Airborne equipment ground safety accident prediction method and system based on space-time factors

CN120995261BActive Publication Date: 2026-06-19CHINA AERO POLYTECH ESTAB
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
CHINA AERO POLYTECH ESTAB
Filing Date
2025-07-22
Publication Date
2026-06-19

AI Technical Summary

Technical Problem

Existing technologies struggle to quantitatively predict the probability of safety accidents in ground testing environments for aviation equipment using small sample test data, especially low failure probability events, resulting in a lack of quantitative data support for assessments.

Method used

By establishing a safety accident prediction method based on spatiotemporal factors, utilizing the influence relationship between safety accidents and time and distance parameters, the probability density function of the time and distance distribution of the test process is derived, and the conditional probability density function and mathematical model are constructed to evaluate the safety of the test environment and process.

Benefits of technology

It enables quantitative prediction based on small sample data, reduces the difficulty of data acquisition, provides a basis for decision-making on quantitative evaluation of experimental environment layout and design optimization, and significantly improves the accuracy of safety analysis.

✦ Generated by Eureka AI based on patent content.

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Abstract

This invention provides a method and system for predicting ground safety accidents of aviation equipment based on spatiotemporal factors, relating to the fields of resource allocation and risk assessment. The method includes: S1, identifying failure modes of safety accidents in the test environment to obtain the failure modes of the ground test environment of aviation equipment; S2, setting safety standards and establishing a mathematical model for the statistical prediction distribution of safety accidents based on historical safety accident data; S3, solving the mathematical model for the statistical prediction distribution of safety accidents based on test data to obtain the parameters of the conditional probability model for the occurrence of safety accidents; S4, predicting the overall safety accident probability of the test environment to determine the safety of the test environment. This invention transforms the problem of estimating the probability of high-risk safety accidents into a testing problem of key parameters such as time and distance under controllable constraints. By establishing mathematical models of the conditional probability density function and the probability density function of safety accidents, quantitative prediction based on small sample data is achieved.
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