Data processing method and device, computer device, readable storage medium and program product

Abstract

The application relates to a data processing method, device, equipment, readable storage medium and product. The method comprises the following steps: obtaining target data corresponding to a code hosting platform, a project management platform and an organization management platform; generating actual task completion indexes and abnormal indexes corresponding to each to-be-evaluated object based on the target data; generating project completion indexes and department completion indexes based on the actual task completion indexes and the abnormal indexes; and generating an evaluation report according to the actual task completion indexes, the abnormal indexes, the project completion indexes and the department completion indexes. By establishing a correlation between research and development activity data and project plan data, the accuracy and consistency of the evaluation result are improved.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular to a data processing method, apparatus, computer equipment, computer-readable storage medium, and computer program product. Background Technology

[0002] With the development of software R&D technology and DevOps (Development and Operations) technology, more and more R&D teams are adopting Git (Global Information Tracker, a distributed version control system) as a code version management tool, and using code hosting platforms such as GitLab (Git Laboratory, a Git-based code hosting platform) and GitHub (a global code hosting and collaborative development platform) to complete R&D activities such as code development, version management, code review, and defect tracking, thereby forming relatively complete R&D activity data.

[0003] Currently, in order to monitor project progress, requirement completion status, and staff work status, R&D teams typically use project management tools to manage projects. While existing project management tools can manage requirements, tasks, and key project milestones, the project management information they rely on mostly requires manual maintenance and cannot automatically generate corresponding project management information directly from the R&D activity data already generated in the code hosting platform.

[0004] Therefore, project plan data in existing project management tools is usually stored separately from R&D activity data in code hosting platforms, resulting in a lack of automatic correlation between the two types of data. When performing project analysis, data from different systems needs to be repeatedly retrieved, matched, and synchronized, which increases the consumption of storage and computing resources during data processing and affects the timeliness and accuracy of project analysis results. Summary of the Invention

[0005] Therefore, it is necessary to provide a data processing method, apparatus, computer equipment, computer-readable storage medium, and computer program product that can improve the real-time performance and accuracy of project management, addressing the aforementioned technical problems.

[0006] In a first aspect, this application provides a data processing method, including:

[0007] Acquire the first target data collected from the code hosting platform, the second target data collected from the project management platform, and the third target data collected from the organization management platform;

[0008] Based on the first target data and the second target data, generate actual task completion indicators and abnormal indicators for each object to be evaluated within the target analysis period;

[0009] By combining the third target data, and performing correlation analysis based on at least one of the actual task completion indicators and the abnormal indicators, departmental completion indicators and project completion indicators are obtained.

[0010] An evaluation report is generated based on the actual task completion indicators and abnormal indicators of the object to be evaluated, the department completion indicators, and the project completion indicators.

[0011] In one embodiment, acquiring the first target data collected from the code hosting platform, the second target data collected from the project management platform, and the third target data collected from the organization management platform includes:

[0012] Load the corresponding data source adapters for the code hosting platform, the project management platform, and the organization management platform, respectively;

[0013] Obtain the synchronization cursors corresponding to the code hosting platform, the project management platform, and the organization management platform respectively; the synchronization cursors are used to determine the synchronization position.

[0014] Through the data source adapter, based on the synchronization location, the first incremental data of the code hosting platform, the second incremental data of the project management platform, and the third incremental data of the organization management platform are read;

[0015] The first target data is obtained based on the first incremental data, the second target data is obtained based on the second incremental data, and the third target data is obtained based on the third incremental data.

[0016] In one embodiment, the step of generating actual task completion indicators and anomaly indicators for each object to be evaluated within the target analysis period based on the first target data and the second target data includes:

[0017] Based on preset matching rules, the first target data and the second target data of each of the objects to be evaluated within the target analysis period are associated to obtain the task association results of each of the objects to be evaluated in each project.

[0018] Obtain the historical task completion indicators of each object to be evaluated corresponding to the target analysis period mentioned above;

[0019] Based on the historical task completion metrics and the task association results, the actual task completion metrics and the abnormal metrics are obtained.

[0020] In one embodiment, obtaining the actual task completion indicator and the anomaly indicator based on the historical task completion indicator and the task association result includes:

[0021] Based on the historical task completion metrics and the task association results, the actual task completion metrics are obtained.

[0022] Calculate the statistical deviation between the actual task completion indicator and the historical task completion indicator, and calculate the abnormal indicator based on the statistical deviation.

[0023] In one embodiment, the actual task completion indicator is implemented through a first preset model; the process by which the first preset model obtains the actual task completion indicator based on the historical task completion indicator and task association results includes:

[0024] For each of the task association results, calculate the matching degree between the first target data and the second target data;

[0025] Based on preset scoring rules, the historical task completion indicators, and the matching degree, the task association results are scored to obtain the actual task completion indicators.

[0026] In one embodiment, the step of calculating the statistical deviation between the actual task completion indicator and the historical task completion indicator, and calculating the abnormal indicator based on the statistical deviation, includes:

[0027] Calculate the statistical deviation between the actual task completion indicator and the historical task completion indicator;

[0028] When the historical task completion indicator meets the preset stability condition and the statistical deviation value is greater than the first preset threshold, the actual task completion indicator is determined as a candidate abnormal indicator.

[0029] When the historical task completion indicator meets the preset stability condition and the statistical deviation value is greater than the second preset threshold, the actual task completion indicator is determined to be a real abnormal indicator.

[0030] In one embodiment, by combining the third target data and performing correlation analysis based on at least one of the actual task completion indicators and the abnormal indicators, departmental completion indicators and project completion indicators are obtained, including:

[0031] The project completion indicators are obtained by analyzing the actual task completion indicators, abnormal indicators, key project nodes, and historical project analysis indicators corresponding to each object to be evaluated in the project member list.

[0032] The departmental completion indicators are obtained by analyzing the project completion indicators and project status information of each project under each department; the project status information is structured summary data.

[0033] In one embodiment, the method further includes:

[0034] Receive a project management request; the project management request carries project identification information;

[0035] Based on the project identification information, query the corresponding project management data in the project management repository;

[0036] If no project management data corresponding to the project identification information is found, a target project management repository is created, and the second target data is generated.

[0037] When project management data corresponding to the project identification information is found, the second target data is read.

[0038] Secondly, this application also provides a data processing apparatus, comprising:

[0039] The acquisition module is used to acquire the first target data collected from the code hosting platform, the second target data collected from the project management platform, and the third target data collected from the organization management platform.

[0040] The first evaluation module is used to generate actual task completion indicators and abnormal indicators for each object to be evaluated within the target analysis period, based on the first target data and the second target data.

[0041] The second evaluation module is used to combine the third target data and perform correlation analysis based on at least one of the actual task completion indicators and the abnormal indicators to obtain the department completion indicators and the project completion indicators.

[0042] The third evaluation module is used to generate an evaluation report based on the actual task completion indicators and abnormal indicators of the object to be evaluated, the department completion indicators, and the project completion indicators.

[0043] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method in any of the above embodiments.

[0044] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method in any of the above embodiments.

[0045] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the methods in any of the above embodiments.

