Performance management system and method based on cloud computing and artificial intelligence

Abstract

The invention belongs to the technical field of enterprise management, and provides a performance management system and method based on cloud computing and artificial intelligence, and the system comprises a user management module, a performance management module, a performance evaluation module, a display module, a message leaving module, an address book module, a reward and punishment module, and a data analysis module. According to the invention, by integrating a plurality of modules such as user management, performance management, performance evaluation, visual display, information exchange, address book management, reward and punishment strategy and data analysis, comprehensive intelligence of performance management is realized; according to the system, data security and efficient access are guaranteed by using a cloud computing technology, performance indicators and weights are dynamically adjusted in combination with an artificial intelligence algorithm, evaluation fairness is improved, prediction accuracy is improved through deep learning and time sequence prediction, powerful support is provided for enterprise decision making and individual development of employees, and the economic benefit is improved. And meanwhile, communication and interaction among employees are enhanced, a reward and punishment mechanism is optimized, and improvement of the overall performance level is promoted.

Description

Technical Field

[0001] The present invention belongs to the technical field of enterprise management, and specifically relates to a performance management system and method based on cloud computing and artificial intelligence. Background Art

[0002] In the current field of enterprise management, performance management systems play a vital role. However, traditional performance management systems often have many shortcomings; for example, most of them rely on local servers to store data, which not only limits the access flexibility and scalability of data, but also increases the risk of data loss and damage; in addition, traditional performance management systems often use fixed weights and indicators when evaluating performance, and cannot be dynamically adjusted according to the actual situation of the enterprise and the development and changes of employees, resulting in inaccurate and unfair evaluation results; in addition, these systems usually lack intelligent analysis and prediction functions and cannot provide valuable decision support for enterprises.

[0003] With the rapid development of cloud computing and artificial intelligence technologies, more and more companies are beginning to explore the application of these two technologies in performance management systems; cloud computing technology can provide efficient and flexible data storage and processing capabilities, while artificial intelligence technology can realize intelligent analysis and prediction; however, there is currently a lack of a performance management system on the market that can fully utilize these two technologies to meet the urgent needs of companies for efficient, accurate and intelligent performance management.

[0004] To this end, those skilled in the art have proposed a performance management system and method based on cloud computing and artificial intelligence to solve the problems raised by the background technology. Summary of the invention

[0005] In order to solve the above technical problems, the present invention provides a performance management system and method based on cloud computing and artificial intelligence, so as to solve the problems that the performance management system in the prior art often adopts fixed weights and indicators when evaluating performance, and cannot be dynamically adjusted according to the actual situation of the enterprise and the development and changes of employees, resulting in inaccurate and unfair evaluation results.

[0006] A performance management system based on cloud computing and artificial intelligence, including a user management module, a performance management module, a performance evaluation module, a display module, a message module, an address book module, a reward and punishment module and a data analysis module, wherein:

[0007] User management module, which is used to store employee information in the cloud server and manage the system permissions of all employees;

[0008] A performance management module, used to issue corresponding assessment tasks according to at least one performance event set by the administrator;

[0009] The performance evaluation module is used to perform performance evaluation according to the assessment task, obtain the self-evaluation score, the other-evaluation score and the leader's evaluation score, and set the evaluation weights corresponding to the self-evaluation score, the other-evaluation score and the leader's evaluation score in the performance event;

[0010] A display module is used to display the performance evaluation results of employees using a visual dynamic chart according to the query request, wherein the dynamic chart includes any one or more of the overall performance evaluation results of employees in different positions, the performance evaluation results of different employees in the same position, the employee performance ranking list, the performance evaluation results of outstanding employees, and the performance evaluation results of employees who do not meet the standards;

[0011] Message module, used to display and forward messages posted by employees and / or leaders;

[0012] Address book module, used to save employees' communication information;

[0013] The reward and punishment module is used to generate reward and punishment strategies for employees based on the performance evaluation results, and to issue early warnings to employees whose performance does not meet the standards. Specifically, a gold coin system is implemented for employees who meet the performance standards, where the next day after the performance meets the standards for consecutive days increases the gold coins by 20% compared to the previous day; the number of gold coins obtained by employees is counted according to the preset cycle, and employees are rewarded according to the number of gold coins; if the performance does not meet the standards, an early warning message is issued in the user's client;

[0014] The data analysis module is used to conduct in-depth analysis of employees' performance data and provide performance improvement suggestions and trend forecasts.

