Intelligent anti-addiction method and system based on game behavior analysis

Abstract

The invention discloses an intelligent anti-addiction method and system based on game behavior analysis, and belongs to the technical field of computer software, and the method comprises the following steps: S1, collecting single game duration and capability parameters of a player in real time; s2, performing dynamic analysis on the acquired data by adopting a behavior pattern analysis algorithm to obtain a capability progress condition and an abnormal frequency condition of the player in a preset period; s3, according to the ability progress condition and the abnormal frequency condition of the player, the game access permission of the player is controlled according to a gradient access limitation strategy; according to the design, a'game effectiveness' evaluation dimension can be introduced, that is, invalid game behaviors of players can be effectively reduced by comprehensively considering two dimensions of'ability progress' and'abnormal game frequency 'of the players, and the design is different from existing most game anti-addiction systems which only depend on a single game duration threshold value to judge whether a limiting mechanism is triggered or not; compared with a traditional system, invalid game duration can be effectively shortened.

Description

Technical Field

[0001] The present invention belongs to the field of computer software technology, and in particular relates to an intelligent anti-addiction method and system based on game behavior analysis. Background Art

[0002] With the rapid development of internet technology, the gaming industry continues to expand, its user base continues to grow, and the problem of game addiction has become increasingly prominent. Against this backdrop, anti-addiction systems have become a crucial tool for the gaming industry to ensure a healthy and sustainable gaming experience for users. Currently, existing anti-addiction systems, such as the technical solution with publication number CN112306408A, often utilize a fixed-time threshold for forced disconnection. However, these mechanisms have exposed numerous significant flaws in practical applications.

[0003] The first point is the lack of effective differentiation of gaming behaviors. The current anti-addiction system cannot accurately distinguish between effective and ineffective gaming behaviors. Players can exploit loopholes in the system rules and circumvent anti-addiction restrictions by frequently logging in for short periods of time. For example, in some online games, players constantly log in and out of the game, each time keeping the duration of the game within the threshold set by the anti-addiction system. This makes it difficult for the anti-addiction system to play its intended role and truly constrain players' gaming behavior.

[0004] The second point is the neglect of incentives for player growth. Existing anti-addiction systems lack positive incentives for player growth, which can easily lead to a negative gaming experience characterized by "ineffective, time-consuming play with no progress." This prolonged state of affairs not only makes it difficult for players to enjoy the game but can also foster boredom, negatively impacting the healthy development of the gaming industry. For example, in role-playing games, some players spend countless hours playing but, due to a lack of incentives, are unable to make substantial progress in improving their character abilities or acquiring equipment, leading to reduced game satisfaction.

[0005] The third point is that it cannot adapt to different game types. Different types of games, such as MOBA (multiplayer cooperative tower defense game), RPG (role-playing game), FPS (first-person shooter game), etc., have their own unique rank evaluation systems and gameplay. The existing anti-addiction system treats all games equally and cannot be personalized according to the characteristics of different game types, resulting in a significant reduction in the anti-addiction effect. For example, MOBA games emphasize teamwork and competitive confrontation, and players' gaming performance is mainly reflected in their ranks; while RPG games focus more on character growth and plot experience, and game progress is usually measured by indicators such as character level and task completion. The existing anti-addiction system finds it difficult to take into account these differences and cannot provide an adaptive anti-addiction solution for all types of games.

[0006] In view of the shortcomings of the existing technology, the present invention provides an intelligent anti-addiction method and system based on game behavior analysis, aiming to solve the above problems. Summary of the Invention

[0007] The purpose of the present invention is to overcome the shortcomings of the existing technology and provide an intelligent anti-addiction method and system based on game behavior analysis, which can break through the traditional simple time control mode, introduce the "game effectiveness" evaluation dimension, and effectively reduce players' invalid gaming behavior.

[0008] To achieve the above objectives, in a first aspect, the present invention provides an intelligent anti-addiction method based on game behavior analysis, the method comprising:

[0009] S1: Real-time collection of players’ single game duration and ability parameters;

[0010] S2: Use behavioral pattern analysis algorithms to dynamically analyze the collected data to obtain the player's ability progress and abnormal frequency within a preset period;

[0011] S3: Control the players’ game access rights according to the gradient access restriction strategy based on the players’ ability progress and abnormal frequency.