[0046] The aforementioned data processing method, apparatus, computer equipment, computer-readable storage medium, and computer program product first acquire first target data from a code hosting platform, second target data from a project management platform, and third target data from an organizational management platform. Then, based on the first and second target data, they generate actual task completion indicators and abnormal indicators for each object to be evaluated. Next, they aggregate and analyze the actual task completion indicators and abnormal indicators for each object to be evaluated, combining them with the third target data, to obtain project completion indicators and departmental completion indicators. Finally, they generate an evaluation report based on the actual task completion indicators, abnormal indicators, project completion indicators, and departmental completion indicators. Through this method, this application can establish an automatic correlation between R&D activity data and project plan data, enabling R&D activity data in the code hosting platform to be automatically converted into project evaluation results. This reduces the data processing and data transmission overhead caused by repeated retrieval, matching, and synchronization processing of data in different systems. Simultaneously, it can update project evaluation results in real time based on actual R&D activities, thereby improving the real-time performance and accuracy of project evaluation results. Attached Figure Description

[0047] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0048] Figure 1 This is a flowchart illustrating a data processing method in one embodiment;

[0049] Figure 2 This is a flowchart illustrating the data processing steps in an exemplary embodiment.

[0050] Figure 3 This is a structural block diagram of a data processing device in one embodiment;

[0051] Figure 4 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0052] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0053] In one embodiment, such as Figure 1 As shown, a data processing method is provided. This embodiment illustrates the method applied to a terminal, but it is understood that the method can also be applied to a server, or to a system including both a terminal and a server, and implemented through interaction between the terminal and the server. In this embodiment, the method includes the following steps:

[0054] Step 102: Obtain the first target data collected from the code hosting platform, the second target data collected from the project management platform, and the third target data collected from the organization management platform.

[0055] In this context, a code hosting platform refers to a platform used to manage source code, version information, and development process data. This can include GitLab (Git Laboratory, a Git-based code hosting platform), GitHub (a global code hosting and collaborative development platform), Gitee (a Git-based code hosting platform), etc. The first target data in this embodiment refers to development activity data generated during the development process, such as code commit records, merge request records, issue tracking records, code review records, branch information, and continuous integration / continuous delivery (CI / CD) status information.

[0056] The project management platform refers to a platform used to manage project requirements, task plans, and project progress. This platform can be Jira (Atlassian project management and defect tracking platform), TAPD (Tencent Agile Product Development platform), ZenTao (ZenTao Project Management Software), etc. The second target data in this embodiment refers to project plan data generated during the project management process, such as project requirement information, task information, key project milestone information, phase target information, requirement status information, task owner information, and project progress information.

[0057] The organizational management platform refers to a platform used to manage organizational structure and personnel information, such as DingTalk, WeCom, and Feishu. In this embodiment, the third target data refers to organizational information generated during the organizational management process, such as department information, job information, personnel information, hierarchical relationships, project member information, and the organizational affiliation of personnel.

[0058] Optionally, the first target data, the second target data, and the third target data can be collected periodically from code hosting platforms, project management platforms, and organization management platforms.

[0059] Furthermore, due to differences in data update frequencies across different platforms, different data collection frequencies are configured for each platform. Specifically, the update frequency of R&D activity data on code hosting platforms is relatively high, thus requiring a shorter data collection cycle; the update frequency of project plan data on project management platforms is relatively low, thus requiring a medium data collection cycle; and the update frequency of organizational structure data on organizational management platforms is low, thus requiring a longer data collection cycle. For example, a first update frequency is used to collect the first target data, a second update frequency is used to collect the second target data, and a third update frequency is used to collect the third target data. The first update frequency is greater than or equal to the second update frequency, and the second update frequency is greater than the third update frequency.

[0060] For example, first-target data and second-target data can be collected according to the frequency of the target analysis cycle. When it is necessary to analyze the first-target data within the target analysis cycle, the second-target data within the corresponding cycle is collected simultaneously. This is because the first-target data mainly reflects the R&D activities of the object being evaluated within the target analysis cycle, while subsequent analysis processes also need to analyze the first-target data in conjunction with second-target data such as project requirements, key project milestones, and stage goals. Since the second-target data may change with changes in requirements, adjustments to project plans, or updates to key project milestones, the second-target data within the corresponding cycle is collected simultaneously to ensure that the data used in subsequent analyses has a consistent time frame.

[0061] Because developers may participate in both internal company projects and open-source projects simultaneously, and internal code repositories typically require submissions using corporate email addresses or employee accounts, while open-source code repositories usually use personal Git accounts or personal email addresses, the code submission records of the same object to be evaluated on different code hosting platforms may correspond to different identity identifiers. Therefore, it is necessary to merge the primary target data of the same object to be evaluated based on its identity identifier.

[0062] Optionally, when merging the first target data of the same object to be evaluated based on identity identifiers, the matching is performed in the following order: First, matching is performed on the project management platform, that is, matching the identity identifiers in the first target data with the personnel information in the project management platform according to the personnel mapping relationships pre-maintained in the project management platform. When the match is successful, it is determined that the corresponding first target data belongs to the same object to be evaluated. When no corresponding personnel mapping relationship exists, exact matching is used with email addresses, that is, determining whether the email addresses in different first target data are consistent. When the email addresses are consistent, it is determined that the corresponding first target data belongs to the same object to be evaluated. When exact matching with email addresses fails, matching is performed with email address rules, that is, matching is performed based on email address prefixes, email address domains, or preset email address mapping rules. When the match is successful, it is determined that the corresponding first target data belongs to the same object to be evaluated. When matching with email address rules fails, matching is performed with name rules, that is, the usernames in different first target data are normalized and matched in combination with the co-occurrence relationships in the corresponding projects. When the normalized usernames are consistent and the corresponding R&D activities appear in the same project, it is determined that the corresponding first target data belongs to the same object to be evaluated. If none of the above matching methods can determine a unique match, the corresponding identity identifier is marked as pending confirmation, and a list of candidate matching results is generated. The project manager or administrator then manually confirms the candidate matching results; upon successful confirmation, the corresponding first target data is associated with the corresponding object to be evaluated.

[0063] For example, a developer uses " Submit your code and use it on the open-source code hosting platform. "When submitting code, the user submits code in another code repository using "Zhang San" as the username. In this case, the system first attempts to match the user through the personnel mapping relationship in the project management platform; if no corresponding mapping relationship exists, it further uses methods such as exact email matching, email rule matching, and name rule matching to determine that the above multiple identity identifiers correspond to the same object to be evaluated, and merges the corresponding first target data."

[0064] Step 104: Based on the first target data and the second target data, generate the actual task completion indicators and abnormal indicators for each object to be evaluated within the target analysis period.

[0065] As mentioned above, the first target data refers to the R&D activity data generated during the R&D process, and the second target data refers to the project data generated during the project management process. Therefore, when evaluating the object to be evaluated, it is necessary to combine the first target data and the second target data for analysis.

[0066] Optionally, the project requirements, key project milestones, and phase goals in the second target data can be used as a reference to analyze the first target data, thereby determining the task execution status of the object to be evaluated within the target analysis period, and generating corresponding actual task completion indicators and anomaly indicators. The actual task completion indicators characterize the execution status of the corresponding tasks of the object to be evaluated within the target analysis period, such as task completion rate, requirement matching rate, goal achievement rate, or task contribution rate. The anomaly indicators characterize abnormal changes in the object to be evaluated within the target analysis period compared to historical analysis periods, such as abnormal R&D activity frequency, abnormal task progress, abnormal requirement completion, or abnormal delays in key project milestones.

[0067] Optionally, since the same object to be evaluated may participate in the development of multiple projects at the same time, it is necessary to associate the first target data with the second target data to obtain the target R&D activity data of the object to be evaluated in each project.

[0068] Optionally, after the association is completed, analysis can be conducted based on the target R&D activity data of each project, as well as the corresponding project requirements, key project nodes, and stage goals, to determine the task completion status of the object to be evaluated within the target analysis period.

