Researches on the applications of information and communication technologies to fisheries and data aggregation systems

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基本資料

Project title

Code/計畫編號

110農科-6.1.3-子-F1

Translated Name/計畫中文名

應用資通訊技術在漁業動態解析研究

Funding Organization/主管機關

Council of Agriculture,Executive Yuan

Co-Investigator(s)/共同執行人

陳科仰(計畫主持人)
張以杰
吳朝榮
謝志豪

Department/Unit

Planning Division,Fisheries Agency,COA,Executive Yuan

Website

https://www.grb.gov.tw/search/planDetail?id=13918734

Year

2021

Start date/計畫起

01-01-2021

Expected Completion/計畫迄

31-12-2021

Co-Investigator(s)

Ting-Chun Kuo
William Wei-Yuan Hsu

Bugetid/研究經費

6050千元

ResearchField/研究領域

漁業

(1)因應歐盟的黃牌警告，行政院農委會漁業署於2015年開始，補助開發新世代海洋資訊系統(DeepSea9)。這個成功的政策改變了漁業管理的方式。截至目前為止，這套系統除了整合漁船船位會報系統(VMS)、電子漁獲日誌(eLogbook)、遠洋漁業作業系統(Torch)、沿近海漁業資料、船舶識別系統(AIS)、各種的圖資以及多方資料來源的氣象、海象、海洋保護區、危險海域等資訊，並可即時對監控之船隻進行預警。這套系統具備資料完善性，且具有智慧型資料分析，除了精進系統原有功能外，本年度將著重結合船位回報以及eLogbook系統，解析人工回報船位及衛星自動回報船位並進行整合，將船位對漁獲資訊自動配對，自動分析異常人工回報船位資訊，且提以警示修正人為錯誤。計畫中將提升使用機動性，設計智慧型自訂船舶、船團清單以及自訂區域範圍搜尋功能，當遇特殊目的(如海上救難、會船監控)時，能迅速鎖定查詢區域或特定船舶資訊狀態。且同時建置針對港內船舶之警示系統、船舶轉載預報資訊加入每日會船資料自動進行比對、加入觀察員詳細資料進行歷史及即時資料呈現、進行24/36小時之船舶動態軌跡呈現、針對運搬船之衛星自動回報船位偵測斷訊功能，以此完善系統功能且更貼合需求。於管理方面，將強化資料視覺化界面，使得資料呈現更為清晰明朗。且將各模組相互連結參照，呈現最即時資訊，提供前線人員決策之使用。(2)秋刀魚漁況資訊系統結合海洋環境因子及秋刀魚漁業資料，預報秋刀魚在北太平洋的時空分佈，並提供即時海洋環境資料及海況預報，達到資料整合與漁場即時預報。秋刀魚漁海況預報系統已經建立，提供預報系統讓秋刀魚漁船實驗性試用，包含教育訓練。本年度計畫主要在系統維持與更新，資料更新，系統推廣，並分析使用者行為。本年度持續整合並更新環境資料，包括表水溫（Sea Surface Temperature），表水鹽度（Sea Surface salinity），水溫鋒面（ocean temperature front），表水溫鋒面頻度（temperature front frequency），海面高度（Sea Level Height anomaly），海洋生產力（ocean color），洋流流向及流速（geostrophic flow），渦旋動量（eddy kinetic energy）及海洋生產力（ocean color），以高解析度圖相，每日更新與發佈。並整合過去漁業資料（CPUE），0.5X0.5度方格解析度，將CPUE的空間分佈，按年月份與上述之各環境參數之歷史資料套疊。提供預報系統給秋刀魚漁船實驗性試用，提供四項資訊：1.每日更新與即時發佈(daily)環境資料。包含彩色與黑白兩個版本，並可以自由放大縮小。另外，考量到漁船上網路流量有限，建置低流量網站，讓船長直接輸入需要的經緯度範圍，並只回傳該範圍的海況圖，每張圖片大小控制在100K左右，以PDF形式，以符合船上網路限制及方便船長操作。提供最相關之歷史資訊。亦即，透過比對歷史和當下環境資料，提供最接近當下之歷史環境資料，並套疊CPUE之分布。利用按月份建立的組合預報模式，代入即時環境資料預測當日秋刀魚的出現機率，並以地圖呈現，漁民可依據出現機率的空間分布決定作業位置。只在主要漁期提供預報。並提供低流量下載網頁。整合國際氣象資料，轉載氣象預報。實驗船公司可以透過網路，進入資料庫伺服器，看到這些資訊，再提供給漁船。或由漁船透過低流量網站下載漁海況資訊。對漁民先行教育訓練。系統將依照業者於上年度填復問卷結果及訪談所得之建議，視可行性做修改。建置網頁使用狀況監測系統，分析使用者行為。 The Fisheries Agency of Taiwan supported the development of the DeepSea 9 fisheries monitoring system as an answer to the yellow card warning in 2015. The success of this policy has lead to a new era of fisheries management. Currently, DeepSea 9 contains data from the VMS system, eLogbook system, Torch management system, automatic identifying system (AIS), and many other marine data such as weather, danger zones, and marine protected areas (MPA), and can issue real-time warnings to violations or dangers on the sea. To make the system even smarter, we will create even stronger bindings between human reports and the current report system and cross-reference it between electronic data to make verdicts. Furthermore, to make fisheries management easier, we