Performance evaluation comprehensive system and platform

Abstract

The invention relates to the technical field of performance evaluation, and particularly discloses a performance evaluation comprehensive system and platform, and the system comprises a data collection module which is used for obtaining multi-source data related to an evaluation subject; the index system building module is used for building an index system according to the type and evaluation purpose of the evaluation subject; the weight determination module is used for determining the weight of each evaluation index by adopting a mode of combining an analytic hierarchy process and an entropy weight method; the evaluation model establishing module is used for determining an evaluation grade set V, constructing a fuzzy relation matrix R, performing fuzzy synthesis to obtain a comprehensive evaluation vector B, and determining the performance grade of an evaluation object according to a maximum membership degree principle; and the evaluation result analysis and feedback module is used for analyzing the evaluation result, calculating and ranking the total performance score of the evaluation object, and generating an evaluation report. According to the invention, subjective deviation of manual scoring can be avoided; the analytic hierarchy process and the entropy weight method are combined to determine the weight, so that the weight distribution is more reasonable.

Description

Technical Field

[0001] The present invention relates to the technical field of performance evaluation, and in particular to a comprehensive performance evaluation system and platform. Background Art

[0002] Performance evaluation plays a vital role in all areas of modern society. In energy enterprise management, accurate performance evaluations can provide a basis for employee salary adjustments, promotions, and training, motivating employees to improve work efficiency and quality, and promoting the sustainable development of energy enterprises. In project management, performance evaluations can monitor project progress in real time, identify issues promptly, adjust strategies, and ensure the successful achievement of project goals.

[0003] However, current performance evaluation methods suffer from numerous flaws. Existing performance evaluation indicator systems are often incomplete, focusing only on certain key indicators while overlooking other important influencing factors. For example, employee performance evaluations at energy companies may focus solely on work performance, neglecting aspects such as work attitude and teamwork. This results inaccurately reflecting employees' true performance, which can easily dampen employee motivation and hinder energy companies' ability to fully understand their employees' abilities and potential.

[0004] In addition, during the evaluation process, data collection methods are limited, data sources are single, and it mainly relies on manual filling of evaluation forms and subjective scoring. This method is not only inefficient, but also easily affected by the subjective factors of the evaluator, making it difficult to guarantee the objectivity and fairness of the evaluation results.

[0005] Furthermore, traditional performance evaluations are mostly conducted periodically, resulting in a static model. This approach fails to track and provide timely feedback on performance changes, and therefore cannot meet the demands of the rapidly evolving modern world. In a rapidly changing market environment, energy companies need to understand employee performance trends in a timely manner to quickly adjust business strategies. During project implementation, they also need to monitor project performance in real time to address any issues that arise. Summary of the Invention

[0006] The purpose of this invention is to provide a comprehensive performance evaluation system and platform to solve the problems existing in existing performance evaluation methods, such as incomplete evaluation indicators, non-objective data collection, and static evaluation process, so as to achieve comprehensive, accurate and dynamic evaluation of the performance of various entities and provide strong support for decision-making.

[0007] The purpose of the present invention can be achieved through the following technical solutions:

[0008] A comprehensive performance evaluation system, comprising:

[0009] The data acquisition module is used to obtain multi-source data related to the evaluation subject, including business system data Db , behavioral data D a and third-party data D t ; The total data set collected D = D b ∪D a ∪D t ;

[0010] The indicator system construction module is used to construct a multi-level and multi-dimensional performance evaluation indicator system according to the type of evaluation subject and evaluation purpose. The indicator system includes basic indicators I b , auxiliary indicator I s and special index I e , complete indicator system I=I b ∪I s ∪I e ;

[0011] The weight determination module is used to determine the weight of each evaluation index by combining the hierarchical analysis method and the entropy weight method; wherein the hierarchical analysis method constructs the judgment matrix A=(a ij ) n×n , calculate the maximum eigenvalue λ max and the eigenvector W A , and normalize it to get the subjective weight vector; the entropy weight method is used to calculate the original data matrix X=(x ij ) m×n Standardization yields Y=(y ij ) m×n , calculate the index entropy value E j and entropy weight W E ; Final weight vector W = αW A +βW E , α is the subjective weight ratio, β is the objective weight ratio, and α+β=1;

[0012] Evaluation model building module, determine the evaluation level set V, and construct the fuzzy relationship matrix R = (r ij ) n×l , perform fuzzy synthesis to obtain the comprehensive evaluation vector The performance level of the evaluation object is determined according to the maximum membership principle, where ° is the fuzzy synthesis operator;

[0013] Evaluation result analysis and feedback module, used to analyze the evaluation results and calculate the total performance score of the evaluation object And rank and generate evaluation reports.

