristics, providing a lightweight and complete basis for subsequent job identification and performance evaluation.

[0039] 2. Job Feature Recognition Based on Rule Engine: Job roles are identified through a rules engine architecture. The job feature definer defines key performance indicator weight matrices for R&D engineers, testing engineers, and operations engineers: R&D engineers have higher weights for code quality and code submission frequency; testing engineers have higher weights for defect discovery rate and test case coverage; and operations engineers have higher weights for system stability and fault repair time. The rules configuration library sets a time utilization threshold of 80% and a task quality standard of bug fix rate ≥95%, etc.

[0040] A multi-level rule matching algorithm was used to calculate the job matching degree of each employee's characteristics. The matching degree of an employee's R&D job was 0.78, which is greater than 0.7, so the employee was identified as an R&D engineer. The matching degree of an employee's testing job was 0.65, which is greater than 0.6, so the employee was identified as a testing engineer.

[0041] Dynamic weight adjustment: The team is currently in the urgent stage of project launch. The Urgency is calculated using the project urgency assessment function as: Urgency = 0.4 × version release countdown weight (0.9) + 0.3 × bug severity weight (0.8) + 0.3 × task priority weight (0.9) = 0.87.

[0042] The Collaboration requirement index is 0.8, the Business Value index is 0.9, the adjustment coefficients are α=0.3, β=0.2, γ=0.5, and the basic weight W for the code submission dimensions of R&D engineers is... i_base =0.2, W is calculated using a dynamic weight adjustment algorithm. i_new =0.2×(1+0.3×0.87+0.2×0.8+0.5×0.9)=0.2×(1+0.261+0.16+0.45)=0.2×1.871=0.3742, which increases the evaluation weight of code submission-related dimensions.

[0043] Multi-level performance evaluation and threshold detection: A tiered evaluation algorithm was used to calculate the overall performance score of employees: A certain R&D engineer's time utilization rate was 85%, attendance rate was 98%, task completion rate was 92%, and response time score was 88. Score 基础 = 0.3×85+0.2×98+0.2×92+0.3×88=89.9 points; Technical competence score = 90 points, innovation contribution score = 85 points, problem-solving ability score = 92 points. Score 能力 = 0.4×90+0.3×85+0.3×92=36+25.5+27.6=89.1 points; Teamwork score = 86 points, Knowledge sharing score = 80 points. Score 协作 = 0.5×86+0.5×80=43+40=83 points; Business impact score = 91 points, User satisfaction score = 88 points. Score 价值 = 0.6×91+0.4×88=54.6+35.2=89.8 points.

[0044] The weights for each level are W1=0.2, W2=0.4, W3=0.2, and W4=0.2. Overall performance score = 0.2×89.9+0.4×89.1+0.2×83+0.2×89.86=88.18 points.

[0045] The dynamic threshold of a certain feature is calculated by the intelligent threshold detection algorithm = mean (85) ± 2 × standard deviation (5), i.e. 75-95. If the value of this feature of an employee is 70, which is lower than the lower threshold, the abnormal detection rule is triggered. The abnormality is analyzed as untimely task response, and the optimization suggestion is to reasonably plan the work time and improve the ability to judge task priority.

[0046] Trend Analysis and Rule Recommendations: The performance data of this R&D engineer over the past three months was analyzed using a sliding window trend analysis algorithm. Trend slope = (average of this period 88 - average of the previous period 82) / 3 = 2, Volatility coefficient = Standard deviation 4 / Mean 88 ≈ 0.045 < 0.3 The trend is determined to be "stable upward".

[0047] The matching degree calculated by the intelligent recommendation algorithm is 0.85, which is greater than 0.8. The recommendation level is "strongly recommended". It is suggested that the company assign more challenging R&D tasks and provide advanced technical training.

[0048] Data security and privacy protection: All collected data is anonymized, and employees' sensitive personal information is stored in encrypted form. Data access permissions are controlled through role-based access control, with only the human resources department and team managers having the corresponding data viewing permissions, thus achieving secure data sharing.

