Game resource position determination method and system based on multi-role historical data

Abstract

The invention discloses a game resource position determination method and system based on multi-role historical data. The method comprises the following steps: acquiring historical playing data of a plurality of game players in a target game area; determining predicted player distribution parameters of the target game area according to the historical playing data and a prediction algorithm; determining a plurality of candidate resource position distributions according to the region parameters of the target game region; and determining the optimal resource distribution of the target game area according to the predicted player distribution parameter and the plurality of candidate resource position distributions. Therefore, reasonable configuration and delivery accuracy of the game resources can be realized, the game experience of game players is enhanced, and meanwhile, the balance and competitiveness of the game area are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a method and system for determining the location of game resources based on multi-character historical data. Background Art

[0002] In the development and operation of multiplayer games, the placement and strategy of game resources are extremely important. Existing game systems usually adopt a fixed distribution or random distribution method for resource placement. Although it can ensure a certain degree of fairness, it fails to fully consider the actual activity patterns of players, resulting in a low match between resource distribution and player needs, which in turn affects the game experience. Although there are some improvement schemes that attempt to optimize resource distribution based on the static characteristics of the game map, due to the failure to dynamically adapt to changes in player behavior, resources may be concentrated in low-activity areas or fail to be reasonably distributed in high-demand areas. In addition, the existing technology lacks accurate prediction of player behavior and cannot optimize resource placement based on player activity patterns, resulting in low resource utilization, making it difficult for some players to obtain key resources, affecting the competitiveness and balance of the game. It can be seen that the existing technology has defects that need to be solved urgently. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a method and system for determining the location of game resources based on multi-character historical data, which can achieve reasonable allocation and delivery accuracy of game resources, enhance the gaming experience of game players, and improve the balance and competitiveness of the game area.

[0004] In order to solve the above technical problems, the first aspect of the present invention discloses a method for determining the location of game resources based on multi-character historical data, the method comprising: Obtain historical game data of multiple game players in the target game area; Determine predicted player distribution parameters of the target game area based on the historical game data and a prediction algorithm; Determining the location distribution of multiple candidate resources according to the area parameters of the target game area; The optimal resource distribution of the target game area is determined based on the predicted player distribution parameters and the multiple candidate resource location distributions.

[0005] As an optional embodiment, in the first aspect of the present invention, the historical play data includes at least one of the game player's game operations, game routes, game positions, game triggered plots, game execution tasks and game character information in a historical time period.

[0006] As an optional implementation, in the first aspect of the present invention, determining the predicted player distribution parameters of the target game area based on the historical play data and a prediction algorithm includes: The historical play data is grouped according to the acquired historical time periods to obtain play data sets corresponding to multiple different historical time periods; Input each of the game data sets into the player distribution recognition algorithm model to obtain the player distribution parameters corresponding to each of the game data sets; the player distribution recognition algorithm model is trained by a training data set including a plurality of training game data and corresponding player distribution parameter annotations; the player distribution parameters include group position, group number, moving direction, moving speed, frequent activity area and internal player density of at least one player group; The time-varying patterns of the player distribution parameters corresponding to all the game data sets are analyzed to obtain the predicted player distribution parameters of the target game area in the future time period.

[0007] As an optional implementation, in the first aspect of the present invention, the analysis of the time-varying patterns of the player distribution parameters corresponding to all the game data sets to obtain the predicted player distribution parameters of the target game area in the future time period includes: According to the corresponding historical time period from early to late, the player distribution parameters of the player groups in all the player distribution parameters are sorted to obtain a player distribution parameter sequence; The player distribution parameter sequence is input into the trained LSTM neural network to obtain the predicted player distribution parameters of the target game area in the future time period; the predicted player distribution parameters include player group position parameters, player group quantity parameters and player group activity area; the LSTM neural network is trained by a training data set including multiple training player distribution parameters sorted over time.

[0008] As an optional embodiment, in the first aspect of the present invention, the regional parameters include at least one of regional shape, regional area, regional location, tasks within the region, triggered plots within the region, and NPC characters within the region.

[0009] As an optional implementation, in the first aspect of the present invention, determining the distribution of multiple candidate resource locations according to the area parameters of the target game area includes: According to the area parameters of the target game area, a plurality of matching resource distribution schemes having all or part of the same parameters are matched in a historical resource distribution database; For each of the matching resource distribution schemes, obtaining historical player complaint records and player play data corresponding to the historical application period of the matching resource distribution scheme; Calculating a first similarity between the player's play data and all the historical play data; Inputting the historical player complaint records into a resource-related complaint identification algorithm model to obtain the resource-related complaint ratio corresponding to the historical player complaint records; the resource-related complaint identification algorithm model is trained by a training data set including a plurality of training player complaint records and corresponding resource-related complaint annotations; Calculate the product of the first similarity and the resource-related complaint ratio to obtain the priority of the matching resource distribution plan; All the matching resource distribution schemes whose priorities are greater than a priority threshold are screened out to obtain a plurality of candidate resource location distributions.

