Project performance management method and device, computer equipment and storage medium

Abstract

The invention relates to the technical field of performance management, in particular to a project performance management method, computer equipment and a storage medium, and the scheme comprises the following steps: obtaining at least two projects and performance data of each of the at least two projects; performing data preprocessing on the performance data of each project to obtain preprocessed first performance data of each project; performing data processing on the first performance data of each project to obtain target performance data of each project; checking and calculating the target performance data of each item to obtain a performance order of each item; and obtaining a performance management report according to the performance order of each project. According to the method, the performance data of a plurality of projects can be processed, and the performance order of each project is further obtained according to the performance data, so that the performance difference between the projects is accurately identified, a high-quality performance management report is provided, and a scientific basis is provided for project combination management of an enterprise.

Description

Technical Field

[0001] The present application relates to the technical field of performance management, and in particular to a project performance management, apparatus, computer device and storage medium. Background Art

[0002] In the practice of project management, the performance evaluation of project portfolio plays a core role in the decision-making process. It is crucial to guide project investment, optimize resource allocation, and improve the overall performance of the organization. However, the traditional performance evaluation methods currently widely used have defects in many aspects, which limit their effectiveness and reliability in practical applications.

[0003] Traditional performance evaluation methods mainly rely on two methods: subjective evaluation and simple financial indicators. These methods are usually based on the personal judgment of project team members or management, or only focus on the financial performance of the project. This approach is difficult to provide a comprehensive and objective perspective to measure the true performance of the project. There are the following problems: incomplete data collection. In the evaluation process, there is often a lack of a comprehensive data collection mechanism, which leads to the neglect of important project information, thus affecting the integrity of the evaluation results; inconsistency of evaluation criteria. Different evaluators may use different evaluation criteria, or there are differences in the understanding and application of the same standard, which makes the evaluation results lack comparability; strong subjectivity. Since the evaluation process relies heavily on subjective judgment, the evaluation results are easily affected by personal bias and emotional factors, resulting in non-objective evaluation results.

[0004] In addition, traditional performance evaluation methods also rely on parametric statistics, which means that the data is required to meet specific distribution assumptions, such as normal distribution. However, in real-world project management, the collected data often does not meet these assumptions and may be non-normally distributed, skewed, or contain outliers. This data characteristic causes traditional parametric statistical methods to produce misleading evaluation results when applied. Summary of the invention

[0005] An object of an embodiment of the present invention is to provide a project performance management, apparatus, computer device and storage medium for solving the technical problems of incompleteness, objectivity and inconsistency of existing project performance evaluation methods.

[0006] In a first aspect, an embodiment of the present invention provides a project performance management method, the method comprising:

[0007] Obtaining performance data of at least two projects and each of the at least two projects;

[0008] Performing data preprocessing on the performance data of each of the projects to obtain preprocessed first performance data of each of the projects;

[0009] Performing data processing on the first performance data of each of the projects to obtain target performance data of each of the projects;

[0010] Performing verification and calculation on the target performance data of each project to obtain the performance ranking of each project;

[0011] A performance management report is obtained based on the performance ranking of each project.

[0012] In a second aspect, an embodiment of the present invention provides a project performance management device, the device comprising:

[0013] An acquisition unit, used for acquiring performance data of at least two projects and each of the at least two projects;

[0014] A processing unit, configured to perform data preprocessing on the performance data of each project to obtain first performance data of each project after preprocessing;

[0015] The processing unit is further used to perform data processing on the first performance data of each project to obtain target performance data of each project;

[0016] A verification unit, used to verify and calculate the target performance data of each project to obtain the performance order of each project;

[0017] The confirmation unit is used to obtain a performance management report according to the performance order of each project.

[0018] In a third aspect, an embodiment of the present invention provides a computer device, including:

[0019] at least one processor; and,

[0020] a memory communicatively connected to the at least one processor; wherein,

[0021] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to the first aspect.

[0022] In a fourth aspect, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by a processor, the processor executes the method as described in the first aspect.

[0023] In the scheme implemented by the above-mentioned project performance management, devices, computer equipment and storage media, the method first ensures the comprehensiveness and accuracy of performance management by obtaining the performance data of at least two projects and each of the at least two projects; then preprocesses the performance data of each project to obtain the first performance data of each project after preprocessing, which helps to clean and standardize data, reduce noise and errors, and improve the accuracy and reliability of subsequent data processing; further processes the first performance data of each project to obtain the target performance data of each project, thereby ensuring the accuracy of data processing and making the final performance analysis more credible; then verifies and calculates the target performance data of each project to obtain the performance rank of each project, and adopts statistical verification and calculation methods to ensure the objectivity and fairness of the performance rank; finally, according to the performance rank of each project, a performance management report is obtained, which provides detailed performance analysis results and optimizes project management efficiency and resource allocation. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.

[0025] Figure 1 It is a flowchart of a project performance management method in one embodiment of the present invention;

[0026] Figure 2 It is a structural schematic diagram of a project performance management device in one embodiment of the present invention;

[0027] Figure 3 It is a schematic diagram of the structure of a computer device in one embodiment of the present invention. DETAILED DESCRIPTION

[0028] In order to make the purpose, technical scheme and advantages of the present invention more clearly understood, the present invention 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 invention and are not intended to limit the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0029] It should be noted that, if there is no conflict, the various features in the embodiments of the present invention can be combined with each other, all within the scope of protection of the present invention. In addition, although the functional modules are divided in the device schematic diagram and the logical order is shown in the flow chart, in some cases, the steps shown or described can be performed in a different order from the module division in the device or the flow chart. Furthermore, the words "first", "second", "third", etc. used in the present invention do not limit the data and execution order, but only distinguish the same items or similar items with basically the same functions and effects.

[0030] The technical solution of this application can be applied to various performance management scenarios.

