Open source project-oriented crowd-sourcing convergence entropy quantification and visualization method and device

Abstract

The invention relates to the technical field of software data and information visualization, and provides a crowd-sourcing convergence entropy quantification and visualization method and device oriented to an open source project in order to solve the technical problems of narrow crowd-sourcing measurement visual angle and single measurement dimension in the prior art. Inquiring or downloading submitted data of the target project and the branch project thereof from the Internet to a local database; dividing the project life cycle according to a preset time window; the normalized submission number distribution is utilized, the difference degree of the submission number distribution between the target software project and the bifurcated project set is combined, and an entropy value synthesis formula is designed to calculate the crowd-sourcing convergence entropy of each time window; and carrying out visualization on the calculated entropy value data by utilizing a server. According to the method, the crowd-sourcing convergence entropy is calculated by designing the entropy formula, the change trend of the crowd-sourcing convergence degree of the open source project is dynamically and intuitively reflected, and visual decision support is provided for management and optimization of the open source project.

Description

Technical Field

[0001] This invention belongs to the field of software data and information visualization technology, specifically relating to a method and apparatus for quantifying and visualizing the collective intelligence convergence entropy of open-source projects. Background Technology

[0002] Open source software has become an indispensable part of the information infrastructure of modern society. Supported by open source software hosting platforms such as GitHub and GitLab, developers worldwide can easily participate in open source projects. The success of open source projects largely depends on the active participation of community members and the integration of collective wisdom—that is, "collective intelligence stimulation" and "collective intelligence convergence" from the perspective of the collective intelligence paradigm. Given the autonomy and diversity of open source development participants, the development process and costs of projects are highly uncertain. Therefore, quantitatively measuring and visualizing the dynamics of collective intelligence in open source projects is crucial for project managers to understand and optimize group collaboration models and achieve the effective emergence of intelligence.

[0003] Existing technologies have attempted to measure collective intelligence by dividing the project's lifecycle into time windows and calculating the information entropy (i.e., collective intelligence evoked entropy) of developer behavior data within each window to quantify the dynamic changes in collective intelligence caused by participant behavior. This method provides a useful tool for monitoring collective intelligence in open-source software. However, this method's measurement perspective is limited to the internal behavior of a single project repository, mainly focusing on the diversity of developers' commits, comments, and other behaviors within the project itself. It equates the uncertainty (entropy) of behavior with the level of collective intelligence, failing to effectively distinguish between disordered "noise" and orderly "collaboration," and even more so failing to measure the process of wisdom aggregation across projects. It is prone to simply misjudging high activity as high collective intelligence.

[0004] Secondly, and more importantly, the perspective for measuring collective intelligence is narrow. Existing methods completely ignore the collective intelligence aggregation process embodied in the core paradigm of "forked projects-merge" in modern open-source collaboration. In the open-source ecosystem, the inspiration of wisdom occurs not only within the main project but also manifests in the dynamic process by which developers create project forks for independent exploration and innovation, and then integrate their results into the main project through pull requests. This mechanism of "inspired by forks, converging in the main repository" is a crucial link in the formation of collective intelligence. However, existing technologies have not incorporated the development activities of forked project networks into their analytical frameworks, making it impossible to measure the efficiency of cross-project wisdom flow, collaboration, and integration, and failing to truly reflect a project's ability to attract and integrate community wisdom.

[0005] Therefore, there is an urgent need in this field for quantitative and visualization methods that can move beyond the perspective of a single project repository and accurately quantify the degree of collaboration and intelligence convergence between the main project and its forked project networks at the level of project community ecosystem, so as to provide a deeper and more effective insight tool for open source project management. Summary of the Invention

[0006] This invention addresses the shortcomings of existing technologies in measuring collective intelligence, which often suffer from narrow perspectives and limited dimensionality. It proposes a method and apparatus for quantifying and visualizing the collective intelligence convergence entropy in open-source projects. Within the framework of the collective intelligence paradigm, this method calculates the collective intelligence convergence entropy using an entropy formula, dynamically and intuitively reflecting the changing trends in the degree of collective intelligence convergence within open-source projects. This provides a quantitative and visual tool based on entropy theory for software development processes that stimulate collective intelligence convergence, thereby quantitatively measuring the effectiveness of collective intelligence convergence and enhancing the transparency and operational efficiency of open-source projects.