[0012] In combination with the first aspect, the ability parameters in step S1 include but are not limited to: game character level, competition rank, equipment score and task completion, and each of the ability parameters is customized through a dynamic parameter configuration engine.

[0013] In conjunction with the first aspect, the behavior pattern analysis algorithm in step S2 is a sliding window algorithm, and its specific steps include:

[0014] S21: Perform window initialization processing on the collected data;

[0015] S22: Calculate the player's ability parameter change value ΔA based on the initialized data i , the calculation formula is: ΔA i =A i，current -A i，previous ;

[0016] Among them, A i，current Indicates the ability parameter value at the end of the current game, A i，previous Indicates the ability parameter value at the end of the last game;

[0017] S23: Assign a weight w to each capability parameter based on its importance and relevance i ;

[0018] S24: Calculate the player's progress score ProgressScore within the sliding window using a weighted summation method; the calculation formula is:

[0019] Where n is the total number of capability parameters, w i is the weight of the i-th capability parameter, ΔA i is the change value of the i-th capability parameter; ΔA i A positive value indicates an improvement in capability, while a negative value indicates a decrease in capability.

[0020] In combination with the first aspect, step S3 specifically includes the following steps:

[0021] When at least one of the following S31 and S32 occurs, the system triggers a prohibited entry operation;

[0022] S31: When the ProgressScore is less than 0, the system determines that the player's progress has stagnated and triggers a ban operation. The ban duration is set according to the gradient access restriction policy;

[0023] S311: Set different capability thresholds for different types of games based on game type and design goals;

[0024] S312: The system monitors the player's ability parameter changes in the last N games in real time, calculates the cumulative change value of the ability parameter, and compares it with the preset ability threshold;

[0025] S313: If the player does not reach the preset ability threshold within N game sessions, the system determines that the player has engaged in invalid gaming behavior and triggers a ban operation. The ban duration is set according to the gradient access restriction policy.

[0026] S32: Monitor the duration of a single game session of the player. When an abnormal behavior pattern of M single game sessions < T minutes is detected, the system triggers a ban operation. The ban duration is also set according to the gradient access restriction strategy.

[0027] In combination with the first aspect, the dynamic parameter configuration engine provides an interface for game developers, supporting game developers to define ability improvement indicators, including but not limited to: rank difference value and experience growth rate, and at the same time set the weight of each ability improvement indicator to adapt to the characteristics of different types of games.

[0028] In combination with the first aspect, the gradient access restriction strategy includes: setting the ban period to 24 hours for the first violation, and extending it to 72 hours for repeated violations or customizing it according to actual needs.

[0029] In a second aspect, the present invention provides an intelligent anti-addiction system based on game behavior analysis, which is used to implement the intelligent anti-addiction method based on game behavior analysis as described in the first aspect, and the system includes:

[0030] Data collection module, used to collect players' single game duration and ability parameters in real time;

[0031] The data analysis module is used to dynamically analyze the collected data using a behavioral pattern analysis algorithm to obtain the player's ability progress and abnormal frequency within a preset period;

[0032] The control module is used to control the player's game access rights according to the player's ability progress and abnormal frequency, in accordance with the gradient access restriction strategy.

[0033] In conjunction with the second aspect, the control module includes:

[0034] A progress stagnation detection unit, configured to trigger a ban if a player fails to reach a preset ability threshold within a set number of N games; wherein the ability threshold includes: rank, level, and achievement points;

[0035] Abnormal frequency detection unit, used to monitor the duration of a player's single game session. When an abnormal behavior pattern of M single game sessions of less than T minutes is detected, the system triggers a ban operation;

[0036] When a player has at least one of the above progress stagnation detection units and abnormal frequency detection units, the system triggers a ban operation.

[0037] In a third aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the intelligent anti-addiction method based on game behavior analysis as described in the first aspect are implemented.

[0038] In a fourth aspect, the present invention provides a device comprising:

[0039] a memory for storing instructions;

[0040] The processor is configured to execute the instructions so that the device performs the steps of implementing the method described in the first aspect.

