Project performance evaluation method, device, equipment, product and storage medium

Abstract

The invention relates to a project performance evaluation method and device, equipment, a product and a storage medium. The method comprises the following steps: acquiring a plurality of performance evaluation indexes of a target project; determining an index weight coefficient of each performance evaluation index and a system weight coefficient of each project management subsystem, and determining a comprehensive weight coefficient of each performance evaluation index according to the index weight coefficient and the system weight coefficient of the corresponding project management subsystem; obtaining a preset score of each performance evaluation index, and determining a comprehensive characteristic value of the target project based on the preset score and the comprehensive weight coefficient; and obtaining a standard cloud picture corresponding to the target project, generating a comprehensive performance cloud picture of the target project based on the comprehensive characteristic value, and comparing the comprehensive performance cloud picture with the standard cloud picture to obtain a total performance evaluation result of the target project. Compared with the prior art, the performance evaluation result is objective and fair, and managers can master the overall situation of project quality management conveniently.

Description

Technical Field

[0001] The present application relates to the field of performance evaluation technology, and in particular to a project performance evaluation method, device, equipment, product and storage medium. Background Art

[0002] Pumped storage power station is a type of energy storage power station. It has the characteristics of large scale, long life and high efficiency. It is a mature and widely used physical energy storage technology. The construction of pumped storage power station can not only effectively balance the supply and demand of electricity, but also improve the stability and reliability of the power grid. It is an important part of building a smart grid.

[0003] The engineering project management of pumped storage power stations is significantly complex in terms of scale, organization, technology, environment, etc. In order to evaluate the performance of each project, a scientific performance evaluation system needs to be established. Performance evaluation is an important part of project management. Through performance evaluation, we can understand the execution of the project and the work performance of the team. In addition, through performance evaluation, we can find problems and deficiencies and take timely measures to improve them. It can not only improve the execution efficiency of the project, but also motivate team members and improve the overall level of the team.

[0004] Most of the evaluation indicators used in the existing performance evaluation methods for pumped storage power stations are qualitative indicators, lacking specific and quantifiable evaluation indicators, making it difficult to accurately reflect the actual performance of the project. Summary of the invention

[0005] Based on this, it is necessary to provide an objective project performance evaluation method, device, equipment, product and storage medium that can quantitatively analyze the evaluation indicators in response to the above technical problems.

[0006] In a first aspect, the present application provides a project performance evaluation method, the method comprising:

[0007] Acquire multiple performance evaluation indicators of the target project; the performance evaluation indicators are obtained based on the decomposition of the project management subsystem;

[0008] Determine the indicator weight coefficient of each performance evaluation indicator and the system weight coefficient of each project management subsystem, and determine the comprehensive weight coefficient of each performance evaluation indicator based on the indicator weight coefficient and the corresponding system weight coefficient of the project management subsystem;

[0009] Obtaining a preset score for each performance evaluation indicator, and determining a comprehensive characteristic value of the target project based on the preset score and the comprehensive weight coefficient;

[0010] Obtain a standard cloud map corresponding to the target project, generate a comprehensive performance cloud map of the target project based on the comprehensive characteristic value, compare the comprehensive performance cloud map with the standard cloud map, and obtain an overall performance evaluation result of the target project.

[0011] In one embodiment, the step of determining the indicator weight coefficient of each performance evaluation indicator and the system weight coefficient of each project management subsystem, and determining the comprehensive weight coefficient of each performance evaluation indicator based on the indicator weight coefficient and the corresponding system weight coefficient of the project management subsystem, includes:

[0012] The performance evaluation indicators of each project management subsystem are sorted from large to small according to the preset importance, and the performance evaluation indicator sequence corresponding to each project management subsystem is obtained; based on the first relative importance parameters of two adjacent performance evaluation indicators in each performance evaluation indicator sequence, the indicator weight coefficient of each performance evaluation indicator is determined; the indicator weight coefficient is the relative weight of the importance of the performance evaluation indicator in the corresponding project management subsystem;

[0013] The project management subsystems are sorted from large to small according to preset importance to obtain a project management subsystem sequence; based on the second relative importance parameters of two adjacent project management subsystems in the project management subsystem sequence, a system weight coefficient of each project management subsystem is determined; the system weight coefficient is the relative weight of the importance of the project management subsystem in the target project;

[0014] According to the indicator weight coefficient and the system weight coefficient, the comprehensive weight coefficient of each performance evaluation indicator is obtained; the comprehensive weight coefficient is the relative weight of the importance of the performance evaluation indicator in the target project.

[0015] In one embodiment, determining the indicator weight coefficient of each performance evaluation indicator based on the first relative importance parameter of two adjacent performance evaluation indicators in the performance evaluation indicator sequence includes:

[0016] Based on the performance evaluation indicator sequence, determining an indicator weight coefficient of an nth performance evaluation indicator in the performance evaluation indicator sequence; the nth performance evaluation indicator is the last performance evaluation indicator in the performance evaluation indicator sequence;

[0017] Obtaining a first relative importance parameter between the nth performance evaluation indicator and the n-1th performance evaluation indicator; the first relative importance parameter is determined according to the relative importance between two adjacent performance evaluation indicators;

[0018] Determine the indicator weight coefficient of the n-1th performance evaluation indicator according to the first relative importance parameter between the n-th performance evaluation indicator and the n-1th performance evaluation indicator and the indicator weight coefficient of the n-1th performance evaluation indicator, and repeat this cycle until the indicator weight coefficient of each performance evaluation indicator is obtained;

[0019] The determining of the system weight coefficient of each project management subsystem based on the second relative importance parameters of two adjacent project management subsystems in the project management subsystem sequence includes:

[0020] Based on the project management subsystem sequence, determining a system weight coefficient of an m-th project management subsystem in the project management subsystem sequence; the m-th project management subsystem is the last project management subsystem in the project management subsystem sequence;

[0021] Obtaining a second relative importance parameter between the m-th project management subsystem and the m-1-th project management subsystem; the second relative importance parameter is determined according to the relative importance between two adjacent project management subsystems;

[0022] The system weight coefficient of the m-1th project management subsystem is determined based on the second relative importance parameter between the m-th project management subsystem and the m-1th project management subsystem and the system weight coefficient of the m-th project management subsystem, and this cycle is repeated until the system weight coefficient of each project management subsystem is obtained.

[0023] In one embodiment, the step of obtaining a preset score for each performance evaluation indicator and determining a comprehensive characteristic value of the target project based on the preset score and the comprehensive weight coefficient includes:

[0024] Obtain the preset scores for each performance evaluation indicator;

[0025] According to the preset scores obtained, the indicator characteristic values ​​of each performance evaluation indicator are determined based on the cloud model; the indicator characteristic values ​​include the indicator expected value, indicator entropy and indicator super entropy of each performance evaluation indicator;

[0026] Determine the comprehensive expected value, comprehensive entropy and comprehensive super entropy of the target project according to the expected value, the entropy and the super entropy of each performance evaluation indicator and the comprehensive weight coefficient of the performance evaluation indicator;

[0027] The comprehensive expected value, comprehensive entropy and comprehensive super entropy are integrated to obtain the comprehensive characteristic value.

[0028] In one embodiment, the indicator characteristic value of each performance evaluation indicator is determined based on the cloud model according to the preset score obtained; the indicator characteristic value includes the indicator expected value, indicator entropy and indicator super entropy of each performance evaluation indicator, and further includes:

[0029] According to the indicator expected value, indicator entropy and indicator super entropy of the performance evaluation indicators corresponding to each project management subsystem and the indicator weight coefficient of the performance evaluation indicator, the system expected value, system entropy and system super entropy of the project management subsystem are determined, and the comprehensive expected value, comprehensive entropy and comprehensive super entropy are integrated to obtain the system characteristic value of the project management subsystem;

[0030] Based on the system characteristic values ​​of each project management subsystem, a system performance evaluation result of each project management subsystem is obtained, and the system performance evaluation result is visualized.