will bring more freedom into this system by allowing arbitrary queries, human-defined vessel groups, focusing on areas of interest, and automatic listing reports. This will provide better support to the monitoring team when handling cases such as transshipment or rescue missions. For vessels docked in harbors, we will include more procedures to identify its purpose, such as supplying, repairing, or resting. We will make the system more flexible to track observers on board and show their presence on the seas. Finally, with the enhanced visual effects, we will allow the monitoring taskforce to easily identify problems by looking at the screen. This includes animated tracks of vessels, event highlights, and even predicted results to support future decisions. Near-nowcast for using environmental data to predict fishing ground for Pacific saury This project aims to maintain the developed near-now-cast system for using environmental data to predict fishing ground for Pacific saury with updated data, in order to provide useful tools for fisheries management and lower the cost of fishery operations. Here, we integrate environmental data, including Sea Surface Temperature, Sea Level Height anomaly, ocean color, sea surface salinity, geostrophic flow velocity and direction, and calculate ocean temperature gradient, frequency of temperature front, and eddy kinetic energy. We will display the ocean conditions with high-resolution maps and update the information daily. We then integrate CPUE data of Pacific saury with the environmental data (monthly data with 0.5 degree resolution). We will work with saury fishermen to test our system, using experimental fishing boats. We will provide four sources of information: First, the daily updated environmental data, with color and black-and-white versions and zoom-in function, as well as downloadable pictures for on-board usage ; second, the most relevant historical environmental data associated with CPUE ; third, the distribution map of occurrence probability of saury predicted by a month-specific ensemble forecast using the updated environmental data; fourth, weather forecast information from international sources. Registered fisheries companies can access the information in our data servers through internet. We will introduce our near-now-cast system to fishermen at least once a year. We will develop a tool to monitor the system usage behavior of fishermen. Finally, we will use questionnaire to request feedbacks from fishermen.

Keyword(s)

漁況預報
秋刀魚
海洋資訊科學
實驗漁船
巨量資料
漁業管理
雲端架構
近期海況預報
電子化行政
人工智慧預測
fishing ground forecasting
Cololabis saira
Oceanic information science
experimental fishing boats
Big data
Fisheries management
Cloud architecture
ocean condition near-nowcast
Electronic administration
Artificial intelligent predicitions

DSpace CRIS

Researches on the applications of information and communication technologies to fisheries and data aggregation systems

基本資料

Description