A resource configuration method and system of a virtual game scene

Abstract

The application discloses a resource configuration method and system for a virtual game scene, and relates to the technical fields of resource configuration and data processing. The application periodically collects and encrypts player behavior data and resource configuration data, ensures the security and privacy of data processing, uses the data, combines preset profitability and fairness indicators, establishes an association model between resource configuration and game indicators, the model can predict the influence of resource configuration on game indicators, optimizes the delivery strategy of in-game resources and props through data analysis and machine learning technology to meet the preset fairness and profitability conditions, not only improves the rationality of resource configuration, but also ensures the fairness of game experience.

Description

Technical Field

[0001] This invention relates to the field of resource allocation and data processing technology, specifically a resource allocation method and system for a virtual game scene. Background Technology

[0002] In virtual games, the proper allocation of virtual resources and the distribution of virtual items are crucial factors affecting player experience and game rewards. Current technologies lack reasonable strategies to ensure fairness and profitability when allocating virtual resources, and are inefficient in generating and moving virtual items, thus impacting user experience. Summary of the Invention

[0003] The purpose of this invention is to provide a resource allocation method and system for virtual game scenes. Through refined data analysis and real-time adjustment of resource allocation strategies, it solves the problems of fairness and profitability in the allocation of resources and props in virtual games, optimizes usage efficiency, and improves player experience.

[0004] The objective of this invention can be achieved through the following technical solutions:

[0005] This application provides a resource configuration method for a virtual game scene, including the following steps:

[0006] Regularly collect resource configuration data and player behavior data from virtual games, encrypt the collected data, and then transmit it to the data processing platform for preprocessing and storage;

[0007] Based on encrypted data analysis and the preset conditions that the profitability and fairness indicators of virtual games must meet, the amount of virtual resources to be invested in task-based game strategies in virtual games is then determined.

[0008] Based on the amount of resources deployed, virtual resources are deployed in the task-based game strategy to ensure that the profitability and fairness indicators of the virtual game meet the preset conditions.

[0009] In the virtual scene, by responding to the player's command to open the 2D map, a 2D map corresponding to the virtual scene is displayed; by responding to the player's command to generate the target virtual item, the player's first position set in the 2D map and the type of the target virtual item are determined.

[0010] During the process of configuring virtual resources and distributing virtual items, the configuration strategy is adjusted in real time, and then the distribution of invalid resources and items to players who have met the conditions is stopped to optimize the efficiency of resource and item usage.

[0011] By collecting all player behavior data, a player behavior dataset is generated based on the behavior data. Combined with the configured resource types and quantities information and the deployed item types and quantities information, a list of players whose resources are satisfied is obtained.

[0012] A model is built using a decision tree algorithm, a threshold for the distribution of resources and items is set, and a supervised machine learning model is used to determine whether the distribution of resources and items to a specific player should be stopped.

[0013] Based on players' resource and item demand tags and the quantities they have acquired, the Gradient Boosting Decision Tree (GBDT) algorithm is used to predict and optimize the distribution of resources and items. A time-series prediction model is then established to predict the consumption trends of resources and items and adjust the distribution strategy. An isolated forest algorithm is used to remove abnormal player data, determine the resource and item distribution list, and execute the actual resource and item distribution operation according to the preset virtual currency threshold and exchange rules.

[0014] Furthermore, resource configuration data and player behavior data from the virtual game are collected periodically. The collected data is encrypted before being transmitted to a data processing platform for preprocessing and storage. This includes:

[0015] According to a preset time interval, the virtual game data collection program is triggered periodically to collect resource configuration data and player behavior data in the virtual game. The collected resource configuration data and player behavior data are then encrypted using an asymmetric encryption algorithm to obtain encrypted data.

[0016] The encrypted resource configuration data and player behavior data are transmitted to the data processing platform through a secure channel. In the data processing platform, the encrypted data is decrypted using a decryption algorithm that matches the encryption algorithm to obtain the original resource configuration data and player behavior data.

[0017] The decrypted resource configuration data and player behavior data are preprocessed using data cleaning and data transformation to obtain standardized data. The preprocessed resource configuration data and player behavior data are then persistently stored using distributed storage technology.

[0018] Furthermore, after determining the amount of virtual resources allocated in a task-based game strategy based on resource allocation data and preset indicator conditions, the specific steps include:

[0019] Acquire multi-dimensional indicator data related to profitability and fairness in virtual games, and determine whether the current indicator meets the preset conditions based on the preset profitability and fairness target thresholds;

[0020] By training historical resource allocation data and corresponding profitability and fairness indicator data using machine learning algorithms, a correlation model between resource allocation and indicators is established, and the corresponding profitability and fairness indicator values ​​are predicted based on the current resource allocation situation.

[0021] When the predicted index value does not meet the preset conditions, the resource allocation scheme is iteratively optimized through the optimization algorithm to obtain the optimized resource allocation scheme.

[0022] Based on the optimized resource allocation scheme, the amount and proportion of each type of virtual resource in different task-based game strategies are determined and dynamically adjusted according to feedback data; then, a lifecycle management mechanism for virtual resources is constructed to dynamically adjust the resource generation speed and recycling mechanism.

