An action game design analysis method, system, device and storage medium

Abstract

This invention belongs to the field of adaptive difficulty analysis and design for competitive games, and discloses a method, system, device, and storage medium for designing and analyzing action games. By constructing a behavioral feature dataset and evaluating habitual confidence levels, this invention can accurately grasp players' behavioral habits during combat, providing a reliable basis for subsequent difficulty adjustments and solving the problem of insufficient player behavior analysis in existing technologies. Based on a large amount of test data and objective classification logic, this invention constructs a mapping relationship between competitive actions and difficulty, making the difficulty classification more consistent with players' actual experiences and overcoming the shortcomings of unscientific difficulty classification in existing technologies. This invention achieves autonomous adjustment of the difficulty of simulated character combat actions by evaluating player adaptability and analyzes the rationality of the adjustment to ensure that the difficulty adjustment is suitable for players, improving game playability and player experience, and making up for the lack of autonomy and rationality evaluation in tactical adjustments in existing technologies.

Description

Technical Field

[0001] This invention belongs to the field of adaptive difficulty analysis and design technology for competitive games, and relates to a design and analysis method, system, device and storage medium for action games. Background Technology

[0002] With the booming development of the gaming industry, action games have become increasingly popular among players due to their intense and exciting combat experience. In action game design, accurately analyzing player behavior and appropriately setting the difficulty of combat to enhance the player experience and the game's replayability is a crucial challenge for game developers.

[0003] Currently, the design of combat between simulated characters and player characters in action games often relies on the developer's experience, lacking in-depth analysis of player behavior characteristics and scientific assessment of the difficulty of the combat. This results in the game difficulty not matching the player's actual skill level, affecting the player's gaming experience. Therefore, research on adaptive analysis and control of difficulty in a combat-based action game is of great significance.

[0004] Existing technical solutions rarely systematically construct datasets of the combat behavior characteristics of player characters and simulated characters, making it difficult to accurately assess the habitual behavior of players in each combat action and failing to provide precise basis for difficulty adjustment.

[0005] Existing technical solutions lack objective standards based on extensive test data for classifying the difficulty of combat actions, relying heavily on subjective judgment, which leads to a discrepancy between the difficulty classification and the player's actual experience.

[0006] Existing technical solutions make it difficult for simulated character combat tactics to adapt to player adaptability. Furthermore, after difficulty adjustments are made, there is a lack of effective analysis of the rationality of the adjustments, which may result in situations where the adjusted difficulty is still unsuitable for players. Summary of the Invention

[0007] In view of this, in order to solve the problems mentioned in the background art above, a design analysis method, system, device and storage medium for action games are proposed.

[0008] The objective of this invention can be achieved through the following technical solution: The first aspect of this invention provides a design analysis method for action games, including player behavior feature analysis, constructing a dataset of behavioral features of simulated characters for each confrontation action, obtaining a dataset of confrontation behavior features of corresponding player characters, and evaluating the habitual confidence of each confrontation action.

[0009] The adversarial strategy graph is constructed, and the actual difficulty of each adversarial action is output based on the pre-constructed adversarial action-difficulty mapping relationship.

[0010] The system's tactics are autonomously evolved. Based on the habitual confidence level and actual difficulty of each combat action, the system assesses the player character's adaptability to combat actions of varying difficulty, determines whether the difficulty of the simulated character's combat actions needs to be adjusted, and if so, identifies the target difficulty and makes adjustments accordingly.

[0011] The tactical adjustments were analyzed reasonably, and data on the combat behavior characteristics of player characters in each combat action after the difficulty adjustment were collected to determine whether the difficulty adjustment was reasonable.

[0012] A second aspect of the present invention provides an action game design analysis system, comprising: a player behavior feature analysis module, which constructs a dataset of behavioral features of simulated characters for each confrontation action, obtains a dataset of confrontation behavior features of corresponding player characters, and evaluates the habitual confidence level of each confrontation action.

