Project state visualization method and device, electronic equipment and storage medium

Abstract

The invention discloses a project state visualization method and device, electronic equipment and a storage medium, and relates to the technical field of computer software, and the method comprises the steps: asynchronously obtaining heterogeneous data from a plurality of independent data sources, and carrying out the format conversion and semantic alignment of the heterogeneous data, thereby constructing a unified and normative multi-dimensional data association structure, and achieving the visualization of a project state. And interactive dynamic visual analysis based on user instructions is supported, so that the technical problems that an existing project management system can only process single dimension in the aspect of data collection, cannot automatically acquire comprehensive data and lacks real-time monitoring and interactive analysis capabilities can be solved; the technical effects of realizing full-automatic, multi-dimensional and real-time acquisition and visual analysis of project data and remarkably improving the comprehensive control capability of quality and progress in project management are achieved.

Description

Technical Field

[0001] This application relates to the field of computer software technology, and in particular to methods, apparatus, electronic devices and storage media for visualizing project status. Background Technology

[0002] With the rapid development of information technology, the demand for data collection and visualization in the project management field is growing. Traditional project management methods can no longer meet the needs of modern project management, especially when projects involve evaluation data of multiple types and dimensions. Most existing project management systems can only provide single-dimensional data collection and visualization, and cannot automatically collect comprehensive project quality data. This fails to provide comprehensive control over project quality and schedule. Furthermore, existing systems lack real-time data monitoring and analysis capabilities, preventing project managers from promptly grasping project progress and potential problems. Summary of the Invention

[0003] This application provides a method, apparatus, electronic device, and storage medium for visualizing project status, in order to at least address the lack of real-time monitoring and analysis capabilities for data in related technologies, which prevents project managers from timely grasping the progress of projects and potential problems.

[0004] This application provides a method for visualizing project status, including:

[0005] Asynchronously retrieve heterogeneous data associated with the target project from at least two independent data sources; The heterogeneous data is subjected to format conversion and semantic alignment to generate standardized data that conforms to a preset data model, which includes multiple dimensions and metrics associated with each dimension. Based on the standardized data, a multidimensional data association structure is constructed that supports combination, slicing, and drill-down operations according to the dimensions. In response to user configuration commands, dynamic visualizations that support interactive analysis are generated and presented based on the selected combination of dimensions and metrics.

[0006] Optionally, the asynchronous acquisition of heterogeneous data associated with the target project from at least two independent data sources includes: Obtain project task and schedule management data from the first type of data source; Obtain project artifacts and version control data from the second type of data source; Obtain project quality and test management data from the third type of data source.

[0007] Optionally, the process of format conversion and semantic alignment of the heterogeneous data includes: Identify and unify fields representing the same event from different data sources; Transform time series data with different time granularities to a unified time base.

[0008] Optionally, the metrics include schedule deviation metrics calculated based on planned data and actual data.

[0009] Optionally, the dimensions include at least two of the following: time dimension, task attribute dimension, personnel dimension, quality attribute dimension, and risk attribute dimension.

[0010] Optionally, the time dimension can be divided and aggregated at least one of the following granularities: year, quarter, iteration cycle, week, and day.

[0011] Optionally, generating and presenting a dynamic visualization view that supports interactive analysis includes: Generate charts to display the progress trend of task completion, which simultaneously show the planned progress curve and the actual progress curve; Generate a matrix diagram to illustrate the distribution of risks, where the location of risk points is determined based on their probability of occurrence and degree of impact.

[0012] Optionally, generating and presenting a dynamic visualization view that supports interactive analysis further includes: In response to interactive operations on target data elements within the first chart of the dynamic visualization view, the data content displayed in the second chart of the view is updated accordingly.

[0013] Optionally, the method further includes: Configure differentiated data access permissions and visualization templates for users with different roles; The dimensions, metrics, and data ranges displayed in the view template configured for the target role are related to the role's functional scope.

[0014] Optionally, the method further includes: The data displayed in the dynamic visualization view is automatically refreshed at a preset frequency or in response to data update events; It stores historical versions of the standardized data and supports restoring the corresponding data state and visualization view based on a specified point in time.