[0049] Example 2: Deployment of a multi-position employee performance evaluation system in a certain XXX company: Deploy the employee performance dynamic evaluation system based on a configurable rule engine as described in this invention. The functions of each module in the system are as follows: The multi-dimensional data acquisition engine integrates statistical API interfaces from enterprise OA systems, project management systems, and code management systems to automate the collection of multi-dimensional work behavior data from employees. Data is stored after anonymization and aggregation. The core module of the rules engine provides a visual configuration interface, allowing HR staff to customize evaluation rules and weighting strategies for different positions. Rules support hot deployment and real-time updates. The job feature definer establishes exclusive performance evaluation templates and indicator systems for positions such as product, R&D, testing, operations, and marketing. The dynamic weight adjuster adjusts evaluation weights in real time based on project stage, business priority, and team situation. The multi-level evaluator achieves comprehensive evaluation at the basic, capability, collaboration, and value levels. The threshold detection module identifies abnormal work patterns and potential risks. The data security coordinator is responsible for cross-departmental data security sharing and privacy protection. The rule recommendation engine provides practical suggestions for employee development and team optimization.

[0050] After the system was put into operation, it enabled accurate assessment of the performance of employees in multiple positions. The accuracy of the assessment was improved by 45% compared with traditional methods, the accuracy of anomaly identification was improved by 60%, and the system response speed was improved by 75%, effectively supporting the optimization of enterprise human resources allocation and employee career development planning.

[0051] The present invention has been described in the above-described embodiments; however, these embodiments are merely examples for implementing the present invention. It must be noted that the disclosed embodiments do not limit the scope of the present invention. Conversely, any modifications and refinements made without departing from the spirit and scope of the present invention are within the scope of patent protection of the present invention.

Claims

1. A method for dynamic evaluation of employee performance based on a configuration rule engine, characterized in that: The specific steps of the method are as follows: S1: Multi-dimensional data acquisition and feature engineering construction: The multi-dimensional data includes development activity statistics, collaboration behavior statistics, knowledge contribution statistics, and task completion statistics; We collect statistical data on employee development activities, collaborative behavior, knowledge contribution, and task completion as raw statistical data. We then use a weighted feature fusion algorithm to standardize the raw statistical data and then use principal component analysis (PCA) dimensionality reduction technology to compress 200+ dimensions of features into 50-dimensional core feature vectors, retaining more than 95% of the information content, to construct a feature engineering profile of employee work behavior. The calculation formula for the standardization process is as follows: Standardized eigenvalue = (Original value - Mean) / Standard deviation; Comprehensive feature score = Σ(W i × standardized eigenvalues ​​i), where W i Let be the weight coefficient of the i-th feature; S2: Job Feature Recognition Based on Rule Engine: Intelligent job identification is achieved through a rules engine architecture; The rule engine architecture includes a job feature definer, a rule configuration library, and a rule execution engine; A multi-level rule matching algorithm is used to calculate the job matching degree of the features; The formula for calculating the job matching degree is: The job matching degree of the i-th feature is Σ(Rj×feature similarity j), where Rj is the confidence weight of the j-th rule, and the employee job type is determined by the job category judgment function. S3: Dynamic weight adjustment: An adaptive weight adjustment algorithm is used to dynamically calculate the weight coefficient of each dimension, taking into account the urgency of the project, the needs of team collaboration, the impact on business value, and the individual's growth stage. The project urgency assessment function is as follows: Urgency = 0.4 × version release countdown weight + 0.3 × bug severity weight + 0.3 × task priority weight The formula for dynamic weight adjustment is: W i_new =W i_base ×(1+α×Urgency+β×Collaboration+γ×BusinessValue), where W i_base The basic weights for the i-th dimension are... α, β, and γ are configurable adjustment coefficients, Urgency is the project urgency index [0,1], Collaboration is the collaboration requirement index [0,1], and BusinessValue is the business value index [0,1]. S4: Multi-level performance evaluation and threshold detection: A hierarchical evaluation system is constructed, consisting of a foundation layer, a capability layer, a collaboration layer, and a value layer. A hierarchical evaluation algorithm is used to calculate the comprehensive performance score, while an intelligent threshold detection algorithm is used to identify data anomalies. The hierarchical evaluation algorithm is as follows: Score 基础 = 0.3 × work hour utilization rate + 0.2 × attendance rate + 0.2 × task completion rate + 0.3 × response time score; Score 能力 =0.4 × Technical Ability Score + 0.3 × Innovation Contribution Score + 0.3 × Problem Solving Ability; Score 协作 =0.5 × Teamwork Score + 0.5 × Knowledge Sharing Score; Score 价值 =0.6 × Business Impact Score + 0.4 × User Satisfaction; Overall Performance Score = W1 × Score 基础 +W2×Score 能力 + W3×Score 协作 + W4×Score 价值 ; In the intelligent threshold detection algorithm, the dynamic threshold = mean ± k × standard deviation. k is a configurable parameter of 2-3; S5: Trend Analysis and Rule Recommendations By employing sliding window trend analysis and rule matching algorithms, the trend slope and fluctuation coefficient are calculated through time series trend analysis algorithms. Combined with trend judgment rules, the efficiency fluctuation pattern is identified. Then, the matching degree is calculated and the recommendation level is determined through intelligent recommendation algorithms, providing decision support for human resource management. The time series trend analysis algorithm is as follows: Trend slope = (average of this period - average of the previous period) / time interval; Volatility coefficient = standard deviation / average. S6: Data Security and Privacy Protection By employing distributed data storage, access control mechanisms, data anonymization, and role-based access control, we ensure that sensitive data is stored and transmitted in an encrypted manner, enabling secure data sharing across departments and projects.

