Player Behavior Detection Using Multi-Dimensional Game Data
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Solution Overview
Problem
Current PVP online games rely heavily on player reporting for negative behavior detection, which is inefficient and inaccurate, leading to undetected negative behaviors and a deteriorating game experience for other players, affecting game trust and developer reputation.
Innovation Solution
A method and system that comprehensively collects and analyzes multi-dimensional game data, including game behavior, performance, and social data to detect negative behaviors, using weighted scoring and heatmap analysis to determine and remind players of negative actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a reporting system relying on player initiative is used, then the system complexity is low, but the detection precision and completeness of negative behaviors deteriorates
Solution Approach 1:
The system automatically detects negative behaviors through multi-dimensional data collection and analysis without requiring player intervention. The detection system serves itself by autonomously gathering game data, analyzing behavior patterns, and identifying negative actions, replacing the manual reporting mechanism while improving detection precision.
Solution Approach 2:
The patent replaces the mechanical player-reporting system with an automated computational detection system. Instead of relying on players to manually report negative behaviors, the system uses data collection modules, analysis modules, and scoring mechanisms to automatically detect and identify negative actions through multi-dimensional game data analysis.
2Ease of manufacture
If player reporting is used for negative behavior detection, then the detection system is simple to implement, but the detection speed and timeliness deteriorates
Solution Approach 1:
The system continuously collects game data and analyzes player behaviors in real-time throughout the game process. Rather than waiting for periodic player reports, the detection system operates continuously, monitoring game data streams and immediately identifying negative behaviors as they occur, thereby improving detection speed and timeliness.
Solution Approach 2:
The system performs preliminary data collection and analysis during the game process itself, before negative behaviors need to be reported. By continuously monitoring game data and pre-analyzing player actions, the system is ready to immediately detect and respond to negative behaviors, eliminating the delay inherent in post-game reporting systems.
3Measurement precision
If comprehensive multi-dimensional data analysis is implemented, then the detection precision improves, but the device complexity increases
Solution Approach 1:
The detection system is divided into distinct functional modules: data collection modules for different data types (game data, chat data, behavior data), analysis modules for processing specific data dimensions, and a scoring module for综合 evaluation. This segmentation allows the complex multi-dimensional analysis to be organized into manageable, independent components that can be developed and maintained separately.
Solution Approach 2:
The detection system employs universal data structures and analysis frameworks that can handle multiple types of game data (game data, chat data, behavior data) through a unified multi-dimensional scoring mechanism. This multi-functionality allows the system to process diverse data types using common analytical approaches, reducing overall system complexity despite the comprehensive nature of the analysis.
Data Source
AI summary
A player negative behavior detection method, a system, an electronic device, and a storage medium are provided, wherein the method includes: comprehensively collecting behavioral data such as the player operation counts, the chat content, the behavioral data, the position information, and the cooperation request responses in the single-round game; processing and analyzing the data by calculating the operation frequency score and the single behavior score, constructing a position heatmap, counting the negative word counts, and calculating the cooperation request response counts, so as to obtain multi-dimensional detection data such as game behavior, game performance, and social behavior; and comparing the comprehensive operation frequency score of the player, the single behavior score, and the expected score, combining the comparison to the scores of the other players in the team, so as to determine the negative behavior, and to output the reminding information.