Game prop multi-channel consumption evaluation method and system based on multi-dimensional weight

Abstract

The invention provides a multi-dimensional weight-based game prop multi-channel consumption evaluation method and system. The method comprises the following steps: collecting behavior activity information, prop consumption records and game economic indexes of each player, carrying out normalization processing to obtain normalized data of the consumption records and game economy, carrying out processing to obtain prop consumption correction weight values, constructing an evaluation matrix according to the data of the game economic indexes and the consumption records, and carrying out evaluation according to the evaluation matrix. Calculating an entropy value to construct a behavior evaluation vector, processing by combining with a prop consumption correction weight value to obtain a consumption total value equivalent, generating a game prop consumption queue list, processing according to index change data of prop consumption to obtain an effect reward parameter, and carrying out annotation adjustment on queue list sorting; therefore, behavior evaluation vectors are constructed on the data of the game by the props, consumption value equivalents are obtained as the core, a prop consumption evaluation queue list is constructed and adjusted, and a multi-dimensional evaluation technology between players and the game props is achieved.

Description

Technical Field

[0001] This application relates to the field of virtual game technology, and more specifically, to a method and system for evaluating the multi-channel consumption of game items based on multi-dimensional weights. Background Technology

[0002] With the rapid development of the virtual game industry, the consumption of game items has become an important tool for measuring player behavior and the game economy. However, traditional analysis of game item consumption is mostly based on a single dimension, such as analyzing only the number of items consumed or the value of item dungeons. But with the emergence and use of various items by multiple players through multiple channels and dimensions, traditional analysis methods cannot reflect the true value of players' consumption of various items through multiple channels. This makes it impossible for existing technology to accurately adapt to and evaluate the relationship between players' item consumption and the game economy in a complex game economic environment. There is a lack of a multi-dimensional evaluation technology that can reflect the relationship between players and game items.

[0003] Effective technical solutions are urgently needed to address the above problems. Summary of the Invention

[0004] The purpose of this application is to provide a method and system for evaluating the multi-channel consumption of game items based on multi-dimensional weights. It can construct a behavioral evaluation vector based on the data of items in the game to obtain the consumption value equivalent, and build and adjust the item consumption evaluation queue list to realize the multi-dimensional evaluation technology between players and game items.

[0005] The first aspect of this application provides a method for evaluating the multi-channel consumption of game items based on multi-dimensional weights, including the following steps: Collect player behavior information and game economic indicators for each player, including player activity information and consumption records, and extract player activity and item preference. The consumption record data and game economic indicator data are cleaned and normalized to obtain normalized consumption record data and normalized game economic data. The normalized data of the consumption records and the normalized data of the game economy are processed to obtain the item consumption weight value, and the corrected item consumption weight value is obtained. An evaluation matrix is ​​constructed based on the game economic index data and consumption record data of each player's consumption of the aforementioned consumable items, and the entropy value of each player's consumption behavior is calculated to construct a behavior evaluation vector. The consumption value equivalent of each consumable item is obtained by combining the behavioral evaluation vector of each player on multiple channels with the item consumption correction weight value, and then aggregated into the total consumption value equivalent, and sorted to generate a game item consumption queue list. Real-time monitoring of economic indicators related to game item consumption, processing of reward parameters, and annotation and adjustment of the game item consumption queue ranking.

[0006] Optionally, in the multi-dimensional weighted multi-channel consumption evaluation method for game items described in this application, the collection of player behavior information and game economic indicator data, including player activity information and consumption record data, and the extraction of player activity and item preference, includes: Collect player behavior information and game economic indicators data from each player; The player behavior information includes player activity information and player consumption records of various consumable items through multiple channels; The game's economic indicators include the market price, inventory, supply and demand ranking, and circulation speed of each consumable item. The player activity information includes player online information, player level, and item preference information; Player activity is extracted based on the player's online information, and item preference is extracted based on the item preference information; The consumption record data includes consumption time, consumption quantity, and consumption amount.

[0007] Optionally, in the multi-dimensional weighted multi-channel consumption evaluation method for game items described in this application, the step of cleaning and normalizing the consumption record data and game economic indicator data to obtain normalized consumption record data and normalized game economic data includes: The consumption record data and game economic indicator data are cleaned and normalized to obtain normalized consumption record data and normalized game economic data, respectively. The normalized data of the consumption records includes consumption time score, consumption quantity score, and consumption game currency value; The game economy normalized data includes item market price score, inventory quantity score, supply and demand ranking score, and average daily circulation rate score.

[0008] Optionally, in the multi-dimensional weighted multi-channel consumption evaluation method for game items described in this application, the step of processing the normalized data of the consumption records and the normalized data of the game economy to obtain item consumption weight values, and then correcting them to obtain corrected item consumption weight values, includes: Each consumable item is processed according to its corresponding market price score and supply and demand ranking score to obtain the first weight value of item consumption; The consumption time score, average daily circulation rate score, and inventory quantity score of each consumable item are processed to obtain the second weight value of item consumption. Based on the consumption score and game currency value corresponding to each consumable item, a third weight value for item consumption is obtained. Based on the player's activity level and item preference within a preset time period, the first weight value, the second weight value, and the third weight value of item consumption are adjusted to obtain the first adjusted weight value, the second adjusted weight value, and the third adjusted weight value of item consumption.

[0009] Optionally, in the multi-dimensional weighted multi-channel consumption evaluation method for game items described in this application, the step of constructing an evaluation matrix based on the game economic indicator data and consumption record data of each player consuming each consumable item, and calculating the consumption behavior entropy value of each player to construct a behavior evaluation vector includes: An evaluation matrix is ​​constructed based on the game economic indicator data and consumption record data of each player within a preset range, including q indicators for m players, and the weights of each indicator are calculated. ; Based on the standardized values ​​of each player's corresponding indicators, combined with the aforementioned weights Process the data to obtain the entropy value T of each player's consumption behavior; Construct a behavior evaluation vector based on the entropy value T of each player's consumption behavior.