[0069] For example, when an object to be evaluated participates in the development of requirements A and B within the target analysis cycle, the system first associates the code submission record, merge request record, and issue tracking record corresponding to the object to be evaluated with requirements A and B respectively; then, based on the planned completion status and actual R&D activities corresponding to requirements A and B, the system determines the actual task completion indicators corresponding to the object to be evaluated; at the same time, combined with the actual task completion indicators corresponding to the historical analysis cycles, the system determines whether there are any abnormal changes in the current target analysis cycle and generates corresponding abnormal indicators.

[0070] Optionally, a pre-trained model can be used to analyze the first and second target data to obtain actual task completion metrics and anomaly metrics.

[0071] Optionally, based on the first target data and the second target data corresponding to multiple target analysis cycles, the changing trend of the task completion status of the object to be evaluated can be determined, and the corresponding actual task completion indicators can be generated according to the changing trend.

[0072] Optionally, the system can also determine the changing trend of the task completion status of the object to be evaluated based on the first target data and the second target data corresponding to multiple target analysis periods. Specifically, the system can separately count the number of code submissions, merge requests, issue closures, and related requirement completions associated with the target requirements or key project nodes for the object to be evaluated within multiple target analysis periods, and calculate the amount or percentage of change between adjacent target analysis periods. When the above amount or percentage of change meets the preset trend conditions, the actual task completion indicators of the object to be evaluated are adjusted according to the corresponding amount or percentage of change.

[0073] Optionally, corresponding anomaly indicators can be generated based on requirement change information and the first target data within the target analysis period. Specifically, when project requirements, key project nodes, or phase goals recorded in the second target data change within the target analysis period, the system obtains the requirement content, responsible person, planned completion time, and associated file paths before and after the requirement change, and redetermines the relationship between the first and second target data based on the information after the requirement change. Then, the system compares the number of associated R&D activities, the number of unclosed issues, the number of unmerged requests, and the planned completion time offset for the same evaluation object before and after the requirement change to obtain a change statistical deviation value. When the change statistical deviation value exceeds a preset statistical deviation threshold, a corresponding anomaly indicator is generated.

[0074] Step 106: Combine the third target data and perform correlation analysis based on at least one actual task completion indicator and abnormal indicator to obtain the department completion indicator and project completion indicator.

[0075] After determining the actual task completion indicators and abnormal indicators for each object to be evaluated, we can further combine the project member information, department affiliation information and organizational relationship information in the third target data to perform aggregate analysis on the actual task completion indicators and abnormal indicators for multiple objects to be evaluated, so as to obtain the project completion indicators and department completion indicators.

[0076] Optionally, aggregate analysis can be performed based on the project completion indicators corresponding to each project under the same department to obtain the departmental completion indicators.

[0077] Optionally, project completion metrics can also be obtained by analyzing the actual task completion metrics, abnormal metrics, key project milestones, and historical project completion metrics of project members.

[0078] Step 108: Generate an evaluation report based on the actual task completion indicators, abnormal indicators, departmental completion indicators, and project completion indicators of the object to be evaluated.

[0079] After obtaining the actual task completion indicators, abnormal indicators, project completion indicators, and departmental completion indicators, these indicators can be summarized and analyzed, and corresponding evaluation reports can be generated based on the analysis results. These evaluation reports characterize the performance of the corresponding evaluated objects, projects, and departments within the target analysis period.

[0080] Optionally, actual task completion indicators, abnormal indicators, project completion indicators, and departmental completion indicators can be input into the first preset model. The first preset model generates corresponding evaluation summaries, risk descriptions, and improvement suggestions, and an evaluation report is generated based on the evaluation summaries, risk descriptions, and improvement suggestions.

[0081] In the above embodiments, firstly, first target data from the code hosting platform, second target data from the project management platform, and third target data from the organization management platform are acquired; then, based on the first and second target data, actual task completion indicators and abnormal indicators corresponding to each object to be evaluated are generated; subsequently, the actual task completion indicators and abnormal indicators corresponding to each object to be evaluated are aggregated and analyzed in conjunction with the third target data to obtain project completion indicators and department completion indicators; finally, an evaluation report is generated based on the actual task completion indicators, abnormal indicators, project completion indicators, and department completion indicators. Through this method, this application can establish an automatic correlation between R&D activity data and project plan data, enabling R&D activity data in the code hosting platform to be automatically converted into project evaluation results, reducing the need for manual maintenance of project management information, and simultaneously enabling real-time updates of project evaluation results based on actual R&D activities, thereby improving the real-time performance and accuracy of project evaluation results.

[0082] In one embodiment, the acquisition of the first target data collected from the code hosting platform, the second target data collected from the project management platform, and the third target data collected from the organization management platform includes: loading corresponding data source adapters for the code hosting platform, the project management platform, and the organization management platform, respectively; acquiring synchronization cursors corresponding to the code hosting platform, the project management platform, and the organization management platform, respectively; the synchronization cursors are used to determine the synchronization position; reading the first incremental data from the code hosting platform, the second incremental data from the project management platform, and the third incremental data from the organization management platform based on the synchronization position through the data source adapters; obtaining the first target data based on the first incremental data, obtaining the second target data based on the second incremental data, and obtaining the third target data based on the third incremental data.

[0083] Because the data structures, access interfaces, and data update methods of code hosting platforms, project management platforms, and organization management platforms differ, it is necessary to configure corresponding data source adapters for each platform. These data source adapters are used to obtain data from the corresponding platform based on its data format and interface specifications.

[0084] As mentioned above, the data update frequencies of code hosting platforms, project management platforms, and organization management platforms typically differ. Therefore, the system maintains a synchronization cursor for each platform. For example, the synchronization cursor records the synchronization position when a data synchronization is completed on the corresponding platform. After completing this data synchronization, the system records the position corresponding to the last successfully processed data in this synchronization process as a new synchronization cursor, serving as the starting position for the next data synchronization. The synchronization position can be one or more of the following: commit time, update time, event sequence number, log offset, or version number. For instance, for a code hosting platform, the commit time corresponding to the last synchronized code commit can be used as the synchronization cursor; for a project management platform, the update time corresponding to the last synchronized requirement record can be used as the synchronization cursor; and for an organization management platform, the event sequence number corresponding to the last synchronized organization change record can be used as the synchronization cursor.

[0085] Optionally, the first incremental data of the code hosting platform is read from the code hosting platform based on the first synchronization position through the data source adapter corresponding to the code hosting platform; the second incremental data of the project management platform is read from the project management platform based on the second synchronization position through the data source adapter corresponding to the project management platform; and the third incremental data of the organization management platform is read from the organization management platform based on the third synchronization position through the data source adapter corresponding to the organization management platform.

[0086] Optionally, for the first incremental data corresponding to the code hosting platform, since the first incremental data mainly includes R&D activity data such as code commit records, merge request records, issue tracking records, and code review records, it can be directly used as the first target data. For the second incremental data corresponding to the project management platform and the third incremental data corresponding to the organization management platform, since data such as project requirement information, key project node information, organizational structure information, and personnel affiliation information usually have continuous validity, the second and third incremental data can be merged with historical stored data respectively to obtain the corresponding second and third target data.

[0087] Optionally, after obtaining the first target data, the second target data, and the third target data, the first target data is written to the Activity table, the second target data is written to the PMSnapshot table, and the third target data is written to the Org table. Simultaneously, the system updates the synchronization cursor corresponding to the current data source and generates the corresponding SyncLog.

[0088] In the above embodiments, by using a data source adapter and a synchronization cursor to incrementally synchronize data from different platforms, the data transmission overhead caused by repeated data collection can be reduced.

