Multi-user cooperative training system in electronic sports virtual training ground
By designing a multi-user collaborative training system in the e-sports virtual training field, and determining the optimal training strategy using personalized analysis and collaborative fitting algorithms, the problem of lack of targeted analysis in the training field in the existing technology is solved, and the training efficiency is significantly improved and immediacy.
Patent Information
- Application Number
- CN202411245497.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-09-06
- Publication Date
- 2024-12-13
AI Technical Summary
The existing e-sports virtual training ground lacks targeted analysis, making it difficult to formulate personalized collaborative training plans for multiple training users, resulting in inefficient training.
A multi-user collaborative training system in the e-sports virtual training field was designed, including data storage module, personalized analysis module and multi-user collaborative analysis module. By conducting personalized analysis and collaborative fitting algorithms on user historical training data, we determine The optimal e-sports virtual training strategy, generate personalized training scenarios and dynamically adjust the strategy.
It realizes personalized state analysis for users and maximizes training benefits for multiple users, improving the training efficiency and immediacy of the e-sports virtual training ground.
Smart Images
Figure CN119130750A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of e-sports training, and in particular to a multi-user collaborative training system in an e-sports virtual training field. Background Art
[0002] Electronic Sports (Electronic Sports) refers to sports events where electronic game competitions have reached the "competitive" level. Electronic sports are competitions between people that combine intelligence and physical strength using electronic devices as sports equipment. Through electronic sports, participants' thinking ability, reaction ability, limb coordination ability and willpower can be trained and improved, team spirit can be cultivated, and professional e-sports also have high physical requirements. Electronic sports is also a profession, similar to non-electronic game competitions such as chess.
[0003] With the rapid development of the e-sports industry, many large clubs have equipped e-sports teams / players with professional e-sports coaches, data analysts, etc., and have arranged targeted training opponents for e-sports teams / players to conduct daily training to improve the training effect. However, for smaller or small e-sports teams, due to limited resources, it usually takes a lot of effort to find suitable training opponents. Based on this situation, the virtual training ground can effectively solve the above problems. By inputting the data of the training opponent into the virtual training ground, an AI opponent similar to the training opponent can be generated in the virtual training ground. However, the existing e-sports virtual training ground lacks targeted analysis of users, and it is difficult to implement personalized collaborative training program formulation for multiple training users. Therefore, this application provides a multi-user collaborative training system in an e-sports virtual training ground to meet the needs. Summary of the invention
[0004] The technical problem to be solved by the present invention is to provide a multi-user collaborative training system in an e-sports virtual training field to solve the problem that the existing e-sports virtual training field lacks targeted analysis of users and is difficult to implement personalized collaborative training plan formulation for multiple training users.
[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: A multi-user collaborative training system in an e-sports virtual training field, comprising: A data storage module, the data storage module is used to store a training database, the training database stores historical training data of each user; A personalized analysis module, the personalized analysis module is electrically connected to the data storage module, and the personalized analysis module is used to determine the influencing factors of each user based on the historical training data of each user; A multi-user collaborative analysis module, the multi-user collaborative analysis module is electrically connected to the personalized analysis module, and the multi-user collaborative analysis module has a collaborative fitting algorithm based on the influencing factors of at least one user participating in the training to determine the optimal e-sports virtual training strategy; A training execution module, wherein the training execution module is electrically connected to the multi-user collaborative analysis module and the data storage module, and the training execution module is used to generate an e-sports virtual training scene based on the optimal e-sports virtual training strategy, and obtain the user's training data and store it in the data storage module.
[0006] Optionally, the personalized analysis module specifically includes: A data extraction unit, the data extraction unit is used to extract the user's historical training data from the training database; A singularity analysis unit, the singularity analysis unit is used to identify the unique data in the user's historical training data; A correlation analysis unit is used to determine the influencing factors of each user based on the specific data in the historical training data of the user.
