Data processing method and apparatus, device, and storage medium

Abstract

This application relates to the field of data processing and discloses a data processing method, apparatus, device, and storage medium. The method includes: acquiring unreleased version datasets and iterative datasets corresponding to each project; matching the unreleased version datasets and iterative datasets with a preset time field to generate a mapping dataset corresponding to the datasets and the unreleased version datasets; arranging the mapping datasets according to a predetermined time order corresponding to the iterative data to obtain a data list; and, based on the data list, associating and storing the unreleased version datasets and iterative datasets according to predetermined attribute parameters as storage labels to generate an associated data report. This application's embodiment establishes a mapping by matching unreleased version data with iterative data, thereby associating and storing iterative data with unreleased version data, achieving unified management and planning of iterative data distributed across various product lines, and improving data management efficiency.

Description

Technical Field

[0001] This application relates to the field of data processing, and more particularly to a data processing method, apparatus, device, and storage medium. Background Technology

[0002] The project development management process involves the entire development cycle from development and testing to official release. During the development cycle, multiple product lines need to collaborate to complete the solution release, and there are certain interdependencies between the product lines. Therefore, before the official release of the project, the project needs to be continuously iterated and upgraded, resulting in multiple unreleased version data, until finally a stable version is generated and delivered.

[0003] During project iteration and upgrades, the fragmented management of iteration data across various product lines makes it impossible to link and manage iteration data at different levels. Consequently, it becomes impossible to conduct unified overall planning for the project during version delivery, resulting in low data management efficiency. Summary of the Invention

[0004] In view of this, in order to overcome the shortcomings of the prior art, this application provides a data processing method, apparatus, device and storage medium.

[0005] Firstly, this application provides a data processing method, including:

[0006] Obtain the unreleased version dataset and the iteration dataset corresponding to each project; wherein, the unreleased version dataset is a collection of version data with the status attribute of unreleased; the iteration dataset is a collection of iteration data;

[0007] The unreleased version dataset is matched with the iterative dataset using a preset time field to generate a mapping dataset corresponding to the iterative dataset and the unreleased version dataset;

[0008] The mapping dataset is arranged according to the predetermined time order corresponding to the iterative data to obtain a data list;

[0009] Based on the data list, the unreleased version dataset and the iterative dataset are associated and stored together using predetermined attribute parameters as storage labels, and an associated data report is generated.

[0010] In an optional implementation, obtaining the unreleased version dataset and iterative dataset corresponding to each project includes:

[0011] Obtain the system configuration file and system version data field from the business interface;

[0012] Extract the project identification code from the system configuration file, obtain the iteration data corresponding to each project based on the project identification code, and generate an iteration dataset;

[0013] The version data is filtered according to the status attributes of each version data in the system version data domain, and all version data in the unreleased state are aggregated to form an unreleased version dataset.

[0014] In an optional implementation, the step of obtaining iterative data corresponding to each project based on the project identification code and generating an iterative dataset includes:

[0015] Based on the project identification code in the system configuration file, obtain the sprint cycle data corresponding to each project;

[0016] Extract the sprint cycle identification code from the sprint cycle data of each project, and obtain and collect the iteration data corresponding to each sprint cycle based on the sprint cycle identification code to generate an iteration dataset.

[0017] In an optional implementation, the step of matching the unreleased version dataset with the iterative dataset using a preset time field to generate a mapping dataset corresponding to the iterative dataset and the unreleased version dataset includes:

[0018] Query the release time field corresponding to each version data in the unreleased version dataset, and the online time field of each iteration data in the iterative dataset;

[0019] The launch time field and the release time field are matched in a funnel pattern. When the launch time field and the release time field are consistent, a mapping relationship is established for the corresponding version data and iteration data, thereby generating a mapping dataset corresponding to the iteration dataset and the unreleased version dataset.