[0089] In one embodiment, the above-mentioned generation of actual task completion indicators and abnormal indicators for each object to be evaluated within the target analysis period based on the first target data and the second target data includes: associating the first target data and the second target data of each object to be evaluated within the target analysis period based on a preset matching rule to obtain the task association results of each object to be evaluated in each project; obtaining the historical task completion indicators of each object to be evaluated corresponding to the previous target analysis period; and obtaining the actual task completion indicators and abnormal indicators based on the historical task completion indicators and the task association results.

[0090] Among them, the preset matching rules refer to the rules used to establish the correspondence between R&D activity data and project plan data.

[0091] As mentioned above, the same entity to be evaluated may participate in multiple projects simultaneously. Therefore, the first target data typically includes multiple first target sub-data. These first target sub-data refer to the data of a single R&D activity corresponding to the entity to be evaluated, such as code commit records, merge request records, issue tracking records, and code review records. Correspondingly, the second target data includes multiple second target sub-data. These second target sub-data refer to the project plan data corresponding to the project, such as project requirements information, key project milestone information, phase target information, and task information.

[0092] Therefore, it is necessary to associate the first target sub-data with the corresponding second target sub-data according to preset matching rules, thereby determining the project requirements corresponding to each R&D activity. One second target sub-data can be associated with multiple first target sub-data. For example, the same project requirement may correspond to multiple code commit records, multiple merge request records, and multiple issue tracking records during development; therefore, the association result can be represented as one second target sub-data corresponding to multiple first target sub-data.

[0093] Optionally, the association between the first target sub-data and the second target sub-data can be established based on one or more of the following: project identifier, requirement identifier, task identifier, branch name, file path, commit description, and issue number. For example, when the requirement number in the code commit record matches the requirement number in the project requirement information, it is determined that the code commit record is associated with the corresponding project requirement.

[0094] Optionally, the association between the first target sub-data and the second target sub-data can be established based on one or more of the following: project identifier, requirement identifier, task identifier, branch name, file path, submission description, and issue number.

[0095] Specifically, when a code commit record contains a requirement number or issue number, a corresponding association is established based on the requirement number or issue number; when the branch name corresponding to a code commit record meets the preset naming rules, a corresponding association is established based on the branch name; when the path of the changed file corresponding to a code commit record and the path of the target module corresponding to the project requirement meet the preset path conditions, a corresponding association is established based on the file path; when the person in charge information corresponding to a code commit record and the person in charge information corresponding to the project requirement meet the preset correspondence, a corresponding association is established based on the person in charge information.

[0096] When a relationship cannot be established based on the above information, or when multiple candidate relationship results are obtained based on the above information, the R&D activity description information corresponding to the first target sub-data and the project description information corresponding to the second target sub-data are obtained, and the matching degree between the R&D activity description information and the project description information is calculated using the first preset model. The R&D activity description information may include one or more of the following: submission instructions, problem descriptions, merge request instructions, and code review content; the project description information may include one or more of the following: requirement descriptions, project architecture descriptions, project key node descriptions, and stage goal descriptions.

[0097] Then, the association score is calculated based on the matching degree of each candidate association result and the preset scoring rules, and the candidate association result with the highest association score is determined as the target association result.

[0098] Optionally, after obtaining the task correlation results, analysis can be performed in conjunction with historical task completion indicators. Since historical task completion indicators can reflect the task execution status of the object under evaluation within the historical analysis period, by combining historical task completion indicators with the task correlation results corresponding to the current target analysis period, the changes in the current task execution status compared to the historical task execution status can be determined, and corresponding actual task completion indicators and abnormal indicators can be generated accordingly.

[0099] In the above embodiments, by establishing a correlation between R&D activity data and project plan data, an automatic mapping between R&D activities and project tasks can be achieved, thereby improving the accuracy of subsequent task evaluation results.

[0100] In one embodiment, obtaining the actual task completion index and the abnormal index based on the historical task completion index and the task association result includes: obtaining the actual task completion index based on the historical task completion index and the task association result; calculating the statistical deviation value between the actual task completion index and the historical task completion index; and calculating the abnormal index based on the statistical deviation value.

[0101] Optionally, historical task completion indicators and task association results can be input into a first preset model, and the actual task completion indicators can be obtained by the first preset model based on the historical task completion indicators and task association results.

[0102] Furthermore, the process by which the aforementioned first preset model obtains actual task completion indicators and abnormal indicators based on historical task completion indicators and task association results includes: calculating the matching degree between the first target data and the second target data for each task association result; and scoring the task association results based on preset scoring rules, historical task completion indicators, and matching degree to obtain actual task completion indicators.

[0103] The first preset model is used to analyze the task execution status of the object to be evaluated within the target analysis period based on the task association results and historical task completion indicators.

[0104] When calculating actual task completion metrics, the first preset model first calculates the matching degree between the first target data and the second target data for each task-related result. The matching degree characterizes the correspondence between R&D activities and project plans in the task-related results. A higher matching degree indicates a greater consistency between the R&D activities corresponding to the first target data and the project requirements, key project milestones, and phase goals in the second target data; a lower matching degree indicates a greater deviation between the corresponding R&D activities and the project plan.

[0105] Optionally, the first preset model can extract feature information corresponding to the first target data and the second target data respectively, and calculate the matching degree based on the corresponding feature information. The feature information corresponding to the first target data may include one or more of the following: code submission instructions, issue tracking records, merge request descriptions, and code review information; the feature information corresponding to the second target data may include one or more of the following: project requirement descriptions, project key node descriptions, phase goal descriptions, and task descriptions.

[0106] Subsequently, the first pre-defined model scores the task association results based on pre-defined scoring rules, historical task completion metrics, and matching degree. The pre-defined scoring rules include corresponding bonus and deduction rules. When the matching degree is higher than a pre-defined matching threshold, the score of the corresponding task association result is increased; when the matching degree is lower than the pre-defined matching threshold, the score of the corresponding task association result is decreased. Simultaneously, the first pre-defined model can also combine historical task completion metrics to determine the degree of change in the current task completion status relative to historical task completion status. When the difference between the current score and historical task completion metrics exceeds a pre-defined difference threshold, a corresponding abnormality indicator is generated.

[0107] For example, when an object to be evaluated completes the code development, code review, and problem handling corresponding to the project requirements within the target analysis period, and the corresponding R&D activities have a high degree of matching with the project requirements, key project nodes, and stage goals, the score of the corresponding task association result is increased; when the matching degree between the corresponding R&D activities and the project plan is low, or the current score shows a significant decline compared to historical task completion indicators, the corresponding abnormal indicator is generated.

[0108] The first preset model can be trained using historical project data. This historical project data includes historical R&D activity data, historical project plan data, and corresponding historical task completion metrics. Through training, the first preset model learns the correspondence between R&D activity data and project plan data, and generates actual task completion metrics and anomaly metrics based on the learned correspondence.

[0109] In this way, by combining historical task completion indicators and task-related results to analyze task execution, we can simultaneously consider the correspondence between current R&D activities and project plans, as well as historical execution, thereby improving the accuracy of actual task completion indicators and abnormal indicators.

[0110] Furthermore, after obtaining the actual task completion indicators, abnormal indicators are calculated based on the actual task completion indicators.

[0111] Optionally, the statistical deviation between the actual task completion indicators and the historical task completion indicators is calculated, and anomaly indicators are calculated based on the statistical deviation.

[0112] The statistical deviation value is used to characterize the degree of change in the current task completion status compared to historical normal levels. For example, if the task completion indicators of an object to be evaluated have generally remained at a high level over a historical period, but the actual task completion indicators for the current target analysis period have decreased significantly, it indicates that there is an abnormal change in the current task execution status compared to the historical state. Therefore, corresponding abnormal indicators can be generated based on this statistical deviation value.