[0010] As an optional implementation, in the first aspect of the present invention, determining the optimal resource distribution of the target game area according to the predicted player distribution parameters and the plurality of candidate resource location distributions includes: Determining an objective function and constraint conditions based on the predicted player distribution parameters and the plurality of candidate resource location distributions; Through the dynamic programming algorithm, the resource distribution plan is iteratively calculated based on the objective function and the constraints until it is optimal, thereby obtaining the optimal resource distribution of the target game area.

[0011] As an optional implementation, in the first aspect of the present invention, the objective function includes: The average value of the second similarity between the resource distribution scheme and each of the candidate resource location distributions reaches a maximum; The distribution uniformity corresponding to the resource distribution scheme reaches a minimum; the distribution uniformity is obtained by calculating the resource positions and resource quantities of multiple arranged resources in the resource distribution scheme through a spatial distribution uniformity algorithm; The restrictions include: The location distance between the resource location of each arranged resource in the resource distribution plan and the location of the nearest player group in the predicted player distribution parameter is less than a distance threshold; The resource quantity of each arranged resource in the resource distribution scheme is less than the average resource quantity corresponding to the predicted player distribution parameter; the average resource quantity is the product of the average value of the player group quantity of all player groups in the predicted player distribution parameter and a preset ratio parameter; The number of arranged resources in the area corresponding to the player group activity area in the predicted player distribution parameter in the resource distribution plan is greater than a preset number threshold.

[0012] A second aspect of an embodiment of the present invention discloses a system for determining a game resource location based on multi-character historical data, the system comprising: An acquisition module is used to acquire historical game data of multiple game players in a target game area; A first determination module, configured to determine predicted player distribution parameters of the target game area based on the historical game data and a prediction algorithm; A second determination module is used to determine the location distribution of multiple candidate resources according to the area parameters of the target game area; The third determination module is used to determine the optimal resource distribution of the target game area according to the predicted player distribution parameters and the plurality of candidate resource location distributions.

[0013] As an optional embodiment, in the second aspect of the present invention, the historical play data includes at least one of the game player's game operations, game routes, game positions, game triggered plots, game execution tasks and game character information in a historical time period.

[0014] As an optional implementation, in the second aspect of the present invention, the first determination module determines the specific manner of predicting the player distribution parameters of the target game area according to the historical play data and the prediction algorithm, including: The historical play data is grouped according to the acquired historical time periods to obtain play data sets corresponding to multiple different historical time periods; Input each of the game data sets into the player distribution recognition algorithm model to obtain the player distribution parameters corresponding to each of the game data sets; the player distribution recognition algorithm model is trained by a training data set including a plurality of training game data and corresponding player distribution parameter annotations; the player distribution parameters include group position, group number, moving direction, moving speed, frequent activity area and internal player density of at least one player group; The time-varying patterns of the player distribution parameters corresponding to all the game data sets are analyzed to obtain the predicted player distribution parameters of the target game area in the future time period.

[0015] As an optional implementation, in the second aspect of the present invention, the first determination module analyzes the time-varying pattern of the player distribution parameters corresponding to all the game data sets to obtain the specific method of predicting the player distribution parameters of the target game area in the future time period, including: According to the corresponding historical time period from early to late, the player distribution parameters of the player groups in all the player distribution parameters are sorted to obtain a player distribution parameter sequence; The player distribution parameter sequence is input into the trained LSTM neural network to obtain the predicted player distribution parameters of the target game area in the future time period; the predicted player distribution parameters include player group position parameters, player group quantity parameters and player group activity area; the LSTM neural network is trained by a training data set including multiple training player distribution parameters sorted over time.

[0016] As an optional embodiment, in the second aspect of the present invention, the regional parameters include at least one of regional shape, regional area, regional location, tasks within the region, triggered plots within the region, and NPC characters within the region.

[0017] As an optional implementation, in the second aspect of the present invention, the second determination module determines the specific manner of distributing the locations of the plurality of candidate resources according to the area parameters of the target game area, including: According to the area parameters of the target game area, a plurality of matching resource distribution schemes having all or part of the same parameters are matched in a historical resource distribution database; For each of the matching resource distribution schemes, obtaining historical player complaint records and player play data corresponding to the historical application period of the matching resource distribution scheme; Calculating a first similarity between the player's play data and all the historical play data; Inputting the historical player complaint records into a resource-related complaint identification algorithm model to obtain the resource-related complaint ratio corresponding to the historical player complaint records; the resource-related complaint identification algorithm model is trained by a training data set including a plurality of training player complaint records and corresponding resource-related complaint annotations; Calculate the product of the first similarity and the resource-related complaint ratio to obtain the priority of the matching resource distribution plan; All the matching resource distribution schemes whose priorities are greater than a priority threshold are screened out to obtain a plurality of candidate resource location distributions.

[0018] As an optional implementation, in the second aspect of the present invention, the third determination module determines the specific manner of the optimal resource distribution of the target game area according to the predicted player distribution parameters and the plurality of candidate resource location distributions, including: Determining an objective function and constraint conditions based on the predicted player distribution parameters and the plurality of candidate resource location distributions; Through the dynamic programming algorithm, the resource distribution plan is iteratively calculated based on the objective function and the constraints until it is optimal, thereby obtaining the optimal resource distribution of the target game area.