[0007] Optionally, the specific data in the historical training data for identifying the user specifically includes: Get the user's performance score in each historical training data; Based on the Grubbs criterion, peak screening conditions were established; Based on the peak screening condition, the abnormal data points in the performance scores of several historical training data that exceed the normal fluctuation are screened out as the specific data in the user's historical training data; Wherein, the expression of the peak screening condition is:
[0008] In the formula, Score the performance in the i-th historical training data, is the average of the performance scores in all historical training data, is the standard deviation of the performance scores across all historical training data, is the Grubbs critical value. If the expression of the peak screening condition is satisfied, then is an outlier; The Grubbs critical value is obtained as follows: Setting a detection level value based on the abnormal sensitivity of the performance scores in the historical training data; Determine the total number of all historical training data; Based on the total number of historical training data and the detection level value, the corresponding value is retrieved from the Grubbs table as the Grubbs critical value.
[0009] Optionally, the determining of the influencing factors of each user based on the specific data in the historical training data of the user specifically includes: Filter out the training strategy of historical training scenarios corresponding to the specific data in the user's historical training data, based on the sample training strategy; Split at least one training factor from the sample training strategy as a factor to be verified; Conduct a comprehensive analysis of each factor to be verified to determine the impact of each factor on the user's performance score; Determine whether the influence index of the factor to be verified on the user's performance score is greater than a preset value. If so, record the factor to be verified as an influencing factor. If not, do not respond.
[0010] Optionally, the comprehensive analysis of each factor to be verified to determine the impact index of each factor to be verified on the user's performance score specifically includes: From the historical training data of the user, select the performance scores of several users when they execute the current factor to be verified to different degrees, and record them as the initial training data; Analyze the initial training data in combination with the user's historical training data to obtain the user's performance score change rate when executing the current factor to be verified to different degrees as standardized data; Based on the standardized data, the impact index between the factor to be verified and the user's performance score is calculated through the correlation calculation formula; The correlation calculation formula is specifically: In the formula, is the impact indicator between the factor to be verified and the user's performance score, is the total number of standardized data, is the execution degree of the factor to be verified in the jth standardized data, is the performance score change rate of the user in the jth standardized data.
[0011] Optionally, the initial training data is analyzed in combination with the user's historical training data to obtain the user's performance score change rate when the current factor to be verified is executed at different degrees, as standardized data, specifically including: Obtain all other training factors executed in the historical training scenarios corresponding to the initial training data of the factor to be verified at each execution level, and record them as comparative training factors; Selecting a number of comparative historical training scenarios that have and only have comparative training factors from the user's historical training data, and determining performance scores of the users for the comparative historical training scenarios; The performance scores of several users in the comparison historical training scenarios are averaged to obtain comparison standard data; Based on the initial training data and the comparison standard data, the user's performance score change rate is calculated according to the standardized calculation formula; The standardized calculation formula is as follows:
[0012] In the formula, The rate of change of the user's performance score, is the initial training data, For comparison with standard data.
[0013] Optionally, the multi-user collaborative analysis module includes: A user focus analysis unit, the user focus analysis unit is used to determine the focus weights of users participating in the training; A benchmark training strategy unit, the benchmark training strategy unit is used to determine a benchmark training strategy for e-sports virtual training, the benchmark training strategy is determined by e-sports events and e-sports opponent data; A factor focus analysis unit, the factor focus analysis unit is used to determine a priority elimination factor from all influencing factors of users participating in the training, and formulate a targeted training strategy based on the priority elimination factor; A strategy determination unit, wherein the strategy determination unit is used to merge the baseline training strategy and the targeted training strategy to obtain the optimal e-sports virtual training strategy.
[0014] Optionally, determining a priority elimination factor from all influencing factors of users participating in the training specifically includes: Determine the maximum influence degree value of all influencing factors of users participating in the training under the actual state; Based on the impact index of the influencing factor on the user's performance score and the maximum impact value of the influencing factor in its actual state, the benefit index of eliminating the influencing factor is calculated through the benefit index formula; Filter out the influencing factors corresponding to the maximum value of the profit index as the priority factors to be eliminated; The profit indicator formula is specifically:
[0015] In the formula, To eliminate the profit index of the kth influencing factor, is the emphasis weight of the lth user participating in the training, is the impact index of the kth influencing factor on the performance score of the lth user participating in the training, is the maximum influence value of the kth influencing factor in the actual state.
