Salary performance evaluation method and system based on AI technology

Abstract

The invention discloses a salary performance evaluation method and system based on an AI technology, and the method comprises the steps: obtaining the performance data of a target employee in response to an evaluation request of an administrator for the salary performance of the target employee, and enabling the performance data to comprise the post performance data and cross-department cooperation performance data of the target employee; inputting the cross-department collaboration performance data into a preset collaboration contribution model to obtain a collaboration contribution parameter of the target employee; inputting the post performance data into a first preset evaluation model, and calculating post performance parameters of the target employee; inputting the cooperation contribution parameter into a second preset evaluation model, and calculating a cross-department cooperation performance parameter of the target employee; and generating a salary performance evaluation result of the target employee according to the post performance parameter, the cross-department cooperation performance parameter and the enterprise salary performance strategy. According to the method, the comprehensiveness of salary performance evaluation results can be improved.

Description

Technical Field

[0001] The present application relates to the field of human resource management, and specifically to a salary performance evaluation method and system based on AI technology. Background Art

[0002] With the continuous development of intelligent enterprise management, salary and performance evaluation has gradually become an important link that affects enterprise operational efficiency and employee enthusiasm. The rapid development of artificial intelligence technology has provided new solutions for salary and performance evaluation, especially in data analysis, modeling and decision support. AI technology can quickly and objectively generate salary and performance evaluation results based on employees' multi-dimensional performance data, thereby improving enterprise management efficiency and employee satisfaction.

[0003] In the existing technology, some systems have tried to assist in salary performance evaluation by introducing intelligent algorithms. These systems usually generate salary performance evaluation results based on the employee's job performance data and some behavioral data, combined with the company's salary performance strategy. Compared with the traditional method of obtaining salary performance results through manual analysis combined with fixed weights or rules, the degree of automation of the evaluation process has been greatly improved, and the data processing efficiency has been significantly improved.

[0004] However, existing technologies mainly evaluate employees based on their job performance data, which makes it difficult to fully reflect the comprehensive value of employees, thus affecting the comprehensiveness of salary and performance evaluation results. Summary of the invention

[0005] This application provides a salary performance evaluation method and system based on AI technology to improve the comprehensiveness of salary performance evaluation results.

[0006] In a first aspect of the present application, a salary performance evaluation method based on AI technology is provided, which is applied to a server. The method includes: In response to the administrator's request for evaluation of the target employee's salary performance, the target employee's performance data is obtained, the performance data including the target employee's current position performance data and cross-departmental collaboration performance data; the cross-departmental collaboration performance data is input into a preset collaboration contribution model to obtain the target employee's collaboration contribution parameters; the current position performance data is input into a first preset evaluation model to calculate the target employee's current position performance parameters; the collaboration contribution parameters are input into a second preset evaluation model to calculate the target employee's cross-departmental collaboration performance parameters; and the target employee's salary performance evaluation results are generated based on the current position performance parameters, cross-departmental collaboration performance parameters and the company's salary performance strategy.

[0007] Optionally, before inputting the cross-departmental collaboration performance data into a preset collaboration contribution model to obtain the collaboration contribution parameters of the target employee, the method further includes: Obtain historical cross-departmental performance data of all target employees in the enterprise within a preset historical time period, the historical cross-departmental performance data including first historical data and second historical data, the first historical data being quantitative data of the target employees' historical cross-departmental work results, and the second historical data being comprehensive evaluation data of the target employees' historical cross-departmental work performance; extract quantitative features based on the first historical data, and extract qualitative features based on the second historical data; fuse the quantitative features with the qualitative features to obtain multi-dimensional feature vectors of all target employees; regard the target employees and their collaboration objects in cross-departmental collaboration as nodes, and regard the collaboration relationships between the target employees and the collaboration objects as edges, to obtain a cross-departmental collaboration network; extract collaboration network features of the cross-departmental collaboration network; fuse the collaboration network features with the multi-dimensional feature vector to obtain a target feature vector; and train a collaboration contribution model based on the target feature vector using a preset machine learning algorithm.

[0008] Optionally, the performance data of the current position is input into the first preset evaluation model to calculate the performance parameters of the target employee in the current position, specifically including: The performance parameters of this position are calculated through the first calculation formula; the first calculation formula is: P job This is the performance parameter of this position. The performance data of this position includes A j , T j , C j 、F j and n, where A j is the target employee's job performance completion rate in the jth task, T j is the performance completion trend of the target employee on the jth task, C j is the job competency parameter of the target employee on the jth task, F j is the innovation parameter of the target employee, n is the total number of tasks in this position, λ is the adjustment parameter used to control the impact of innovation on the formula, ω j is the importance parameter of item j.

[0009] Optionally, the collaboration contribution parameter is input into a second preset evaluation model to calculate the target employee's cross-department collaboration performance parameter, specifically including: The cross-departmental collaboration performance parameters are calculated using the second calculation formula; the second calculation formula is: Among them, P collab is the cross-departmental collaboration performance parameter, α is the collaboration contribution parameter, β i is the importance parameter of collaborative project i, C i is the contribution weight of the target employee in the i-th collaborative project, Ri is the actual impact of the i-th collaborative project, S i The comprehensive feedback score of the target employee in the i-th collaborative project, Q i Score employees' problem-solving abilities during collaboration, E i is the target employee’s participation in the i-th collaborative project, L i is the complexity coefficient of the ith collaborative project, n is the total number of cross-departmental collaborative projects, δ is the adjustment parameter used to control the impact of problem-solving ability on collaboration, and γ is the adjustment parameter reflecting the importance that the enterprise attaches to cross-departmental collaboration.

[0010] Optionally, generate the target employee's salary performance evaluation results based on the position performance parameters, cross-departmental collaboration performance parameters and the company's salary performance strategy, including: Determine the first weight of the performance parameters of this position and the second weight of the cross-departmental collaboration performance parameters based on the enterprise's salary and performance strategy; based on the first weight and the second weight, input the performance parameters of this position and the cross-departmental collaboration performance parameters and performance data into the preset analysis model for analysis to obtain analysis results, which include performance analysis results, weight adaptability analysis results and performance rankings of target employees; generate salary performance evaluation results for target employees based on the analysis results.