[0010] Optionally, in the multi-dimensional weighted multi-channel consumption evaluation method for game items described in this application, the step of processing the consumption value equivalent of each consumable item by combining the behavioral evaluation vector of each player on multiple channels with the item consumption correction weight value, aggregating them into a total consumption value equivalent, and sorting to generate a game item consumption queue list includes: Based on the player's behavior evaluation vector for each consumable item across multiple channels, combined with the player's level, and the corresponding first, second, and third corrected weight values ​​for item consumption, the equivalent value of each player's consumption of each consumable item across multiple channels is obtained. The system aggregates the consumption value of each consumable item by each player within a preset range, obtains the total consumption value of each consumable item, sorts them, and generates a game item consumption queue list.

[0011] Optionally, in the multi-dimensional weighted multi-channel consumption evaluation method for game items described in this application, the real-time monitoring of economic indicator changes in game item consumption, processing of reward parameters, and labeling and adjusting the ranking of the game item consumption queue include: Real-time monitoring of economic indicators for each player's consumption of game items, including item price volatility, circulation volatility, consumption time change rate, consumption quantity change rate, and player ratings for item consumption; The effect reward parameters of each consumed item are obtained by processing the item price volatility, circulation volatility, consumption time change rate, consumption number change rate, and score input into a preset item consumption reinforcement learning model. The ranking of the game item consumption queue is marked and adjusted based on the effect reward parameters.

[0012] Secondly, this application provides a multi-dimensional weighted multi-channel consumption evaluation system for game items. The system includes a memory and a processor. The memory includes a program for a multi-dimensional weighted multi-channel consumption evaluation method for game items. When the program for the multi-dimensional weighted multi-channel consumption evaluation method for game items is executed by the processor, it performs the following steps: Collect player behavior information and game economic indicators for each player, including player activity information and consumption records, and extract player activity and item preference. The consumption record data and game economic indicator data are cleaned and normalized to obtain normalized consumption record data and normalized game economic data. The normalized data of the consumption records and the normalized data of the game economy are processed to obtain the item consumption weight value, and the corrected item consumption weight value is obtained. An evaluation matrix is ​​constructed based on the game economic index data and consumption record data of each player's consumption of the aforementioned consumable items, and the entropy value of each player's consumption behavior is calculated to construct a behavior evaluation vector. The consumption value equivalent of each consumable item is obtained by combining the behavioral evaluation vector of each player on multiple channels with the item consumption correction weight value, and then aggregated into the total consumption value equivalent, and sorted to generate a game item consumption queue list. Real-time monitoring of economic indicators related to game item consumption, processing of reward parameters, and annotation and adjustment of the game item consumption queue ranking.

[0013] Optionally, in the game item multi-channel consumption evaluation system based on multi-dimensional weights described in this application, the collection of player behavior information and game economic indicator data, including player activity information and consumption record data, and the extraction of player activity and item preference, includes: Collect player behavior information and game economic indicators data from each player; The player behavior information includes player activity information and player consumption records of various consumable items through multiple channels; The game's economic indicators include the market price, inventory, supply and demand ranking, and circulation speed of each consumable item. The player activity information includes player online information, player level, and item preference information; Player activity is extracted based on the player's online information, and item preference is extracted based on the item preference information; The consumption record data includes consumption time, consumption quantity, and consumption amount.

[0014] Optionally, in the game item multi-channel consumption evaluation system based on multi-dimensional weights described in this application, the step of cleaning and normalizing the consumption record data and game economic indicator data to obtain normalized consumption record data and normalized game economic data includes: The consumption record data and game economic indicator data are cleaned and normalized to obtain normalized consumption record data and normalized game economic data, respectively. The normalized data of the consumption records includes consumption time score, consumption quantity score, and consumption game currency value; The game economy normalized data includes item market price score, inventory quantity score, supply and demand ranking score, and average daily circulation rate score.

[0015] As can be seen from the above, the game item multi-channel consumption evaluation method and system based on multi-dimensional weights provided in this application collects information on various player behaviors, item consumption records, and game economic indicators, and performs normalization processing to obtain normalized data of consumption records and game economy. This data is then processed to obtain item consumption correction weight values. An evaluation matrix is ​​constructed based on the game economic indicators and consumption record data, and entropy values ​​are calculated to construct a behavior evaluation vector. Finally, the total consumption value equivalent is obtained by combining the item consumption correction weight values, and a game item consumption queue ranking is generated. Based on the changes in item consumption indicators, effect reward parameters are obtained to adjust the ranking of the queue. Thus, with the core function of constructing a behavior evaluation vector from item data to obtain the consumption value equivalent, the system constructs and adjusts the item consumption evaluation queue ranking, realizing a multi-dimensional evaluation technology between players and game items.