[0014] As a further solution of the present invention: in the data acquisition module, business system data acquisition is achieved by connecting with the business system of the energy enterprise or institution and using database connection technology or API interface to obtain data; behavioral data acquisition is achieved by deploying sensors in the workplace and installing data acquisition software on computer terminals; third-party data acquisition is achieved by establishing a cooperative relationship with a third-party data platform and using a data interface to obtain data.

[0015] As a further solution of the present invention: in the indicator system construction module, basic indicators are determined according to the core business of the evaluation subject, auxiliary indicators are used to supplement and refine the evaluation, and special indicators are set in combination with specific industries or evaluation scenarios.

[0016] As a further solution of the present invention: in the weight determination module, the judgment matrix a of the hierarchical analysis method ij The value range is {1,2,...,9,1 / 2,1 / 3,...,1 / 9}. The consistency index is calculated during the consistency test. The average random consistency index RI is obtained by looking up the table, and the consistency ratio CR=CI / RI. When CR<0.1, the judgment matrix has satisfactory consistency. The normalization treatment in the entropy weight method is effective for positive indicators. For negative indicators Indicator entropy When p ij = 0, p ij lnp ij =0, entropy weight

[0017] As a further solution of the present invention: in the evaluation model building module, the fuzzy synthesis operator includes the maximum-minimum synthesis operator b j =max 1≤i≤n {min(w i ,r ij )} or weighted average synthesis operator

[0018] As a further solution of the present invention: in the evaluation result analysis and feedback module, the evaluation report includes basic information of the evaluation subject, performance score, score of each indicator, comparative analysis with the same industry or similar evaluation objects, existing problems and their causes.

[0019] A comprehensive performance evaluation platform includes the comprehensive performance evaluation system, a user interface module for providing an interface for user interaction with the system, including functions such as data input, evaluation result viewing, and report downloading; and a data storage module for storing collected multi-source data, evaluation indicator system, weights, evaluation results and other information.

[0020] As a further solution of the present invention: the user interface module adopts a visual design to intuitively display the evaluation results and related information in the form of charts and tables; the data storage module adopts a database management system to ensure the security and reliability of the data.

[0021] The beneficial effects of this invention are: through multi-source data collection and the construction of a multi-level, multi-dimensional indicator system, it can comprehensively cover all factors affecting performance, so that the evaluation results can better reflect the actual performance level of the evaluation subject. In the performance evaluation of energy enterprise employees, not only work performance is considered, but also factors such as work attitude and teamwork are incorporated to fully demonstrate the comprehensive ability of employees;

[0022] The use of advanced data collection technology and scientific weight determination methods reduces interference from human factors and improves the objectivity and fairness of the evaluation results. The use of sensors to collect behavioral data avoids the subjective bias of manual scoring; the combination of hierarchical analysis method and entropy weight method to determine weights makes the weight distribution more reasonable.

[0023] Real-time monitoring and automatic adjustment functions can track performance changes in a timely manner, dynamically adjust evaluation strategies based on actual conditions, and adapt to rapidly changing environments. During project implementation, the system can adjust evaluation indicators and weights in real time based on project progress, identifying and resolving problems in a timely manner.

[0024] Detailed evaluation reports and targeted improvement suggestions provide the evaluation subject with a clear development direction, helping to improve performance and providing strong support for managers' decision-making. Energy enterprise managers can use the evaluation results to formulate reasonable human resource management strategies to promote the development of energy enterprises. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The present invention will be further described below with reference to the accompanying drawings.