[0011] Optionally, after generating the target employee's salary performance evaluation results based on the analysis results, the method also includes: obtaining the target employee's historical performance data, and generating career development suggestions for the target employee through a preset artificial intelligence algorithm based on the historical performance data and the analysis results.

[0012] Optionally, after generating the target employee's salary performance evaluation result according to the position performance parameter, the cross-department collaboration performance parameter and the enterprise salary performance strategy, the method further includes: Obtain reward preference feedback information of target employees, including reward method preference and reward cycle preference; generate personalized incentive strategy for target employees based on salary performance evaluation results and reward preference feedback information; if the target employee's first performance meets the preset reward rules, adjust the salary performance evaluation results based on the personalized salary incentive strategy and the first performance, where the first performance is the target employee's performance within the preset historical period.

[0013] In a second aspect of the present application, a salary performance evaluation system based on AI technology is provided, comprising: An acquisition module, used to respond to the administrator's request for evaluation of the target employee's salary performance, and acquire the target employee's performance data, the performance data including the target employee's current position performance data and cross-departmental collaboration performance data; An input module is used to input the cross-departmental collaboration performance data into a preset collaboration contribution model to obtain the collaboration contribution parameters of the target employees; A first calculation module, used to input the performance data of the current position into a first preset evaluation model to calculate the performance parameters of the current position of the target employee; A second calculation module, used to input the collaboration contribution parameter into a second preset evaluation model to calculate the cross-department collaboration performance parameter of the target employee; The generation module is used to generate the target employee's salary performance evaluation results based on the performance parameters of the position, cross-departmental collaboration performance parameters and the company's salary performance strategy.

[0014] In the third aspect of the present application, an electronic device is provided, including a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are both used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device performs any of the methods described above.

[0015] In a fourth aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores instructions, and when the instructions are executed, any of the above methods is executed.

[0016] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. The performance data of the position is calculated into the performance parameters of the position through the preset first evaluation model, reflecting the direct contribution of the employee within the job responsibilities; the cross-departmental collaboration data is quantified into collaboration contribution parameters through the collaboration contribution model, and further calculated into the cross-departmental collaboration performance parameters through the second evaluation model, which effectively solves the problem that the value of collaboration is difficult to quantify, and at the same time avoids the evaluation bias that may be caused by traditional methods such as strong subjectivity or simple weighting. By combining the performance parameters of the position with the cross-departmental collaboration performance parameters, the comprehensive performance of the target employee within the job responsibilities and in collaboration can be fully covered, thereby improving the comprehensiveness of the salary performance evaluation results.

[0017] 2. By obtaining the first historical data (quantitative work results) and the second historical data (comprehensive evaluation) of the target employees in the historical time period, the historical collaborative performance of the target employees can be fully covered, thus providing a rich data foundation for subsequent feature extraction. Based on these two types of historical data, quantitative features and qualitative features are extracted respectively, and they are merged into multi-dimensional feature vectors, which can effectively combine the objective data and subjective evaluation of employee collaboration results, and solve the one-sided problem of collaborative contribution evaluation caused by a single data dimension in traditional methods. In addition, the target employees and the collaborative objects are constructed into a cross-departmental collaborative network, and the collaborative network features are extracted, which can characterize the role and influence of the target employees in team collaboration from the perspective of network relationships, and further enhance the comprehensiveness of collaborative contribution evaluation. By merging the collaborative network features with the multi-dimensional feature vector into the target feature vector, and using the machine learning algorithm to train the collaborative contribution model, the model can make full use of historical data and network relationship features, and improve the adaptability and prediction accuracy of the collaborative contribution model to complex collaborative scenarios.

[0018] 3. In the first preset evaluation model, the performance parameters of this position are calculated by the first calculation formula, and the important parameters in the performance data of this position are integrated, including the performance completion rate of the position, the performance completion trend, the position competency parameters and the innovation parameters, and the task importance parameters are used to assign differentiated weights to different tasks, so that the evaluation results can more accurately reflect the direct contribution of employees within their job responsibilities. At the same time, by introducing the innovation adjustment parameters, the impact of innovation on the evaluation results can be flexibly controlled, so as to adapt to the degree of emphasis on innovation capabilities of different enterprises, which not only reflects the flexibility of the evaluation, but also enhances the comprehensiveness of the evaluation of employee job performance.

[0019] 4. In the second preset evaluation model, the cross-departmental collaboration performance parameters are calculated by the second calculation formula. The collaboration contribution parameters obtained by the collaboration contribution model and the important parameters in the cross-departmental collaboration performance data are combined with the collaboration project complexity coefficient to reflect the multi-dimensional performance of the target employees in cross-departmental collaboration. At the same time, by introducing the problem-solving ability adjustment parameters and the adjustment parameters that reflect the degree of emphasis on collaboration by the enterprise, the enterprise can flexibly adjust the impact of collaboration-related factors on performance evaluation according to its own management needs, further enhancing the applicability and flexibility of the method.

[0020] 5. Through weight distribution and analysis models, combined with the company's compensation and performance strategy, an evaluation result including performance, weight adaptability and employee ranking is generated to ensure the scientificity and adaptability of the evaluation. Then, the historical performance data of the target employees is further utilized to generate career development suggestions through artificial intelligence algorithms to provide intelligent support for employee capacity building. At the same time, based on the feedback information of employees' reward preferences, personalized incentive strategies are formulated, and the evaluation results are dynamically adjusted when the employee's performance meets the reward rules, which improves the pertinence and effectiveness of the incentive. By combining weight adjustment, historical data analysis and personalized incentives, it not only accurately reflects the overall performance of employees, but also effectively motivates employees and optimizes compensation and performance management. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 This is a flowchart of a salary performance evaluation method based on AI technology in an embodiment of the present application; Figure 2 This is a structural diagram of a salary performance evaluation system based on AI technology in an embodiment of the present application; Figure 3 It is a schematic diagram of the structure of an electronic device in an embodiment of the present application.