[0016] Other features and advantages of this application will be set forth in the following description and will be apparent in part from the description or may be learned by practicing embodiments of this application. The objectives and other advantages of this application may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A flowchart of a multi-channel consumption evaluation method for game items based on multi-dimensional weights provided in this application embodiment; Figure 2 A flowchart illustrating the process of obtaining modified weight values ​​for item consumption in a modified game item multi-channel consumption evaluation method based on multi-dimensional weights, as provided in this application embodiment. Figure 3 A flowchart illustrating the construction behavior evaluation vector of the game item multi-channel consumption evaluation method based on multi-dimensional weights provided in this application embodiment; Figure 4 A system diagram of a game item multi-channel consumption evaluation system based on multi-dimensional weights provided in this application embodiment. Detailed Implementation

[0019] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0020] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, the terms "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0021] Please refer to Figure 1 , Figure 1 This is a flowchart of a multi-channel consumption evaluation method for game items based on multi-dimensional weights, as described in some embodiments of this application. This multi-dimensional weights-based multi-channel consumption evaluation method for game items is used in terminal devices, such as computers and mobile phones. The multi-dimensional weights-based multi-channel consumption evaluation method for game items includes the following steps: S11. Collect player behavior information and game economic indicators for each player, including player activity information and consumption records, and extract player activity and item preference. S12. Clean and normalize the consumption record data and game economic indicator data to obtain normalized consumption record data and normalized game economic data. S13. Process the normalized data of the consumption record and the normalized data of the game economy to obtain the item consumption weight value, and then correct the obtained item consumption correction weight value. S14. Construct an evaluation matrix based on the game economic index data and consumption record data of each player's consumption of each consumable item, and calculate the consumption behavior entropy value of each player to construct a behavior evaluation vector. S15. Based on the behavioral evaluation vectors of each player on each consumable item across multiple channels, combined with the item consumption correction weight value, the consumption value equivalent of each consumable item is obtained, aggregated into the total consumption value equivalent, and sorted to generate a game item consumption queue list. S16. Monitor the changes in economic indicators of game item consumption in real time, process the parameters of obtained effect rewards, and mark and adjust the ranking of the game item consumption queue.

[0022] This process involves cleaning and normalizing game economic indicator data and consumption record data to transform the activity-related data of different players for various game items from different channels into unified measurement data. Weighting is then applied to obtain the dynamic weight between game activity behavior and item economy. Simultaneously, an evaluation matrix is ​​constructed based on the core data of game economic indicators and consumption records, and the entropy value of each player's consumption behavior is gradually calculated to construct a behavioral evaluation vector for each player's consumption of game items. Further, the consumption value of each item is adjusted by combining the item consumption weight value to obtain the consumption value equivalent. The consumption value equivalents from each channel are aggregated into a total consumption value equivalent, and a game item consumption queue list is generated, reflecting the dynamic consumption of each item. Finally, based on the effect reward parameters obtained from the monitored changes in game item consumption economic indicators, the queue list is labeled and its order is adjusted, thus realizing the ranking and dynamic reordering of the game item consumption queue list, reflecting a multi-dimensional weighted evaluation method for item consumption.

[0023] According to an embodiment of the present invention, the collection of player behavior information and game economic indicator data, including player activity information and consumption record data, and the extraction of player activity and item preference, includes: Collect player behavior information and game economic indicators data from each player; The player behavior information includes player activity information and player consumption records of various consumable items through multiple channels; The game's economic indicators include the market price, inventory, supply and demand ranking, and circulation speed of each consumable item. The player activity information includes player online information, player level, and item preference information; Player activity is extracted based on the player's online information, and item preference is extracted based on the item preference information; The consumption record data includes consumption time, consumption quantity, and consumption amount.

[0024] The system collects player behavior and metrics data on the game server side, including player behavior information such as player activity information and player consumption records of various consumable items through multiple channels. Player activity information includes player online status, player level, and item usage preferences. It also extracts player activity and item preference scores, which are the system's evaluation of player online activity rate and item usage status scores. Consumption record data covers detailed records of each player's consumption of items through various channels such as in-game store purchases, task exchanges, and event rewards, including consumption time, number of items consumed, and consumption amount. At the same time, the collected game economic metrics data include the current market price, inventory, supply and demand ranking, and circulation speed data of each item.

[0025] According to an embodiment of the present invention, the step of cleaning and normalizing the consumption record data and game economic indicator data to obtain normalized consumption record data and normalized game economic data includes: The consumption record data and game economic indicator data are cleaned and normalized to obtain normalized consumption record data and normalized game economic data, respectively. The normalized data of the consumption records includes consumption time score, consumption quantity score, and consumption game currency value; The game economy normalized data includes item market price score, inventory quantity score, supply and demand ranking score, and average daily circulation rate score.

[0026] To avoid errors or anomalies in the collected data and overcome differences in scales across different ranges, the data for different items from different channels were further analyzed to obtain data with the same scale. Consumption record data and game economic indicator data were cleaned to remove duplicate, missing, and erroneous data points, such as eliminating unreasonable consumption records caused by system malfunctions or abnormal player operations. Then, the data was normalized to convert data with different scales and ranges to the same scale. For example, all consumption amounts were converted to game currency values ​​in the game's universal currency unit, consumption time was converted to points, and item market prices were converted to "gold coin" value points to eliminate differences in scale between data. The normalization process for each data point is: |Parameter of this item - Minimum parameter among all item categories| / |Maximum parameter among all item categories - Minimum parameter among all item categories|. For example, item market price points = |Market price of this item - Market price of the cheapest item| / |Market price of the most expensive item - Market price of the cheapest item|.

[0027] Please refer to Figure 2 , Figure 2 This is a flowchart illustrating the process of obtaining modified item consumption weight values ​​using a multi-dimensional weighted method for evaluating multi-channel consumption of game items, as described in some embodiments of this application. According to embodiments of the present invention, the step of processing the normalized consumption record data and the normalized game economy data to obtain item consumption weight values, and then modifying the obtained item consumption weight values, includes: S21. Process each consumable item according to its corresponding market price score and supply and demand ranking score to obtain the first weight value of item consumption. S22. Process the consumption time score, average daily circulation rate score and inventory quantity score of each consumable item to obtain the second weight value of item consumption. S23. Process the consumption score and game currency value corresponding to each consumable item to obtain the third weight value of item consumption. S24. Based on the player's activity level and item preference within a preset time period, the first weight value, the second weight value, and the third weight value of item consumption are adjusted respectively to obtain the first adjusted weight value, the second adjusted weight value, and the third adjusted weight value of item consumption.