[0015] Preferably, in the performance evaluation module, in order to make the evaluation results more reasonable and fair, an intelligent weight allocation algorithm is introduced, which automatically adjusts the weights of different performance indicators according to historical performance data and business logic;

[0016] At the same time, a dynamic adjustment algorithm for performance indicators is introduced, which automatically adjusts performance indicators and their weights based on factors such as changes in corporate strategic goals, fluctuations in the market environment, and the growth of employee capabilities.

[0017] Preferably, in the message module, a sentiment analysis algorithm is introduced to analyze the information posted by employees and leaders in the message module, identify emotional tendencies, and provide management with insights into the emotional state of employees;

[0018] At the same time, an employee satisfaction prediction algorithm is introduced, which predicts employee satisfaction by analyzing the content, frequency, sentiment and other information of employees' messages in the message module.

[0019] Preferably, in the reward and punishment module, in order to more effectively warn employees whose performance does not meet the standards, a risk warning algorithm is added. The algorithm predicts the risks faced by employees' future performance by analyzing their performance evaluation results, historical performance data, market environment and other information, and issues early warnings.

[0020] Preferably, in the data analysis module, a time series prediction algorithm is introduced to use historical performance data to predict employee performance trends in the future to provide support for corporate decision-making.

[0021] Preferably, in the data analysis module, deep learning algorithms are used to conduct in-depth mining and analysis of performance data to improve evaluation accuracy and predictive capabilities.

[0022] A performance management method based on cloud computing and artificial intelligence, using the above-mentioned performance management system based on cloud computing and artificial intelligence, comprising:

[0023] S1. Manage employee information through the user management module and control employee system access rights;

[0024] S2. Issue assessment tasks through the performance management module and evaluate employee performance through the performance evaluation module;

[0025] S3. Display the employee's performance evaluation results in the form of visual dynamic charts through the display module;

[0026] S4. Realize information exchange and feedback between employees and / or leaders through the message module;

[0027] S5. Facilitate communication and contact between employees through the address book module;

[0028] S6. Reward and punish employees according to the performance evaluation results through the reward and punishment module, and issue early warning reminders to employees whose performance does not meet the standards;

[0029] S7. Conduct in-depth analysis of employees’ performance data through the data analysis module to provide decision support for the company’s performance management.

[0030] A processor is configured to execute a performance management system based on cloud computing and artificial intelligence according to the above.

[0031] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the above-mentioned performance management system based on cloud computing and artificial intelligence.

[0032] Compared with the prior art, the present invention has the following beneficial effects:

[0033] 1. The present invention uses cloud computing technology to store employee information in a cloud server, thereby realizing centralized storage and efficient processing of data; this not only improves the access flexibility and scalability of data, but also reduces the risk of data loss and damage; at the same time, cloud computing technology can also dynamically allocate resources according to the actual needs of the enterprise, thereby reducing the operation and maintenance costs of the enterprise.

[0034] 2. The present invention introduces an intelligent weight allocation algorithm and a performance indicator dynamic adjustment algorithm in the performance evaluation module; these two algorithms can automatically adjust the weights of different performance indicators and the performance indicators themselves according to factors such as historical performance data, business logic, changes in corporate strategic goals, fluctuations in the market environment, and growth in employee capabilities, making the evaluation results more reasonable and fair.

[0035] 3. The present invention introduces time series prediction algorithm and deep learning algorithm into the data analysis module; these two algorithms can use historical performance data to predict employee performance trends in the future and provide valuable decision support for enterprises; at the same time, the deep learning algorithm can also conduct in-depth mining and analysis of performance data to improve evaluation accuracy and prediction ability.

