Academic virtual community user social contact driving method and device, equipment and storage medium

Abstract

The invention relates to the technical field of data processing, and discloses an academic virtual community user social driving method, device and equipment and a storage medium. A subject section popularity change trend of a target operation area in a target social driving time period and hardware resource allocation information of a previous social driving time period are considered, a constraint condition set is constructed by using hardware configuration parameters and position deployment information, a hardware resource scheduling sub-strategy is optimized and solved by using a minimum server switching influence as an optimization target, and meanwhile, a hardware resource scheduling sub-strategy is optimized and solved. By considering the availability of the candidate subject social driving activities and the remaining hardware resources, the candidate subject social driving activities are added into the user social driving of the target academic virtual community as much as possible, so that the influence of regional activity differences of subject sections on stable operation of the community is avoided; and high-activity, high-heat and high-stability continuous operation of the community is kept.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a method, device, equipment and storage medium for driving user social interaction in an academic virtual community. Background Art

[0002] Academic virtual communities are online platforms built on Internet technology, aiming to provide a space for communication and collaboration across time and space for academic groups such as scholars, researchers, and students. Their core features include: (1) focusing on academic fields (such as specific disciplines and research directions), with content centered around knowledge production, dissemination, and application; (2) supporting user interaction through posting, commenting, private messaging, and conferences, promoting the exchange of ideas and collaboration; (3) providing storage and sharing functions for academic resources such as literature, data, and tools; and (4) building a user system through mechanisms such as points, levels, and certification to enhance a sense of belonging and activity.

[0003] User-driven social interaction refers to the design of social mechanisms, functions, or content to stimulate users to actively participate in community interactions, forming sustained social behaviors, thereby enhancing community activity, cohesion, and academic value. Therefore, in order to achieve the ultimate goal of academic virtual communities to promote knowledge dissemination, academic cooperation, and innovation through social connections, it is necessary to maximize the activity of academic virtual communities. However, the operation of existing academic virtual communities and user-driven social interaction methods have the following limitations: First, there are many ways to drive user social interaction in academic virtual communities (such as academic hot events such as the Nobel Prize announcement, community-driven activities, and external traffic diversion of section content, etc.). Different user social driving methods will form different degrees of high-traffic access to academic virtual communities in different subject sections and at different durations, which will bring different pressures to the hardware resources of the academic virtual community (mainly various hardware parameters of the server for processing concurrent data). In order to ensure the stable operation and continuous activity of the community, when conducting or responding to user social driving in academic virtual communities, it is necessary to consider the limitations brought by hardware resources to avoid problems such as community crashes and unexpected delays.

[0004] Second, when academic virtual communities are driven by user social interaction in different academic disciplines, regional differences in community activity often occur. For example, the highly active disciplines in the Yangtze River Delta region are biomedical engineering and blockchain technology, the highly active disciplines in the Beijing-Tianjin-Hebei region are artificial intelligence and aerospace technology, the highly active disciplines in the Guangdong-Hong Kong-Macao Greater Bay Area are semiconductors and cross-border data flows, and the highly active disciplines in the Chengdu-Chongqing region are electronic information and automotive engineering. When driving user social interaction in academic virtual communities, it is also necessary to pay attention to the regional activity differences in different academic disciplines, which may cause server overloads in some areas.

[0005] Third, due to the regional differences in community activity in academic virtual communities, when allocating calls to servers and subject sections, the communication distance between the server and the active area corresponding to the subject section also needs to be considered. If the server allocated to a certain subject section has sufficient hardware resources and processing power, but the server is deployed far away from the community active area corresponding to the subject section, this situation will also cause high latency in the community interaction process and affect the social experience.

[0006] Fourth, when there are many user social-driven activities, in order to maximize the overall activity of the academic virtual community, it is necessary to run as many user social-driven activities as possible within the scope of server hardware resources. How to arrange and run user social-driven activities under the above-mentioned multiple restrictions has become a difficult problem.

[0007] Therefore, how to reasonably and scientifically implement user social driving in academic virtual communities, intelligently allocate scheduling and allocation strategies for different subject sections and servers under user social driving when server hardware resources permit, avoid the impact of regional activity differences in different subject sections on the stable operation of the community, and possibly run more user social driving activities to maintain the community's high activity, high popularity and high stability, is a technical problem that needs to be solved urgently. Summary of the Invention

[0008] The present invention provides a method, apparatus, device and storage medium for driving user social interaction in an academic virtual community, aiming to solve at least one of the above-mentioned technical problems.

[0009] To achieve the above-mentioned object, the present invention provides a method for driving user social interaction in an academic virtual community, comprising the following steps: Obtaining community attributes of the target academic virtual community; wherein the community attributes include subject section division information and hardware resource deployment information; Based on the subject section division information, the subject association information of each subject section in the target operating area is collected and a set of popularity driving factors is constructed. The popularity driving factor set is used to predict the trend of changes in the subject section popularity in the target operating area during the target social driving period. Based on the hardware resource information and deployment location information of the plurality of distributed community servers in the hardware resource deployment information, taking into account the subject section popularity value and the heat impact area of ​​each social driving cycle in the subject section popularity change trend of the target operating area during the target social driving period and the hardware resource allocation information of the previous social driving period, a user social driving strategy for the target social driving period is generated; The hardware resource scheduling sub-strategy and the social driving sub-strategy in the user social driving strategy are extracted to respectively execute the server resource allocation and user social driving of the subject section.

