首页 | 官方网站   微博 | 高级检索  
     

基于VMS的张网渔船捕捞努力量与网位坐标提取方法
引用本文:裴凯洋,张胜茂,樊伟,朱文斌,汤先峰.基于VMS的张网渔船捕捞努力量与网位坐标提取方法[J].上海海洋大学学报,2021,30(1):179-188.
作者姓名:裴凯洋  张胜茂  樊伟  朱文斌  汤先峰
作者单位:上海海洋大学信息学院,中国水产科学研究院东海水产研究所,农业农村部东海渔业资源开发利用重点实验室,中国水产科学研究院东海水产研究所,农业农村部东海渔业资源开发利用重点实验室,浙江省海洋水产研究所,浙江省海洋渔业资源可持续利用技术研究重点实验室,上海海洋大学信息学院
基金项目:国家自然科学基金项目(面上项目,重点项目,重大项目);上海市自然科学基金项目;中国水产科学研究院基本科研业务费
摘    要:捕捞努力量是渔业资源管理和评估领域的重要参数之一,传统捕捞努力量计算方法无法满足实时、大范围、快速统计的需要。以我国近海作业的某张网渔船为研究对象,采用BP(back propagation)神经网络模型,对张网船155在2016年和2017年北斗渔船监控系统所获取的若干连续航次的经纬度坐标、航速和航向等信息进行分析和判断,提取各航次作业的网位坐标,通过阈值筛选渔船布网位置和时间,计算放网时长,把网口迎流面积与放网时长的乘积作为网次的捕捞努力量。结合BP神经网络和阈值分析的判断结果,网位判断准确率为82%,4个航次累计捕捞时长3562.62 h,累计捕捞努力量712524(m2·h)。设计的张网渔船状态判断、确定网位、放网时长提取和捕捞努力量计算方法为张网作业分析和其捕捞强度量化提供新的研究思路。

关 键 词:张网渔船  渔船监控系统  网位坐标  捕捞努力量  BP神经网络  阈值分析
收稿时间:2019/5/13 0:00:00
修稿时间:2019/8/13 0:00:00

Extraction method of fishing effort and net position in stow net vessels based on vessel monitoring system data
PEI Kaiyang,ZHANG Shengmao,FAN Wei,ZHU Wenbin,TANG Xianfeng.Extraction method of fishing effort and net position in stow net vessels based on vessel monitoring system data[J].Journal of Shanghai Ocean University,2021,30(1):179-188.
Authors:PEI Kaiyang  ZHANG Shengmao  FAN Wei  ZHU Wenbin  TANG Xianfeng
Affiliation:College of Information, Shanghai Ocean University,Key Laboratory of East China Sea Fishery Resources Exploitation & Utilization, Ministry of Agriculture and Rural Affairs; East China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences,Key Laboratory of East China Sea Fishery Resources Exploitation & Utilization, Ministry of Agriculture and Rural Affairs; East China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences,Key Laboratory of Sustainable Utilization of Technology Research for Fishery Resource of Zhejiang Province, Marine Fisheries Research Institute of Zhejiang,College of Information, Shanghai Ocean University, shanghai
Abstract:Fishing effort is one of the important parameters in the field of fishery resource management and assessment. Traditional fishing effort calculation methods cannot satisfy the needs of real-time, large-scale and rapid statistics. This study takes the stow net vessels operating offshore in China as the research object, and adopts the BP (Back Propagation) neural network model. Through the state judgment of the latitude and longitude coordinates, speed, heading and other information of the five consecutive voyages acquired by the Beidou Fishing Vessel Monitoring System in 2016 and 2017 of Zhangwangchuan155, the net position coordinates of each voyage operation are obtained, Filter the position and time of the fishing net through the threshold and calculate the fishing time, and fishing effort can be calculated by the fishing net opening area multiplied the fishing time. Combined with the judgment results of BP neural network and threshold analysis, the accuracy of obtaining the network position data is 82%, The cumulative fishing time of the four voyages was 3563.62h, and the accumulated fishing effort was 712524m2·h.The state judgment, net position identification, time of release time, and calculation method of fishing effort designed by this paper provide new research ideas for the analysis of the net operation and the quantification of its fishing intensity.
Keywords:stow net vessels  vessel monitoring system  net position coordinates  fishing effort  BP neural network  threshold analysis
本文献已被 维普 等数据库收录!
点击此处可从《上海海洋大学学报》浏览原始摘要信息
点击此处可从《上海海洋大学学报》下载全文
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司    京ICP备09084417号-23

京公网安备 11010802026262号