Virtual game interaction data analysis and management system

Abstract

The invention relates to the field of game resource management, in particular to a virtual game interaction data analysis and management system which comprises the following steps: acquiring basic information such as game universal resource scenes and user interaction data; classifying and dividing the interaction data according to the universal resource scene, and calculating an operation coefficient of a user in each scene through multi-dimensional analysis; identifying trigger nodes in the scene, splitting to form a local link scene, collecting basic trigger information and performing quantitative analysis to obtain a scene trigger coefficient and a scene change curve; finally, the real-time operation coefficient of the user is accurately matched with the scene change curve, an adaptive final data scene is determined, and real-time intelligent resource loading is achieved based on the scene position. User capability description and scene characteristic quantification are more accurate through multi-dimensional data collection, matching logic breaks through static adaptation limitation, and real-time intelligent resource loading is achieved. Meanwhile, redundant occupation is reduced through dynamic resource loading, and the equipment operation efficiency and the game fluency are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of game resource management, and particularly relates to a virtual game interactive data analysis management system. BACKGROUND

[0002] With the rapid development of the virtual game industry, the complexity of game scenes, the interaction dimension and the diversity of user groups continue to improve, and users' requirements for game personalized experience, smooth operation and resource utilization efficiency are increasingly stringent.

[0003] The prior art CN117138329A discloses a virtual game design system based on distribution, which comprises a data processing module for obtaining real information of a user based on a tracking device, processing the real information based on a cloud platform server to obtain virtual information; a resource management module for distributing tasks and memory resources for device nodes based on distributed computing; and a network communication module for transmitting real information and virtual information.

[0004] However, the current scene resource loading in virtual games mostly adopts static preloading or distance trigger loading mode, without considering the adaptation relationship between real-time operation ability of users and scene characteristics: on the one hand, the static loading mode will pre-load a large amount of scene resources that users do not need to touch at present, causing waste of device computing power and high memory occupation; on the other hand, simple distance trigger loading may cause high-difficulty scene resource loading to be not timely, causing lag, or resource loading redundancy after a low-capability user mistakenly touches a high-difficulty scene, seriously affecting game running performance and user experience. SUMMARY

[0005] The purpose of the present application is to solve the problems in the background art and provide a virtual game interactive data analysis management system.

[0006] In order to achieve the above-mentioned purpose, the present application adopts the following technical scheme: A virtual game interactive data analysis management system, comprising: a game information collection module for collecting basic information of a virtual game, the basic information including general resource scenes; a user data collection module for collecting interactive data of a target user, the interactive data including operation frequency, pass time, operation hit rate and key decision time consumption; an operation analysis module for obtaining the interactive data of the target user, setting different scene blocks according to different general resource scenes, dividing the interactive data according to the scene blocks, determining a block data set, and analyzing the data in each block data set to determine an operation coefficient of the target user in each scene block; The scene analysis module is configured to acquire scene blocks of the virtual game, identify trigger nodes of each scene block, set local link scenes based on the trigger nodes, collect basic trigger information of the local link scenes, perform data analysis on the basic trigger information, and determine a scene trigger coefficient and a scene change curve of each local link scene. The resource matching module is configured to comprehensively match the current operation coefficient of the target user and the scene change curve of each scene block of the virtual game, and determine a final data scene at present. The data execution module is configured to perform real-time resource loading on the virtual game according to a scene position where the final data scene is located.

[0007] As a further scheme of the present application, the interaction data specifically includes an operation frequency, a pass time, an operation hit rate, and a key decision time consumption, the operation frequency refers to a motion frequency of continuous operation motions of the target user in the virtual game, the operation hit rate refers to a result of an operation instruction interacting with a target in the game, and the key decision time consumption refers to a decision time consumption of a scene key node.