[0041] Compared with the prior art, the present invention has the following beneficial effects:

[0042] 1. Improve game effectiveness: This invention breaks through the traditional simple time control model and introduces the "game effectiveness" evaluation dimension, that is, it proposes a "dual-factor trigger mechanism". It comprehensively considers the two dimensions of "ability improvement" and "abnormal game frequency" of players to effectively reduce players' invalid game behavior. Unlike most existing game anti-addiction systems that only rely on a single game time threshold to determine whether to trigger the restriction mechanism; compared with traditional systems, it can effectively reduce invalid game time.

[0043] 2. Optimize user experience: The gradient access restriction strategy designed by this invention can not only serve as a warning to players who violate the rules, but also avoid overly harsh penalties. While ensuring the anti-addiction effect, it also takes into account the user experience, which helps to improve user satisfaction and loyalty to the game; and the data-driven dynamic analysis model replaces the traditional mechanical control, making the anti-addiction system more scientific and intelligent, and can accurately manage according to the actual game behavior of players. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 It is a flow chart of the method of the present invention.

[0045] Figure 2 It is the overall architecture diagram of the system of the present invention. DETAILED DESCRIPTION

[0046] The present invention will be further described below in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention and are not intended to limit the scope of protection of the present invention.

[0047] Example 1

[0048] refer to Figure 1 This embodiment provides an intelligent anti-addiction method based on game behavior analysis, the method comprising:

[0049] S1: Real-time collection of players’ single game duration and ability parameters;

[0050] S2: Use behavioral pattern analysis algorithms to dynamically analyze the collected data to obtain the player's ability progress and abnormal frequency within a preset period;

[0051] S3: Control the player's game access rights according to the gradient access restriction strategy based on the player's ability progress and abnormal frequency.

[0052] Furthermore, the ability parameters in S1 include but are not limited to: game character level, competition rank, equipment score and task completion, and each of the ability parameters is customized through a dynamic parameter configuration engine.

[0053] In addition, the dynamic parameter configuration engine provides an interface for game developers, supporting game developers to define ability improvement indicators, including but not limited to: rank difference value and experience growth rate, and setting the weight of each ability improvement indicator to adapt to the characteristics of different types of games.

[0054] Furthermore, the behavior pattern analysis algorithm in step S2 is a sliding window algorithm, and its specific steps include:

[0055] S21: Perform window initialization processing on the collected data;

[0056] S22: Calculate the player's ability parameter change value ΔA based on the initialized data i , the calculation formula is: ΔA i =A i，current -A i，previous ;

[0057] Among them, A i，current Indicates the ability parameter value at the end of the current game, A i，previous Indicates the ability parameter value at the end of the last game;

[0058] S23: Assign a weight w to each capability parameter based on its importance and relevance i ;

[0059] Weight w i The settings follow the following principles:

[0060] 1. Based on indicator importance: Highly important ability parameters are assigned higher weights. For example, in competitive games, the weight of changes in competitive rank may be higher than the weight of changes in equipment rating.

[0061] 2. Consider indicator correlation: If certain capability parameters are correlated or complementary, weights should be assigned appropriately to avoid double counting or overemphasizing certain aspects. For example, role level and task completion may be correlated, so weighting should balance the impact of both.

[0062] 3. Game Objective Setting: Based on the game's design goals and gameplay characteristics, assign higher weights to ability parameters related to the game's core objectives. For example, if the game focuses on developing the player's combat abilities, then achievement indicators related to combat can be given a relatively higher weight.

[0063] 4. Determine weights based on the game's different progression levels and their importance: For different game types (such as MOBA, RPG, FPS, etc.), dynamically adjust weights based on the game's progression level and the importance of each indicator. For example, in a MOBA game, rank advancement may be weighted higher than equipment rating; while in an RPG game, character level and quest completion may be weighted more highly.

[0064] For example, in a MOBA game, the weight of rank increase is set to 0.3, the weight of equipment score increase is set to 0.2, and the weight of task completion is set to 0.1. In an RPG game, the weight of character level increase is set to 0.4, the weight of task completion is set to 0.3, the weight of equipment score increase is set to 0.2, and so on.

[0065] S24: Calculate the player's progress score ProgressScore within the sliding window using a weighted summation method; the calculation formula is:

[0066] Where n is the total number of capability parameters, w i is the weight of the i-th capability parameter, ΔA i is the change value of the i-th capability parameter; ΔA i A positive value indicates an improvement in capability, while a negative value indicates a decrease in capability.