[0007] A method for quantifying and visualizing the entropy of collective intelligence aggregation in open-source projects, the method comprising: Step 110: The server receives a user request containing the name of the target software project; queries the local database to see if the target software project and its forked project commit data exist; if the target software project and its forked project commit data do not exist in the local database, proceed to step 120; otherwise, jump directly to step 150. Step 120: Use the server to search for and download all commit data of the target software project and its forks from the open source platform to the local database; Step 130: Divide the deadline period into multiple time windows according to the preset time window length; the deadline period is set with the creation time of the target software project as the starting point and the current time as the ending point. Step 140: Process each time window of the deadline period in chronological order to obtain the collective intelligence convergence entropy of each time window; the collective intelligence convergence entropy is used to measure the degree of collaboration and convergence of software project development activities. By comprehensively considering the total number of submissions within the time window and the degree of difference in the number of submissions between the target software project and the set of branch projects, the entropy value is synthesized and the collective intelligence convergence entropy of the current window is calculated and stored in the local database. Step 150: Visualize the time series data containing the collective intelligence entropy in all time windows, generate and output the collective intelligence entropy change curve of the target software project over time, and return it to the user.

[0008] Preferably, the method for quantifying and visualizing the collective intelligence convergence entropy for open-source projects provided by the present invention further includes step 160, which involves automatically checking the commit data of the software projects and corresponding forked projects stored in the local database at a preset period using a server, and recalculating the collective intelligence convergence entropy according to step 140 to update the stored software projects.

[0009] On the other hand, the present invention also provides a device for quantifying and visualizing entropy aggregation in open source projects, the device comprising: The first module is used by the server to receive user requests containing the name of the target software project; query the local database to see if the target software project and its forked project commit data exist; if the target software project and its forked project commit data do not exist in the local database, continue to the steps of the second module; otherwise, directly jump to the steps of the fifth module. The second module is used to search for and download all commit data of the target software project and its forks from the open source platform to the local database using the server. The third module is used to divide the deadline period into multiple time windows according to a preset time window length; the deadline period is set with the creation time of the target software project as the starting point and the current time as the ending point. The fourth module is used to process each time window of the deadline period in chronological order and obtain the collective intelligence convergence entropy of each time window. The collective intelligence convergence entropy is used to measure the degree of collaboration and convergence of software project development activities. By comprehensively considering the total number of submissions within the time window and the degree of difference in the number of submissions between the target software project and the set of branch projects, the entropy value is synthesized and the collective intelligence convergence entropy of the current window is calculated and stored in the local database. The fifth module is used to visualize the time series data containing the collective intelligence entropy in all time windows, generate and output the collective intelligence entropy change curve of the target software project over time, and return it to the user.

[0010] Preferably, the collective intelligence convergence entropy quantification and visualization device for open source projects provided by the present invention further includes a sixth module, which is used to automatically check the submission data of the software projects and corresponding forked projects stored in the local database at a preset period using the server, and recalculate the collective intelligence convergence entropy according to the steps of the fourth module to update the stored software projects.

[0011] Compared with the prior art, the technical effects of the present invention include: (1) This invention proposes a novel concept of “collective intelligence convergence entropy” measurement, which shifts the focus of collective intelligence measurement from “behavioral stimulation” of a single project to “wisdom convergence” of a project community, which is more in line with the essence of open source collaboration.

[0012] (2) By systematically incorporating the submitted data of the forked project network into the analysis framework, an ecological evaluation model for the collaboration of primary and secondary projects was constructed, expanding the breadth of the collective intelligence measurement object.

[0013] (3) This invention introduces Jensen-Shannon divergence to quantify the activity consistency between the main project and the branch project community, and integrates "overall activity" and "primary-secondary synergy" into a comprehensive index through entropy synthesis model. Compared with the single Shannon entropy, it can more finely and accurately depict the convergence process of intelligence, and solve the problem of existing technologies misjudging disorder and noise as effective collective intelligence. It is not only easy to operate and highly flexible, but also provides an effective monitoring tool for open source project managers to track and evaluate the degree of convergence of collective intelligence in the software development process. With the help of the method of this invention, project managers can more intuitively understand and promote the convergence and productivity of group collaboration. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.

[0015] Figure 1 This is a schematic diagram illustrating the steps of a method for quantifying and visualizing entropy in the collective intelligence of open-source projects according to one embodiment of the present invention. Figure 2 This is a flowchart of quantitative analysis and visualization in one embodiment of the present invention when the local database is an existing software project; Figure 3 This is a flowchart of the entropy quantification analysis and visualization process for collective intelligence aggregation in one embodiment of the present invention when the local server has not yet downloaded the complete resource library of projects. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. It should be noted that the reference to "embodiment" in this application means that a specific feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the present invention. The appearance of this phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments.