[0023] Furthermore, based on the deployment volume, virtual resources are deployed in the task-based game strategy to ensure that the profitability and fairness indicators of the virtual game meet preset conditions, specifically including:

[0024] Historical data of task-based games are obtained, and target optimization values ​​for profitability and fairness indicators are determined based on preset conditions. Machine learning algorithms are used to establish a correlation model between virtual resource allocation and profitability and fairness indicators.

[0025] By using a correlation model, the changes in profitability and fairness indicators under different virtual resource deployment volumes are predicted. When the predicted changes in indicators meet the preset conditions, the corresponding virtual resource deployment volume is output as a candidate optimization value.

[0026] The optimized virtual resource allocation is selected from the candidate optimized values ​​after comprehensive evaluation and used as the basis for adjusting the game strategy. The optimized virtual resource allocation is then applied to the task-based game to dynamically adjust the resource allocation so that the game's profitability and fairness indicators meet the expected requirements.

[0027] Furthermore, by responding to the player's command to open a 2D map, a 2D map corresponding to the virtual scene is displayed; by responding to the player's command to generate a target virtual item, the player's initial location on the 2D map and the type of the target virtual item are determined, specifically including:

[0028] Based on the player's map instructions, the system retrieves the scene map of the current virtual scene from the storage device. If there is no scene map for the virtual scene, the system triggers a construction process to create a two-dimensional top-down view based on the scene model. By obtaining the player's position, the system calculates the player's three-dimensional coordinate information using the game engine's spatial coordinate system and maps it onto the scene map to form corresponding points.

[0029] Based on the attribute data of each game object on the scene map, the data is labeled to obtain an updated marked scene. A multi-scale edge detection algorithm is applied to the marked scene. The obtained marked scene is divided into several regions using a region growing algorithm. Then, the relationship between the regions is determined. The region skeleton is extracted using a skeleton extraction algorithm, and the obtained spatial geometric structure is used.

[0030] The system obtains the player's target item generation command, extracts item type data and first position information from the target item generation command to obtain the item data determined by the user. The first position information includes the two-dimensional coordinate information of the first position. Based on the two-dimensional coordinate information of the first position, the system calls the kd-tree nearest neighbor search algorithm to query the spatial units close to the first position through the layout relationship database of the indoor scene to obtain the second position in the three-dimensional coordinate system. The system then determines the third position and the orientation of the item in the three-dimensional space.

[0031] In the game engine, based on the item data and the third location, the instantiation model of the target virtual item is loaded in the virtual scene. The item type determines the form information and interaction information of the item instantiation.

[0032] Furthermore, it also includes: determining a second position in the virtual scene based on the first position; generating and displaying a target virtual prop at the second position according to the prop type; and displaying a movement animation of the target virtual prop moving from the second position to the third position.

[0033] Further, based on the first position, a second position in the virtual scene is determined; according to the prop type, a target virtual prop is generated and displayed at the second position; and a movement animation of the target virtual prop moving from the second position to the third position is displayed, specifically including:

[0034] Based on the first position information, the spatial relationship mapping algorithm is used to determine and calibrate the second position three-dimensional coordinates within the pre-established three-dimensional virtual scene spatial coordinate system;

[0035] Obtain the 3D model data of the preset target prop; based on the prop type attributes and corresponding category information, use a hash table structure to find the corresponding data and read it into the 3D model to render the target prop;

[0036] After rendering, the target virtual prop and other objects in the scene are combined into a unified image layer, and the rendering results are combined with the pre-calculated ambient lighting data to render the two-dimensional display plane.

[0037] The position data of the target virtual prop in the two-dimensional image space is calculated and recorded based on the spatial information of the target virtual prop. A smooth movement trajectory is generated in the three-dimensional space using the continuous space cubic Bézier curve algorithm based on the image position data. The image position data is used to form the display effect of image hierarchy in the two-dimensional space.

[0038] Then, based on the coordinate parameters determined by the spatial cubic Bézier curve algorithm, the coordinates of the third position of the spatial motion are obtained. The average gray value L0 in the unit volume of the third position is collected and compared with the average gray value L1 in the unit volume of the target virtual prop.

[0039] The mean-standard deviation normalization method is used to process the two sets of gray values. The final data difference is greater than the gray value threshold. When the difference result is greater than the gray value threshold, a prediction is made and the rendering output color value is changed.

[0040] The color information of the third position is changed according to the calculation results. The three-dimensional spatial position of the target virtual prop is updated in each frame according to the three-dimensional movement trajectory function. The two-dimensional display position of the target virtual prop is updated by continuously calling the rendering interface through image position data.

[0041] Furthermore, after optimizing the efficiency of resource and item usage, it also includes: regularly evaluating the configuration effect, continuously optimizing strategies, and achieving an efficient balance between resource allocation and item usage.

[0042] Furthermore, real-time data on resource allocation and item usage are acquired and comprehensively analyzed to obtain key indicators. Based on preset evaluation rules and thresholds, the effectiveness of the current resource allocation strategy is judged. If the expected goal is not achieved, the strategy optimization process is triggered. Heuristic algorithms and reinforcement learning algorithms are used to find the optimal combination of resource allocation and item usage in massive historical data to form candidate optimization strategies.

[0043] This invention provides a resource allocation system for a virtual game scene, used to implement a resource allocation method for a virtual game scene, including:

[0044] The data acquisition and processing module periodically collects resource configuration data and player behavior data from the virtual game, encrypts the collected data, and then transmits it to the data processing platform for preprocessing and storage.