[0031] In view of this, this application proposes a project performance management method to solve the above problems, which is introduced in detail below.

[0032] See also Figure 1 , Figure 1 A schematic diagram of a project performance management method provided by an embodiment of the present invention includes the following steps:

[0033] S10. Obtain performance data of at least two projects and each of the at least two projects.

[0034] Among them, the projects may come from the same department, different departments, or different business units, and there is no sole limitation here.

[0035] For each selected project, collect relevant performance data. Performance data may include but are not limited to the following: project progress, project cost, project quality, resource utilization, risk management, other key performance indicators (KPI): Based on the specific characteristics of the project, select other relevant performance indicators, such as sales growth rate, market share, etc.

[0036] Specifically, project progress: project start time, expected completion time, actual completion time, etc.; project cost: project budget, actual expenditure, cost overruns, etc.; project quality: project quality indicators, defect rate, customer satisfaction, etc.; resource utilization: project human resources, material resource utilization, etc.; risk management: project risk identification, assessment, response measures, etc.; other key performance indicators (KPI): based on the specific characteristics of the project, select other relevant performance indicators, such as sales growth rate, market share, etc.

[0037] The acquisition of performance data may include but is not limited to: project management system, financial system, human resources system, customer feedback system, etc. Ensure the diversity and reliability of acquisition methods to improve the comprehensiveness and accuracy of data.

[0038] It can be seen that in this embodiment, the required project performance data can be systematically obtained, providing a solid data foundation for subsequent data preprocessing, data processing, performance analysis and performance reporting.

[0039] S20: preprocess the performance data of each project to obtain the first performance data of each project after preprocessing.

[0040] The data preprocessing may include but is not limited to data cleaning and outlier processing.

[0041] Among them, data cleaning refers to organizing and cleaning the collected raw data to remove or correct errors, missing values ​​and inconsistencies in the data.

[0042] Optionally, during the data cleaning process, exclude project delay data due to delays from external suppliers to ensure that the data set only contains the impact of controllable factors within the project and avoid interference from external uncontrollable factors on the data analysis results. For example, if a project is delayed due to a supplier's failure to deliver materials on time, this situation should be excluded or marked as an external factor during the data cleaning process so as not to affect the evaluation of the project's internal performance.

[0043] Among them, outlier processing refers to identifying and processing outliers or outliers in the data to ensure the accuracy and consistency of the data. Outliers are those values ​​that deviate significantly from other data points, which may be caused by data entry errors, equipment failures, etc.

[0044] Specifically, for the identified outliers, the following processing methods can be adopted: delete outliers, directly delete outliers; correct outliers, correct outliers according to business rules or context information to make them fit within the expected range; replace outliers, use mean, median or other reasonable values ​​to replace outliers to ensure data continuity and consistency.

[0045] For example, if there is an abnormally high value in the cost data of a project, which is significantly higher than the costs of other projects, the outlier identification method can be used to confirm whether the value is an outlier and take appropriate processing measures.

[0046] It can be seen that the pre-processed first performance data obtained after data cleaning and outlier processing in this embodiment is more accurate, standardized and consistent, providing a reliable basis for subsequent data processing and analysis.

[0047] In one embodiment, the data preprocessing is performed on the performance data of each project to obtain the first performance data of each project after preprocessing, including: data cleaning is performed on the performance data of each project to obtain second performance data that meets the first preset performance verification rule; in the second performance data, third performance data that meets the second verification rule is identified, and the third performance data is the first performance data of each project after preprocessing.

[0048] The first preset performance inspection rule is a set of standards or conditions for evaluating and filtering performance data. The first preset performance inspection rule can be set according to business needs and data quality requirements to ensure that the cleaned data meets the expected quality standards, and is not limited here.

[0049] For example, the first preset performance verification rule can be: 1. Ensure that the key fields of each data record (such as project name, project progress, project cost, etc.) are not empty; 2. Ensure that the format of the data meets expectations (such as the date format is YYYY-MM-DD, the cost is a numeric type, etc.); 3. Ensure that the data value is within a reasonable range (such as the project progress percentage is between 0-100, the cost is not a negative value, etc.).

[0050] Among them, the second performance data is performance data that has passed data cleaning and meets the first preset performance inspection rules.

[0051] The second verification rule is a set of standards or conditions for further screening and verifying data to ensure high quality and reliability of data. The second verification rule is set for specific business needs or data analysis requirements.

[0052] For example, the second verification rule can be: ensuring the consistency of data between different records and fields (such as the consistency of project progress and project completion time); ensuring the accuracy of data through cross-validation or confirmation by reference to other data sources (such as the consistency of project cost and financial system data); ensuring the rationality of data through business logic or industry standards (such as the rationality of project progress and actual construction period).

[0053] Among them, the third performance data is performance data that meets the second preset performance verification rules.

[0054] It can be seen that after two rounds of preprocessing in this embodiment, all preset inspection rules are obtained to ensure the accuracy, completeness, consistency and rationality of the first performance.

[0055] S30: Process the first performance data of each project to obtain target performance data of each project.

[0056] The purpose of data processing is to further convert and calculate the pre-processed first performance data to obtain the target performance data of each project. The target performance data can be used for performance evaluation, comparison and decision support.

[0057] Specifically, data processing includes data processing of each item in a single dimension and data processing of each item in multiple dimensions. If it is in multiple dimensions, the data of multiple dimensions are converted into a unified scoring standard.

[0058] Data processing under a single dimension refers to processing and calculating a certain performance indicator of each project. Each dimension represents a specific performance indicator, such as project progress, project cost, project quality, etc.

