Design And Implementation Of Precise Environmental Monitoring And Early Warning System

In recent years,with the gradual deepening of environmental monitoring informatization construction,environmental monitoring and early warning systems have become increasingly important in environmental protection construction,pollution prevention and other fields.However,it is still challenging to obtain accurate early warning and monitoring data.Due to the influence of adverse environment,emergencies and other factors,monitoring data anomalies occur from time to time,which makes the quality of monitoring data decline sharply and brings great obstacles to environmental monitoring warning and related management decisions.The traditional environmental monitoring and early warning system uses sensor technology to realize real-time monitoring of environmental information data.It simply judges whether the monitoring data value is normal depends on manual thresholds,but it is difficult to detect the hidden abnormal data.At the same time,the traditional anomaly detection method is not suitable for the dynamic data flow environment in the environmental monitoring and early warning system,and traditional anomaly detection methods are difficult to detect abnormal data in data flow.Therefore,it is of great significance to explore the abnormal detection method for data flow and develop an accurate environmental monitoring and warning system based on this method to improve the emergency prevention and management decision-making ability of the system.This thesis studies the anomaly detection method for the data flow of the environmental monitoring and early warning system.The main task is as follows:(1)This thesis carries on the demand analysis and design of environment monitoring and early warning system.Firstly,the functional requirements and non-functional requirements are analyzed according to the demand background.Secondly,the system architecture and functional modules are designed according to the requirements analysis.Finally,the main functional modules are designed in detail.(2)This thesis studies the data flow anomaly detection method based on adaptive density parameter clustering.Firstly,the sensor historical data is preprocessed according to the time correlation of the sensor data stream,and the data clustering is realized by introducing the initial point to realize the adaptive clustering of global density parameter.Secondly,the data stream is obtained by sliding window and its spatial characteristics are extracted.Finally,the detection of various types of abnormal data is completed through the comparison of linear traversal and the judgment of spatial eigenvalues.(3)This thesis implements and tests the environmental monitoring and early warning system.Firstly,this thesis completes the deployment of the basic modules of the system according to the system design.Secondly,this thesis combines Vue and Spring Boot technology to complete the front-end and backend coding of system functions.Finally this thesis carries out functional and non-functional testing of the environmental monitoring and early warning system.The test results show that the environmental monitoring and early warning system designed in this thesis has good availability and stability,and it is suitable for the scene of precise early warning and management of environmental monitoring.