Crowdsourcing monitoring method, device and equipment in open source project

Abstract

The application relates to a crowd wisdom inspiration monitoring method, device and equipment in an open source project. By introducing a crowd wisdom inspiration measurement method based on an entropy theory and fully mining and utilizing long-term developer behavior data of the project, crowd wisdom dynamic changes caused by participant behaviors in a development process of an open source software project can be captured and quantified in detail. The technology is easy to operate and has high flexibility, is suitable for software project supervision developed in an open source manner, and provides an effective monitoring tool for managers of the open source software project to track and evaluate a crowd wisdom inspiration degree in a real time manner.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of computers, and relates to a crowd intelligence inspiration monitoring method, device and equipment in an open source project. BACKGROUND

[0002] Open source software has become an indispensable part of modern social infrastructure. With the support of open source software hosting platforms, developers all over the world can easily publish and participate in open source software projects. The success of an open source software project depends largely on the active participation and integration of wisdom of community members, that is, crowd intelligence inspiration and convergence from the perspective of crowd intelligence paradigm. Due to the autonomy and diversity of open source software development participants, the development time, labor intensity and cost of the project are highly uncertain. Therefore, quantitative measurement and visualization of crowd intelligence in open source software projects are crucial for managers to grasp and optimize the pattern of group intelligence and achieve effective emergence of group intelligence.

[0003] Existing crowd intelligence measurement methods are mainly applied to the scenario of robot groups in reinforcement learning tasks, and entropy theory is used to measure the level of wisdom reflected in group behavior. The robot group is regarded as a group intelligence system, and the emergence process of crowd intelligence is regarded as an iterative process of crowd intelligence inspiration and convergence. In the process of crowd intelligence inspiration and convergence, the change in the complexity of the behavior of the robot group corresponds to the process of entropy increase and decrease in the group intelligence system.

[0004] For example, in a search task scenario on a complex graph structure, the task is for an agent to discover unknown nodes on a given graph structure through a walk strategy, and the task is completed if all nodes are discovered. The agent will receive a reward in the process of executing the task, and the reward is related to the number of new unknown nodes discovered by the agent and the total number of walks. Initially, only a single agent is executing the task, but as the reward value increases, new individuals will be inspired to join the task. Then, as the task difficulty increases, the number of available new nodes decreases, and individuals begin to share information to complete the task more efficiently and obtain more rewards, presenting a phenomenon of crowd intelligence emergence. In this scenario, the agent selects a neighbor node for search with a certain probability, and according to the information entropy theory, the average uncertainty of the intelligent group behavior can be used to measure the intelligence exhibited by the intelligent group behavior. Using the crowd intelligence emerging from this intelligent group as a measurement tool for the training effect of multi-agent reinforcement learning can effectively speed up convergence and improve the effect of reinforcement learning.

[0005] However, in the context of open source software development, there is an urgent need for corresponding adaptive crowd wisdom measurement methods. Unlike the task scenario of robot swarm reinforcement learning, in the context of open source software development, the influencing factors involved in the open source software development process are complex and diverse, and the behavior of developers as a kind of special intelligent agent with subjective ideology is unpredictable, which brings different challenges to the crowd wisdom measurement of open source software development. SUMMARY

[0006] In view of the problems in the above-mentioned traditional method, the present application provides a crowd wisdom stimulation monitoring method in an open source project, a crowd wisdom stimulation monitoring device in an open source project and a computer device, which can effectively monitor the crowd wisdom stimulation degree of an open source software project.

[0007] In order to achieve the above-mentioned purpose, the embodiments of the present application adopt the following technical solutions:

[0008] On the one hand, a crowd wisdom stimulation monitoring method in an open source project is provided, comprising the steps of:

[0009] obtaining a crowd wisdom stimulation monitoring request sent by a user end; the crowd wisdom stimulation monitoring request comprises the name of a target software project;

[0010] finding the latest developer behavior data of the target software project within a set time period in the background server database according to the crowd wisdom stimulation monitoring request;

[0011] when there is no latest developer behavior data in the background server database, searching and downloading the latest developer behavior data of the target software project from the Internet to the background server database;

[0012] dividing the life cycle of the target software project into time windows according to a set time window size; the life cycle is the starting point of the creation time of the target software project to the current time;

[0013] filtering out the developer behavior data in each time window in order of time from early to late, and calculating the crowd wisdom stimulation entropy corresponding to each time window according to the developer behavior data;

[0014] generating a crowd wisdom stimulation entropy-time curve of the target software project according to the crowd wisdom stimulation entropy corresponding to each time window and returning it to the user end.