[0013] The adversarial strategy graph construction module outputs the actual difficulty of each adversarial action based on the pre-built adversarial action-difficulty mapping relationship.

[0014] The system's tactical auto-evolution module assesses the player character's adaptability to combat actions of varying difficulty based on the habitual confidence level and actual difficulty of each combat action. It then determines whether the difficulty of the simulated character's combat actions needs adjustment, and if so, identifies the target difficulty and makes adjustments accordingly.

[0015] The tactical adjustment rationality analysis module collects data on the combat behavior characteristics of player characters in each combat action after the difficulty adjustment, and determines whether the difficulty adjustment is reasonable.

[0016] A third aspect of the present invention provides an action game design and analysis device, the action game design and analysis device comprising: a memory, a processor, and an action game design and analysis program stored in the memory and executable on the processor, wherein the action game design and analysis program, when executed by the processor, implements the steps of the action game design and analysis method.

[0017] A fourth aspect of the present invention provides a storage medium that stores one or more programs that can be executed by one or more processors to implement the steps in the above-described action game design and analysis method.

[0018] Compared with the prior art, the beneficial effects of the present invention are as follows: (1) By constructing a behavioral feature dataset and evaluating habitual confidence, the present invention can accurately grasp the behavioral habits of players in the process of confrontation, providing a reliable basis for subsequent difficulty adjustment, and solving the problem of insufficient player behavior analysis in the prior art.

[0019] (2) Based on a large amount of test data and objective division logic, this invention constructs a mapping relationship between adversarial actions and difficulty, making the difficulty division more in line with the actual experience of players and overcoming the shortcomings of unscientific difficulty division in the prior art.

[0020] (3) This invention achieves autonomous adjustment of the difficulty of simulated character combat actions by assessing the player's adaptability and analyzes the rationality of the adjustment to ensure that the difficulty adjustment is suitable for the player, thereby improving the playability of the game and the player's gaming experience, and making up for the lack of autonomy and rationality assessment in the existing technology of tactical adjustment. Attached Figure Description

[0021] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a schematic diagram illustrating the implementation steps of the method of the present invention.

[0023] Figure 2 This is a schematic diagram showing the connections of the various modules in the system of the present invention.

[0024] Figure 3 This is a block diagram of a computer system for implementing some embodiments of the present invention.

[0025] Figure 4 This is a schematic diagram of a storage medium structure provided by the present invention.

[0026] Reference numerals: 100--Computer system, 110--Memory, 120--Processor, 130--Bus, 140--Input / output interface, 150--Network interface, 160--Storage interface. Detailed Implementation

[0027] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0028] Please see Figure 1 As shown, the first aspect of the present invention provides a design analysis method for action games, including: player behavior feature analysis, constructing a behavior feature dataset of simulated characters for each confrontation action, obtaining a confrontation behavior feature dataset of the corresponding player character, and evaluating the habitual confidence of each confrontation action.

[0029] It's important to note that constructing the behavioral feature dataset provides fundamental data support for subsequent analysis of simulated character combat actions. By recording their behavior in different combat scenarios, it provides a reference for evaluating player responses and adjusting the difficulty level. Obtaining the player character's combat behavioral feature dataset is crucial because it contains key parameters such as dodge distance, which directly assess the player's habitual response to various combat actions. This reflects the player's proficiency and provides accurate data support for judging player adaptability and adjusting the difficulty of simulated character combat.

[0030] In a preferred embodiment of the present invention, the specific method for evaluating the habitual confidence level of each confrontation action is as follows: extracting the confrontation behavior feature dataset of each confrontation action, wherein the confrontation behavior feature dataset includes dodging distance, response delay and device operation pressure.

[0031] It should be noted that extracting the combat behavior feature dataset for each combat action is to accurately capture the core operational performance of players when responding to simulated character combat actions. The three key parameters included in this dataset each have a clear meaning: the dodge distance reflects the displacement range of the player to avoid combat; too far or too close may indicate instability in the response.