[0045] The indicator determination module obtains the preset conditions that the profitability and fairness indicators of virtual games must meet;

[0046] The virtual resource configuration module determines the amount of virtual resources to be deployed in the task-based game strategy based on resource configuration data and preset indicator conditions; based on the deployment amount, virtual resources are deployed in the task-based game strategy to ensure that the profitability and fairness indicators of the virtual game meet the preset conditions.

[0047] The virtual scene and prop generation module displays a two-dimensional map in the virtual scene. In response to the player's command to open the two-dimensional map, it displays a two-dimensional map corresponding to the virtual scene. In response to the player's command to generate a target virtual prop, it determines the player's first position in the two-dimensional map and the type of the target virtual prop.

[0048] Specifically, based on the first position, a second position in the virtual scene is determined; according to the prop type, a target virtual prop is generated and displayed at the second position; and a movement animation of the target virtual prop moving from the second position to the third position is displayed.

[0049] The integration optimization and control module adjusts the configuration strategy in real time during the virtual resource allocation and virtual item delivery process; it then stops the delivery of invalid resources and items to players who have met the conditions, optimizes the efficiency of resource and item usage, and regularly evaluates the configuration effect to continuously optimize the strategy.

[0050] The beneficial effects of this invention are as follows:

[0051] This invention effectively solves the issues of fairness and profitability in resource allocation within virtual games through refined resource allocation and item distribution strategies. By regularly collecting and encrypting player behavior data and resource allocation data, it ensures the security and privacy of data processing. Using this data, combined with preset profitability and fairness indicators, a correlation model between resource allocation and game indicators is established. This model can predict the impact of resource allocation on game indicators and optimize the distribution strategies of in-game resources and items through data analysis and machine learning techniques to meet preset fairness and profitability conditions. In this way, not only is the rationality of resource allocation improved, but the fairness of the game experience and the economic benefits for game operators are also ensured.

[0052] By monitoring and adjusting resource allocation strategies in real time, the efficiency of resource and item usage was optimized, improving the user experience. Based on players' resource availability, decision trees and supervised machine learning models were used to set distribution thresholds and determine whether to stop distributing resources and items to specific players. Simultaneously, the GBDT algorithm and time-series prediction model were employed to predict resource and item consumption trends, dynamically adjusting the distribution strategy. Anomalies were eliminated using the Isolation Forest algorithm, ensuring the accuracy of resource distribution. Finally, based on preset virtual currency thresholds and exchange rules, actual resource and item distribution operations were executed, thus solving the problem of low efficiency in generating and moving virtual items mentioned in the background technology, and improving the efficiency and effectiveness of virtual item distribution. Attached Figure Description

[0053] To better understand and implement this application, the technical solution is described in detail below with reference to the accompanying drawings.

[0054] Figure 1 A flowchart illustrating a resource allocation method for a virtual game scene provided in Embodiment 1 of this application;

[0055] Figure 2 This is a flowchart illustrating the process of encrypting, preprocessing, and storing the collected data in a resource allocation method for a virtual game scene provided in Embodiment 1 of this application.

[0056] Figure 3 This is a schematic diagram of the structure of a resource allocation system for a virtual game scene provided in Embodiment 2 of this application. Detailed Implementation

[0057] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, exemplary embodiments will be described in detail below, examples of which are illustrated in the accompanying drawings. In the following description relating to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of methods and systems consistent with some aspects of this application as detailed in the appended claims.

[0058] The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The singular forms “a,” “the,” and “the” used in this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0059] The following detailed description of the specific implementation methods, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided in detail.

[0060] Example 1

[0061] Please see Figures 1-2 This embodiment provides a resource configuration method for a virtual game scene, including the following steps:

[0062] S1. Regularly collect resource configuration data and player behavior data in virtual games, encrypt the collected data, and then transmit it to the data processing platform for preprocessing and storage;

[0063] Furthermore, resource configuration data and player behavior data from the virtual game are collected periodically. The collected data is encrypted before being transmitted to a data processing platform for preprocessing and storage. This includes:

[0064] S11. According to the preset time interval, the virtual game data collection program is triggered periodically to collect resource configuration data and player behavior data in the virtual game. The collected resource configuration data and player behavior data are encrypted using an asymmetric encryption algorithm, such as the RSA algorithm, to obtain encrypted data.

[0065] S12. The encrypted resource configuration data and player behavior data are transmitted to the data processing platform through a secure channel. In the data processing platform, the encrypted data is decrypted using a decryption algorithm that matches the encryption algorithm to obtain the original resource configuration data and player behavior data.

[0066] S13. The decrypted resource configuration data and player behavior data are preprocessed using methods such as data cleaning and data transformation to obtain standardized data. The preprocessed resource configuration data and player behavior data are then persistently stored using distributed storage technology for subsequent data analysis and mining.

[0067] Specifically, by regularly collecting resource configuration and player behavior data, using asymmetric encryption algorithms (such as RSA) to ensure the security of data transmission, and then decrypting, cleaning, transforming and standardizing the data on the data processing platform, the preprocessed data is finally persistently stored through distributed storage technology for effective data analysis and mining, thereby improving data security, availability and processing efficiency.