[0008] As a further scheme of the present application, the operation coefficient of the target user in each scene block is determined by the following method: Basic information of the virtual game is acquired, and a general resource scene in the basic information is extracted, the general resource scene is divided according to different scene modules to obtain different scene blocks, then interaction data of the target user is acquired, and the interaction data is divided according to the scene blocks, and the interaction data in one scene block is integrated to obtain a block data set; The block data set is sequentially marked as a target set, subsets in the target set are identified, and representative data of single-item classes in the subsets are calculated, and the representative data are used to determine abnormal data and normal data in the single-item classes; Based on the representative data of the single-item classes, the representative data in the same subset are integrated into the same array, and the number of abnormal data in the array is identified to determine a normal array; All the normal arrays are acquired, the representative data of the same single-item classes in the normal arrays are subjected to mean value processing to obtain single-item characteristic values, and the different single-item classes are comprehensively processed to determine the operation coefficient of the target user in the scene block.

[0009] As a further scheme of the present application, the abnormal data and the normal data are determined by the following method: The block data set is sequentially marked as a target set, data in the target set is divided into several subsets according to a single interaction mode of the target user, specifically, the target user starts from logging in the virtual game to the end of logging out the virtual game as one single interaction mode; arbitrarily selecting a subset Z1, extracting single-item number classes in the subset Z1, and identifying data quantity of the single-item number classes, if the data quantity of the single-item number classes is 1, then marking the single-item number classes as representative data of the data type in the corresponding subset Z1, otherwise, if the data quantity of the single-item number classes is greater than 1, then obtaining all the single-item number classes, and performing mean value processing on the single-item number classes, and marking the mean value processing result as representative data of the single-item number classes in the subset Z1; obtaining representative data of single-item number classes in all subsets, then arbitrarily selecting a single-item number class a1, obtaining representative data of the single-item number class a1 in all subsets, and marking as Di, wherein i represents different subsets; using a normal distribution algorithm to respectively calculate characteristic mean value and standard deviation of the single-item number class a1, setting a normal interval comparing the representative data Di with the normal interval, if Di , then marking the corresponding representative data Di as normal data, otherwise, if Di , then marking the corresponding representative data Di as abnormal data, and k1 is set as 2.

[0010] As a further scheme of the present application, the method for determining single-item characteristic values comprises: based on the representative data of the single-item number classes, integrating the representative data in the same subset into the same array, that is, one subset corresponds to one array; obtaining all arrays in the target set, identifying abnormal data in each array, and simultaneously counting the number of abnormal data in a single array, if the number of abnormal data is greater than or equal to an abnormal threshold X1, then marking the array as an abnormal array, otherwise, if the number of abnormal data is less than the abnormal threshold X1, then marking the array as a normal array; obtaining all normal arrays, performing mean value processing on the representative data of the same single-item number classes in all normal arrays, and marking the obtained mean value processing result as a single-item characteristic value TCj, j represents different single-item number classes, and j simultaneously identifying a scene block of the target set, obtaining standard operation data TBj of the scene block, then using a calculation formula obtaining an operation coefficient CXj of the target user in the scene block, wherein, represents a weight coefficient of different single-item number classes j.

[0011] As a further scheme of the present application, the method for determining a scene triggering coefficient and a scene change curve comprises: Mark each scene block as a target scene in sequence, acquire a scene change sequence of the target scene, and identify trigger nodes in the scene change sequence, wherein the scene change sequence refers to a set of state changes of the target scene triggered by an interactive operation in a game running process and arranged in time sequence, and the trigger node refers to a set of state changes of the target scene triggered by the interactive operation in the game running process and arranged in time sequence; The obtained local link scene is arranged in time sequence again, and then the first local link scene is selected, and basic trigger information of the first local link scene is collected, wherein the basic trigger information includes operation step number Z1, input device dimension Z2, and skill combination number Z3; Acquire upper limit trigger information of the virtual game, wherein the upper limit trigger information refers to a maximum value of data in the virtual game, and a calculation formula is used Obtain a scene trigger coefficient CF1 of the first local link scene, wherein ZB1 represents upper limit trigger information of the operation step number, ZB2 represents upper limit trigger information of the input device dimension, ZB3 represents an upper limit trigger value of the skill combination number, and , and are proportional coefficients, respectively, ; According to the above method, the scene trigger coefficients CFn of each local link scene are calculated in sequence, wherein n represents different local link scenes. Set time as the horizontal coordinate and set the scene trigger coefficient as the vertical coordinate, and establish a two-dimensional coordinate system, and mark the scene trigger coefficient of each local link scene in the two-dimensional coordinate system, and after the marking is completed, fit all the points into a curve to obtain a scene change curve.