[0020] In an optional implementation, the step of performing funnel-style matching between the launch time field and the release time field, and establishing a mapping relationship between the corresponding version data and iteration data when the launch time field and the release time field are consistent, includes:

[0021] The online time field of each of the iterative data is matched with the release time field of the merged version data in the unreleased version dataset for the first time. If the first match is successful, an association is established between the corresponding iterative data and the merged version data; wherein, the merged version data is version data that completes multiple functional requirements.

[0022] If the first match fails, the launch time field of each of the iterative data is matched a second time with the release time field of the independent version data in the unreleased version dataset. If the second match is successful, an association is established between the corresponding iterative data and the independent version data; wherein, the independent version data is version data that completes a functional requirement.

[0023] In an optional implementation, after obtaining the unreleased version dataset corresponding to each project, the method further includes:

[0024] Based on the version requirement attributes, identify the number of functional requirements to be completed for each version in the unreleased version dataset;

[0025] The various version data are categorized according to the number of functional requirements to obtain the merged version data and the independent version data.

[0026] In an optional implementation, the method further includes:

[0027] Set an automatic update time. When the automatic update time is reached, automatically retrieve the unreleased version dataset and iteration dataset corresponding to each project within the automatic update time to generate a new related data report; or,

[0028] Upon receiving a report update request, the system retrieves the unreleased version dataset and iterative dataset corresponding to each of the aforementioned projects at the current moment, in order to generate new related data reports accordingly.

[0029] Secondly, this application provides a data processing apparatus, comprising:

[0030] The acquisition module is used to acquire the unreleased version dataset and the iteration dataset corresponding to each project; wherein, the unreleased version dataset is a collection of version data with the status attribute of unreleased; and the iteration dataset is a collection of iteration data.

[0031] The matching module is used to match the unreleased version dataset with the iterative dataset using a preset time field to generate a mapping dataset corresponding to the iterative dataset and the unreleased version dataset;

[0032] The sorting module is used to sort the mapping dataset according to the predetermined time order corresponding to the iterative data to obtain a data list;

[0033] The association module is used to associate and store the unreleased version dataset and the iterative dataset according to the data list, using predetermined attribute parameters as storage labels, and generate an association data report.

[0034] Thirdly, this application provides a computer device, the computer device including a memory and at least one processor, the memory storing a computer program, and the processor executing the computer program to implement the aforementioned data processing method.

[0035] Fourthly, this application provides a computer-readable storage medium storing a computer program, which, when executed, implements the aforementioned data processing method.

[0036] The embodiments of this application have the following beneficial effects:

[0037] This application embodiment obtains the unreleased version dataset and iterative dataset corresponding to each project; matches the unreleased version dataset and iterative dataset with a preset time field to generate a mapping dataset corresponding to the iterative dataset and the unreleased version dataset; arranges the mapping dataset according to the predetermined time order corresponding to the iterative data to obtain a data list; based on the data list, the unreleased version dataset and iterative dataset are associated and stored according to predetermined attribute parameters as storage tags to generate an associated data report. This application embodiment establishes a mapping by matching unreleased version data and iterative data, thereby associating and storing iterative data with unreleased version data, realizing unified management and planning of iterative data distributed across various product lines, improving data management efficiency; furthermore, it can achieve data transparency across product lines and break down business barriers between product lines. Attached Figure Description

[0038] To more clearly illustrate the technical solutions of this application, the accompanying drawings used in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of this application and should not be considered as a limitation on the scope of protection of this application. In the various drawings, similar components are numbered similarly.

[0039] Figure 1 A schematic diagram of the first embodiment of the data processing method in this application is shown;

[0040] Figure 2 A schematic diagram of a second embodiment of the data processing method in this application is shown;

[0041] Figure 3 A schematic diagram of a third embodiment of the data processing method in this application is shown;

[0042] Figure 4 A schematic diagram of a fourth embodiment of the data processing method in this application is shown;

[0043] Figure 5A schematic diagram of the fifth embodiment of the data processing method in this application is shown;

[0044] Figure 6 This illustration shows a funnel-shaped matching process between iterative data and unpublished version data in an embodiment of this application.