[0015] In one of the embodiments, the step of filtering out the developer behavior data in each time window in order of time from early to late, and calculating the crowd wisdom stimulation entropy corresponding to each time window according to the developer behavior data, comprises the steps of:

[0016] selecting a current time window in order of time from early to late;

[0017] checking whether there is developer behavior data in the time window;

[0018] if there is developer behavior data, filtering out the developer behavior data in the time window and calculating the crowd-sourced inspiration entropy corresponding to the time window according to the developer behavior data; the crowd-sourced inspiration entropy corresponding to the time window is stored in the background server database;

[0019] selecting a next time window in the order from early to late, and if the next time window exists, returning to the step of checking whether there is developer behavior data in the time window;

[0020] if the next time window does not exist, jumping to the step of generating the crowd-sourced inspiration entropy-time curve of the target software project according to the crowd-sourced inspiration entropy corresponding to each time window and returning to the user terminal.

[0021] In one embodiment, after the step of checking whether there is developer behavior data in the time window, the method further includes the steps of:

[0022] if there is no developer behavior data, recording the crowd-sourced inspiration entropy corresponding to the time window as 0 and storing it in the background server database.

[0023] In one embodiment, when there is the latest developer behavior data in the background server database, the crowd-sourced inspiration monitoring method in the open source project further includes the steps of:

[0024] finding the crowd-sourced inspiration entropy-time curve of the target software project from the background server database and returning it to the user terminal.

[0025] In one embodiment, the crowd-sourced inspiration monitoring method in the open source project further includes the steps of:

[0026] periodically checking each software project in the background server database and updating the calculation of the crowd-sourced inspiration entropy of each software project.

[0027] On the other hand, a crowd-sourced inspiration monitoring device in an open source project is also provided, which includes:

[0028] a request receiving module configured to obtain a crowd-sourced inspiration monitoring request sent by a user terminal; the crowd-sourced inspiration monitoring request includes the name of a target software project;

[0029] a database searching module configured to search for the latest developer behavior data of the target software project within a set period in the background server database according to the crowd-sourced inspiration monitoring request;

[0030] a project obtaining module configured to search and download the latest developer behavior data of the target software project from the Internet to the background server database when there is no latest developer behavior data in the background server database.

[0031] a time window division module, configured to divide a life cycle of the target software project into time windows according to a set time window size, the life cycle being from a starting time of creation of the target software project to a current time;

[0032] an entropy calculation module, configured to filter out developer behavior data in each time window in chronological order and calculate a corresponding crowd intelligence inspiration entropy of each time window according to the developer behavior data;

[0033] an entropy returning module, configured to generate a crowd intelligence inspiration entropy curve of the target software project according to the corresponding crowd intelligence inspiration entropy of each time window and return the curve to a user terminal.

[0034] In one of the embodiments, the entropy calculation module comprises:

[0035] a time window selection submodule, configured to select a current time window in chronological order;

[0036] a behavior checking submodule, configured to check whether there is developer behavior data in the time window;

[0037] a filtering and calculation submodule, configured to filter out the developer behavior data in the time window and calculate the corresponding crowd intelligence inspiration entropy of the time window according to the developer behavior data when there is the developer behavior data; and the corresponding crowd intelligence inspiration entropy of the time window is stored in a background server database;

[0038] The time window selection submodule is further configured to select a next time window in chronological order and trigger the behavior checking submodule when the next time window exists.

[0039] The time window selection submodule is further configured to trigger the entropy returning module when the next time window does not exist.

[0040] In one of the embodiments, the filtering and calculation submodule is further configured to record the corresponding crowd intelligence inspiration entropy of the time window as 0 and store the value in the background server database when there is no developer behavior data.