[0032] Response latency records the time difference between when the simulated character initiates an action and when the player makes an effective response, which is directly related to the player's reaction speed and proficiency.

[0033] Equipment operation stress measures the intensity of a player's operation of the equipment and reflects the operational burden.

[0034] The dodging distance, response delay, and device operating pressure are compared with the corresponding preset thresholds. If any parameter exceeds the corresponding threshold, one point is accumulated. The habitual confidence level of the current confrontation action is obtained by comparison and accumulation.

[0035] Preferably, the specific setting method for the thresholds based on dodging distance, response latency, and device operation pressure is as follows: collect a large number of historical combat records of players of different skill levels in various combat scenarios, extract the dodging distance, response latency, and device operation pressure data corresponding to all effective combat actions, and form a historical dataset of three types of parameters.

[0036] Statistical analysis is performed on historical datasets for each parameter type to calculate key statistics such as the median and quartiles. Based on the game design goals' expectation of player behavior rationality, appropriate quantiles are selected as basic threshold references. For example, the 75th percentile is used as the initial upper limit threshold; exceeding this value is considered a deviation from the normal operating range of most players.

[0037] Further screening of successful player responses to combat actions in historical records, extraction of corresponding parameter data and calculation of mean values, and correction of the initial thresholds based on the distribution characteristics of successful operations, ultimately determining the corresponding thresholds for the three types of parameters to ensure that the thresholds can effectively distinguish between the normal and abnormal states of player operations.

[0038] It should be noted that a higher habitual confidence score indicates a greater deviation from the player's usual approach, and a weaker habituality and proficiency in that action; a lower score indicates a more stable and proficient approach. This quantitative result provides a direct basis for subsequent analysis of player adaptability and adjustment of game difficulty.

[0039] It should be noted that by constructing a behavioral feature dataset and evaluating habitual confidence, this invention can accurately grasp the behavioral habits of players during combat, providing a reliable basis for subsequent difficulty adjustments and solving the problem of insufficient player behavior analysis in existing technologies.

[0040] The adversarial strategy graph is constructed, and the actual difficulty of each adversarial action is output based on the pre-constructed adversarial action-difficulty mapping relationship.

[0041] It's important to note that establishing a mapping relationship between combat actions and difficulty is to provide a quantitative basis for the difficulty of combat actions in action games. By collecting test records of simulated characters and test data from players of different skill levels, and combining average success rate, average latency, and preset logic, high, medium, and low difficulties are defined, ensuring a one-to-one correspondence between each combat action and its difficulty. This mapping relationship provides a standard for subsequently outputting the actual difficulty of each combat action, supports the system in assessing players' adaptability to actions of different difficulty levels, and thus provides a basis for adjusting the difficulty of simulated character combat actions, ensuring that difficulty adjustments are systematic and improving the scientific and rational nature of game design analysis.

[0042] In a preferred embodiment of the present invention, the specific construction method of the combat action-difficulty mapping relationship is as follows: collect a large number of test records of combat actions of simulated characters in different scenarios, covering the operation steps, angles, displacement distances and speeds corresponding to each combat action.

[0043] A large number of player characters of different skill levels were selected and tested on various combat actions. The average success rate and average latency of each player character for each combat action in each test were statistically analyzed.

[0044] The average success rate and average delay of the aforementioned combat actions are arranged in descending order, and the difficulty is divided according to a preset classification logic, including high, medium, and low difficulty.

[0045] A mapping relationship between combat actions and difficulty is constructed by mapping each combat action to its difficulty.

[0046] The preset partitioning logic is as follows:

[0047] The average success rate is lower than the median of the corresponding average success rate.

[0048] The average latency is greater than the median average latency.

[0049] If a combat action meets both of the above conditions, its difficulty is determined to be high. If a combat action meets either of the above two conditions, its difficulty is determined to be medium. If a combat action does not meet either of the above two conditions, its difficulty is determined to be low.