[0016] Optionally, the training execution module specifically includes: A training monitoring unit, the training monitoring unit is used to monitor the performance score of each user participating in the training under the optimal e-sports virtual training strategy; A training result feedback unit is used to determine whether the performance scores of all users participating in the training have reached their performance score average. If so, a training completion signal is output to the multi-user collaborative analysis module, and the multi-user collaborative analysis module regenerates the optimal e-sports virtual training strategy. If not, training continues based on the current optimal e-sports virtual training strategy.
[0017] Compared with the prior art, the present invention has at least the following beneficial effects: In the above scheme, a personalized analysis module is set up to conduct personalized analysis on the data during the user's training process to determine the factors that each user needs to overcome that affect their performance in the game. In this way, personalized status analysis of the user can be achieved to ensure that the formulated e-sports virtual training strategy is compatible with the user's status.
[0018] By setting up a multi-user collaborative analysis module, collaborative training of multiple users is planned to ensure that the executed e-sports virtual training strategy can achieve the maximum training benefits of multiple users, thereby effectively improving the training efficiency of the e-sports virtual training field.
[0019] By setting up a training execution module to monitor the training process and dynamically feedback the training results, it is convenient for the technology to instantly adjust the e-sports virtual training strategy based on the training results, thereby effectively ensuring the immediacy of e-sports training adjustments and further improving the training efficiency of the e-sports virtual training field. BRIEF DESCRIPTION OF THE DRAWINGS
[0020] The accompanying drawings, which are incorporated herein and constitute a part of the specification, illustrate embodiments of the invention and, together with the description, further serve to explain the principles of the invention and to enable those skilled in the relevant art to make and use the invention.
[0021] Figure 1 This is a structural block diagram of the multi-user collaborative training system in the e-sports virtual training field proposed by the present invention; Figure 2 A flow chart of a method for identifying specific data in a user's historical training data in the present invention; Figure 3 A flow chart of a method for determining the influencing factors of each user in the present invention; Figure 4 A flow chart of a method for determining an impact index of each factor to be verified on a user's performance score in the present invention; Figure 5A flow chart of a method for obtaining a user's performance score change rate when the current factor to be verified is executed at different degrees in the present invention; Figure 6 The flowchart of the method for determining a priority elimination factor from all influencing factors of users participating in training in the present invention. DETAILED DESCRIPTION
[0022] The following is a detailed description of the multi-user collaborative training system in the e-sports virtual training field provided by the present invention in combination with the accompanying drawings and specific embodiments. At the same time, it is explained here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments, and those skilled in the art may also adopt other alternative methods to implement some known technologies; and the accompanying drawings are only for a more specific description of the embodiments, and are not intended to specifically limit the present invention.
[0023] It should be noted that the references to "one embodiment", "embodiment", "exemplary embodiments", "some embodiments" and the like in the specification indicate that the embodiments described may include specific features, structures or characteristics, but not every embodiment may include the specific features, structures or characteristics. In addition, when a specific feature, structure or characteristic is described in conjunction with an embodiment, it should be within the knowledge of a person skilled in the art to implement such feature, structure or characteristic in conjunction with other embodiments (whether or not explicitly described).
[0024] In general, a term can be understood, at least in part, from its use in context. For example, depending, at least in part, on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending, at least in part, on the context, allow for the presence of other factors that are not necessarily explicitly described.
[0025] It will be understood that the meanings of “on,” “over,” and “above” in the present invention should be interpreted in the broadest manner, so that “on” not only means “directly on” something, but also includes the meaning of being “on” something with intervening features or layers therebetween, and “on” or “over” not only means “on” or “above” something, but also includes the meaning of being “on” or “above” something with no intervening features or layers therebetween.
