RFID Route Stitching for Real-Time Race Data Mapping
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Solution Overview
Problem
Conventional race timing systems lack the ability to provide real-time, interactive, and comprehensive race data aggregation and display, limiting athlete and fan engagement during and after races.
Innovation Solution
A platform that aggregates and displays race data using RFID stitching to create a race route, incorporating big data analytics, streaming data, and cloud infrastructure for real-time athlete tracking, media broadcasting, and interactive features, with scalable microservices and GraphQL APIs for data flexibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional race timing systems with transponders and antenna arrays are used, then race timing and monitoring can be achieved, but real-time interactive display and comprehensive data aggregation are limited
Solution Approach 1:
The system divides the race timing infrastructure into independent segments: RFID readers at timing points, a central server for data aggregation, and client devices for display. This segmentation allows comprehensive data collection while distributing complexity across multiple manageable components rather than a monolithic system.
Solution Approach 2:
The patent introduces an intermediary platform (the race timing server) that sits between the RFID reading system and the display system. This intermediary aggregates data from multiple timing points, processes it, and makes it available to various clients, thereby improving data accessibility without requiring direct complex connections between all system components.
2Measurement precision
If RFID transponders and antenna arrays are deployed at multiple timing points, then racer tracking capability is improved, but system complexity and infrastructure requirements increase
Solution Approach 1:
The system employs universal RFID timing points that can serve multiple racing events and different types of races (running, cycling, swimming). Each timing point is designed to be multi-functional, reading various RFID tag types and providing data that can be used for timing, tracking, and analytics across different race configurations, thereby reducing overall infrastructure complexity.
Solution Approach 2:
The system allows flexible configuration of timing point parameters such as reading distance, tag types supported, and data collection frequency. By adjusting these parameters based on specific race requirements, the system achieves high measurement precision when needed while avoiding unnecessary complexity in standard racing scenarios.
3Productivity
If comprehensive race data is collected and displayed in real-time, then athlete and fan engagement is enhanced, but data processing and system resource requirements increase
Solution Approach 1:
The system implements partial data aggregation by collecting only the essential race data needed for real-time display (timing, position, race results) while storing comprehensive detailed data on-demand or in batches. This approach provides real-time engagement capabilities without requiring continuous full-scale data processing, thereby reducing energy consumption while maintaining productivity.
4Reliability
If a centralized backend system processes all race data, then data consistency is maintained, but system scalability and flexibility are reduced
Solution Approach 1:
The system architecture is segmented into a centralized data aggregation layer and distributed display layers. The central server maintains data consistency by being the single source of truth for race data, while multiple independent client devices (mobile apps, web browsers, display screens) can simultaneously access and display data without interfering with each other. This segmentation enables both data consistency and system scalability.
Solution Approach 2:
The system dynamically adapts to different race configurations, numbers of participants, and display requirements without requiring structural changes. The centralized backend can handle varying data loads by adjusting processing priorities, and the distributed client architecture naturally scales to accommodate more users and devices as needed.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables real-time race data aggregation and interactive display, enhancing athlete and fan engagement through comprehensive event information, live tracking, and easy scalability, overcoming limitations of traditional systems.
Implementation Method 1
Often, the transponder is a Radio Frequency Identification (RFID) transponder, which can be active or passive
Data Source
AI summary
A method comprising using at least one hardware processor to: selecting race data API's on a race results third party platform; creating an event or race and connecting with a race results third party platform; connecting to the race data associated with the created event or race via the corresponding API's; accessing timer point names via the API's; creating a route using a route creation tool on a map; matching the timing point names/ID's to rank names/ID's and Decimal times with distance markers; placing representations of the distance markers on the map; receiving race result data associated with the timing points; correlating the race result data with the distance markers; and displaying, recording, or both the race result data with respect to the map.