[0068] S2. Analyze encrypted data and obtain the preset conditions that the profitability and fairness indicators of the virtual game must meet; determine the amount of virtual resources to be deployed in the task-based game strategy based on resource allocation data and preset indicator conditions.

[0069] Furthermore, after determining the amount of virtual resources deployed in task-based game strategies within the virtual game, the specific details include:

[0070] Acquire multi-dimensional indicator data related to profitability and fairness in virtual games, including user activity, payment rate, resource consumption rate, etc., and determine whether the current indicators meet the preset conditions based on the preset profitability and fairness target thresholds.

[0071] By training historical resource allocation data and corresponding profitability and fairness indicator data using machine learning algorithms, a correlation model between resource allocation and indicators is established, and the corresponding profitability and fairness indicator values ​​are predicted based on the current resource allocation situation.

[0072] When the predicted indicator values ​​do not meet the preset conditions, the resource allocation scheme is iteratively optimized through the optimization algorithm. While meeting the profitability and fairness indicators, the game balance and user experience are also taken into account to obtain the optimized resource allocation scheme.

[0073] Based on the optimized resource allocation scheme, the amount and allocation ratio of each type of virtual resource in different task-based game strategies are determined. The effects of different configuration schemes are compared through A / B testing and other methods, and adjustments are made dynamically based on feedback data.

[0074] Construct a lifecycle management mechanism for virtual resources, and dynamically adjust the generation speed and recycling mechanism of resources based on factors such as resource scarcity and consumption rate to ensure the sustainability and balance of resources;

[0075] Design diverse task-based game strategies and gameplay to guide users to acquire and consume virtual resources through different methods, thereby increasing user participation and activity while avoiding excessive impact on game fairness; establish a virtual resource trading and circulation mechanism to allow users to exchange and transfer resources within a reasonable scope, thereby improving resource liquidity and utilization, while setting appropriate transaction taxes and restrictions to prevent problems such as resource monopolies and inflation.

[0076] Specifically, by analyzing multi-dimensional indicator data, using machine learning algorithms to establish and optimize resource allocation models, dynamically adjusting resource allocation and distribution ratios, and constructing resource lifecycle management mechanisms and diversified game strategies, the aim is to achieve a balance between game profitability, fairness, and user experience, while improving resource sustainability and game activity, avoiding resource monopolies and inflation, thereby enhancing the overall economic benefits of the game and player satisfaction.

[0077] S3. Based on the deployment volume, deploy virtual resources in the task-based game strategy to ensure that the profitability and fairness indicators of the virtual game meet the preset conditions.

[0078] Furthermore, based on the deployment volume, virtual resources are deployed in the task-based game strategy to ensure that the profitability and fairness indicators of the virtual game meet preset conditions, specifically including:

[0079] Acquire historical data for task-based games, including game attribute data such as virtual resource allocation, profitability indicators, and fairness indicators;

[0080] Based on preset conditions, the target optimization values ​​of profitability and fairness indicators are determined, and machine learning algorithms, such as reinforcement learning, are used to establish a correlation model between the amount of virtual resources allocated and the profitability and fairness indicators.

[0081] This correlation model predicts the changes in profitability and fairness indicators under different virtual resource deployment volumes. When the predicted indicator changes meet the preset conditions, the corresponding virtual resource deployment volume is output as a candidate optimization value.

[0082] The optimal allocation of virtual resources is selected from the candidate optimization values ​​based on the comprehensive evaluation effect. This serves as the basis for adjusting the game strategy. The optimized allocation of virtual resources is then applied to task-based games to dynamically adjust resource allocation, ensuring that the game's profitability and fairness indicators meet the expected requirements.

[0083] Specifically, machine learning algorithms, especially reinforcement learning, are used to establish a correlation model between the amount of virtual resources deployed and the game's profitability and fairness indicators. By predicting and optimizing the changes in indicators under different deployment amounts, resource allocation strategies are dynamically adjusted to ensure that the game's profitability and fairness indicators meet the expected requirements, thereby optimizing in-game resource allocation and improving resource utilization efficiency and game balance.

[0084] S4. In the virtual scene, by responding to the player's command to open the 2D map, display the 2D map corresponding to the virtual scene; by responding to the player's command to generate the target virtual item, determine the player's first position set in the 2D map and the type of the target virtual item;

[0085] Furthermore, by responding to the player's command to open a 2D map, a 2D map corresponding to the virtual scene is displayed; by responding to the player's command to generate a target virtual item, the player's initial location on the 2D map and the type of the target virtual item are determined, specifically including:

[0086] Based on the player's map instructions, the system retrieves the scene map of the current virtual scene from the storage device. If there is no scene map for the virtual scene, the system triggers a construction process to create a two-dimensional top-down view based on the scene model. By obtaining the player's position, the system calculates the player's three-dimensional coordinate information using the game engine's spatial coordinate system and maps it onto the scene map to form corresponding points.

[0087] Based on the attribute data of each game object on the scene map, the data is labeled to obtain an updated marked scene. A multi-scale edge detection algorithm is applied to the marked scene. The obtained marked scene is divided into several regions using a region growing algorithm. Then, the relationship between the regions is determined. The region skeleton is extracted using a skeleton extraction algorithm, and the obtained spatial geometric structure is used.