[0031] In one embodiment, obtaining a standard cloud map corresponding to the target project includes:

[0032] Determine a preset score range for the performance evaluation indicator, and determine a standard cloud characteristic value corresponding to each preset score range according to the preset score range;

[0033] A standard cloud map is generated based on the standard cloud feature values ​​corresponding to each preset score range.

[0034] In a second aspect, the present application also provides a project performance evaluation device, the device comprising:

[0035] An indicator acquisition module is used to acquire multiple performance evaluation indicators of a target project; the performance evaluation indicators are obtained based on the decomposition of the project management subsystem;

[0036] A weight coefficient determination module is used to determine the indicator weight coefficient of each performance evaluation indicator and the system weight coefficient of each project management subsystem, and determine the comprehensive weight coefficient of each performance evaluation indicator based on the indicator weight coefficient and the corresponding system weight coefficient of the project management subsystem;

[0037] A feature determination module, used to obtain a preset score for each performance evaluation indicator, and determine a comprehensive feature value of the target project based on the preset score and the comprehensive weight coefficient;

[0038] The performance evaluation module is used to obtain the standard cloud map corresponding to the target project, generate a comprehensive performance cloud map of the target project based on the comprehensive characteristic value, compare the comprehensive performance cloud map with the standard cloud map, and obtain the overall performance evaluation result of the target project.

[0039] In a third aspect, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above-mentioned method when executing the computer program.

[0040] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the method described above are implemented.

[0041] In a fifth aspect, the present application also provides a computer program product, including a computer program, which implements the steps of the method described above when executed by a processor.

[0042] The above-mentioned project performance evaluation method, device, equipment, product and storage medium, when a certain engineering management project has a performance evaluation demand, it is determined as a target project, multiple performance evaluation indicators of the target project are obtained, and the indicator weight coefficient of each performance evaluation indicator and the system weight coefficient of each project management subsystem are determined, and the comprehensive weight coefficient of each performance evaluation indicator is determined according to the indicator weight coefficient and the system weight coefficient of the corresponding project management subsystem, so that the indicator weight coefficient of the performance evaluation indicator for the project management subsystem can be transferred to the target project through the system weight coefficient of the project management subsystem for the target project, so as to obtain the comprehensive weight system of the performance evaluation indicator for the target project, which is conducive to improving the consistency of the importance evaluation results of the performance evaluation indicator in the entire target project; then, by obtaining the preset score of each performance evaluation indicator, the comprehensive characteristic value of the target project is determined based on the preset score and the comprehensive weight coefficient, and the standard cloud map corresponding to the target project is obtained, and the comprehensive performance cloud map of the target project is generated based on the comprehensive characteristic value, and the comprehensive performance cloud map is compared with the standard cloud map to obtain the total performance evaluation result of the target project, and the qualitative indicators are converted into quantitative indicators, so that managers can grasp the overall situation of engineering quality management and provide data support for special improvements to weak management sections. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0044] Figure 1 An application environment diagram of a project performance evaluation method in an embodiment;

[0045] Figure 2 is a flowchart of a project performance evaluation method in one embodiment;

[0046] Figure 3 A schematic diagram of a flow chart for determining the comprehensive weight coefficients of various performance evaluation indicators in one embodiment;

[0047] Figure 4 A schematic diagram of a process for determining a comprehensive characteristic value of each performance evaluation indicator in an embodiment;

[0048] Figure 5 is a schematic diagram of a characteristic cloud map in one embodiment;

[0049] Figure 6 A radar chart for evaluating the performance of a project management subsystem of a target project in one embodiment;

[0050] Figure 7 A radar chart for evaluating the performance of a project management subsystem for multiple target projects in one embodiment;

[0051] Figure 8 A comparison diagram of a standard cloud map and a comprehensive performance cloud map in an embodiment;

[0052] Fig. 9 is a structural block diagram of a project performance evaluation device in one embodiment;

[0053] Fig.10 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0054] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0055] The project performance evaluation method provided in the embodiment of the present application can be applied to Figure 1In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be integrated on the server 104, or it can be placed on the cloud or other network servers. The server 104 obtains multiple performance evaluation indicators of the target project, introduces system thinking, integrates the performance evaluation indicators into each project management subsystem, and sorts each project management subsystem and its corresponding performance evaluation indicators according to the degree of importance. According to the adjacent relationship between the performance evaluation indicators, the indicator weight coefficient of each performance evaluation indicator is determined one by one, and according to the adjacent relationship between the project management subsystems, the system weight coefficient of each project management subsystem is determined one by one, and then the comprehensive weight coefficient of each performance evaluation indicator in the target project is determined according to the indicator weight coefficient and the system weight coefficient, so as to obtain the comprehensive characteristic value of the performance evaluation indicator, and then the qualitative indicator is converted into a quantitative indicator. The performance evaluation result of the target project can be obtained by comparing the comprehensive characteristic value with the standard value.

[0056] The terminal 102 may be, but is not limited to, various personal computers, laptops, smart phones, tablet computers, IoT devices, and portable wearable devices. The IoT devices may be smart speakers, smart TVs, smart air conditioners, smart car devices, projection devices, etc. The portable wearable devices may be smart watches, smart bracelets, etc. The server 104 may be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides cloud computing services.

[0057] In an exemplary embodiment, Figure 2 As shown in the figure, a project performance evaluation method is provided. Figure 1 The server in the example is used for explanation. It is understandable that the method of this embodiment can also be applied to Figure 1 The method of this embodiment includes the following steps S201 to S204. Among them:

[0058] Step S201, obtaining multiple performance evaluation indicators of a target project.

[0059] Among them, target projects mainly refer to engineering management projects that require performance evaluation, such as construction engineering management projects, operation engineering management projects, etc.

[0060] Among them, performance evaluation indicators refer to key indicators that ensure project success and efficient operation in engineering management projects; performance evaluation indicators are decomposed based on the management elements of the project management subsystem. Project management subsystem refers to the management elements obtained by dividing the system thinking introduced in the process of engineering project management; project management subsystem is obtained based on the horizontal dimension division of the entire engineering management project. Each target project has multiple project management subsystems, and each project management subsystem has multiple corresponding performance evaluation indicators.

[0061] Exemplarily, when there is a need for project performance evaluation, the server obtains the project management subsystem corresponding to the target project and the performance evaluation indicators corresponding to each project management subsystem. In this step, the project management subsystem and the performance evaluation indicators are both qualitative parameters, which are usually stored in the data storage system in the form of fields or strings, or input into the server in real time based on the project performance evaluation needs.

[0062] Step S202, determining the indicator weight coefficient of each performance evaluation indicator and the system weight coefficient of each project management subsystem, and determining the comprehensive weight coefficient of each performance evaluation indicator based on the indicator weight coefficient and its corresponding system weight coefficient of the project management subsystem.

[0063] Among them, the indicator weight coefficient refers to the relative weight of the importance of the performance evaluation indicator in the corresponding project management subsystem; the indicator weight coefficient is used to measure the importance of the performance evaluation indicator in the corresponding project management subsystem, and the sum of the indicator weight coefficients of each performance evaluation indicator in the same project management subsystem is one. The system weight coefficient refers to the relative weight of the importance of the project management subsystem in the target project; the system weight coefficient is used to measure the importance of the project management subsystem in the target project, and the sum of the system weight coefficients of each project management subsystem is one. The comprehensive weight coefficient refers to the relative weight of the importance of the performance evaluation indicator in the target project; the comprehensive weight coefficient is used to measure the importance of the performance evaluation indicator in the target project, and the sum of the comprehensive weight coefficients of all performance evaluation indicators is one.