[0036] 4. The present invention realizes information exchange and feedback between employees and / or leaders through the message module, thereby enhancing the interaction and communication between employees; at the same time, the message module also introduces a sentiment analysis algorithm and an employee satisfaction prediction algorithm, which provides management with insights into the emotional state of employees and predictions of employee satisfaction, helping management to better understand the needs and dynamics of employees and formulate more reasonable management strategies.

[0037] 5. The present invention generates reward and punishment strategies for employees in the reward and punishment module according to the performance evaluation results, and issues early warning reminders to employees whose performance does not meet the standards; this not only helps to stimulate the enthusiasm and creativity of employees and improve the overall performance level, but also can timely discover and solve potential problems to avoid greater losses to the enterprise. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 This is a framework diagram of the performance management system based on cloud computing and artificial intelligence of the present invention;

[0039] Figure 2 This is a flow chart of the performance management method based on cloud computing and artificial intelligence of the present invention. DETAILED DESCRIPTION

[0040] The following embodiments of the present invention are described in further detail in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0041] Embodiment: The present invention provides a performance management system based on cloud computing and artificial intelligence, such as Figure 1 As shown, it includes a user management module, a performance management module, a performance evaluation module, a display module, a message module, an address book module, a reward and punishment module and a data analysis module, among which:

[0042] User management module, which is used to store employee information in the cloud server and manage the system permissions of all employees;

[0043] A performance management module, used to issue corresponding assessment tasks according to at least one performance event set by the administrator;

[0044] The performance evaluation module is used to perform performance evaluation according to the assessment task, obtain the self-evaluation score, the other-evaluation score and the leader's evaluation score, and set the evaluation weights corresponding to the self-evaluation score, the other-evaluation score and the leader's evaluation score in the performance event;

[0045] A display module is used to display the performance evaluation results of employees using a visual dynamic chart according to the query request, wherein the dynamic chart includes any one or more of the overall performance evaluation results of employees in different positions, the performance evaluation results of different employees in the same position, the employee performance ranking list, the performance evaluation results of outstanding employees, and the performance evaluation results of employees who do not meet the standards;

[0046] Message module, used to display and forward messages posted by employees and / or leaders;

[0047] Address book module, used to save employees' communication information;

[0048] The reward and punishment module is used to generate reward and punishment strategies for employees based on the performance evaluation results, and to issue early warnings to employees whose performance does not meet the standards. Specifically, a gold coin system is implemented for employees who meet the performance standards, where the next day after the performance meets the standards for consecutive days increases the gold coins by 20% compared to the previous day; the number of gold coins obtained by employees is counted according to the preset cycle, and employees are rewarded according to the number of gold coins; if the performance does not meet the standards, an early warning message is issued in the user's client;

[0049] The data analysis module is used to conduct in-depth analysis of employees' performance data and provide performance improvement suggestions and trend forecasts.

[0050] As can be seen from the above, the system has achieved comprehensive intelligent performance management by integrating multiple modules such as user management, performance management, performance evaluation, visual display, information exchange, address book management, reward and punishment strategies and data analysis. The system uses cloud computing technology to ensure data security and efficient access, combines artificial intelligence algorithms to dynamically adjust performance indicators and weights, improves evaluation fairness, and improves prediction accuracy through deep learning and time series prediction, providing strong support for corporate decision-making and employee personal development, while enhancing communication and interaction among employees, optimizing the reward and punishment mechanism, and promoting the improvement of overall performance levels.

[0051] Furthermore, in the performance evaluation module, in order to make the evaluation results more reasonable and fair, an intelligent weight allocation algorithm is introduced, which automatically adjusts the weights of different performance indicators according to historical performance data and business logic. The formula of the intelligent weight allocation algorithm includes:

[0052] w i,new =f(w i,old );

[0053] Among them, w i,new represents the updated weight, w i,old represents the original weight, and f represents the weight update function;

[0054] At the same time, a dynamic adjustment algorithm for performance indicators is introduced. The algorithm automatically adjusts performance indicators and their weights according to factors such as changes in corporate strategic goals, fluctuations in the market environment, and growth in employee capabilities. The formula for the dynamic adjustment algorithm for performance indicators includes:

[0055] I new =f(I old ,ΔS,ΔM,ΔE);

[0056] Among them, I new represents the adjusted performance indicator, I old represents the original performance indicator, ΔS represents the change in the enterprise's strategic goals, ΔM represents the change in the market environment, ΔE represents the growth in employee capabilities, and f represents the dynamic adjustment function.

