環保專案成果報告查詢系統

專案計畫編號	1081113090
經費年度	109
計畫經費	7,900 千元
專案開始日期	2020/01/01
專案結束日期	2020/12/31
專案主持人	陳恬恬
主辦單位	臺中市政府環境保護局
承辦人	朱家瑩
執行單位	振興發科技有限公司
專案分類	環境資訊系統、大氣空氣
中文關鍵字	固定污染源、公害陳情、環境監控
英文關鍵字	stationary sources, nuisance petition, environmental monitoring
協同主持人	何平世
共同主持人
計畫聯絡人	陳恬恬
計畫聯絡電話	04-22933250#
計畫聯絡信箱	allison@mail.pstcom.com.tw

中文摘要	本計畫先藉由環保署公害陳情案件管理系統篩選出臺中空氣污染陳情案件熱區，搭配空氣品質監測站、移動式監測車及微型感測器等資料，找出可以合適場域後，於每個場域內選擇3個特定建築物制高點架設雲端影像監控設備，設備之影像以4G網路回傳至系統中。回傳影像透過各項環境參數之整合與機器學習，提供能見度及黑煙辨識判定，於影像異常如黑煙或能見度不佳時，提供即時通訊軟體警示服務。另智慧環境監控平台亦整合事業機構、陳情案件資料、重大污染案件位置、污染熱區、空氣品質監測資料、水質測站等資訊，使用者可以自行至平台進行定位操作，透過地理資訊系統將相對位置展示於平台上。另外本系統亦可調閱能見度及黑煙判定異常之歷史影像，同時亦可透過能見度影像判定儀表板觀察與空氣品質及其他氣象因子之相對關係。
中文關鍵字	固定污染源、公害陳情、環境監控

中文摘要

本計畫先藉由環保署公害陳情案件管理系統篩選出臺中空氣污染陳情案件熱區，搭配空氣品質監測站、移動式監測車及微型感測器等資料，找出可以合適場域後，於每個場域內選擇3個特定建築物制高點架設雲端影像監控設備，設備之影像以4G網路回傳至系統中。回傳影像透過各項環境參數之整合與機器學習，提供能見度及黑煙辨識判定，於影像異常如黑煙或能見度不佳時，提供即時通訊軟體警示服務。另智慧環境監控平台亦整合事業機構、陳情案件資料、重大污染案件位置、污染熱區、空氣品質監測資料、水質測站等資訊，使用者可以自行至平台進行定位操作，透過地理資訊系統將相對位置展示於平台上。另外本系統亦可調閱能見度及黑煙判定異常之歷史影像，同時亦可透過能見度影像判定儀表板觀察與空氣品質及其他氣象因子之相對關係。

中文關鍵字

固定污染源、公害陳情、環境監控

類型	檔名	檔案大小	說明	最新版公開日期
期末報告	01_AI_期末報告書(定稿本)_公開版_V2.pdf	1MB		2021/1/19 上午 12:00:00

摘要	The project team has screened the hot spot with serious pollution in Taichung by using the Public Nuisance Petition Cases Management System database from EPA. The project team also matching environmental data such as air quality monitoring station, air quality monitoring vehicle, air micro sensor et., al. We use the hot spot layer to find suitable fields for experimentation, and then we choose 3 tall buildings to set up IP CAM in each experimental field. Then the dynamic real-time images are sent back to the EPB system through the 4G network. The dynamic images have been provided the abnormal discrimination result of visibility and black smoke recognition through various parameters and machine learning process. When the system detects abnormal image information, the system will provide active push messages through communication software. The smart environment monitoring platform also integrates the coordinates of business institutions, pollution complaints, and major pollution cases, pollution hot zone layer, the data of air quality monitoring stations and water quality monitoring stations. Users can locate the location through the platform and find the relative location of important information through the geographic information system. The system can also observe the relative relationship between air quality, visibility and several meteorological factors through the visibility dashboard.
英文關鍵字	stationary sources, nuisance petition, environmental monitoring

摘要

The project team has screened the hot spot with serious pollution in Taichung by using the Public Nuisance Petition Cases Management System database from EPA. The project team also matching environmental data such as air quality monitoring station, air quality monitoring vehicle, air micro sensor et., al. We use the hot spot layer to find suitable fields for experimentation, and then we choose 3 tall buildings to set up IP CAM in each experimental field. Then the dynamic real-time images are sent back to the EPB system through the 4G network. The dynamic images have been provided the abnormal discrimination result of visibility and black smoke recognition through various parameters and machine learning process. When the system detects abnormal image information, the system will provide active push messages through communication software. The smart environment monitoring platform also integrates the coordinates of business institutions, pollution complaints, and major pollution cases, pollution hot zone layer, the data of air quality monitoring stations and water quality monitoring stations. Users can locate the location through the platform and find the relative location of important information through the geographic information system. The system can also observe the relative relationship between air quality, visibility and several meteorological factors through the visibility dashboard.

英文關鍵字

stationary sources, nuisance petition, environmental monitoring