[0010] Optionally, the step of obtaining the community attributes of the target academic virtual community includes: Querying the backend management system of the target academic virtual community, parsing the subject section architecture information stored in the backend management system, and extracting several subject section categories of the target academic virtual community; Querying first server deployment information of the target academic virtual community in the public cloud service platform and second server deployment information in the self-built IDC computer room, and determining a deployment server set of the target academic virtual community based on the first server deployment information and the second server deployment information; Several subject section categories are used as subject section division information, and a deployment server set is used as hardware resource deployment information to generate community attributes of a target academic virtual community.

[0011] Optionally, based on the subject section division information, collecting subject association information of each subject section in the target operating area and constructing a set of popularity driving factors specifically includes: Based on the plurality of subject section categories in the subject section division information, a subject keyword set is generated for each subject section category by adopting a data expansion method; Using the subject keyword set, a plurality of subject-related information are matched in a subject-related information database by adopting a keyword matching method, and popularity driving factors in the plurality of subject-related information are extracted to construct a popularity driving factor set; The plurality of subject-related information is configured to be at least one of academic hot events, community operation events, or external channel diversion events published on a plurality of subject hot event publishing pages or publishing platforms recorded in the subject-related information database; Among them, the popularity driving factors are configured as the popularity impact characteristics of academic hot events, community operation activities or external channel diversion events.

[0012] Optionally, the step of using the popularity driving factor set to predict the popularity change trend of the subject section in the target operating area during the target social driving period specifically includes: Obtaining the subject section popularity change information within a historical period, extracting the subject section popularity value and popularity influence area of ​​each subject section category in the subject section popularity change information, and constructing the subject section popularity value change information, popularity influence area change information, and popularity influence features into a popularity training sample; Call the pre-built initial neural network model and train it using the heat training samples. When the training reaches the target number of times or converges, a heat prediction model for the completion of the pilgrimage is obtained. By utilizing several heat-influencing features in the heat-driving factor set, a heat-influencing feature set is constructed and used as a heat prediction sample. The heat prediction sample is input into the heat prediction model to predict the heat change trend of the subject section in the target operating area during the target social driving period.

[0013] Optionally, based on the hardware resource information and deployment location information of several distributed community servers in the hardware resource deployment information, taking into account the subject section popularity value and the popularity impact area of ​​each social driving cycle in the target operating area during the target social driving period and the hardware resource allocation information of the previous social driving period, the user social driving strategy steps for the target social driving period are generated, specifically including: Extracting the hardware resource information and deployment location information of several distributed community servers from the hardware resource deployment information, considering the subject section popularity value and the heat impact area of ​​each social driving cycle in the target operating area during the target social driving period, and the hardware resource allocation information of the previous social driving period; The first constraint condition is that the sum of the hardware resource demand parameters corresponding to the subject section heat of the several subject sections assigned to each distributed community server for service in each social drive cycle of the target social drive period is higher than the hardware resource configuration parameters corresponding to the hardware resource information of the distributed community server. The second constraint condition is that the heat influence area corresponding to the several subject sections assigned to each distributed community server for service in each social drive cycle of the target social drive period covers the deployment location information of the distributed community server. The optimization objective is to minimize the cumulative value of the sum of the subject section heat of two adjacent social drive cycles corresponding to all subject sections in the hardware resource allocation information of the last social drive cycle in the previous social drive period to the hardware resource allocation information of the last social drive cycle in the target social drive period when they are assigned to different distributed community servers. The hardware resource allocation information of the several subject sections assigned to the several distributed community servers in each social drive cycle of the target social drive period is optimized and solved. A user social driving strategy is generated according to hardware resource allocation information of several subject sections allocated to several distributed community servers in each social driving cycle during a target social driving period.

[0014] Optionally, the step of generating a user social driving strategy according to hardware resource allocation information of several subject sections allocated to several distributed community servers in each social driving cycle during the target social driving period specifically includes: Generate hardware resource scheduling sub-strategies for the distributed community servers according to hardware resource allocation information of the discipline sections allocated to the distributed community servers in each social driving cycle of the target social driving period; Calculate the difference between the hardware resource configuration parameters of each distributed community server and the sum of the hardware resource requirement parameters of each social driving cycle in the target social driving period under the hardware resource allocation information, and generate a list of remaining hardware resource parameters for each social driving cycle in the target social driving period for each distributed community server; Obtain a candidate set of disciplinary social-driven activities, extract a number of candidate disciplinary social-driven activities that are introduced chronologically from the candidate set, predict the disciplinary section popularity change trend of each candidate disciplinary social-driven activity based on the popularity influence characteristics of each candidate disciplinary social-driven activity, and determine the disciplinary section popularity value and popularity influence area for each social-driven cycle; Considering the subject section popularity value and popularity influence area of ​​each social driving cycle, the deployment location information of each distributed community server, and the remaining hardware resource parameter list, under the conditions that the hardware resource requirements of the candidate subject social driving activities and the deployment location of the distributed community server fall into the popularity influence area requirements, repeatedly perform the action of adding the candidate subject social driving activities to the target social driving period in chronological order until the requirements can no longer be met, and obtain the allocation information of each candidate subject social driving activity and the distributed community server in the target social driving period; Based on the selected candidate subject social driving activities and the allocation information of each candidate subject social driving activity and the distributed community server in the target social driving period, a plurality of social driving sub-strategies of the distributed community servers are generated.