[0026] Figure 1 It is a flow chart of a comprehensive performance evaluation system of the present invention. DETAILED DESCRIPTION

[0027] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0028] See also Figure 1 As shown, the present invention is a comprehensive performance evaluation system, comprising:

[0029] Data acquisition module:

[0030] Business System Data Collection: Connect with various business systems of energy enterprises or institutions, acquiring relevant data through interface development or data sharing agreements. For energy enterprise financial systems, database connection technology is used to regularly extract employee sales performance, cost control, and other data. For office automation systems, API interfaces are used to obtain employee attendance records, task completion progress, and other information.

[0031] Behavioral data collection: Deploy sensors in the workplace, such as cameras and smart wristbands, to collect employee behavioral data. Cameras monitor employee work hours and status; smart wristbands collect information such as employee movement data and work intensity. Simultaneously, install data collection software on computer terminals to record employee operational behavior, such as file processing frequency and system login duration.

[0032] Third-party data collection: Establish partnerships with third-party data platforms and access industry and market data through data interfaces. Collaborate with market research organizations to obtain performance data, market share, and other information from companies in the same industry. Obtain macroeconomic data from financial data platforms for external environmental analysis. When collecting data from business systems, different systems have different data characteristics and interface specifications. For example, energy enterprise resource planning (ERP) systems contain core business data such as finance, procurement, production, and sales. By connecting to the ERP system's database and using standardized SQL queries, key data such as employee sales performance, order processing volume, and inventory turnover can be regularly retrieved. Customer relationship management (CRM) systems record customer information, communication records, sales opportunities, and other data. Using the API provided by the CRM system, data such as employee-customer interaction frequency and customer satisfaction survey results can be obtained. This data is crucial for evaluating employee customer service capabilities and sales follow-up effectiveness.

[0033] In terms of behavioral data collection, sensors deployed in the workplace can employ various technical principles. For example, cameras can utilize computer vision technology to analyze human behavior. By processing video images captured by the cameras, information such as employees' work posture, operating movements, and whether they are within their work area can be identified. Smart wristbands can communicate with data collection servers via Bluetooth or Wi-Fi, transmitting real-time data such as employees' steps, heart rate, and sleep quality. This data can indirectly reflect their work intensity and work status. Data collection software installed on computer terminals can utilize hook technology to monitor employee mouse clicks, keyboard input, application usage time, and other operational behaviors, providing a basis for analyzing employee work efficiency and focus.

[0034] When collecting third-party data, different collaboration models and data usage rules apply to different third-party data platforms. When collaborating with market research organizations, a data usage agreement is typically required, ensuring that industry data, market share data, and other information provided by them are used within the scope and manner specified in the agreement. Obtaining macroeconomic data from financial data platforms may incur fees and adhere to the data platform's interface specifications and data update frequency requirements. This third-party data can provide macroeconomic and industry benchmarking information for performance evaluation, making the results more objective and comparable.

[0035] The specific implementation steps are as follows:

[0036] Acquire multi-source data related to the evaluation subject, including but not limited to business system data, behavior data, third-party data, etc.; let the business system data set be D b ={d b1 ,d b2 ,...,d bm}, the behavioral data set is D a ={d a1 ,d a2 ,...,d an}, the third-party data set is D t ={d t1 ,d t2 ,...,d tp}, then the total data set collected is D=D b ∪D a ∪D t .

[0037] Indicator system building modules:

[0038] Determine basic indicators: Core basic indicators are determined for different evaluation entities. In energy enterprise employee performance evaluations, performance indicators may include sales volume and number of completed projects; quality indicators may include error rates and customer complaint rates.

[0039] Determine auxiliary indicators: Determine auxiliary indicators based on evaluation needs. For energy enterprise employees, work attitude indicators may include work enthusiasm and sense of responsibility; teamwork ability indicators can be reflected in team project participation and peer evaluation.

[0040] Determine special indicators: Determine special indicators based on specific industries or evaluation scenarios. In energy companies, user activity and user retention rates can be used as special indicators.

[0041] For different types of evaluation entities, the determination of basic indicators requires full consideration of their core business and key performance factors. In energy enterprise employee performance evaluations, sales figures, sales growth rate, and sales profit margin are important basic indicators for sales positions, directly reflecting the employee's sales performance and the value they create for the company. For R&D positions, core basic indicators such as the number of new product developments, R&D project completion rate, and number of patent applications reflect an employee's R&D capabilities and innovative achievements.