[0026] Additionally, spatially relative terms such as "under," "beneath," "lower," "above," "upper," and the like may be used herein for descriptive convenience to describe the relationship of one element or feature to another element or features, as shown in the accompanying drawings. Spatially relative terms are intended to encompass different orientations of the device in use or operation in addition to the orientation depicted in the accompanying drawings. The device may be oriented in other ways, and the spatially relative descriptors used herein may be similarly interpreted accordingly.
[0027] like Figure 1 As shown, an embodiment of the present invention provides a multi-user collaborative training system in an e-sports virtual training field, including: A data storage module, the data storage module is used to store a training database, the training database stores historical training data of each user; A personalized analysis module, the personalized analysis module is electrically connected to the data storage module, and the personalized analysis module is used to determine the influencing factors of each user based on the historical training data of each user; The personalized analysis module specifically includes: A data extraction unit, the data extraction unit is used to extract the user's historical training data from the training database; A singularity analysis unit, the singularity analysis unit is used to identify the unique data in the user's historical training data; A correlation analysis unit is used to determine the influencing factors of each user based on the specific data in the historical training data of the user.
[0028] By setting up a personalized analysis module, a personalized analysis is performed on the data during the user's training process to determine the factors that affect each user's performance in the game and that need to be overcome. In this way, personalized status analysis for the user can be achieved.
[0029] A multi-user collaborative analysis module, the multi-user collaborative analysis module is electrically connected to the personalized analysis module, and the multi-user collaborative analysis module has a collaborative fitting algorithm based on the influencing factors of at least one user participating in the training to determine the optimal e-sports virtual training strategy; The multi-user collaborative analysis module includes: A user focus analysis unit, the user focus analysis unit is used to determine the focus weights of users participating in the training; A benchmark training strategy unit, the benchmark training strategy unit is used to determine a benchmark training strategy for e-sports virtual training, the benchmark training strategy is determined by e-sports events and e-sports opponent data; A factor focus analysis unit, the factor focus analysis unit is used to determine a priority elimination factor from all influencing factors of users participating in the training, and formulate a targeted training strategy based on the priority elimination factor; A strategy determination unit, wherein the strategy determination unit is used to merge the baseline training strategy and the targeted training strategy to obtain the optimal e-sports virtual training strategy.
[0030] By setting up a multi-user collaborative analysis module, collaborative training of multiple users is planned to ensure that the executed e-sports virtual training strategy can achieve the maximum training benefits of multiple users, thereby effectively improving the training efficiency of the e-sports virtual training field.
[0031] A training execution module, wherein the training execution module is electrically connected to the multi-user collaborative analysis module and the data storage module, and the training execution module is used to generate an e-sports virtual training scene based on the optimal e-sports virtual training strategy, and obtain the user's training data and store it in the data storage module.
[0032] The training execution module specifically includes: A training monitoring unit, the training monitoring unit is used to monitor the performance score of each user participating in the training under the optimal e-sports virtual training strategy; A training result feedback unit is used to determine whether the performance scores of all users participating in the training have reached their performance score average. If so, a training completion signal is output to the multi-user collaborative analysis module, and the multi-user collaborative analysis module regenerates the optimal e-sports virtual training strategy. If not, training continues based on the current optimal e-sports virtual training strategy.
[0033] By setting up a training execution module to monitor the training process and dynamically feedback the training results, it is convenient for the technology to instantly adjust the e-sports virtual training strategy based on the training results, thereby effectively ensuring the immediacy of e-sports training adjustments and further improving the training efficiency of the e-sports virtual training field.