[0088] The system obtains the player's target item generation command, extracts item type data and first position information from the target item generation command to obtain the item data determined by the user. The first position information includes the two-dimensional coordinate information of the first position. Based on the two-dimensional coordinate information of the first position, the system calls the kd-tree nearest neighbor search algorithm through the layout relationship database of the indoor scene to query the spatial unit closest to the first position to obtain the second position in the three-dimensional coordinate system. The system then determines the third position and the orientation of the item in the three-dimensional space.

[0089] In the game engine, based on the item data and the third location, the instantiation model of the target virtual item is loaded in the virtual scene. The item type determines the form information and interaction information of the item instantiation.

[0090] Furthermore, it also includes: determining a second position in the virtual scene based on the first position; generating and displaying a target virtual prop at the second position according to the prop type; and displaying a movement animation of the target virtual prop moving from the second position to the third position.

[0091] Further, based on the first position, a second position in the virtual scene is determined; according to the prop type, a target virtual prop is generated and displayed at the second position; and a movement animation of the target virtual prop moving from the second position to the third position is displayed, specifically including:

[0092] Based on the first position information, the spatial relationship mapping algorithm is used to determine and calibrate the second position three-dimensional coordinates within the pre-established three-dimensional virtual scene spatial coordinate system;

[0093] Obtain the 3D model data of the preset target prop; based on the prop type attributes and corresponding category information, use a hash table structure to find the corresponding data and read it into the 3D model to render the target prop;

[0094] After rendering, the target virtual prop and other objects in the scene are combined into a unified image layer, and the rendering results are combined with the pre-calculated ambient lighting data to render the two-dimensional display plane.

[0095] Regenerate a random number r. When the random number r conforms to the gamma distribution, add a new object to the 3D scene based on the calculation result and calculate its visibility.

[0096] The gamma distribution is a continuous probability distribution in statistics, which includes two parameters: shape and scale. The probability density function of the gamma distribution is: ,in and These are two parameters of the gamma distribution. It is the gamma function, used to represent the shape parameter of the gamma distribution. This represents the possible values ​​of a random variable; it is a non-negative real number. .

[0097] Get the size A1 of the rendered object and the average value A2 of the curvature k of each point on the object outline. Make a prediction based on A1 and A2.

[0098] The position data of the target virtual prop in the two-dimensional image space is calculated and recorded based on the spatial information of the target virtual prop. A smooth movement trajectory is generated in the three-dimensional space using the continuous space cubic Bézier curve algorithm based on the image position data. The image position data is used to form the display effect of image hierarchy in the two-dimensional space.

[0099] Then, based on the coordinate parameters determined by the spatial cubic Bézier curve algorithm, the coordinates of the third position of the spatial motion are obtained. The average gray value L0 in the unit volume of the third position is collected and compared with the average gray value L1 in the unit volume of the target virtual prop.

[0100] The mean-standard deviation normalization method is used to process the two sets of gray values. The final data difference is greater than the gray threshold A. When the difference result is greater than the gray threshold A, a prediction is made and the rendering output color value is changed to ensure that the spatial information is not distorted when it is synthesized into the image.

[0101] Regenerate random numbers. If they conform to an exponential distribution, change the color information of the third position according to the calculation result. Update the three-dimensional spatial position of the target virtual prop in each frame according to the three-dimensional movement trajectory function. Continuously call the rendering interface through image position data to obtain the updated two-dimensional display position of the target virtual prop. Construct the movement process of the target virtual prop on the two-dimensional plane to form a complete movement animation effect.

[0102] Specifically, users can adjust the viewing angle in three-dimensional space, determine the angle with the three-dimensional coordinate system based on the different viewing angles, change the angle parameters according to the spatial coordinate mapping transformation relationship of the three-dimensional coordinate system, and use a machine learning spatial object interpolation model to train an interpolation network to obtain a smooth transition interpolated image sequence with predicted viewpoint.

[0103] Specifically, by responding to player commands, the system can acquire and display a two-dimensional map, determine the player's set location and item type, and accurately generate and position virtual items in the virtual scene. Utilizing technologies such as spatial coordinate systems, data labeling, multi-scale edge detection, region growing algorithms, and skeleton extraction, the system can create and render three-dimensional models of items. It also uses a continuous space cubic Bézier curve algorithm to generate smooth movement trajectories for the items. Furthermore, it generates random numbers through gamma and exponential distributions to enhance the dynamism and realism of the scene, ultimately achieving realistic movement animation effects of items on a two-dimensional plane. At the same time, it allows users to observe three-dimensional space from different angles, improving the game's interactivity and visual experience.

[0104] S5. During the process of configuring virtual resources and distributing virtual items, adjust the configuration strategy in real time to ensure the rational use of resources, and then stop distributing invalid resources and items to players who have met the conditions, thereby optimizing the efficiency of resource and item usage.

[0105] Furthermore, during the allocation of virtual resources and the distribution of virtual items, the allocation strategy is adjusted in real time to ensure the rational use of resources. Then, the distribution of ineffective resources and items to players who have already met the requirements is stopped, optimizing the efficiency of resource and item usage. Specifically, this includes:

[0106] Collect all player behavior data, generate a player behavior dataset based on the behavior data, combine the configured resource types and quantities information, and the deployed item types and quantities information, and obtain the satisfied player data and unsatisfied player data corresponding to the player dataset through processing. The unsatisfied player data includes the corresponding unsatisfied player list, and thus obtain the player resource satisfied list.