[0045] Figure 7 A schematic diagram of the sixth embodiment of the data processing method in this application is shown;

[0046] Figure 8 A schematic diagram of the data processing apparatus in an embodiment of this application is shown. Detailed Implementation

[0047] The technical solutions in 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.

[0048] The components of the embodiments of this application described and illustrated in the accompanying drawings can be arranged and designed in a variety of different configurations. Therefore, the following detailed description of the embodiments of this application provided in the drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0049] In the following, the terms “comprising,” “having,” and their cognates, which may be used in various embodiments of this application, are intended only to indicate a particular feature, number, step, operation, element, component, or combination thereof, and should not be construed as excluding, firstly, the presence of one or more other features, numbers, steps, operations, elements, components, or combinations thereof, or adding the possibility of one or more features, numbers, steps, operations, elements, components, or combinations thereof.

[0050] Furthermore, the terms "first," "second," and "third" are used only to distinguish descriptions and should not be interpreted as indicating or implying relative importance.

[0051] Unless otherwise specified, all terms used herein (including technical and scientific terms) shall have the same meaning as commonly understood by one of ordinary skill in the art to which the various embodiments of this application pertain. Terms (such as those defined in commonly used dictionaries) shall be interpreted as having the same meaning as in their contextual meaning in the relevant technical field and shall not be construed as having an idealized or overly formal meaning, unless clearly defined in the various embodiments of this application.

[0052] In the process of software R&D management, there are various management scenarios in R&D project management, such as iteration management scenario and version management scenario.

[0053] Iteration typically refers to a series of activities undertaken by an organization after project activities have commenced, including adjusting and enriching functionality to ensure the project's features meet user needs, or defining the project's features. In software products, a version refers to the continuous iterative upgrades of a project. A version can be one-to-one or one-to-many with an iteration; conventionally, it's a collection of iterations, corresponding to the underlying software logic of "product upgrade," and based on the "version number." In other words, the iteration of one or more features must occur within a specific version, which is reflected in the version number.

[0054] A work cycle from development and testing to official release involves a version, which is the actual output of the development process. Iteration tasks are planned according to the actual situation; after multiple rounds of development and testing, a stable version is finally delivered to the customer, completing one iteration. In project development management, version management is the management of a collection of specific functions or the results of specific code builds during software development.

[0055] Agile Release Train (ART) is a method for developing and releasing solutions across multiple product lines using a series of fixed-length iterations. Products developed using integrated product development are typically large-scale, requiring collaboration between multiple product lines to complete the solution release. These product lines often have interdependencies, hence the ART approach is commonly used for releases. The ART release method effectively coordinates the progress of each product line, ensuring the overall solution release is successful. However, currently, there is no project management tool that can automatically collect data from the iteration hierarchy and automatically plan ART releases.

[0056] In addition, the current version train consists of multiple iterations, and the management of each iteration version is scattered, which makes it impossible to link and manage the iteration data at different levels, so that the version train cannot uniformly plan the project as a whole.

[0057] To better and faster plan version trains and to more quickly understand the data of each iteration, it is necessary to uniformly summarize the iteration data so as to quickly understand the information and progress of each iteration. Therefore, this application provides a data processing method to realize the association management between unreleased version data and iteration data, thereby improving the data management efficiency in the project development process.

[0058] Example 1

[0059] Please refer to Figure 1This application provides a data processing method, which will be described in detail below.

[0060] S10: Obtain the unreleased version dataset and iterative dataset for each project.

[0061] During project development, unreleased version datasets and iterative datasets are acquired for each development project. The unreleased version dataset is a collection of version data with the status attribute of "unreleased." The iterative dataset is a collection of data generated during the continuous iteration and upgrade process of each project. Furthermore, the version data in the unreleased version dataset can be further subdivided into monthly versions or other versions according to iteration time and other rules; this embodiment does not impose such limitations.