[0034] like Figure 2 As shown, the specific data in the historical training data for identifying the user specifically includes: Get the user's performance score in each historical training data; Based on the Grubbs criterion, peak screening conditions were established; Based on the peak screening condition, the abnormal data points in the performance scores of several historical training data that exceed the normal fluctuation are screened out as the specific data in the user's historical training data; Wherein, the expression of the peak screening condition is:
[0035] In the formula, Score the performance in the i-th historical training data, is the average of the performance scores in all historical training data, is the standard deviation of the performance scores across all historical training data, is the Grubbs critical value. If the expression of the peak screening condition is satisfied, then is an outlier; The Grubbs critical value is obtained as follows: Setting a detection level value based on the abnormal sensitivity of the performance scores in the historical training data; Determine the total number of all historical training data; Based on the total number of historical training data and the detection level value, the corresponding value is retrieved from the Grubbs table as the Grubbs critical value.
[0036] It is understandable that under normal performance, the user's performance level fluctuations should be within the range of the normal distribution. Therefore, when the user's performance level is at an abnormal trough value that does not conform to the normal distribution range, it means that the user's performance is abnormal at this time. At this time, exploring the factors that lead to the user's abnormal performance has greater reference significance for formulating targeted training plans. In this way, finding out the influencing factors that cause negative abnormal fluctuations in the user's performance level can effectively ensure the accuracy of the subsequent determination of the e-sports virtual training strategy.
[0037] like Figure 3 As shown, the specific data in the user's historical training data is used to determine the influencing factors of each user, including: Filter out the training strategy of historical training scenarios corresponding to the specific data in the user's historical training data, based on the sample training strategy; Split at least one training factor from the sample training strategy as a factor to be verified; Conduct a comprehensive analysis of each factor to be verified to determine the impact of each factor on the user's performance score; Determine whether the influence index of the factor to be verified on the user's performance score is greater than a preset value. If so, record the factor to be verified as an influencing factor. If not, do not respond.
[0038] like Figure 4 As shown, the comprehensive analysis of each factor to be verified to determine the impact index of each factor to be verified on the user's performance score specifically includes: From the historical training data of the user, select the performance scores of several users when they execute the current factor to be verified to different degrees, and record them as the initial training data; Analyze the initial training data in combination with the user's historical training data to obtain the user's performance score change rate when executing the current factor to be verified to different degrees as standardized data; Based on the standardized data, the impact index between the factor to be verified and the user's performance score is calculated through the correlation calculation formula; The correlation calculation formula is specifically: In the formula, is the impact indicator between the factor to be verified and the user's performance score, is the total number of standardized data, is the execution degree of the factor to be verified in the jth standardized data, is the performance score change rate of the user in the jth standardized data.
[0039] In this scheme, by calculating the linear correlation between the execution degree of each factor and the change in the user's performance score as the influencing indicator, the influencing factors that have a great impact on the change in the user's performance score can be quickly and accurately identified, making it easier to formulate corresponding training plans based on these influencing factors.
[0040] like Figure 5 As shown, the initial training data is analyzed in combination with the user's historical training data to obtain the user's performance score change rate when the current factor to be verified is executed at different degrees, which specifically includes as standardized data: Obtain all other training factors executed in the historical training scenarios corresponding to the initial training data of the factor to be verified at each execution level, and record them as comparative training factors; Selecting a number of comparative historical training scenarios that have and only have comparative training factors from the user's historical training data, and determining performance scores of the users for the comparative historical training scenarios; The performance scores of several users in the comparison historical training scenarios are averaged to obtain comparison standard data; Based on the initial training data and the comparison standard data, the user's performance score change rate is calculated according to the standardized calculation formula; The standardized calculation formula is as follows:
[0041] In the formula, The rate of change of the user's performance score, is the initial training data, For comparison with standard data.
[0042] It is understandable that in real environments, there are usually multiple influencing factors occurring simultaneously during training. Therefore, when analyzing a single influencing factor, it is necessary to eliminate the influence of other factors. Therefore, in this scheme, after a single influencing factor is added, the user's performance level is correlated with the performance level decline rate before the addition. The greater the linear correlation between the performance level decline rate and the execution degree of the factor, the more the factor will affect the change in the user's performance level.