[0028] Among these factors, the weights of item value, consumption contribution, and consumption value have a significant impact on the measurement of the relationship between item consumption and game value. This is because the supply-demand ratio of consumption volume to total resources and consumption value to total value affects economic expansion, and some consumption incentives indirectly contribute to the economic value of item consumption. Therefore, a weight analysis of item value, consumption contribution, and consumption value is necessary. This is achieved by multiplying the item market price score by the supply-demand ranking score, the consumption time score, the average daily circulation rate score by the inventory quantity score, and the consumption quantity score by the consumed game currency value, respectively. These first, second, and third weight values ​​for item consumption are then obtained. Finally, these weight values ​​are multiplied by the arithmetic mean of player activity and item preference, respectively, to obtain corrected weight values ​​for item consumption. These weights reflect the actual impact on the game economy and provide a basis for subsequent quantitative evaluation.

[0029] Please refer to Figure 3 , Figure 3 This is a flowchart illustrating the construction of a behavior evaluation vector for a multi-channel consumption evaluation method for game items based on multi-dimensional weights, as described in some embodiments of this application. According to embodiments of the present invention, the step of constructing an evaluation matrix based on game economic indicator data and consumption record data of each player consuming each consumable item, and calculating the consumption behavior entropy value of each player to construct a behavior evaluation vector, includes: S31. Construct an evaluation matrix based on the game economic indicator data and consumption record data of each player within a preset range, including q indicators for m players, and calculate the weight of each indicator. ; S32. Based on the standardized values ​​of each player's corresponding indicators and the aforementioned weights... Process the data to obtain the entropy value T of each player's consumption behavior; S33. Construct a behavior evaluation vector based on the entropy value T of each player's consumption behavior.

[0030] First, a matrix is ​​constructed based on the game economic indicators and consumption records of multiple players. The rows represent different players, and the columns represent various consumption and economic indicators. For example, for 50 players, the 4 game economic indicators and 3 consumption records, a total of 7 indicators, are combined into a matrix of 50 rows and 7 columns. Then, calculate the indicator weights. : Calculate the proportion of the nth player to all players for that indicator under the i-th indicator. For each metric, calculate the proportion of each player's value for that metric to the total value of all players for that metric. ,in Let m be the value of the nth player under the i-th indicator, and m be the total number of players. Calculate the entropy value of the i-th indicator. Entropy values ​​reflect the degree of dispersion of each indicator: , where k = 1 / ln m is the standardized coefficient of the entropy value; Calculate the coefficient of difference for the i-th indicator. The coefficient of variation indicates the usefulness of the indicator: The larger the entropy value of an indicator, the smaller its dispersion and the smaller its coefficient of variation, and the weaker its role in evaluation. Conversely, the smaller the entropy value and the larger the coefficient of variation, the more important the indicator is in evaluation. Calculate the weight of the i-th indicator : , where q is the total number of indicators; The obtained weights This indicates the relative importance of the i-th indicator in measuring player spending behavior, with a weight sum of 1; Finally, the entropy value T of the player's spending behavior is calculated. For each player, the standardized values ​​of each indicator are multiplied by their corresponding weights, and then these weighted values ​​are summed to obtain the comprehensive entropy value of the player's spending behavior. Consumption behavior entropy ,in is the standardized value of the nth player under the i-th indicator, and q is the total number of indicators. It is the weight of the i-th indicator; The consumption behavior entropy value can be used to measure the differences and fluctuations in players' consumption behavior characteristics, reflecting the differences and fluctuations in the impact of players' consumption behavior on game economic indicators across various channels. By calculating the entropy value, corresponding weights are determined for each indicator in the game's economic indicators and consumption record data. The entropy values ​​of each indicator are combined in a certain order to construct a behavior evaluation vector. This evaluation vector can effectively integrate the originally scattered indicators of different dimensions, providing a quantitative basis for subsequent cross-channel consumption value equivalent assessment, and enabling indicators of different dimensions to be comprehensively evaluated under a unified framework.

[0031] According to an embodiment of the present invention, the step of processing the behavior evaluation vectors of each player on each consumable item across multiple channels and combining them with the item consumption correction weight value to obtain the consumption value equivalent of each consumable item, aggregating them into a total consumption value equivalent, and sorting them to generate a game item consumption queue list includes: Based on the player's behavior evaluation vector for each consumable item across multiple channels, combined with the player's level, and the corresponding first, second, and third corrected weight values ​​for item consumption, the equivalent value of each player's consumption of each consumable item across multiple channels is obtained. The system aggregates the consumption value of each consumable item by each player within a preset range, obtains the total consumption value of each consumable item, sorts them, and generates a game item consumption queue list.

[0032] The system utilizes a pre-defined consumption equivalence conversion model, using behavioral evaluation vectors, player levels, and weight correction values ​​as model parameters. Through mathematical mapping, it converts player consumption behavior across various channels into a unified value measured in multi-channel consumption value equivalents. For example, if a player purchases items from the in-game store, the model converts the purchase amount into a corresponding value equivalent based on the store's evaluation vector, the player's level, and three correction weight values. This conversion transforms previously fragmented and incomparable item consumption data from multiple channels into comparable comprehensive consumption value data, achieving equivalent quantification of item consumption behavior across different channels. This allows for accurate measurement of different players' contributions to the game's economic value across different channels. The system then aggregates the consumption value equivalents of each player's items across different channels to obtain the total consumption value equivalent of items across all channels, and ranks the players. The ranked game item consumption queue not only displays the overall consumption value ranking of each item but also provides detailed information on each player's specific consumption details and contribution percentage across each channel, offering a comprehensive ranking display of item consumption.