[0022] Explanation of the accompanying drawings: 201, acquisition module; 202, input module; 203, first calculation module; 204, second calculation module; 205, generation module; 206, training module; 207, excitation module; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. DETAILED DESCRIPTION

[0023] In order to enable technicians in this field to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0024] In the description of the embodiments of the present application, words such as "for example" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "for example" or "for example" is intended to present related concepts in a specific way.

[0025] In the description of the embodiments of the present application, the meaning of the term "multiple" refers to two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "include", "comprise", "have" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0026] Figure 1 It is a flow chart of a salary performance evaluation method based on AI technology in an embodiment of the present application.

[0027] See also Figure 1 In an embodiment of the present application, a salary performance evaluation method based on AI technology is applied to a server, and the method includes: S101, in response to an administrator's request for evaluation of a target employee's salary performance, obtaining performance data of the target employee, the performance data including the target employee's current position performance data and cross-departmental collaboration performance data; The administrator's evaluation request contains the identity information of the target employee and the time range for evaluation. Based on the administrator's evaluation request for the target employee's salary performance, the system can obtain the target employee's performance data through the company's relevant data management systems (such as human resource management system, project management system and work order system, etc.). The performance data includes the target employee's performance data of this position and cross-departmental collaboration performance data within the time range required for evaluation. Among them, the performance data of this position includes quantitative performance data of this position and qualitative performance data of this position. The quantitative performance data of this position is mainly the quantitative results of the target employee's position within the time range, including at least the number of completed projects, the key performance indicators (KPI) achieved, the efficiency of completing tasks, the quantity or quality of output results, etc. The qualitative performance data of this position is the comprehensive evaluation data of the target employee's performance in cross-departmental work within the time range, mainly including the evaluation records of supervisors or colleagues, feedback on teamwork ability, description of innovation ability, skill level, job competency, leadership ability and subjective rating of problem-solving ability, etc. The qualitative performance data of this position usually exists in the form of text description or grade rating. Cross-departmental collaboration performance data includes quantitative cross-departmental collaboration performance data and qualitative cross-departmental collaboration performance data. Quantitative cross-departmental collaboration performance data refers to the quantitative data of the results achieved by the target employee in cross-departmental work within the time frame required for evaluation, such as the number of completed projects, key performance indicators (KPIs) achieved, efficiency of completing tasks, quantity or quality of output results, etc. These data are usually presented in specific numerical forms, such as percentages, scores or absolute values. Qualitative cross-departmental performance data refers to the comprehensive evaluation data of the target employee's cross-departmental work performance within the time frame required for evaluation, mainly including evaluation records of supervisors or colleagues, feedback on teamwork ability, description of innovation ability, subjective scores of leadership and problem-solving ability, etc. These data are usually in the form of text descriptions or graded scores.

[0028] S102, inputting the cross-departmental collaboration performance data into a preset collaboration contribution model to obtain the collaboration contribution parameters of the target employee; In a possible implementation, before step S102, the method further includes: obtaining historical cross-departmental performance data of all target employees in the enterprise within a preset historical time period, the historical cross-departmental performance data including first historical data and second historical data, the first historical data being quantitative data of the target employees' historical cross-departmental work results, and the second historical data being comprehensive evaluation data of the target employees' historical cross-departmental work performance; extracting quantitative features based on the first historical data, and extracting qualitative features based on the second historical data; fusing the quantitative features with the qualitative features to obtain multi-dimensional feature vectors of all target employees; regarding the target employees and their collaborative objects in cross-departmental collaboration as nodes, and regarding the collaborative relationships between the target employees and the collaborative objects as edges, to obtain a cross-departmental collaboration network; extracting collaborative network features of the cross-departmental collaboration network; fusing the collaborative network features with the multi-dimensional feature vector to obtain a target feature vector; and training the collaborative contribution model based on the target feature vector by a preset machine learning algorithm.

[0029] Specifically, the historical cross-departmental collaboration performance data of all target employees within a preset historical time period is obtained from the relevant data management systems of the enterprise (such as the human resources management system, project management system, and work order system, etc.). The preset historical time period can be flexibly determined based on the actual situation of the enterprise, for example, based on the business cycle of the enterprise, the active time period of cross-departmental collaboration, the importance of tasks or projects, etc. The preset historical time period can be one or more. The historical cross-departmental collaboration performance data includes first historical data and second historical data. The first historical data is the quantitative data of the historical cross-departmental work results of the target employees, that is, the quantitative cross-departmental collaboration performance data within the preset historical time period. The second historical data is the comprehensive evaluation data of the historical cross-departmental work performance of the target employees, that is, the qualitative cross-departmental collaboration performance data within the preset historical time period.

[0030] For the first historical data, data cleaning is first performed, including processing missing values ​​(e.g., filling with mean values ​​or removing abnormal data) and data standardization (e.g., converting data of different units to the same range through Z-score standardization or Min-Max normalization). Then, quantitative features are extracted according to specific business needs, such as calculating the average, maximum, and minimum values ​​of task completion efficiency, counting the fluctuations of KPI achievement rates, or extracting performance indicators directly related to target positions and cross-departmental collaborative tasks. In order to further optimize the extracted quantitative features, machine learning techniques, such as principal component analysis (PCA) or feature importance ranking, can also be used to screen out key quantitative features that can most effectively reflect employee capabilities and performance. For the second historical data, data in the form of text descriptions need to be structured. For example, natural language processing (NLP) technology can be combined to perform word segmentation, sentiment analysis, or topic extraction on supervisor evaluations and colleague feedback, and extract key feature keywords related to employee performance, such as "strong communication skills" and "outstanding innovation ability". At the same time, for qualitative data in the form of scoring, the average scores, fluctuations and relative rankings of employees in different dimensions can be calculated to construct structured qualitative characteristics that fully reflect the soft power and comprehensive performance of employees in cross-departmental collaboration.