[0043] like Figure 6 As shown, the step of determining a priority elimination factor from all influencing factors of users participating in the training specifically includes: Determine the maximum influence degree value of all influencing factors of users participating in the training under the actual state; Based on the impact index of the influencing factor on the user's performance score and the maximum impact value of the influencing factor in its actual state, the benefit index of eliminating the influencing factor is calculated through the benefit index formula; Filter out the influencing factors corresponding to the maximum value of the profit index as the priority factors to be eliminated; The profit indicator formula is specifically:
[0044] In the formula, To eliminate the profit index of the kth influencing factor, is the emphasis weight of the lth user participating in the training, is the impact index of the kth influencing factor on the performance score of the lth user participating in the training, is the maximum influence value of the kth influencing factor in the actual state.
[0045] In e-sports clubs, different players usually have different training priorities. Therefore, this solution combines the user's training priority and the user's sensitivity to changes in the performance of various influencing factors to conduct a comprehensive analysis of the training priority for overcoming each influencing factor, thereby effectively ensuring that the formulated e-sports virtual training strategy can achieve maximum training benefits.
[0046] The advantages of the present invention are: through targeted analysis of users, personalized collaborative training plans can be formulated for multiple training users, greatly improving the training level received by users in the e-sports virtual training field.
[0047] The present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention. In order to make the public have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, but those skilled in the art can fully understand the present invention without the description of these details. In addition, in order to avoid unnecessary confusion about the essence of the present invention, well-known methods, processes, procedures, components and circuits are not described in detail.
[0048] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.
Claims
1. A multi-user collaborative training system in an e-sports virtual training field, characterized in that: include: A data storage module, the data storage module is used to store a training database, the training database stores historical training data of each user; A personalized analysis module, the personalized analysis module is electrically connected to the data storage module, and the personalized analysis module is used to determine the influencing factors of each user based on the historical training data of each user; A multi-user collaborative analysis module, the multi-user collaborative analysis module is electrically connected to the personalized analysis module, and the multi-user collaborative analysis module has a collaborative fitting algorithm based on the influencing factors of at least one user participating in the training to determine the optimal e-sports virtual training strategy; A training execution module, wherein the training execution module is electrically connected to the multi-user collaborative analysis module and the data storage module, and the training execution module is used to generate an e-sports virtual training scene based on the optimal e-sports virtual training strategy, and obtain the user's training data and store it in the data storage module.
2. The multi-user collaborative training system in the e-sports virtual training field according to claim 1, characterized in that: The personalized analysis module specifically includes: A data extraction unit, the data extraction unit is used to extract the user's historical training data from the training database; A singularity analysis unit, the singularity analysis unit is used to identify the unique data in the user's historical training data; A correlation analysis unit is used to determine the influencing factors of each user based on the specific data in the historical training data of the user.
3. The multi-user collaborative training system in the e-sports virtual training field according to claim 2, characterized in that: The specific data in the historical training data for identifying the user specifically includes: Get the user's performance score in each historical training data; Based on the Grubbs criterion, peak screening conditions were established; Based on the peak screening condition, the abnormal data points in the performance scores of several historical training data that exceed the normal fluctuation are screened out as the specific data in the user's historical training data; Wherein, the expression of the peak screening condition is: In the formula, Score the performance in the i-th historical training data, is the average of the performance scores in all historical training data, is the standard deviation of the performance scores across all historical training data, is the Grubbs critical value. If the expression of the peak screening condition is satisfied, then is an outlier; The Grubbs critical value is obtained as follows: Setting a detection level value based on the abnormal sensitivity of the performance scores in the historical training data; Determine the total number of all historical training data; Based on the total number of historical training data and the detection level value, the corresponding value is retrieved from the Grubbs table as the Grubbs critical value.
4. The multi-user collaborative training system in the e-sports virtual training field according to claim 3, characterized in that: The determining of the influencing factors of each user based on the specific data in the historical training data of the user specifically includes: Filter out the training strategy of historical training scenarios corresponding to the specific data in the user's historical training data, based on the sample training strategy; Split at least one training factor from the sample training strategy as a factor to be verified; Conduct a comprehensive analysis of each factor to be verified to determine the impact of each factor on the user's performance score; Determine whether the influence index of the factor to be verified on the user's performance score is greater than a preset value. If so, record the factor to be verified as an influencing factor. If not, do not respond.