[0019] As an optional implementation, in the second aspect of the present invention, the objective function includes: The average value of the second similarity between the resource distribution scheme and each of the candidate resource location distributions reaches a maximum; The distribution uniformity corresponding to the resource distribution scheme reaches a minimum; the distribution uniformity is obtained by calculating the resource positions and resource quantities of multiple arranged resources in the resource distribution scheme through a spatial distribution uniformity algorithm; The restrictions include: The location distance between the resource location of each arranged resource in the resource distribution plan and the location of the nearest player group in the predicted player distribution parameter is less than a distance threshold; The resource quantity of each arranged resource in the resource distribution scheme is less than the average resource quantity corresponding to the predicted player distribution parameter; the average resource quantity is the product of the average value of the player group quantity of all player groups in the predicted player distribution parameter and a preset ratio parameter; The number of arranged resources in the area corresponding to the player group activity area in the predicted player distribution parameter in the resource distribution plan is greater than a preset number threshold.

[0020] The third aspect of the present invention discloses another system for determining the location of game resources based on multi-character historical data, the system comprising: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute part or all of the steps in the method for determining the location of game resources based on multi-character historical data disclosed in the first aspect of the present invention.

[0021] The fourth aspect of the present invention discloses a computer storage medium, which stores computer instructions. When the computer instructions are called, they are used to execute some or all of the steps in the method for determining the location of game resources based on multi-character historical data disclosed in the first aspect of the present invention.

[0022] Compared with the prior art, the embodiments of the present invention have the following beneficial effects: The present invention can determine the predicted player distribution parameters of the target game area based on historical play data and prediction algorithms, screen the candidate resource location distribution in combination with the area parameters of the target game area, and further optimize the resource distribution according to the predicted player distribution parameters, thereby achieving reasonable allocation and delivery accuracy of game resources, enhancing the game experience of game players, and improving the balance and competitiveness of the game area. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0024] Figure 1 It is a flow chart of a method for determining the location of game resources based on multi-character historical data disclosed in an embodiment of the present invention.

[0025] Figure 2 It is a structural diagram of a system for determining the location of game resources based on multi-character historical data disclosed in an embodiment of the present invention.

[0026] Figure 3 It is a structural schematic diagram of another system for determining the location of game resources based on multi-character historical data disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0027] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0028] The terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, device, product or equipment that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units that are not listed, or may optionally include other steps or units that are inherent to these processes, methods, products or equipment.

[0029] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0030] The present invention discloses a method and system for determining the location of game resources based on multi-role historical data, which can determine the predicted player distribution parameters of the target game area based on historical play data and a prediction algorithm, screen the candidate resource location distribution in combination with the regional parameters of the target game area, and further optimize the resource distribution according to the predicted player distribution parameters, thereby achieving reasonable configuration and delivery accuracy of game resources, enhancing the game experience of game players, and improving the balance and competitiveness of the game area. The following are detailed descriptions.

[0031] Embodiment 1 See also Figure 1 , Figure 1 1 is a flow chart of a method for determining the location of game resources based on multi-role historical data disclosed in an embodiment of the present invention. Figure 1 The method for determining the location of game resources based on multi-character historical data described above can be applied to a data processing system / data processing device / data processing server (wherein the server includes a local processing server or a cloud processing server). Figure 1 As shown, the method for determining the location of game resources based on multi-role historical data may include the following operations: 101. Obtain historical game data of multiple game players in a target game area.

[0032] 102. Determine the predicted player distribution parameters for the target game area based on historical play data and prediction algorithms.

[0033] 103. Determine the location distribution of multiple candidate resources according to the area parameters of the target game area.

[0034] 104. Determine the optimal resource distribution of the target game area based on the predicted player distribution parameters and the distribution of multiple candidate resource locations.

[0035] Optionally, the resource location distribution or resources in the resource distribution referred to in the present invention can be defined as data objects in the game that can be collected, consumed, converted or used by players to improve character performance. The type can be a single resource type or a combination of multiple resource types. The present invention does not explicitly limit the number of resource types because the method of the present invention does not involve the determination and identification of resource types. However, even if the method of the present invention is applied to the distribution determination of multiple types of resources, the application is deemed to fall within the scope of protection of the present invention.

[0036] It can be seen that the above-mentioned embodiments of the invention can determine the predicted player distribution parameters of the target game area based on historical play data and prediction algorithms, screen the candidate resource location distribution in combination with the regional parameters of the target game area, and further optimize the resource distribution according to the predicted player distribution parameters, thereby achieving reasonable allocation and delivery accuracy of game resources, enhancing the game experience of game players, and improving the balance and competitiveness of the game area.

[0037] As an optional embodiment, in the above steps, the historical play data includes at least one of the game player's game operations, game routes, game locations, game triggered plots, game execution tasks and game character information in a historical time period.

[0038] Specifically, in actual development, players' historical play data is usually collected and stored in JSON format, which can be saved in a database (such as MongoDB) or log file for replaying players' historical behaviors or analyzing game data. It can also be used in multiplayer online games to synchronize player status to the server. Later, developers can use this data to trace back bugs or optimize the game experience.

