At present, in the application of Bayesian network (BN) structure learning algorithm for structure learning, the network scale increases with the increase of number of nodes, resulting in a large scale of structure search space, which is difficult to calculate, and the existing learning algorithms are inefficient, making BN structure learning difficulty increase. To solve this problem, a BN structure optimization method based on local information is proposed. Firstly, it proposes to construct an initial network framework with local information and uses the Max-Min Parents and Children (MMPC) algorithm to construct an undirected network framework to reduce the search space. Then the particle swarm optimization (PSO) algorithm is used to strengthen the algorithm's optimization ability by constructing a new position and velocity update rule and improve the efficiency of the algorithm. Experimental results show that under the same sample data set, the algorithm can obtain a more accurate BN structure while converging quickly, which verifies the correctness and effectiveness of the algorithm.

The location of distress object in the maritime search area is difficult to determine, which has brought great difficulties to the search path planning. Aiming at this problem, a search path planning algorithm based on the probability of containment (POC) model for a distress object is proposed. This algorithm divides the area to be searched into several subareas by grid method and dynamically evaluates the POC of the distress object in each subarea using the Monte Carlo random particle method to build the POC model. On this basis, the POC is dynamically updated by employing the Bayes criterion within the constraint of the time window. Then, the sum of the POC of the object in the subareas is regarded as the weight of the search path. And the proposed algorithm dynamically executes the search path planning according to the maximum path weight. In comparison with the parallel line search path planning algorithm given in the “International Aeronautical and Maritime Search and Rescue Manual,” the simulation results show that the search path planning algorithm based on the POC model of the distress object can effectively improve the search efficiency and the probability of search success of the distress object.