AI Cycling Performance Analysis System with Dynamic Track Control
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing cycling sport performance analysis systems fail to integrate and analyze multiple characteristic information of cyclists, lack the use of track information for apparatus control, and do not employ artificial intelligence for comprehensive training and evaluation, resulting in insufficient analysis and evaluation of performance levels.
Innovation Solution
A cycling sport performance level analysis system incorporating an artificial intelligence classification analysis module, a bicycle apparatus, and a cyclist information module, which collects and analyzes sport sensing information, track information, and historical riding data to generate performance level analysis results, and recommends training plans and merchandise based on these analyses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple characteristic information of the cyclist is integrated and analyzed using AI models, then the accuracy of performance level analysis is improved, but the device complexity increases
Solution Approach 1:
The system divides the performance analysis into multiple independent modules: an AI classification analysis module for processing sensing data, a classification knowledge rule module for evaluating performance levels, and a cyclist information module for managing personal data. Each module handles specific tasks separately, improving analysis accuracy while keeping individual module complexity manageable.
Solution Approach 2:
The classification knowledge rule module acts as an intermediary between the AI classification analysis module and the cyclist information module. It receives classified results from the AI module, combines them with cyclist basic information and historical data, and generates comprehensive performance evaluations, thereby integrating multiple information sources systematically.
2Adaptability or versatility
If track information is used to control the sport apparatus, then the adaptability of the training system is improved, but the device complexity increases
Solution Approach 1:
The sport apparatus transitions from a static configuration to a dynamic one by incorporating track information from the classification test task rule. The apparatus automatically adjusts its operation based on the specific track characteristics, enabling adaptive training while maintaining straightforward control through pre-programmed track profiles.
3Reliability
If comprehensive sport sensing information is collected and analyzed, then the reliability of performance evaluation is improved, but the loss of time in data processing increases
Solution Approach 1:
The system performs preliminary classification of sensing information using the AI classification analysis module during the test execution. By pre-processing and categorizing data in real-time according to established classification rules, the system ensures comprehensive evaluation reliability while minimizing post-processing time through structured data organization from the outset.
Data Source
AI summary
A cycling sport performance level analysis system includes an artificial intelligence classification analysis module, a bicycle apparatus, a classification knowledge rule module and a cyclist information module. The classification knowledge rule module transmits a classification test task rule including a track information to the artificial intelligence classification analysis module and the bicycle apparatus. The bicycle apparatus performs the classification test task rule and collects a sport sensing information which is sensed to transmit the sport sensing information to the artificial intelligence classification analysis module. The cyclist information module transmits a cyclist basic information and a track historical riding information to the artificial intelligence classification analysis module. The artificial intelligence classification analysis module analyzes the sport sensing information, the track information, the cyclist basic information and the track historical riding information to generate a cycling sport performance level analysis result.