[0059] Specifically, the performance data of different projects under the same dimension are standardized to make them comparable. Common methods include normalization and Z-score standardization; according to business needs, key performance indicators (KPIs) are calculated, such as the average value of project costs and the completion percentage of project progress. For example, for the project progress dimension, the progress percentage of each project is calculated and standardized to make the progress data of different projects comparable.

[0060] Among them, multi-dimensional data processing refers to the comprehensive processing and calculation of data of multiple performance indicators to obtain a comprehensive performance order. Each dimension represents a specific performance indicator, such as project progress, project cost, project quality, etc.

[0061] Specifically, select multiple performance dimensions to ensure that the comprehensive score can fully reflect the performance of the project; assign weights to each dimension based on business needs and the focus of performance evaluation. The weight assignment can be determined through expert scoring, historical data analysis, and other methods; convert the data of each dimension into a unified scoring standard. Common methods include linear transformation and percentage conversion; calculate the comprehensive performance order of each project based on the weights and scoring standards of each dimension. Common methods include weighted average method, TOPSIS method, etc. For example, for project performance evaluation, select three dimensions: project progress, project cost, and project quality, and assign weights of 0.4, 0.3, and 0.3 respectively. Convert the data of each dimension into a percentage score, and then calculate the comprehensive performance order.

[0062] Among them, the target performance data is the final performance data obtained after single-dimensional or multi-dimensional data processing. The target performance data reflects the performance of each project in each performance indicator, and integrates data from multiple dimensions to provide a unified performance ranking standard.

[0063] It can be seen that, according to data processing, this embodiment can transform complex, multi-dimensional performance data into a simple, intuitive performance ranking to facilitate project management and decision-making.

[0064] S40, verifying and calculating the target performance data of each project to obtain the performance ranking of each project.

[0065] The test calculation may be a Kruskal-Wallis test and a Nemenyi test. The test calculation of the target performance data is to further verify and calculate the target performance data that has been obtained to ensure the accuracy and rationality of the data.

[0066] Specifically, the Kruskal-Wallis test is a nonparametric test method used to compare whether there are significant differences in the medians of multiple groups of data. In this solution, it can be used to determine whether there are statistically significant differences in the performance of multiple projects; if the results of the Kruskal-Wallis test show that there are significant differences between the projects, then the Nemenyi test can be further performed to determine which specific project groups have differences.

[0067] Among them, the Nemenyi test is a multiple comparison test, usually used after the Kruskal-Wallis test finds significant differences. It is used to identify which specific project groups have significant performance differences. The Nemenyi test calculates the mean rank differences between groups to determine whether these differences are beyond the range of random variation.

[0068] For example, assuming there are target performance data for three projects A, B, and C, the Kruskal-Wallis test can be used to determine whether there are significant differences in the performance data of the three projects; if the Kruskal-Wallis test finds that there are significant differences in the performance data of projects A, B, and C, the Nemenyi test can be used to further determine whether there are significant differences between projects A and B, A and C, or B and C.

[0069] Specifically, first, a Kruskal-Wallis test is performed on the target performance data of each project to determine whether there is at least one project whose performance is significantly different from other projects; if the Kruskal-Wallis test results show that there are significant differences between the projects, then the Nemenyi test is used to determine which specific project groups have differences; based on the results of the Nemenyi test, a performance rank can be assigned to each project, and this score reflects the relative performance level of the project compared with other projects.

[0070] Among them, the performance order can be based on the rank or average rank of the project. The performance order refers to the order of arrangement according to the performance data of the project. The performance order usually ranks the projects from best to worst according to the performance of these indicators. It can be seen that in this embodiment, by testing and calculating the target performance data of each project, such as using the Kruskal-Wallis test and the Nemenyi test, the accuracy and rationality of the data can be ensured, and significant differences between different projects can be identified, helping managers understand the performance of each project, and then obtaining the performance order of each project. This performance order can not only be used for performance evaluation and decision support, but also provide a basis for reward and punishment mechanisms and resource allocation, helping managers to better manage and optimize projects.

[0071] S50. Obtain a performance management report according to the performance order of each project.

[0072] The performance management report is a written report based on the performance ranking of each project, which comprehensively analyzes and summarizes the performance of each project. The performance management report aims to provide comprehensive performance evaluation results and analysis, provide a basis for decision-making, and help improve project management and performance.

[0073] Optionally, the content of the performance management report is as follows: Project Overview, which briefly introduces the goals, scope and key milestones of each project; Data Analysis, which analyzes the data behind the performance ranking, including the strengths and weaknesses of the projects, and factors that may affect performance; Comparison and Trends, if applicable, compares the performance of different projects and provides a time series analysis to show the trend of performance over time; Conclusions and Recommendations, based on the performance ranking and analysis, presents conclusions and provides specific recommendations for improving project performance; Attachments and Appendices, which may include detailed data tables, charts, survey results or other supporting materials.

[0074] It can be seen that in this embodiment, the performance management report can be visualized, making the performance order of each project more intuitive and easy to understand, which helps to promptly discover problems in the project execution process and provide a scientific basis for project decision-making.

[0075] This method firstly ensures the comprehensiveness and accuracy of performance management by obtaining the performance data of at least two projects and each of the at least two projects; then performs data preprocessing on the performance data of each project to obtain the first performance data of each project after preprocessing, which is helpful to clean and standardize data, reduce noise and errors, and improve the accuracy and reliability of subsequent data processing; further performs data processing on the first performance data of each project to obtain the target performance data of each project, thereby ensuring the accuracy of data processing and making the final performance analysis more credible; then performs inspection and calculation on the target performance data of each project to obtain the performance rank of each project, and adopts statistical inspection and calculation methods to ensure the objectivity and fairness of the performance rank; finally, according to the performance rank of each project, a performance management report is obtained, which provides detailed performance analysis results and optimizes project management efficiency and resource allocation.

