In 2022, the results of establishing the Visualization Information Platform for inspection data are as follows:
(A) Study and analyze the multiple primary methods for establishing modular intelligent visualization
1. The development method of the visualization platform used responsive web design (RWD) to provide users with appropriate visualization platforms on various devices.
2. This project studied and analyzed the permission and report data to select those to be used and presented on the visualization platform.
3. Based on actual inspections, this project presented special issues of concern, including suspected bypasses, hot spots of hidden pipe discharge, case tracking by inspectors, distribution of industry-specific clusters, industrial areas that should be further managed, and distribution of waste disposal enterprises, as well as relevant information from industry organizations and hot spots of relevant industrial clusters on the visualization platform as the Seven Thematic Visualization Dashboard.
4. When presenting the related themes on the visualization platform, the geospatial map was used as the main visualization screen, and the business locations and basic information were used as the display format.
5. Set industry, waste code, raw material code, industrial area, and watershed as search criteria in the system query interface, and the search results were presented by business locations.
6. Based on the number of inspection cases handled by inspectors and the total number of people who contributed to the system in the past years, the query criteria were determined based on counties, cities, and pollution categories to control the inspection status of each county and city.
7. AI technology was used to retrieve and search financial-related information by automating the word processing of semi-structured column data. The Seven Thematic Visualization Dashboard was used as the foundation to propose AI and IoT application planning methods.
(B) Analyze the linkage method for specific inspection cases. This project analyzed the linkage method for specific types of pollution inspection cases (e.g. by business, volume, item of concern, time, or place). The linkage content should include the following data content:
1. This project inventoried the data of inspection and punishment in the past years, extracted the essential information and label them as event characteristics, and established models with 61 sets of labels by machine learning. A total of 34 models were obtained.
2. The 34 models were presented in a social network analysis (SNA) diagram to demonstrate the event characteristic labels associated with the same enterprise, or the event characteristic labels of each contamination marked by a single enterprise.
(C) Analyze the feasibility of using financial data to assist inspection
1. This project used automatic web crawlers to retrieve open financial data to establish a database of historical financial data of listed companies. These data were compared with water system reporting data to create a total of 1,404 enterprise data tables.
2. A total of 24 sets of comparison patterns were established using four types of financial data and six types of water system reporting data. The relatively high correlation coefficients between financial data and water pollution reporting data among the 24 comparison patterns were identified by regression analysis and listed as the factors of interest.
(D) Related tasks assigned by the authorities
1. This project cooperated with the authorities to complete the assigned tasks and organize regular progress meetings, participated in project implementation meetings, and completed designated documents.
2. This project assisted in the presentation of system screens at press conferences and continued to meet the needs of the authorities.