An engineering blasting safety monitoring and early warning system and method
By simultaneously collecting and processing vibration waveforms and video image data in the engineering blasting area, and combining peak particle velocity prediction and flying rock trajectory recognition, a comprehensive safety early warning command is generated. This solves the problems of lagging assessment and insufficient judgment accuracy of single data sources in existing technologies, and realizes multi-dimensional quantitative early warning of blasting risks.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CHENGDU TIANYOU TANGYUAN ENG TESTING CONSULTING CO LTD
- Filing Date
- 2026-06-11
- Publication Date
- 2026-07-17
AI Technical Summary
In existing engineering blasting safety monitoring, vibration data and video data are collected and analyzed independently without establishing a unified time reference. This results in blasting risk assessment relying on the judgment of exceeding limits based on a single vibration data point. Early warning signals only reflect the current state and cannot quantitatively predict subsequent vibration trends. Furthermore, video data does not automatically extract the trajectory and spread range of flying rocks, and the single-dimensional monitoring information has limitations.
By deploying sensor nodes in the engineering blasting area to synchronously collect vibration waveform data and video image data, and performing time stamp alignment and preprocessing, combined with peak particle velocity prediction algorithm and video image analysis, the trajectory and diffusion range of flying rocks are identified, and a fusion decision-making mechanism is established to generate comprehensive early warning instructions.
It enables quantitative prediction of the trend of blasting vibration changes, and generates comprehensive safety early warning instructions by combining the trajectory and diffusion range of flying rocks, which improves the pertinence and completeness of the early warning and reduces the bias of judgment based on a single data source.
Smart Images
Figure CN122416699A_ABST