[0015] Optionally, the method further includes: Exchange data with external business systems through predefined application programming interfaces; This involves obtaining budget or cost data from the external business system and comparing and displaying it in conjunction with project execution data.

[0016] This application also provides a visualization device for project status, including: The acquisition unit is also used to asynchronously acquire heterogeneous data associated with the target project from at least two independent data sources; The generation unit is also used to perform format conversion and semantic alignment processing on the heterogeneous data to generate standardized data that conforms to a preset data model, the data model including multiple dimensions and metrics associated with each dimension; The construction unit is also used to construct a multi-dimensional data association structure based on the normalized data, which supports combination, slicing and drill-down operations according to the dimensions. The response unit is also used to respond to user configuration commands and generate and present dynamic visualizations that support interactive analysis based on the selected combination of dimensions and metrics.

[0017] Optionally, the acquisition unit is further configured to: Obtain project task and schedule management data from the first type of data source; Obtain project artifacts and version control data from the second type of data source; Obtain project quality and test management data from the third type of data source.

[0018] Optionally, the generation unit is further configured to: Identify and unify fields representing the same event from different data sources; Transform time series data with different time granularities to a unified time base.

[0019] Optionally, the metrics include schedule deviation metrics calculated based on planned data and actual data.

[0020] Optionally, the dimensions include at least two of the following: time dimension, task attribute dimension, personnel dimension, quality attribute dimension, and risk attribute dimension.

[0021] Optionally, the time dimension can be divided and aggregated at least one of the following granularities: year, quarter, iteration cycle, week, and day.

[0022] Optionally, the response unit is further configured to: Generate charts to display the progress trend of task completion, which simultaneously show the planned progress curve and the actual progress curve; Generate a matrix diagram to illustrate the distribution of risks, where the location of risk points is determined based on their probability of occurrence and degree of impact.

[0023] Optionally, the response unit is further configured to: In response to interactive operations on target data elements within the first chart of the dynamic visualization view, the data content displayed in the second chart of the view is updated accordingly.

[0024] Optional, also includes: The configuration unit is also used to configure differentiated data access permissions and visualization view templates for users with different roles; The dimensions, metrics, and data ranges displayed in the view template configured for the target role are related to the role's functional scope.

[0025] Optional, also includes: The refresh unit is also used to automatically refresh the data displayed in the dynamic visualization view at a preset frequency or in response to a data update event; The storage unit is also used to store historical versions of the normalized data and supports the restoration of the corresponding data state and visualization view based on a specified point in time.

[0026] Optional, also includes: The switching unit is also used to exchange data with external business systems through a predefined application programming interface; This involves obtaining budget or cost data from the external business system and comparing and displaying it in conjunction with project execution data.

[0027] This application also provides an electronic device, including: a memory for storing a computer program; and a processor for implementing the visualization method of any of the above-described project states when executing the computer program.

[0028] This application also provides a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, it implements the steps of any of the above-described methods for visualizing project states.

[0029] This application also provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of a visualization method for any of the above-described project states.

[0030] This application addresses the technical problems of existing project management systems in data collection. By asynchronously acquiring heterogeneous data from multiple independent data sources and performing format conversion and semantic alignment, a unified and standardized multidimensional data association structure is constructed, supporting interactive dynamic visualization analysis based on user commands. This solves the problem that existing project management systems can only handle single-dimensional data collection, cannot automatically acquire comprehensive data, and lack real-time monitoring and interactive analysis capabilities. It achieves fully automatic, multi-dimensional, and real-time acquisition and visualization analysis of project data, significantly improving the ability to comprehensively control quality and schedule in project management. Attached Figure Description

[0031] To more clearly illustrate the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0032] Figure 1 A flowchart illustrating a method for visualizing project status provided in an embodiment of this disclosure; Figure 2 A schematic diagram of the structure of a project status visualization device provided in an embodiment of this disclosure; Figure 3 This is a schematic diagram of the structure of a project status visualization device provided in an embodiment of the present disclosure. Detailed Implementation

[0033] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the protection scope of this application.

[0034] It should be noted that, in the description of this application, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. The terms "first," "second," etc., in this application are used to distinguish similar objects and not to describe the order or sequence of objects.