[0076] In one embodiment, the data processing of the first performance data of each project to obtain the target performance data of each project includes: obtaining each sub-data in the first performance data, wherein the sub-data corresponds to a sub-project under each project; determining the degree of completion of the sub-project under each project based on each sub-data; obtaining preset sub-project weights and preset deviation coefficients; calculating the degree of completion of the sub-project under each project, the preset sub-project weights and the preset deviation coefficients to obtain the sub-performance data of the sub-project under each project; and combining the sub-performance data of the sub-projects under each project to obtain the target performance data of each project.

[0077] Each sub-data is extracted from the first performance data, and the sub-data represents the performance information of the sub-project under each project. Each project may contain multiple sub-projects, and each sub-project has its own independent performance data.

[0078] The degree of completion can be a percentage, score or other quantitative indicators, indicating the degree of achievement of the sub-project goals. For example, if the planned progress of sub-project A1 is 100% and the actual completion progress is 80%, then its degree of completion is 80%.

[0079] The preset sub-project weight refers to the importance ratio of each sub-project in the overall project. The preset sub-project weight is usually pre-set by the project manager based on the importance and influence of the sub-project, and is not limited here.

[0080] For example, if sub-item A1 is of higher importance, the weight may be 0.5, while sub-items A2 and A3 may have weights of 0.3 and 0.2, respectively.

[0081] The preset deviation coefficient refers to the degree of deviation between the actual completion of a sub-project and the expected completion. The deviation coefficient is used to measure the gap between the actual performance of a sub-project and the target performance.

[0082] For example, if subproject A1 is planned to be 100% complete and is actually 80% complete, the coefficient of variation might be 0.8.

[0083] Specifically, the progress completion degree of each sub-project ni = completed task volume / total task volume*100%; set a preset deviation coefficient βi; preset the weight of each sub-project to ζi; then the sub-performance data Yi=ni*ζi / (1-βi).

[0084] The target performance data of each project is the sub-performance data of all sub-projects under each project.

[0085] For example, sub-project A1: completion rate 80%, weight 0.5, deviation coefficient 0.8; sub-project A2: completion rate 90%, weight 0.3, deviation coefficient 1.0; sub-project A3: completion rate 70%, weight 0.2, deviation coefficient 0.9; therefore, the sub-performance data of A1 is 80%*0.5*0.8=0.32; the sub-performance data of A2 is 90%*0.3*1.0=0.27; the sub-performance data of A3 is 70%*0.2*0.9=0.126.

[0086] Therefore, in the process of combining the sub-performance data of the sub-items under each project to obtain the target performance data of each project, the combination can be to add the sub-performance data of all sub-items under each project to obtain a target performance data. According to the above case, the target performance can be = 0.32 + 0.27 + 0.126 = 0.716 or 71.6%. Or,

[0087] The combination can also be to put the sub-performance data of all sub-projects under each project into one set, that is, the target performance data of each project can be a set. According to the above case, the target performance can be {0.32, 0.27, 0.126}.

[0088] It can be seen that in this embodiment, the target performance data of the project is calculated through the completion, weight and deviation coefficient of the sub-projects in a single dimension, which helps the project manager to evaluate the overall performance of the project and make decisions accordingly.

[0089] In one embodiment, the performance data includes performance data in multiple dimensions, and the data processing of the first performance data of each project to obtain the target performance data of each project includes: obtaining the performance data in each dimension of the first performance data; standardizing the performance data in each dimension of the first performance data to obtain the standardized standard performance value in each dimension; using each dimension as a query identifier, querying in a preset weight database to obtain the weight value corresponding to each dimension; performing weighted average calculation on the standard performance value in each dimension of the first performance data and the weight value corresponding to each dimension to obtain the target performance data of each project, wherein the target performance data is the indicator value of the performance data of all dimensions under each project.

[0090] The performance data may include multiple dimensions, each of which represents a different performance indicator. For example, project performance may include time dimension, cost dimension, quality dimension, etc.

[0091] For example, the performance data of Project A may include multiple dimensions such as time completion, cost control, and quality compliance rate.

[0092] In obtaining the performance data under each dimension of the first performance data, the specific performance data under each dimension is extracted from the first performance data. For example, the time completion rate of project A is 85%, the cost control is 90%, and the quality compliance rate is 80%.

[0093] Among them, the standardization process is to eliminate the dimensional effects between data of different dimensions so that the data can be compared on the same scale. Standardization processing may include but is not limited to minimum-maximum standardization (scaling the data to between 0 and 1) and Z-score standardization (converting the data to a distribution with a mean of 0 and a standard deviation of 1). The data in each dimension after standardization is called the standard performance value.

[0094] Among them, the preset weight database stores the weight value of each dimension, and these weight values ​​are usually set by experts or managers based on actual conditions and experience.

[0095] Among them, the weight value reflects the importance of each dimension in the overall performance evaluation, and different weight allocations can significantly affect the final target performance data.

[0096] Among them, the standardized performance value is multiplied by the corresponding weight value, and then all dimensions are summed to obtain a comprehensive performance order, namely the target performance data. The weighted average calculation formula can be expressed as: target performance data = Σ (standard performance value * corresponding weight value), where Σ represents the sum of all dimensions.

[0097] Among them, the target performance data is the comprehensive performance evaluation result of each project in all dimensions, reflecting the overall performance level of the project.

[0098] It can be seen that in this embodiment, by systematically processing multi-dimensional performance data, the target performance data of each project is obtained, providing strong support for performance evaluation and decision-making.

[0099] In one embodiment, the performance data includes performance data in multiple dimensions, and the data processing of the first performance data of each project to obtain the target performance data of each project includes: obtaining the performance data in each dimension of the first performance data; inputting the performance data in each dimension into a preset performance model to obtain the target performance data of each project output by the preset performance model, wherein the preset performance model is a model for performing performance evaluation on the performance data in multiple dimensions of each project, and the target performance data is the indicator value of the performance data of all dimensions of each project.

