Visual supervision of large‐scope heat source factories based on knowledge graph

Abstract As a result of limited resources and economic development acting as dual bottleneck constraints, optimizing industrial layout structures has been a general trend in heavy industry. The visual supervision of heat source factories based on integrated multidimensional information is important for optimizing an industrial layout. Based on Visible Infrared Imaging Radiometer Suite (VIIRS) I‐band 375‐m active fire product (VNP14IMG) data, point of interest data, enterprise attribute information and other data, combined with clustering regression, knowledge graphs (KGs), 3D geographic information systems, and other technical methods, the temporal and spatial variations in China's heat source industries are macroscopically analyzed, and a visual supervision platform for heat source industries with functions such as visualization, time‐series analysis, and knowledge discovery is established. The results show that: (1) overall, heat source factories exhibit a spatial pattern of dense in the east and sparse in the west, and the number of industrial heat source objects and the number of industrial fire hotspots decreased from 2013 to 2021, with rates of decline of 22.0 and 27.3%, respectively; (2) the enterprise KG, which contains basic enterprise information, dynamic enterprise risk information and enterprise equity structure information, can provide users with accurate and reliable enterprise knowledge; and (3) the remote sensing monitoring information platform for heat source factories performs well in terms of the discovery and management of heat source factories at large scale. In general, the platform constructed in this study can support the rapid monitoring and positioning of industrial heat sources over large areas to improve supervision in terms of finding problems and preventing risks and to provide a necessary reference for optimizing industrial spatial patterns and environmental governance.