[0107] The decision tree algorithm is used to model the system. Based on the player behavior data and game progress data of the players in the resource satisfaction list, the resource and item distribution threshold is set. The game resource threshold parameter includes a numerical variable A and a categorical variable B. The game item threshold parameter includes a numerical variable and a categorical variable. The game resource numerical variable and the game item numerical variable are set as the distribution stop parameters. The list of players who meet the stop distribution criteria is obtained by combining the stop parameters.

[0108] By determining when a player reaches the stop parameter, a supervised machine learning model is used to make the next judgment for that player to determine whether to stop distributing resources and items to them. The distribution content parameters include numerical variables and categorical variables, and the player's resource and item demand tags are obtained.

[0109] By comparing the number of resources and items already acquired by players, and combining the player resource and item demand tags obtained in the previous process, the Gradient Boosting Decision Tree (GBDT) algorithm is used to train and obtain the predicted values ​​for resource and item delivery. A classifier for accurately delivering resource and item content is constructed, and then player data below a set threshold is obtained as the data to be delivered, thus obtaining the recommended content and the number of recommendations.

[0110] By establishing a time-series prediction model based on existing player behavior patterns and deployment data, the consumption trends of resources and items in a short period of time can be predicted. Based on the changing trends, adjustment plans can be formulated, and the frequency of plan modification can be set to N. By using a series of historical observations of previously obtained values, a time series model can be constructed through processing to obtain the predicted deployment modification plan, acquire real-time monitoring data, and obtain the immediate modification content.

[0111] By adjusting the content in real time and combining it with the recommended content, if the number of resource and item types exceeds the set number, the system obtains the player's historical satisfaction data, trains the system using the isolated forest algorithm based on the player's historical data, determines whether there is abnormal player data, removes abnormal players, and then determines the final list of resource and item distribution.

[0112] Obtain the resource and item data from the final list, perform actual deployment operations, use a preset virtual currency quantity threshold to determine the relationship between the resource value and the preset virtual currency quantity threshold, and set a resource conversion ratio P1 for values ​​greater than the currency threshold. Use preset exchange rules to determine the relationship between resources and items, determine the item conversion rules, and P1 and preset exchange rule parameters include numerical variables and categorical variables. Obtain the resource and item deployment execution strategy based on the converted data.

[0113] Furthermore, after optimizing the efficiency of resource and item usage, it also includes: regularly evaluating the configuration effect, continuously optimizing strategies, and achieving an efficient balance between resource allocation and item usage.

[0114] Furthermore, real-time data on resource allocation and item usage are acquired and comprehensively analyzed to obtain key indicators such as resource utilization and item usage efficiency. Based on preset evaluation rules and thresholds, the effectiveness of the current resource allocation strategy is judged. If the expected goal is not achieved, the strategy optimization process is triggered. Heuristic algorithms and reinforcement learning algorithms are used to find the optimal combination of resource allocation and item usage in massive historical data to form candidate optimization strategies.

[0115] Specifically, by collecting and analyzing player behavior data in real time, resource allocation strategies are dynamically adjusted to accurately deploy resources and items, optimize resource utilization efficiency, and utilize advanced technologies such as supervised machine learning models, gradient boosting decision tree (GBDT) algorithms, time series prediction models, and isolated forest algorithms to predict consumption trends, eliminate abnormal data, and continuously optimize deployment strategies according to preset rules to achieve an efficient balance between resource allocation and item usage, thereby significantly improving the game experience and the economic benefits for game operators.

[0116] By integrating virtual resource allocation and virtual item delivery optimization technologies, the rational utilization rate of virtual resources and the efficiency of virtual item usage have been significantly improved. Through big data analysis and real-time strategy adjustments, the profitability and fairness of the game have been ensured, resource waste has been reduced, the player's gaming experience and the overall system's operational efficiency have been enhanced, and users' various behaviors in the game have been better managed and controlled, ensuring the healthy development of the game.

[0117] Example 2

[0118] Please see Figure 3 This embodiment provides a resource allocation system for a virtual game scene, used to implement a resource allocation method for a virtual game scene, including:

[0119] The data acquisition and processing module periodically collects resource configuration data and player behavior data from the virtual game, encrypts the collected data, and then transmits it to the data processing platform for preprocessing and storage.

[0120] The indicator determination module obtains the preset conditions that the profitability and fairness indicators of virtual games must meet;

[0121] The virtual resource configuration module determines the amount of virtual resources to be deployed in the task-based game strategy based on resource configuration data and preset indicator conditions; based on the deployment amount, virtual resources are deployed in the task-based game strategy to ensure that the profitability and fairness indicators of the virtual game meet the preset conditions.

[0122] The virtual scene and prop generation module displays a two-dimensional map in the virtual scene. In response to the player's command to open the two-dimensional map, it displays a two-dimensional map corresponding to the virtual scene. In response to the player's command to generate a target virtual prop, it determines the player's first position in the two-dimensional map and the type of the target virtual prop.

[0123] Specifically, based on the first position, a second position in the virtual scene is determined; according to the prop type, a target virtual prop is generated and displayed at the second position; and a movement animation of the target virtual prop moving from the second position to the third position is displayed.