[0012] As a further scheme of the application, the basic trigger information refers to a minimum limit condition required for entering a next local link scene from a current local link scene, the operation step number refers to a minimum step number required from entering a current local link environment to completing interaction, and the input device dimension refers to a number of input channels participating in the interaction.

[0013] As a further scheme of the application, the determination method of the final data scene includes: Identify a scene block where a target user is currently located, and acquire a scene change curve of the current scene block, and identify a maximum value CFmax of the scene trigger coefficient in the scene change curve. Meanwhile, a scene operation matching table is set, a current operation coefficient of the target user is identified in the operation matching table, a corresponding scene trigger coefficient is identified, the scene trigger coefficient is marked as a target limit coefficient CFb, then the target limit coefficient is compared with a maximum value CFmax of the scene trigger coefficient, if CFb<CFmax, a cut-off signal is generated, otherwise, if CFb≥CFmax, a full resource signal is generated; The target limit coefficient corresponds to a local link scene, when the cut-off signal is detected, the local link scene is taken as the final data scene, when the full resource signal is detected, a scene change sequence of a corresponding scene block is acquired, and a last local link scene in the scene change sequence is marked as the final data scene.

[0014] Compared with the prior art, the present application has the following advantages: The present application collects basic information such as general resource scenes of games, and core interactive data such as user operation frequency, time for passing through, operation hit rate, and time consumption for key decisions, classifies and divides the interactive data according to the general resource scenes, calculates operation coefficients of the user in each scene through multi-dimensional analysis, identifies trigger nodes in the scene, splits to form local link scenes, collects basic trigger information and performs quantitative analysis to obtain scene trigger coefficients and scene change curves, and finally accurately matches real-time operation coefficients of the user with the scene change curves to determine the final data scene that is adapted, and realizes real-time intelligent loading of resources based on the scene position. BRIEF DESCRIPTION OF DRAWINGS

[0015] Figure 1 The figure is a schematic diagram of the system structure of the present application. DETAILED DESCRIPTION

[0016] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application.