[0050] It needs further explanation that when a combat action simultaneously meets both of the above conditions—that is, the player's average success rate in completing the action is low and the average response delay is long—it indicates that it demands a high level of player skill and is therefore classified as high difficulty. If only one condition is met, it indicates moderate difficulty and is classified as medium difficulty. If neither condition is met, it indicates that the action is easy to complete and is classified as low difficulty. This rule clarifies the difficulty levels through quantitative data, providing a standardized basis for classifying the mapping relationship between combat actions and difficulty.

[0051] The specific method for outputting the actual difficulty of each adversarial action is as follows: match each adversarial action with a pre-constructed adversarial action-difficulty mapping relationship to obtain the actual difficulty of each adversarial action.

[0052] It should be noted that this invention constructs a mapping relationship between adversarial actions and difficulty based on a large amount of test data and objective classification logic, making the difficulty classification more in line with the actual experience of players and overcoming the shortcomings of unscientific difficulty classification in existing technologies.

[0053] The system's tactics are autonomously evolved. Based on the habitual confidence level and actual difficulty of each combat action, the system assesses the player character's adaptability to combat actions of varying difficulty, determines whether the difficulty of the simulated character's combat actions needs to be adjusted, and if so, identifies the target difficulty and makes adjustments accordingly.

[0054] In a preferred embodiment of the present invention, the specific method for evaluating the adaptability of the player character to combat actions of various difficulty levels is as follows: classify each combat action according to the actual difficulty of the action to obtain each combat action corresponding to each difficulty level.

[0055] The habitual confidence level of each combat action corresponding to each difficulty level is compared with a pre-set habitual confidence level threshold, and combat actions with a habitual confidence level greater than the habitual confidence level threshold are selected.

[0056] It should be noted that the above-mentioned habitual confidence threshold is based on statistical data of habitual confidence in various combat actions across different skill levels, with the median or mean of the distribution calculated as the core reference. If the game design goal is to filter out actions that players have developed stable operating habits, the threshold can be set slightly higher than the average habitual confidence of novice players, while being lower than the common value of experienced players. Alternatively, the threshold can be dynamically set according to the desired proficiency level of actions players should achieve at different stages, such as lowering the threshold initially to accommodate novices and raising it later to filter highly proficient actions. This ensures that the selected actions accurately reflect the player's adaptation to the corresponding difficulty level, providing a reasonable benchmark for subsequent adaptation index calculations.

[0057] The actual operation time of each adversarial action with a habitual confidence level greater than the habitual confidence level threshold is obtained, the interval between the actual operation time and the current time is obtained, and the time adjustment coefficient is obtained by calculating the ratio between the interval and the preset reference time and taking the reciprocal.

[0058] It's important to note that the time adjustment coefficient is used to eliminate the interference of the time elapsed between actions on a player's habitual confidence level, ensuring the assessment results more closely reflect the player's current actual performance. A player's habits and abilities may change over time. Assessing adaptability solely based on the habitual confidence level of past actions could lead to inaccurate results due to discrepancies between earlier and current skill levels. By calculating the interval between the actual action and the current moment, comparing it to a preset reference time, and taking the reciprocal, the time adjustment coefficient is obtained. This allows for dynamic adjustment of the weighting of actions at different time points—a shorter interval results in a coefficient closer to 1, with less correction to confidence and more information about the current state; a longer interval results in a smaller coefficient, reducing its impact on the results. This makes the subsequently calculated corrected habitual confidence and adaptability index more accurate, providing a reliable basis for assessing a player's true adaptability to combat actions of varying difficulty.

[0059] The corrected habitual confidence level for adversarial actions is obtained by multiplying the time adjustment coefficient by the habitual confidence level of the corresponding adversarial action.

[0060] The player character's adaptability index for combat actions of various difficulty levels is obtained by averaging the corrected habitual confidence scores.

[0061] In a preferred embodiment of the present invention, the specific method for determining whether the difficulty of the simulated character's combat action needs to be adjusted is as follows: the adaptability index of each difficulty combat action is adjusted with a preset adaptability index threshold, the difficulty of the adaptability index being less than the adaptability index threshold is identified, and the difficulty is arranged in ascending order.