[0062] Furthermore, this embodiment can automate the matching and association of iterative data and version data through a custom tool, thereby enabling the management of iterative and version data during project development. Exemplarily, this custom tool can be defined as a version train information collection and planning tool.

[0063] This custom tool demonstrates how to manage iterative and version data. Specifically, based on the iterative planning of each business line during project development, the tool acquires iterative data and version data from each business line's version management, retrieves and organizes the iterative data source and version data, and then associates and stores the iterative and version data to generate associated data reports.

[0064] In one implementation, such as Figure 2 As shown, step S10 specifically includes the following steps:

[0065] S11 retrieves the system configuration file and system version data field from the business interface.

[0066] S12: Extract the project identification code from the system configuration file, obtain the iteration data corresponding to each project based on the project identification code, and generate the iteration dataset.

[0067] S13. According to the status attributes of each version data in the system version data field, filter the version data and gather all version data in the unreleased state to form an unreleased version dataset.

[0068] The system configuration file (config file) and version data field (version_fields data) are retrieved from the system's business interfaces. The configuration file stores the system's runtime environment configuration, while the version data field contains all actual output data from the project development process. Furthermore, the project identifier (also denoted as project-uuid) is extracted from the configuration file and used as a parameter to obtain the iteration data for each project, which is then aggregated to form an iteration dataset.

[0069] In this embodiment, the status attributes corresponding to each version data in the system version data domain are identified. These status attributes include released status and unreleased status. Based on the status attributes corresponding to each version data, the version data is filtered to extract all version data with the status attribute of unreleased, and the dataset is formed as an unreleased version dataset.

[0070] In one implementation, such as Figure 3 As shown, step S12 above specifically includes the following steps:

[0071] S121: Obtain the sprint cycle data corresponding to each project based on the project identification code in the system configuration file.

[0072] S122, extract the sprint cycle identification code from the sprint cycle data of each project, and obtain and collect the iteration data corresponding to each sprint cycle based on the sprint cycle identification code to generate an iteration dataset.

[0073] Specifically, a custom tool can be invoked to access the business system through its business interface, thereby obtaining the system's config file and the project identifier (project-uuid) range specified in the system configuration file. All sprint data (i.e., sprint data) for this project set can be retrieved using query statements (such as the GraphQL command), and the sprint identifier (also denoted as sprint-uuid) can be extracted from the sprint data. Then, using the sprint identifier as a parameter, all sprint data fields (i.e., sprint_fields data) can be obtained. The corresponding iteration data from each sprint is then retrieved and aggregated from these fields to form an iteration data set. Each sprint cycle must contain at least one iteration.

[0074] As an alternative solution, such as Figure 4 As shown, after step S10, this embodiment of the application further includes the following steps:

[0075] S51, based on the version requirement attributes, identify the number of functional requirements to be completed for each version in the unreleased version dataset.

[0076] S52 categorizes the data of each version according to the number of functional requirements, resulting in merged version data and independent version data.

[0077] The version requirement attribute corresponding to the version data indicates the number of functional requirements completed for that version data. Therefore, based on the version requirement attribute corresponding to this version data, the number of functional requirements completed for each version data in the unreleased version dataset can be identified.

[0078] The unreleased version dataset is divided into merged version data (e.g., version data with type=1) and independent version data (e.g., version data with type=2) based on the number of functional requirements completed. Merged version data represents versions that complete multiple functional requirements, while independent version data represents versions that complete only one functional requirement.

[0079] It should be noted that the data for each version in the unreleased version dataset can be categorized according to actual needs, and no limitation is made here. This embodiment is only an example, dividing the data for each version into two main categories: independent version data and merged version data; in addition, independent version data and merged version data can be further subdivided.

[0080] S20: Match the unreleased version dataset with the iterative dataset using a preset time field to generate a mapping dataset corresponding to the iterative dataset and the unreleased version dataset.