[0031] After the extraction of quantitative and qualitative features is completed, the feature weights of the qualitative and quantitative features of target employees in different departments are determined according to the preset rules of the enterprise, and the qualitative and quantitative features are weighted and fused to generate a multi-dimensional feature vector for each target employee. The preset rules of the enterprise usually set the weights of certain features in combination with the nature of work, job responsibilities and key needs in cross-departmental collaboration of different departments. For example, for employees in the technical research and development department, they may pay more attention to task completion efficiency and innovation ability, so the weights of efficiency dimension and innovation ability dimension are higher, while the weights of communication ability and teamwork dimension are relatively low; for employees in the marketing department, they may pay more attention to communication ability and teamwork ability, so the weights of these dimensions will be higher, and the weight of efficiency dimension may be appropriately reduced.

[0032] After generating the multidimensional feature vector of the target employee, the target employee and his / her collaborative partner in the cross-department collaboration are regarded as nodes, and the collaborative relationship between the target employee and the collaborative partner is regarded as an edge to construct a cross-department collaboration network. Then, the collaboration network features are extracted from the collaboration network, such as the degree of the node (indicating the collaboration frequency and association strength of the employee), the clustering coefficient of the node (indicating the closeness of the employee in the collaboration network), and the betweenness centrality of the node (indicating the degree to which the employee acts as an important bridge in the collaboration network). These collaboration network features can reflect the structural position and collaboration contribution of the employee in the cross-department collaboration network.

[0033] Next, the collaborative network features extracted from the collaborative network are fused with the employee's multidimensional feature vector to obtain the target feature vector. The target feature vector combines the employee's historical cross-departmental performance (represented by the multidimensional feature vector) and his structural information in the collaborative network (represented by the collaborative network features). For example, the target feature vector of an employee may be expressed as [90, 85, 80, 5, 0.3, 0.7], where the first three dimensions are multidimensional feature vectors (efficiency, innovation ability, communication ability), and the last three dimensions are collaborative network features (degree, clustering coefficient, betweenness centrality).

[0034] Finally, based on the target feature vectors of all employees, the collaborative contribution model is trained through a preset machine learning algorithm. The machine learning algorithm (such as support vector machine, random forest or deep learning model) can learn the collaborative contribution rules of employees based on the target feature vector, thereby predicting the contribution level of employees in future cross-departmental collaboration.

[0035] In step S102, the target employee's cross-departmental collaboration performance data is input into a preset collaboration contribution model. The input data includes the target employee's cross-departmental collaboration performance within a specific time period, such as the number of projects completed during the cross-departmental work process, the key performance indicators (KPIs) achieved, the efficiency of completing tasks, the quantity or quality of output results, and comprehensive evaluation data on cross-departmental work performance, mainly including evaluation records of supervisors or colleagues, feedback on teamwork ability, description of innovation ability, skill level, job competency, leadership ability and problem-solving ability, etc.

[0036] After the cross-departmental collaborative performance data is input into the preset collaborative contribution model, the input data is first preprocessed, including standardizing the data of different dimensions to ensure that the numerical ranges between features are consistent, and extracting key features for subsequent calculations. Next, the preset collaborative contribution model will weight and fuse the input features according to the rules learned during training. For example, the preset collaborative contribution model will give higher weights to the importance and complexity of the project, thereby highlighting the contribution of core projects; it will feature-weight the individual performance data of employees (such as task completion rate, problem-solving ability score, etc.) to comprehensively evaluate the performance of employees in specific tasks. At the same time, the model will use the global pattern of collaborative network features to combine the structural role of the target employee in the collaborative network (such as the role of a bridge for cross-departmental communication) with actual performance, and further improve the calculation of collaborative contributions.

[0037] After completing feature weighting and fusion, the preset collaborative contribution model calculates the complex relationship between input features through built-in nonlinear algorithms (such as random forests, gradient boosting decision trees, or deep neural networks). Determine the interactive contribution of target employees in different projects and collaborative environments, such as the amplification effect of high participation in high-priority projects on overall contribution. Finally, the preset collaborative contribution model outputs the collaborative contribution parameter of the target employee, which is a quantitative value used to reflect the overall collaborative contribution level of the employee in a specific time period.

[0038] S103, inputting the performance data of the current position into the first preset evaluation model to calculate the performance parameters of the current position of the target employee; Calculate the performance parameters of this position through the first calculation formula; The first calculation formula is: P job This is the performance parameter of this position. The performance data of this position includes A j 、T j , C j 、F j and n, where A j is the target employee's job performance completion rate in the jth task, T j is the performance completion trend of the target employee on the jth task, C j is the job competency parameter of the target employee on the jth task, F j is the innovation parameter of the target employee, n is the total number of tasks in this position, λ is the adjustment parameter used to control the impact of innovation on the formula, ω j is the importance parameter of item j.

[0039] The first preset evaluation model can calculate the performance parameters of this position through the performance data of this position, where: Is the performance completion trend T j Nonlinear mapping of , used to enhance sensitivity to trend changes; is the innovative parameter F j The comprehensive performance of each task is determined by A j ,T j ,C j ,F j Decide together, by the importance of the task j Weighted, ω j Reflects the weight of the task in the overall performance evaluation. The importance of different tasks can be set according to the company's strategic goals or the priority of job responsibilities. λ represents the adjustment parameter used to control the impact of innovation on the formula. By adjusting the value of λ, the contribution of the innovation parameter to the overall performance evaluation results can be controlled.