[0033] According to an embodiment of the present invention, the real-time monitoring of economic indicator changes in game item consumption, processing of reward parameters, and labeling and adjusting the ranking of the game item consumption queue include: Real-time monitoring of economic indicators for each player's consumption of game items, including item price volatility, circulation volatility, consumption time change rate, consumption quantity change rate, and player ratings for item consumption; The effect reward parameters of each consumed item are obtained by processing the item price volatility, circulation volatility, consumption time change rate, consumption number change rate, and score input into a preset item consumption reinforcement learning model. The ranking of the game item consumption queue is marked and adjusted based on the effect reward parameters.

[0034] Finally, to achieve dynamic adjustment of the queue rankings and reflect the real-time consumption of various game items by each player, the system monitors the in-game economic dynamics and player behavior trends in real time. This is achieved by monitoring and collecting real-time changes in in-game economic indicators, such as item price volatility and consumption time variation, as well as player ratings of item usage, including player satisfaction with item usage and reward allocation. These changes and feedback data are used as inputs to the reinforcement learning model, while the current item effect reward parameters are used as outputs to obtain effect reward parameters. This generates an evaluation result that adjusts the queue rankings. The reward parameters include the increase value and the economic balance index, thus enabling the construction and adjustment of the item consumption evaluation queue rankings.

[0035] Please refer to Figure 4 , Figure 4 This is a system diagram of a game item multi-channel consumption evaluation system based on multi-dimensional weights in some embodiments of this application.

[0036] Secondly, the present invention also discloses a game item multi-channel consumption evaluation system 4 based on multi-dimensional weights. The system includes a memory 401 and a processor 402. The memory includes a game item multi-channel consumption evaluation method program based on multi-dimensional weights. When the game item multi-channel consumption evaluation method program based on multi-dimensional weights is executed by the processor, it performs the following steps: Collect player behavior information and game economic indicators for each player, including player activity information and consumption records, and extract player activity and item preference. The consumption record data and game economic indicator data are cleaned and normalized to obtain normalized consumption record data and normalized game economic data. The normalized data of the consumption records and the normalized data of the game economy are processed to obtain the item consumption weight value, and the corrected item consumption weight value is obtained. An evaluation matrix is ​​constructed based on the game economic index data and consumption record data of each player's consumption of the aforementioned consumable items, and the entropy value of each player's consumption behavior is calculated to construct a behavior evaluation vector. The consumption value equivalent of each consumable item is obtained by combining the behavioral evaluation vector of each player on multiple channels with the item consumption correction weight value, and then aggregated into the total consumption value equivalent, and sorted to generate a game item consumption queue list. Real-time monitoring of economic indicators related to game item consumption, processing of reward parameters, and annotation and adjustment of the game item consumption queue ranking.

[0037] This process involves cleaning and normalizing game economic indicator data and consumption record data to transform the activity-related data of different players for various game items from different channels into unified measurement data. Weighting is then applied to obtain the dynamic weight between game activity behavior and item economy. Simultaneously, an evaluation matrix is ​​constructed based on the core data of game economic indicators and consumption records, and the entropy value of each player's consumption behavior is gradually calculated to construct a behavioral evaluation vector for each player's consumption of game items. Further, the consumption value of each item is adjusted by combining the item consumption weight value to obtain the consumption value equivalent. The consumption value equivalents from each channel are aggregated into a total consumption value equivalent, and a game item consumption queue list is generated, reflecting the dynamic consumption of each item. Finally, based on the effect reward parameters obtained from the monitored changes in game item consumption economic indicators, the queue list is labeled and its order is adjusted, thus realizing the ranking and dynamic reordering of the game item consumption queue list, reflecting a multi-dimensional weighted evaluation method for item consumption.

[0038] According to an embodiment of the present invention, the collection of player behavior information and game economic indicator data, including player activity information and consumption record data, and the extraction of player activity and item preference, includes: Collect player behavior information and game economic indicators data from each player; The player behavior information includes player activity information and player consumption records of various consumable items through multiple channels; The game's economic indicators include the market price, inventory, supply and demand ranking, and circulation speed of each consumable item. The player activity information includes player online information, player level, and item preference information; Player activity is extracted based on the player's online information, and item preference is extracted based on the item preference information; The consumption record data includes consumption time, consumption quantity, and consumption amount.

[0039] The system collects player behavior and metrics data on the game server side, including player behavior information such as player activity information and player consumption records of various consumable items through multiple channels. Player activity information includes player online status, player level, and item usage preferences. It also extracts player activity and item preference scores, which are the system's evaluation of player online activity rate and item usage status scores. Consumption record data covers detailed records of each player's consumption of items through various channels such as in-game store purchases, task exchanges, and event rewards, including consumption time, number of items consumed, and consumption amount. At the same time, the collected game economic metrics data include the current market price, inventory, supply and demand ranking, and circulation speed data of each item.

[0040] According to an embodiment of the present invention, the step of cleaning and normalizing the consumption record data and game economic indicator data to obtain normalized consumption record data and normalized game economic data includes: The consumption record data and game economic indicator data are cleaned and normalized to obtain normalized consumption record data and normalized game economic data, respectively. The normalized data of the consumption records includes consumption time score, consumption quantity score, and consumption game currency value; The game economy normalized data includes item market price score, inventory quantity score, supply and demand ranking score, and average daily circulation rate score.