[0017] Referring to Figure 1 A virtual game interactive data analysis management system includes a game information collection module, a user data collection module, an operation analysis module, a scene analysis module, a resource matching module, and a data execution module. The game information collection module is configured to collect the basic information of the virtual game, and then establish a one-way communication connection between the game information collection module and the operation analysis module and the scene analysis module, and transmit the basic information to the operation analysis module and the scene analysis module, respectively. Further, the basic information includes operating parameters, basic UI components, and general resource scenes. The user data collection module is configured to collect the interaction data of the target user, wherein the target user refers to a user player who interacts in the virtual game, and the interaction data refers to the data generated by the target user when operating in the virtual game. The interaction data specifically includes operation frequency, time to complete, operation hit rate, and key decision time consumption. The operation frequency refers to the action frequency of the continuous operation actions of the target user in the virtual game. The operation hit rate refers to the result of the operation instruction interacting with the target in the game, such as hitting, missing, or invalid operation. The key decision time consumption refers to the decision time consumption of the scene key node. Then, the user data collection module and the operation analysis module establish a one-way communication connection, and transmit the interaction data of the target user to the operation analysis module. The operation analysis module is configured to obtain the interaction data of the target user and analyze the interaction data of the target user to determine the operation coefficient of the target user. The specific determination method of the operation coefficient includes: S1: Obtain the basic information of the virtual game, and extract the general resource scene in the basic information. Divide the general resource scene into different scene modules to obtain different scene blocks. Then, obtain the interaction data of the target user, and divide the interaction data according to the scene blocks. At the same time, integrate the interaction data in one scene block to obtain a block data set. Further, one scene block corresponds to one block data set. S2: Arbitrarily select one block data set, and take this block data set as an example. Label this block data set as a target set. Then, divide the data in the target set into several subsets according to the single interaction mode of the target user. Specifically, the single interaction mode of the target user starts from logging into the virtual game and ends when the target user exits the virtual game. Further, each subset contains all the interaction data in the single interaction mode. Arbitrarily select one subset Z1, and take this subset Z1 as an example. Extract the single item class in this subset Z1, and identify the data quantity of the single item class. If the data quantity of the single item class is 1, then label this single item class as the representative data of this data type in the corresponding subset Z1. Otherwise, if the data quantity of the single item class is greater than 1, then obtain all the single item classes, and perform mean value processing on the single item classes. Label the mean value processing result as the representative data of the single item class in the subset Z1. It needs to be further explained that the single number class refers to a type of data, for example, in the subset Z1, the key decision time consumption is taken as a single number class, and the customs clearance time is taken as another single number class, and in a single number class, the data collection result of this type of data may be 0 due to the operation of the target user and other factors, and at this time, 0 is taken as the representative data of this single number in the subset Z1; S3: Obtain the representative data of the single number class in all subsets, and then randomly select a single number class a1, obtain the representative data of the single number class a1 in all subsets, and mark it as Di, wherein i represents different subsets; The characteristic mean value of the single number class a1 is calculated by using the normal distribution algorithm And the standard deviation ; Set the normal interval Compare the representative data Di with the normal interval, if Di∈ , the corresponding representative data Di is marked as normal data, otherwise, if Di , the corresponding representative data Di is marked as abnormal data, wherein in the embodiment, k1 is set to 2; Further, the normal distribution algorithm belongs to the prior art, and the specific calculation process will not be described here; The remaining single number classes are processed according to the above method to determine the normal data and abnormal data in each single number class; S4: Based on the representative data of the single number class, the representative data in the same subset is integrated into the same array, that is, one subset corresponds to one array, and in the array, each data corresponds to a single number class; It needs to be further explained that the difference between the array and the subset is that in the subset, according to the different operation time sequences of the target user, multiple data may be generated in a single number class, but in the array, there is only one representative data corresponding to a single number class; Then obtain all the arrays in the target set, identify the abnormal data in each array, and simultaneously count the number of abnormal data in a single array, if the number of abnormal data is greater than or equal to the abnormal threshold X1, the array is marked as an abnormal array, otherwise, if the number of abnormal data is less than the abnormal threshold X1, the array is marked as a normal array, wherein the specific value of the abnormal threshold X1 is set by the person skilled in the art according to the big data experience; S5: Obtain all the normal arrays, perform mean processing on the representative data of the same single number class in all normal arrays, and mark the obtained mean processing result as a single feature value TCj, j represents different single number classes, and j∈[1, J], indicating that there are J single number classes; Meanwhile, the scene block of the target set is identified, the standard operation data TBj of the scene block is obtained, and then the calculation formula is used to obtain the operation coefficient CXj of the target user in the scene