5. The multi-user collaborative training system in the e-sports virtual training field according to claim 4, characterized in that: The comprehensive analysis of each factor to be verified to determine the impact of each factor to be verified on the user's performance score specifically includes: From the historical training data of the user, select the performance scores of several users when they execute the current factor to be verified to different degrees, and record them as the initial training data; Analyze the initial training data in combination with the user's historical training data to obtain the user's performance score change rate when executing the current factor to be verified to different degrees as standardized data; Based on the standardized data, the impact index between the factor to be verified and the user's performance score is calculated through the correlation calculation formula; The correlation calculation formula is specifically: In the formula, is the impact indicator between the factor to be verified and the user's performance score, is the total number of standardized data, is the execution degree of the factor to be verified in the jth standardized data, is the performance score change rate of the user in the jth standardized data.
6. The multi-user collaborative training system in the e-sports virtual training field according to claim 5, characterized in that: The initial training data is analyzed in combination with the historical training data of the user to obtain the user's performance score change rate when the current factor to be verified is executed at different degrees, which specifically includes as standardized data: Obtain all other training factors executed in the historical training scenarios corresponding to the initial training data of the factor to be verified at each execution level, and record them as comparative training factors; Selecting a number of comparative historical training scenarios that have and only have comparative training factors from the user's historical training data, and determining performance scores of the users for the comparative historical training scenarios; The performance scores of several users in the comparison historical training scenarios are averaged to obtain comparison standard data; Based on the initial training data and the comparison standard data, the user's performance score change rate is calculated according to the standardized calculation formula; The standardized calculation formula is as follows: In the formula, The rate of change of the user's performance score, is the initial training data, For comparison with standard data.
7. The multi-user collaborative training system in the e-sports virtual training field according to claim 6, characterized in that: The multi-user collaborative analysis module includes: A user focus analysis unit, the user focus analysis unit is used to determine the focus weights of users participating in the training; A benchmark training strategy unit, the benchmark training strategy unit is used to determine a benchmark training strategy for e-sports virtual training, the benchmark training strategy is determined by e-sports events and e-sports opponent data; A factor focus analysis unit, the factor focus analysis unit is used to determine a priority elimination factor from all influencing factors of users participating in the training, and formulate a targeted training strategy based on the priority elimination factor; A strategy determination unit, wherein the strategy determination unit is used to merge the baseline training strategy and the targeted training strategy to obtain the optimal e-sports virtual training strategy.
8. The multi-user collaborative training system in the e-sports virtual training field according to claim 7, characterized in that: Determining a priority elimination factor from all influencing factors of users participating in the training specifically includes: Determine the maximum influence degree value of all influencing factors of users participating in the training under the actual state; Based on the impact index of the influencing factor on the user's performance score and the maximum impact value of the influencing factor in its actual state, the benefit index of eliminating the influencing factor is calculated through the benefit index formula; Filter out the influencing factors corresponding to the maximum value of the profit index as the priority factors to be eliminated; The profit indicator formula is specifically: In the formula, To eliminate the profit index of the kth influencing factor, is the emphasis weight of the lth user participating in the training, is the impact index of the kth influencing factor on the performance score of the lth user participating in the training, is the maximum influence value of the kth influencing factor in the actual state.
9. The multi-user collaborative training system in the e-sports virtual training field according to claim 8, characterized in that: The training execution module specifically includes: A training monitoring unit, the training monitoring unit is used to monitor the performance score of each user participating in the training under the optimal e-sports virtual training strategy; A training result feedback unit is used to determine whether the performance scores of all users participating in the training have reached their performance score average. If so, a training completion signal is output to the multi-user collaborative analysis module, and the multi-user collaborative analysis module regenerates the optimal e-sports virtual training strategy. If not, training continues based on the current optimal e-sports virtual training strategy.