[0113] Optionally, before calculating abnormal indicators based on historical task completion indicators, it can be determined whether the historical task completion indicators meet preset stability conditions. Preset stability conditions may include one or more of the following: the number of historical samples is greater than or equal to a preset quantity threshold, the fluctuation range of historical task completion indicators is less than a preset fluctuation threshold, and the standard deviation of historical task completion indicators is less than a preset standard deviation threshold. Optionally, preset stability conditions may also include that the rate of change corresponding to the historical task completion indicators meets preset change conditions, and the data integrity rate corresponding to the historical task completion indicators meets preset integrity rate conditions, etc.

[0114] When historical task completion indicators meet preset stability conditions, it indicates that these indicators reflect the task execution level of the evaluated object under normal conditions. At this point, anomalies can be identified based on the statistical deviation between the current actual task completion indicator and the historical indicator. Specifically, when the statistical deviation exceeds a first preset threshold, the current actual task completion indicator is marked as a candidate anomaly; when the statistical deviation exceeds a second preset threshold, or when multiple consecutive target analysis periods exceed the first preset threshold, the current actual task completion indicator is marked as a true anomaly. The second preset threshold is greater than the first preset threshold.

[0115] Optionally, the first preset threshold and the second preset threshold can be determined based on the statistical distribution of historical task completion indicators.

[0116] For example, a statistical deviation value can be calculated based on the mean and / or standard deviation corresponding to historical task completion indicators. The first preset threshold can be set as a first multiple of the corresponding mean and / or standard deviation, and the second preset threshold can be set as a second multiple of the corresponding mean and / or standard deviation, where the second multiple is greater than the first multiple.

[0117] For example, the first preset threshold can be set to 2 standard deviations, and the second preset threshold can be set to 3 standard deviations. In this case, when the statistical deviation value exceeds 2 standard deviations, the corresponding actual task completion indicator is marked as a candidate anomaly; when the statistical deviation value exceeds 3 standard deviations, the corresponding actual task completion indicator is marked as a true anomaly.

[0118] Optionally, the first preset threshold and the second preset threshold can also be configured by the administrator according to the department type, project type or indicator type; or dynamically adjusted based on historical data corresponding to historical completed indicators. This application does not limit this.

[0119] When historical task completion indicators do not meet the preset stability conditions, it indicates that the number of historical samples is insufficient or the historical task completion indicators themselves fluctuate significantly, making them difficult to use as a stable benchmark. In this case, instead of using historical statistical deviation values ​​to calculate abnormal indicators, a fixed threshold rule can be used for anomaly detection.

[0120] Among them, the fixed threshold rule refers to the pre-configured business judgment rule. The system can determine whether the corresponding abnormal conditions are met based on the task association results, actual task completion indicators, and project plan data.

[0121] For example, when the actual completion time of a key project node lags behind the planned completion time by more than a preset number of days, a corresponding abnormal indicator is generated; or when the number of related R&D activities corresponding to a key requirement within the target analysis period is zero, a corresponding abnormal indicator is generated; or when the actual task completion indicator is lower than a preset indicator threshold, a corresponding abnormal indicator is generated; or when the deviation of the planned project completion time from the historical planned completion time exceeds a preset deviation threshold, a corresponding abnormal indicator is generated.

[0122] Optionally, the fixed threshold rule may also include: generating a corresponding abnormal indicator when the number of unclosed issues exceeds a preset threshold within the target analysis period; or generating a corresponding abnormal indicator when the number of incomplete R&D activities exceeds a preset threshold within the target analysis period.

[0123] In the above embodiments, by combining historical task completion indicators with statistical deviation analysis of the current actual task completion indicators, the accuracy of anomaly identification results can be improved; at the same time, by setting different anomaly judgment thresholds, false alarms can be reduced and the reliability of anomaly identification results can be improved.

[0124] In one embodiment, by combining the third target data and performing correlation analysis based on at least one actual task completion indicator and anomaly indicator, departmental completion indicators and project completion indicators are obtained. This includes: analyzing the actual task completion indicators, anomaly indicators, key project nodes, and historical project analysis indicators corresponding to each evaluation object in the project member list to obtain project completion indicators; and analyzing the project completion indicators of each department and project, as well as the project status information corresponding to each project, to obtain departmental completion indicators.

[0125] The project member list refers to the set of entities to be evaluated who participated in the development of the corresponding project. This may include one or more of the following: project leader, developers, testers, and operations personnel. Project completion metrics are used to characterize the execution status of the corresponding project within the target analysis period, such as one or more of the following: project completion rate, project health status, project risk level, project progress statistical deviation value, and project goal achievement status.

[0126] Optionally, the actual task completion indicators, abnormal indicators, key project nodes, and historical project analysis indicators corresponding to each evaluation object in the project member list are input into the second preset model. The second preset model generates corresponding project completion indicators based on the task completion status, abnormal situations, and key project node completion status of each evaluation object.

[0127] The second preset model, when generating project completion indicators, can first take the actual task completion indicators, abnormal indicators, key project node information, and historical project analysis indicators corresponding to each object to be evaluated in the project member list as inputs to obtain the project analysis results of the corresponding project.

[0128] For example, the second preset model first generates an execution summary based on the actual task completion indicators corresponding to each object to be evaluated, which is used to characterize the overall project execution status within the target analysis period; then, it determines the critical path analysis results based on the project's key node information and task completion status, which is used to characterize the key tasks and bottleneck tasks affecting project progress; next, it determines the plan statistical deviation value analysis results based on the project's key node information, actual task completion indicators, and historical project analysis indicators, which is used to characterize the statistical deviation value of the current project execution status relative to the project plan and historical project status; finally, it generates risk analysis results based on abnormal indicators, which is used to characterize the risk items and risk levels existing in the current project.

[0129] Optionally, the project analysis results may also include a project execution score and a project progress percentage. The project execution score characterizes the overall project execution quality, while the project progress percentage characterizes the overall project completion level.

[0130] Optionally, after obtaining the project analysis results, the format of the results is validated. If the project analysis results meet the preset data format requirements, the results are stored in the project analysis database; otherwise, the corresponding project analysis results are regenerated.

[0131] Among them, project status information refers to project-level structured summary data generated by project-level analysis, including one or more of the following: execution summary, critical path / bottleneck, plan statistics deviation, risk matrix, project execution score, and progress percentage.

[0132] Optionally, the project completion indicators and corresponding project status information of each project are input into the third preset model. The data corresponding to each project are aggregated and analyzed by the third preset model to obtain the departmental completion indicators.

[0133] Among them, the third preset model can separately calculate the project scale indicators, project health indicators, project progress indicators, project risk indicators, project workload indicators, project collaboration indicators, and project anomaly indicators for each project under the department when generating department completion indicators.

[0134] Among them, the project scale indicator is used to represent the number of projects, the number of project members, and the number of active projects under the department; the project health indicator is used to represent the distribution of project completion indicators for each project under the department; the project progress indicator is used to represent the number of key milestones of completed projects, the number of key milestones of delayed projects, and the completion status of requirements; the project risk indicator is used to represent the number of risk events and the distribution of risk levels; the project workload indicator is used to represent the number of R&D activities and the number of requirements processed by project members; the project collaboration indicator is used to represent the cross-project collaboration and the response status of R&D activities; and the project anomaly indicator is used to represent the number of anomaly indicators and the distribution of anomaly types.