block, wherein, represents the weight coefficient of different single number class j, and the weight coefficient of single number class j is determined by the operation center requirement of the corresponding scene block; Further, the standard operation data is set by the person skilled in the art according to the operation technical requirement of the scene block; Further, the standard operation data is set by the person skilled in the art according to the operation technical requirement of the scene block; The block data set is sequentially marked as a target set and processed according to the above method to determine the operation coefficient of each scene block; Then, a one-way communication connection is established between the operation analysis module and the resource matching module, and the operation coefficient of the target user in each scene block is transmitted to the resource matching module; The scene analysis module is used to obtain the game scene of the virtual game, and set a scene change curve for the game scene. The specific determination method of the scene change curve includes: SS1: arbitrarily select a scene block and mark it as a target scene, obtain the scene change sequence of the target scene, and identify the trigger nodes in the scene change sequence, and mark the scenes between adjacent trigger nodes as local link scenes, wherein the scene change sequence refers to a set of state changes of the target scene triggered by interactive operations in the game running process, and the trigger nodes refer to a set of state changes of the target scene triggered by interactive operations in the game running process in time sequence; SS2: The obtained local link scenes are arranged in time sequence again, and then the first local link scene is selected, and the basic trigger information of the first local link scene is collected, including the number of operation steps Z1, the input device dimension Z2 and the number of skill combinations Z3; Further, the basic trigger information refers to the minimum limit condition required to enter the next local link scene under the current local link scene, the number of operation steps refers to the minimum number of steps required from entering the current local link environment to completing the interaction, the input device dimension refers to the number of input channels participating in the interaction, for example, when only the mouse is clicked, the input device dimension is 1, when the handle rocker is moved and the trigger key is confirmed, the input device dimension is 2, and the number of skill combinations refers to the number of skills required for interaction, for example, when the number of skill combinations is 1, only one basic skill is required, when the number of skill combinations is 2, two skill combinations are required to form an advanced skill, and so on; obtaining an upper limit trigger information of the virtual game, wherein the upper limit trigger information refers to a maximum value of data in the virtual game, for example, the dimension of the input device, if the handle rocker movement + trigger key is confirmed as the highest dimension, the upper limit value of the input device dimension is 2, and further, the specific value of the upper limit trigger information is determined by the person skilled in the art according to the game parameters of the corresponding virtual game; SS3: using the calculation formula obtaining a scene trigger coefficient CF1 of the first local link scene, wherein ZB1 represents the upper limit trigger information of the operation step number, ZB2 represents the upper limit trigger information of the input device dimension, ZB3 represents the upper limit trigger value of the skill combination number, and 、 and are proportional coefficients, , 、 and The specific values are obtained by the person skilled in the art according to big data operation; SS4: according to the above method, the scene trigger coefficient CFn of each local link scene is calculated in turn, wherein n represents different local link scenes; setting time as the horizontal coordinate and setting the scene trigger coefficient as the vertical coordinate, and establishing a two-dimensional coordinate system, and marking the scene trigger coefficient of each local link scene in the two-dimensional coordinate system, when the point marking is completed, fitting all points into a curve to obtain a scene change curve; After that, a one-way communication connection is established between the scene analysis module and the resource matching module, and the scene change curve is transmitted to the resource matching module; The resource matching module is used to obtain the current operation coefficient of the target user and the scene change curve of each scene block of the virtual game, and to comprehensively analyze and determine the final data scene, and further, the determination method of the final data scene includes: identifying the scene block where the target user is currently located, and obtaining the scene change curve of the current scene block, and identifying the maximum value CFmax of the scene trigger coefficient in the scene change curve; At the same time, a scene operation matching comparison table is set, the operation coefficient of the target user is identified in the operation matching comparison table, the corresponding scene trigger coefficient is marked as the target limit coefficient CFb, and then the target limit coefficient is compared with the maximum value CFmax of the scene trigger coefficient, if CFb < CFmax, a truncated signal is generated, otherwise, if CFb ≥ CFmax, a full resource signal is generated; It needs to be further explained that the scene operation matching table is a mapping relationship established according to the operation coefficient of the user and the scene trigger coefficient, and the principle is "ability-difficulty equivalence", so as to ensure that the user faces moderate challenges, and the specific scene operation matching table is set by the person skilled in the art according to the big data operation. The local link scene corresponding to the target limit coefficient is identified, when the truncation signal is detected, the local link scene is taken as the final data scene, when the full resource signal is detected, the scene change sequence of the corresponding scene block is obtained, and the last local link scene in the scene change sequence is marked as the final data scene; Then the one-way communication connection is carried out between the resource matching module and the data execution module, and the final data scene is transmitted to the data execution module; The data execution module carries out real-time resource loading for the virtual game according to the scene position where the final data scene is located.

