Machine learning method for realizing ship anthropomorphic intelligent collision prevention decision

Abstract

The invention discloses a machine learning method for realizing ship anthropomorphic intelligent collision prevention decision. An analog source and an example source are generated by off-line artificial learning, and a collision prevention model for on-line acquiring new collision avoidance knowledge, and a database for storing ship parameters are constructed, and an automatic reasoning mechanism, a calculation unit and an evaluation system are designed. The collision prevention model and the automatic reasoning mechanism are used, knowledge discovery and approximate reinforcement learning strategies are realized through online machine learning, and new collision prevention knowledge is acquired, and a dynamic collision avoidance knowledge base is constructed. An inference engine is usedto invoke the ship parameters and the PIDVCA algorithm of the database through the automatic inference mechanism to realize the intelligent collision prevention decision of the machine. The machine iscapable of acquiring the information and the formalized collision prevention domain knowledge on site through the guidance of the automatic reasoning mechanism, is used to learn and solve new knowledge of collision prevention problems of any meeting scene, and has a perception target and a cognitive target to further formulate a scientific and reasonable collision prevention decision scheme, andfinally has a thinking mode for simulating and surpassing the human to solve complex collision prevention problems.

Description

technical field [0001] The invention relates to the research field of intelligent ship navigation technology derived from the cross-discipline of traffic information engineering and control and vehicle utilization engineering, and in particular refers to a machine learning method for realizing anthropomorphic intelligent collision avoidance decision-making of ships. Background technique [0002] The "International Rules for Preventing Collisions at Sea" (hereinafter referred to as the rules) is a summary of the experience of navigators' sailing practice for thousands of years. The guarantee of orderly navigation, prevention and reduction of collisions is the marine traffic rules that ship drivers should abide by. However, collision avoidance accidents caused by human factors such as crew decision-making and operation errors and improper emergency response still occur from time to time, and more than 96% of them are caused by the duty officer failing to take reasonable action...

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to provide a machine learning method for realizing anthropomorphic intelligent collision avoidance decision-making of ships, so that the machine can obtain information and formalized collision avoidance field knowledge from the scene under the guidance of a preset reasoning mechanism, and learn to solve any collision. The new knowledge of scene collision avoidance enables the machine to perceive the target, recognize the target and then formulate a scientific, economical and reasonable collision avoidance decision-making plan, and finally enables the machine to have a thinking mode that simulates and surpasses humans to solve complex collision avoidance problems

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