[0039] It can be seen that through the above optional embodiments, the content of historical game data is limited to comprehensively characterize the player's game-related characteristics, so as to facilitate subsequent accurate player distribution prediction and resource distribution prediction, assist in realizing the reasonable allocation and delivery accuracy of game resources, enhance the game experience of game players, and at the same time improve the balance and competitiveness of the game area.

[0040] As an optional embodiment, in the above step, determining the predicted player distribution parameters of the target game area based on the historical game data and the prediction algorithm includes: The historical play data is grouped according to the acquired historical time periods to obtain play data sets corresponding to multiple different historical time periods; Input each play data set into the player distribution identification algorithm model to obtain the player distribution parameters corresponding to each play data set; optionally, the player distribution identification algorithm model is trained by a training data set including a plurality of training play data and corresponding player distribution parameter annotations; the player distribution parameters include group position, group number, moving direction, moving speed, frequent activity area and internal player density of at least one player group; The time-varying patterns of the player distribution parameters corresponding to all game data sets are analyzed to obtain the predicted player distribution parameters of the target game area in the future time period.

[0041] In a specific implementation scheme, the player distribution identification algorithm model includes a data extraction model and a distribution information prediction model. The data extraction model is used to filter out data related to player distribution in historical gameplay data, while the distribution information prediction model is used to predict player distribution parameters based on relevant data. Specifically, the data extraction model is a multi-classifier model, and the distribution information prediction model is a CNN neural network model. Both models are trained and optimized using the same training data set.

[0042] It can be seen that through the above optional embodiments, it is possible to group the historical play data according to time periods, and use the player distribution recognition algorithm model to extract the player distribution parameters corresponding to each time period, and further analyze the time-varying patterns of the player distribution parameters to predict the player distribution in future time periods, thereby accurately identifying the dynamic characteristics of the player group in the game environment, improving the understanding of the player activity patterns, making the distribution of resources more in line with the actual needs of the players, and assisting in the reasonable allocation and delivery accuracy of game resources, enhancing the game experience of game players, and at the same time improving the balance and competitiveness of the game area.

[0043] As an optional embodiment, in the above step, analyzing the time-varying patterns of the player distribution parameters corresponding to all the game data sets to obtain the predicted player distribution parameters of the target game area in the future time period includes: According to the corresponding historical time period from early to late, the player distribution parameters of the player groups in all the player distribution parameters are sorted to obtain a player distribution parameter sequence; The player distribution parameter sequence is input into the trained LSTM neural network to obtain the predicted player distribution parameters of the target game area in the future time period; optionally, the predicted player distribution parameters include player group position parameters, player group quantity parameters and player group activity area; the LSTM neural network is trained by a training data set including multiple training player distribution parameters sorted over time.

[0044] Specifically, in the training data set of the LSTM neural network, multiple training player distribution parameters sorted by time are obtained by statistical analysis of player distribution parameters at multiple historical time points in the same game area, which are used to show the changing pattern of player distribution in the game area. Based on the training of the LSTM neural network, the LSTM neural network can accurately predict the player distribution parameters of the target game area in the future time period.

[0045] It can be seen that through the above optional embodiments, it is possible to sort the player distribution parameter sequence based on the historical time period, and use the trained LSTM neural network to predict the player distribution parameters of the target game area in the future time period, including the location, number and activity area of ​​the player group, so as to accurately predict the player's aggregation trend and activity pattern, improve the pertinence and timeliness of resource delivery, assist in the reasonable allocation and delivery accuracy of game resources, enhance the game experience of game players, and at the same time improve the balance and competitiveness of the game area.

[0046] As an optional embodiment, in the above steps, the regional parameters include at least one of regional shape, regional area, regional location, tasks within the region, triggered plots within the region, and NPC roles within the region.

[0047] It can be seen that through the above optional embodiments, the content of the regional parameters is limited to effectively characterize the relevant characteristics of the game area, so as to facilitate the subsequent accurate resource distribution prediction, assist in the reasonable allocation and delivery accuracy of game resources, enhance the game experience of game players, and at the same time improve the balance and competitiveness of the game area.

[0048] As an optional embodiment, in the above step, determining the location distribution of multiple candidate resources according to the area parameters of the target game area includes: According to the regional parameters of the target game area, multiple matching resource distribution schemes with all or part of the same parameters are matched in the historical resource distribution database; For each matching resource distribution scheme, obtain the historical player complaint records and player play data corresponding to the historical application period of the matching resource distribution scheme; Calculate the first similarity between the player's play data and all historical play data; Inputting historical player complaint records into a resource-related complaint identification algorithm model to obtain a resource-related complaint ratio corresponding to the historical player complaint records; optionally, the resource-related complaint identification algorithm model is trained by a training data set including a plurality of training player complaint records and corresponding resource-related complaint annotations; Calculate the product of the first similarity and the resource-related complaint ratio to obtain the priority of the matching resource distribution plan; All matching resource distribution schemes with priorities greater than a priority threshold are screened out to obtain multiple candidate resource location distributions.