[0042] Supplementary indicators can supplement and refine the performance of the evaluation subject from multiple perspectives. In energy enterprise employee evaluations, supplementary indicators for work attitude may include initiative, responsibility, and professionalism. These indicators can be quantified through employees' daily work performance, peer evaluations, and feedback from superiors. Supplementary indicators for teamwork skills can be measured through factors such as role contribution in team projects, frequency of communication and collaboration, and team conflict resolution skills.

[0043] The setting of special indicators needs to be closely aligned with the characteristics of specific industries or evaluation scenarios. In the internet industry, in addition to traditional business indicators, important special indicators include user activity, user retention rate, and user growth rate. These indicators reflect a product's market competitiveness and development potential. In the healthcare industry, special indicators such as a doctor's surgical success rate, patient cure rate, and medical dispute rate are directly related to the quality and safety of medical services.

[0044] The specific implementation steps are as follows:

[0045] According to the type of evaluation subject and evaluation purpose, a multi-level and multi-dimensional performance evaluation index system is constructed. The index system is divided into basic indicators I b ={i b1 ,i b2 ,...,i bq}、Auxiliary indicator I s ={i s1 ,i s2 ,...,i sr} and special indicator I e ={i e1 ,i e2 ,...,i es}, then the complete index system I=I b ∪I s ∪I e .

[0046] Weight determination module:

[0047] The AHP process involves inviting experts in related fields to conduct pairwise comparisons of the relative importance of indicators at different levels, constructing a judgment matrix. For example, in the performance evaluation of energy company employees, experts conduct pairwise comparisons of indicators such as work performance, work quality, and work attitude, determining which is more important, performance or quality, and to what degree, to form a judgment matrix. The eigenvectors and maximum eigenvalue of the judgment matrix are then calculated and tested for consistency to ensure the rationality of the judgment.

[0048] Entropy weighting method steps: Standardize the collected data and calculate the entropy value and entropy weight of each indicator. The entropy value reflects the degree of dispersion of the indicator data. The greater the degree of data dispersion, the greater the entropy weight, indicating that the indicator has a greater impact on the evaluation results. The entropy weighting method objectively determines the weight of each indicator.

[0049] Weighted synthesis: The subjective weights obtained by the analytic hierarchy process and the objective weights obtained by the entropy weight method are combined, and the final weights of each indicator are obtained using a linear weighting method. For example, the subjective weight accounts for 40% and the objective weight accounts for 60%. The weights of each indicator are calculated comprehensively.

[0050] The specific implementation steps are as follows:

[0051] The weight of each evaluation index is determined by combining the analytic hierarchy process (AHP) and the entropy weight method.

[0052] Analytic Hierarchy Process (AHP):

[0053] Construct judgment matrix A=(a ij ) n×n , where a ij Indicates the importance of indicator i relative to indicator j, usually a ij ∈{1,2,...,9,1 / 2,1 / 3,...,1 / 9}.

[0054] Calculate the maximum eigenvalue λ of the judgment matrix max , obtained by solving the equation |A-λI|=0, where I is the identity matrix.

[0055] Calculate eigenvectors Meet AW A =λ max W A , and normalize it so that Get the subjective weight vector W A .

[0056] Perform consistency test and calculate consistency index The average random consistency index RI can be obtained by looking up the table, and the consistency ratio CR = CI / RI. When CR < 0.1, it is considered that the judgment matrix has satisfactory consistency.

[0057] Entropy Weight Method:

[0058] Let the original data matrix X=(x ij ) m×n , where m is the number of evaluation objects and n is the number of indicators. The data is standardized to obtain the standardized matrix Y=(y ij ) m×n .

[0059] For positive indicators:

[0060] For negative indicators:

[0061] Calculate the entropy value of the jth indicator in (When p ij = 0, define p ij lnp ij =0).

[0062] Calculate the entropy weight W of the jth indicator E =(w E1 ,w E2 ,...,w En ) T ,in

[0063] Weighted synthesis:

[0064] Assume that the subjective weight ratio is α, the objective weight ratio is β, and α+β=1, then the final weight vector W=αW A +βW E .

[0065] Evaluation model building module:

[0066] Determine the evaluation level: Determine the performance evaluation level based on the evaluation requirements. For example, the performance of energy enterprise employees can be divided into four levels: excellent, good, qualified, and unqualified, with each level corresponding to a certain score range.

