A method for detecting the braking performance of a sightseeing vehicle

By using virtual scene calibration and multi-sensor fusion technology, combined with a thermal fade compensation model, the accuracy and adaptability issues of sightseeing vehicle braking detection were solved, achieving high-precision, safe, and reliable detection results and continuous optimization.

CN122242237APending Publication Date: 2026-06-19湖南省特种设备检验检测研究院
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
湖南省特种设备检验检测研究院
Filing Date
2026-03-20
Publication Date
2026-06-19

AI Technical Summary

Technical Problem

Existing methods for testing the brakes of sightseeing vehicles mainly rely on static parameter testing and lack sensor data system calibration, resulting in a large deviation between the test results and the actual performance, making them unsuitable for complex driving environments and road conditions.

Method used

By combining the inherent parameters of the sightseeing vehicle, multi-source environmental sensor data, and a virtual scene library, the sensor calibration coefficients are fitted using VIL virtual scene calibration technology to construct a basic parameter dataset. Multi-sensor data fusion is used to identify braking intentions. Combined with a thermal fade compensation model and a dynamic weighted evaluation system, friction loss is corrected, braking life is predicted, and detection data is transmitted via AES-256 encryption and NB-IoT dual links.

Benefits of technology

Improve detection accuracy, enhance scenario adaptability, provide clear maintenance guidance, ensure secure and reliable data transmission, and achieve continuous optimization of detection capabilities.

✦ Generated by Eureka AI based on patent content.

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Abstract

This invention discloses a method for testing the braking performance of sightseeing vehicles, belonging to the field of vehicle braking performance testing. The method includes the following steps: S1, constructing a basic parameter dataset for the sightseeing vehicle; S2, obtaining a calibrated multi-dimensional braking process dataset; S3, calculating the comprehensive braking performance score after thermal fade compensation; S4, predicting the remaining braking life of the sightseeing vehicle and constructing a corrected final comprehensive performance score; S5, determining whether the vehicle is qualified, requires maintenance, or is unqualified, and outputting the determination result and specific reasons; S6, transmitting the data to the server and IoT platform via dual-link redundancy of NB-IoT and 4G; S7, dynamically updating the data to form a closed-loop iterative optimization throughout the entire process. This sightseeing vehicle braking performance testing method achieves a synergistic improvement in testing accuracy, scenario adaptability, data security, and iterative capabilities.
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