Construction environment-oriented multistage safety early warning method

Abstract

The invention discloses a construction environment-oriented multistage safety early warning method, and belongs to the field of safety early warning. According to the construction environment-oriented multi-level safety early warning method, a hierarchical early warning mechanism is adopted, and systematicness, accuracy and reliability of construction big data multi-level safety early warning are improved; labeling and danger division are carried out on the collected data, and the early warning cost is effectively saved on the premise that the accuracy and timeliness are guaranteed by deeply mining the incidence relation between the multi-dimensional multi-element data; in the detection process, a visual depth analysis scheme suitable for a project construction scene is provided in combination with an existing advanced algorithm, the situation of dead angles in safety detection is effectively prevented, and the system is endowed with the ability of dynamically adjusting the early warning level.

Description

technical field [0001] The invention belongs to the field of safety early warning, in particular to a construction environment-oriented multi-level safety early warning method. Background technique [0002] With the continuous advancement of industrial information technology, how to carry out early warning and prevention and control for safety in the process of project construction in multiple directions, angles and levels has become one of the problems to be solved urgently in safety production. The data of the early warning mechanism mainly comes from the big data of the construction site, which focuses on the safety management and control of the construction site on the construction site and focuses on key elements such as personnel, machinery, materials, and the environment. However, the existing related early warning models suffer from single data, insufficient data linkage, The small scale of data processing, poor technical scalability, unclear hierarchical management ...

AI Technical Summary

Problems solved by technology

The data of the early warning mechanism mainly comes from the big data of the construction site, which focuses on the safety management and control of the construction site, and focuses on key elements such as personnel, machinery, materials, and the environment. However, the existing relevant early warning models have single data, insufficient data linkage, Various problems such as small data processing scale, poor technical scalability, unclear boundaries of hierarchical management and control, and high cost lead to insufficient system migration and scalability, low accuracy and reliability, and lack of timeliness and linkage. Therefore, starting from specific construction scenarios, it is particularly important to design a universal, reliable, systematic, and scalable early warning method

[0003] Different from the general early warning system, the reasons for the insignificant effect and poor usability of the project construction early warning mechanism are mainly divided into the following three aspects: First, the construction scene is complex and changeable, and the data collected only by sensors results in a single source, and Due to its own limitations, there are loopholes in detection, the acquisition rate of effective information is low, and the data redundancy is large, so it is impossible to accurately divide, extract and judge the danger level of early warning information; secondly, people and the environment, objects (materials and equipment, etc.) The relationship between the environment and people and things (materials, equipment, etc.) Increased the difficulty of the early warning system; finally, in the construction scene, it is difficult to detect abnormal behaviors such as fighting, smoking, chatting, etc. in a conventional way. The existing early warning system lacks overall monitoring and large-scale image capture, processing, and analysis. capacity, seriously affecting the effect of construction big data security early warning

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