[0064] Among them, the larger the weight coefficient of an indicator or subsystem is, the more important the indicator is in the corresponding subsystem or the subsystem is in the target project, and the greater the impact on the overall performance evaluation results, and vice versa.

[0065] Exemplarily, after the server obtains each project management subsystem and the performance evaluation index, the server determines the system weight coefficient of each project management subsystem in the target project and the index weight coefficient of each performance evaluation index in the corresponding project management subsystem; then, based on the determined index weight system and the system weight coefficient of the corresponding project management subsystem in the target project, the server can map the impact of the performance evaluation index on the management project subsystem to the target project to obtain the comprehensive weight coefficient of each performance evaluation index in the target project. Since each performance evaluation index has a direct impact on the corresponding project management subsystem during performance evaluation, and indirectly affects the overall performance evaluation of the target project through the performance evaluation of the corresponding project management subsystem, therefore, by determining the system weight system, the impact of the performance evaluation index on the management project subsystem can be transferred to the target project to determine the importance of the obtained performance evaluation index in the target project.

[0066] Step S203, obtaining a preset score for each performance evaluation indicator, and determining a comprehensive characteristic value for each performance evaluation indicator based on the preset score and a comprehensive weight coefficient.

[0067] Among them, the preset score refers to the score used to evaluate the completion or operation of the performance evaluation indicators of the target project.

[0068] The comprehensive characteristic value refers to the quantitative characteristic value corresponding to the target project obtained by converting the qualitative performance evaluation index.

[0069] Exemplarily, the server obtains preset scores for each performance evaluation indicator generated externally, and then determines the comprehensive characteristic value of the target project based on the preset score of each performance evaluation indicator and the corresponding comprehensive weight coefficient to convert the qualitative performance evaluation indicator into a quantitative comprehensive characteristic value.

[0070] Step S204, obtain the standard cloud map corresponding to the target project, generate a comprehensive performance cloud map of the target project based on the comprehensive characteristic value, compare the comprehensive performance cloud map with the standard cloud map, and obtain the overall performance evaluation result of the target project.

[0071] Among them, the standard cloud map refers to the performance evaluation standard determined according to the performance management requirements of the target project, and the feature cloud generated based on the performance evaluation standard for evaluation reference. In the standard cloud map, according to the completion or operation of the performance evaluation indicators, there are multiple standard evaluation levels, and each evaluation level is an independent feature cloud.

[0072] Among them, the comprehensive performance cloud map refers to the characteristic cloud generated according to the comprehensive characteristic values ​​of the target project to characterize the actual completion or operation status of the target project. By comparing the comprehensive performance cloud map with the characteristic clouds of different levels in the standard cloud map, the corresponding overall performance evaluation results can be intuitively obtained.

[0073] Exemplarily, the server obtains the standard cloud map corresponding to the target project according to the performance evaluation requirements of the target project, and generates the comprehensive performance cloud map of the target project based on the comprehensive characteristic value; then, the server compares the comprehensive performance cloud map with the standard cloud map to obtain the overall performance evaluation result of the target project. In specific implementation, the standard cloud map and the comprehensive performance cloud map can be generated based on the cloud generator.

[0074] In the above project performance evaluation method, when a certain engineering management project has a performance evaluation demand, it is determined as a target project, multiple performance evaluation indicators of the target project are obtained, and the indicator weight coefficient of each performance evaluation indicator and the system weight coefficient of each project management subsystem are determined, and the comprehensive weight coefficient of each performance evaluation indicator is determined according to the indicator weight coefficient and the system weight coefficient of the corresponding project management subsystem, so that the indicator weight coefficient of the performance evaluation indicator for the project management subsystem can be transferred to the target project through the system weight coefficient of the project management subsystem for the target project, so as to obtain the comprehensive weight system of the performance evaluation indicator for the target project, which is conducive to improving the consistency of the importance evaluation results of the performance evaluation indicator in the entire target project; then, by obtaining the preset score of each performance evaluation indicator, the comprehensive characteristic value of the target project is determined based on the preset score and the comprehensive weight coefficient, and the standard cloud map of the target project is obtained, and the comprehensive performance cloud map of the target project is generated based on the comprehensive characteristic value, and the comprehensive performance cloud map is compared with the standard cloud map to obtain the total performance evaluation result of the target project, and the qualitative indicators are converted into quantitative indicators, so that managers can grasp the overall situation of engineering quality management and provide data support for special improvements to weak management sections.

[0075] In one embodiment, Figure 3 As shown in the figure, the comprehensive weight coefficients for determining each performance evaluation indicator include:

[0076] Step S301, sorting the performance evaluation indicators of each project management subsystem from large to small according to the preset importance, and obtaining the performance evaluation indicator sequence corresponding to each project management subsystem; based on the first relative importance parameter of two adjacent performance evaluation indicators in each performance evaluation indicator sequence, determining the indicator weight coefficient of each performance evaluation indicator.

[0077] Among them, the performance evaluation indicator sequence refers to the sequence obtained by sorting the performance evaluation indicators in each project management subsystem from large to small according to the preset importance. In the performance evaluation indicator sequence, the first performance evaluation indicator is usually more important than the corresponding project management subsystem, and accordingly, its indicator weight coefficient is the largest. Conversely, the last performance evaluation indicator is less important, and its indicator weight coefficient is the smallest.

[0078] Among them, the first relative importance parameter refers to which performance evaluation indicator is more important between a certain performance evaluation indicator and its preceding or following performance evaluation indicator in the performance evaluation sequence, and the parameter obtained by quantitatively assigning the importance of the two according to certain judgment criteria. The first relative importance parameter is determined according to the relative importance between two adjacent performance evaluation indicators; for example, the importance between a performance evaluation indicator A1 and another adjacent performance evaluation indicator A2 is determined as "A1 is as important as A2", "A1 is slightly more important than A2", "A1 is more important than A2", "A1 is obviously more important than A2" and "A1 is more important than A2", and the importance of each level is quantitatively assigned according to the aforementioned degree to obtain the relative importance parameter of each level, such as 1.0, 1.2, 1.4, 1.6 and 1.8, etc. This embodiment is only used as an example, and can be adjusted according to different performance evaluation requirements. In specific implementation, the aforementioned importance degree and the corresponding first relative importance parameter can be obtained by using an empirical method or a large amount of existing data to train a prediction model based on machine learning, neural network, etc., and this embodiment is not limited.

[0079] Exemplarily, the server sorts each performance evaluation indicator in each project management subsystem from large to small according to their importance relative to each other, and obtains a performance evaluation indicator sequence for each project management subsystem; then, the server determines which of two adjacent performance evaluation indicators in each performance evaluation indicator sequence is more important, and quantitatively assigns the importance between the two, and obtains several first relative importance parameters, and then determines the indicator weight coefficient of each performance evaluation indicator.

[0080] Step S302, sorting the project management subsystems from large to small according to preset importance to obtain a project management subsystem sequence; determining a system weight coefficient of each project management subsystem based on the second relative importance parameter of two adjacent project management subsystems in the project management subsystem sequence.

[0081] Among them, the project management subsystem sequence refers to the sequence obtained by sorting all the project management subsystems in the target project from large to small according to their importance. In the project management subsystem sequence, the first project management subsystem is usually more important than the target project, and accordingly, its system weight coefficient is the largest. Conversely, the last project management subsystem is less important, and its system weight coefficient is the smallest.

[0082] Among them, the second relative importance parameter refers to which one of a certain project management subsystem and its preceding or following project management subsystem is more important in performance evaluation in the project management subsystem sequence, and the parameter obtained by quantitatively assigning the importance of the two according to certain judgment criteria. The second relative importance parameter is determined based on the relative importance between two adjacent project management subsystems; the determination process of the second relative importance parameter can refer to the determination process of the first relative importance parameter mentioned above, and will not be repeated here.

