航道视图处理算法框架的设计与实现

Design and implementation of algorithmic framework for channel chart processing

目前,利用AI技术提高人员工作效率已在各行各业得到普遍应用,但是内河由于船型标准化低、监测设备安装条件恶劣等,导致视频图形算法的应用水平较低,也降低了相关系统使用的便捷性。针对视图信息的缺失使得信息处理易受干扰的问题,进行分析研究。在济宁智慧港航图处理算法中采用多视图聚类技术,构建一套基于不完备多视图聚类的图处理算法框架。该算法的搭建有效提高了航道视频AI识别的应用水平,提高了识别的准确性和高效性。同时,由于系统架构优良的可扩展性和开放性,也为基于此框架解决更多水运监管业务提供了基础性的技术开发思路。

Currently,the use of AI technology to improve the efficiency of personnel work has been commonly applied in various industries.However,due to the low standardization of ship types and poor installation conditions of monitoring equipment in inland waterways,which leads to a low level of application of video graphic algorithms and reduces the convenience of using related systems.This paper addresses the problem that the lack of view information makes information processing susceptible to interference.Multi-view clustering technology is used in the Jining smart harbor navigation graph processing algorithm,and a set of graph processing algorithm framework based on incomplete multi-view clustering is constructed.The construction of this algorithm effectively improves the application level of AI recognition of navigation video,and improves the accuracy and efficiency of recognition.Meanwhile,due to the excellent scalability and openness of the system architecture,it also provides a foundational technology development idea for solving more water transport regulatory business based on this framework.