[0040] S104, inputting the collaboration contribution parameter into the second preset evaluation model to calculate the cross-department collaboration performance parameter of the target employee; Specifically, the cross-departmental collaboration performance parameter is calculated by a second calculation formula; the second calculation formula is: Among them, P collab is the cross-departmental collaboration performance parameter, α is the collaboration contribution parameter, β i is the importance parameter of collaborative project i, C i is the contribution weight of the target employee in the i-th collaborative project, R i is the actual impact of the i-th collaborative project, S i The comprehensive feedback score of the target employee in the i-th collaborative project, Q i Score employees' problem-solving abilities during collaboration, E i is the target employee’s participation in the i-th collaborative project, L i is the complexity coefficient of the ith collaborative project, n is the total number of cross-departmental collaborative projects, δ is the adjustment parameter used to control the impact of problem-solving ability on collaboration, and γ is the adjustment parameter reflecting the importance that the enterprise attaches to cross-departmental collaboration.

[0041] The second preset evaluation model can calculate the cross-departmental collaboration performance parameters through the collaboration contribution parameters and other parameters obtained from the cross-departmental collaboration performance data. Among them, the collaboration contribution parameter α is usually based on 1.0. When the collaboration contribution parameter is greater than 1.0, it means that the contribution is higher than the average, and when the collaboration contribution parameter is less than 1.0, it means that the contribution is lower than the average; β i is the importance parameter of the collaborative project, reflecting the impact of the ith collaborative project on the overall collaborative performance. It is set by the enterprise based on factors such as the strategic value, resource input, and business priority of the project. For example, a project that contributes more to the enterprise's strategic goals can be given a higher weight; C i is the contribution weight of the target employee in the i-th collaborative project, which is obtained by analyzing the proportion of tasks completed by the employee in the project to the total tasks or the importance of his role. For example, if the target employee completes 80% of the tasks as the person in charge in the project, then C i =0.8; R i is the actual impact of the i-th collaborative project, reflecting the ultimate impact of the project on the enterprise's business. It can be quantified by data such as customer satisfaction improvement, cost savings, or revenue growth. For example, if a project directly brings 15% cost savings, then R i =0.85; S iis the comprehensive feedback score of the target employee in the i-th collaborative project, which is based on the evaluation data of superiors, colleagues or customers. The evaluation dimensions include communication ability, execution ability and collaborative attitude. For example, if the comprehensive score of superiors and colleagues is 87.5, then S i =87.5;Q i is the problem-solving ability score of the target employee in the collaboration process, which is obtained by analyzing the employee's ability to deal with unexpected problems or solve key challenges. For example, if an employee successfully solves an important technical problem in a project, his problem-solving ability score is 0.8, and the degree of influence of problem-solving ability on the formula result is controlled by adjusting the parameter δ; γ is an adjustment parameter that reflects the importance of cross-departmental collaboration by the enterprise, which is dynamically set by the enterprise according to strategic goals. For example, when the enterprise is highly dependent on cross-departmental collaboration at the current stage, γ can be set to a higher value to increase the collaboration weight. In addition, the formula uses the collaboration project complexity coefficient L i The mapping value 1+e -Li To quantify the impact of complexity on collaborative performance.

[0042] S105. Generate the target employee's salary performance evaluation results based on the position performance parameters, cross-departmental collaboration performance parameters and the company's salary performance strategy; Specifically, the first weight of the performance parameters of this position and the second weight of the cross-departmental collaboration performance parameters are determined according to the enterprise's salary and performance strategy; based on the first weight and the second weight, the performance parameters of this position and the cross-departmental collaboration performance parameters are input into the preset analysis model with the performance data for analysis to obtain analysis results, which include performance analysis results, weight adaptability analysis results and performance rankings of target employees; based on the analysis results, the salary performance evaluation results of the target employees are generated.

[0043] Among them, according to the compensation and performance strategy, the enterprise system assigns different weights to the target employee's performance in the position and the performance of cross-departmental collaboration. These weights reflect the degree of attention that the enterprise pays to different aspects of employee performance in compensation and performance evaluation. Specifically, the first weight of the performance parameter of the position refers to the importance of the employee's performance in his or her position. The more core the job responsibilities are, the higher the weight of the employee's performance in the position is usually. For example, the sales performance of sales personnel may account for a large proportion, while for management positions, it may be necessary to consider not only the completion of job tasks, but also factors such as team management and decision-making influence. The second weight of the cross-departmental collaboration performance parameter reflects the importance of the employee's performance in teamwork and cross-departmental collaboration. If the enterprise emphasizes teamwork and collaborative work, it may give a higher weight to cross-departmental collaboration. For example, if an employee plays a key role in a project jointly carried out by multiple departments, then the weight of his or her cross-departmental collaboration may be high.

[0044] Next, the performance parameters of the position and the performance parameters of cross-department collaboration are input into the preset analysis model, and the performance data of the target employee is analyzed according to the first weight and the second weight. The performance analysis results, weight adaptability analysis results and performance ranking of the target employee can be obtained. First, the performance analysis results will combine the employee's performance in the position and the cross-department collaboration performance to obtain a comprehensive performance score. For example, an employee may complete all tasks in the position, but perform averagely in cross-department collaboration. In this case, the performance analysis results may show that the employee performs well in the position, but is relatively weak in cross-department collaboration. Secondly, the weight adaptability analysis results verify the rationality of the weights to ensure that the set weights match the actual work contribution of the employee. If the company finds that the employee's performance in cross-department collaboration far exceeds the performance of the position, but the weight is low, the system may make adjustment suggestions. Finally, the performance ranking of the target employee will be generated based on the performance data of all employees. For example, if an employee performs well in cross-department collaboration, but other employees perform well in their positions, then this employee may be in the middle of the overall ranking, although the score for cross-department collaboration is high. Performance rankings help administrators understand employees' relative contributions and further provide data support for compensation decisions.