[0017] Those skilled in the art will understand that the embodiments described herein can be combined with other embodiments. The term "and / or" as used in this application refers to any combination of one or more of the associated listed items, and all possible combinations, including such combinations. The embodiments described herein are only a portion of the embodiments of the present invention, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0018] In one embodiment, the present invention provides a method for quantifying and visualizing the entropy of collective intelligence aggregation in open-source projects, such as... Figure 1 As shown, the method includes: Step 110: The server receives a user request containing the name of the target software project; queries the local database to see if the target software project and its forked project commit data exist; if the target software project and its forked project commit data do not exist in the local database, proceed to step 120; otherwise, jump directly to step 150. Step 120: Use the server to search for and download all commit data of the target software project and its forks from the open source platform to the local database; Step 130: Divide the deadline period into multiple time windows according to the preset time window length; the deadline period is set with the creation time of the target software project as the starting point and the current time as the ending point. Step 140: Process each time window of the deadline period in chronological order to obtain the collective intelligence convergence entropy of each time window; the collective intelligence convergence entropy is used to measure the degree of collaboration and convergence of software project development activities. By comprehensively considering the total number of submissions within the time window and the degree of difference in the number of submissions between the target software project and the set of branch projects, the entropy value is synthesized and the collective intelligence convergence entropy of the current window is calculated and stored in the local database. Step 150: Visualize the time series data containing the collective intelligence entropy in all time windows, generate and output the collective intelligence entropy change curve of the target software project over time, and return it to the user.

[0019] Specifically, in step 110, the user sends a collective intelligence monitoring request to the server through the client. This request includes the name of the target software project, such as "project-X". After receiving the request, the server first queries the local database to determine whether the target software project and its forked projects have commit data. If the local database does not yet store complete commit data for "project-X" and its forked projects, the server proceeds to step 120; otherwise, the server jumps to step 150 and directly executes the visualization step.

[0020] Furthermore, in step 120, the server uses APIs from open-source platforms such as GitHub and Gitee to search for and identify all forked repositories of the main project, "project-X". Then, it downloads all commit data (including commit hashes, authors, times, modified files, etc.) of the main project and all forked projects in batches to a local database, forming a complete commit dataset covering the entire project community.

[0021] Next, in step 130, the time period from the creation time of the target software project to the current time is divided into multiple time windows according to the preset time window length.

[0022] Let the preset time window length be... The server retrieves the creation time of "project-X". and current time Deadline period Divided into several consecutive time windows with a length of Time window: ; It is the deadline period. The total number of time windows within the time frame.

[0023] The step of defining and calculating the collective intelligence convergence entropy in step 140 is the core content of the technical solution of this invention. The collective intelligence convergence entropy is used to precisely measure the synergy and integration efficiency of development activities between the main project and branch project clusters within a specific time window. The calculation of the collective intelligence convergence entropy does not simply rely on the number of behaviors, but is obtained through an innovative entropy value synthesis model that comprehensively considers the overall activity of the project cluster and the synergistic consistency between the main and secondary projects.

[0024] In step 140, each time window of the cutoff time period is processed sequentially according to time order. , Calculate the collective intelligence convergence entropy for each time window. Specifically, it includes: Step 141, check the current time window Check if there are any forked projects and corresponding submission data. If so, proceed to step 142; otherwise, record the collective intelligence convergence entropy as 0 and proceed to step 143.

[0025] Step 142: By comprehensively considering the total commit distribution within the current time window and the degree of difference in commit distribution between the target software project and the forked project set, the collective intelligence convergence entropy of the current time window is calculated after entropy synthesis, including: Perform data extraction and normalized distribution calculation: For the current time window Each forked project within , The number of raw submissions generated in the statistical submission data , The current time window Total number of internal forks; based on all forks in the current time window. This forms a distribution vector, which is then normalized to obtain a submission distribution that reflects the original contribution strength of each bifurcation project. ; Calculate the number of merge commits that were merged back into the main project from the submitted data. Based on all forked projects in the current time window This forms a distribution vector, which is then normalized to obtain the merged commit distribution that reflects the strength of the effective convergence of each bifurcation project back to the main project. ; Calculate the total number of original commits within the current time window. and total number of merged submissions ; Calculate the consistency index between the main project and the branch projects: using distribution and The Jensen-Shannon divergence (JSD) is used to calculate the synergy and consistency index between the master project and the forked project. ; Using the total number of original commits, the total number of merged commits, and the coordination consistency index between the main project and the forked projects, we design a formula for the entropy synthesis model to calculate the collective intelligence convergence entropy for the current time window.