[0041] In one of the embodiments, the crowd intelligence inspiration monitoring device in the open source project further comprises:

[0042] a checking and updating module, configured to periodically check each software project in the background server database and update the crowd intelligence inspiration entropy of each software project.

[0043] In another aspect, a computer device is provided, comprising a memory and a processor, the memory storing a computer program, and the processor implementing the steps of the crowd intelligence inspiration monitoring method in the open source project when executing the computer program.

[0044] One of the above technical solutions has the following advantages and beneficial effects:

[0045] The open source project group wisdom stimulation monitoring method, device and equipment has the advantages that when the group wisdom stimulation monitoring request sent by the user end is acquired, the latest developer behavior data in the set period of the target software project is first searched in the background server database, when there is no latest developer behavior data in the background server database, the latest developer behavior data of the target software project is searched and downloaded from the Internet to the background server database, then the life cycle of the target software project is divided into time windows, then the developer behavior data in each time window is filtered out in order from early to late, the group wisdom stimulation entropy corresponding to each time window is calculated according to the developer behavior data, and finally the group wisdom stimulation entropy-time curve of the target software project is generated according to the group wisdom stimulation entropy corresponding to each time window and returned to the user end.

[0046] Compared with the existing group wisdom measurement method, by introducing the group wisdom stimulation measurement method based on the entropy theory and fully mining and utilizing the long-term developer behavior data of the project, the dynamic changes of the group wisdom caused by the behaviors of the participants in the development process of the open source software project can be captured and quantified in detail. The technology is easy to operate and has high flexibility, is suitable for the supervision of the software project developed in an open source manner, and provides an effective monitoring tool for the managers of the open source software project to track and evaluate the stimulation degree of the collective wisdom in the development process of the open source software in real time. BRIEF DESCRIPTION OF DRAWINGS

[0047] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0048] Figure 1 The flowchart of the open source project group wisdom stimulation monitoring method in one embodiment;

[0049] Figure 2 The overall application flowchart of the open source project group wisdom stimulation monitoring method in one embodiment;

[0050] Figure 3 The flowchart of the open source project group wisdom stimulation monitoring method in another embodiment;

[0051] Figure 4 The module structure schematic diagram of the open source project group wisdom stimulation monitoring device in one embodiment. DETAILED DESCRIPTION

[0052] In order to make the purpose, technical scheme and advantages of the present application more clear, the present application will be further described in detail below in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and not to limit the present application. Unless otherwise defined, all technical and scientific terms used in the present application have the same meaning as understood by those skilled in the art to which the present application belongs. The terms used in the specification of the present application are only for the purpose of describing the specific embodiments and are not intended to limit the present application.

[0053] It should be noted that the term "embodiment" mentioned in the present application means that the specific features, structures or characteristics described in combination with the embodiment can be included in at least one embodiment of the present application. The phrase is shown at various places in the specification and does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment to other embodiments. Those skilled in the art can understand that the embodiments described in the present application can be combined with other embodiments. The term "and / or" used in the specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes these combinations.

[0054] The present application is directed to the above-mentioned problems existing in the scenario of open source software development, and provides a technology for quantitatively analyzing and visualizing the crowd wisdom stimulation in an open source project. By introducing a measurement method based on entropy theory, the dynamic changes of crowd wisdom caused by the behavior of participants in the open source software development process can be captured and quantified in detail. It is not only easy to operate and has high flexibility, but also can provide an effective monitoring tool for the manager of the open source project to track and evaluate the degree of stimulation of collective wisdom in the software development process. With the help of the technology of the present application, the manager of the open source project can more intuitively understand and promote the activity and innovation of group collaboration.

[0055] The embodiments of the present application will be described in detail below in combination with the drawings in the embodiment diagram of the present application.

[0056] Please refer to Figure 1 In one embodiment, a crowd wisdom stimulation monitoring method in an open source project is provided, which can include the following processing steps S10 to S20:

[0057] S10, acquiring a crowd wisdom stimulation monitoring request sent by a user end; the crowd wisdom stimulation monitoring request includes the name of a target software project; it can be understood that after the server receives the crowd wisdom stimulation monitoring request sent by the user end (which can include the name of the target software project), the server can first query whether there is the latest user behavior data of the target software project in the background server database according to the name of the target software project, so as to determine the storage situation of the complete user behavior data of the target software project in the background server database about which the current user needs to monitor.