[0018] The above is only the preferred specific embodiment of the present application, but the protection scope of the present application is not limited to this, any person skilled in the art can make equivalent replacement or change according to the technical scheme and the inventive concept of the present application within the technical range disclosed by the present application, which should be covered in the protection scope of the present application.

Claims

1. A virtual game interaction data analysis management system, characterized by, The application relates to a virtual game resource matching method and device. The game information collection module is used for collecting basic information of a virtual game, and the basic information comprises a general resource scene; The user data collection module is used for collecting interactive data of a target user, and the interactive data comprises operation frequency, time for passing through a game, operation hit rate and key decision time consumption; The operation analysis module is used for obtaining the interactive data of the target user, setting different scene blocks according to different general resource scenes, dividing the interactive data according to the scene blocks, determining block data sets, analyzing the data in each block data set and determining operation coefficients of the target user in each scene block; The scene analysis module is used for obtaining scene blocks of the virtual game, identifying trigger nodes of each scene block, setting local link scenes based on the trigger nodes, collecting basic trigger information of the local link scenes, analyzing the basic trigger information, determining scene trigger coefficients and scene change curves of each local link scene; The resource matching module is used for comprehensively matching the operation coefficients of the target user and the scene change curves of each scene block of the virtual game, and determining a final data scene. The data execution module performs real-time resource loading on the virtual game according to a scene position of the final data scene.

2. The virtual game interaction data analysis management system of claim 1, wherein, The interactive data specifically comprises operation frequency, time for passing through a game, operation hit rate and key decision time consumption, the operation frequency refers to the action frequency of continuous operation actions of the target user in the virtual game, the operation hit rate refers to the result of operation instructions and target interaction in the game, and the key decision time consumption refers to decision time consumption of a scene key node.

3. The virtual game interaction data analysis management system of claim 1, wherein, The operation coefficients of the target user in each scene block are determined by the following method: Basic information of the virtual game is obtained, general resource scenes in the basic information are extracted, the general resource scenes are divided according to different scene blocks, different scene blocks are obtained, interactive data of the target user is obtained, the interactive data is divided according to the scene blocks, interactive data in a scene block is integrated, and block data sets are obtained; The block data sets are sequentially marked as target sets, subsets in the target sets are identified, representative data of single item classes in the subsets are calculated, abnormal data and normal data in the single item classes are determined based on the representative data; Based on the representative data of the single item classes, the representative data in the same subset are integrated into the same array, the number of abnormal data in the array is identified, and normal arrays are determined; All the normal arrays are obtained, the representative data of the same single item classes in the normal arrays are processed by mean value, single characteristic values are obtained, different single item classes are comprehensively processed, and the operation coefficients of the target user in the scene block are determined.

4. The virtual game interaction data analysis management system of claim 3, wherein, The abnormal data and the normal data are determined by the following method: The block data sets are sequentially marked as target sets, data in the target sets is divided into several subsets according to a single interactive mode of the target user, specifically, the target user starts from logging in the virtual game to exiting the virtual game and ends as a single interactive mode; Arbitrarily select a subset Z1, extract the single-item number class in the subset Z1, and identify the data quantity of the single-item number class. If the data quantity of the single-item number class is 1, the single-item number class is marked as the representative data of the data type in the corresponding subset Z1. Otherwise, if the data quantity of the single-item number class is greater than 1, all single-item number classes are obtained, the single-item number classes are processed by mean value, and the mean value processing result is marked as the representative data of the single-item number class in the subset Z1; Obtain the representative data of the single-item number class in all subsets, then arbitrarily select a single-item number class a1, obtain the representative data of the single-item number class a1 in all subsets, and mark it as Di, where i represents different subsets; Using normal distribution algorithm, the characteristic mean value of the single item class a1 is calculated and the standard deviation ; Setting normal interval The representative data Di is compared with the normal interval, if Di∈ The corresponding representative data Di is marked as normal data, otherwise, if Di The corresponding representative data Di is marked as abnormal data, and k1 is set as 2.