[0135] Subsequently, the third preset model performs aggregate analysis based on project size indicators, project health indicators, project schedule indicators, project risk indicators, project workload indicators, project collaboration indicators, and project anomaly indicators to obtain departmental completion indicators.

[0136] Optionally, after obtaining the above aggregated indicators, the aggregated indicators and the corresponding project status information can be input into the third preset model to generate departmental-level project execution status, departmental-level risk status, and departmental-level project progress status. The project status information may include one or more of the following: execution summary, critical path analysis results, plan statistical deviation value analysis results, and risk analysis results.

[0137] For example, when a department includes Project A, Project B, and Project C, the system first generates project completion indicators for Project A, Project B, and Project C respectively; then, by combining the project status information for Project A, Project B, and Project C, the system determines the overall project completion status, risk status, and progress status of the department, and generates corresponding departmental completion indicators.

[0138] In the above embodiments, by performing project-level aggregate analysis on the actual task completion indicators and abnormal indicators corresponding to the evaluation object, and further performing department-level aggregate analysis, it is possible to achieve step-by-step analysis from the individual dimension to the project dimension and the department dimension, thereby improving the accuracy of the evaluation results on the overall project execution.

[0139] In one embodiment, the method further includes: receiving a project management request; the project management request carrying project identification information; querying the corresponding project management data in the project management repository based on the project identification information; when no project management data corresponding to the project identification information is found, creating a target project management repository and generating second target data; and when project management data corresponding to the project identification information is found, reading the second target data.

[0140] For example, when a project manager needs to generate an evaluation report for a target project, they can initiate a project management request through the project management platform. This request includes the target project's identification information, such as the project name, project number, project code, or associated code repository address. Upon receiving the request, the system checks the project management repository for the existence of corresponding project management data based on the project identification information.

[0141] When the corresponding project management data is found, it means that a project management repository has been established for the target project in the project management platform. At this point, the second target data in the project management repository can be read directly. The second target data may include project requirements information, key project milestone information, phase goal information, and project manager information, etc.

[0142] It should be noted that the process of querying project management data based on project identification information and generating the second target data corresponds to a different operational phase than the aforementioned process of periodically collecting the second target data. Specifically, the process of querying project management data based on project identification information and generating the second target data corresponds to the project initialization phase; the process of periodically collecting the second target data corresponds to the project's daily operation phase.

[0143] For example, when a target project is first connected to the system, the system first checks whether a corresponding project management repository exists based on the project identifier information. If no corresponding project management repository is found, it means that the current target project has not yet completed its initial configuration. In this case, the corresponding project management repository is created, and initial secondary target data is generated. If a corresponding project management repository is found, it means that the current target project has completed its initial configuration. In this case, the corresponding secondary target data is read directly.

[0144] If no corresponding project management data is found, it means that the target project has not yet established a project management repository in the project management platform. In this case, a target project management repository can be created in the project management platform, and corresponding second target data can be generated based on the project description information, project manager information, and associated code repository information in the project management request. Subsequently, based on this second target data and the first target data collected from the code hosting platform, a corresponding evaluation report is generated.

[0145] In the above embodiments, by automatically creating a target project management repository and generating corresponding second target data in the project management platform, it can be ensured that the subsequent analysis process has complete project plan data, thereby improving the completeness of the evaluation report generation process.

[0146] Furthermore, the process of generating the second target data includes: generating a basic project configuration file based on the project description information in the project management request; generating a project requirements document based on the project description information and the basic project configuration file; generating a project architecture document based on the basic project configuration file and the project requirements document; generating key project nodes based on the basic project configuration file, the project requirements document, and the project architecture document; and generating the second target data based on the basic project configuration file, the project requirements document, the project architecture document, and the key project nodes.

[0147] The project basic configuration file is used to record basic configuration information such as project name, project manager, project type, development cycle, and code repository information; the project requirements document is used to describe project requirements and functional planning information; the project architecture document is used to describe project technical architecture information and module division information; and the project key nodes are used to describe phase goal information.

[0148] For example, the project's basic configuration file can be stored in the form of a structured configuration file, such as a project.yaml file. The project.yaml file can include fields such as project name, technology stack information (tech_stack), team size information (team_size), code repository information (code_repos), and project owner information (owner).

[0149] Project requirements documents can be stored in the form of requirements description files, such as the REQ-001.md file. The REQ-001.md file can include fields such as requirements description information, user story information, acceptance criteria information, and requirements priority information.

[0150] Project architecture documentation can be stored in the form of an architecture description file, such as an architecture.md file. The architecture.md file can include fields such as information on data acquisition modules, analysis and processing modules, storage modules, and deployment architecture.

[0151] Key project milestones can be stored in a structured configuration file, such as an M1.yaml file. The M1.yaml file can include fields such as deliverables, planned completion date, dependencies, and acceptance criteria.

[0152] In this embodiment, the aforementioned project basic configuration file, project requirements document, project architecture document, and project key nodes all serve as intermediate data carriers in the process of generating the second target data. They are used to gradually extract and improve project requirements information, project key node information, and stage target information, thereby generating the second target data required for subsequent task correlation analysis.

[0153] Optionally, when generating the project requirements document, the project description information and the project basic configuration file are input into the first language model to generate the corresponding project requirements document; when generating the project architecture document, the project basic configuration file and the project requirements document are input into the second language model to generate the corresponding project architecture document; when generating the project key nodes, the project basic configuration file, the project requirements document, and the project architecture document are input into the third language model to generate the corresponding project key nodes.

[0154] It should be noted that subsequent stages of the generation process can build upon the results of previous stages to construct an input context, thereby progressively refining the generated content. For example, the generation process of the project architecture document can be based on the requirements in the project requirements document; the generation process of key project nodes can be based on the content in both the project requirements document and the project architecture document.

[0155] Furthermore, after each stage of generation is completed, a corresponding quality review process can be executed. Specifically, the completeness, consistency, implementability, verifiability, and format compliance of the content generated in the current stage are scored, and it is checked whether the content generated in the current stage is missing any preset key fields.

[0156] Specifically, the content generated in the current stage is input into the independent review model. This independent review model is independent of the generation model that generated the content in the current stage. The independent review model evaluates the content generated in the current stage from five dimensions: completeness, consistency, implementability, verifiability, and format compliance, generating corresponding scores and justifications for each dimension. The scores for each dimension can range from 0 to 100. Simultaneously, the independent review model also detects whether the content generated in the current stage is missing any preset key fields and generates a list of corresponding missing key fields. This list records the key fields missing from the content generated in the current stage.

[0157] For example, when the current stage corresponds to a project requirements document, the preset key fields may include one or more of the following: requirement description, user story, acceptance criteria, and requirement priority; when the current stage corresponds to a project architecture document, the preset key fields may include one or more of the following: module division information, data flow information, and deployment architecture information.

[0158] After obtaining scores for each dimension, a corresponding quality score is determined based on those scores. This quality score can be a weighted average or the sum of the scores for each dimension. When the quality score is greater than or equal to a preset quality threshold and the list of missing key items is empty, the content generated in this stage is deemed to have passed quality review. Conversely, when the quality score is lower than the preset quality threshold or the list of missing key items is not empty, the content generated in this stage is deemed to have failed quality review.

[0159] If the content generated in the current stage fails the quality review, the scoring reasons and a list of key missing items will be fed back to the generation model as correction information, and the content of the current stage will be regenerated based on the correction information. If the content still fails the quality review after the number of regenerations reaches a preset number, the content generated in the current stage will be marked as pending manual review.