[0041] To avoid errors or anomalies in the collected data and overcome differences in scales across different ranges, the data for different items from different channels were further analyzed to obtain data with the same scale. Consumption record data and game economic indicator data were cleaned to remove duplicate, missing, and erroneous data points, such as eliminating unreasonable consumption records caused by system malfunctions or abnormal player operations. Then, the data was normalized to convert data with different scales and ranges to the same scale. For example, all consumption amounts were converted to game currency values ​​in the game's universal currency unit, consumption time was converted to points, and item market prices were converted to "gold coin" value points to eliminate differences in scale between data. The normalization process for each data point is: |Parameter of this item - Minimum parameter among all item categories| / |Maximum parameter among all item categories - Minimum parameter among all item categories|. For example, item market price points = |Market price of this item - Market price of the cheapest item| / |Market price of the most expensive item - Market price of the cheapest item|.

[0042] According to an embodiment of the present invention, the step of processing the normalized data of the consumption records and the normalized data of the game economy to obtain item consumption weight values, and then correcting the obtained item consumption correction weight values, includes: Each consumable item is processed according to its corresponding market price score and supply and demand ranking score to obtain the first weight value of item consumption; The consumption time score, average daily circulation rate score, and inventory quantity score of each consumable item are processed to obtain the second weight value of item consumption. Based on the consumption score and game currency value corresponding to each consumable item, a third weight value for item consumption is obtained. Based on the player's activity level and item preference within a preset time period, the first weight value, the second weight value, and the third weight value of item consumption are adjusted to obtain the first adjusted weight value, the second adjusted weight value, and the third adjusted weight value of item consumption.

[0043] Among these factors, the weights of item value, consumption contribution, and consumption value have a significant impact on the measurement of the relationship between item consumption and game value. This is because the supply-demand ratio of consumption volume to total resources and consumption value to total value affects economic expansion, and some consumption incentives indirectly contribute to the economic value of item consumption. Therefore, a weight analysis of item value, consumption contribution, and consumption value is necessary. This is achieved by multiplying the item market price score by the supply-demand ranking score, the consumption time score, the average daily circulation rate score by the inventory quantity score, and the consumption quantity score by the consumed game currency value, respectively. These first, second, and third weight values ​​for item consumption are then obtained. Finally, these weight values ​​are multiplied by the arithmetic mean of player activity and item preference, respectively, to obtain corrected weight values ​​for item consumption. These weights reflect the actual impact on the game economy and provide a basis for subsequent quantitative evaluation.

[0044] According to an embodiment of the present invention, the step of constructing an evaluation matrix based on the game economic index data and consumption record data of each player consuming each consumable item, and calculating the consumption behavior entropy value of each player to construct a behavior evaluation vector includes: An evaluation matrix is ​​constructed based on the game economic indicator data and consumption record data of each player within a preset range, including q indicators for m players, and the weights of each indicator are calculated. ; Based on the standardized values ​​of each player's corresponding indicators, combined with the aforementioned weights Process the data to obtain the entropy value T of each player's consumption behavior; Construct a behavior evaluation vector based on the entropy value T of each player's consumption behavior.

[0045] First, a matrix is ​​constructed based on the game economic indicators and consumption records of multiple players. The rows represent different players, and the columns represent various consumption and economic indicators. For example, for 50 players, the 4 game economic indicators and 3 consumption records, a total of 7 indicators, are combined into a matrix of 50 rows and 7 columns. Then, calculate the indicator weights. : Calculate the proportion of the nth player to all players for that indicator under the i-th indicator. For each metric, calculate the proportion of each player's value for that metric to the total value of all players for that metric. ,in Let m be the value of the nth player under the i-th indicator, and m be the total number of players. Calculate the entropy value of the i-th indicator. Entropy values ​​reflect the degree of dispersion of each indicator: , where k = 1 / ln m is the standardization coefficient of the entropy value; Calculate the coefficient of difference for the i-th indicator. The coefficient of variation indicates the usefulness of the indicator: The larger the entropy value of an indicator, the smaller its dispersion and the smaller its coefficient of variation, and the weaker its role in evaluation. Conversely, the smaller the entropy value and the larger the coefficient of variation, the more important the indicator is in evaluation. Calculate the weight of the i-th indicator : , where q is the total number of indicators; The obtained weights This indicates the relative importance of the i-th indicator in measuring player spending behavior, with a weight sum of 1; Finally, the entropy value T of the player's spending behavior is calculated. For each player, the standardized values ​​of each indicator are multiplied by their corresponding weights, and then these weighted values ​​are summed to obtain the comprehensive entropy value of the player's spending behavior. Consumption behavior entropy ,in is the standardized value of the nth player under the i-th indicator, and q is the total number of indicators. It is the weight of the i-th indicator; The consumption behavior entropy value can be used to measure the differences and fluctuations in players' consumption behavior characteristics, reflecting the differences and fluctuations in the impact of players' consumption behavior on game economic indicators across various channels. By calculating the entropy value, corresponding weights are determined for each indicator in the game's economic indicators and consumption record data. The entropy values ​​of each indicator are combined in a certain order to construct a behavior evaluation vector. This evaluation vector can effectively integrate the originally scattered indicators of different dimensions, providing a quantitative basis for subsequent cross-channel consumption value equivalent assessment, and enabling indicators of different dimensions to be comprehensively evaluated under a unified framework.

[0046] According to an embodiment of the present invention, the step of processing the behavior evaluation vectors of each player on each consumable item across multiple channels and combining them with the item consumption correction weight value to obtain the consumption value equivalent of each consumable item, aggregating them into a total consumption value equivalent, and sorting them to generate a game item consumption queue list includes: Based on the player's behavior evaluation vector for each consumable item across multiple channels, combined with the player's level, and the corresponding first, second, and third corrected weight values ​​for item consumption, the equivalent value of each player's consumption of each consumable item across multiple channels is obtained. The system aggregates the consumption value of each consumable item by each player within a preset range, obtains the total consumption value of each consumable item, sorts them, and generates a game item consumption queue list.