[0083] Exemplarily, the server sorts each project management subsystem in the target project from large to small according to their importance relative to each other, and obtains a project management subsystem sequence; then, the server determines which of the two adjacent project management subsystems in each project management subsystem sequence is more important, and quantitatively assigns the importance between the two, and obtains several second relative importance parameters, and then determines the system weight coefficient of each project management subsystem.

[0084] Step S303, obtaining the comprehensive weight coefficient of each performance evaluation indicator according to the indicator weight coefficient and the system weight coefficient.

[0085] Among them, the comprehensive weight coefficient is calculated using the following formula:

[0086]

[0087] Among them, W' is a matrix composed of comprehensive weight coefficients corresponding to each performance evaluation indicator, W' = [w 1,1 ,w 1,2 ,...,w m,n ],w 1,1 ,w 1,2 ,...,w m,n are the comprehensive weight coefficients of each performance evaluation index, w m,n It refers to the comprehensive weight coefficient of the nth performance evaluation indicator in the mth project management subsystem, m=1,2,...,M, n=1,2,...,N, M and N are the number of project management subsystems and performance evaluation indicators respectively; is the transpose of the matrix composed of the system weight coefficients of each project management subsystem, are the system weight coefficients of each project management subsystem; W B is a matrix composed of the indicator weight coefficients of the performance evaluation indicators corresponding to each project management subsystem. They are the indicator weight coefficients of the performance evaluation indicators in the project management subsystem.

[0088] Exemplarily, the server calculates the comprehensive weight coefficient of each performance evaluation indicator according to the indicator weight coefficient and the system weight coefficient and adopts the above formula (1).

[0089] In this embodiment, the project management subsystems and their corresponding performance evaluation indicators are sorted according to their importance, and the indicator weight coefficient is determined based on the first relative importance parameter of each performance evaluation indicator in the corresponding sequence, and the system weight coefficient is determined based on the second relative importance parameter of each project management subsystem in the corresponding sequence. Then, the comprehensive weight coefficient of each performance evaluation indicator is obtained according to the indicator weight coefficient and the system weight coefficient. The whole process determines the relative importance by comparing the indicators or subsystems. The results have good consistency and there is no need to construct a judgment matrix and perform consistency checks. This ensures that the comprehensive weight coefficient is accurate and objective while improving the determination efficiency.

[0090] In one embodiment, determining the indicator weight coefficient of each performance evaluation indicator includes:

[0091] Based on the performance evaluation indicator sequence, determine the indicator weight coefficient of the nth performance evaluation indicator in the performance evaluation indicator sequence; obtain the first relative importance parameter between the nth performance evaluation indicator and the n-1th performance evaluation indicator; determine the indicator weight coefficient of the n-1th performance evaluation indicator according to the first relative importance parameter between the nth performance evaluation indicator and the n-1th performance evaluation indicator and the indicator weight coefficient of the nth performance evaluation indicator, and repeat the above process until the indicator weight coefficient of each performance evaluation indicator is obtained.

[0092] Among them, the nth performance evaluation indicator refers to the last performance evaluation indicator at the end of the sequence in the performance evaluation indicator sequence corresponding to a certain project management subsystem. The last performance evaluation indicator is the indicator with the smallest indicator weight coefficient in the project management subsystem, that is, the indicator that is the least important for performance evaluation in the project management subsystem. Starting the calculation from the smallest weight coefficient can avoid frequent adjustments to the weight value in subsequent calculations, thereby simplifying the calculation process to reduce the computing burden of the server. It is understandable that in some other embodiments, when the performance evaluation indicator sequence is sorted differently, the selected performance evaluation indicator may also be the first or other ranking indicator, and it only needs to be the indicator with the smallest weight coefficient. Of course, in some scenarios where the computing burden does not need to be considered, other indicators such as the one with the largest weight coefficient can also be selected to start the calculation.

[0093] Among them, the indicator weight coefficient of the last performance evaluation indicator (i.e. the nth performance evaluation indicator) is determined by the following formula:

[0094]

[0095] Among them, r i B is the i-th first relatively important parameter.

[0096] Among them, the indicator weight coefficient of the n-1th performance evaluation indicator is determined by the following formula:

[0097]

[0098] in, is the indicator weight coefficient of the n-1th performance evaluation indicator, is the first relative importance parameter between the nth performance evaluation indicator and the n-1th performance evaluation indicator.

[0099] Exemplarily, the server determines the performance evaluation indicator with the smallest indicator weight coefficient in the performance evaluation indicator sequence, i.e., the nth performance evaluation indicator, based on the performance evaluation indicator sequence, and uses the above formula (2) to calculate the indicator weight coefficient of the nth performance evaluation indicator; then, the server obtains the first relative importance parameter between the nth performance evaluation indicator and the n-1th performance evaluation indicator, and uses the above formula (3) to calculate the indicator weight coefficient of the n-1th performance evaluation indicator based on the first relative importance parameter between the nth performance evaluation indicator and the n-1th performance evaluation indicator and the indicator weight coefficient of the nth performance evaluation indicator, and repeats the above process until the indicator weight coefficient of each performance evaluation indicator is obtained.

[0100] Determine the system weight coefficient of each project management subsystem, including:

[0101] Based on the project management subsystem sequence, determine the system weight coefficient of the mth project management subsystem in the project management subsystem sequence; obtain the second relative importance parameter between the mth project management subsystem and the m-1th project management subsystem; determine the system weight coefficient of the m-1th project management subsystem based on the second relative importance parameter between the mth project management subsystem and the m-1th project management subsystem and the system weight coefficient of the mth project management subsystem, and repeat the above process until the system weight coefficient of each project management subsystem is obtained.

[0102] Among them, the mth project management subsystem refers to the last project management subsystem in the project management subsystem sequence; the last project management subsystem is similar to the last performance evaluation indicator mentioned above, and the details can be found in the above records, which will not be repeated here.

[0103] Among them, the system weight coefficient of the last project management subsystem (i.e., the mth project management subsystem) is determined by the following formula:

[0104]

[0105] in, is the jth second relatively important parameter.

[0106] Among them, the system weight coefficient of the m-1th project management subsystem is determined by the following formula:

[0107]

[0108] in, is the system weight coefficient of the m-1th project management subsystem, It is the second relative importance parameter between the m-th project management subsystem and the m-1-th project management subsystem.

[0109] Exemplarily, based on the project management subsystem sequence, the server determines the project management subsystem with the smallest system weight coefficient in the project management subsystem sequence, that is, the mth performance evaluation indicator, and uses the above formula (4) to calculate the system weight coefficient of the mth project management subsystem; then, the server obtains the second relative importance parameter between the mth project management subsystem and the m-1th project management subsystem, and uses the above formula (5) to calculate the system weight coefficient of the m-1th project management subsystem based on the second relative importance parameter between the mth project management subsystem and the m-1th project management subsystem and the system weight coefficient of the mth project management subsystem, and repeats the above process until the system weight coefficient of each project management subsystem is obtained.

[0110] In one embodiment, Figure 4As shown, the preset score of each performance evaluation indicator is obtained, and the comprehensive characteristic value of each performance evaluation indicator is determined based on the preset score and the comprehensive weight coefficient, including:

[0111] Step S401, obtaining a preset score for each performance evaluation indicator.

[0112] Among them, the preset score is generated externally. The preset score can be pre-generated and stored in the data storage system, or it can be generated externally in real time based on the current performance evaluation needs and input into the server. Usually, the preset score can be determined by empirical methods, such as having professionals in multiple industries score each performance evaluation indicator and then taking the average value to obtain the preset score. The average value can be a weighted average (the score of more authoritative or senior personnel is more important, so it is given a greater weight) or a simple average, etc.; in addition, the preset score can also be obtained by training a scoring model based on a large amount of existing data based on machine learning, neural networks, etc., to increase the objectivity of the preset score.