[0015] Optionally, extract the hardware resource scheduling sub-strategy and the social driving sub-strategy in the user social driving strategy, and respectively execute the server resource allocation and user social driving steps of the subject section, specifically including: Extracting the hardware resource scheduling sub-strategy and the social driving sub-strategy in the user social driving strategy, and using the hardware resource scheduling sub-strategy to control a plurality of distributed community servers to execute server resource allocation for each subject section in each social driving cycle of a target social driving period; By utilizing the social driving sub-strategy, the virtual community management terminal is driven to execute user social driving for candidate subject social driving activities on the distributed community servers and corresponding subject sections, and control the server resource allocation of several distributed community servers to the corresponding subject sections of the newly added candidate subject social driving activities in each social driving cycle during the target social driving period.

[0016] In addition, in order to achieve the above-mentioned purpose, the present invention also provides a user social driving device for an academic virtual community, comprising: An acquisition module, configured to acquire community attributes of a target academic virtual community; wherein the community attributes include subject section division information and hardware resource deployment information; A prediction module is used to collect subject-related information of each subject section in the target operating area based on the subject section division information and construct a set of popularity driving factors, and use the set of popularity driving factors to predict the popularity change trend of the subject section in the target operating area during the target social driving period; A generation module is configured to generate a user social driving strategy for a target social driving period based on the hardware resource information and deployment location information of the plurality of distributed community servers in the hardware resource deployment information, taking into account the subject section popularity value and the heat impact area of ​​each social driving cycle in the subject section popularity change trend of the target operating area during the target social driving period, and the hardware resource allocation information of the previous social driving period; The execution module is used to extract the hardware resource scheduling sub-strategy and the social driving sub-strategy in the user social driving strategy, and respectively execute the server resource allocation and user social driving of the subject section.

[0017] In addition, in order to achieve the above-mentioned purpose, the present invention also provides an academic virtual community user social driving device, which includes: a memory, a processor, and an academic virtual community user social driving program stored on the memory and runnable on the processor. When the academic virtual community user social driving program is executed by the processor, the steps of the academic virtual community user social driving method described above are implemented.

[0018] In addition, in order to achieve the above-mentioned purpose, the present invention also provides a storage medium, on which an academic virtual community user social driving program is stored. When the academic virtual community user social driving program is executed by a processor, the steps of the above-mentioned academic virtual community user social driving method are implemented.

[0019] The beneficial effects of the present invention are: a method, device, equipment and storage medium for user social driving of an academic virtual community are proposed, which predicts the popularity change trend of the subject section in the target social driving period, and then considers the popularity change trend of the subject section in the target operating area in the target social driving period and the hardware resource allocation information of the previous social driving period based on the hardware resource deployment information, constructs a constraint condition set with hardware configuration parameters and location deployment information, takes the minimum server switching impact as the optimization goal, and optimizes and solves the hardware resource scheduling sub-strategy. At the same time, considering the candidate subject social driving activities and the availability of the remaining hardware resources, as many candidate subject social driving activities as possible are added to the user social drive of the target academic virtual community, avoiding the impact of the regional activity differences of the subject section on the stable operation of the community, and maintaining the continuous operation of the community with high activity, high popularity and high stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 A schematic diagram of the device structure of the hardware operating environment involved in the embodiment of the present invention; Figure 2 Schematic diagram of the process of driving user social interaction in an academic virtual community according to an embodiment of the present invention; Figure 3 This is a structural block diagram of the user social driving device of the academic virtual community in an embodiment of the present invention. DETAILED DESCRIPTION

[0021] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0022] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0023] like Figure 1 As shown, Figure 1 It is a schematic diagram of the device structure of the hardware operating environment involved in the embodiment of the present invention.

[0024] like Figure 1As shown, the device may include: a processor 1001, such as a CPU, a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Among them, the communication bus 1002 is used to realize the connection and communication between these components. The user interface 1003 may include a display screen (Display), an input unit such as a keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface and a wireless interface. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a WI-FI interface). The memory 1005 may be a high-speed RAM memory or a stable memory (non-volatile memory), such as a disk memory. The memory 1005 may optionally be a storage device independent of the aforementioned processor 1001.

[0025] Those skilled in the art will understand that Figure 1 The structure of the device shown in the figure does not constitute a limitation of the device, and the device may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0026] like Figure 1 As shown, the memory 1005 as a computer storage medium may include an operating system, a network communication module, a user interface module, and an academic virtual community user social driving program.