[0049] Optionally, the resource distribution plan in the historical resource distribution database can be a resource allocation plan pre-generated by the operator according to instructions or algorithm models, or it can be a plan actually referenced after the operator or algorithm model adjusts the resource plan in the game according to actual player needs or game plot needs.

[0050] Optionally, the calculation of similarity in the present invention may be implemented by a data identical ratio algorithm or a vector distance algorithm, and the technicians may select an existing algorithm according to actual conditions.

[0051] Specifically, the resource-related complaint identification algorithm model is implemented as a text classification model in an actual scenario, which is used to identify and classify text in complaint records to determine whether it is a complaint text related to dissatisfaction with resource distribution.

[0052] It can be seen that through the above-mentioned optional embodiments, it is possible to match multiple similar resource distribution schemes from the historical resource distribution database based on the regional parameters of the target game area, and calculate the priority of the matching scheme in combination with historical player complaint records and game data to screen out high-priority candidate resource location distributions, thereby comprehensively considering historical data and player feedback, improving the rationality and adaptability of the resource distribution scheme, assisting in the reasonable allocation and delivery accuracy of game resources, enhancing the game experience of game players, and at the same time improving the balance and competitiveness of the game area.

[0053] As an optional embodiment, in the above step, determining the optimal resource distribution of the target game area according to the predicted player distribution parameters and the distribution of multiple candidate resource locations includes: Determine the objective function and constraints based on the predicted player distribution parameters and multiple candidate resource location distributions; Through the dynamic programming algorithm, the resource distribution plan is iteratively calculated based on the objective function and constraints until it is optimal, and the optimal resource distribution of the target game area is obtained.

[0054] It can be seen that through the above-mentioned optional embodiments, it is possible to construct objective functions and constraints based on the predicted player distribution parameters and multiple candidate resource location distributions, and perform iterative optimization through a dynamic programming algorithm to calculate the optimal resource distribution of the target game area, thereby being able to dynamically adjust resource allocation according to player distribution trends, improve resource utilization, optimize player interaction experience, and enhance the fairness and strategy of the game environment.

[0055] As an optional embodiment, in the above steps, the objective function includes: The average value of the second similarity between the resource distribution scheme and each candidate resource location distribution reaches a maximum; The distribution uniformity corresponding to the resource distribution scheme is minimized; optionally, the distribution uniformity is obtained by calculating the resource positions and resource quantities of multiple arranged resources in the resource distribution scheme through a spatial distribution uniformity algorithm; Restrictions include: The location distance between the resource location of each resource arrangement in the resource distribution plan and the location of the nearest player group in the predicted player distribution parameter is less than a distance threshold; The resource quantity of each arranged resource in the resource distribution scheme is less than the average resource quantity corresponding to the predicted player distribution parameter; optionally, the average resource quantity is the product of the average value of the player group quantity of all player groups in the predicted player distribution parameter and a preset ratio parameter; The number of arranged resources in the area corresponding to the player group activity area in the predicted player distribution parameters in the resource distribution plan is greater than a preset number threshold.

[0056] It can be seen that through the above optional embodiments, the objective function constructed takes into account the similarity and distribution uniformity of the resource distribution scheme and the candidate resource distribution scheme, and the restriction conditions ensure that the resource distribution scheme conforms to the spatial distribution characteristics and resource needs of the player group, thereby improving the accuracy and rationality of resource allocation, optimizing the fairness and strategy of the game environment, and enhancing the player's gaming experience.

[0057] Embodiment 2 See also Figure 2 , Figure 2 : is a schematic diagram of a system for determining the location of game resources based on multi-role historical data disclosed in an embodiment of the present invention. Figure 2 The game resource location determination system based on multi-character historical data described above can be applied to a data processing system / data processing device / data processing server (wherein the server includes a local processing server or a cloud processing server). Figure 2 As shown, the game resource location determination system based on multi-role historical data may include: The acquisition module 201 is used to acquire historical game data of multiple game players in a target game area.

[0058] The first determination module 202 is used to determine the predicted player distribution parameters of the target game area based on the historical game data and the prediction algorithm.

[0059] The second determination module 203 is used to determine the location distribution of multiple candidate resources according to the area parameters of the target game area.

[0060] The third determination module 204 is used to determine the optimal resource distribution of the target game area according to the predicted player distribution parameters and the multiple candidate resource location distributions.

[0061] It can be seen that the above-mentioned embodiments of the invention can determine the predicted player distribution parameters of the target game area based on historical play data and prediction algorithms, screen the candidate resource location distribution in combination with the regional parameters of the target game area, and further optimize the resource distribution according to the predicted player distribution parameters, thereby achieving reasonable allocation and delivery accuracy of game resources, enhancing the game experience of game players, and improving the balance and competitiveness of the game area.

[0062] As an optional embodiment, the historical play data includes at least one of the game player's game operations, game routes, game locations, game triggered plots, game execution tasks, and game character information in a historical time period.