[0058] S12, searching for the latest developer behavior data of the target software project within the set time period in the background server database according to the crowd inspiration monitoring request; it can be understood that the server searches the background server database maintained by the server to determine whether there is updated data within the set time period (which can be recorded as N hours) of the target software project, which can include all developer behavior data of the project up to the current time, and the integer N can be set by the user or the relevant system administrator according to the actual monitoring needs, for example, N = 24 represents that the expiration time limit of the data is 1 day, N = 48 represents that the expiration time limit of the data is 2 days, and so on.

[0059] S14, when there is no latest developer behavior data in the background server database, searching and downloading the latest developer behavior data of the target software project from the Internet to the background server database; it can be understood that when the server fails to find the latest developer behavior data of the target software project from the background server database, it is determined that the background server database does not have complete data of the target software project, so the data search of the target software project can be directly performed from the Internet, and the latest developer behavior data of the target software project is downloaded from the found open source platform to the background server database and stored in the background server database for backup.

[0060] S16, dividing the life cycle of the target software project into time windows according to the set time window size; the life cycle is the starting point of the creation time of the target software project to the current time; it can be understood that the server can divide the life cycle of the target software project into several time windows according to the time window size T days (the integer T can be set by the user or other relevant system administrator according to the actual monitoring needs, for example, T = 1 represents that the time window length is 1 day, T = 2 represents that the time window length is 2 days, and so on) from the creation time of the target software project to the current time, so as to be used for accurate calculation in the subsequent steps.

[0061] S18, filtering out the developer behavior data in each time window in order of time from early to late, and calculating the crowd inspiration entropy corresponding to each time window according to the developer behavior data; it can be understood that the server can calculate and process each time window in order of time from early to late: filtering out the developer behavior data in each time window, and then calculating the crowd inspiration entropy corresponding to the time window according to the crowd inspiration entropy calculation formula based on the filtered developer behavior data, and so on until the crowd inspiration entropy of all time windows is calculated.

[0062] Specifically, the crowd inspiration entropy calculation formula can be as follows:

[0063] H = -npi log(p i )

[0064] wherein n represents the total number of developer behaviors, p i represents the proportion of each developer behavior type i, and H represents the crowd wisdom stimulation entropy of the time window.

[0065] S20, generating a crowd wisdom stimulation entropy curve of the target software project over time according to the crowd wisdom stimulation entropy of each time window and returning the curve to the user terminal.

[0066] It can be understood that after the server calculates the crowd wisdom stimulation entropy of all time windows based on the entropy theory, since each time window is divided according to the set time window size and has a time sequence, the crowd wisdom stimulation entropy of each time window also strictly corresponds to the time sequence. The server can directly visualize the crowd wisdom stimulation entropy of these time windows, such as generating a crowd wisdom stimulation entropy curve of the target software project over time and returning the curve to the user terminal, so that the user terminal can visualize the curve through the display interface.

[0067] The above open source project crowd wisdom stimulation monitoring method, when receiving a crowd wisdom stimulation monitoring request sent by the user terminal, first searches the latest developer behavior data of the target software project within the set time period in the background server database. When there is no latest developer behavior data in the background server database, searches and downloads the latest developer behavior data of the target software project from the Internet to the background server database, then divides the life cycle of the target software project into time windows, then filters out the developer behavior data in each time window in order from early to late, calculates the crowd wisdom stimulation entropy corresponding to each time window according to the developer behavior data, and finally generates a crowd wisdom stimulation entropy curve of the target software project over time according to the crowd wisdom stimulation entropy corresponding to each time window and returns the curve to the user terminal.

[0068] Compared with existing crowd wisdom measurement methods, by introducing an entropy theory-based crowd wisdom stimulation measurement method and fully exploiting and utilizing long-term developer behavior data of the project, the dynamic changes in crowd wisdom caused by participant behavior in the development process of open source software projects can be captured and quantified in detail. This technology is easy to operate and has high flexibility, and is suitable for monitoring software projects developed in an open source manner, providing an effective monitoring tool for managers of open source software projects to track and evaluate the degree of crowd wisdom stimulation in real time.