[0067] Furthermore, the step S3 specifically includes the following steps:

[0068] When at least one of the following S31 and S32 occurs, the system triggers a prohibited entry operation;

[0069] S31: When the ProgressScore is less than 0, the system determines that the player's progress has stagnated and triggers a ban operation. The ban duration is set according to the gradient access restriction policy;

[0070] That is, when a player fails to reach a preset ability threshold within a set number of N games, the system triggers a ban operation; wherein, the ability threshold includes but is not limited to: rank, level, and achievement points. The specific implementation steps are as follows:

[0071] S311: Set different capability thresholds for different types of games based on game type and design goals;

[0072] S312: The system monitors the player's ability parameter changes in the last N games in real time, calculates the cumulative change value of the ability parameter, and compares it with the preset ability threshold;

[0073] S313: If the player does not reach the preset ability threshold within N game sessions, the system determines that the player has engaged in invalid gaming behavior and triggers a ban operation. The ban duration is set according to the gradient access restriction policy.

[0074] S32: Monitor the duration of a player's single game session. When an abnormal behavior pattern of M single game sessions < T minutes is detected, the system triggers a ban operation. The ban duration is also set according to the gradient access restriction policy.

[0075] Wherein, step S32 includes the following steps:

[0076] S321: The system monitors the player's single game duration in real time, records the start and end time of each game, and calculates the single game duration;

[0077] S322: The system counts the number of times M that a player's single game session duration is less than T minutes within a certain time window (e.g., 24 hours). For example, for a MOBA game, T is set to 15 minutes. If a player's single game session duration is less than 15 minutes in 7 out of 10, this is considered an abnormal behavior pattern.

[0078] S323: If M abnormal behavior patterns are detected, the system determines that the player has invalid gaming behaviors such as frequent short-term logins, and triggers a ban operation; the ban duration is also set according to the gradient access restriction strategy.

[0079] Specifically, the gradient access restriction strategy includes: setting the ban period to 24 hours for the first violation, and extending it to 72 hours for repeated violations or customizing it according to actual needs.

[0080] Preferably, based on the above step S3, in order to adapt to a variety of game types, in this embodiment, the present invention uses a configurable rule engine to flexibly set anti-addiction rules according to the characteristics of different game types, such as MOBA, RPG, etc., to meet diverse gaming needs; specifically as follows:

[0081] 1. MOBA game applications

[0082] In this type of game, this embodiment sets K=15 because the player abilities in this type of game change frequently and short-term performance has a greater impact on overall ability evaluation.

[0083] Set the rank difference threshold: 30 ranked matches without improving by ≥1 small rank;

[0084] Abnormal frequency detection: 7 out of 10 games lasted less than 15 minutes (including sudden quits);

[0085] Penalty strategy: 24-hour ban for the first violation, escalated to 72 hours for three consecutive violations.

[0086] 2.RPG game applications

[0087] In this type of game, this embodiment sets K=25 because the player's ability in this type of game usually improves slowly, and a larger K value can more accurately reflect the long-term progress.

[0088] Defined ability indicators: Experience value growth rate <5% / time or no improvement in equipment score;

[0089] Combined with the task completion rate (copy clearance rate < 30%) as an auxiliary judgment condition;

[0090] Penalty strategy: 24-hour ban for the first violation, escalated to 72 hours for three consecutive violations.

[0091] 3. FPS game applications

[0092] In this type of game, this embodiment sets K=15 because the player abilities in this type of game change frequently and short-term performance has a greater impact on overall ability evaluation.

[0093] Set the ability improvement indicator: KD value (Kill / Death Ratio) does not increase by ≥ 0.1 in 20 games, and headshot rate does not increase significantly;

[0094] Abnormal frequency detection: 5 out of 8 games with a duration of less than 18 minutes, including behaviors such as voluntarily quitting the game and hanging up;

[0095] Penalty strategy: A warning will be given for the first violation, a 12-hour ban for the second violation, and a 36-hour ban for three consecutive violations.

[0096] 4. Casual puzzle game apps

[0097] In this type of game, this embodiment sets K=8 because the single game duration of this type of game is relatively short, and changes in player abilities may be reflected in a relatively short period of time. A smaller K value can reflect the player's progress more promptly.