[0081] Based on the release time field in the version data and the launch time field in the iteration data, the time fields of each data in the unreleased version dataset and the iteration dataset are matched. When the time fields match, a mapping relationship is established for the corresponding iteration data and version data, thereby generating a mapping dataset corresponding to the iteration dataset and the unreleased version dataset.

[0082] In one implementation, such as Figure 5 As shown, step S20 specifically includes the following steps:

[0083] S21, query the release time field corresponding to each version data in the unreleased version dataset, and the launch time field of each iteration data in the iterative dataset.

[0084] From the unreleased version dataset, identify and extract the release time field corresponding to each version data and the launch time field from each iteration data in the iterative dataset.

[0085] S22 performs funnel-style matching between the launch time field and the release time field, and when the launch time field and the release time field are consistent, establishes a mapping relationship between the corresponding version data and the iteration data, thereby generating a mapping dataset corresponding to the iteration dataset and the unreleased version dataset.

[0086] Version data and iteration data are matched in a funnel pattern according to the corresponding release time field and launch time field, that is, the iteration data and version data are matched step by step. Specifically, if the launch time field and release time field are consistent, a relationship is established between the corresponding version data and iteration data, thereby generating a mapping dataset corresponding to the iteration dataset and the unreleased version dataset.

[0087] For example, in one implementation, merged version data with the version requirement attribute of merged version is selected from the unreleased version dataset. The launch time field of each iteration data can be matched with the release time field of the merged version data in the unreleased version dataset for the first time. If the first match is successful, an association relationship is established between the corresponding iteration data and the merged version data. The merged version data is version data that completes multiple functional requirements.

[0088] If the first match is successful, that is, the online time field of the iterative data matches the release time field of the merged version data, then a mapping relationship is established between the corresponding iterative data and the merged version data.

[0089] Furthermore, matching the online time field and the release time field involves calculating the character similarity between them. If the similarity is greater than a predetermined similarity threshold, the online time field and the release time field are considered a match; otherwise, they are not. The character similarity can be calculated using algorithms such as the edit distance algorithm or the longest common subsequence (LCS) algorithm.

[0090] If the first match fails, indicating a mismatch between the launch time field of the iterative data and the release time field of the merged version data, then independent version data with the "independent version" attribute is selected from the unreleased version dataset. A second match is then performed between the launch time field of each iterative data set and the release time field of the independent version data in the unreleased version dataset. If the second match succeeds, a relationship is established between the corresponding iterative data and the independent version data. Here, independent version data refers to version data that completes a single functional requirement.

[0091] Specifically, the launch time field is matched with the release time field of the merged version data (e.g., version data with type=1). If a match is found, a mapping relationship is established between the sprint cycle identifier of the corresponding iteration data and the version identifier in the merged version data. Conversely, if the launch time field fails to match the release time field of the merged version data, the launch time field is matched with the independent version data (e.g., version data with type=2). If a match is found, a mapping relationship is established between the sprint cycle identifier of the corresponding iteration data and the version identifier in the independent version data.

[0092] Optionally, the funnel-shaped matching process between this iterative data and the unreleased version data is as follows: Figure 6 As shown, by using a funnel-shaped, step-by-step matching method between iterative data and unreleased version data, the amount of data that needs to be matched later can be reduced, thus improving matching efficiency. Specifically, Figure 6 In the diagram, type1 version A and type1 version B are versions of data in the merged version data; type2 version C and type2 version D are versions of data in the independent version data; iteration 1, iteration 2, ..., iteration 10 are iterations of data in the iterative dataset.

[0093] S30, arrange the mapping dataset according to the predetermined time order corresponding to the iteration data to obtain a data list.

[0094] In this embodiment, the data in the mapping dataset are arranged according to a predetermined time order corresponding to the iterative data, thereby generating a data list. That is, the iterative data in the mapping dataset and the unreleased version data associated with the iterative data are arranged in a predetermined time order. The predetermined time order can be the release time order, iteration completion time order, acceptance time order, etc., and is not limited here.