[0069] In one embodiment, when there is latest developer behavior data in the background server database, the above open source project crowd wisdom stimulation monitoring method can further include the following processing steps:

[0070] The system retrieves the curve of the collective intelligence excitation entropy of the target software project over time from the backend server database and returns it to the user.

[0071] Understandably, since the server has already found complete data for the target software project in the backend server database, it can use this data to directly calculate the collective intelligence activating entropy for all time windows of the target software project. Then, it can directly generate a curve showing the change of collective intelligence activating entropy over time and return it to the user for visualization. This eliminates the need for internet data searching, resulting in more efficient computation and lower computational resource consumption.

[0072] Specifically, after the user sends a request to the server to obtain the graph of the change of the collective intelligence activating entropy of the target software project over time, the server finds the existing graph of the change of the collective intelligence activating entropy of the target software project over time in the background server database and returns it to the user, thus ending the process of quantitative analysis and visualization of the collective intelligence activating entropy of the target software project.

[0073] The system automates the acquisition of open-source software quality evidence on the Internet, greatly simplifying the tedious and repetitive manual acquisition process. It makes full use of existing local calculation results, significantly reducing the workload of staff, and offers good flexibility, effectively helping open-source platforms cope with massive user access.

[0074] In one embodiment, such as Figure 2 As shown, step S18 above can specifically include the following processing steps:

[0075] Select the current time window in chronological order;

[0076] Check if there is developer behavior data in the time window;

[0077] If developer behavior data exists, the developer behavior data within the time window is filtered out and the collective intelligence activation entropy corresponding to the time window is calculated based on the developer behavior data; the collective intelligence activation entropy corresponding to the time window is stored in the backend server database.

[0078] Select the next time window in chronological order. If the next time window exists, return to the steps above to check if developer behavior data exists in the time window.

[0079] If the next time window does not exist, proceed to step S20 above.

[0080] It is understandable that when the server calculates the corresponding collective intelligence activation entropy for each time window, it can specifically calculate the current time window (which can be denoted as [t]). k-1 , t kthe next time window (which can be recorded as [t k , t k+1 ]) is selected in the order of time from the first to the last for calculation; if the next time window does not exist, it means that the server has calculated the crowd inspired entropy of all time windows, and at this time, the crowd inspired entropy of the target software project over time curve can be directly returned to the user end for visual display.

[0081] Conversely, if the next time window exists, the developer behavior data in the next time window is filtered out, the proportion p i of each developer behavior type i is calculated, the total number n of developer behaviors is calculated, the crowd inspired entropy of the next time window is calculated according to the above crowd inspired entropy calculation formula based on entropy theory, the data is stored in the background server database, and then the next time window is selected in the order of time from the first to the last for calculation and processing, and the process is repeated until the crowd inspired entropy of all time windows is calculated.

[0082] In an embodiment, after the step of checking whether there is developer behavior data in the time window, the following processing steps can be further included:

[0083] If there is no developer behavior data, the crowd inspired entropy corresponding to the time window is recorded as 0 and stored in the background server database.

[0084] It can be understood that when the server checks whether there is user behavior data in the time window, if there is no user behavior data in the time window, the inspired entropy of the time window can be directly recorded as 0, and then the inspired entropy data is stored in the background server database, that is, the calculation of the remaining time windows is continued, the real record of the crowd inspired entropy of the time period without user behavior data in the life cycle of the target software project is realized, and the situation of no accurate prompt due to data blank is avoided.

[0085] In an embodiment, as shown in Figure 3 , the crowd inspired monitoring method in the open source project can further include the following steps:

[0086] S22, periodically checking each software project in the background server database, and updating the calculation of the crowd inspired entropy of each software project.