[0057] As can be seen from the above, the intelligent weight allocation algorithm and performance indicator dynamic adjustment algorithm introduced by the present invention can automatically and flexibly adjust the weights of different performance indicators and the performance indicators themselves by comprehensively considering multiple factors such as historical performance data, business logic, changes in corporate strategic goals, market environment fluctuations, and employee capability growth. It not only significantly improves the accuracy and fairness of performance evaluation, but also ensures that the performance management system can closely fit the actual development needs of the enterprise, effectively stimulate the enthusiasm and creativity of employees, and thus promote the continuous optimization and improvement of the overall performance level.

[0058] Furthermore, in the message module, a sentiment analysis algorithm is introduced to analyze the information posted by employees and leaders in the message module, identify sentiment tendencies, and provide management with insights into the emotional state of employees. The formula of the sentiment analysis algorithm includes:

[0059]

[0060] Among them, S represents the sentiment score, w i Represents the sentiment weight of the word, s i Indicates the sentiment of the word (positive or negative);

[0061] At the same time, an employee satisfaction prediction algorithm is introduced. The algorithm predicts employee satisfaction by analyzing information such as the content, frequency, and sentiment of messages left by employees in the message module. The formula of the employee satisfaction prediction algorithm includes:

[0062] S = g(C, F, E);

[0063] Among them, S represents employee satisfaction, C represents message content, F represents message frequency, E represents message sentiment, and g represents the prediction function.

[0064] As can be seen from the above, the present invention realizes in-depth mining and analysis of information released by employees and leaders by introducing sentiment analysis algorithms and employee satisfaction prediction algorithms. The sentiment analysis algorithm can accurately identify the emotional tendencies in the information, provide management with intuitive insights into the emotional state of employees, and help to better understand employee needs and psychological dynamics. The employee satisfaction prediction algorithm effectively predicts employee satisfaction by analyzing multi-dimensional information such as message content, frequency, and emotions, providing an important basis for corporate management to formulate and adjust management strategies. The application of these two algorithms not only enhances communication and understanding between employees and management, but also helps to improve employee satisfaction and loyalty, thereby promoting the harmonious and stable development of the enterprise.

[0065] Furthermore, in the reward and punishment module, in order to more effectively warn employees whose performance does not meet the standards, a risk warning algorithm is added. The algorithm predicts the risks faced by employees' future performance by analyzing their performance evaluation results, historical performance data, market environment and other information, and issues early warnings. The formula of the risk warning algorithm includes:

[0066] R = j(P,H,M);

[0067] Among them, R represents the risk warning result, P represents the performance evaluation result, H represents the historical performance data, M represents the market environment, and j represents the warning function.

[0068] As can be seen from the above, by comprehensively analyzing employees' performance evaluation results, historical performance data, market environment and other multi-dimensional information, it is possible to accurately predict the risks faced by employees' future performance and issue early warnings. This not only helps corporate management to promptly identify and respond to potential performance issues, but also provides timely guidance and support to employees whose performance does not meet standards to prevent the problem from further deteriorating. Through the application of risk warning algorithms, companies can more effectively manage employee performance, promote personal growth and development of employees, and maintain the overall operational stability and performance improvement of the company.

[0069] Furthermore, in the data analysis module, a time series prediction algorithm is introduced to use historical performance data to predict employee performance trends in the future to provide support for corporate decision-making. The formula of the time series prediction algorithm includes:

[0070] Y t =φ1Y t-1 +φ2Y t-2 +...+φ p Y t-p +e t -θ1e t-1 -...-θ q e t-q ;

[0071] Among them, Y t represents the performance value at time t, φ and θ represent model parameters, and e t represents the error term.