[0035] To enable those skilled in the art to better understand the present application, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0036] The embodiments of this application provide a method for visualizing project status, and the method is described in detail in conjunction with the execution flow of the method for visualizing project status. Figure 1 This is a flowchart illustrating a method for visualizing project status provided in an embodiment of this disclosure.

[0037] like Figure 1 As shown, the method includes the following steps: Step 101: Asynchronously obtain heterogeneous data associated with the target project from at least two independent data sources; Independent data sources refer to project-related systems that have no direct data dependency on each other and have different functional orientations. Examples include systems focused on task allocation and progress tracking, systems responsible for code submission and version management, systems for automated testing and defect logging, and systems for managing project finances and resource allocation. These data sources store different dimensions of project information, and their operating logic is independent. Heterogeneous data refers to data from different data sources with different data formats, types, and structures, including but not limited to project task allocation status and priority data, code submission records and file modification information, test pass rates and defect details, resource usage and financial budget data, etc. Asynchronous acquisition is implemented through preset interface call mechanisms, timed triggering strategies, or event-driven patterns. During the acquisition process, it does not affect the normal operation of each data source or cause blocking of the system. It can complete data acquisition operations from multiple data sources in parallel or in a time-sharing manner, ensuring efficient and complete acquisition of various data related to the target project even when multiple data sources generate data simultaneously or the data volume is large. This provides a comprehensive and reliable data foundation for subsequent data integration, processing, and display.

[0038] Step 102: Perform format conversion and semantic alignment processing on the heterogeneous data to generate standardized data that conforms to a preset data model. The data model includes multiple dimensions and metrics associated with each dimension. Format conversion adapts heterogeneous data from different data sources, converting data in various formats scattered across different systems into a pre-defined standard data format. This eliminates data compatibility issues caused by differences in storage formats, ensuring that data can be smoothly recognized and processed by subsequent modules. Semantic alignment focuses on resolving the consistency of meaning in data from different data sources. By establishing unified semantic mapping rules, data with the same business meaning but different expressions and calculation logics are calibrated, achieving semantic unity for related data from different systems and avoiding ambiguity. The generated standardized data must strictly conform to a pre-defined data model. This data model is a structured abstraction of project management-related information. Its dimensions cover various classification dimensions that need to be considered in the project management process, and the metrics are quantifiable data indicators corresponding to each dimension. Through the association between dimensions and metrics, a data organization form that can comprehensively reflect the project status is constructed, providing standardized data support for subsequent multi-dimensional data display and analysis.

[0039] Step 103: Based on the normalized data, construct a multi-dimensional data association structure that supports combination, slicing, and drill-down operations according to the dimensions; Based on standardized data that has undergone format conversion and semantic alignment, and combined with multiple dimensions and corresponding metrics in a pre-defined data model, a multi-dimensional data association structure is constructed. This structure breaks down the isolation of data by clearly defining the logical relationships between dimensions and between dimensions and metrics, forming a data organization form that supports multi-dimensional operations. Specifically, combination operations allow for flexible pairing of different dimensions according to project management needs, enabling data presentation from multiple cross-perspectives; slicing operations can fix the target values ​​of one or more dimensions, filtering out specific subsets of data that meet the criteria from the overall data; and drill-down operations allow for in-depth exploration from higher-level dimensions to lower-level dimensions, obtaining more detailed data analysis results. This multi-dimensional data association structure fully leverages the value of standardized data, allowing users to flexibly adjust their data viewing perspective according to actual business scenarios, quickly locate key information, and provide more comprehensive data analysis support for project management decisions.

[0040] Step 104: In response to the user's configuration instructions, generate and present a dynamic visualization view that supports interactive analysis based on the selected combination of dimensions and metrics.

[0041] User configuration commands are operations initiated based on their own project management needs, covering core aspects such as selecting dimension combinations and specifying metrics. The system will respond to these commands in real time and initiate the view generation process. Based on the previously built multi-dimensional data association structure, the system will accurately retrieve standardized data matching the selected dimension combinations and metrics, and generate a dynamic visualization view through preset visualization rendering logic. This view is not a fixed display format but can be adjusted in real time according to user operations, supporting interactive analysis functions. Users can trigger data linkage presentation, perspective switching, and other effects through operations to quickly focus on key information. The dynamic visualization view presents project-related data in an intuitive visual form, satisfying the personalized data display needs of different users, and lowering the threshold for data exploration through interactive operations. It helps users efficiently uncover the project status, trends, and potential problems behind the data, providing direct and powerful support for project management decisions.