[0062] Match the minimum difficulty level with the current difficulty level of the combat actions.

[0063] It should be explained that matching the minimum difficulty with the difficulty setting of the combat action currently being used by the simulated character is intended to determine whether the player's worst-case scenario difficulty matches the current action difficulty: if they match, it means that the player has already shown signs of being unaccustomed to the current difficulty, but there is no need to adjust the difficulty, because a lower difficulty does not exist or does not conform to the design logic; if they do not match, it means that the player is not yet accustomed to actions with lower difficulty, and the current difficulty setting exceeds their ability range, so the difficulty needs to be adjusted.

[0064] If the match is consistent, it is determined that no adjustment to the difficulty of the combat actions is needed; otherwise, it is determined that the difficulty of the combat actions needs to be adjusted.

[0065] In a preferred embodiment of the present invention, the specific method for identifying the target difficulty is as follows: the difficulties with an adaptability index less than the adaptability index threshold are arranged in ascending order, and the minimum difficulty is selected as the target difficulty.

[0066] It's important to explain why the lowest difficulty level was chosen as the target difficulty: Difficulties where the player's adaptability index is below a preset threshold—meaning difficulties where players' adaptability is insufficient—were sorted from lowest to highest level. This sorting focuses on the difficulty range players haven't yet adapted to, prioritizing players' weaknesses in adaptability according to a logic from low to high. The lowest difficulty level in the sorted list was chosen as the target difficulty because it represents the base difficulty level that players currently find most difficult to adapt to. Setting it as the adjustment target ensures that the difficulty adjustment is tilted towards the player's weakest point, reducing the difficulty of the simulated character's combat actions to a level that players are more likely to adapt to. This avoids hindering the player experience due to excessive difficulty, while also reserving room for gradually increasing the difficulty later, ultimately achieving a precise match between difficulty and player ability.

[0067] The tactical adjustments were analyzed reasonably, and data on the combat behavior characteristics of player characters in each combat action after the difficulty adjustment were collected to determine whether the difficulty adjustment was reasonable.

[0068] In a preferred embodiment of the present invention, the specific method for determining whether the difficulty adjustment is reasonable is as follows: collect the combat behavior feature data of the player character for each combat action after the difficulty adjustment, and compare the dodge distance, response delay and device operation pressure corresponding to multiple sets of combat behavior feature data with preset thresholds respectively.

[0069] The difficulty adjustment is deemed unreasonable if any of the following conditions exist:

[0070] Condition 1: The proportion of adversarial behavior feature data sets exceeding the corresponding threshold is excessive, which includes any one of the following: evasion distance, response delay, and equipment operation pressure.

[0071] Condition 2: The proportion of adversarial behavior feature data groups where all of the following parameters exceed the corresponding thresholds: dodge distance, response delay, and equipment operation pressure.

[0072] It's worth noting that the percentage of data sets where any single parameter exceeds the threshold is typically set at a relatively high limit, such as 30%. This is because exceeding the limit for a single parameter may only reflect a localized adaptation problem in a player's skill level, rather than an overall lack of ability; allowing some margin for error prevents over-adjustment. Conversely, the percentage of data sets where all three parameters exceed the threshold is typically set at a lower limit, such as 15%. This is because exceeding the limit for all three parameters simultaneously indicates a severe lack of adaptation in the player's overall operational ability. If the percentage of such data is even slightly high, it could significantly impact the gaming experience, necessitating a more stringent adjustment mechanism.

[0073] It needs further explanation that if the percentage of data sets showing combat behavior characteristics exceeding the corresponding threshold in any of the parameters—dodge distance, response latency, or device operational pressure—exceeds the preset limit (e.g., exceeding 30%), it indicates that players generally find it difficult to adapt to the adjusted difficulty in that parameter dimension. For example, most players experience excessively long response times, reflecting that the rhythm of the actions exceeds their reaction time. If the percentage of data sets showing combat behavior characteristics exceeding the corresponding threshold in all three parameters exceeds the preset limit (e.g., exceeding 15%), it indicates that some players are completely unable to cope with the adjusted difficulty in terms of overall operational ability, resulting in excessive overall operational pressure.