[0063] It can be seen that through the above optional embodiments, the content of historical game data is limited to comprehensively characterize the player's game-related characteristics, so as to facilitate subsequent accurate player distribution prediction and resource distribution prediction, assist in realizing the reasonable allocation and delivery accuracy of game resources, enhance the game experience of game players, and at the same time improve the balance and competitiveness of the game area.

[0064] As an optional embodiment, the first determination module determines the specific method of predicting the player distribution parameters of the target game area according to the historical game data and the prediction algorithm, including: The historical play data is grouped according to the acquired historical time periods to obtain play data sets corresponding to multiple different historical time periods; Input each play data set into the player distribution identification algorithm model to obtain the player distribution parameters corresponding to each play data set; optionally, the player distribution identification algorithm model is trained by a training data set including a plurality of training play data and corresponding player distribution parameter annotations; the player distribution parameters include group position, group number, moving direction, moving speed, frequent activity area and internal player density of at least one player group; The time-varying patterns of the player distribution parameters corresponding to all game data sets are analyzed to obtain the predicted player distribution parameters of the target game area in the future time period.

[0065] It can be seen that through the above optional embodiments, it is possible to group the historical play data according to time periods, and use the player distribution recognition algorithm model to extract the player distribution parameters corresponding to each time period, and further analyze the time-varying patterns of the player distribution parameters to predict the player distribution in future time periods, thereby accurately identifying the dynamic characteristics of the player group in the game environment, improving the understanding of the player activity patterns, making the distribution of resources more in line with the actual needs of the players, and assisting in the reasonable allocation and delivery accuracy of game resources, enhancing the game experience of game players, and at the same time improving the balance and competitiveness of the game area.

[0066] As an optional embodiment, the first determination module analyzes the time-varying pattern of the player distribution parameters corresponding to all the game data sets to obtain the specific method of predicting the player distribution parameters of the target game area in the future time period, including: According to the corresponding historical time period from early to late, the player distribution parameters of the player groups in all the player distribution parameters are sorted to obtain a player distribution parameter sequence; The player distribution parameter sequence is input into the trained LSTM neural network to obtain the predicted player distribution parameters of the target game area in the future time period; optionally, the predicted player distribution parameters include player group position parameters, player group quantity parameters and player group activity area; the LSTM neural network is trained by a training data set including multiple training player distribution parameters sorted over time.

[0067] It can be seen that through the above optional embodiments, it is possible to sort the player distribution parameter sequence based on the historical time period, and use the trained LSTM neural network to predict the player distribution parameters of the target game area in the future time period, including the location, number and activity area of ​​the player group, so as to accurately predict the player's aggregation trend and activity pattern, improve the pertinence and timeliness of resource delivery, assist in the reasonable allocation and delivery accuracy of game resources, enhance the game experience of game players, and at the same time improve the balance and competitiveness of the game area.

[0068] As an optional embodiment, the regional parameters include at least one of the regional shape, regional area, regional location, tasks within the region, triggered plots within the region, and NPC roles within the region.

[0069] It can be seen that through the above optional embodiments, the content of the regional parameters is limited to effectively characterize the relevant characteristics of the game area, so as to facilitate the subsequent accurate resource distribution prediction, assist in the reasonable allocation and delivery accuracy of game resources, enhance the game experience of game players, and at the same time improve the balance and competitiveness of the game area.

[0070] As an optional embodiment, the second determination module determines the specific manner of distributing the locations of the plurality of candidate resources according to the area parameters of the target game area, including: According to the regional parameters of the target game area, multiple matching resource distribution schemes with all or part of the same parameters are matched in the historical resource distribution database; For each matching resource distribution scheme, obtain the historical player complaint records and player play data corresponding to the historical application period of the matching resource distribution scheme; Calculate the first similarity between the player's play data and all historical play data; Inputting historical player complaint records into a resource-related complaint identification algorithm model to obtain a resource-related complaint ratio corresponding to the historical player complaint records; optionally, the resource-related complaint identification algorithm model is trained by a training data set including a plurality of training player complaint records and corresponding resource-related complaint annotations; Calculate the product of the first similarity and the resource-related complaint ratio to obtain the priority of the matching resource distribution plan; All matching resource distribution schemes with priorities greater than a priority threshold are screened out to obtain multiple candidate resource location distributions.

[0071] Optionally, the calculation of similarity in the present invention can be implemented by calculating the vector distance of vector data after vectorizing the data, or by other optional data similarity algorithms.

[0072] Optionally, the resource-related complaint identification algorithm model may be a pre-trained BERT model or other text processing algorithm model, such as an LLM model.

[0073] It can be seen that through the above-mentioned optional embodiments, it is possible to match multiple similar resource distribution schemes from the historical resource distribution database based on the regional parameters of the target game area, and calculate the priority of the matching scheme in combination with historical player complaint records and game data to screen out high-priority candidate resource location distributions, thereby comprehensively considering historical data and player feedback, improving the rationality and adaptability of the resource distribution scheme, assisting in the reasonable allocation and delivery accuracy of game resources, enhancing the game experience of game players, and at the same time improving the balance and competitiveness of the game area.