[0042] In some embodiments, asynchronously acquiring heterogeneous data associated with the target project from at least two independent data sources includes: Obtain project task and schedule management data from the first type of data source; Obtain project artifacts and version control data from the second type of data source; Obtain project quality and test management data from the third type of data source.

[0043] The first type of data source focuses on project task and schedule management. The project task and schedule management data acquired from this system covers information such as task creation, allocation, priority setting, execution status updates, and phased records of the overall project progress, providing a clear timeline and completion status of project tasks. The second type of data source focuses on project deliverables and version control. The acquired project deliverables and version control data includes code commit records, committer information, number of modified lines, version iteration records, etc., completely preserving the evolution process of core project deliverables and providing a basis for version traceability. The third type of data source emphasizes project quality and test management. The acquired project quality and test management data includes indicators such as automated test pass rate, number of defects, detailed information, code coverage, and test case execution status, directly related to the quality level assessment of project deliverables. These three types of data sources are independent yet complementary in function. Asynchronous acquisition is achieved through preset triggering mechanisms and interface interactions, ensuring that data collection does not interfere with the normal operation of each data source while efficiently integrating project-related data from different dimensions, providing rich and accurate raw data support for subsequent data processing and display.

[0044] In some embodiments, the format conversion and semantic alignment processing of the heterogeneous data includes: Identify and unify fields representing the same event from different data sources; Transform time series data with different time granularities to a unified time base.

[0045] Different data sources may use different field names or representations for the same project event. For example, for the event of task completion status, some data sources may name the field "task completion status," some "task execution result," and others "task completion marker." It is necessary to accurately identify these fields that point to the same event through pre-defined field mapping rules and business logic analysis, and then unify them into standardized field names and data formats to eliminate semantic ambiguity caused by field differences, ensuring that data related to the same event from different data sources can be accurately linked and integrated. Based on this, a unified time base transformation is performed on time series data with different time granularities. Time granularity refers to the time interval of data collection or recording, including different types such as daily, weekly, monthly, and quarterly. The unified time base is a pre-defined standard time interval. During the transformation process, appropriate processing methods are adopted according to the data characteristics. When transforming fine-grained data to a coarse-grained base, data aggregation calculations are performed; when transforming coarse-grained data to a fine-grained base, reasonable interpolation methods are used to supplement the data, ensuring that the transformed time series data maintains consistency within the same time dimension, providing unified time dimension support for subsequent construction of multi-dimensional data association structures and data display.

[0046] In some embodiments, the metrics include a schedule deviation metric calculated based on planned data and actual data.

[0047] Planned data refers to the baseline data related to task completion preset before project initiation or during execution. It covers core information such as the planned workload, work value, and timeframes, providing a clear reference standard for progress assessment. Actual data refers to the real-time recorded execution results during project progress, including objective data such as the actual number of tasks completed, realized work value, and actual time consumed, accurately reflecting the project's actual progress. The schedule deviation index is calculated by comparing corresponding dimensions of planned and actual data. It intuitively presents the degree of deviation between the actual and planned progress, allowing users to quickly determine whether the project is ahead of schedule or behind schedule. This index provides direct data support for project managers to promptly identify schedule issues, adjust resource allocation, and optimize execution strategies, helping to ensure the project progresses as expected and improving the scientific and efficient nature of project management.

[0048] In some embodiments, the dimensions include at least two of the following: time dimension, task attribute dimension, personnel dimension, quality attribute dimension, and risk attribute dimension.

[0049] The dimensions are designed to comprehensively cover the core concerns in project management, providing a foundation for multi-dimensional combined analysis and personalized data visualization. These dimensions include at least two of the following: time, task attribute, personnel, quality attribute, and risk attribute. The time dimension focuses on the timeline of project progress, covering different stages throughout the project lifecycle and reflecting data trends over time. The task attribute dimension revolves around the core characteristics of project tasks, covering attributes such as task type, affiliated functional module, and work package, used to differentiate between different types of project tasks. The personnel dimension centers on the personnel involved in the project, including classifications such as department, position, and role, facilitating the association of personnel-related project data. The quality attribute dimension focuses on the quality evaluation of project deliverables, covering quality-related attribute classifications such as defect type, severity, and code complexity level, supporting the analysis of quality-related data. The risk attribute dimension addresses potential risks during project execution, including attributes such as risk type and impact level, providing support for the integrated visualization of risk-related data. Choosing to combine at least two dimensions can break the limitations of a single dimension, connect project data from different perspectives, ensure the comprehensiveness of data display and the flexibility of analysis, and help users gain a more comprehensive understanding of the project status.