[0095] In one implementation, based on the mapping dataset obtained after the iterative dataset and the unreleased version dataset are successfully matched, the iterative data in the mapping dataset is arranged in a predetermined time order, and the sprint cycle identifier code and the version identifier code are used as parameters to call the system's version management iteration interface to manage the iterative data and the unreleased version data, and generate a data list.

[0096] S40: Based on the data list, associate and store the unreleased version dataset and the iterative dataset with predefined attribute parameters as storage labels, and generate an associated data report.

[0097] As an example, predefined attribute parameters are used as storage labels, and the unreleased version dataset and iterative dataset are associated and stored according to the order in the data list, generating an associated data report. Specifically, based on the corresponding association and order between the unreleased version dataset and the iterative dataset in the data list, the mapped unreleased version data and iterative data are associated and stored in the location of the predefined attribute parameter corresponding to the file archive location. This predefined attribute parameter serves as the storage label. Subsequently, iterative data and version data can be retrieved using this storage label (i.e., the predefined attribute parameter). The predefined attribute parameter can be a version requirement attribute, release time, etc.

[0098] Optionally, version requirement attributes and release time attributes can be pre-specified as storage tags. A third-party reporting tool (such as Excelize) can be called through a custom tool to add custom report styles. Related data reports can be automatically generated based on the iterative dataset and the unreleased version dataset. At the same time, the custom tool can automatically find the storage location corresponding to the storage tag based on the archive location directory in the system (auto-addressing) to insert the corresponding related data report.

[0099] In one implementation, such as Figure 7 As shown, the embodiments of this application also include the following steps:

[0100] S61, set the automatic update time. When the automatic update time is reached, automatically obtain the unreleased version dataset and iteration dataset corresponding to each project within the automatic update time, and generate new related data reports accordingly.

[0101] An automatic update time is set so that the system can automatically update the associated data report according to the set time. Specifically, when an automatic update time is set, the system automatically obtains the unreleased version dataset and iteration dataset corresponding to each project within the automatic update time when it detects that the automatic update time has arrived, and executes steps S10-S40 again to generate a new associated data report. The new associated data report replaces the original associated data report, thereby realizing the automatic update of the associated data report.

[0102] S62, upon receiving a report update request, retrieves the unreleased version dataset and iteration dataset corresponding to each project at the current moment, in order to generate new related data reports accordingly.

[0103] Upon receiving a report update request, the system retrieves the unopened version dataset and iteration dataset corresponding to each project at the current moment, and executes steps S10-S40 again to generate a new related data report. This new related data report replaces the original related data report, thereby enabling on-demand updates of the related data report, i.e., updating the related data report according to the actual update requirements.

[0104] Furthermore, this custom tool can be used to update and execute the connection to the created Jenkins job. By executing the `cd-job` within the Jenkins job, related data reports can be automatically updated according to actual needs. Jenkins is an open-source software project, a continuous integration tool developed based on Java, used to monitor repetitive tasks. It aims to provide an open and easy-to-use software platform that enables continuous integration for software projects.

[0105] This application embodiment establishes a mapping by matching unreleased version data with iteration data, thereby associating and storing iteration data with unreleased version data to generate associated data reports, which can be automatically updated as needed. Firstly, it achieves unified management and planning of iteration data distributed across various product lines, as well as data transparency for each product line, breaking down business barriers and improving data management efficiency. Furthermore, the method provided by this application embodiment has a certain degree of universality and high scalability. Secondly, by associating iteration data with version data, version planning information can be automatically generated based on iteration planning information, improving version train planning efficiency. Thirdly, the steps in this application embodiment can be executed through a version train information collection and planning tool, thereby enhancing the applicability and versatility of the version train information collection and planning tool, making it applicable to any project management software based on iterations.