[0087] It can be understood that the server can also periodically check the locally existing open source software projects, check the local developer behavior data storage of the projects and can update the corresponding local data (resource) library by itself (or triggered according to the set update time interval), calculate the new crowd inspired entropy of each open source software project and store for standby, so that when other users initiate a crowd inspired monitoring request, the crowd inspired entropy change curve over time required by the user can be returned in the first time, thereby further improving the monitoring efficiency, and the checking and updating can be performed in the server idle time period to optimize the allocation and use of the server computing resources.

[0088] It should be understood that although the above flow Figures 1 to 3 shows the steps in sequence according to the direction of the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this application, the execution of these steps has no strict order limitation, and these steps can be executed in other orders. Moreover, at least part of the steps of the above flow Figures 1 to 3 may include multiple sub-steps or multiple stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution order of these sub-steps or stages is not necessarily sequential, but can be alternately executed with at least part of other steps or sub-steps or stages of other steps.

[0089] In one embodiment, as Figure 4 shown, a crowd inspired monitoring device 100 for the above open source project is also provided, which includes a request receiving module 11, a database searching module 13, a project obtaining module 15, a time window dividing module 17, an entropy calculating module 19 and an entropy returning module 21. The request receiving module 11 is used to obtain a crowd inspired monitoring request sent by a user end; the crowd inspired monitoring request includes the name of a target software project. The database searching module 13 is used to search for the latest developer behavior data of the target software project within a set time period in the background server database according to the crowd inspired monitoring request. The project obtaining module 15 is used to search and download the latest developer behavior data of the target software project from the Internet to the background server database when there is no latest developer behavior data in the background server database. The time window dividing module 17 is used to divide the life cycle of the target software project into time windows according to a set time window size; the life cycle is the starting point of the creation time of the target software project to the current time. The entropy calculating module 19 is used to filter out the developer behavior data in each time window in sequence from the beginning to the end of time, and calculate the crowd inspired entropy corresponding to each time window according to the developer behavior data. The entropy returning module 21 is used to generate a crowd inspired entropy change curve over time of the target software project according to the crowd inspired entropy corresponding to each time window and return it to the user end.

[0090] When the crowd intelligence stimulation monitoring device 100 in the open source project obtains the crowd intelligence stimulation monitoring request sent by the user end, it first searches for the latest developer behavior data in the set period of the target software project in the background server database. When there is no latest developer behavior data in the background server database, it searches and downloads the latest developer behavior data of the target software project from the Internet to the background server database, then divides the life cycle of the target software project into time windows, then filters out the developer behavior data in each time window in order of time from early to late, calculates the crowd intelligence stimulation entropy corresponding to each time window according to the developer behavior data, and finally generates the crowd intelligence stimulation entropy-time curve of the target software project according to the crowd intelligence stimulation entropy corresponding to each time window and returns it to the user end.

[0091] Compared with the existing crowd intelligence measurement method, by introducing the crowd intelligence stimulation measurement method based on entropy theory and fully mining and utilizing the long-term developer behavior data of the project, the dynamic changes of crowd intelligence caused by the behavior of participants in the development process of open source software projects can be captured and quantified in detail. This technology is easy to operate and has high flexibility, and is suitable for the supervision of software projects developed in an open source manner, providing an effective monitoring tool for the managers of open source software projects to track and evaluate the degree of stimulation of collective wisdom in the development process of open source software in real time.

[0092] In one embodiment, the entropy calculation module 19 includes a time window selection submodule, a behavior checking submodule, and a filtering calculation submodule. The time window selection submodule is used to select a current time window in order of time from early to late. The behavior checking submodule is used to check whether there is developer behavior data in the time window. The filtering calculation submodule is used to filter out the developer behavior data in the time window and calculate the crowd intelligence stimulation entropy corresponding to the time window according to the developer behavior data when there is developer behavior data; the crowd intelligence stimulation entropy corresponding to the time window is stored in the background server database. The time window selection submodule can also be used to select the next time window in order of time from early to late, and trigger the behavior checking submodule when the next time window exists. The time window selection submodule can also be used to trigger the entropy return module when the next time window does not exist.

[0093] In one embodiment, the filtering calculation submodule can also be used to record the crowd intelligence stimulation entropy corresponding to the time window as 0 and store it in the background server database when there is no developer behavior data.