[0074] As an optional embodiment, the third determination module determines a specific method of optimal resource distribution of the target game area according to the predicted player distribution parameters and the distribution of multiple candidate resource locations, including: Determine the objective function and constraints based on the predicted player distribution parameters and multiple candidate resource location distributions; Through the dynamic programming algorithm, the resource distribution plan is iteratively calculated based on the objective function and constraints until it is optimal, and the optimal resource distribution of the target game area is obtained.

[0075] It can be seen that through the above-mentioned optional embodiments, it is possible to construct objective functions and constraints based on the predicted player distribution parameters and multiple candidate resource location distributions, and perform iterative optimization through a dynamic programming algorithm to calculate the optimal resource distribution of the target game area, thereby being able to dynamically adjust resource allocation according to player distribution trends, improve resource utilization, optimize player interaction experience, and enhance the fairness and strategy of the game environment.

[0076] As an optional embodiment, the objective function includes: The average value of the second similarity between the resource distribution scheme and each candidate resource location distribution reaches a maximum; The distribution uniformity corresponding to the resource distribution scheme is minimized; optionally, the distribution uniformity is obtained by calculating the resource positions and resource quantities of multiple arranged resources in the resource distribution scheme through a spatial distribution uniformity algorithm; Restrictions include: The location distance between the resource location of each resource arrangement in the resource distribution plan and the location of the nearest player group in the predicted player distribution parameter is less than a distance threshold; The resource quantity of each arranged resource in the resource distribution scheme is less than the average resource quantity corresponding to the predicted player distribution parameter; optionally, the average resource quantity is the product of the average value of the player group quantity of all player groups in the predicted player distribution parameter and a preset ratio parameter; The number of arranged resources in the area corresponding to the player group activity area in the predicted player distribution parameters in the resource distribution plan is greater than a preset number threshold.

[0077] Optionally, the spatial distribution uniformity algorithm can be obtained by calculating the similarity between multiple layout resources in the resource distribution scheme and a preset uniform distribution scheme. For example, a preset uniform distribution scheme specifies the location and quantity of different layout resources, and the location distance and quantity difference between each layout resource in the resource distribution scheme and the nearest layout resource in the uniform distribution scheme are calculated and weighted summed to obtain a distinguishability parameter, and the inverse of the distinguishability parameter is calculated to obtain the distribution uniformity. Optionally, a trained uniformity recognition algorithm model can be used to directly identify the uniformity of the resource distribution scheme.

[0078] It can be seen that through the above optional embodiments, the objective function constructed takes into account the similarity and distribution uniformity of the resource distribution scheme and the candidate resource distribution scheme, and the restriction conditions ensure that the resource distribution scheme conforms to the spatial distribution characteristics and resource needs of the player group, thereby improving the accuracy and rationality of resource allocation, optimizing the fairness and strategy of the game environment, and enhancing the player's gaming experience.

[0079] Embodiment 3 See also Figure 3 , Figure 3 This is another game resource location determination system based on multi-character historical data disclosed in an embodiment of the present invention. Figure 3 The game resource location determination system based on multi-character historical data is applied to a data processing system / data processing device / data processing server (wherein the server includes a local processing server or a cloud processing server). Figure 3 As shown, the game resource location determination system based on multi-role historical data may include: A memory 301 storing executable program codes; a processor 302 coupled to the memory 301; The processor 302 calls the executable program code stored in the memory 301 to execute the steps of the method for determining the location of game resources based on multi-character historical data described in the first embodiment.

[0080] Embodiment 4 An embodiment of the present invention discloses a computer-readable storage medium storing a computer program for electronic data exchange, wherein the computer program enables a computer to execute the steps of the method for determining the location of game resources based on multi-character historical data described in the first embodiment.

[0081] Embodiment 5 An embodiment of the present invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to enable a computer to execute the steps of the method for determining the location of game resources based on multi-character historical data described in the first embodiment.

[0082] The above describes specific embodiments of the present specification, and other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily have to be performed in the specific order or sequential order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0083] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0084] For the convenience of description, the above device is described in various units according to their functions. Of course, when implementing this specification, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0085] Those skilled in the art will appreciate that the embodiments of this specification may be provided as methods, systems, or computer program products. Therefore, the embodiments of this specification may be in the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the embodiments of this specification may be in the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0086] This specification is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of this specification. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0087] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0088] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0089] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0090] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0091] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0092] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0093] This specification may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. This specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media including storage devices.

[0094] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0095] Finally, it should be noted that the method and system for determining the location of game resources based on multi-character historical data disclosed in the embodiment of the present invention only discloses the preferred embodiments of the present invention, which are only used to illustrate the technical scheme of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical schemes described in the aforementioned embodiments can still be modified, or some of the technical features therein can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical schemes from the spirit and scope of the technical schemes of the various embodiments of the present invention.

Claims

1. A method for determining the location of game resources based on multi-character historical data, characterized in that: The method comprises: Obtain historical game data of multiple game players in the target game area; Determine predicted player distribution parameters of the target game area based on the historical game data and a prediction algorithm; Determining the location distribution of multiple candidate resources according to the area parameters of the target game area; The optimal resource distribution of the target game area is determined based on the predicted player distribution parameters and the multiple candidate resource location distributions.