[0113] Exemplarily, the server obtains preset scores for each performance evaluation indicator from the data storage system or obtains externally generated preset scores based on performance evaluation requirements.

[0114] Step S402, according to the preset score obtained, determine the indicator characteristic value of each performance evaluation indicator based on the cloud model.

[0115] Among them, the cloud model refers to an uncertainty conversion model that converts each qualitative performance evaluation indicator into a quantitative characteristic value. The cloud model usually consists of two parts: the digital characteristics of the cloud and the cloud generator. A cloud is composed of many cloud droplets. A cloud droplet is the quantitative implementation of the qualitative value, that is, the digital characteristics of the cloud, that is, the indicator characteristic value of this step, and the comprehensive characteristic value below. The cloud generator is used to generate the corresponding cloud map according to the digital characteristics of the cloud. The cloud includes three characteristic values, namely the expected value (Ex), entropy (En) and super entropy (He); the expected value is the most representative and typical sample of the qualitative indicator, which reflects the central value of the corresponding qualitative indicator; entropy represents the uncertainty of the qualitative indicator, and its size directly determines the margin of the qualitative value; super entropy represents the uncertainty measure of entropy, and its size indirectly reflects the thickness of the cloud (such as Figure 5 As shown). The cloud generator is used to generate a cloud map. The cloud generator includes a forward cloud generator and a reverse cloud generator. The forward cloud generator generates cloud droplets that meet the requirements according to the digital characteristic values ​​of the cloud, thereby realizing the conversion from qualitative concepts to quantitative values. Conversely, the specific sample cloud droplet values ​​of the reverse cloud generator are processed to obtain the qualitative concept represented by the sample characteristic values.

[0116] The indicator characteristic value refers to the digital characteristics of the cloud corresponding to each performance evaluation indicator obtained based on the cloud model, and the indicator characteristic value includes the indicator expected value, indicator entropy and indicator super entropy of each performance evaluation indicator.

[0117] Among them, the preset score is recorded as Z p,q ={z p,1 ,z p,2 ,...,z p,q}, thus, the expected value of the indicator is determined by the following formula:

[0118]

[0119] Among them, E X,q is the expected value of the qth performance evaluation indicator among all performance evaluation indicators, z p,q is the pth preset score of the qth performance evaluation indicator, q = 1, 2, ..., Q, Q is the number of all performance evaluation indicators, Q = M × N, p = 1, 2, ..., P, P is the number of preset scores for each performance evaluation indicator.

[0120] Among them, the indicator entropy is determined by the following formula:

[0121]

[0122] Among them, E N,q is the index entropy of the qth performance evaluation index among all performance evaluation indicators.

[0123] Among them, the indicator super entropy is determined by the following formula:

[0124]

[0125] Among them, H E,q is the super entropy of the qth performance evaluation indicator among all performance evaluation indicators,

[0126] Exemplarily, the server obtains a pre-built cloud model, and based on the cloud model and according to the obtained preset scores, uses the above formulas (6)(7)(8) to calculate the indicator characteristic values ​​of each performance evaluation indicator.

[0127] Step S403, determining the comprehensive expected value, comprehensive entropy and comprehensive super entropy of the target project according to the expected value, indicator entropy and indicator super entropy of each performance evaluation indicator and the comprehensive weight coefficient of the performance evaluation indicator.

[0128] Among them, the comprehensive expected value refers to the digital characteristics of the cloud corresponding to all performance evaluation indicators obtained based on the cloud model. The comprehensive expected value includes the comprehensive expected value, comprehensive entropy and comprehensive super entropy of the target project.

[0129] Among them, the comprehensive expected value is determined based on the expected value of each performance evaluation indicator and the comprehensive weight coefficient of the corresponding performance evaluation indicator. The comprehensive expected value is expressed as:

[0130]

[0131] Among them, E X is the comprehensive expected value of the target project, w q is the comprehensive weight coefficient of the qth performance evaluation indicator among all performance evaluation indicators, w q =W' m,n , which is the comprehensive weight coefficient of the nth performance evaluation indicator in the mth project management subsystem.

[0132] Among them, the comprehensive entropy is determined based on the index entropy of each performance evaluation index and the comprehensive weight coefficient of the corresponding performance evaluation index. The comprehensive entropy is expressed as:

[0133]

[0134] Among them, E N is the comprehensive entropy of the target project.

[0135] Among them, the comprehensive super entropy is determined based on the indicator super entropy of each performance evaluation indicator and the comprehensive weight coefficient of the corresponding performance evaluation indicator. The comprehensive super entropy is expressed as:

[0136]

[0137] Among them, H E is the comprehensive super entropy of the target project.

[0138] Exemplarily, the server determines the comprehensive expected value of the target project based on the expected value of each performance evaluation indicator and the corresponding comprehensive weight coefficient of the performance evaluation indicator and adopts the above formula (9), determines the comprehensive entropy of the target project based on the indicator entropy of each performance evaluation indicator and the corresponding comprehensive weight coefficient of the performance evaluation indicator and adopts the above formula (10), and determines the comprehensive super entropy of the target project based on the indicator super entropy of each performance evaluation indicator and the corresponding comprehensive weight coefficient of the performance evaluation indicator and adopts the above formula (11).

[0139] Step S403, integrating the comprehensive expected value, the comprehensive entropy and the comprehensive super entropy to obtain a comprehensive characteristic value.

[0140] Exemplarily, the server integrates the comprehensive expected value, the comprehensive entropy and the comprehensive super entropy to obtain a comprehensive eigenvalue, which represents C U =(E X ,E N ,H E ).

[0141] In this embodiment, by introducing the cloud model, qualitative performance evaluation indicators can be converted into quantitative comprehensive characteristic values, so as to facilitate quantitative analysis of the operation and completion of the target project, which is conducive to objective performance evaluation of the target project and increases the accuracy and objectivity of the evaluation results.

[0142] In one embodiment, after determining the indicator characteristic value of each performance evaluation indicator based on the cloud model according to the acquired preset score, it also includes:

[0143] According to the indicator expected value, indicator entropy and indicator super entropy of the performance evaluation indicators corresponding to each project management subsystem and the indicator weight coefficient of the performance evaluation indicator, the system expected value, system entropy and system super entropy of the project management subsystem are determined, and the comprehensive expected value, comprehensive entropy and comprehensive super entropy are integrated to obtain the system characteristic value of the project management subsystem; based on the system characteristic value of each project management subsystem, the system performance evaluation results of each project management subsystem are obtained, and the system performance evaluation results are visualized.

[0144] Among them, the system expected value refers to the digital characteristics of the cloud corresponding to each project management subsystem obtained based on the cloud model. The system expected value includes the system expected value, system entropy and system super entropy of each project management subsystem. Specifically, when determining the system expected value, system entropy and system super entropy, the above formulas (9)(10)(11) can be used, and the value of P in the formula can be replaced with the number of performance evaluation indicators in the corresponding project management subsystem. The specific process can be referred to the records of the above embodiment, which will not be repeated in this embodiment.

[0145] Exemplarily, the server determines the system expected value of each project management subsystem according to the expected value of each performance evaluation indicator and the corresponding comprehensive weight coefficient of the performance evaluation indicator and adopts the modified above formula (9), determines the system entropy of each project management subsystem according to the indicator entropy of each performance evaluation indicator and the corresponding comprehensive weight coefficient of the performance evaluation indicator and adopts the modified above formula (10), and determines the system super entropy of each project management subsystem according to the indicator super entropy of each performance evaluation indicator and the corresponding comprehensive weight coefficient of the performance evaluation indicator and adopts the modified above formula (11). Subsequently, the server integrates the system expected value, system entropy and system super entropy to obtain the system characteristic value of each project management subsystem; finally, the server obtains the system performance evaluation result of each project management subsystem based on the system characteristic value of each project management subsystem, and visualizes the system performance evaluation result.

