Design Of Construction Site Personnel Safety Monitoring System Based On Embedded Technolog

The construction industry is a high-risk industry.Its internal environment is complex and there are many high-altitude operations.In addition,the overall quality of the construction workers is not high due to their high mobility,and some unfavorable factors that are difficult to avoid in the construction process of the site lead to frequent accidents.How to prevent and reduce the safety accidents of workers on the site has attracted wide attention.Relevant data show that in the construction process of the site,heavy objects fall and hurt workers,workers fall at high altitude,and workers fall is a common kind of injury on the site,resulting in casualties.In many cases,it is difficult for the injured to be found in time,resulting in missing the best treatment time.Effective safety monitoring of site personnel is therefore one of the measures to reduce casualties.Based on this background,this paper designs a site personnel safety monitoring system based on embedded technology,which includes embedded equipment installed on the worker helmet and background management system.The embedded device collects the workers ’ activity data and three-dimensional coordinate data through the nine-axis inertial sensor and the positioning module,and judges whether there is an abnormal situation of falling and falling at high altitude through the algorithm.When the abnormal situation is detected,the embedded device sends out sound and light alarm to remind people around.At the same time,it automatically sends text messages to the preset emergency contacts.Managers or emergency contacts can view the location of abnormal people in the mobile phone or computer browser.Based on the analysis of the functional requirements of the system,an embedded device with small size and low power consumption is designed.Through the analysis and research on the feature information of normal activities and abnormal situations,a multi-threshold detection algorithm based on traditional threshold method and an anomaly detection algorithm based on machine learning personnel are proposed.Due to the weak computing and storage ability of low power embedded system,the threshold method is combined with machine learning algorithm to reduce the computing pressure of embedded devices,and it is applied to embedded devices to realize real-time monitoring of personnel anomalies.Finally,through the design of background management system,realize the management of equipment and personnel safety monitoring.The test results show that the system runs stably,the data acquisition is convenient,the judgment of abnormal situation is accurate and reliable,and has good engineering application value.It is of great significance to reduce the casualty rate after the accident and ensure the personal safety of workers.