[0026] Step 143: Store the collective intelligence convergence entropy of the current time window in the local database; Step 144: Repeat steps 141-143 until all time windows of the cutoff period have been traversed.

[0027] In step 142, the core idea of ​​calculating the collective intelligence convergence entropy is that an ideal state of intelligence convergence requires not only active exploration in the branching projects (original number of submissions), but also that the results of these explorations can be efficiently and consistently merged into the main project (merged number of submissions).

[0028] In one embodiment, step 142 is used to calculate the collective intelligence convergence entropy of the current time window, and the entropy value synthesis model formula used is: .

[0029] In the above formula, It depicts the coordination and consistency between the main project and the forked project; when their behaviors are completely consistent, A value of 1 indicates the highest degree of synergy. It characterizes the aggregation efficiency of project clusters, that is, the proportion of original data submissions that are accepted by the main project. and The product composition implies that a high collective intelligence aggregation entropy value will only be generated when a project cluster is both highly collaborative and has high aggregation efficiency. This effectively distinguishes it from simply high activity (high...). ) and high-quality intelligent integration (high collaboration, high efficiency).

[0030] In step 150, the time series data containing the collective intelligence entropy in all time windows are visualized, and the collective intelligence entropy curve of the target software project over time is generated and output, and returned to the user.

[0031] The server uses time as its horizontal axis, and collective intelligence converges on entropy. Using the vertical axis as the ordinate, a time-series data curve is generated and returned to the user's client for display. Project managers can clearly see the key periods and bottleneck stages of community wisdom aggregation during the project's development through this curve.

[0032] In one embodiment, such as Figure 1 As shown, the method for quantifying and visualizing the collective intelligence entropy for open-source projects further includes step 160, which involves automatically checking the commit data of the software projects and corresponding forked projects stored in the local database using a server at a preset period, and recalculating the collective intelligence entropy according to step 140, thereby updating the stored software project resource library.

[0033] In one embodiment, the present invention provides a method for quantifying and visualizing the entropy of collective intelligence aggregation in open-source projects, such as... Figure 2 The diagram shown is a flowchart for the quantitative analysis and visualization of existing software projects in the local database. The specific steps are as follows: Step 101: The user makes a request to the server, requesting to obtain the graph of the collective intelligence aggregation entropy of software project P over time. Step 102: The server finds an existing graph of the collective intelligence entropy over time for software project P locally and returns it to the user. Step 103 concludes the process of quantitative analysis and visualization of the collective intelligence convergence entropy of project P.

[0034] The method in this embodiment automates the acquisition of open-source software quality evidence on the Internet, simplifies the tedious and repetitive manual acquisition process, makes full use of existing calculation results, greatly reduces the workload of users, has good flexibility, and can help open-source platforms cope with massive user access.

[0035] In one embodiment, the present invention provides a method for quantifying and visualizing the collective intelligence entropy of open-source projects, such as... Figure 3 The diagram shows the entropy quantification and visualization flowchart for projects where the local server has not yet downloaded the complete resource library. The specific steps are as follows: Step 201: The user makes a request to the server, requesting to obtain the graph of the collective intelligence entropy of software project Q over time. Step 202: The server checks the local database but finds no complete data about software project Q; Step 203: The server searches the Internet and downloads the latest forks and commit data of software project Q from open source platform A to its local database; Step 204, obtain the creation time of the target software project. Get the current time as .from Initially, based on the time window size... ( It can be configured by the system administrator, for example It can represent a time window length of 1 day, etc., and be divided into several time windows; Step 205, for each time window The forks and commit data in the time window are filtered out. By comprehensively considering the total number of commits in the current time window and the degree of difference in the number of commits between the target software project and the fork project set, the entropy value is synthesized and the collective intelligence convergence entropy of the current time window is calculated. Step 206: The server visualizes the data and provides the curve of the collective intelligence entropy of the target software project changing over time, and returns it to the user.

[0036] Step 207 concludes the process of quantitative analysis and visualization of the collective intelligence convergence entropy of Project Q.