2. The method for determining the location of game resources based on multi-role historical data according to claim 1, characterized in that: The historical play data includes at least one of the game operations, game routes, game locations, game triggered plots, game execution tasks and game character information of the game player in a historical time period.

3. The method for determining the location of game resources based on multi-role historical data according to claim 1, characterized in that: Determining predicted player distribution parameters of the target game area based on the historical game data and a prediction algorithm includes: The historical play data is grouped according to the acquired historical time periods to obtain play data sets corresponding to multiple different historical time periods; Input each of the game data sets into the player distribution recognition algorithm model to obtain the player distribution parameters corresponding to each of the game data sets; the player distribution recognition algorithm model is trained by a training data set including a plurality of training game data and corresponding player distribution parameter annotations; the player distribution parameters include group position, group number, moving direction, moving speed, frequent activity area and internal player density of at least one player group; The time-varying patterns of the player distribution parameters corresponding to all the game data sets are analyzed to obtain the predicted player distribution parameters of the target game area in the future time period.

4. The method for determining the location of game resources based on multi-role historical data according to claim 3, characterized in that: The analyzing the time-varying patterns of the player distribution parameters corresponding to all the game data sets to obtain the predicted player distribution parameters of the target game area in the future time period includes: According to the corresponding historical time period from early to late, the player distribution parameters of the player groups in all the player distribution parameters are sorted to obtain a player distribution parameter sequence; The player distribution parameter sequence is input into the trained LSTM neural network to obtain the predicted player distribution parameters of the target game area in the future time period; the predicted player distribution parameters include player group position parameters, player group quantity parameters and player group activity area; the LSTM neural network is trained by a training data set including multiple training player distribution parameters sorted over time.

5. The method for determining the location of game resources based on multi-role historical data according to claim 1, characterized in that: The regional parameters include at least one of regional shape, regional area, regional location, tasks within the region, triggered plots within the region, and NPC roles within the region.

6. The method for determining the location of game resources based on multi-role historical data according to claim 1, characterized in that: The determining, according to the area parameters of the target game area, a plurality of candidate resource location distributions includes: According to the area parameters of the target game area, a plurality of matching resource distribution schemes having all or part of the same parameters are matched in a historical resource distribution database; For each of the matching resource distribution schemes, obtaining historical player complaint records and player play data corresponding to the historical application period of the matching resource distribution scheme; Calculating a first similarity between the player's play data and all the historical play data; Inputting the historical player complaint records into a resource-related complaint identification algorithm model to obtain the resource-related complaint ratio corresponding to the historical player complaint records; the resource-related complaint identification algorithm model is trained by a training data set including a plurality of training player complaint records and corresponding resource-related complaint annotations; Calculate the product of the first similarity and the resource-related complaint ratio to obtain the priority of the matching resource distribution plan; All the matching resource distribution schemes whose priorities are greater than a priority threshold are screened out to obtain a plurality of candidate resource location distributions.

7. The method for determining the location of game resources based on multi-role historical data according to claim 1, characterized in that: The step of determining the optimal resource distribution of the target game area according to the predicted player distribution parameters and the plurality of candidate resource location distributions includes: Determining an objective function and constraint conditions based on the predicted player distribution parameters and the plurality of candidate resource location distributions; Through the dynamic programming algorithm, the resource distribution plan is iteratively calculated based on the objective function and the constraints until it is optimal, thereby obtaining the optimal resource distribution of the target game area.

8. The method for determining the location of game resources based on multi-role historical data according to claim 7, characterized in that: The objective function includes: The average value of the second similarity between the resource distribution scheme and each of the candidate resource location distributions reaches a maximum; The distribution uniformity corresponding to the resource distribution scheme reaches a minimum; the distribution uniformity is obtained by calculating the resource positions and resource quantities of multiple arranged resources in the resource distribution scheme through a spatial distribution uniformity algorithm; The restrictions include: The location distance between the resource location of each arranged resource in the resource distribution plan and the location of the nearest player group in the predicted player distribution parameter is less than a distance threshold; The resource quantity of each arranged resource in the resource distribution scheme is less than the average resource quantity corresponding to the predicted player distribution parameter; the average resource quantity is the product of the average value of the player group quantity of all player groups in the predicted player distribution parameter and a preset ratio parameter; The number of arranged resources in the area corresponding to the player group activity area in the predicted player distribution parameter in the resource distribution plan is greater than a preset number threshold.

9. A system for determining the location of game resources based on multi-character historical data, characterized in that: The system comprises: An acquisition module is used to acquire historical game data of multiple game players in a target game area; A first determination module, configured to determine predicted player distribution parameters of the target game area based on the historical game data and a prediction algorithm; A second determination module is used to determine the location distribution of multiple candidate resources according to the area parameters of the target game area; The third determination module is used to determine the optimal resource distribution of the target game area according to the predicted player distribution parameters and the plurality of candidate resource location distributions.

10. A game resource location determination system based on multi-character historical data, characterized in that: The system comprises: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the method for determining the location of game resources based on multi-character historical data as described in any one of claims 1-8.

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