[0100] Among them, performance data can include multiple dimensions, each dimension represents a different performance indicator. For example, project performance can include time dimension, cost dimension, quality dimension, etc. For example, the performance data of project A may include multiple dimensions such as time completion, cost control, and quality compliance rate.

[0101] Among them, the preset performance model is a model used to comprehensively evaluate multi-dimensional performance data. The preset performance model can be a rule-based model, a statistical model, a machine learning model, etc. Specifically, the rule-based model calculates and evaluates multi-dimensional performance data through predefined rules and formulas; or, the statistical model uses statistical methods such as weighted average and regression analysis to comprehensively evaluate multi-dimensional performance data; or, the machine learning model predicts and evaluates multi-dimensional performance data through training data and algorithms such as decision trees and neural networks.

[0102] Specifically, the performance data under each dimension is input into the preset performance model for data processing and evaluation. For example, the time completion, cost control, and quality compliance rate are input into the preset performance model respectively.

[0103] Optionally, the preset performance model can be adjusted and optimized according to different needs and conditions to suit different projects and performance evaluation requirements. For example, the parameters and weights in the model can be adjusted according to the importance of a specific project.

[0104] The target performance data is a comprehensive indicator that reflects the overall performance of the project in all performance dimensions. This indicator value can be a simple total score, a weighted average score, or other forms of scoring, depending on the settings of the preset performance model.

[0105] It can be seen that in this embodiment, a preset performance model is used to process and evaluate multi-dimensional performance data, so as to ensure the objectivity and consistency of the evaluation process, reduce the influence of human bias, and further automatically process a large amount of multi-dimensional performance data through the preset performance model to obtain target performance data, thereby improving efficiency and accuracy.

[0106] In one embodiment, the target performance data of each project is tested and calculated to obtain the performance order of each project, including: performing statistical analysis on the target performance data of each project according to a preset Kruskal-Wallis test to determine the performance difference between each project and obtain a first performance difference between each project; determining whether the first performance difference between each project is the same; if not, performing a preset Nemenyi test on the target performance data of each project to obtain the performance order of each project; or, if the same, determining that the target performance data of each project is the same.

[0107] The preset Kruskal-Wallis test is as described in S40 and will not be repeated here.

[0108] Among them, the target performance data of each project was statistically analyzed through the Kruskal-Wallis test to determine whether there were significant differences in performance among the projects.

[0109] In the process of statistical analysis of the target performance data of each project, the target performance data of each project is collected and organized into independent samples; the data of all samples are sorted and assigned ranks; the Kruskal-Wallis statistic is calculated based on the number of samples and ranks; the calculated statistic is compared with the critical value to determine whether there is a significant difference.

[0110] The first performance difference refers to the preliminary performance difference results between the projects obtained through the Kruskal-Wallis test. According to the significance test results, it is determined whether there is a significant difference in the first performance difference between the projects.

[0111] Among them, if the Kruskal-Wallis test results show that the performance differences between projects are not significant, it is considered that the performance order of each project is the same.

[0112] If the Kruskal-Wallis test results show that there are significant differences between the items, a further Nemenyi test is required.

[0113] It can be seen that in this embodiment, through the preset Kruskal-Wallis test, the target performance data of each project can be objectively evaluated, the influence of subjective bias on the evaluation results can be avoided, and the performance differences between projects can be further accurately identified. By performing detailed pairwise comparisons through the Nemenyi test again, it can be clearly pointed out which specific projects have significant differences, and then the performance order can be accurately assigned to each project.

[0114] In one embodiment, the target performance data of each project is subjected to a preset Nemenyi test calculation to obtain the performance order of each project, including: performing a preset Nemenyi test calculation on the target performance data of each project to obtain a performance difference value between any two projects; obtaining a second performance difference situation between any two projects based on the performance difference value; and determining the performance order of each project in the at least two projects based on the second performance difference situation between any two projects.

[0115] The preset Nemenyi test is as described in S40 and will not be repeated here.

[0116] Among them, the target performance data of each project is compared through the Nemenyi test, and the performance difference value between two projects is calculated.

[0117] Among them, the performance difference value is a numerical value that indicates the degree of difference between the performance of two projects.

[0118] Among them, in the process of performing a preset Nemenyi test calculation on the target performance data of each project to obtain the performance difference value between each pair of projects, after the Kruskal-Wallis test finds a significant difference, each project is compared pairwise, and for each pair of projects, their average rank difference is calculated; based on the sample size and significance level, the critical difference is calculated; the calculated average rank difference is compared with the critical difference to determine whether there is a significant difference; the performance difference value between each pair of projects is recorded to form a matrix or list.

[0119] The second performance difference refers to the detailed performance difference results between the projects obtained by Nemenyi test. The project pairs with significant differences are identified and their difference values ​​are recorded, and the project pairs without significant differences are identified and their difference values ​​are recorded.

[0120] Specifically, in the process of determining the performance order of each of the at least two projects according to the second performance difference between the two projects, the project performance is ranked according to the rank sum, such as the order of E>B>C>D>A; then, according to the comparison value between the difference between the average ranks and the critical value CD, it is judged whether the above ranking is reasonable. If the average rank difference between BC is less than the critical value CD, it is considered that the difference between BC is not significant, and then it is judged that the performance of B = the performance of C, so the final ranking should be E>B = C>D>A.

[0121] Therefore, the specific values ​​of the mean rank and its component differences in the Nemenyi test are used to test whether the differences between groups are significant.

