Research On Urban Risk Monitoring Network Based On Machine Learning Algorithm

With the emergence of various types of intelligent algorithms,artificial intelligence has penetrated into all aspects of life,from home appliances to decisionmaking and management in cities,all with the shadow of artificial intelligence.The risk monitoring network of a smart city is an important construction to prevent urban risks.Since machine learning can simulate the learning behavior of humans by machines,reorganize and improve existing knowledge,it is an artificial intelligence technology that best reflects the "wisdom" of a smart city.Therefore,applying machine learning to the risk monitoring network is the most appropriate choice.According to the current research status,in order to improve the speed of risk warning,this paper divides the city into eight functional areas,then uses the video obtained by the camera,uses the Gaussian mixture model to preprocess the video,and uses the YOLO algorithm for target recognition and prediction.Finally,Realize the risk prediction of different functional areas according to the test results.This paper is mainly based on the target detection theory,centering on the YOLO algorithm to study the risks of various functional areas of the city,and finally realize the risk prediction of each functional area.This article uses different versions of the YOLO algorithm to analyze the picture samples and obtain the analysis results;through comparative experiments,to find the most suitable algorithm,it is expected to provide a more scientific and practical method for urban risk monitoring and provide relevant departments for work Technical support.The improved YOLO algorithm proposed in this paper has a high recognition rate of urban risk categories,and has high practical value,and finally realizes the classification of urban risks,which provides scientific guidance for the monitoring and resolution of urban risks.This article uses machine learning algorithms to help cities think and make decisions,so that all city data can be configured and dispatched in the most reasonable way through the risk monitoring network,so as to more effectively ensure the safety of the city.