[0146] In an optional embodiment, if Figure 6As shown in FIG. 1 , the radar chart can be used to visualize the system characteristic values ​​(mainly the system expected values) of each project management subsystem. Moreover, when it is necessary to compare the performance evaluation of multiple target projects, radar charts of multiple target projects can be generated and the radar charts of each target project can be superimposed in the same radar network (such as Figure 7 shown).

[0147] In this embodiment, by visualizing the system characteristic values ​​of each project management subsystem, it is possible to directly understand the advantages and disadvantages of the target project in which project management subsystems, which is beneficial for managers to grasp the performance management of the target project and make special improvements to weak sections. In addition, by comparing and classifying the visualization icons of multiple target projects, it is also possible to intuitively display the differences in management levels of project management subsystems of different target projects, further facilitating managers to control projects.

[0148] In one embodiment, obtaining a standard cloud map of a target project includes:

[0149] Determine the preset score range of the performance evaluation indicator, and determine the standard cloud characteristic value corresponding to each preset score range based on the preset score range; generate a standard cloud map based on the standard cloud characteristic value corresponding to each preset score range.

[0150] Among them, the preset score range refers to the division of various performance evaluation indicators into several quantitative score intervals according to preset standards based on the performance evaluation requirements of management objectives, and each score interval corresponds to a qualitative evaluation level.

[0151] Among them, the standard cloud characteristic value refers to the digital characteristics of the cloud corresponding to the standard preset score range obtained based on the cloud model. Similarly, the standard cloud characteristic value includes the standard expected value, standard entropy and standard super entropy.

[0152] In an exemplary embodiment, the evaluation level of the performance evaluation indicator of a certain target project is qualitatively classified from high to low as "excellent", "excellent", "good", "medium", and "qualified" according to the performance evaluation requirements. For each qualitative evaluation level, its standard score is determined respectively. For example, for the aforementioned evaluation level, the preset score range obtained by the standard of each level is respectively determined as [95, 100], [90, 95), [80, 90), [70, 80), and [60, 70]. In specific implementation, each score range can be arbitrarily divided according to the performance evaluation requirements, and this embodiment does not make specific restrictions.

[0153] The standard expected value is determined based on the endpoint values ​​of each preset score range, and the standard expected value is:

[0154]

[0155] Among them, E X ' is the standard expected value, G max ,G min They are respectively the maximum and minimum values ​​(i.e., endpoint values) of the above-mentioned preset score ranges.

[0156] The standard entropy is determined based on the endpoint values ​​of each preset score range, and the standard entropy is:

[0157]

[0158] Among them, E N ' is the standard entropy.

[0159] Among them, the standard super entropy is a constant, that is, the standard super entropy is:

[0160] H E '=c (14)

[0161] Among them, H E ' is the standard super entropy, c is a constant.

[0162] Based on the above, the standard characteristic values ​​corresponding to each preset score range are respectively expressed as (97.5000, 0.8333, 0.50), (92.5000, 0.8333, 0.50), (85.0000, 1.6777, 0.50), (75.0000, 1.6777, 0.50), and (65.0000, 1.6777, 0.50).

[0163] The server generates a standard cloud image according to the above standard characteristic values ​​and based on the forward cloud generator.

[0164] like Figure 8 As shown in the figure, it is a schematic diagram of the standard cloud map and the comprehensive performance cloud map. Both the standard cloud map and the comprehensive performance cloud map are generated based on the forward cloud generator; the black dot part in the figure represents the characteristic cloud corresponding to each preset score range, and the red dot part represents the comprehensive performance cloud map. Therefore, it can be intuitively seen which level of characteristic cloud the comprehensive performance cloud map is closer to, thereby obtaining the results and level of performance evaluation, so as to achieve objective performance evaluation of project management.

[0165] In this embodiment, a forward cloud generator is used to generate cloud droplets that meet the requirements according to the digital characteristic values ​​of the cloud, and corresponding standard cloud maps and comprehensive performance cloud maps are obtained, thereby realizing the conversion from qualitative concepts to quantitative values. By comparing the cloud maps, the performance evaluation results and grades of the target projects can be determined intuitively and quickly, and the results are accurate and the evaluation efficiency is high.

[0166] In an application example, this embodiment proposes a project performance evaluation method, including:

[0167] I. Construction of the indicator system

[0168] When dividing the performance evaluation indicators, in accordance with the principles of independence, representativeness, difference and operability, based on the three horizontal dimensions of full process management, multi-objective control and full factor guarantee of the engineering management project, the entire management project is divided into multiple project management subsystems, including but not limited to the structural decomposition subsystem and construction procedure subsystem in the full process management dimension, the quality management subsystem, safety and health subsystem, progress management subsystem and investment management subsystem in the multi-objective control dimension, and the organization subsystem, resource subsystem and technology innovation subsystem in the full factor guarantee dimension. Each subsystem contains multiple performance evaluation indicators (see Table 2 below).

[0169] 2. Determination of indicator weights

[0170] After completing the evaluation index system, it is necessary to determine the weight of each indicator. Due to the large number of indicators and the complex relationships between them that are difficult to describe, the subjective weighting method is more applicable at this time. The most commonly used subjective weighting method is the analytic hierarchy process (AHP), but it may face the problem that the judgment matrix does not meet the consistency requirements. In order to solve this problem, the G1 method can be used, which avoids its shortcomings by improving the AHP method. The G1 method calculates the relative importance of each indicator by comparing two indicators. The calculation results have good consistency and there is no need to construct a judgment matrix and perform consistency checks. The specific steps are as follows:

[0171] Step 1: Determine the order of the nine subsystems and their indicators. The indicators are represented as X = {x1, x2, ..., x n}, where n is the number of indicators. The most important indicator in X is determined by the expert group and marked as x1'. Next, the most important factor is selected from the remaining indicators and marked as x2'. The above steps are repeated until only one indicator is left, which is marked as x n '. So far, the importance order of all indicators has been obtained, denoted as X′={x1′,x2′,…,x n ′}.

[0172] Step 2: Determine x k-1 ′ and x k According to the standards in Table 1, the relative importance of adjacent indicators is obtained.

[0173] It can be expressed according to formula (15).

[0174]

[0175] Among them, w k-1 and w k Represents the index x k-1 ′ and xk ′’s weight.

[0176] Table 1

[0177]

[0178] Step 3: Calculate the weights of the nine subsystems and each indicator. k After obtaining the value of (k=2,3,…,n), use formula (16) to calculate the weight of index k.

[0179]

[0180] According to the calculation result of the weight of indicator k, the weights of other indicators can be calculated using formula (17).

[0181] w k-1 =r k w k (17)

[0182] Step 4: Calculate the comprehensive weight of indicators in each subsystem. The comprehensive weight can be calculated using formula (18).

[0183]

[0184] Among them, W a and W b Represent the subsystem weight vector and the corresponding indicator weight vector respectively.

[0185] In order to ensure the accuracy of the evaluation and reduce the subjective influence of the G1 method, the importance ranking and scoring of each indicator in this evaluation were generated externally. The results are shown in Table 2.

[0186] Table 2

[0187]

[0188]

[0189] 3. Comprehensive Assessment Method

[0190] The cloud model is an uncertainty conversion model that can achieve the conversion between qualitative and quantitative. Cloud digital features and cloud generators are the two core theories of the model.

[0191] 1. Digital characteristics of the cloud

[0192] A cloud is composed of many cloud droplets, and a cloud droplet is the quantitative realization of a qualitative value. The abscissa value represents the quantitative value corresponding to the qualitative concept, and the ordinate value represents the membership degree of the qualitative concept represented by the quantitative value. The three characteristic values ​​of the cloud involve the following:

[0193] Expected value (Ex): It is the most representative and typical sample of a qualitative concept, reflecting the central value of the corresponding qualitative concept.