[0045] Combined with the above analysis results, the system will generate the target employee's salary performance evaluation results based on the employee's comprehensive performance in the position and cross-departmental collaboration. The salary performance evaluation results may include salary adjustment suggestions, bonus distribution and other incentives. Salary adjustments will be determined based on the overall performance of the employee. If the employee performs well in both job tasks and cross-departmental collaboration, his basic salary will be increased; conversely, if the performance in one aspect is poor, the salary adjustment may be more conservative or reduced. At the same time, bonus distribution will be based on the employee's performance and the company's reward policy. The system will allocate corresponding bonuses based on the employee's performance in different cycles, such as annual or quarterly. For example, an employee's outstanding performance in cross-departmental collaboration may result in the company giving additional rewards to recognize his contribution to teamwork. In addition to cash rewards and bonuses, other incentives will also be considered, especially for the long-term development of employees. Companies may provide employees with incentives such as stock options, career development opportunities, and training programs, which are intended to inspire employees to make more contributions to the company's long-term goals, while helping employees improve their personal abilities and further promote their career development.

[0046] In a possible implementation, after generating the target employee's salary performance evaluation results based on the analysis results, the method further includes: obtaining the target employee's historical performance data, and generating career development suggestions for the target employee through a preset artificial intelligence algorithm based on the historical performance data and the analysis results.

[0047] Specifically, firstly, the historical performance data of the target employees are extracted from the enterprise's data management system. These data include quantitative historical performance data (such as the number of completed projects, key performance indicator achievement rate, task completion efficiency, etc.) and qualitative historical performance data (such as evaluations from superiors, feedback from colleagues, comprehensive scores of innovation or leadership capabilities, etc.), as well as the employee's personal career information (such as current rank, years of tenure, training experience, etc.). Subsequently, the historical performance data of the target employees are comprehensively analyzed with their latest salary and performance evaluation results. By comparing historical performance with current performance levels, the core strengths (such as technical expertise, teamwork ability, etc.) and shortcomings (such as lack of leadership or innovation) of the employees are identified, thereby clarifying the employee's ability characteristics and development potential. Then, using preset artificial intelligence algorithms (such as decision trees, recommendation systems or deep learning models), combined with the employee's job requirements and the enterprise's career development path, personalized career development suggestions are generated for the target employees. Career development suggestions include: clear career development direction (such as recommending employees to develop into management positions or technical experts), specific short-term skill improvement plans (such as recommending to participate in certain training courses or obtain relevant professional certifications), and long-term career promotion path planning (such as phased tasks and time nodes for target positions). Ultimately, the generated career development suggestions will be stored in the company's human resources management system for employees and managers to refer to and use for developing personalized training plans, task allocation and promotion plans, thereby helping employees clarify their future development direction.

[0048] Optional, in Figure 1 After step S105 of the illustrated embodiment, the following steps may be performed: Specifically, the reward preference feedback information of the target employee is obtained, and the reward preference feedback information includes the reward method preference and the reward cycle preference; the personalized incentive strategy of the target employee is generated according to the salary performance evaluation result and the reward preference feedback information; if the target employee's first performance meets the preset reward rules, the salary performance evaluation result is adjusted according to the personalized salary incentive strategy and the first performance, and the first performance is the performance of the target employee within the preset historical period.

[0049] Among them, after generating the salary performance evaluation results, the reward preference feedback information of the target employees can be obtained through the information feedback system. The reward preference feedback information mainly includes two aspects: reward method preference and reward cycle preference. Reward method preference refers to which type of reward employees prefer. For example, some employees may prefer cash rewards because they believe that money can meet their needs immediately, while some employees prefer non-monetary rewards, such as additional vacations, recognition, or enhanced career development opportunities. Reward cycle preference refers to employees' preference for the time of reward distribution. Some employees prefer to receive rewards in the short term, such as quarterly bonuses, while others may prefer long-term rewards such as year-end bonuses or stock options. Understanding these preferences can help companies formulate more accurate incentive strategies and ensure that the incentive method matches the needs of employees, thereby stimulating employees' work enthusiasm and long-term contributions.

[0050] After obtaining employee reward preference feedback, generate personalized incentive strategies based on employee salary performance evaluation results and their reward preference information. The goal of this step is to tailor an incentive plan for each employee that is consistent with their personal preferences and work performance. Personalized incentive strategies need to comprehensively consider employee performance data, preferred reward methods, and company compensation policies. For example, if an employee performs well and prefers short-term monetary rewards, the company can motivate the employee through quarterly bonuses; if the employee performs well and prefers long-term rewards, they may be motivated through equity incentives, year-end bonuses, etc. In this way, the personalized incentive strategy not only takes into account the employee's work performance, but also ensures that the reward method and cycle meet the expectations of employees, thereby improving the incentive effect and employee satisfaction.

[0051] The system will adjust the salary performance evaluation results based on the target employee's performance in the preset historical period (i.e., first performance) and the personalized salary incentive strategy. If the target employee's first performance meets the preset reward rules, the company will adjust its salary evaluation results based on the incentive strategy. This means that if an employee performs well in a specific period of time and meets the reward conditions, the company can reward the employee's performance by increasing bonuses, increasing salaries, providing more benefits, etc.

[0052] See also Figure 2 , is a structural diagram of a salary performance evaluation system based on AI technology provided in an embodiment of the present application, and a salary performance evaluation system based on AI technology 200 specifically includes: The acquisition module 201 is used to respond to the administrator's request for evaluating the salary performance of the target employee and acquire the performance data of the target employee, the performance data including the target employee's current position performance data and cross-departmental collaboration performance data; An input module 202 is used to input the cross-departmental collaboration performance data into a preset collaboration contribution model to obtain the collaboration contribution parameters of the target employee; The first calculation module 203 is used to input the performance data of the current position into the first preset evaluation model to calculate the performance parameters of the current position of the target employee; A second calculation module 204 is used to input the collaboration contribution parameter into a second preset evaluation model to calculate the cross-department collaboration performance parameter of the target employee; The generation module 205 is used to generate the target employee's salary performance evaluation result according to the position performance parameters, the cross-departmental collaboration performance parameters and the enterprise's salary performance strategy.