[0027] exist Figure 1 In the terminal shown, the network interface 1004 is mainly used to connect to the backend server and communicate data with the backend server; the user interface 1003 is mainly used to connect to the client (user end) and communicate data with the client; and the processor 1001 can be used to call the academic virtual community user social driver stored in the memory 1005 and perform the following operations: Obtaining community attributes of the target academic virtual community; wherein the community attributes include subject section division information and hardware resource deployment information; Based on the subject section division information, the subject association information of each subject section in the target operating area is collected and a set of popularity driving factors is constructed. The popularity driving factor set is used to predict the trend of changes in the subject section popularity in the target operating area during the target social driving period. Based on the hardware resource information and deployment location information of the plurality of distributed community servers in the hardware resource deployment information, taking into account the subject section popularity value and the heat impact area of ​​each social driving cycle in the subject section popularity change trend of the target operating area during the target social driving period and the hardware resource allocation information of the previous social driving period, a user social driving strategy for the target social driving period is generated; The hardware resource scheduling sub-strategy and the social driving sub-strategy in the user social driving strategy are extracted to respectively execute the server resource allocation and user social driving of the subject section.

[0028] The specific embodiments of the present invention applied to the device are basically the same as the embodiments of the following application of the academic virtual community user social driving method, and will not be described in detail here.

[0029] The embodiment of the present invention provides a method for driving user social interaction in an academic virtual community. Figure 2 , Figure 2 This is a flow chart of an embodiment of the method for driving user social interaction in an academic virtual community according to the present invention.

[0030] In this embodiment, a method for driving user social interaction in an academic virtual community includes the following steps: S100: Obtaining community attributes of a target academic virtual community; wherein the community attributes include subject section division information and hardware resource deployment information; S200: Based on the subject section division information, collecting subject association information of each subject section in the target operating area and constructing a popularity driving factor set, and using the popularity driving factor set to predict the subject section popularity change trend in the target operating area during the target social driving period; S300: Based on the hardware resource information and deployment location information of the plurality of distributed community servers in the hardware resource deployment information, taking into account the subject section popularity value and the popularity impact area of ​​each social driving cycle in the target operating area in the target social driving period and the hardware resource allocation information of the previous social driving period, a user social driving strategy for the target social driving period is generated; S400: extracting the hardware resource scheduling sub-strategy and the social driving sub-strategy in the user social driving strategy, and executing the server resource allocation and user social driving of the subject section respectively.

[0031] It should be noted that the operation of existing academic virtual communities and the user-social driven approach have the following limitations: First, there are multiple methods for user-social driven academic virtual communities (such as academic hot events such as the Nobel Prize announcement, community-initiated operations, and external diversion of section content, etc.), and different user-social driven approaches will result in different degrees of high-traffic access to the academic virtual community in different subject sections and at different durations, which will bring different pressures to the hardware resources of the academic virtual community (mainly various hardware parameters of the server for processing concurrent data). In order to ensure the stable operation and continuous activity of the community, when conducting or responding to user-social driven academic virtual communities, it is necessary to consider the limitations brought by hardware resources to avoid problems such as community collapse and unexpected delays. Second, when academic virtual communities are driven by user social interaction across different disciplinary sections, regional differences in community activity often emerge. For example, the highly active sections in the Yangtze River Delta region are biomedical engineering and blockchain technology, the highly active sections in the Beijing-Tianjin-Hebei region are artificial intelligence and aerospace technology, the highly active sections in the Guangdong-Hong Kong-Macao Greater Bay Area are semiconductors and cross-border data flow, and the highly active sections in the Chengdu-Chongqing region are electronic information and automotive engineering. When driving user social interaction within academic virtual communities, it is also necessary to pay attention to the regional differences in activity across different disciplinary sections, which may cause server overloads in some areas. Third, due to regional differences in community activity within academic virtual communities, when allocating calls to servers and disciplinary sections, it is also necessary to consider the communication distance between the server and the corresponding active area of ​​the disciplinary section. If a server assigned to a disciplinary section has sufficient hardware resources and processing power, but the server is deployed far away from the community active area corresponding to that disciplinary section, this will also cause high latency during community interaction, affecting the social experience. Fourth, when there are many user social-driven activities, in order to maximize the overall activity of the academic virtual community, it is necessary to run as many user social-driven activities as possible within the scope of server hardware resources. How to arrange and run user social-driven activities under the above-mentioned multiple restrictions has become a difficult problem.

[0032] In order to solve the above problems, this embodiment predicts the popularity change trend of the subject section in the target social driving period, and then considers the popularity change trend of the subject section in the target operating area in the target social driving period and the hardware resource allocation information of the previous social driving period based on the hardware resource deployment information, and constructs a constraint condition set with hardware configuration parameters and location deployment information. With the minimum server switching impact as the optimization goal, the hardware resource scheduling sub-strategy is optimized and solved. At the same time, considering the candidate subject social driving activities and the availability of remaining hardware resources, as many candidate subject social driving activities as possible are added to the user social drive of the target academic virtual community to avoid the impact of regional activity differences in subject sections on the stable operation of the community, and maintain the continuous operation of the community with high activity, high popularity and high stability.