[0072] As can be seen from the above, by building an accurate prediction model, it is possible to scientifically predict employee performance trends in the future. It provides valuable forward-looking insights for corporate decision-makers, helping them to better plan human resource allocation, formulate performance improvement strategies, and optimize salary incentive systems. By predicting the changing trends of employee performance in advance, companies can respond to market changes more flexibly, seize development opportunities, and ensure that performance management strategies are highly consistent with corporate strategic goals, thereby promoting the sustainable and healthy development of the company.

[0073] Furthermore, in the data analysis module, a deep learning algorithm is used to deeply mine and analyze the performance data to improve the evaluation accuracy and prediction ability. The formula of the deep learning algorithm includes:

[0074] a l =σ(W l a l-1 +b l );

[0075] Among them, a l represents the activation value of the lth layer, W l represents the weight matrix of the lth layer, b lrepresents the bias term of the lth layer, and σ represents the activation function.

[0076] As can be seen from the above, the present invention uses deep learning algorithms to conduct in-depth mining and analysis of performance data, significantly improving the accuracy of performance evaluation and the ability to predict future trends. Through deep learning algorithms, the system can automatically learn and capture complex patterns and potential connections in performance data, thereby more accurately evaluating employee performance and predicting its future development trends. It not only provides companies with a more scientific and reliable basis for performance management, but also helps companies better identify high-potential employees, discover performance bottlenecks, and then formulate targeted improvement measures to promote the continuous improvement of overall performance levels.

[0077] Furthermore, the effects of a performance management system based on cloud computing and artificial intelligence in the embodiment and a traditional performance management system (comparative example) are compared to obtain the following table:

[0078]

[0079]

[0080]

[0081] As can be seen from the above table, the performance management system based on cloud computing and artificial intelligence is significantly superior to the traditional performance management system in terms of data storage and processing, performance evaluation, data analysis and prediction, information exchange and feedback, reward and punishment management, system intelligence, and resource utilization. It can bring more efficient, accurate, and intelligent performance management experience to enterprises, thereby promoting the sustainable and healthy development of enterprises.

[0082] A performance management method based on cloud computing and artificial intelligence, such as Figure 2 As shown, using the above-mentioned performance management system based on cloud computing and artificial intelligence, including:

[0083] S1. Manage employee information through the user management module and control employee system access rights;

[0084] S2. Issue assessment tasks through the performance management module and evaluate employee performance through the performance evaluation module;

[0085] S3. Display the employee's performance evaluation results in the form of visual dynamic charts through the display module;

[0086] S4. Realize information exchange and feedback between employees and / or leaders through the message module;

[0087] S5. Facilitate communication and contact between employees through the address book module;

[0088] S6. Reward and punish employees according to the performance evaluation results through the reward and punishment module, and issue early warning reminders to employees whose performance does not meet the standards;

[0089] S7. Conduct in-depth analysis of employees’ performance data through the data analysis module to provide decision support for the company’s performance management.

[0090] Working principle:

[0091] The embodiment of the present application provides an electronic device, which is applicable to the above-mentioned performance management system based on cloud computing and artificial intelligence, including:

[0092] Memory, used to protect computer programs and data;

[0093] Processor, used to run system programs.

[0094] An embodiment of the present application provides a computer storage medium, which is applicable to the above-mentioned performance management system based on cloud computing and artificial intelligence, and performs hierarchical confidentiality management on the above-mentioned system and data in accordance with confidentiality management requirements.

[0095] Those skilled in the art will appreciate that the embodiments of the present application may be provided as a system or a computer program product. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0096] The present application is described with reference to the flowcharts and / or block diagrams of the devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0097] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0098] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0099] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0100] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0101] Computer readable media include permanent and non-permanent, removable and non-removable media, and can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include transitory media such as modulated data signals and carrier waves.

[0102] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, commodity or device including the elements.