[0047] The system utilizes a pre-defined consumption equivalence conversion model, using behavioral evaluation vectors, player levels, and weight correction values ​​as model parameters. Through mathematical mapping, it converts player consumption behavior across various channels into a unified value measured in multi-channel consumption value equivalents. For example, if a player purchases items from the in-game store, the model converts the purchase amount into a corresponding value equivalent based on the store's evaluation vector, the player's level, and three correction weight values. This conversion transforms previously fragmented and incomparable item consumption data from multiple channels into comparable comprehensive consumption value data, achieving equivalent quantification of item consumption behavior across different channels. This allows for accurate measurement of different players' contributions to the game's economic value across different channels. The system then aggregates the consumption value equivalents of each player's items across different channels to obtain the total consumption value equivalent of items across all channels, and ranks the players. The ranked game item consumption queue not only displays the overall consumption value ranking of each item but also provides detailed information on each player's specific consumption details and contribution percentage across each channel, offering a comprehensive ranking display of item consumption.

[0048] According to an embodiment of the present invention, the real-time monitoring of economic indicator changes in game item consumption, processing of reward parameters, and labeling and adjusting the ranking of the game item consumption queue include: Real-time monitoring of economic indicators for each player's consumption of game items, including item price volatility, circulation volatility, consumption time change rate, consumption quantity change rate, and player ratings for item consumption; The effect reward parameters of each consumed item are obtained by processing the item price volatility, circulation volatility, consumption time change rate, consumption number change rate, and score input into a preset item consumption reinforcement learning model. The ranking of the game item consumption queue is marked and adjusted based on the effect reward parameters.

[0049] Finally, to achieve dynamic adjustment of the queue rankings and reflect the real-time consumption of various game items by each player, the system monitors the in-game economic dynamics and player behavior trends in real time. This is achieved by monitoring and collecting real-time changes in in-game economic indicators, such as item price volatility and consumption time variation, as well as player ratings of item usage, including player satisfaction with item usage and reward allocation. These changes and feedback data are used as inputs to the reinforcement learning model, while the current item effect reward parameters are used as outputs to obtain effect reward parameters. This generates an evaluation result that adjusts the queue rankings. The reward parameters include the increase value and the economic balance index, thus enabling the construction and adjustment of the item consumption evaluation queue rankings.

[0050] This invention discloses a multi-dimensional weighted method and system for evaluating the multi-channel consumption of game items. It collects information on player behavior, item consumption records, and game economic indicators, and performs normalization processing to obtain normalized data of consumption records and game economy. Further processing yields corrected weight values ​​for item consumption. An evaluation matrix is ​​constructed based on the game economic indicators and consumption records, and entropy values ​​are calculated to construct a behavior evaluation vector. Finally, the corrected weight values ​​are combined to obtain the total consumption value equivalent, and a game item consumption queue ranking is generated. Based on changes in item consumption indicators, effect reward parameters are obtained to adjust the ranking of the queue. Thus, with the core function of constructing a behavior evaluation vector from item data to obtain the consumption value equivalent, the system constructs and adjusts the item consumption evaluation queue ranking, achieving a multi-dimensional evaluation technology between players and game items.

[0051] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.

[0052] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units. They may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of this embodiment according to actual needs.

[0053] In addition, in the various embodiments of the present invention, each functional unit can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0054] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0055] Alternatively, if the integrated units of this invention are implemented as software functional modules and sold or used as independent products, they can also be stored in a readable storage medium. Based on this understanding, the technical solutions of the embodiments of this invention, or the parts that contribute to the prior art, can be embodied in the form of a software product. This software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROM, RAM, magnetic disks, or optical disks.

Claims

1. A method for evaluating the multi-channel consumption of game items based on multi-dimensional weights, characterized in that, Includes the following steps: Collect player behavior information and game economic indicators for each player, including player activity information and consumption records, and extract player activity and item preference. The consumption record data and game economic indicator data are cleaned and normalized to obtain normalized consumption record data and normalized game economic data. The normalized data of the consumption records and the normalized data of the game economy are processed to obtain the item consumption weight value, and the corrected item consumption weight value is obtained. An evaluation matrix is ​​constructed based on the game economic index data and consumption record data of each player's consumption of the aforementioned consumable items, and the entropy value of each player's consumption behavior is calculated to construct a behavior evaluation vector. The consumption value equivalent of each consumable item is obtained by combining the behavioral evaluation vector of each player on multiple channels with the item consumption correction weight value, and then aggregated into the total consumption value equivalent, and sorted to generate a game item consumption queue list. Real-time monitoring of economic indicators related to game item consumption, processing of reward parameters, and annotation and adjustment of the game item consumption queue ranking.

2. The method for evaluating the multi-channel consumption of game items based on multi-dimensional weights according to claim 1, characterized in that, The collection of player behavior information and game economic indicators, including player activity information and consumption records, and the extraction of player activity and item preference, include: Collect player behavior information and game economic indicators data from each player; The player behavior information includes player activity information and player consumption records of various consumable items through multiple channels; The game's economic indicators include the market price, inventory, supply and demand ranking, and circulation speed of each consumable item. The player activity information includes player online information, player level, and item preference information; Player activity is extracted based on the player's online information, and item preference is extracted based on the item preference information; The consumption record data includes consumption time, consumption quantity, and consumption amount.