[0033] In a preferred embodiment, the step of obtaining the community attributes of the target academic virtual community specifically includes: S110: querying the backend management system of the target academic virtual community, parsing the subject section architecture information stored in the backend management system, and extracting several subject section categories of the target academic virtual community; S120: Querying first server deployment information of the target academic virtual community in the public cloud service platform and second server deployment information in the self-built IDC computer room, and determining a deployment server set of the target academic virtual community based on the first server deployment information and the second server deployment information; S130: Using several subject section categories as subject section division information and using a deployment server set as hardware resource deployment information to generate community attributes of a target academic virtual community.

[0034] In this embodiment, by querying the background management system of the target academic virtual community, several subject section categories of the target academic virtual community are parsed out, and then by querying the first server deployment information in the public cloud service platform of the target academic virtual community and the second server deployment information in the self-built IDC computer room, the deployment server set of the target academic virtual community is determined, thereby constructing the community attributes of the target academic virtual community, analyzing and summarizing the hardware architecture and software architecture of the target academic virtual community, and providing data support for the subsequent scheduling and connection between the subject sections and servers.

[0035] In a preferred embodiment, based on the subject section division information, collecting subject association information of each subject section in the target operating area and constructing a set of heat driving factors specifically includes: S210: Based on the plurality of subject section categories in the subject section division information, a subject keyword set is generated for each subject section category by using a data expansion method; S220: using the subject keyword set, matching a plurality of subject-related information in a subject-related information database by using a keyword matching method, extracting popularity driving factors from the plurality of subject-related information, and constructing a popularity driving factor set; The plurality of subject-related information is configured to be at least one of academic hot events, community operation events, or external channel diversion events published on a plurality of subject hot event publishing pages or publishing platforms recorded in the subject-related information database; Among them, the popularity driving factors are configured as the popularity impact characteristics of academic hot events, community operation activities or external channel diversion events.

[0036] On this basis, using the aforementioned popularity driving factor set, the steps for predicting the popularity trend of the subject section in the target operating area during the target social driving period include: S230: Obtaining subject section popularity change information within a historical period, extracting subject section popularity values ​​and popularity impact areas of each subject section category from the subject section popularity change information, and constructing subject section popularity value change information, popularity impact area change information, and popularity impact features into popularity training samples; S240: calling a pre-built initial neural network model, training the initial neural network model using the heat training sample, and obtaining a heat prediction model for the completion of the pilgrimage when the training reaches the target number of times or converges; S250: Utilize several heat-influencing features in the heat-driving factor set to construct a heat-influencing feature set and use it as a heat prediction sample. Input the heat prediction sample into the heat prediction model to predict the heat change trend of the subject section in the target operating area during the target social driving period.

[0037] In this embodiment, the acquired subject section division information is used to generate a subject keyword set by data expansion, and then subject-related information is extracted from academic hot events, community operation activity time and external channel drainage events in the subject-related information database, and a heat driving factor set is constructed based on this. The pre-trained heat prediction model is then used to predict the heat change trend of the subject section. The heat change trend of the subject section can reflect the changes in the heat value of each subject section and the changes in the heat influence area. When considering the scheduling and connection of the subject section and the server, the relevant information on the heat change trend of the subject section can play a significant guiding role, so that the target academic virtual community can ensure high activity and high heat for a longer period of time under the premise of hardware support.

[0038] In a preferred embodiment, based on the hardware resource information and deployment location information of the plurality of distributed community servers in the hardware resource deployment information, taking into account the subject section popularity value and the heat impact area of ​​each social driving cycle in the target operating area in the target social driving period and the hardware resource allocation information of the previous social driving period, the user social driving strategy steps for the target social driving period are generated, specifically including: S310: Extracting hardware resource information and deployment location information of several distributed community servers from the hardware resource deployment information, considering the subject section popularity value and popularity impact area of ​​each social driving cycle in the target operating area during the target social driving period, and the hardware resource allocation information of the previous social driving period; S320: The first constraint condition is that the sum of the hardware resource requirement parameters corresponding to the subject section heat of the several subject sections assigned to each distributed community server for service in each social drive cycle of the target social drive period is higher than the hardware resource configuration parameters corresponding to the hardware resource information of the distributed community server; the second constraint condition is that the heat influence area corresponding to the several subject sections assigned to each distributed community server for service in each social drive cycle of the target social drive period covers the deployment location information of the distributed community server; the optimization objective is to minimize the cumulative value of the sum of the subject section heat of two adjacent social drive cycles corresponding to all subject sections in the hardware resource allocation information of the last social drive cycle in the previous social drive period to the hardware resource allocation information of the last social drive cycle in the target social drive period when they are assigned to different distributed community servers; the hardware resource allocation information of the several subject sections assigned to the several distributed community servers in each social drive cycle of the target social drive period is optimized; S330: Generate a user social driving strategy according to hardware resource allocation information of a plurality of subject sections allocated to a plurality of distributed community servers in each social driving cycle during a target social driving period.

