Detection method based on operation state of video image evidence-obtaining device

Abstract

A detection method based on an operation state of a video image evidence-obtaining device comprises the steps of (1) performing anomaly detection of a communication interface, wherein a front-end device sends a heartbeat data package at set frequency, if the heartbeat data package of the front-end device is received within a time period of the frequency, the communication interface is normal, and if not, the communication interface is judged to be abnormal; (2) performing fault detection of application software, wherein the application software sends a heartbeat data package to a central client-side at set frequency, if the central client-side does not receive the heartbeat data package beyond the time period of the frequency, the application software is judged to be abnormal, and if not, the application software is judged to be normal; (3) detecting insufficiency of network bandwidth, wherein the bandwidth required by normal communication is first calculated, then bandwidth required by actual communication is calculated,, if the actual bandwidth is larger than the bandwidth required by the normal communication, the network bandwidth is judged to be sufficient, and if not, the network bandwidth is judged to be insufficient; and (4) performing detection of video signal loss. The detection method effectively solves the detection problem of the operation state of the video image evidence-obtaining device related in '995 standard' and is good in reliability.

Description

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AI Technical Summary

Benefits of technology

This technology allows users to easily detect their own status on videos from security systems without having them shut down or pay extra money every few hours. It can be used with both older devices that were previously installed and newer ones that had been upgraded overtime.

Problems solved by technology

The technical problem addressed in this patents relates to improving vehicle safety systems' response times during emergency situations where there may exist hidden dangers like roadside obstacles or other obstructions affecting safe driving behavior. Current solutions involve expensive hardware installations, manual troubleshooting, and relying upon visual observation techniques. These approaches result in low operational accuracy and incomplete diagnoses of malfunctions. There is a demand for better ways to identify and track these issues without requiring extensive infrastructure investments or having extra components attached to them.

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