[0067] Constructing a fuzzy relationship matrix: Evaluate each evaluation indicator, determine the degree of membership of the evaluated object to each evaluation level, and construct a fuzzy relationship matrix. For example, for an employee's work performance indicator, determine their membership to the four levels of excellent, good, qualified, and unqualified based on their actual performance, and form a fuzzy relationship matrix.

[0068] Perform fuzzy synthesis: Fuzzy synthesis is performed on the fuzzy relationship matrix and the weight vector of each indicator to obtain the comprehensive evaluation result of the evaluated object. Through fuzzy transformation, the comprehensive membership of the evaluated object to each evaluation level is calculated, thereby determining its final performance level.

[0069] The specific implementation steps are as follows:

[0070] According to the evaluation index system and weight, a performance evaluation model is established. Using the fuzzy comprehensive evaluation method, the evaluation level set V = {v1, v2, ..., v l}.

[0071] Construct the fuzzy relationship matrix R=(r ij ) n×l , where r ij It represents the membership of the i-th indicator to the j-th evaluation level.

[0072] Perform fuzzy synthesis to obtain a comprehensive evaluation vector B = W°R, where ° is the fuzzy synthesis operator. Commonly used operators include the maximum-minimum synthesis operator b j =max 1≤i≤n {min(w i ,r ij )} or weighted average synthesis operator

[0073] According to the principle of maximum membership, the performance level of the evaluation object is determined. That is, if b k =max{b1,b2,...,b l}, then the evaluation object belongs to the kth evaluation level v k .

[0074] Evaluation result analysis and feedback module:

[0075] Evaluation Results Analysis: Conduct an in-depth analysis of the evaluation results, calculating the scores for each indicator and the overall performance score, and then conducting ranking and comparative analysis. In energy enterprise employee performance evaluations, analyze each employee's scores on various indicators and compare them with employees in the same department or position to identify strengths and weaknesses. Additionally, conduct trend analysis to observe changes in employee performance.

[0076] Report Generation: Based on the evaluation results, a detailed evaluation report is generated. The report includes basic information about the evaluation subject, performance scores, scores for each indicator, comparative analysis with peers in the same industry or category, and existing issues and their causes. The report uses a combination of charts and text to intuitively present the evaluation results.

[0077] Feedback and Suggestions: Based on the evaluation report, we provide the evaluation subject with targeted improvement suggestions and development plans. For employees with outstanding performance, we provide recommendations for promotions and rewards. For employees whose performance needs improvement, we develop personalized training plans and improvement measures to help them improve their performance.

[0078] The specific implementation steps are as follows:

[0079] Analysis of evaluation results:

[0080] Assume that there are m evaluation objects and n indicators, and the score of the i-th evaluation object on the j-th indicator is x ij , then the total performance score of the i-th evaluation object where w j is the weight of the j-th indicator.

[0081] Ranking calculation:

[0082] According to the total performance score S i Sort the evaluation objects and set the ranking function as Rank(S i ), if S i >S k , then Rank(S i ) <Rank(S k ).

[0083] Dynamic monitoring and adjustment module:

[0084] Data monitoring: Real-time monitoring of performance data changes of the evaluation subject and setting thresholds for key indicators. For energy enterprise employee performance evaluations, thresholds for key indicators such as sales and customer satisfaction are set. If an employee's sales fall below the threshold for two consecutive months, the system automatically issues an alert.

[0085] Adjustment triggers: When performance data changes exceed preset thresholds or meet specific rules, the performance evaluation adjustment process is automatically triggered. For example, when the market environment undergoes significant changes, the evaluation indicator system and weights are adjusted; when an employee's position changes, the corresponding evaluation indicators and standards are adjusted.

[0086] Adjustment implementation: Adjust the evaluation indicators, weights, evaluation cycles, etc. according to the adjustment requirements. Recalculate the performance evaluation results and provide timely feedback to the evaluation subject to ensure the timeliness and accuracy of the evaluation.

[0087] The specific implementation steps are as follows:

[0088] Assume the threshold of key indicator i is T i , the value of key indicator i at time t is x i (t). When |x i (t)-x i (t-1)|>T i When , the performance evaluation adjustment process is triggered. The adjusted indicator weight is set to W′=(w1′,w2′,...,w n ′), can be calculated according to the preset adjustment rules, such as w j ′=wj+Δw j , where Δw j is the weight adjustment amount.