[0037] In summary, this invention, through the aforementioned methods for quantifying and visualizing the collective intelligence convergence entropy of open-source projects, proposes a novel measurement concept: "collective intelligence convergence entropy." This shifts the focus of collective intelligence assessment from the behavioral stimulation of individual projects to the overall convergence of intelligence across project communities, thus better aligning with the fundamental characteristics of open-source collaboration. By incorporating the commit data of forked project networks into a unified analysis framework, an ecological assessment model covering the collaborative relationship between the main project and forked projects is constructed, broadening the scope of collective intelligence measurement. Furthermore, Jensen-Shannon divergence is introduced to quantify the activity consistency between the main project and forked project communities. Using an entropy synthesis model, "overall activity" and "primary-secondary synergy" are integrated into a single comprehensive indicator. Compared to using only Shannon entropy, this invention can more precisely and accurately describe the process of intelligence convergence, effectively avoiding the misjudgment of disordered noise as effective collective intelligence. The aforementioned methods are simple to operate and highly flexible, providing open-source project managers with effective monitoring tools to help them track and evaluate the accumulation of collective intelligence during software development, thereby more intuitively understanding and promoting the convergence effect and productivity improvement of group collaboration.

[0038] In one embodiment, the present invention also provides a device for quantifying and visualizing the entropy of collective intelligence in open-source projects, the device comprising: The first module is used by the server to receive user requests containing the name of the target software project; query the local database to see if the target software project and its forked project commit data exist; if the target software project and its forked project commit data do not exist in the local database, continue to the steps of the second module; otherwise, directly jump to the steps of the fifth module. The second module is used to search for and download all commit data of the target software project and its forks from the open source platform to the local database using the server. The third module is used to divide the deadline period into multiple time windows according to a preset time window length; the deadline period is set with the creation time of the target software project as the starting point and the current time as the ending point. The fourth module is used to process each time window of the deadline period in chronological order and obtain the collective intelligence convergence entropy of each time window. The collective intelligence convergence entropy is used to measure the degree of collaboration and convergence of software project development activities. By comprehensively considering the total number of submissions within the time window and the degree of difference in the number of submissions between the target software project and the set of branch projects, the entropy value is synthesized and the collective intelligence convergence entropy of the current window is calculated and stored in the local database. The fifth module is used to visualize the time series data containing the collective intelligence entropy in all time windows, generate and output the collective intelligence entropy change curve of the target software project over time, and return it to the user.

[0039] In one embodiment, the collective intelligence convergence entropy quantification and visualization device for open source projects provided by the present invention further includes a sixth module, which is used to automatically check the submission data of the software projects and corresponding forked projects stored in the local database at a preset period using the server, and recalculate the collective intelligence convergence entropy according to the steps of the fourth module to update the stored software projects.

[0040] On the other hand, the present invention provides a computer device, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps of the crowd intelligence aggregation entropy quantification and visualization method for open-source projects provided in any of the above embodiments. The computer device can be a server. The computer device includes a processor, a memory, a network interface, and a database connected via a system bus. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store sample data. The network interface of the computer device is used to communicate with external terminals via a network connection.

[0041] On the other hand, the present invention provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the crowd intelligence convergence entropy quantification and visualization method for open-source projects provided in any of the above embodiments.

[0042] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0043] Matters not covered in this invention are common knowledge.

[0044] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0045] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these modifications and improvements all fall within the protection scope of this application.

[0046] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for quantifying and visualizing the entropy of collective intelligence in open-source projects, characterized in that, The method includes: Step 110: The server receives a user request containing the name of the target software project; queries the local database to see if the target software project and its forked project commit data exist; if the target software project and its forked project commit data do not exist in the local database, proceed to step 120; otherwise, jump directly to step 150. Step 120: Use the server to search for and download all commit data of the target software project and its forks from the open source platform to the local database; Step 130: Divide the deadline period into multiple time windows according to the preset time window length; the deadline period is set with the creation time of the target software project as the starting point and the current time as the ending point. Step 140: Process each time window of the deadline period in chronological order to obtain the collective intelligence convergence entropy of each time window; the collective intelligence convergence entropy is used to measure the degree of collaboration and convergence of software project development activities. By comprehensively considering the total number of submissions within the time window and the degree of difference in the number of submissions between the target software project and the set of branch projects, the entropy value is synthesized and the collective intelligence convergence entropy of the current window is calculated and stored in the local database. Step 150: Visualize the time series data containing the collective intelligence entropy in all time windows, generate and output the collective intelligence entropy change curve of the target software project over time, and return it to the user.