[0050] In some embodiments, the time dimension supports division and aggregation at least one of the following granularities: year, quarter, iteration cycle, week, and day.

[0051] Annual granularity focuses on the long-term time span of the entire project lifecycle, presenting the overall progress and trend changes of the project over multiple years, providing data support for strategic decision-making. Quarterly granularity uses three-month time units, suitable for summarizing phased results and adjusting mid-term plans, helping managers grasp the project's progress pace in different quarters. Iteration cycle granularity aligns with agile project management models, using preset iteration durations to accurately reflect the task completion status and goal achievement of each iteration phase. Weekly granularity focuses on short-term timeframes, facilitating the tracking of weekly work progress and task execution efficiency, and promptly identifying progress deviations within short periods. Daily granularity provides the finest time perspective, recording daily project data changes and supporting precise traceability of daily work results and issues. Granularity aggregation can aggregate fine-grained data upwards, such as aggregating daily data into weekly or quarterly data to form a macro-level time dimension view; it also supports selecting target granularity for data splitting based on needs, extracting fine-grained information from coarse-grained data to meet the differentiated viewing needs of time dimension data in different scenarios, improving the flexibility and accuracy of project time management.

[0052] In some embodiments, generating and presenting a dynamic visualization view that supports interactive analysis includes: Generate charts to display the progress trend of task completion, which simultaneously show the planned progress curve and the actual progress curve; Generate a matrix diagram to illustrate the distribution of risks, where the location of risk points is determined based on their probability of occurrence and degree of impact.

[0053] When generating and presenting dynamic visualizations that support interactive analysis, a chart is first generated to display the task completion progress trend. This chart is based on the time dimension and progress-related metrics in the normalized data, simultaneously presenting the planned progress curve and the actual progress curve. The planned progress curve is generated based on the project's preset task completion time nodes, workload allocation, and stage goals, clearly outlining the expected progress trajectory; the actual progress curve is drawn based on real-time updated task completion data, truly reflecting the actual progress status of the project. Displaying both on the same screen allows users to intuitively compare the differences between the planned and actual progress, quickly determining whether the project is ahead of schedule or behind schedule.

[0054] Simultaneously, a matrix diagram is generated to display the risk distribution. This matrix diagram constructs a two-dimensional coordinate system with the probability of risk occurrence as the horizontal axis and the degree of risk impact as the vertical axis. The location of each risk point is precisely determined by its corresponding probability of occurrence and degree of impact. The distribution of different risk points in the matrix intuitively presents the priority level of various risks. Both views support interactive analysis operations. Users can click on the target time node in the progress trend chart to view the detailed task completion data of the corresponding stage, or click on the risk point in the risk matrix diagram to retrieve the specific description of the risk, related dimension information, and response suggestions. Through dynamic display and interactive operation, users are provided with more in-depth progress control and risk management support, helping to make accurate and efficient project management decisions.

[0055] In some embodiments, generating and presenting a dynamic visualization view that supports interactive analysis further includes: In response to interactive operations on target data elements within the first chart of the dynamic visualization view, the data content displayed in the second chart of the view is updated accordingly.

[0056] When a user interacts with a target data element in the first chart of the dynamic visualization view, the system responds in real time and triggers a linkage mechanism. Interactive operations include common forms such as clicking, selecting, and hovering. The target data element refers to the visualization unit in the first chart that carries specific dimensions and metrics. The first and second charts are built on the same multi-dimensional data association structure, sharing some dimension or metric relationships. After responding to the interactive operation, the system extracts the dimension conditions or data features corresponding to the target data element, filters out the target data associated with that data element according to preset association rules, and then synchronously updates the data content displayed in the second chart. This linkage update mechanism ensures that the data in the two charts always maintains correlation and consistency. Users can quickly jump from one data perspective to the association perspective without manually switching or reconfiguring, intuitively view the inherent connections between data, and deeply explore the association logic of data in different dimensions. This further enhances the depth and efficiency of interactive analysis, helping users gain a more comprehensive understanding of the business information behind the project data.