[0089] The above is a detailed description of an embodiment of the present invention. However, the content described is only a preferred embodiment of the present invention and should not be considered to limit the scope of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A comprehensive performance evaluation system, characterized in that: include: The data acquisition module is used to obtain multi-source data related to the evaluation subject, including business system data D b , behavioral data D a and third-party data D t ; The total data set collected D = D b ∪D a ∪D t ; The indicator system construction module is used to construct a multi-level and multi-dimensional performance evaluation indicator system according to the type of evaluation subject and evaluation purpose. The indicator system includes basic indicators I b , auxiliary indicator I s and special index I e , complete indicator system I=I b ∪I s ∪I e ; The weight determination module is used to determine the weight of each evaluation indicator by combining the hierarchical analysis method and the entropy weight method; The analytic hierarchy process constructs the judgment matrix A=(a ij ) n×n , calculate the maximum eigenvalue λ max and the eigenvector W A , and normalize it to get the subjective weight vector; the entropy weight method is used to calculate the original data matrix X=(x ij ) m×n Standardization yields Y=(y ij ) m×n , calculate the index entropy value E j and entropy weight W E ; Final weight vector W = αW A +βW E , α is the subjective weight ratio, β is the objective weight ratio, and α+β=1; Evaluation model building module, determine the evaluation level set V, and construct the fuzzy relationship matrix R = (r ij ) n×l , perform fuzzy synthesis to obtain the comprehensive evaluation vector B = W°R, and determine the performance level of the evaluation object according to the maximum membership principle, where ° is the fuzzy synthesis operator; Evaluation result analysis and feedback module, used to analyze the evaluation results and calculate the total performance score of the evaluation object And rank and generate evaluation reports.

2. A comprehensive performance evaluation system according to claim 1, characterized in that: In the data acquisition module, business system data is collected by connecting with the business system of energy enterprises or institutions and using database connection technology or API interface to obtain data; behavioral data collection is achieved by deploying sensors in the workplace and installing data acquisition software on computer terminals; Third-party data collection establishes a cooperative relationship with a third-party data platform and uses data interfaces to obtain data.

3. A comprehensive performance evaluation system according to claim 1, characterized in that: In the indicator system construction module, basic indicators are determined according to the core business of the evaluation subject, auxiliary indicators are used to supplement and refine the evaluation, and special indicators are set in combination with specific industries or evaluation scenarios.

4. A comprehensive performance evaluation system according to claim 1, characterized in that: In the weight determination module, the judgment matrix a of the hierarchical analysis method ij The value range is {1,2,...,9,1 / 2,1 / 3,...,1 / 9}. The consistency index is calculated during the consistency test. The average random consistency index RI is obtained by looking up the table, and the consistency ratio CR=CI / RI. When CR<0.1, the judgment matrix has satisfactory consistency. The normalization treatment in the entropy weight method is effective for positive indicators. For negative indicators Indicator entropy When p ij = 0, p ij lnp ij =0, entropy weight 5. A comprehensive performance evaluation system according to claim 1, characterized in that: In the evaluation model building module, the fuzzy synthesis operator includes the maximum-minimum synthesis operator b j =max 1≤i≤n {min(w i ,r ij )} or weighted average synthesis operator 6. A comprehensive performance evaluation system according to claim 1, characterized in that: In the evaluation result analysis and feedback module, the evaluation report includes the basic information of the evaluation subject, performance score, score of each indicator, comparative analysis with the same industry or similar evaluation objects, existing problems and their causes.

7. A comprehensive performance evaluation platform, characterized by: The comprehensive performance evaluation system includes any one of claims 1-6, and further includes a user interface module for providing an interface for user interaction with the system, including functions such as data input, evaluation result viewing, and report downloading; and a data storage module for storing collected multi-source data, evaluation indicator system, weights, evaluation results and other information.

8. A comprehensive performance evaluation platform according to claim 7, characterized in that: The user interface module adopts a visual design to intuitively display the evaluation results and related information in the form of charts and tables; the data storage module adopts a database management system to ensure the security and reliability of the data.

Images