[0074] When the difficulty adjustment is deemed unreasonable, the difficulty of the current adversarial behavior should be lowered by one level and the analysis should continue.

[0075] It's important to note that an unreasonable difficulty adjustment means the current difficulty exceeds the player's comfort level. Lowering the difficulty by one level is the most direct relief measure, quickly reducing the player's operational pressure and making it easier for them to reach a reasonable skill level at the adjusted difficulty. However, a single level reduction may not solve the problem immediately. Therefore, it's necessary to continuously collect data on combat behavior characteristics after the difficulty is lowered, repeating the above analysis and judgment process until the conditions for an unreasonable difficulty adjustment are no longer met. Through cyclical analysis, it's ensured that the difficulty of combat actions remains stable within the player's comfort range, avoiding gaps in the player experience due to excessive difficulty fluctuations. This also provides a foundation for gradually increasing the difficulty in the future, achieving a dynamic balance between difficulty and player ability.

[0076] It should be noted that this invention achieves autonomous adjustment of the difficulty of simulated character combat actions by assessing the player's adaptability, and analyzes the rationality of the adjustment to ensure that the difficulty adjustment is suitable for the player, thereby improving the game's playability and the player's gaming experience, and making up for the shortcomings of existing technologies in tactical adjustments lacking autonomy and rationality assessment.

[0077] Please see Figure 2As shown, a second aspect of the present invention provides an action game design and analysis system, including a player behavior feature analysis module, an adversarial strategy graph construction module, a system tactical auto-evolution module, and a tactical adjustment rationality analysis module, wherein the player behavior feature analysis module is connected to the adversarial strategy graph construction module, the adversarial strategy graph construction module is connected to the system tactical auto-evolution module, and the system tactical auto-evolution module is connected to the tactical adjustment rationality analysis module.

[0078] The player behavior feature analysis module constructs a dataset of behavioral features of simulated characters for each combat action, obtains the corresponding player character's combat behavior feature dataset, and evaluates the habitual confidence level of each combat action.

[0079] The adversarial strategy graph construction module outputs the actual difficulty of each adversarial action based on the pre-built adversarial action-difficulty mapping relationship.

[0080] The system's tactical auto-evolution module assesses the player character's adaptability to combat actions of varying difficulty based on the habitual confidence level and actual difficulty of each combat action. It then determines whether the difficulty of the simulated character's combat actions needs adjustment, and if so, identifies the target difficulty and makes adjustments accordingly.

[0081] The tactical adjustment rationality analysis module collects data on the combat behavior characteristics of player characters in each combat action after the difficulty adjustment, and determines whether the difficulty adjustment is reasonable.

[0082] A third aspect of the present invention provides an action game design and analysis device, the action game design and analysis device comprising: a memory, a processor, and an action game design and analysis program stored in the memory and executable on the processor, wherein the action game design and analysis program, when executed by the processor, implements the steps of the action game design and analysis method.

[0083] Please see Figure 3 The diagram shows a block diagram of a computer system for implementing some embodiments of the present disclosure. The computer system 100 may be represented in the form of a general computing device. The computer system 100 includes a memory 110, a processor 120, and a bus 130 connecting different system components.

[0084] The memory 110 may include, for example, system memory, non-volatile storage media, etc. The system memory may store, for example, an operating system, application programs, a bootloader, and other programs. The system memory may include volatile storage media, such as random access memory (RAM) and / or cache memory. The non-volatile storage media may store, for example, instructions for executing at least one of the corresponding embodiments of the body torsional stiffness improvement method. Non-volatile storage media include, but are not limited to, disk storage, optical storage, flash memory, etc.