[0106] Example 2

[0107] Please refer to Figure 8 This application provides a data processing apparatus, which includes:

[0108] The acquisition module 81 is used to acquire the unreleased version dataset and the iteration dataset corresponding to each project; wherein, the unreleased version dataset is a collection of version data with the status attribute of unreleased; and the iteration dataset is a collection of iteration data.

[0109] Matching module 82 is used to match the unreleased version dataset with the iterative dataset using a preset time field to generate a mapping dataset corresponding to the iterative dataset and the unreleased version dataset;

[0110] The arrangement module 83 is used to arrange the mapping dataset according to the predetermined time order corresponding to the iterative data to obtain a data list;

[0111] The association module 84 is used to associate and store the unreleased version dataset and the iterative dataset according to the data list, using predetermined attribute parameters as storage labels, and generate an association data report.

[0112] It is understood that the above-described data processing apparatus corresponds to the data processing method of Embodiment 1; any option in Embodiment 1 is also applicable to this embodiment, and will not be described in detail here.

[0113] This application also provides a computer device, which may be, but is not limited to, a desktop computer, a laptop, etc. Its form is not limited, mainly depending on whether it needs to support the interface display function of a web browser. Exemplarily, the computer device includes a memory and at least one processor. The memory stores a computer program, and the processor executes the computer program to implement the data processing method of the above embodiments, thereby realizing the associated management of iterative data and version data, and improving data management efficiency.

[0114] The processor can be an integrated circuit chip with signal processing capabilities. The processor can be a general-purpose processor, including at least one of a Central Processing Unit (CPU), Graphics Processing Unit (GPU), Network Processor (NP), Digital Signal Processor (DSP), Application-Specific Integrated Circuit (ASIC), Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. The general-purpose processor can be a microprocessor or any conventional processor, capable of implementing or executing the methods, steps, and logic block diagrams disclosed in the embodiments of this application.

[0115] The memory can be, but is not limited to, Random Access Memory (RAM), Read Only Memory (ROM), Programmable Read-Only Memory (PROM), Erasable Programmable Read-Only Memory (EPROM), and Electrically Erasable Programmable Read-Only Memory (EEPROM). The memory stores computer programs, and the processor, upon receiving execution instructions, can execute the computer programs accordingly.

[0116] Furthermore, the memory may include a stored program area and a stored data area, wherein the stored program area may store the operating system and application programs required for at least one function; the stored data area may store data created based on the use of the computer device (such as iterative data, version data, etc.). In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0117] This application also provides a computer-readable storage medium storing computer-executable instructions. When called and executed by a processor, the computer-executable instructions cause the processor to perform the steps of the data processing method described in the above embodiments. The method includes: acquiring unreleased version datasets and iterative datasets corresponding to each project; wherein the unreleased version dataset is a collection of version data with the status attribute of "unreleased"; the iterative dataset is a collection of iterative data; matching the unreleased version dataset and the iterative dataset with a preset time field to generate a mapping dataset corresponding to the iterative dataset and the unreleased version dataset; arranging the mapping dataset according to a predetermined time order corresponding to the iterative data to obtain a data list; and, based on the data list, associating and storing the unreleased version dataset and the iterative dataset with predetermined attribute parameters as storage tags to generate an associated data report.

[0118] It is understood that the optional methods in the data processing method of the above embodiments are also applicable to this embodiment, so they will not be described again here.