[0122] It can be seen that in this embodiment, the Nemenyi test can be used to obtain accurate performance difference values ​​between projects, and the performance difference can be further quantified into specific numerical values, making the comparison between projects more specific and intuitive. According to the second performance difference situation, the projects can be ranked to clarify which projects perform best and which perform worst. It can also be clearly pointed out which specific projects have significant differences, and each project can be accurately ranked in terms of performance to obtain a performance order.

[0123] For example, suppose a company needs to conduct a performance evaluation on the progress achievement rate of its multiple development projects to determine which projects require additional resources or intervention. The method of the present invention is used for evaluation. The performance data of different development projects are collected from the company's project management system, including the completed task volume, total task volume, project weight of all sub-projects in each project, and a preset deviation coefficient. The collected data is processed by S20-S30 in this embodiment. Assuming there are five projects A, B, C, D, and E, the sample size and target performance data of each project are as follows:

[0124]

[0125] Assume that the performance data of five projects are processed by S20-S30 in this embodiment to obtain the following data:

[0126]

[0127] The following process is the specific implementation process of S40-S50 in this embodiment.

[0128] First is the Kruskal-Wallis rank sum test stage: Assuming that after statistical analysis, the above data meets the conditions of the Kruskal-Wallis test, sorting and ranking are performed, and then the Kruskal-Wallis test is performed. The Kruskal-Wallis rank sum test is used to conduct a preliminary evaluation of the performance of the project portfolio to determine whether there are significant differences in the performance of different projects or project stages. Determine the hypothesis and significance level. The significance level α = 0.05; determine the hypothesis conditions H0: Ya = Yb = Yc = Yd = Ye; H1: at least one pair Yi≠Yj; Rank calculation: merge the comprehensive achievement rate data of all projects, sort them from small to large, and assign a rank (i.e. ranking) to each data.

[0129]

[0130] Assign ranks to sorted data:

[0131]

[0132] Rank Sum Calculation: Calculate the rank sum of each item (that is, the sum of all data ranks for that item):

[0133] Project A: R A =2+6+14.5+17+19=58.5;

[0134] Project B: R B =11+21.5+24.5+26.5+29.5+32=145;

[0135] Project C: R C =1+6+8+17+21.5+24.5+32=110;

[0136] Project D: R D =3+4+6+9.5+12.5+17+21.5=73.5;

[0137] Project E: R E =9.5+12.5+14.5+21.5+26.5+28+29.5+32=174;

[0138] Calculate the Kruskal-Wallis test statistic H,

[0139]

[0140] Determine the critical value

[0141] 1. Determine the degrees of freedom: The degrees of freedom (df) is equal to the number of groups (k) minus 1. With 5 groups of data, the degrees of freedom is 4.

[0142] 2. Choose the significance level: The significance level (α) is usually set at 0.05, which means there is a 5% chance of rejecting a true null hypothesis.

[0143] 3. Find the critical value: Using the degrees of freedom and significance level, find the corresponding critical value in the chi-square distribution table. This value is the value that the test statistic H must exceed in order to reject the null hypothesis.

[0144] Secondly, the number of groups K = 5, N = 33, the degree of freedom df = K-1 = 4, and the critical value of the Kruskal-Wallis test = 9.49. When H>9.49, H0 is rejected.

[0145] Calculate the test statistic H: Based on the H value and the degrees of freedom (k-1=4), look up the critical value table for the Kruskal-Wallis test or use statistical software to calculate the p-value.

[0146]

[0147] Among them, N = 5 + 6 + 7 + 7 = 8 = 33 (total sample size); k = 5 (number of groups), R j and n j are the rank sum and sample size of the j-th item respectively.

[0148] H0 is rejected, indicating that there are significant differences in the performance of at least two of the five projects.

[0149] Further analysis was performed using the Nemenyi test. The items were initially ranked according to the rank sum in the Kruskal-Wallis test. E >R B >R C >R D >R A .

[0150] Calculate the critical difference (CD) of Nemenyi test:

[0151]

[0152] Calculate the average rank: Item A: 58.5 / 5 = 11.7; Item B: 145 / 6 = 24.2; Item C: 110 / 7 = 15.7; Item D: 73.5 / 7 = 10.5; Item E: 174 / 8 = 21.8; Determine the difference between groups: AB: |11.7-24.2| = 12.5; AC: |11.7-15.7| = 4; AD: |11.7-10.5 |=1.2; AE:|11.7-21.8|=10.1; BC:|24.2-15.7|=8.5; BD:|24.2-10.5|=13.7; BE:|24 .2-21.8|=2.4; CD:|15.7-10.5|=5.2; CE:|15.7-21.8|=6.1; DE:|10.5-21.8|=11.3; Multiple comparisons were performed to compare the differences between the groups with the critical value range: AB: 12.5 > CD, so there is a significant difference between A and B; AC: 4 > CD, so there is a significant difference between A and C; AD: 1.2 > CD, so there is a significant difference between A and D; AE: 10.1 > CD, so there is a significant difference between A and E; BC: 8.5 > CD, so there is a significant difference between B and C; BD: 13.7 > CD, so there is a significant difference between B and D; BE: 2.4 > CD, so there is a significant difference between B and E; CD: 5.2 > CD, so there is a significant difference between C and D; CE: 6.1 > CD, so there is a significant difference between C and E; DE: 11.3 > CD, so there is a significant difference between D and E; According to the multiple comparison table, the performance order of each project can be obtained: E (highest rank sum, best performance) > B > C > D > A (lowest rank sum, worst performance). In the performance management report, resources can be added to project A, and stricter progress control measures can be introduced for project C. For projects B, C, and D, new performance management strategies were implemented to improve project performance.

[0153] Optionally, if the calculated inter-group differences are all less than the critical value CD, then it is considered that all component differences are not significant. Then the judgment E>B>C>D>A is not true, and it should be E=B=C=D=A.