5. The virtual game interaction data analysis management system of claim 3, wherein, The single-item characteristic value determination method comprises: Based on the representative data of the single-item number class, the representative data in the same subset is integrated into the same array, that is, one subset corresponds to one array; Obtain all arrays in the target set, identify the abnormal data in each array, and simultaneously count the number of abnormal data in a single array. If the number of abnormal data is greater than or equal to the abnormal threshold X1, the array is marked as an abnormal array. Otherwise, if the number of abnormal data is less than the abnormal threshold X1, the array is marked as a normal array; Obtain all normal arrays, process the representative data of the same single-item number class in all normal arrays by mean value, and mark the obtained mean value processing result as a single-item characteristic value TCj, where j represents different single-item number classes, and j [1, J] represents that there are J single-item number classes; At the same time, the scene block of the target set is identified, standard operation data TBj of the scene block is obtained, and then the calculation formula is used to obtain the operation coefficient CXj of the target user in the scene block, wherein, represents the weight coefficient of different single item number class j.

6. The virtual game interaction data analysis management system of claim 1, wherein, The scene trigger coefficient and scene change curve determination method comprises: Each scene block is marked as a target scene in turn, the scene change sequence of the target scene is obtained, and the trigger nodes in the scene change sequence are identified. The scenes in adjacent trigger nodes are marked as local link scenes. The scene change sequence refers to a state change set of the target scene triggered by an interactive operation in the game running process and arranged in time sequence. The trigger node refers to a state change set of the target scene triggered by an interactive operation in the game running process and arranged in time sequence. The obtained local link scenes are arranged in time sequence again, then the first local link scene is selected, and the basic trigger information of the first local link scene is collected. The basic trigger information includes the number of operation steps Z1, the input device dimension Z2, and the number of skill combinations Z3. obtaining upper limit trigger information of the virtual game, wherein the upper limit trigger information refers to a maximum value of data in the virtual game, and a calculation formula is used obtaining a scene trigger coefficient CF1 of a first local link scene, wherein ZB1 represents upper limit trigger information of the number of operation steps, ZB2 represents upper limit trigger information of the dimension of an input device, ZB3 represents an upper limit trigger value of the number of skill combinations, and , and are proportional coefficients, respectively, ; According to the above method, the scene trigger coefficient CFn of each local link scene is calculated in turn, where n represents different local link scenes. The time is set as the horizontal coordinate, the scene trigger coefficient is set as the vertical coordinate, a two-dimensional coordinate system is established, and the scene trigger coefficient of each local link scene is marked in the two-dimensional coordinate system. After the point marking is completed, all points are fitted into a curve to obtain the scene change curve.

7. The virtual game interaction data analysis management system of claim 6, wherein, The basic trigger information refers to the minimum limit condition required to enter the next local link scene under the current local link scene. The number of operation steps refers to the minimum number of steps required to complete the interaction from entering the current local link environment. The input device dimension refers to the number of input channels participating in the interaction.

8. The virtual game interaction data analysis management system of claim 1, wherein, The final data scene determination method comprises: Identify the scene block where the target user is currently located, and obtain the scene change curve of the current scene block, identify the maximum value CFmax of the scene trigger coefficient in the scene change curve; At the same time, set up a scene operation matching comparison table, identify the corresponding scene trigger coefficient of the target user's current operation coefficient in the operation matching comparison table, and mark this scene trigger coefficient as the target limit coefficient CFb, then compare the target limit coefficient with the maximum value CFmax of the scene trigger coefficient, if CFb<CFmax, generate a truncated signal, otherwise, if CFb≥CFmax, generate a full resource signal; Identify the local link scene corresponding to the target limit coefficient, when the truncated signal is detected, the local link scene is the final data scene, when the full resource signal is detected, obtain the scene change sequence of the corresponding scene block, and mark the last local link scene in the scene change sequence as the final data scene.

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