[0094] In one embodiment, the crowd intelligence stimulation monitoring device 100 in the open source project described above further includes an inspection and update module for periodically inspecting each software project already in the background server database and updating the crowd intelligence stimulation entropy of each software project.

[0095] It can be understood that the above explanation of the features of each embodiment of the crowd-sensing monitoring device 100 in the open source project can be understood in the same way as the explanation of the corresponding features of each embodiment of the crowd-sensing monitoring method in the open source project, which will not be repeated here. Each module in the crowd-sensing monitoring device 100 in the open source project can be realized by software, hardware, and their combinations. The above modules can be embedded in or independent of a device with data processing function in hardware form, or stored in the memory of the device in software form, so that the processor can call and execute the operations corresponding to each module. The device can be, but is not limited to, various data computing and processing devices in the art.

[0096] In one embodiment, a computer device is also provided, including a memory and a processor, the memory stores a computer program, and the processor implements the following processing steps when executing the computer program: obtaining a crowd-sensing monitoring request sent by a user terminal; the crowd-sensing monitoring request includes the name of a target software project; searching for the latest developer behavior data of the target software project within a set time period in the background server database according to the crowd-sensing monitoring request; when there is no latest developer behavior data in the background server database, searching and downloading the latest developer behavior data of the target software project from the Internet to the background server database; dividing the life cycle of the target software project into time windows according to a set time window size; the life cycle is the starting point of the creation time of the target software project to the current time; filtering out the developer behavior data in each time window in order from the beginning to the end according to time, and calculating the crowd-sensing entropy corresponding to each time window according to the developer behavior data; generating a crowd-sensing entropy-time curve of the target software project according to the crowd-sensing entropy corresponding to each time window and returning it to the user terminal.

[0097] It can be understood that the above computer device can include other software and hardware components not listed in the specification in addition to the above-mentioned memory and processor. The specific data processing device model can be determined according to different application scenarios, and the specification will not be listed in detail.

[0098] In one embodiment, the processor executing the computer program can also implement the steps or sub-steps added in each embodiment of the crowd-sensing monitoring method in the open source project.

[0099] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when the computer program is executed, the processes of the above-mentioned embodiments of the methods can be included. Any reference to memory, storage, database or other medium used in the embodiments provided in the present application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration but not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus dynamic random access memory (Rambus DRAM, RDRAM for short) and interface dynamic random access memory (DRDRAM).

[0100] The technical features of the above embodiments can be combined in any way. In order to make the description simple, not all possible combinations of the technical features in the above embodiments are described, however, as long as the combination of the technical features does not exist contradictory, it should be considered as the scope of the present application. The above embodiments only express several embodiments of the present application, and the description is more specific and detailed, but it should not be understood as a limitation on the scope of the patent. It should be noted that for those skilled in the art, without departing from the concept of the present application, some modifications and improvements can be made, which are all within the scope of the present application. Therefore, the scope of the patent protection of the present application should be subject to the appended claims.

Claims

1. A crowd intelligence inspiration monitoring method in an open source project, characterized in that, The method comprises the steps of: acquiring a crowd-sourcing monitoring request sent by a user terminal; the crowd-sourcing monitoring request comprises the name of a target software project; finding the latest developer behavior data of the target software project within a set time period in a background server database according to the crowd-sourcing monitoring request; when there is no latest developer behavior data in the background server database, searching and downloading the latest developer behavior data of the target software project from the Internet to the background server database; dividing the life cycle of the target software project into time windows according to a set time window size; the life cycle is the starting point of the creation time of the target software project to the current time; Filtering out the developer behavior data in each of the time windows in chronological order from early to late, and calculating the crowd inspired entropy corresponding to each of the time windows according to the developer behavior data; the crowd inspired entropy calculation formula is: H = -np i log(p i ), wherein n represents the total number of developer behaviors, p i represents the proportion of each developer behavior type i, and H represents the crowd inspired entropy of the time window. generating a crowd-sourcing entropy curve of the target software project according to the crowd-sourcing entropy corresponding to each time window and returning the curve to the user terminal.