[0057] In some embodiments, the method further includes: Configure differentiated data access permissions and visualization templates for users with different roles; The dimensions, metrics, and data ranges displayed in the view template configured for the target role are related to the role's functional scope.

[0058] Differentiated configurations are based on the core responsibilities of each role in project management. Data access permissions are set according to the information scope required for each role's work, ensuring that different roles can only access data relevant to their own work. This avoids information overload caused by irrelevant data and ensures the security of project data. The configuration of visualization view templates also revolves around role functions. The dimensions, metrics, and data scope displayed in the templates customized for the target role are closely related to that role's functional scope. For example, a project manager, as the project coordinator, will have a view template covering multiple dimensions such as global time, task attributes, and risk attributes. Metrics include global control indicators such as schedule deviation and risk exposure value, and the data scope covers the entire project. Developers' view templates focus on personnel, task attributes, and quality attributes. Metrics emphasize code submission status, defect count, and other content directly related to their work, and the data scope is limited to the task modules or iteration cycles they are responsible for. Testers' view templates are centered on quality and task attributes, with metrics focusing on professional data such as test coverage and defect distribution, and the data scope revolves around test-related work. This differentiated configuration allows different roles to obtain their own precise views without manually filtering data, greatly improving work efficiency while ensuring the standardization and security of data access, and helping each role to perform its duties efficiently.

[0059] In some embodiments, the method further includes: The data displayed in the dynamic visualization view is automatically refreshed at a preset frequency or in response to data update events; It stores historical versions of the standardized data and supports restoring the corresponding data state and visualization view based on a specified point in time.

[0060] Automatic refresh is achieved through preset frequencies or in response to data update events. The preset frequency can be set to different time intervals according to project management needs to ensure that the view data is updated synchronously according to a regular pattern. Data update events include scenarios such as adding data sources, modifying data, and deleting data. When such events are triggered, the system responds immediately and automatically updates the relevant data displayed in the dynamic visualization view. This ensures the timeliness of the data without requiring manual operation by the user, allowing users to obtain the latest project status information in real time.

[0061] Simultaneously, the system continuously stores historical versions of standardized data, each associated with a corresponding timestamp, allowing users to initiate data status recovery requests based on specified time points. When users need to trace past project statuses, the system can retrieve the corresponding historical version data based on a specified time point, restoring the data status and corresponding visualization at that time. This fully restores the project data display at that time, providing reliable support for scenarios such as project progress retrospective analysis, historical data comparison, and improving the traceability of project management and the comprehensiveness of data analysis.

[0062] In some embodiments, the method further includes: Exchange data with external business systems through predefined application programming interfaces; This involves obtaining budget or cost data from the external business system and comparing and displaying it in conjunction with project execution data.

[0063] Predefined application programming interfaces (APIs) are standardized data exchange channels pre-configured by the system. They possess unified data formats and interaction protocols, ensuring compatibility and efficient data transmission with various external business systems and avoiding data exchange obstacles caused by inconsistent interfaces. External business systems encompass various project-related professional management systems. The budget or cost data obtained from these systems includes core financial information such as the total budget amount established before project initiation, budget allocations for each stage, and cost control targets. This data serves as a crucial basis for project financial management. Project execution data, on the other hand, is standardized data generated by the system through preliminary data collection and processing. This includes quantitative data related to execution status, such as work hours consumed, resource usage, expenses incurred, and task completion progress.

[0064] The system will link and integrate budget or cost data obtained from external business systems with project execution data, and display the comparison in a dynamic visualization view. For example, it will compare the actual cost expenditures at each stage with the preset budget amount in real time, and present the changing trend of budget execution rate. This will allow users to intuitively understand the use of budget and the effectiveness of cost control, and promptly identify problems such as budget overruns or resource waste. It will provide accurate data support for project financial decisions and help optimize resource allocation and cost control strategies.

[0065] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method.