[0154] It should be noted that, in each of the above-mentioned embodiments, there is not necessarily a certain order between the above-mentioned steps. A person skilled in the art can understand, based on the description of the embodiments of the present application, that in different embodiments, the above-mentioned steps may have different execution orders, that is, they may be executed in parallel, may be executed interchangeably, and so on.

[0155] As another aspect of the embodiment of the present application, the embodiment of the present application provides a project performance management device, wherein the project performance management device can be a software module, the software module includes a number of instructions, which are stored in a memory, and the processor can access the memory and call the instructions for execution to complete the project performance management method described in each of the above embodiments.

[0156] See also Figure 2 , Figure 2 Schematic diagram of a project performance management device provided in an embodiment of the present application. Figure 2 As shown, the project performance management device 200 includes:

[0157] An acquisition unit 201 is used to acquire at least two projects and performance data of each of the at least two projects;

[0158] A processing unit 202 is used to perform data preprocessing on the performance data of each project to obtain preprocessed first performance data of each project;

[0159] The processing unit 202 is further configured to perform data processing on the first performance data of each project to obtain target performance data of each project;

[0160] A verification unit 203 is used to perform verification calculation on the target performance data of each project to obtain the performance order of each project;

[0161] The confirmation unit 204 is used to obtain a performance management report according to the performance order of each project.

[0162] This method firstly ensures the comprehensiveness and accuracy of performance management by obtaining the performance data of at least two projects and each of the at least two projects; then performs data preprocessing on the performance data of each project to obtain the first performance data of each project after preprocessing, which is helpful to clean and standardize data, reduce noise and errors, and improve the accuracy and reliability of subsequent data processing; further performs data processing on the first performance data of each project to obtain the target performance data of each project, thereby ensuring the accuracy of data processing and making the final performance analysis more credible; then performs inspection and calculation on the target performance data of each project to obtain the performance rank of each project, and adopts statistical inspection and calculation methods to ensure the objectivity and fairness of the performance rank; finally, according to the performance rank of each project, a performance management report is obtained, which provides detailed performance analysis results and optimizes project management efficiency and resource allocation.

[0163] In one embodiment, in the data processing of the first performance data of each project to obtain the target performance data of each project, the processing unit 202 is further used to: obtain each sub-data in the first performance data, the sub-data corresponding to the sub-project under each project; determine the completion degree of the sub-project under each project based on each sub-data; obtain the preset sub-project weight and the preset deviation coefficient; calculate the completion degree of the sub-project under each project, the preset sub-project weight and the preset deviation coefficient to obtain the sub-performance data of the sub-project under each project; and combine the sub-performance data of the sub-project under each project to obtain the target performance data of each project.

[0164] In one embodiment, the performance data includes performance data in multiple dimensions. In the data processing of the first performance data of each project to obtain the target performance data of each project, the processing unit 202 is further used to: obtain the performance data in each dimension of the first performance data; standardize the performance data in each dimension of the first performance data to obtain the standardized standard performance value in each dimension; use each dimension as a query identifier to query in a preset weight database to obtain the weight value corresponding to each dimension; perform weighted average calculation on the standard performance value in each dimension of the first performance data and the weight value corresponding to each dimension to obtain the target performance data of each project, wherein the target performance data is the indicator value of the performance data of all dimensions under each project.

[0165] In one embodiment, the performance data includes performance data in multiple dimensions. In the data processing of the first performance data of each project to obtain the target performance data of each project, the processing unit 202 is further used to: obtain the performance data in each dimension of the first performance data; input the performance data in each dimension into a preset performance model to obtain the target performance data of each project output by the preset performance model, wherein the preset performance model is a model for performing performance evaluation on the performance data in multiple dimensions of each project, and the target performance data is the indicator value of the performance data of all dimensions of each project.

[0166] In one embodiment, in the process of verifying and calculating the target performance data of each project to obtain the performance order of each project, the verification unit 203 is further used to: perform statistical analysis on the target performance data of each project according to a preset Kruskal-Wallis test to determine the performance difference between each project to obtain a first performance difference between each project; determine whether the first performance difference between each project is the same; if not, perform a preset Nemenyi test calculation on the target performance data of each project to obtain the performance order of each project; or, if the same, determine that the target performance data of each project is the same.

[0167] In one embodiment, in performing a preset Nemenyi test calculation on the target performance data of each project to obtain the performance order of each project, the test unit 203 is further used to: perform a preset Nemenyi test calculation on the target performance data of each project to obtain a performance difference value between any two projects; obtain a second performance difference situation between any two projects based on the performance difference value; and determine the performance order of each project among the at least two projects based on the second performance difference situation between any two projects.

[0168] In one embodiment, in the data preprocessing of the performance data of each project to obtain the preprocessed first performance data of each project, the processing unit 202 is further used to: perform data cleaning on the performance data of each project to obtain second performance data that meets the first preset performance verification rule; in the second performance data, identify the third performance data that meets the second verification rule, and the third performance data is the preprocessed first performance data of each project.

[0169] It should be noted that the above-mentioned project performance management device can execute the project performance management method provided in the implementation mode of this application, and has the corresponding functional modules and beneficial effects of the implementation method. For technical details not fully described in the implementation mode of the project performance management device, please refer to the project performance management method provided in the implementation mode of this application.

[0170] See also Figure 3 , Figure 3 3 is a schematic diagram of the structure of a computer device provided in an embodiment of the present application. The computer device includes one or more processors 31 and a memory 32. The memory 32 is connected to the one or more processors 31, for example, connected to the processor 31 via a bus.

[0171] The processor 31 is configured to support the computer device to perform the corresponding functions in the method in the above method embodiment. The processor 31 can be a central processing unit (CPU), a network processor (NP), a hardware chip or any combination thereof. The above hardware chip can be an application specific integrated circuit (ASIC), a programmable logic device (PLD) or a combination thereof. The above PLD can be a complex programmable logic device (CPLD), a field-programmable gate array (FPGA), a generic array logic (GAL) or any combination thereof.