[0053] Optionally, the first calculation module 203 is specifically configured to: The performance parameters of this position are calculated through the first calculation formula; the first calculation formula is: P job This is the performance parameter of this position. The performance data of this position includes A j , T j , C j 、F j and n, where A j is the target employee's job performance completion rate in the jth task, T j is the performance completion trend of the target employee on the jth task, C j is the job competency parameter of the target employee on the jth task, F j is the innovation parameter of the target employee, n is the total number of tasks in this position, λ is the adjustment parameter used to control the impact of innovation on the formula, ω j is the importance parameter of item j.

[0054] Optionally, the second calculation module 204 is specifically configured to: The cross-departmental collaboration performance parameters are calculated using the second calculation formula; the second calculation formula is: Among them, P collab is the cross-departmental collaboration performance parameter, α is the collaboration contribution parameter, β i is the importance parameter of collaborative project i, C i is the contribution weight of the target employee in the i-th collaborative project, R i is the actual impact of the i-th collaborative project, S i The comprehensive feedback score of the target employee in the i-th collaborative project, Q i Score employees' problem-solving abilities during collaboration, E i is the target employee’s participation in the i-th collaborative project, Li is the complexity coefficient of the ith collaborative project, n is the total number of cross-departmental collaborative projects, δ is the adjustment parameter used to control the impact of problem-solving ability on collaboration, and γ is the adjustment parameter reflecting the importance that the enterprise attaches to cross-departmental collaboration.

[0055] Optionally, the generating module 205 is specifically used for: Determine the first weight of the performance parameters of this position and the second weight of the cross-departmental collaboration performance parameters based on the enterprise's salary and performance strategy; based on the first weight and the second weight, input the performance parameters of this position and the cross-departmental collaboration performance parameters and performance data into the preset analysis model for analysis to obtain analysis results, which include performance analysis results, weight adaptability analysis results and performance rankings of target employees; generate salary performance evaluation results for target employees based on the analysis results.

[0056] Optionally, the generating module 205 is further specifically used for: Obtain the historical performance data of target employees, and generate career development suggestions for target employees through preset artificial intelligence algorithms based on the historical performance data and analysis results.

[0057] Optionally, the system further includes a training module 206, specifically configured to: Obtain historical cross-departmental performance data of all target employees in the enterprise within a preset historical time period, the historical cross-departmental performance data including first historical data and second historical data, the first historical data being quantitative data of the target employees' historical cross-departmental work results, and the second historical data being comprehensive evaluation data of the target employees' historical cross-departmental work performance; extract quantitative features based on the first historical data, and extract qualitative features based on the second historical data; fuse the quantitative features with the qualitative features to obtain multi-dimensional feature vectors of all target employees; regard the target employees and their collaboration objects in cross-departmental collaboration as nodes, and regard the collaboration relationships between the target employees and the collaboration objects as edges, to obtain a cross-departmental collaboration network; extract collaboration network features of the cross-departmental collaboration network; fuse the collaboration network features with the multi-dimensional feature vector to obtain a target feature vector; and train a collaboration contribution model based on the target feature vector using a preset machine learning algorithm.

[0058] Optionally, the system further includes an incentive module 207, which is specifically used to: Obtain reward preference feedback information of target employees, including reward method preference and reward cycle preference; generate personalized incentive strategy for target employees based on salary performance evaluation results and reward preference feedback information; if the target employee's first performance meets the preset reward rules, adjust the salary performance evaluation results based on the personalized salary incentive strategy and the first performance, where the first performance is the target employee's performance within the preset historical period.

[0059] It should be noted that: when the device provided in the above embodiment realizes its function, only the division of the above functional modules is used as an example. In actual application, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0060] This embodiment also discloses an electronic device, referring to Figure 3 The electronic device may include: at least one processor 301 , at least one communication bus 302 , a user interface 303 , a network interface 304 , and at least one memory 305 .

[0061] The communication bus 302 is used to realize the connection and communication between these components.

[0062] The user interface 303 may include a display screen (Display) and a camera (Camera). The optional user interface 303 may also include a standard wired interface and a wireless interface.

[0063] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0064] Among them, the processor 301 may include one or more processing cores. The processor 301 uses various interfaces and lines to connect various parts in the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 305, and calling data stored in the memory 305. Optionally, the processor 301 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 301 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 301, and it can be implemented separately through a chip.

[0065] Among them, the memory 305 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 305 includes a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 305 may optionally be at least one storage device located away from the aforementioned processor 301. As Figure 3 As shown, the memory 305 as a computer storage medium may include an operating system, a network communication module, a user interface module, and an application program of a salary performance evaluation method based on AI technology.

[0066] exist Figure 3 In the electronic device shown, the user interface 303 is mainly used to provide an input interface for the user and obtain data input by the user; and the processor 301 can be used to call an application program storing a salary performance evaluation method based on AI technology in the memory 305. When executed by one or more processors 301, the electronic device executes one or more methods in the above-mentioned embodiments.

[0067] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the order of the actions described, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application.

[0068] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0069] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0070] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0071] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0072] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory 305. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory 305 and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the various embodiments of the present application. The aforementioned memory 305 includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a magnetic disk or an optical disk.

[0073] The above are only exemplary embodiments of the present disclosure and cannot be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification, those skilled in the art will easily think of other embodiments of the present disclosure. This application is intended to cover any modification, use or adaptive change of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the technical field that are not recorded in the present disclosure. The description and examples are only regarded as exemplary, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A salary performance evaluation method based on AI technology, characterized in that: Applied in a server, the method comprises: in response to an administrator's request for evaluation of a target employee's salary performance, obtaining performance data of the target employee, the performance data comprising the target employee's current position performance data and cross-departmental collaboration performance data; The cross-departmental collaboration performance data is input into a preset collaboration contribution model to obtain the collaboration contribution parameters of the target employee; the current position performance data is input into a first preset evaluation model to calculate the current position performance parameters of the target employee; the collaboration contribution parameters are input into a second preset evaluation model to calculate the cross-departmental collaboration performance parameters of the target employee; and the salary performance evaluation results of the target employee are generated based on the current position performance parameters, the cross-departmental collaboration performance parameters and the enterprise's salary performance strategy.