2. The method for dynamic evaluation of employee performance based on a configuration rule engine as described in claim 1, characterized in that: In step S1, the statistical data of development activities includes the average number of code commits per day, the average rate of change of lines of code, the distribution of code complexity levels, and the statistical indicators of refactoring frequency. The collaborative behavior statistics include email response time statistics, meeting participation duration statistics, and anonymized indicators of cross-departmental collaboration frequency. The knowledge contribution statistics include the number of technical documents, the number of knowledge sharing sessions, and the participation rate in internal training. The task completion statistics include task completion time distribution, bug fixing efficiency statistics, and requirement response speed level.

3. The method for dynamic evaluation of employee performance based on a configuration rule engine as described in claim 1, characterized in that: In step S2, the job feature definer defines a key performance indicator weight matrix for R&D, testing, operation and maintenance, and product jobs; The rule configuration library supports user-defined evaluation rules and threshold parameters; The rule execution engine adopts a condition-action-based rule pattern, supports complex logical judgments and multi-level rule nesting, and the rules support hot deployment and real-time adjustment.

4. The method for dynamic evaluation of employee performance based on a configuration rule engine as described in claim 1, characterized in that: In step S2, the job category determination function is specifically as follows: If the matching degree 研发 If the value is greater than 0.7, then the job title is Research and Development Engineer. If the matching degree 测试 If the value is greater than 0.6, then the job title is Test Engineer. If the matching degree 运维 If the value is greater than 0.6, then the job title is Operations Engineer. Other cases will be determined according to the preset job classification rules.

5. The method for dynamic evaluation of employee performance based on a configuration rule engine as described in claim 1, characterized in that: In step S4, when the feature value is less than the lower threshold or the feature value is greater than the upper threshold, the anomaly detection rule is triggered, and an anomaly report and optimization suggestions are generated.

6. The method for dynamic evaluation of employee performance based on a configuration rule engine as described in claim 1, characterized in that: In step S5, the specific rules for trend judgment are as follows: If the trend slope is greater than 0.1 and the volatility coefficient is less than 0.3, then the trend is "stable upward". If the trend slope is less than -0.1 and the volatility coefficient is less than 0.3, then the trend is "stable downward". If the volatility coefficient is greater than 0.5, the trend is "unstable"; otherwise, the trend is "stable".

7. The method for dynamic evaluation of employee performance based on a configuration rule engine as described in claim 1, characterized in that: In step S5, the matching degree of the intelligent recommendation algorithm = Σ(similarity i × success case weight i); The recommendation level is determined as follows: if the matching degree is >0.8, the recommendation level is "strongly recommended"; if the matching degree is >0.6, the recommendation level is "suggested recommendation"; otherwise, the recommendation level is "optional recommendation".

8. A dynamic employee performance evaluation system based on a configurable rule engine, characterized in that: The system comprises a security layer, a data layer, a rule engine layer, an analysis and processing layer, and an output layer. The security layer includes a data security coordinator. The data layer includes a multi-dimensional data acquisition engine. The rule engine layer includes a rule engine core module and a job feature definer. The analysis and processing layer includes a dynamic weight adjuster, a multi-level evaluator, and a threshold detection module. The data layer includes a rule recommendation engine and a report generation system.

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