[0124] The integrated optimization and control module adjusts the configuration strategy in real time during the virtual resource allocation and virtual item distribution process to ensure the rational use of resources; it stops the distribution of ineffective resources and items to players who have met the conditions, thereby optimizing the efficiency of resource and item usage; and it regularly evaluates the configuration effect and continuously optimizes the strategy to achieve an efficient balance between resource allocation and item usage.

[0125] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A resource allocation method for a virtual game scene, characterized in that: Includes the following steps: Regularly collect resource configuration data and player behavior data from virtual games, encrypt the collected data, and then transmit it to the data processing platform for preprocessing and storage; Based on encrypted data analysis and the preset conditions that the profitability and fairness indicators of virtual games must meet, the amount of virtual resources to be invested in task-based game strategies in virtual games is then determined. Based on the amount of resources deployed, virtual resources are deployed in the task-based game strategy to ensure that the profitability and fairness indicators of the virtual game meet the preset conditions. In the virtual scene, a two-dimensional map corresponding to the virtual scene is displayed in response to the player's command to open the two-dimensional map; By responding to the player's command to generate the target virtual item, the system determines the player's initial location on the 2D map and the type of the target virtual item. During the process of configuring virtual resources and distributing virtual items, the configuration strategy is adjusted in real time, and then the distribution of invalid resources and items to players who have met the conditions is stopped to optimize the efficiency of resource and item usage. By collecting all player behavior data, a player behavior dataset is generated based on the behavior data. Combined with the configured resource types and quantities information and the deployed item types and quantities information, a list of players whose resources are satisfied is obtained. A model is built using a decision tree algorithm, a threshold for the distribution of resources and items is set, and a supervised machine learning model is used to determine whether the distribution of resources and items to a specific player should be stopped. Based on players' resource and item demand tags and the quantities they have acquired, the Gradient Boosting Decision Tree (GBDT) algorithm is used to predict and optimize the distribution of resources and items. Then, a time-series prediction model is established to predict the consumption trend of resources and items and adjust the distribution strategy. Abnormal player data is removed by the Isolation Forest algorithm to determine the resource and item distribution list. Finally, the actual resource and item distribution operation is executed according to the preset virtual currency threshold and exchange rules. After optimizing the efficiency of resource and item usage, the process also includes: regularly evaluating the configuration effect and continuously optimizing the strategy; obtaining key indicators by acquiring real-time data on resource configuration and item usage and conducting comprehensive analysis; judging the effect of the current resource configuration strategy based on preset evaluation rules and thresholds; and triggering the strategy optimization process when the expected goal is not achieved; and using heuristic algorithms and reinforcement learning algorithms to find combinations of resource configuration and item usage in massive historical data to form candidate optimization strategies. After determining the amount of virtual resources to be deployed in a task-based game strategy within a virtual game, the following is included: Acquire multi-dimensional indicator data related to profitability and fairness in virtual games, and determine whether the current indicator meets the preset conditions based on the preset profitability and fairness target thresholds; By training historical resource allocation data and corresponding profitability and fairness indicator data using machine learning algorithms, a correlation model between resource allocation and indicators is established, and the corresponding profitability and fairness indicator values ​​are predicted based on the current resource allocation situation. When the predicted index value does not meet the preset conditions, the resource allocation scheme is iteratively optimized through the optimization algorithm to obtain the optimized resource allocation scheme. Based on the optimized resource allocation scheme, the amount and proportion of each type of virtual resource in different task-based game strategies are determined and dynamically adjusted according to feedback data; then, a lifecycle management mechanism for virtual resources is constructed to dynamically adjust the resource generation speed and recycling mechanism. It also includes: determining a second position in the virtual scene based on the first position; generating and displaying a target virtual prop at the second position according to the prop type; and displaying a movement animation of the target virtual prop moving from the second position to the third position. Based on the first position, determine the second position in the virtual scene; according to the prop type, generate and display the target virtual prop at the second position; and display a movement animation of the target virtual prop moving from the second position to the third position, including: Based on the first position information, the spatial relationship mapping algorithm is used to determine and calibrate the second position three-dimensional coordinates within the pre-established three-dimensional virtual scene spatial coordinate system; Obtain the 3D model data of the preset target prop; based on the prop type attributes and corresponding category information, use a hash table structure to find the corresponding data and read it into the 3D model to render the target prop; After rendering, the target virtual prop and other objects in the scene are combined into a unified image layer, and the rendering results are combined with the pre-calculated ambient lighting data to render the two-dimensional display plane. The position data of the target virtual prop in the two-dimensional image space is calculated and recorded based on the spatial information of the target virtual prop. A smooth movement trajectory is generated in the three-dimensional space using the continuous space cubic Bézier curve algorithm based on the image position data. The image position data is used to form the display effect of image hierarchy in the two-dimensional space.