[0039] Furthermore, according to the hardware resource allocation information of the several subject sections allocated to the several distributed community servers in each social driving cycle during the target social driving period, a user social driving strategy step is generated, specifically including: S331: Generate hardware resource scheduling sub-strategies for the distributed community servers according to hardware resource allocation information of the discipline sections allocated to the distributed community servers in each social driving cycle of the target social driving period; S332: Calculate the difference between the hardware resource configuration parameters of each distributed community server and the sum of the hardware resource requirement parameters of each social driving cycle in the target social driving period according to the hardware resource allocation information, and generate a list of remaining hardware resource parameters of each distributed community server in each social driving cycle in the target social driving period; S333: Obtain a candidate set of disciplinary social-driven activities, extract a number of candidate disciplinary social-driven activities from the candidate set of disciplinary social-driven activities that are introduced in chronological order, predict the disciplinary section popularity change trend of each candidate disciplinary social-driven activity based on the popularity influence characteristics of each candidate disciplinary social-driven activity, and determine the disciplinary section popularity value and popularity influence area for each social-driven cycle; S334: Considering the popularity value and popularity influence area of ​​the subject section in each social driving cycle, the deployment location information of each distributed community server, and the remaining hardware resource parameter list, repeatedly adding the candidate subject social driving activities to the target social driving period in chronological order, under the condition that the hardware resource requirements of the candidate subject social driving activities and the deployment location of the distributed community server fall within the popularity influence area, until the requirements are no longer met, and obtaining the allocation information of each candidate subject social driving activity and the distributed community server in the target social driving period; S335: Generate social driving sub-strategies for several distributed community servers based on the selected candidate subject social driving activities and the allocation information between each candidate subject social driving activity and the distributed community servers in the target social driving period.

[0040] In this embodiment, by obtaining the subject section division information and hardware resource deployment information of the target academic virtual community, collecting the subject association information of each subject section in the target operating area and constructing a heat driving factor set, predicting the subject section heat change trend in the target social driving period, and then based on the hardware resource information and deployment location information of several distributed community servers in the hardware resource deployment information, considering the subject section heat value and heat impact area of ​​each social driving cycle in the subject section heat change trend in the target operating area in the target social driving period and the hardware resource allocation information of the previous social driving period, constructing a constraint condition set with hardware configuration parameters and location deployment information, taking the minimum server switching impact as the optimization goal, optimizing and solving the hardware resource scheduling sub-strategy, at the same time, considering the candidate subject social driving activities and the availability of remaining hardware resources, as many candidate subject social driving activities as possible are added to the user social drive of the target academic virtual community.

[0041] In a preferred embodiment, the hardware resource scheduling sub-strategy and the social driving sub-strategy in the user social driving strategy are extracted to respectively execute the server resource allocation and user social driving steps of the subject section, specifically including: S410: extracting a hardware resource scheduling sub-strategy and a social driving sub-strategy from the user social driving strategy, and using the hardware resource scheduling sub-strategy to control a plurality of distributed community servers to execute server resource allocation for each subject section in each social driving cycle during a target social driving period; S420: Utilizing the social driving sub-strategy, the virtual community management terminal is driven to execute user social driving for candidate subject social driving activities on the distributed community servers and corresponding subject sections, and control the allocation of server resources of several distributed community servers to the corresponding subject sections of the newly added candidate subject social driving activities in each social driving cycle during the target social driving period.

[0042] In this embodiment, by reasonably and scientifically executing the user social drive of the academic virtual community, the scheduling and allocation strategies of different subject sections and servers under the user social drive are intelligently allocated when the server hardware resources permit, thereby avoiding the impact of regional activity differences in different subject sections on the stable operation of the community, and making it possible to run more user social drive activities to maintain the continuous operation of the community with high activity, high popularity and high stability.

[0043] Reference Figure 3 , Figure 3 This is a structural block diagram of an embodiment of the academic virtual community user social driving device of the present invention.

[0044] like Figure 3 As shown, the academic virtual community user social driving device proposed in the embodiment of the present invention includes: An acquisition module 10 is used to acquire community attributes of a target academic virtual community; wherein the community attributes include subject section division information and hardware resource deployment information; Prediction module 20, for collecting subject-related information of each subject section in the target operating area based on the subject section division information and constructing a set of popularity driving factors, and using the set of popularity driving factors to predict the trend of subject section popularity changes in the target operating area during the target social driving period; A generation module 30 is configured to generate a user social driving strategy for a target social driving period based on the hardware resource information and deployment location information of the plurality of distributed community servers in the hardware resource deployment information, taking into account the subject section popularity value and the heat impact area of ​​each social driving cycle in the subject section popularity change trend of the target operating area during the target social driving period, and the hardware resource allocation information of the previous social driving period; The execution module 40 is used to extract the hardware resource scheduling sub-strategy and the social driving sub-strategy in the user social driving strategy, and respectively execute the server resource allocation and user social driving of the subject section.

[0045] Other embodiments or specific implementations of the academic virtual community user social driving device of the present invention can refer to the above-mentioned method embodiments and will not be repeated here.

[0046] In addition, the present invention also proposes an academic virtual community user social driving device, which includes: a memory, a processor, and an academic virtual community user social driving program stored on the memory and runnable on the processor. When the academic virtual community user social driving program is executed by the processor, the steps of the academic virtual community user social driving method described above are implemented.