[0160] Optionally, while performing the above quality review, the format of the content generated in the current stage can also be validated according to preset data format rules. If there are missing fields, incorrect field types, or incorrect field structures, the content generated in the current stage is determined to have failed the quality review.

[0161] Thus, by separating the generation model from the independent review model and combining key field detection and regeneration mechanisms, automatic review and correction of the generation results at each stage can be achieved, thereby improving the integrity, consistency, and reliability of the second target data. Simultaneously, by limiting problem handling to the current stage, the probability of errors propagating to subsequent stages can be reduced, improving the stability and fault tolerance of the project initialization process.

[0162] Optionally, the above quality review process can be executed after each stage of generation is completed; if the quality review fails, the corresponding regeneration process is executed, which will not be elaborated here.

[0163] In the above embodiments, by adopting a staged generation approach to generate the second target data and using the results of the previous stage as input constraints for the next stage, the scale of context that needs to be processed in a single generation process can be reduced, and the consistency between generated content can be improved. Simultaneously, by storing the generation results of each stage independently, only the corresponding stage can be re-executed in case of generation failure, thereby improving the fault tolerance and recovery efficiency of the generation process. Furthermore, by performing quality reviews on the generation results of each stage separately, corrections can be completed before problems propagate to subsequent stages, improving the efficiency of problem localization and review. Further, by combining quality review, key field detection, and regeneration mechanisms, the integrity, consistency, and reliability of the second target data can be improved.

[0164] In one embodiment, combined Figure 2 , Figure 2 This is a flowchart of the steps of a data processing method in one embodiment.

[0165] Step 202: Receive project management request; the project management request carries project identification information.

[0166] Step 204: If no project management data corresponding to the project identification information is found, create a target project management repository and generate second target data.

[0167] The process of generating the second target data includes: generating a basic project configuration file based on the project description information in the project management request; generating a project requirements document based on the project description information and the basic project configuration file; generating a project architecture document based on the basic project configuration file and the project requirements document; generating key project nodes based on the basic project configuration file, the project requirements document, and the project architecture document; and generating the second target data based on the basic project configuration file, the project requirements document, the project architecture document, and the key project nodes.

[0168] Step 206: Obtain the first target data collected from the code hosting platform, the second target data collected from the project management platform, and the third target data collected from the organization management platform.

[0169] Specifically, the process involves loading the corresponding data source adapters for the code hosting platform, project management platform, and organization management platform; obtaining the corresponding synchronization cursors for the code hosting platform, project management platform, and organization management platform; using the synchronization cursors to determine the synchronization position; reading the first incremental data from the code hosting platform, the second incremental data from the project management platform, and the third incremental data from the organization management platform based on the synchronization position using the data source adapters; obtaining the first target data based on the first incremental data, the second target data based on the second incremental data, and the third target data based on the third incremental data.

[0170] Step 208: Based on the first target data and the second target data, generate the actual task completion indicators and abnormal indicators of each object to be evaluated within the target analysis period.

[0171] Specifically, based on preset matching rules, the first target data and the second target data of each object to be evaluated within the target analysis period are associated to obtain the task association results of each object to be evaluated in each project; the historical task completion indicators of each object to be evaluated corresponding to the previous target analysis period are obtained; and based on the historical task completion indicators and task association results, the actual task completion indicators and abnormal indicators are obtained.

[0172] Specifically, based on historical task completion indicators and task association results, actual task completion indicators and abnormal indicators are obtained, including: obtaining actual task completion indicators based on historical task completion indicators and task association results; calculating the statistical deviation between actual task completion indicators and historical task completion indicators; and calculating abnormal indicators based on the statistical deviation.

[0173] The actual task completion indicator is achieved through a first preset model. The process of obtaining the actual task completion indicator based on historical task completion indicators and task association results includes: calculating the matching degree between the first target data and the second target data for each task association result; scoring the task association results based on preset scoring rules, historical task completion indicators and matching degree to obtain the actual task completion indicator.

[0174] The process involves calculating the statistical deviation between the actual task completion indicator and the historical task completion indicator, and then calculating abnormal indicators based on the statistical deviation. This includes: calculating the statistical deviation between the actual task completion indicator and the historical task completion indicator; when the historical task completion indicator meets a preset stability condition and the statistical deviation is greater than a first preset threshold, the actual task completion indicator is identified as a candidate abnormal indicator; when the historical task completion indicator meets a preset stability condition and the statistical deviation is greater than a second preset threshold, the actual task completion indicator is identified as a real abnormal indicator.

[0175] Step 210: Combining the third target data, perform correlation analysis based on at least one actual task completion indicator and abnormal indicator to obtain departmental completion indicators and project completion indicators.

[0176] Specifically, the project completion indicators are obtained by analyzing the actual task completion indicators, abnormal indicators, key project nodes, and historical project analysis indicators of each candidate in the project member list; and the departmental completion indicators are obtained by analyzing the project completion indicators of each project under each department and the corresponding project status information.

[0177] Step 212: Generate an evaluation report based on the actual task completion indicators, abnormal indicators, departmental completion indicators, and project completion indicators of the object to be evaluated.

[0178] Through the above methods, this application can establish an automatic correlation between R&D activity data and project plan data, enabling R&D activity data in the code hosting platform to be automatically converted into project evaluation results, reducing the need for manual maintenance of project management information, and at the same time, updating project evaluation results in real time based on actual R&D activities, thereby improving the timeliness and accuracy of project evaluation results.

[0179] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0180] Based on the same inventive concept, this application also provides a data processing apparatus for implementing the data processing method described above. The solution provided by this apparatus is similar to the implementation scheme described in the above method; therefore, the specific limitations in one or more data processing apparatus embodiments provided below can be found in the limitations of the data processing method described above, and will not be repeated here.

[0181] In one exemplary embodiment, such as Figure 3 As shown, a data processing apparatus is provided, including: an acquisition module 100, a first evaluation module 200, a second evaluation module 300, and a third evaluation module 400, wherein:

[0182] The acquisition module is used to acquire the first target data collected from the code hosting platform, the second target data collected from the project management platform, and the third target data collected from the organization management platform.

[0183] The first evaluation module is used to generate actual task completion indicators and abnormal indicators for each object to be evaluated within the target analysis period, based on the first target data and the second target data.

[0184] The second evaluation module is used to combine the third target data and perform correlation analysis based on at least one actual task completion indicator and abnormal indicator to obtain departmental completion indicators and project completion indicators.

[0185] The third evaluation module is used to generate an evaluation report based on the actual task completion indicators, abnormal indicators, departmental completion indicators, and project completion indicators of the object to be evaluated.

[0186] In one embodiment, the above-mentioned acquisition module includes:

[0187] The adapter unit is used to load the corresponding data source adapters for the code hosting platform, project management platform, and organization management platform, respectively.

[0188] The synchronization position unit is used to obtain the synchronization cursors corresponding to the code hosting platform, project management platform, and organization management platform, respectively; the synchronization cursor is used to determine the synchronization position.

[0189] The incremental acquisition unit is used to read the first incremental data from the code hosting platform, the second incremental data from the project management platform, and the third incremental data from the organization management platform based on the synchronization location through the data source adapter.

[0190] The data processing unit is used to obtain first target data based on first incremental data, second target data based on second incremental data, and third target data based on third incremental data.

[0191] In one embodiment, the first evaluation module includes:

[0192] The management unit is used to associate the first target data and the second target data of each object to be evaluated within the target analysis period based on preset matching rules, so as to obtain the task association results of each object to be evaluated in each project.

[0193] The historical data acquisition unit is used to acquire the historical task completion indicators of each object to be evaluated corresponding to the previous target analysis period.