2. The method for quantifying and visualizing entropy aggregation in open-source projects according to claim 1, characterized in that, Step 140 includes: Step 141: Check if there are any forked projects and corresponding commit data in the current time window. If they exist, proceed to step 142; otherwise, record the collective intelligence entropy as 0 and proceed to step 143. Step 142: Taking into account the total number of commits within the current time window and the degree of difference in the number of commits between the target software project and the set of forked projects, calculate the collective intelligence convergence entropy of the current time window after synthesizing the entropy values. Step 143: Store the collective intelligence convergence entropy of the current time window in the local database; Step 144: Repeat steps 141-143 until all time windows of the cutoff period have been traversed.

3. The method for quantifying and visualizing entropy aggregation in open-source projects according to claim 2, characterized in that, Step 142 includes: Perform data extraction and normalized distribution calculation: For each forked project in the current time window, count the number of raw commits generated in the commit data; based on the number of raw commits of all forked projects in the current time window, form a distribution vector and normalize it to obtain the commit distribution; Calculate the number of merge commits that were merged back into the main project from the commit data; based on the number of merge commits of all forked projects in the current time window, form a distribution vector and normalize it to obtain the merge commit distribution; Calculate the total number of original commits and the total number of merged commits within the current time window; Calculate the coordination and consistency metrics between the main project and the branch projects; Using the total number of original commits, the total number of merged commits, and the coordination consistency index between the main project and the forked projects, we design a formula for the entropy synthesis model to calculate the collective intelligence convergence entropy for the current time window.

4. The method for quantifying and visualizing entropy aggregation in open-source projects according to claim 3, characterized in that, The total number of original submissions is given by the following formula: ； in, The current time window A forked project within The original number of submissions, ; The current time window The total number of internally branched projects; The total number of merged submissions is given by the following formula: ； in, The current time window A forked project within The number of merged submissions.

5. The method for quantifying and visualizing entropy aggregation in open-source projects according to claim 4, characterized in that, The coordination and consistency index between the main project and the forked project is obtained by calculating the Jensen-Shannon divergence between the commit distribution and the merged commit distribution, and is given by the following formula: ； in, The current time window Indicators of coordination and consistency between the main project and its branch projects. Indicates the submission distribution Combined submission distribution The Jensen-Shannon divergence between them.

6. The method for quantifying and visualizing entropy aggregation for open-source projects according to claim 4 or 5, characterized in that, The formula for the design entropy synthesis model, which calculates the collective intelligence convergence entropy for the current time window, is given by the following formula: ； in, It is the collective entropy of the current time window. It is a metric for the coordination and consistency between the main project and the branch projects.

7. The method for quantifying and visualizing entropy aggregation in open-source projects according to claim 1, characterized in that, It also includes step 160, which uses the server to automatically check the submission data of the software projects and corresponding fork projects stored in the local database at a preset period, and recalculates the collective intelligence entropy according to step 140 to update the stored software projects.

8. A device for quantifying and visualizing entropy aggregation in open-source projects, characterized in that, The device includes: The first module is used by the server to receive user requests containing the name of the target software project; query the local database to see if the target software project and its forked project commit data exist; if the target software project and its forked project commit data do not exist in the local database, continue to the steps of the second module; otherwise, directly jump to the steps of the fifth module. The second module is used to search for and download all commit data of the target software project and its forks from the open source platform to the local database using the server. The third module is used to divide the deadline period into multiple time windows according to a preset time window length; the deadline period is set with the creation time of the target software project as the starting point and the current time as the ending point. The fourth module is used to process each time window of the deadline period in chronological order and obtain the collective intelligence convergence entropy of each time window. The collective intelligence convergence entropy is used to measure the degree of collaboration and convergence of software project development activities. By comprehensively considering the total number of submissions within the time window and the degree of difference in the number of submissions between the target software project and the set of branch projects, the entropy value is synthesized and the collective intelligence convergence entropy of the current window is calculated and stored in the local database. The fifth module is used to visualize the time series data containing the collective intelligence entropy in all time windows, generate and output the collective intelligence entropy change curve of the target software project over time, and return it to the user.

9. The crowd intelligence aggregation entropy quantification and visualization device for open-source projects according to claim 8, characterized in that, The device also includes a sixth module, which is used to automatically check the submission data of the software projects and corresponding fork projects stored in the local database at a preset period using the server, and recalculate the collective intelligence convergence entropy according to the steps of the fourth module to update the stored software projects.

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