[0047] The specific implementation of the academic virtual community user social driving device of the present application is basically the same as the various embodiments of the above-mentioned academic virtual community user social driving method, and will not be repeated here.

[0048] In addition, the present invention also proposes a readable storage medium, which includes a computer-readable storage medium on which a social driving program for users of an academic virtual community is stored. The readable storage medium may be Figure 1 The memory 1005 in the terminal may also be at least one of a ROM (Read-Only Memory) / RAM (Random Access Memory), a magnetic disk, and an optical disk. The readable storage medium includes a number of instructions for enabling an academic virtual community user social driving device with a processor to execute the academic virtual community user social driving method described in various embodiments of the present invention.

[0049] The specific implementation methods in the readable storage medium of this application are basically the same as the various embodiments of the above-mentioned academic virtual community user social driving method, and will not be repeated here.

[0050] It should be understood that, in the description of this specification, reference to terms such as "one embodiment," "another embodiment," "other embodiments," or "first to Nth embodiments" means that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any appropriate manner in any one or more embodiments or examples.

[0051] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or system comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or system. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or system comprising the element.

[0052] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus the necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better embodiment. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes a number of instructions for enabling a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in each embodiment of the present invention.

[0053] The above are only preferred embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention description and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for driving user social interaction in an academic virtual community, characterized in that: The following steps are involved: Obtaining community attributes of the target academic virtual community; wherein the community attributes include subject section division information and hardware resource deployment information; Based on the subject section division information, the subject association information of each subject section in the target operating area is collected and a set of popularity driving factors is constructed. The popularity driving factor set is used to predict the trend of changes in the subject section popularity in the target operating area during the target social driving period. Based on the hardware resource information and deployment location information of the plurality of distributed community servers in the hardware resource deployment information, taking into account the subject section popularity value and the heat impact area of ​​each social driving cycle in the subject section popularity change trend of the target operating area during the target social driving period and the hardware resource allocation information of the previous social driving period, a user social driving strategy for the target social driving period is generated; The hardware resource scheduling sub-strategy and the social driving sub-strategy in the user social driving strategy are extracted to respectively execute the server resource allocation and user social driving of the subject section.

2. The method for driving user social interaction in an academic virtual community according to claim 1, wherein: The steps for obtaining the community attributes of the target academic virtual community include: Querying the backend management system of the target academic virtual community, parsing the subject section architecture information stored in the backend management system, and extracting several subject section categories of the target academic virtual community; Querying first server deployment information of the target academic virtual community in the public cloud service platform and second server deployment information in the self-built IDC computer room, and determining a deployment server set of the target academic virtual community based on the first server deployment information and the second server deployment information; Several subject section categories are used as subject section division information, and a deployment server set is used as hardware resource deployment information to generate community attributes of a target academic virtual community.

3. The method for driving user social interaction in an academic virtual community according to claim 1, wherein: Based on the subject section division information, the subject association information of each subject section in the target operating area is collected and a set of heat driving factors is constructed, specifically including: Based on the plurality of subject section categories in the subject section division information, a subject keyword set is generated for each subject section category by adopting a data expansion method; Using the subject keyword set, a plurality of subject-related information are matched in a subject-related information database by adopting a keyword matching method, and popularity driving factors in the plurality of subject-related information are extracted to construct a popularity driving factor set; The plurality of subject-related information is configured to be at least one of academic hot events, community operation events, or external channel diversion events published on a plurality of subject hot event publishing pages or publishing platforms recorded in the subject-related information database; Among them, the popularity driving factors are configured as the popularity impact characteristics of academic hot events, community operation activities or external channel diversion events.

4. The method for driving user social interaction in an academic virtual community according to claim 1, wherein: Using the popularity driving factor set, the steps for predicting the popularity change trend of the subject section in the target operating area during the target social driving period specifically include: Obtaining the subject section popularity change information within a historical period, extracting the subject section popularity value and popularity influence area of ​​each subject section category in the subject section popularity change information, and constructing the subject section popularity value change information, popularity influence area change information, and popularity influence features into a popularity training sample; Call the pre-built initial neural network model and train it using the heat training samples. When the training reaches the target number of times or converges, a heat prediction model for the completion of the pilgrimage is obtained. By utilizing several heat-influencing features in the heat-driving factor set, a heat-influencing feature set is constructed and used as a heat prediction sample. The heat prediction sample is input into the heat prediction model to predict the heat change trend of the subject section in the target operating area during the target social driving period.