[0098] Defined ability indicators: Level pass rate < 60%, and failure to unlock new levels during 15 game sessions;

[0099] Taking into account the growth of game scores, if the growth of game scores is less than 10% for 10 consecutive times, it is considered as stagnant progress;

[0100] Punishment strategy: When the anti-addiction mechanism is triggered, players are restricted to playing the tutorial level game for 3 hours to guide them to improve their gaming skills.

[0101] 5. Business simulation game applications

[0102] Set operating performance indicators: within a specific period in the game (such as one month of game time), the asset growth rate is less than 8%, and the construction progress of key buildings lags behind;

[0103] Abnormal operation detection: Frequently resetting game progress more than 3 times / day, attempting to circumvent growth problems by restarting the game;

[0104] Penalty strategy: The first time a violation is detected, the player will be restricted from performing game acceleration operations within that day; if the violation occurs again, the player will be banned for 24 hours.

[0105] For example, assume that the player's ability change values ΔAi in the last 15 (MOBA or FPS) game applications are shown in Table 1 below (K=15):

[0106] Table 1:

[0107] Number of games 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 ΔAi (capacity change value) 2 -1 3 0 1 -2 2 1 0 3 -1 2 1 0 2

[0108] According to Table 1, assuming that the weight wi of each capability indicator is 1, the calculation of ProgressScore is as follows:

[0109] ProgressScore=∑(wi×ΔAi)=2-1+3+0+1-2+2+1+0+3-1+2+1+0+2=13>0,

[0110] Since ProgressScore>0, it indicates that the player's overall ability has improved in the last 15 games, and the system will not trigger restrictions.

[0111] In this embodiment, the present invention flexibly sets personalized anti-addiction rules based on the characteristics of different types of games (such as MOBA, RPG, FPS, etc.), which can accurately adapt to the needs of various games, effectively improve the pertinence and effectiveness of the anti-addiction system, and at the same time take into account the player's gaming experience.

[0112] Example 2

[0113] refer to Figure 2 Based on the first embodiment, the present invention provides an intelligent anti-addiction system based on game behavior analysis, which is used to implement the intelligent anti-addiction method based on game behavior analysis as described in the first embodiment. The system includes:

[0114] Data collection module, used to collect players' single game duration and ability parameters in real time;

[0115] The data analysis module is used to dynamically analyze the collected data using a behavioral pattern analysis algorithm to obtain the player's ability progress and abnormal frequency within a preset period;

[0116] The control module is used to control the player's game access rights according to the player's ability progress and abnormal frequency, in accordance with the gradient access restriction strategy.

[0117] Specifically, in this embodiment, the control module includes:

[0118] A progress stagnation detection unit, configured to trigger a ban if a player fails to reach a preset ability threshold within a set number of N games; wherein the ability threshold includes: rank, level, and achievement points;

[0119] Abnormal frequency detection unit, used to monitor the duration of a player's single game session. When an abnormal behavior pattern of M single game sessions of less than T minutes is detected, the system triggers a ban operation;

[0120] When a player meets at least one of the above progress stagnation detection unit and abnormal frequency detection unit, the system triggers the ban operation.

[0121] Example 3

[0122] Based on Example 1, the present invention provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the intelligent anti-addiction method based on game behavior analysis as described in Example 1 are implemented.

[0123] Example 4

[0124] Based on the first embodiment, the present invention provides a device, including:

[0125] a memory for storing instructions;

[0126] The processor is configured to execute the instructions so that the device performs the steps of implementing the method described in the first embodiment.

[0127] In summary, the present invention breaks through the limitations of the traditional simple time control mode by introducing the "game effectiveness" evaluation dimension and the dual-factor trigger mechanism, which can effectively reduce players' invalid gaming behaviors and improve the effectiveness of the game; at the same time, with the help of the dynamic parameter configuration engine and the gradient access restriction strategy, it realizes data-driven precise management and optimizes the user experience, taking into account the anti-addiction effect and player satisfaction, and providing strong support for the healthy development of the game industry.

[0128] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0129] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0130] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0131] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0132] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. An intelligent anti-addiction method based on game behavior analysis, characterized in that: include: S1: Real-time collection of players’ single game duration and ability parameters; S2: Use behavioral pattern analysis algorithms to dynamically analyze the collected data to obtain the player's ability progress and abnormal frequency within a preset period; S3: Control the player's game access rights according to the gradient access restriction strategy based on the player's ability progress and abnormal frequency.