[0085] The processor 120 can be implemented using general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs) or other programmable logic devices, discrete gates, or transistors, among other discrete hardware components. Correspondingly, tasks such as constructing large-scale models for new energy meteorological analysis, forecasting meteorological data, generating power generation feasibility evaluation results, and determining power generation feasibility can all be implemented by executing instructions from the central processing unit (CPU)'s memory, or by using dedicated circuits to execute those steps.

[0086] Bus 130 can use any of the various bus architectures. For example, bus architectures include, but are not limited to, Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MCA) bus, and Peripheral Component Interconnect (PCI) bus.

[0087] The computer system 100 may also include an input / output interface 140, a network interface 150, and a storage interface 160. These interfaces 140, 150, and 160, as well as the memory 110 and processor 120, can be connected via a bus 130. The input / output interface 140 provides a connection interface for input / output devices such as a monitor, mouse, and keyboard. The network interface 150 provides a connection interface for various networked devices. The storage interface 160 provides a connection interface for external storage devices such as floppy disks, USB flash drives, and SD cards.

[0088] Please see Figure 4 As shown, a fourth aspect of the present invention provides a storage medium that stores one or more programs that can be executed by one or more processors to implement the steps in the above-described action game design and analysis method.

[0089] The storage media described herein include random access memory (RAM), memory, read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), registers, hard disks, removable disks, or any other form of storage media known in the art.

[0090] The above content is merely an example and illustration of the concept of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, and all such modifications and additions should fall within the protection scope of the present invention.

Claims

1. A design and analysis method for action games, characterized in that, include: Player behavior feature analysis: construct a dataset of behavioral features of simulated characters for each combat action, obtain the corresponding player character's combat behavior feature dataset, and evaluate the habitual confidence of each combat action; The adversarial strategy graph is constructed, and the actual difficulty of each adversarial action is output based on the pre-constructed adversarial action-difficulty mapping relationship. The system's tactics are autonomously evolved. Based on the habitual confidence level and actual difficulty of each combat action, the system assesses the player character's adaptability to combat actions of varying difficulty, determines whether the difficulty of the simulated character's combat actions needs to be adjusted, and if so, identifies the target difficulty and makes adjustments accordingly. The tactical adjustments were analyzed reasonably, and data on the combat behavior characteristics of player characters in each combat action after the difficulty adjustment were collected to determine whether the difficulty adjustment was reasonable. The specific method for assessing the habitual confidence level of each confrontation action is as follows: Extract the adversarial behavior feature dataset for each adversarial action, which includes dodging distance, response latency, and equipment operation pressure; The dodging distance, response delay, and device operating pressure are compared with the corresponding preset thresholds. If any parameter exceeds the corresponding threshold, one point is accumulated. The habitual confidence level of the current confrontation action is obtained by comparison and accumulation. The specific methods for assessing a player character's adaptability to combat actions at various difficulty levels are as follows: The combat actions are categorized according to their actual difficulty, resulting in each difficulty level corresponding to a specific combat action. The habitual confidence level of each combat action corresponding to each difficulty level is compared with a pre-set habitual confidence level threshold, and combat actions with habitual confidence levels greater than the habitual confidence level threshold are selected. The actual operation time of each adversarial action with a habitual confidence level greater than the habitual confidence level threshold is obtained, the interval between the actual operation time and the current time is obtained, and the time adjustment coefficient is obtained by calculating the ratio between the interval and the preset reference time and taking the reciprocal. The corrected habitual confidence of the confrontation action is obtained by multiplying the time adjustment coefficient with the habitual confidence of the corresponding confrontation action. The player character's adaptability index for combat actions of various difficulty levels is obtained by averaging the corrected habitual confidence scores.

