台州市-生态保护和环境治理业-船舶协同调度废弃物预测模型

模型基于政府和企业的渔船定位信息、船舶运动轨迹、渔船进出港以及船员关联信息等多维度数据，进行信息获取加工和数据属性特征分析，过滤剔除过期、冗余的相关变量后，提取船舶定位坐标信息变量、船舶运动轨迹趋势，通过路径规划算法模拟渔船运动轨迹，实现对海洋垃圾“船收”环节的最佳路径规划。该模型数据可便于运营单位、收集单位提前对于船舶产生的废弃物进行预测，提升运营单位收集效率，降低企业运行成本。This model is constructed using multi-dimensional data sourced from government and enterprise entities, including fishing vessel positioning information, ship movement trajectories, port entry and exit records of fishing vessels, and crew-associated information. It first conducts data acquisition, processing and attribute feature analysis, filters out expired and redundant relevant variables, and extracts variables such as ship positioning coordinate data and ship movement trajectory trends. By simulating fishing vessel movement trajectories via path planning algorithms, the model realizes optimal path planning for the "vessel collection" link of marine waste cleanup. The data from this model enables operating and collection units to predict ship-generated waste in advance, improving the collection efficiency of operating units and reducing the operating costs of enterprises.

搜集汇总

背景与挑战

背景概述

该模型利用渔船定位、运动轨迹等多维度数据，通过路径规划算法优化海洋垃圾收集路径，旨在预测船舶废弃物，以提升收集效率并降低运营成本。