[0119] The computer-readable storage medium can be either a non-volatile storage medium or a volatile storage medium. For example, the computer-readable storage medium may include, but is not limited to, various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0120] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings show the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that, in alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0121] In addition, the functional modules or units in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0122] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a smartphone, personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0123] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A data processing method, characterized in that, include: Obtain the unreleased version dataset and the iteration dataset corresponding to each project; wherein, the unreleased version dataset is a collection of version data with the status attribute of unreleased; the iteration dataset is a collection of iteration data; Based on the version requirement attributes, identify the number of functional requirements completed for each version in the unreleased version dataset; classify each version according to the number of functional requirements to obtain merged version data and independent version data; wherein, the merged version data is version data that completes multiple functional requirements; and the independent version data is version data that completes one functional requirement. Query the release time field corresponding to each version data in the unreleased version dataset, and the online time field of each iteration data in the iterative dataset; The online time field of each of the iterative data is matched with the release time field of the merged version data in the unreleased version dataset for the first time. If the first match is successful, an association relationship is established between the corresponding iterative data and the merged version data. If the first match fails, the online time field of each of the iterative data is matched a second time with the release time field of the independent version data in the unreleased version dataset. If the second match is successful, an association is established between the corresponding iterative data and the independent version data, thereby generating a mapping dataset corresponding to the iterative dataset and the unreleased version dataset. The mapping dataset is arranged according to the predetermined time order corresponding to the iterative data to obtain a data list; Based on the data list, the unreleased version dataset and the iterative dataset are associated and stored together using predetermined attribute parameters as storage labels, and an associated data report is generated.

2. The data processing method according to claim 1, characterized in that, The process of obtaining the unreleased version dataset and iterative dataset corresponding to each project includes: Obtain the system configuration file and system version data field from the business interface; Extract the project identification code from the system configuration file, obtain the iteration data corresponding to each project based on the project identification code, and generate an iteration dataset; Based on the status attributes of each version data in the system version data domain, the version data is filtered, and all version data in the unreleased state are aggregated to form an unreleased version dataset.

3. The data processing method according to claim 2, characterized in that, The step of obtaining iterative data corresponding to each project based on the project identification code and generating an iterative dataset includes: Based on the project identification code in the system configuration file, obtain the sprint cycle data corresponding to each project; Extract the sprint cycle identification code from the sprint cycle data of each project, and obtain and collect the iteration data corresponding to each sprint cycle based on the sprint cycle identification code to generate an iteration dataset.

4. The data processing method according to any one of claims 1-3, characterized in that, The method further includes: Set an automatic update time. When the automatic update time is reached, automatically retrieve the unreleased version dataset and iteration dataset corresponding to each project within the automatic update time to generate a new related data report; or, Upon receiving a report update request, the system retrieves the unreleased version dataset and iterative dataset corresponding to each of the aforementioned projects at the current moment, in order to generate new related data reports accordingly.

5. A data processing apparatus, characterized in that, include: The acquisition module is used to acquire the unreleased version dataset and the iteration dataset corresponding to each project; wherein, the unreleased version dataset is a collection of version data with the status attribute of unreleased; and the iteration dataset is a collection of iteration data. The identification module is used to identify the number of functional requirements completed for each version data in the unreleased version dataset according to the version requirement attributes; classify each version data according to the number of functional requirements to obtain merged version data and independent version data; wherein, the merged version data is version data that completes multiple functional requirements; and the independent version data is version data that completes one functional requirement. The matching module is used to query the release time field corresponding to each version data in the unreleased version dataset, and the online time field of each iteration data in the iterative dataset; The online time field of each of the iterative data is matched with the release time field of the merged version data in the unreleased version dataset for the first time. If the first match is successful, an association relationship is established between the corresponding iterative data and the merged version data. If the first match fails, the online time field of each of the iterative data is matched a second time with the release time field of the independent version data in the unreleased version dataset. If the second match is successful, an association is established between the corresponding iterative data and the independent version data, thereby generating a mapping dataset corresponding to the iterative dataset and the unreleased version dataset. The sorting module is used to sort the mapping dataset according to the predetermined time order corresponding to the iterative data to obtain a data list; The association module is used to associate and store the unreleased version dataset and the iterative dataset according to the data list, using predetermined attribute parameters as storage labels, and generate an association data report.

6. A computer device, characterized in that, The computer device includes a memory and at least one processor, the memory storing a computer program, and the processor executing the computer program to implement the data processing method according to any one of claims 1-4.

7. A computer-readable storage medium, characterized in that, It stores a computer program, which, when executed, implements the data processing method according to any one of claims 1-4.

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