[0172] The memory 32 is used to store program codes, etc. The memory 32 may include a volatile memory (VM), such as a random access memory (RAM); the memory 32 may also include a non-volatile memory (NVM), such as a read-only memory (ROM), a flash memory, a hard disk drive (HDD) or a solid-state drive (SSD); the memory 32 may also include a combination of the above-mentioned types of memories.

[0173] The memory 32 may be used to store non-volatile software programs, non-volatile computer executable programs and modules, such as program instructions / modules corresponding to the project performance management method in the embodiment of the present application. The processor 31 executes various functional applications and data processing of the project performance management method and the project performance management device by running the non-volatile software programs, instructions and modules stored in the memory 32, that is, realizes the functions of the project performance management method and the various modules or units of the project performance management device provided in the above method embodiment.

[0174] The memory 32 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and an application required by at least one function. The data storage area may store data created according to the use of the project performance management device, etc. In some embodiments, the memory 32 may optionally include a memory 32 remotely arranged relative to the processor 31, and these remote memories 32 may be connected to the project performance management device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0175] The one or more modules are stored in the memory 32, and when executed by the one or more processors 31, the project performance management method in any of the above method embodiments is executed, for example, the method steps described in the above method embodiments are executed to realize the functions of the modules described in the above device embodiments.

[0176] An embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by a computer, the computer executes the method described in the above embodiment.

[0177] A person skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above-mentioned methods. The storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc.

[0178] The above disclosure is only the preferred embodiment of the present application, which certainly cannot be used to limit the scope of rights of the present application. Therefore, equivalent changes made according to the claims of the present application are still within the scope covered by the present application.

Claims

1. A project performance management method, characterized in that: include: Obtaining performance data of at least two projects and each of the at least two projects; Performing data preprocessing on the performance data of each of the projects to obtain preprocessed first performance data of each of the projects; Performing data processing on the first performance data of each project to obtain target performance data of each project; Performing verification and calculation on the target performance data of each project to obtain the performance ranking of each project; Based on the performance ranking of each project, a performance management report is obtained.

2. The method according to claim 1, characterized in that The performing data processing on the first performance data of each project to obtain the target performance data of each project includes: Obtain each sub-data in the first performance data, where the sub-data corresponds to a sub-item under each item; Determine the completion degree of the sub-projects under each project according to each sub-data; Obtain preset sub-item weights and preset deviation coefficients; Calculating the degree of completion of the sub-items under each project, the preset sub-item weights and the preset deviation coefficients to obtain sub-performance data of the sub-items under each project; The sub-performance data of the sub-projects under each project are combined to obtain the target performance data of each project.

3. The method according to claim 1, characterized in that The performance data includes performance data in multiple dimensions, and the data processing of the first performance data of each project to obtain the target performance data of each project includes: Obtaining performance data under each dimension of the first performance data; Standardizing the performance data in each dimension of the first performance data to obtain a standardized standard performance value in each dimension; Using each dimension as a query identifier, querying in a preset weight database to obtain a weight value corresponding to each dimension; The standard performance value under each dimension in the first performance data and the weight value corresponding to each dimension are weighted averaged to obtain the target performance data of each project, and the target performance data is the indicator value of the performance data of all dimensions under each project.

4. The method according to claim 1, characterized in that: The performance data includes performance data in multiple dimensions, and the data processing of the first performance data of each project to obtain the target performance data of each project includes: Obtaining performance data under each dimension of the first performance data; The performance data under each dimension is input into a preset performance model to obtain the target performance data of each project output by the preset performance model. The preset performance model is a model for performance evaluation of the performance data under multiple dimensions of each project, and the target performance data is the indicator value of the performance data of all dimensions under each project.

5. The method according to claim 1, characterized in that The checking and calculating of the target performance data of each project to obtain the performance order of each project includes: According to the preset Kruskal-Wallis test, statistical analysis is performed on the target performance data of each project to determine the performance difference between each project and obtain the first performance difference between each project; Determine whether the first performance difference between each of the projects is the same; If they are not the same, a preset Nemenyi test calculation is performed on the target performance data of each project to obtain the performance order of each project; or, If they are the same, it is determined that the target performance data of each project is the same.

6. The method according to claim 5, characterized in that The Nemenyi test calculation is performed on the target performance data of each project to obtain the performance order of each project, including: Performing a preset Nemenyi test calculation on the target performance data of each project to obtain the performance difference value between any two projects; According to the performance difference value, obtaining the second performance difference between the two items; The performance order of each of the at least two projects is determined according to the second performance difference between the two projects.

7. The method according to claim 1, characterized in that The performing data preprocessing on the performance data of each project to obtain the preprocessed first performance data of each project includes: Cleaning the performance data of each project to obtain second performance data that meets the first preset performance inspection rule; In the second performance data, third performance data that meets the second inspection rule is identified, and the third performance data is the first performance data of each item after preprocessing.

8. A project performance management device, comprising: An acquisition unit, used for acquiring at least two projects and performance data of each of the at least two projects; A processing unit, configured to perform data preprocessing on the performance data of each project to obtain first performance data of each project after preprocessing; The processing unit is further used to perform data processing on the first performance data of each project to obtain target performance data of each project; A verification unit, used to verify and calculate the target performance data of each project to obtain the performance order of each project; The confirmation unit is used to obtain a performance management report according to the performance order of each project.

9. A computer device, comprising a memory and a processor, wherein the memory is connected to the processor, and the processor is used to execute one or more computer programs stored in the memory, and when the processor executes the one or more computer programs, the computer device implements the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by a processor, the processor is caused to perform the method according to any one of claims 1 to 7.

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