3. The method for evaluating the multi-channel consumption of game items based on multi-dimensional weights according to claim 2, characterized in that, The step of cleaning and normalizing the consumption record data and game economic indicator data to obtain normalized consumption record data and normalized game economic data includes: The consumption record data and game economic indicator data are cleaned and normalized to obtain normalized consumption record data and normalized game economic data, respectively. The normalized data of the consumption records includes consumption time score, consumption quantity score, and consumption game currency value; The game economy normalized data includes item market price score, inventory quantity score, supply and demand ranking score, and average daily circulation rate score.

4. The method for evaluating the multi-channel consumption of game items based on multi-dimensional weights according to claim 3, characterized in that, The process of processing the normalized data of the consumption records and the normalized data of the game economy to obtain item consumption weight values ​​and then correcting them to obtain item consumption correction weight values ​​includes: Each consumable item is processed according to its corresponding market price score and supply and demand ranking score to obtain the first weight value of item consumption; The consumption time score, average daily circulation rate score, and inventory quantity score of each consumable item are processed to obtain the second weight value of item consumption. Based on the consumption score and game currency value corresponding to each consumable item, a third weight value for item consumption is obtained. Based on the player's activity level and item preference within a preset time period, the first weight value, the second weight value, and the third weight value of item consumption are adjusted to obtain the first adjusted weight value, the second adjusted weight value, and the third adjusted weight value of item consumption.

5. The method for evaluating the multi-channel consumption of game items based on multi-dimensional weights according to claim 4, characterized in that, The process involves constructing an evaluation matrix based on the game economic indicators and consumption records of each player's consumption of various consumable items, calculating the entropy value of each player's consumption behavior, and constructing a behavior evaluation vector, including: An evaluation matrix is ​​constructed based on the game economic indicator data and consumption record data of each player within a preset range, including q indicators for m players, and the weights of each indicator are calculated. ; Based on the standardized values ​​of each player's corresponding indicators, combined with the aforementioned weights Process the data to obtain the entropy value T of each player's consumption behavior; Construct a behavior evaluation vector based on the entropy value T of each player's consumption behavior.

6. The method for evaluating the multi-channel consumption of game items based on multi-dimensional weights according to claim 5, characterized in that, The process involves processing player behavior evaluation vectors across multiple channels for each consumable item, combined with item consumption adjustment weights, to obtain the consumption value equivalent of each consumable item. This value is then aggregated into a total consumption value equivalent, and a ranking list of game item consumption queues is generated, including: Based on the player's behavior evaluation vector for each consumable item across multiple channels, combined with the player's level, and the corresponding first, second, and third corrected weight values ​​for item consumption, the equivalent value of each player's consumption of each consumable item across multiple channels is obtained. The system aggregates the consumption value of each consumable item by each player within a preset range, obtains the total consumption value of each consumable item, sorts them, and generates a game item consumption queue list.

7. The method for evaluating the multi-channel consumption of game items based on multi-dimensional weights according to claim 6, characterized in that, The system monitors real-time changes in economic indicators related to game item consumption, processes reward parameters, and annotates and adjusts the ranking of the game item consumption queue, including: Real-time monitoring of economic indicators for each player's consumption of game items, including item price volatility, circulation volatility, consumption time change rate, consumption quantity change rate, and player ratings for item consumption; The effect reward parameters of each consumed item are obtained by processing the item price volatility, circulation volatility, consumption time change rate, consumption number change rate, and score input into a preset item consumption reinforcement learning model. The ranking of the game item consumption queue is marked and adjusted based on the effect reward parameters.

8. A game item multi-channel consumption evaluation system based on multi-dimensional weights, characterized in that, The system includes a memory and a processor. The memory contains a program for evaluating the multi-channel consumption of game items based on multi-dimensional weights. When the program for evaluating the multi-channel consumption of game items based on multi-dimensional weights is executed by the processor, it performs the following steps: Collect player behavior information and game economic indicators for each player, including player activity information and consumption records, and extract player activity and item preference. The consumption record data and game economic indicator data are cleaned and normalized to obtain normalized consumption record data and normalized game economic data. The normalized data of the consumption records and the normalized data of the game economy are processed to obtain the item consumption weight value, and the corrected item consumption weight value is obtained. An evaluation matrix is ​​constructed based on the game economic index data and consumption record data of each player's consumption of the aforementioned consumable items, and the entropy value of each player's consumption behavior is calculated to construct a behavior evaluation vector. The consumption value equivalent of each consumable item is obtained by combining the behavioral evaluation vector of each player on multiple channels with the item consumption correction weight value, and then aggregated into the total consumption value equivalent, and sorted to generate a game item consumption queue list. Real-time monitoring of economic indicators related to game item consumption, processing of reward parameters, and annotation and adjustment of the game item consumption queue ranking.

9. The game item multi-channel consumption evaluation system based on multi-dimensional weights according to claim 8, characterized in that, The collection of player behavior information and game economic indicators, including player activity information and consumption records, and the extraction of player activity and item preference, include: Collect player behavior information and game economic indicators data from each player; The player behavior information includes player activity information and player consumption records of various consumable items through multiple channels; The game's economic indicators include the market price, inventory, supply and demand ranking, and circulation speed of each consumable item. The player activity information includes player online information, player level, and item preference information; Player activity is extracted based on the player's online information, and item preference is extracted based on the item preference information; The consumption record data includes consumption time, consumption quantity, and consumption amount.

10. The game item multi-channel consumption evaluation system based on multi-dimensional weights according to claim 9, characterized in that, The step of cleaning and normalizing the consumption record data and game economic indicator data to obtain normalized consumption record data and normalized game economic data includes: The consumption record data and game economic indicator data are cleaned and normalized to obtain normalized consumption record data and normalized game economic data, respectively. The normalized data of the consumption records includes consumption time score, consumption quantity score, and consumption game currency value; The game economy normalized data includes item market price score, inventory quantity score, supply and demand ranking score, and average daily circulation rate score.

Images