2. The action game design and analysis method as described in claim 1, characterized in that: The specific method for constructing the adversarial action-difficulty mapping relationship is as follows: We collected a large number of test records of simulated characters’ combat actions in different scenarios, covering the operation steps, angles, displacement distances and speeds corresponding to each combat action; A large number of player characters of different skill levels were selected and tested on various combat actions. The average success rate and average latency of each player character for each combat action were statistically analyzed for each combat action in each test. The average success rate and average delay of the aforementioned combat actions are arranged in descending order, and the difficulty is divided according to a preset classification logic, including high, medium, and low difficulty. Construct a one-to-one mapping relationship between combat actions and difficulty levels; The preset partitioning logic is as follows: The average success rate is lower than the median average success rate. The average latency is greater than the corresponding median average latency. If a combat action meets both of the above conditions, its difficulty is determined to be high; if a combat action meets either of the above two conditions, its difficulty is determined to be medium; if a combat action does not meet either of the above two conditions, its difficulty is determined to be low. The specific method for outputting the actual difficulty of each adversarial action is as follows: match each adversarial action with a pre-constructed adversarial action-difficulty mapping relationship to obtain the actual difficulty of each adversarial action.

3. The action game design and analysis method as described in claim 1, characterized in that: The specific method for determining whether the difficulty of the simulated character's combat actions needs to be adjusted is as follows: The adaptability index of each difficulty level combat action is adjusted with a pre-set adaptability index threshold. Difficulty levels with an adaptability index less than the adaptability index threshold are identified and arranged in ascending order of difficulty. Match the minimum difficulty level with the current difficulty level of the combat action. If the match is consistent, it is determined that no adjustment to the difficulty of the combat actions is needed; otherwise, it is determined that the difficulty of the combat actions needs to be adjusted.

4. The action game design and analysis method as described in claim 3, characterized in that: The specific method for identifying the difficulty of the target is as follows: The difficulty levels with an adaptability index less than the adaptability index threshold are arranged in ascending order, and the lowest difficulty level is selected as the target difficulty level.

5. The action game design and analysis method as described in claim 1, characterized in that: The specific method for determining whether the difficulty adjustment is reasonable is as follows: Collect combat behavior characteristic data of player characters for each combat action after the difficulty adjustment, and compare the dodge distance, response delay and device operation pressure corresponding to multiple sets of combat behavior characteristic data with preset thresholds respectively; The difficulty adjustment is deemed unreasonable if any of the following conditions exist: Condition 1: The proportion of adversarial behavior feature data sets exceeding the corresponding threshold is excessive, including dodge distance, response delay, and equipment operation pressure. Condition 2: The proportion of adversarial behavior feature data sets where each of the following parameters exceeds the corresponding threshold: dodge distance, response delay, and equipment operating pressure; When the difficulty adjustment is deemed unreasonable, the difficulty of the current adversarial behavior should be lowered by one level and the analysis should continue.

6. An action game design analysis system, used to perform the steps of the action game design analysis method as described in any one of claims 1-5, characterized in that, include: The player behavior feature analysis module constructs a dataset of behavioral features of simulated characters for each combat action, obtains the corresponding player character's combat behavior feature dataset, and evaluates the habitual confidence of each combat action. The adversarial strategy graph construction module outputs the actual difficulty of each adversarial action based on the pre-built adversarial action-difficulty mapping relationship; The system's tactical auto-evolution module assesses the player character's adaptability to combat actions of varying difficulty based on the habitual confidence level and actual difficulty of each combat action, determines whether the difficulty of the simulated character's combat actions needs to be adjusted, and if so, identifies the target difficulty and makes adjustments accordingly. The tactical adjustment rationality analysis module collects data on the combat behavior characteristics of player characters in each combat action after the difficulty adjustment, and determines whether the difficulty adjustment is reasonable.

7. A design and analysis device for action games, characterized in that, The action game design and analysis device includes: a memory, a processor, and an action game design and analysis program stored in the memory and executable on the processor. When the action game design and analysis program is executed by the processor, it implements the steps of the action game design and analysis method as described in any one of claims 1 to 5.

8. A storage medium, characterized in that, The storage medium stores one or more programs, which can be executed by one or more processors to implement the steps in the action game design and analysis method as described in any one of claims 1-6.

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