[0194] Entropy (En): It represents the uncertainty of qualitative concepts, and its size directly determines the margin of qualitative values.

[0195] Excess entropy (He): A measure of entropy uncertainty, whose magnitude indirectly reflects the thickness of the cloud.

[0196] 2. Cloud Generator

[0197] There are two main types of cloud generators: forward cloud generator and reverse cloud generator. The forward cloud generator generates cloud droplets that meet the requirements according to the digital characteristic values ​​of the cloud, thereby realizing the conversion from qualitative concepts to quantitative values. In contrast, the specific sample cloud droplet values ​​of the reverse cloud generator are processed to obtain the qualitative concept represented by the sample characteristic values.

[0198] The cloud model method requires calculation and determination of the characteristic values ​​of the evaluation standard cloud and the evaluation comprehensive cloud. The evaluation results and levels are determined by comparing the standard cloud and the comprehensive cloud. The specific steps are as follows:

[0199] (1) Classification of evaluation criteria. The performance evaluation of a power plant engineering system management is incentive-oriented. Therefore, the score range of each indicator is set to [60,100] and further divided into five different levels. The comment set is "excellent", "excellent", "good", "medium", and "qualified". The classification criteria for each level and the corresponding standard cloud feature parameters are shown in Table 3.

[0200] Table 3

[0201]

[0202] (2) Determination of standard cloud. The evaluation interval is divided according to the evaluation target, and each evaluation level interval is recorded as [Amin, Amax]. The digital characteristics of the standard cloud model of each evaluation level are calculated using the following formula, and the evaluation standard cloud is generated by a one-dimensional normal inverse cloud generator. The characteristic values ​​of the standard clouds of the five levels are calculated using formula (19), as shown in Table 3 above.

[0203]

[0204] In the formula, b is a constant and can be adjusted according to needs.

[0205] (3) Calculation of the index evaluation cloud characteristic value. The scoring result is recorded as Z pq ={z p1 ,z p2 ,…,zp 39}, (p represents the score number; q represents the index number), calculate the digital eigenvalues ​​of the 39 evaluation index evaluation clouds in the pumped storage power station engineering system management and control performance evaluation index system, and realize the conversion of the scoring results from quantitative to qualitative expression. The calculation formula is as follows:

[0206]

[0207]

[0208] Where n is the nth index; E xn is the expected value of the nth indicator; E nn is the entropy value of the nth indicator; H en is the super entropy of the nth index.

[0209] (4) Evaluation of comprehensive cloud generation. Substituting the obtained indicator weights and indicator cloud characteristic values ​​into the following formula (24), the evaluation comprehensive cloud characteristic value C' can be obtained: U =(E' X , E' N , H' E ), and then generate the evaluation comprehensive cloud through the forward cloud generator.

[0210]

[0211] Based on the same inventive concept, the embodiment of the present application also provides a project performance evaluation device for implementing the project performance evaluation method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more project performance evaluation device embodiments provided below can refer to the limitations on the project performance evaluation method above, and will not be repeated here.

[0212] In one embodiment, Fig. 9 As shown, a project performance evaluation device is provided, including: an indicator acquisition module 901, a weight coefficient determination module 902, a feature determination module 903 and a performance evaluation module 904. Among them:

[0213] The indicator acquisition module 901 is used to acquire multiple performance evaluation indicators of the target project; the performance evaluation indicators are obtained based on the decomposition of the project management subsystem;

[0214] The weight coefficient determination module 902 is used to determine the indicator weight coefficient of each performance evaluation indicator and the system weight coefficient of each project management subsystem, and determine the comprehensive weight coefficient of each performance evaluation indicator according to the indicator weight coefficient and the corresponding system weight coefficient of the project management subsystem;

[0215] The feature determination module 903 is used to obtain the preset score of each performance evaluation indicator and determine the comprehensive feature value of the target project based on the preset score and the comprehensive weight coefficient;

[0216] The performance evaluation module 904 is used to obtain the standard cloud map corresponding to the target project, generate a comprehensive performance cloud map of the target project based on the comprehensive characteristic value, compare the comprehensive performance cloud map with the standard cloud map, and obtain the overall performance evaluation result of the target project.

[0217] In an optional embodiment, the weight coefficient determination module 902 is also used to sort the performance evaluation indicators of each project management subsystem from large to small according to importance, and obtain a performance evaluation indicator sequence corresponding to each project management subsystem; determine the indicator weight coefficient of each performance evaluation indicator based on the first relative importance parameters of two adjacent performance evaluation indicators in each performance evaluation indicator sequence; the indicator weight coefficient is the relative weight of the performance evaluation indicator in the corresponding project management subsystem; sort the project management subsystems from large to small according to importance, and obtain a project management subsystem sequence; determine the system weight coefficient of each project management subsystem based on the second relative importance parameters of two adjacent project management subsystems in the project management subsystem sequence; the system weight coefficient is the relative weight of the project management subsystem in the target project; obtain the comprehensive weight coefficient of each performance evaluation indicator based on the indicator weight coefficient and the system weight coefficient; the comprehensive weight coefficient is the relative weight of the performance evaluation indicator in the target project.

[0218] In an optional embodiment, the weight coefficient determination module 902 is also used to determine the indicator weight coefficient of the nth performance evaluation indicator in the performance evaluation indicator sequence based on the performance evaluation indicator sequence; the nth performance evaluation indicator is the last performance evaluation indicator in the performance evaluation indicator sequence; obtain the first relative importance parameter between the nth performance evaluation indicator and the n-1th performance evaluation indicator; the first relative importance parameter is determined based on the relative importance between two adjacent performance evaluation indicators; determine the indicator weight coefficient of the n-1th performance evaluation indicator based on the first relative importance parameter between the nth performance evaluation indicator and the n-1th performance evaluation indicator and the indicator weight coefficient of the nth performance evaluation indicator, and repeat this cycle until the indicator weight coefficient of each performance evaluation indicator is obtained. And for determining the system weight coefficient of the mth project management subsystem in the project management subsystem sequence based on the project management subsystem sequence; the mth project management subsystem is the last project management subsystem in the project management subsystem sequence; obtaining the second relative importance parameter between the mth project management subsystem and the m-1th project management subsystem; the second relative importance parameter is determined according to the relative importance between two adjacent project management subsystems; according to the second relative importance parameter between the mth project management subsystem and the m-1th project management subsystem and the system weight coefficient of the mth project management subsystem, determining the system weight coefficient of the m-1th project management subsystem, and repeating this until the system weight coefficient of each project management subsystem is obtained.

[0219] In an optional embodiment, the feature determination module 903 is also used to obtain a preset score for each performance evaluation indicator; based on the cloud model and according to the preset score obtained, the indicator characteristic value of each performance evaluation indicator is determined; the indicator characteristic value includes the indicator expected value, indicator entropy and indicator super entropy of each performance evaluation indicator; according to the indicator expected value, indicator entropy and indicator super entropy of each performance evaluation indicator and the comprehensive weight coefficient of the performance evaluation indicator, the comprehensive expected value, comprehensive entropy and comprehensive super entropy of the target project are determined, and the comprehensive expected value, comprehensive entropy and comprehensive super entropy are integrated to obtain the comprehensive characteristic value.

[0220] In an optional embodiment, the characteristic determination module 903 is also used to determine the system expected value, system entropy and system super entropy of the project management subsystem according to the indicator expected value, indicator entropy and indicator super entropy of the performance evaluation indicators corresponding to each project management subsystem and the indicator weight coefficient of the performance evaluation indicator, and integrate the comprehensive expected value, comprehensive entropy and comprehensive super entropy to obtain the system characteristic value of the project management subsystem; based on the system characteristic value of each project management subsystem, obtain the system performance evaluation results of each project management subsystem, and visualize the system performance evaluation results.