2. The intelligent anti-addiction method based on game behavior analysis according to claim 1 is characterized in that: The ability parameters in step S1 include but are not limited to: game character level, competition rank, equipment score and task completion, and each of the ability parameters is customized through a dynamic parameter configuration engine.

3. The intelligent anti-addiction method based on game behavior analysis according to claim 1 is characterized in that: The behavior pattern analysis algorithm in step S2 is a sliding window algorithm, and its specific steps include: S21: Perform window initialization processing on the collected data; S22: Calculate the player's ability parameter change value ΔA based on the initialized data i , the calculation formula is: ΔA i =A i，current -A i，previous ; Among them, A i，current Indicates the ability parameter value at the end of the current game, A i，previous Indicates the ability parameter value at the end of the last game; S23: Assign a weight w to each capability parameter based on its importance and relevance i ; S24: Calculate the player's progress score ProgressScore within the sliding window using a weighted summation method; the calculation formula is: Where n is the total number of capability parameters, w i is the weight of the i-th capability parameter, ΔA i is the change value of the i-th capability parameter; ΔA i A positive value indicates an improvement in capability, while a negative value indicates a decrease in capability.

4. The intelligent anti-addiction method based on game behavior analysis according to claim 3 is characterized in that: The step S3 specifically includes the following steps: When at least one of the following S31 and S32 occurs, the system triggers a prohibited entry operation; S31: When the ProgressScore is less than 0, the system determines that the player's progress has stagnated and triggers a ban operation. The ban duration is set according to the gradient access restriction policy. The specific implementation steps are as follows: S311: Set different capability thresholds for different types of games based on game type and design goals; S312: The system monitors the player's ability parameter changes in the last N games in real time, calculates the cumulative change value of the ability parameter, and compares it with the preset ability threshold; S313: If the player does not reach the preset ability threshold within N game sessions, the system determines that the player has engaged in invalid gaming behavior and triggers a ban operation. The ban duration is set according to the gradient access restriction policy. S32: Monitor the duration of a single game session of the player. When an abnormal behavior pattern of M single game sessions < T minutes is detected, the system triggers a ban operation. The ban duration is also set according to the gradient access restriction strategy.

5. The intelligent anti-addiction method based on game behavior analysis according to claim 2 is characterized in that: The dynamic parameter configuration engine provides an interface for game developers, supporting them to define ability improvement indicators, including but not limited to: rank difference value and experience growth rate, and setting the weight of each ability improvement indicator to adapt to the characteristics of different types of games.

6. The intelligent anti-addiction method based on game behavior analysis according to claim 1 is characterized in that: The gradient access restriction strategy includes: setting the ban period for the first violation to 24 hours, and for repeated violations, it can be extended to 72 hours or customized according to actual needs.

7. An intelligent anti-addiction system based on game behavior analysis, characterized in that: For implementing the intelligent anti-addiction method based on game behavior analysis as described in any one of claims 1 to 6, the system includes: Data collection module, used to collect players' single game duration and ability parameters in real time; The data analysis module is used to dynamically analyze the collected data using a behavioral pattern analysis algorithm to obtain the player's ability progress and abnormal frequency within a preset period; The control module is used to control the player's game access rights according to the player's ability progress and abnormal frequency, in accordance with the gradient access restriction strategy.

8. The intelligent anti-addiction system based on game behavior analysis according to claim 7 is characterized in that: The control module includes: A progress stagnation detection unit, configured to trigger a ban if a player fails to reach a preset ability threshold within a set number of N games; wherein the ability threshold includes: rank, level, and achievement points; Abnormal frequency detection unit, used to monitor the duration of a player's single game session. When an abnormal behavior pattern of M single game sessions of less than T minutes is detected, the system triggers a ban operation; When a player has at least one of the above progress stagnation detection units and abnormal frequency detection units, the system triggers a ban operation.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the intelligent anti-addiction method based on game behavior analysis as described in any one of claims 1 to 6 are implemented.

10. A device, characterized in that include: a memory for storing instructions; A processor is used to execute the instructions so that the device performs the steps of the intelligent anti-addiction method based on game behavior analysis as described in any one of claims 1 to 6.

Images