[0066] Embodiments of this application also provide a visualization device for project status. Figure 2 A schematic diagram of the structure of a project status visualization device provided in an embodiment of this disclosure, as shown below. Figure 2 As shown, it includes: The acquisition unit 21 is also used to asynchronously acquire heterogeneous data associated with the target project from at least two independent data sources; The generation unit 22 is also used to perform format conversion and semantic alignment processing on the heterogeneous data to generate standardized data that conforms to a preset data model, the data model including multiple dimensions and metrics associated with each dimension; The construction unit 23 is also used to construct a multi-dimensional data association structure based on the normalized data, which supports combination, slicing and drill-down operations according to the dimensions. The response unit 24 is also used to respond to the user's configuration instructions and generate and present a dynamic visualization view that supports interactive analysis based on the selected combination of dimensions and metrics.

[0067] Furthermore, in one possible implementation of this disclosure, the acquisition unit 21 is further configured to: Obtain project task and schedule management data from the first type of data source; Obtain project artifacts and version control data from the second type of data source; Obtain project quality and test management data from the third type of data source.

[0068] Furthermore, in one possible implementation of this disclosure embodiment, the generation unit 22 is further configured to: Identify and unify fields representing the same event from different data sources; Transform time series data with different time granularities to a unified time base.

[0069] Furthermore, in one possible implementation of this disclosure, the metric includes a schedule deviation metric calculated based on planned data and actual data.

[0070] Furthermore, in one possible implementation of this disclosure embodiment, the dimension includes at least two of the following: time dimension, task attribute dimension, personnel dimension, quality attribute dimension, and risk attribute dimension.

[0071] Furthermore, in one possible implementation of this disclosure embodiment, the time dimension supports division and aggregation at least one granularity among year, quarter, iteration cycle, week, and day.

[0072] Furthermore, in one possible implementation of this disclosure, the response unit 24 is further configured to: Generate charts to display the progress trend of task completion, which simultaneously show the planned progress curve and the actual progress curve; Generate a matrix diagram to illustrate the distribution of risks, where the location of risk points is determined based on their probability of occurrence and degree of impact.

[0073] Furthermore, in one possible implementation of this disclosure, the response unit 24 is further configured to: In response to interactive operations on target data elements within the first chart of the dynamic visualization view, the data content displayed in the second chart of the view is updated accordingly.

[0074] Furthermore, in one possible implementation of the embodiments of this disclosure, such as Figure 3 As shown, it also includes: Configuration unit 25 is also used to configure differentiated data access permissions and visualization view templates for users with different roles; The dimensions, metrics, and data ranges displayed in the view template configured for the target role are related to the role's functional scope.

[0075] Furthermore, in one possible implementation of the embodiments of this disclosure, such as Figure 3 As shown, it also includes: The refresh unit 26 is also used to automatically refresh the data displayed in the dynamic visualization view at a preset frequency or in response to a data update event; Storage unit 27 is also used to store historical versions of the normalized data and supports the restoration of the corresponding data state and visualization view based on a specified point in time.

[0076] Furthermore, in one possible implementation of the embodiments of this disclosure, such as Figure 3 As shown, it also includes: The switching unit 28 is also used to exchange data with external business systems through a predefined application programming interface; This involves obtaining budget or cost data from the external business system and comparing and displaying it in conjunction with project execution data.

[0077] For a description of the features in the embodiment corresponding to the visualization device for project status, please refer to the relevant description of the embodiment corresponding to the visualization method for project status, which will not be repeated here.

[0078] Embodiments of this application also provide an electronic device including a memory and a processor, the memory storing a computer program, the processor being configured to run the computer program to perform the steps in any of the above-described visualization method embodiments of project status.

[0079] Embodiments of this application also provide a computer-readable storage medium storing a computer program configured to execute the steps in any of the above-described visualization method embodiments of project states when run.

[0080] In one exemplary embodiment, the aforementioned computer-readable storage medium may include, but is not limited to, various media capable of storing computer programs, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard disk, magnetic disk, or optical disk.

[0081] Embodiments of this application also provide a computer program product, which includes a computer program that, when executed by a processor, implements the steps in any of the above-described visualization method embodiments for project states.