[0221] In an optional embodiment, the performance evaluation module 904 is also used to determine the preset score range of the performance evaluation indicator, and determine the standard cloud characteristic values ​​corresponding to each preset score range based on the preset score range; generate a standard cloud map based on the standard cloud characteristic values ​​corresponding to each preset score range; generate a comprehensive performance cloud map of the target project based on the comprehensive characteristic values, compare the comprehensive performance cloud map with the standard cloud map, and obtain the overall performance evaluation result of the target project based on the standard cloud map corresponding to the preset score range.

[0222] Each module in the above-mentioned project performance evaluation device can be implemented in whole or in part by software, hardware or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute the operations corresponding to each module.

[0223] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Fig.10 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store performance evaluation indicators, project management subsystems, various weight coefficients, preset scores, various characteristic values ​​and other indicators. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a project performance evaluation method is implemented.

[0224] Those skilled in the art will understand that Fig.10 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0225] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the project performance evaluation method of the above embodiment is implemented.

[0226] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the project performance evaluation method of the above embodiment is implemented.

[0227] In one embodiment, a computer program product is provided, including a computer program, which implements the project performance evaluation query method of the above embodiment when executed by a processor.

[0228] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0229] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0230] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0231] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A project performance evaluation method, characterized in that: The method comprises: Acquire multiple performance evaluation indicators of the target project; the performance evaluation indicators are obtained based on the decomposition of the project management subsystem; Determine the indicator weight coefficient of each performance evaluation indicator and the system weight coefficient of each project management subsystem, and determine the comprehensive weight coefficient of each performance evaluation indicator based on the indicator weight coefficient and the corresponding system weight coefficient of the project management subsystem; Obtaining a preset score for each performance evaluation indicator, and determining a comprehensive characteristic value of the target project based on the preset score and the comprehensive weight coefficient; Obtain a standard cloud map corresponding to the target project, generate a comprehensive performance cloud map of the target project based on the comprehensive characteristic value, compare the comprehensive performance cloud map with the standard cloud map, and obtain an overall performance evaluation result of the target project.

2. The method according to claim 1, characterized in that The step of determining the indicator weight coefficient of each performance evaluation indicator and the system weight coefficient of each project management subsystem, and determining the comprehensive weight coefficient of each performance evaluation indicator based on the indicator weight coefficient and the corresponding system weight coefficient of the project management subsystem, includes: The performance evaluation indicators of each project management subsystem are sorted from large to small according to the preset importance, and the performance evaluation indicator sequence corresponding to each project management subsystem is obtained; based on the first relative importance parameters of two adjacent performance evaluation indicators in each performance evaluation indicator sequence, the indicator weight coefficient of each performance evaluation indicator is determined; the indicator weight coefficient is the relative weight of the importance of the performance evaluation indicator in the corresponding project management subsystem; The project management subsystems are sorted from large to small according to preset importance to obtain a project management subsystem sequence; based on the second relative importance parameters of two adjacent project management subsystems in the project management subsystem sequence, a system weight coefficient of each project management subsystem is determined; the system weight coefficient is the relative weight of the importance of the project management subsystem in the target project; According to the indicator weight coefficient and the system weight coefficient, the comprehensive weight coefficient of each performance evaluation indicator is obtained; the comprehensive weight coefficient is the relative weight of the importance of the performance evaluation indicator in the target project.

3. The method according to claim 2, characterized in that The step of determining the indicator weight coefficient of each performance evaluation indicator based on the first relative importance parameter of two adjacent performance evaluation indicators in the performance evaluation indicator sequence includes: Based on the performance evaluation indicator sequence, determining an indicator weight coefficient of an nth performance evaluation indicator in the performance evaluation indicator sequence; the nth performance evaluation indicator is the last performance evaluation indicator in the performance evaluation indicator sequence; Obtaining a first relative importance parameter between the nth performance evaluation indicator and the n-1th performance evaluation indicator; the first relative importance parameter is determined according to the relative importance between two adjacent performance evaluation indicators; Determine the indicator weight coefficient of the n-1th performance evaluation indicator according to the first relative importance parameter between the n-th performance evaluation indicator and the n-1th performance evaluation indicator and the indicator weight coefficient of the n-1th performance evaluation indicator, and repeat this cycle until the indicator weight coefficient of each performance evaluation indicator is obtained; The determining of the system weight coefficient of each project management subsystem based on the second relative importance parameters of two adjacent project management subsystems in the project management subsystem sequence includes: Based on the project management subsystem sequence, determining a system weight coefficient of an m-th project management subsystem in the project management subsystem sequence; the m-th project management subsystem is the last project management subsystem in the project management subsystem sequence; Obtaining a second relative importance parameter between the m-th project management subsystem and the m-1-th project management subsystem; the second relative importance parameter is determined according to the relative importance between two adjacent project management subsystems; The system weight coefficient of the m-1th project management subsystem is determined based on the second relative importance parameter between the m-th project management subsystem and the m-1th project management subsystem and the system weight coefficient of the m-th project management subsystem, and this cycle is repeated until the system weight coefficient of each project management subsystem is obtained.

4. The method according to claim 1, characterized in that: The step of obtaining a preset score for each performance evaluation indicator and determining a comprehensive characteristic value of the target project based on the preset score and the comprehensive weight coefficient includes: Obtain the preset scores for each performance evaluation indicator; According to the preset scores obtained, the indicator characteristic values ​​of each performance evaluation indicator are determined based on the cloud model; the indicator characteristic values ​​include the indicator expected value, indicator entropy and indicator super entropy of each performance evaluation indicator; Determine the comprehensive expected value, comprehensive entropy and comprehensive super entropy of the target project according to the expected value, the entropy and the super entropy of each performance evaluation indicator and the comprehensive weight coefficient of the performance evaluation indicator; The comprehensive expected value, comprehensive entropy and comprehensive super entropy are integrated to obtain the comprehensive characteristic value.

5. The method according to claim 4, characterized in that After determining the indicator characteristic value of each performance evaluation indicator based on the cloud model according to the obtained preset score, it also includes: According to the indicator expected value, indicator entropy and indicator super entropy of the performance evaluation indicators corresponding to each project management subsystem and the indicator weight coefficient of the performance evaluation indicator, the system expected value, system entropy and system super entropy of the project management subsystem are determined, and the comprehensive expected value, comprehensive entropy and comprehensive super entropy are integrated to obtain the system characteristic value of the project management subsystem; Based on the system characteristic values ​​of each project management subsystem, a system performance evaluation result of each project management subsystem is obtained, and the system performance evaluation result is visualized.

6. The method according to claim 1, characterized in that The step of obtaining a standard cloud map corresponding to the target project includes: Determine a preset score range for the performance evaluation indicator, and determine a standard cloud characteristic value corresponding to each preset score range according to the preset score range; A standard cloud map corresponding to the target project is generated based on the standard cloud feature values ​​corresponding to each preset score range.

7. A project performance evaluation device, characterized in that: The device comprises: An indicator acquisition module is used to acquire multiple performance evaluation indicators of a target project; the performance evaluation indicators are obtained based on the decomposition of the project management subsystem; A weight coefficient determination module is used to determine the indicator weight coefficient of each performance evaluation indicator and the system weight coefficient of each project management subsystem, and determine the comprehensive weight coefficient of each performance evaluation indicator based on the indicator weight coefficient and the corresponding system weight coefficient of the project management subsystem; A feature determination module, used to obtain a preset score for each performance evaluation indicator, and determine a comprehensive feature value of the target project based on the preset score and the comprehensive weight coefficient; The performance evaluation module is used to obtain the standard cloud map corresponding to the target project, generate a comprehensive performance cloud map of the target project based on the comprehensive characteristic value, compare the comprehensive performance cloud map with the standard cloud map, and obtain the overall performance evaluation result of the target project.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product comprising a computer program, wherein: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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