[0194] The indicator generation unit is used to obtain the actual task completion indicators and abnormal indicators based on historical task completion indicators and task association results.

[0195] In one embodiment, the above-mentioned indicator generation unit includes:

[0196] The first indicator sub-unit is used to obtain the actual task completion indicator based on historical task completion indicators and task association results.

[0197] The statistical deviation calculation subunit is used to calculate the statistical deviation between the actual task completion indicators and the historical task completion indicators, and to calculate the abnormal indicators based on the statistical deviation values.

[0198] In one embodiment, the above-mentioned statistical deviation value calculation subunit includes:

[0199] The matching degree subunit is used to calculate the matching degree between the first target data and the second target data for each task association result.

[0200] The task indicator sub-unit is used to score the task association results based on preset scoring rules, historical task completion indicators, and matching degree to obtain the actual task completion indicators.

[0201] In one embodiment, the above-mentioned statistical deviation value calculation subunit includes:

[0202] The statistical deviation calculation unit is used to calculate the statistical deviation between the actual task completion target and the historical task completion target.

[0203] The first abnormal indicator subunit is used to determine the actual task completion indicator as a candidate abnormal indicator when the historical task completion indicator meets the preset stability conditions and the statistical deviation value is greater than the first preset threshold.

[0204] The second abnormal indicator subunit is used to determine the actual task completion indicator as a real abnormal indicator when the historical task completion indicator meets the preset stability conditions and the statistical deviation value is greater than the second preset threshold.

[0205] In one embodiment, the second evaluation module includes:

[0206] The project indicator calculation unit is used to analyze the actual task completion indicators, abnormal indicators, key project nodes, and historical project analysis indicators of each object to be evaluated in the project member list to obtain the project completion indicators.

[0207] The departmental indicator calculation unit is used to analyze the project completion indicators of each project under each department and the corresponding project status information to obtain the departmental completion indicators; the project status information is structured summary data.

[0208] In one embodiment, the above-described apparatus further includes a data generation module, which includes:

[0209] The request receiving unit is used to receive project management requests; the project management request carries project identification information.

[0210] The query unit is used to retrieve corresponding project management data from the project management repository based on project identification information.

[0211] The creation unit is used to create a target project management repository and generate a second target data when no project management data corresponding to the project identification information is found.

[0212] The reading unit is used to read the second target data when the project management data corresponding to the project identification information is found.

[0213] Each module in the aforementioned data processing device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the operations corresponding to each module.

[0214] In one exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 4 As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores first target data, second target data, and third target data. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communicating with external terminals via a network connection. When the computer program is executed by the processor, it implements a data processing method.

[0215] Those skilled in the art will understand that Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0216] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method in any of the above embodiments.

[0217] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of the method in any of the above embodiments.

[0218] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps of the method in any of the above embodiments.

[0219] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this 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. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0220] The technical features of the above embodiments can be combined in any way. For the sake of brevity, 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.

[0221] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A data processing method, characterized in that, The method includes: Acquire the first target data collected from the code hosting platform, the second target data collected from the project management platform, and the third target data collected from the organization management platform; Based on the first target data and the second target data, generate actual task completion indicators and abnormal indicators for each object to be evaluated within the target analysis period; By combining the third target data, and performing correlation analysis based on at least one of the actual task completion indicators and the abnormal indicators, departmental completion indicators and project completion indicators are obtained. An evaluation report is generated based on the actual task completion indicators and abnormal indicators of the object to be evaluated, the department completion indicators, and the project completion indicators.

2. The method according to claim 1, characterized in that, The acquisition of the first target data collected from the code hosting platform, the second target data collected from the project management platform, and the third target data collected from the organization management platform includes: Load the corresponding data source adapters for the code hosting platform, the project management platform, and the organization management platform, respectively; Obtain the synchronization cursors corresponding to the code hosting platform, the project management platform, and the organization management platform respectively; the synchronization cursors are used to determine the synchronization position. Through the data source adapter, based on the synchronization location, the first incremental data of the code hosting platform, the second incremental data of the project management platform, and the third incremental data of the organization management platform are read; The first target data is obtained based on the first incremental data, the second target data is obtained based on the second incremental data, and the third target data is obtained based on the third incremental data.

3. The method according to claim 1, characterized in that, The step of generating actual task completion indicators and abnormal indicators for each object to be evaluated within the target analysis period based on the first target data and the second target data includes: Based on preset matching rules, the first target data and the second target data of each of the objects to be evaluated within the target analysis period are associated to obtain the task association results of each of the objects to be evaluated in each project. Obtain the historical task completion indicators of each object to be evaluated corresponding to the target analysis period mentioned above; Based on the historical task completion metrics and the task association results, the actual task completion metrics and the abnormal metrics are obtained.

4. The method according to claim 3, characterized in that, The process of obtaining the actual task completion metrics and the abnormal metrics based on the historical task completion metrics and the task association results includes: Based on the historical task completion metrics and the task association results, the actual task completion metrics are obtained. Calculate the statistical deviation between the actual task completion indicator and the historical task completion indicator, and calculate the abnormal indicator based on the statistical deviation.

5. The method according to claim 4, characterized in that, The actual task completion indicator is achieved through a first preset model; the process by which the first preset model obtains the actual task completion indicator based on the historical task completion indicator and task association results includes: For each of the task association results, calculate the matching degree between the first target data and the second target data; Based on preset scoring rules, the historical task completion indicators, and the matching degree, the task association results are scored to obtain the actual task completion indicators.

6. The method according to claim 4, characterized in that, The calculation of the statistical deviation between the actual task completion indicator and the historical task completion indicator, and the calculation of the abnormal indicator based on the statistical deviation, includes: Calculate the statistical deviation between the actual task completion indicator and the historical task completion indicator; When the historical task completion indicator meets the preset stability condition and the statistical deviation value is greater than the first preset threshold, the actual task completion indicator is determined as a candidate abnormal indicator. When the historical task completion indicator meets the preset stability condition and the statistical deviation value is greater than the second preset threshold, the actual task completion indicator is determined to be a real abnormal indicator.

7. The method according to any one of claims 1 to 6, characterized in that, Combining the third target data, and based on at least one of the actual task completion indicators and the abnormal indicators, a correlation analysis is performed to obtain departmental completion indicators and project completion indicators, including: The project completion indicators are obtained by analyzing the actual task completion indicators, abnormal indicators, key project nodes, and historical project analysis indicators corresponding to each object to be evaluated in the project member list. The departmental completion indicators are obtained by analyzing the project completion indicators and project status information of each project under each department; the project status information is structured summary data.

8. The method according to any one of claims 1 to 6, characterized in that, The method further includes: Receive a project management request; the project management request carries project identification information; Based on the project identification information, query the corresponding project management data in the project management repository; If no project management data corresponding to the project identification information is found, a target project management repository is created, and the second target data is generated. When project management data corresponding to the project identification information is found, the second target data is read.

9. A data processing apparatus, characterized in that, The device includes: The acquisition module is used to acquire the first target data collected from the code hosting platform, the second target data collected from the project management platform, and the third target data collected from the organization management platform. The first evaluation module is used to generate actual task completion indicators and abnormal indicators for each object to be evaluated within the target analysis period, based on the first target data and the second target data. The second evaluation module is used to combine the third target data and perform correlation analysis based on at least one of the actual task completion indicators and the abnormal indicators to obtain the department completion indicators and the project completion indicators. The third evaluation module is used to generate an evaluation report based on the actual task completion indicators and abnormal indicators of the object to be evaluated, the department completion indicators, and the project completion indicators.

10. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 8.

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