2. The method of claim 1, wherein the method further comprises: The step of filtering out the developer behavior data in each time window in chronological order from early to late and calculating the crowd-sourcing entropy corresponding to each time window according to the developer behavior data comprises the steps of: selecting a current time window in chronological order from early to late; checking whether there is developer behavior data in the time window; if there is developer behavior data, filtering out the developer behavior data in the time window and calculating the crowd-sourcing entropy corresponding to the time window according to the developer behavior data; the crowd-sourcing entropy corresponding to the time window is stored in a background server database; selecting a next time window in chronological order from early to late, and if the next time window exists, returning to the step of checking whether there is developer behavior data in the time window; if the next time window does not exist, jumping to the step of generating a crowd-sourcing entropy curve of the target software project according to the crowd-sourcing entropy corresponding to each time window and returning the curve to the user terminal. 3.The open source project crowd-sourcing monitoring method of claim 2, wherein, The step of checking whether there is developer behavior data in the time window further comprises the step of: if there is no developer behavior data, recording the crowd-sourcing entropy corresponding to the time window as 0 and storing it in the background server database.

4. The open source project crowd-sourcing monitoring method according to any one of claims 1 to 3, characterized in that, When the background server database already has the latest developer behavior data, the crowd-sourcing monitoring method of the open source project further comprises the step of: finding the crowd-sourcing entropy curve of the target software project from the background server database and returning the curve to the user terminal.

5. The method of claim 4, wherein the method further comprises: The method further comprises the steps of: periodically checking each software project in the background server database and updating the crowd-sourcing entropy of each software project.

6. A crowd intelligence inspiration monitoring device in an open source project, characterized by, The method comprises: a request receiving module, configured to acquire a crowd-sourcing monitoring request sent by a user terminal; the crowd-sourcing monitoring request comprises the name of a target software project; a database finding module, configured to find the latest developer behavior data of the target software project within a set time period in a background server database according to the crowd-sourcing monitoring request; An item acquisition module is configured to search and download the latest developer behavior data of the target software item from the Internet and store the latest developer behavior data in the background server database when the latest developer behavior data is not in the background server database. A time window division module is configured to divide the life cycle of the target software item into time windows according to a set time window size, wherein the life cycle is from the creation time of the target software item to the current time. An entropy calculation module is configured to filter out the developer behavior data in each time window in chronological order from early to late, and calculate the crowd inspired entropy corresponding to each time window according to the developer behavior data. The crowd inspired entropy calculation formula is: H = -np i log(p i ), wherein n represents the total number of developer behaviors, p i represents the proportion of each developer behavior type i, and H represents the crowd inspired entropy of the time window. An entropy return module is configured to generate a group intelligence stimulation entropy curve of the target software item according to the group intelligence stimulation entropy corresponding to each time window and return the group intelligence stimulation entropy curve to the user terminal.

7. The open source project crowd-sourcing monitoring apparatus according to claim 6, wherein, The entropy calculation module comprises: A time window selection submodule is configured to select a current time window in chronological order. A behavior check submodule is configured to check whether there is developer behavior data in the time window. A filtering calculation submodule is configured to filter out the developer behavior data in the time window and calculate the group intelligence stimulation entropy corresponding to the time window according to the developer behavior data when there is developer behavior data in the time window, wherein the group intelligence stimulation entropy corresponding to the time window is stored in the background server database. The time window selection submodule is further configured to select a next time window in chronological order and trigger the behavior check submodule when the next time window exists. The time window selection submodule is further configured to trigger the entropy return module when the next time window does not exist.

8. The open source project crowd-sourcing monitoring apparatus according to claim 7, wherein, The filtering calculation submodule is further configured to record the group intelligence stimulation entropy corresponding to the time window as 0 and store the group intelligence stimulation entropy in the background server database when there is no developer behavior data in the time window.

9. The open source project crowd-sourcing monitoring apparatus of claim 6, wherein, Further comprising: An inspection and update module is configured to periodically inspect and update the group intelligence stimulation entropy of each software item in the background server database. 10.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is configured to perform the method according to any one of claims 1-9. The processor executes the computer program to realize the steps of the open source item group intelligence stimulation monitoring method in any one of claims 1 to 5.

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