Alcohol-based fuel full life cycle safety monitoring and internet of things management platform

By constructing a four-dimensional feature vector and calculating the toxicity index using Mahalanobis distance, the sensitivity decay problem of electrochemical gas sensors caused by alcohol poisoning was solved, enabling real-time health assessment and accurate early warning of the alcohol-based fuel safety monitoring system, and improving the system's reliability and maintainability.

CN122306920APending Publication Date: 2026-06-30BEIJING JINGXIN CLEAN ENERGY CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
BEIJING JINGXIN CLEAN ENERGY CO LTD
Filing Date
2026-03-30
Publication Date
2026-06-30

AI Technical Summary

Technical Problem

Existing technologies cannot detect in real time the degree of sensitivity decay of electrochemical gas sensors caused by alcohol poisoning, resulting in the inability to identify hidden health decline states and a decrease in the reliability of alcohol-based fuel safety monitoring systems.

Method used

By acquiring the impedance spectrum parameters, background current-temperature data pairs, and cumulative exposure of the electrochemical gas sensor during the idle window, a four-dimensional feature vector is constructed. The toxicity index is calculated using Mahalanobis distance, enabling accurate assessment and early warning of the sensor's health status.

Benefits of technology

It enables real-time identification of the sensitivity decay trend of sensors in the early stage of poisoning, avoids misjudgment and false alarms, and improves the reliability and maintainability of the full life cycle safety monitoring system for alcohol-based fuels.

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Abstract

This invention belongs to the field of alcohol-based fuel safety monitoring technology. It addresses the problem that existing technologies cannot detect the sensitivity decay of electrochemical gas sensors caused by alcohol poisoning in real time, thus failing to identify their hidden health decline. Specifically, it is an alcohol-based fuel full life cycle safety monitoring and IoT management platform, including a raw data acquisition module, a data feature extraction module, a feature data integration module, a poisoning status assessment module, and a poisoning monitoring and early warning module. By mining the sensor's impedance spectrum parameters, background current-temperature response characteristics, and historical cumulative exposure during idle windows, it transforms the traditional indirect health judgment relying on periodic manual calibration into direct state quantification based on multi-dimensional data fusion. This allows the system to identify the sensitivity decay trend of the sensor in the early stages of poisoning, fundamentally avoiding the risk of hidden failure caused by false calibration results.
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