[0103] The embodiments of the present invention are provided for the purpose of illustration and description. Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations of the present invention. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A performance management system based on cloud computing and artificial intelligence, characterized in that: It includes user management module, performance management module, performance evaluation module, display module, message module, address book module, reward and punishment module and data analysis module, among which: User management module, which is used to store employee information in the cloud server and manage the system permissions of all employees; A performance management module, used to issue corresponding assessment tasks according to at least one performance event set by the administrator; The performance evaluation module is used to perform performance evaluation according to the assessment task, obtain the self-evaluation score, the other-evaluation score and the leader's evaluation score, and set the evaluation weights corresponding to the self-evaluation score, the other-evaluation score and the leader's evaluation score in the performance event; A display module is used to display the performance evaluation results of employees using a visual dynamic chart according to the query request, wherein the dynamic chart includes any one or more of the overall performance evaluation results of employees in different positions, the performance evaluation results of different employees in the same position, the employee performance ranking list, the performance evaluation results of outstanding employees, and the performance evaluation results of employees who do not meet the standards; Message module, used to display and forward messages posted by employees and / or leaders; Address book module, used to save employees' communication information; The reward and punishment module is used to generate reward and punishment strategies for employees based on the performance evaluation results, and to issue early warnings to employees whose performance does not meet the standards. Specifically, a gold coin system is implemented for employees who meet the performance standards, where the next day after the performance meets the standards for consecutive days increases the gold coins by 20% compared to the previous day; the number of gold coins obtained by employees is counted according to the preset cycle, and employees are rewarded according to the number of gold coins; if the performance does not meet the standards, an early warning message is issued in the user's client; The data analysis module is used to conduct in-depth analysis of employees' performance data and provide performance improvement suggestions and trend forecasts.

2. A performance management system based on cloud computing and artificial intelligence as claimed in claim 1, characterized in that: In the performance evaluation module, in order to make the evaluation results more reasonable and fair, an intelligent weight allocation algorithm is introduced, which automatically adjusts the weights of different performance indicators based on historical performance data and business logic; At the same time, a dynamic adjustment algorithm for performance indicators is introduced, which automatically adjusts performance indicators and their weights based on factors such as changes in corporate strategic goals, fluctuations in the market environment, and the growth of employee capabilities.

3. A performance management system based on cloud computing and artificial intelligence as claimed in claim 1, characterized in that: In the message module, a sentiment analysis algorithm is introduced to analyze the information posted by employees and leaders in the message module, identify emotional tendencies, and provide management with insights into the emotional state of employees; At the same time, an employee satisfaction prediction algorithm is introduced, which predicts employee satisfaction by analyzing the content, frequency, sentiment and other information of employees' messages in the message module.

4. A performance management system based on cloud computing and artificial intelligence as claimed in claim 1, characterized in that: In the reward and punishment module, in order to more effectively warn employees whose performance does not meet the requirements, a risk warning algorithm is added. This algorithm predicts the risks faced by employees' future performance by analyzing their performance evaluation results, historical performance data, market environment and other information, and issues early warnings.

5. A performance management system based on cloud computing and artificial intelligence as claimed in claim 1, characterized in that: In the data analysis module, a time series prediction algorithm is introduced to use historical performance data to predict employee performance trends in the future and provide support for corporate decision-making.

6. A performance management system based on cloud computing and artificial intelligence as claimed in claim 5, characterized in that: In the data analysis module, deep learning algorithms are used to conduct in-depth mining and analysis of performance data to improve evaluation accuracy and predictive capabilities.

7. A performance management method based on cloud computing and artificial intelligence, characterized by: A performance management system based on cloud computing and artificial intelligence as described in any one of claims 1 to 6, comprising: S1. Manage employee information through the user management module and control employee system access rights; S2. Issue assessment tasks through the performance management module and evaluate employee performance through the performance evaluation module; S3. Display the employee's performance evaluation results in the form of visual dynamic charts through the display module; S4. Realize information exchange and feedback between employees and / or leaders through the message module; S5. Facilitate communication and contact between employees through the address book module; S6. Reward and punish employees according to the performance evaluation results through the reward and punishment module, and issue early warning reminders to employees whose performance does not meet the standards; S7. Conduct in-depth analysis of employees’ performance data through the data analysis module to provide decision support for the company’s performance management.

8. A processor, characterized in that: The method is configured to execute a cloud computing and artificial intelligence-based performance management system according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed by a processor, a performance management system based on cloud computing and artificial intelligence as described in any one of claims 1 to 6 is implemented.

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