[0082] Embodiments of this application also provide another computer program product, including a non-volatile computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps in any of the above-described visualization method embodiments for project states.

[0083] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the target application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each target application, but such implementation should not be considered beyond the scope of this application.

[0084] The foregoing has provided a detailed description of a method, apparatus, electronic device, and storage medium for visualizing project status provided in this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the embodiments above are merely for the purpose of helping to understand the method and core ideas of this application. It should be noted that those skilled in the art can make various improvements and modifications to this application without departing from its principles, and these improvements and modifications also fall within the protection scope of the claims of this application.

Claims

1. A method for visualizing project status, characterized in that, include: Asynchronously retrieve heterogeneous data associated with the target project from at least two independent data sources; The heterogeneous data is subjected to format conversion and semantic alignment to generate standardized data that conforms to a preset data model, which includes multiple dimensions and metrics associated with each dimension. Based on the standardized data, a multidimensional data association structure is constructed that supports combination, slicing, and drill-down operations according to the dimensions. In response to user configuration commands, dynamic visualizations that support interactive analysis are generated and presented based on the selected combination of dimensions and metrics.

2. The method according to claim 1, characterized in that, The asynchronous acquisition of heterogeneous data associated with the target project from at least two independent data sources includes: Obtain project task and schedule management data from the first type of data source; Obtain project artifacts and version control data from the second type of data source; Obtain project quality and test management data from the third type of data source.

3. The method according to claim 2, characterized in that, The process of format conversion and semantic alignment of the heterogeneous data includes: Identify and unify fields representing the same event from different data sources; Transform time series data with different time granularities to a unified time base.

4. The method according to claim 1, characterized in that, The metrics include schedule deviation metrics calculated based on planned data and actual data.

5. The method according to claim 1, characterized in that, The dimensions include at least two of the following: time dimension, task attribute dimension, personnel dimension, quality attribute dimension, and risk attribute dimension.

6. The method according to claim 5, characterized in that, The time dimension supports division and aggregation by at least one of the following granularities: year, quarter, iteration cycle, week, and day.

7. The method according to claim 1, characterized in that, The generation and presentation of dynamic visualizations that support interactive analysis includes: Generate charts to display the progress trend of task completion, which simultaneously show the planned progress curve and the actual progress curve; Generate a matrix diagram to illustrate the distribution of risks, where the location of risk points is determined based on their probability of occurrence and degree of impact.

8. The method according to claim 1, characterized in that, The generation and presentation of dynamic visualizations that support interactive analysis also includes: In response to interactive operations on target data elements within the first chart of the dynamic visualization view, the data content displayed in the second chart of the view is updated accordingly.

9. The method according to claim 1, characterized in that, The method further includes: Configure differentiated data access permissions and visualization templates for users with different roles; The dimensions, metrics, and data ranges displayed in the view template configured for the target role are related to the role's functional scope.

10. The method according to claim 1, characterized in that, The method further includes: The data displayed in the dynamic visualization view is automatically refreshed at a preset frequency or in response to data update events; It stores historical versions of the standardized data and supports restoring the corresponding data state and visualization view based on a specified point in time.

11. The method according to claim 1, characterized in that, The method further includes: Exchange data with external business systems through predefined application programming interfaces; This involves obtaining budget or cost data from the external business system and comparing and displaying it in conjunction with project execution data.

12. A visualization device for project status, characterized in that, include: The acquisition unit is also used to asynchronously acquire heterogeneous data associated with the target project from at least two independent data sources; The generation unit is also used to perform format conversion and semantic alignment processing on the heterogeneous data to generate standardized data that conforms to a preset data model, the data model including multiple dimensions and metrics associated with each dimension; The construction unit is also used to construct a multi-dimensional data association structure based on the normalized data, which supports combination, slicing and drill-down operations according to the dimensions. The response unit is also used to respond to user configuration commands and generate and present dynamic visualizations that support interactive analysis based on the selected combination of dimensions and metrics.

13. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor, used to implement the steps of the visualization method for the project status as described in any one of claims 1 to 11 when executing the computer program.

14. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, wherein when the computer program is executed by a processor, it implements the steps of the method for visualizing the project status as described in any one of claims 1 to 11.

15. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method for visualizing the project status as described in any one of claims 1 to 11.

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