2. The resource allocation method for a virtual game scene according to claim 1, characterized in that: Regularly collect resource configuration data and player behavior data from virtual games, encrypt the collected data, and then transmit it to a data processing platform for preprocessing and storage, including: According to a preset time interval, the virtual game data collection program is triggered periodically to collect resource configuration data and player behavior data in the virtual game. The collected resource configuration data and player behavior data are then encrypted using an asymmetric encryption algorithm to obtain encrypted data. The encrypted resource configuration data and player behavior data are transmitted to the data processing platform through a secure channel. In the data processing platform, the encrypted data is decrypted using a decryption algorithm that matches the encryption algorithm to obtain the original resource configuration data and player behavior data. The decrypted resource configuration data and player behavior data are preprocessed using data cleaning and data transformation to obtain standardized data. The preprocessed resource configuration data and player behavior data are then persistently stored using distributed storage technology.

3. The resource allocation method for a virtual game scene according to claim 1, characterized in that: Based on the deployment volume, virtual resources are deployed in task-based game strategies to ensure that the profitability and fairness indicators of the virtual game meet preset conditions, including: Historical data of task-based games are obtained, and target optimization values ​​for profitability and fairness indicators are determined based on preset conditions. Machine learning algorithms are used to establish a correlation model between virtual resource allocation and profitability and fairness indicators. By using a correlation model, the changes in profitability and fairness indicators under different virtual resource deployment volumes are predicted. When the predicted changes in indicators meet the preset conditions, the corresponding virtual resource deployment volume is output as a candidate optimization value. The optimized virtual resource allocation is selected from the candidate optimized values ​​after comprehensive evaluation and used as the basis for adjusting the game strategy. The optimized virtual resource allocation is then applied to the task-based game to dynamically adjust the resource allocation so that the game's profitability and fairness indicators meet the expected requirements.

4. The resource allocation method for a virtual game scene according to claim 1, characterized in that: By responding to the player's command to open a 2D map, a 2D map corresponding to the virtual scene is displayed; By responding to the player's command to generate the target virtual item, the system determines the player's initial location on the 2D map and the type of the target virtual item, specifically including: Based on the player's map instructions, the system retrieves the scene map of the current virtual scene from the storage device. If there is no scene map for the virtual scene, the system triggers a construction process to create a two-dimensional top-down view based on the scene model. By obtaining the player's position, the system calculates the player's three-dimensional coordinate information using the game engine's spatial coordinate system and maps it onto the scene map to form corresponding points. Based on the attribute data of each game object on the scene map, the data is labeled to obtain an updated marked scene. A multi-scale edge detection algorithm is applied to the marked scene. The obtained marked scene is divided into several regions using a region growing algorithm. Then, the relationship between the regions is determined. The region skeleton is extracted using a skeleton extraction algorithm, and the obtained spatial geometric structure is used. The system obtains the player's target item generation command, extracts item type data and first position information from the target item generation command to obtain the item data determined by the user. The first position information includes the two-dimensional coordinate information of the first position. Based on the two-dimensional coordinate information of the first position, the system calls the kd-tree nearest neighbor search algorithm to query the spatial units close to the first position through the layout relationship database of the indoor scene to obtain the second position in the three-dimensional coordinate system. The system then determines the third position and the orientation of the item in the three-dimensional space. In the game engine, based on the item data and the third location, the instantiation model of the target virtual item is loaded in the virtual scene. The item type determines the form information and interaction information of the item instantiation.

5. The resource allocation method for a virtual game scene according to claim 1, characterized in that: The coordinates of the third position in the spatial motion are obtained by using the coordinate parameters determined by the spatial cubic Bézier curve algorithm. The average gray value L0 in the unit volume of the third position is collected and compared with the average gray value L1 in the unit volume of the target virtual prop. The mean-standard deviation normalization method is used to process the two sets of gray values. If the difference between the data is greater than the gray value threshold, a prediction is made and the rendering output color value is changed when the difference is greater than the gray value threshold. The color information of the third position is changed according to the calculation results. The three-dimensional spatial position of the target virtual prop is updated in each frame according to the three-dimensional movement trajectory function. The two-dimensional display position of the target virtual prop is updated by continuously calling the rendering interface through image position data.

6. A resource allocation system for a virtual game scene, used to implement the resource allocation method for a virtual game scene as described in any one of claims 1-5, characterized in that: include: The data acquisition and processing module periodically collects resource configuration data and player behavior data from the virtual game, encrypts the collected data, and then transmits it to the data processing platform for preprocessing and storage. The indicator determination module obtains the preset conditions that the profitability and fairness indicators of virtual games must meet; The virtual resource configuration module determines the amount of virtual resources to be deployed in the task-based game strategy based on resource configuration data and preset indicator conditions; based on the deployment amount, virtual resources are deployed in the task-based game strategy to ensure that the profitability and fairness indicators of the virtual game meet the preset conditions. The virtual scene and prop generation module displays a two-dimensional map in the virtual scene. In response to the player's command to open the two-dimensional map, it displays a two-dimensional map corresponding to the virtual scene. In response to the player's command to generate a target virtual prop, it determines the player's first position in the two-dimensional map and the type of the target virtual prop. Specifically, based on the first position, a second position in the virtual scene is determined; according to the prop type, the target virtual prop is generated and displayed at the second position; Displays an animation showing the target virtual item moving from the second position to the third position; The integration optimization and control module adjusts the configuration strategy in real time during the virtual resource allocation and virtual item delivery process; it then stops the delivery of invalid resources and items to players who have met the conditions, optimizes the efficiency of resource and item usage, and regularly evaluates the configuration effect to continuously optimize the strategy.

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