Dynamic Control System of Automatic Monitoring Equipment for Waste Gas Pollution Source

In order to improve the quality of automatic monitoring data of pollution sources and apply the automatic monitoring data to verify the environmental tax,Shandong Province took the lead in adopting the Internet of Things technology and drawing on the successful experience of air automatic monitoring stations and surface water automatic monitoring stations in management,and developed a dynamic management and control system for automatic monitoring equipment of pollution sources to improve and strengthen the quality audit of automatic monitoring data,further improve the quality of automatic monitoring data and better provide a basis for environmental management and decision making.The system realizes the simultaneous monitoring of monitoring data,running state and parameters of the automatic monitoring equipment,eliminates the phenomenon of falsification by modifying equipment parameters,and judges the validity of the collected data by acquiring the working state of the equipment remotely and randomly.After the actual operation test of the Department of Ecological Environment of Shandong Province,the system is proved to have the characteristics of practicality,real time and high efficiency,and be able to make up for low frequency and narrow coverage of manual inspection,with good application prospect in the field of environment and pollution source monitoring.

Power Incomplete Data Clustering Based on Fuzzy Fusion Algorithm

With the rapid development of the economy,the scale of the power grid is expanding.The number of power equipment that constitutes the power grid has been very large,which makes the state data of power equipment grow explosively.These multi-source heterogeneous data have data differences,which lead to data variation in the process of transmission and preservation,thus forming the bad information of incomplete data.Therefore,the research on data integrity has become an urgent task.This paper is based on the characteristics of random chance and the Spatio-temporal difference of the system.According to the characteristics and data sources of the massive data generated by power equipment,the fuzzy mining model of power equipment data is established,and the data is divided into numerical and non-numerical data based on numerical data.Take the text data of power equipment defects as the mining material.Then,the Apriori algorithm based on an array is used to mine deeply.The strong association rules in incomplete data of power equipment are obtained and analyzed.From the change trend of NRMSE metrics and classification accuracy,most of the filling methods combined with the two frameworks in this method usually show a relatively stable filling trend,and will not fluctuate greatly with the growth of the missing rate.The experimental results show that the proposed algorithm model can effectively improve the filling effect of the existing filling methods on most data sets,and the filling effect fluctuates greatly with the increase of the missing rate,that is,with the increase of the missing rate,the improvement effect of the model for the existing filling methods is higher than 4.3%.Through the incomplete data clustering technology studied in this paper,a more innovative state assessment of smart grid reliability operation is carried out,which has good research value and reference significance.