5. The method for driving user social interaction in an academic virtual community according to claim 1, wherein: Based on the hardware resource information and deployment location information of the plurality of distributed community servers in the hardware resource deployment information, taking into account the subject section popularity value and the heat impact area of ​​each social driving cycle in the target operating area in the target social driving period and the hardware resource allocation information of the previous social driving period, the user social driving strategy steps for the target social driving period are generated, specifically including: Extracting the hardware resource information and deployment location information of several distributed community servers from the hardware resource deployment information, considering the subject section popularity value and the heat impact area of ​​each social driving cycle in the target operating area during the target social driving period, and the hardware resource allocation information of the previous social driving period; The first constraint condition is that the sum of the hardware resource demand parameters corresponding to the subject section heat of the several subject sections assigned to each distributed community server for service in each social drive cycle of the target social drive period is higher than the hardware resource configuration parameters corresponding to the hardware resource information of the distributed community server. The second constraint condition is that the heat influence area corresponding to the several subject sections assigned to each distributed community server for service in each social drive cycle of the target social drive period covers the deployment location information of the distributed community server. The optimization objective is to minimize the cumulative value of the sum of the subject section heat of two adjacent social drive cycles corresponding to all subject sections in the hardware resource allocation information of the last social drive cycle in the previous social drive period to the hardware resource allocation information of the last social drive cycle in the target social drive period when they are assigned to different distributed community servers. The hardware resource allocation information of the several subject sections assigned to the several distributed community servers in each social drive cycle of the target social drive period is optimized and solved. A user social driving strategy is generated according to hardware resource allocation information of several subject sections allocated to several distributed community servers in each social driving cycle during a target social driving period.

6. The method for driving user social interaction in an academic virtual community according to claim 5, wherein: According to the hardware resource allocation information of several subject sections allocated to several distributed community servers in each social driving cycle during the target social driving period, a user social driving strategy step is generated, specifically including: Generate hardware resource scheduling sub-strategies for the distributed community servers according to hardware resource allocation information of the discipline sections allocated to the distributed community servers in each social driving cycle of the target social driving period; Calculate the difference between the hardware resource configuration parameters of each distributed community server and the sum of the hardware resource requirement parameters of each social driving cycle in the target social driving period under the hardware resource allocation information, and generate a list of remaining hardware resource parameters for each social driving cycle in the target social driving period for each distributed community server; Obtain a candidate set of disciplinary social-driven activities, extract a number of candidate disciplinary social-driven activities that are introduced chronologically from the candidate set, predict the disciplinary section popularity change trend of each candidate disciplinary social-driven activity based on the popularity influence characteristics of each candidate disciplinary social-driven activity, and determine the disciplinary section popularity value and popularity influence area for each social-driven cycle; Considering the subject section popularity value and popularity influence area of ​​each social driving cycle, the deployment location information of each distributed community server, and the remaining hardware resource parameter list, under the conditions that the hardware resource requirements of the candidate subject social driving activities and the deployment location of the distributed community server fall into the popularity influence area requirements, repeatedly perform the action of adding the candidate subject social driving activities to the target social driving period in chronological order until the requirements can no longer be met, and obtain the allocation information of each candidate subject social driving activity and the distributed community server in the target social driving period; Based on the selected candidate subject social driving activities and the allocation information of each candidate subject social driving activity and the distributed community server in the target social driving period, a plurality of social driving sub-strategies of the distributed community servers are generated.

7. The method for driving user social interaction in an academic virtual community according to claim 6, wherein: Extract the hardware resource scheduling sub-strategy and social driving sub-strategy in the user social driving strategy, and respectively execute the server resource allocation and user social driving steps of the subject section, specifically including: Extracting the hardware resource scheduling sub-strategy and the social driving sub-strategy in the user social driving strategy, and using the hardware resource scheduling sub-strategy to control a plurality of distributed community servers to execute server resource allocation for each subject section in each social driving cycle of a target social driving period; By utilizing the social driving sub-strategy, the virtual community management terminal is driven to execute user social driving for candidate subject social driving activities on the distributed community servers and corresponding subject sections, and control the server resource allocation of several distributed community servers to the corresponding subject sections of the newly added candidate subject social driving activities in each social driving cycle during the target social driving period.

8. A device for driving user social interaction in an academic virtual community, characterized in that: include: An acquisition module, configured to acquire community attributes of a target academic virtual community; wherein the community attributes include subject section division information and hardware resource deployment information; A prediction module is used to collect subject-related information of each subject section in the target operating area based on the subject section division information and construct a set of popularity driving factors, and use the set of popularity driving factors to predict the popularity change trend of the subject section in the target operating area during the target social driving period; A generation module is configured to generate a user social driving strategy for a target social driving period based on the hardware resource information and deployment location information of the plurality of distributed community servers in the hardware resource deployment information, taking into account the subject section popularity value and the heat impact area of ​​each social driving cycle in the subject section popularity change trend of the target operating area during the target social driving period, and the hardware resource allocation information of the previous social driving period; The execution module is used to extract the hardware resource scheduling sub-strategy and the social driving sub-strategy in the user social driving strategy, and respectively execute the server resource allocation and user social driving of the subject section.

9. A device for driving user social interaction in an academic virtual community, characterized in that: The academic virtual community user social driving device includes: a memory, a processor, and an academic virtual community user social driving program stored on the memory and executable on the processor. When the academic virtual community user social driving program is executed by the processor, the steps of the academic virtual community user social driving method as described in any one of claims 1 to 7 are implemented.

10. A storage medium, characterized in that: The storage medium stores an academic virtual community user social driving program, which, when executed by a processor, implements the steps of the academic virtual community user social driving method according to any one of claims 1 to 7.

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