2. The method according to claim 1, characterized in that Before inputting the cross-departmental collaboration performance data into a preset collaboration contribution model to obtain the collaboration contribution parameter of the target employee, the method further includes: Obtaining historical cross-department performance data of all the target employees in the enterprise within a preset historical time period, wherein the historical cross-department performance data includes first historical data and second historical data, wherein the first historical data is quantitative data of the target employees' historical cross-department work results, and the second historical data is comprehensive evaluation data of the target employees' historical cross-department work performance; extracting quantitative features based on the first historical data, and extracting qualitative features based on the second historical data; Fusing the quantitative features with the qualitative features to obtain multi-dimensional feature vectors of all the target employees; The target employee and the target employee's collaboration object in the cross-department collaboration are regarded as nodes, and the collaboration relationship between the target employee and the collaboration object is regarded as an edge, to obtain a cross-department collaboration network; Extracting collaborative network features of the cross-departmental collaborative network; Performing feature fusion on the collaborative network feature and the multi-dimensional feature vector to obtain a target feature vector; Based on the target feature vector, the collaborative contribution model is trained by a preset machine learning algorithm.

3. The method according to claim 1, characterized in that The step of inputting the performance data of the current position into the first preset evaluation model to calculate the performance parameters of the current position of the target employee specifically includes: Calculate the performance parameter of the position by using the first calculation formula; The first calculation formula is: P job is the performance parameter of this position, and the performance data of this position includes A j 、T j , C j 、F j and n, where A j is the target employee's job performance completion rate in the jth task, T j is the performance completion trend of the target employee on the jth task, C j is the job competency parameter of the target employee on the jth task, F j is the innovation parameter of the target employee, n is the total number of tasks in this position, λ is the adjustment parameter used to control the impact of innovation on the formula, ω j is the importance parameter of item j.

4. The method according to claim 1, characterized in that The step of inputting the collaboration contribution parameter into a second preset evaluation model to calculate the cross-department collaboration performance parameter of the target employee specifically includes: Calculate the cross-departmental collaboration performance parameter by a second calculation formula; The second calculation formula is: Among them, P collab is the cross-departmental collaboration performance parameter, α is the collaboration contribution parameter, β i is the importance parameter of collaborative project i, C i is the contribution weight of the target employee in the i-th collaborative project, R i is the actual impact of the i-th collaborative project, S i The comprehensive feedback score of the target employee in the i-th collaborative project, Q i Score employees' problem-solving abilities during collaboration, E i is the participation of the target employee in the i-th collaborative project, L i is the complexity coefficient of the ith collaborative project, n is the total number of cross-departmental collaborative projects, δ is the adjustment parameter used to control the impact of problem-solving ability on collaboration, and γ is the adjustment parameter reflecting the importance that the enterprise attaches to cross-departmental collaboration.

5. The method according to claim 1, characterized in that The generating of the target employee's salary performance evaluation result according to the performance parameters of the position, the cross-departmental collaboration performance parameters and the enterprise's salary performance strategy specifically includes: Determine the first weight of the performance parameter of the position and the second weight of the cross-departmental collaboration performance parameter according to the enterprise salary performance strategy; Based on the first weight and the second weight, the performance parameter of the current position and the cross-departmental collaboration performance parameter and the performance data are input into a preset analysis model for analysis to obtain analysis results, which include performance analysis results, weight adaptability analysis results, and the performance ranking of the target employee; Based on the analysis results, a salary performance evaluation result of the target employee is generated.

6. The method according to claim 5, characterized in that After generating the target employee's salary performance evaluation result according to the analysis result, the method further includes: The historical performance data of the target employee is obtained, and based on the historical performance data and the analysis result, a career development suggestion for the target employee is generated by a preset artificial intelligence algorithm.

7. The method according to claim 1, characterized in that After generating the target employee's salary performance evaluation result according to the position performance parameter, the cross-departmental collaboration performance parameter and the enterprise salary performance strategy, the method further includes: Acquiring reward preference feedback information of the target employee, wherein the reward preference feedback information includes reward method preference and reward cycle preference; Generate a personalized incentive strategy for the target employee based on the salary performance evaluation result and the reward preference feedback information; if the first performance of the target employee meets the preset reward rules, adjust the salary performance evaluation result based on the personalized salary incentive strategy and the first performance, and the first performance is the performance of the target employee within a preset historical period.

8. A salary performance evaluation system based on AI technology, characterized in that: include: An acquisition module, configured to acquire performance data of a target employee in response to an administrator's request for evaluation of the target employee's salary performance, wherein the performance data includes the target employee's current position performance data and cross-departmental collaboration performance data; An input module, used to input the cross-departmental collaboration performance data into a preset collaboration contribution model to obtain the collaboration contribution parameters of the target employee; A first calculation module, used for inputting the performance data of the current position into a first preset evaluation model to calculate the performance parameters of the current position of the target employee; A second calculation module, used for inputting the collaboration contribution parameter into a second preset evaluation model to calculate the cross-department collaboration performance parameter of the target employee; A generation module is used to generate the salary performance evaluation result of the target employee according to the performance parameters of the position, the cross-departmental collaboration performance parameters and the enterprise salary performance strategy.

9. A salary performance evaluation device based on AI technology, characterized in that: include: One or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the AI-based salary performance evaluation device to perform the method described in any one of claims 1-7.

10. A computer-readable storage medium comprising instructions, characterized in that: When the instruction is executed on a salary performance evaluation device based on AI technology, the salary performance evaluation device based on AI technology executes the method as described in any one of claims 1 to 7.

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