The social force model is one of the most commonly used models for microscopic modeling of pedestrian movement and crowd evacuation. A large number of researchers have conducted continuous research on it, which makes the social force model suitable for pedestrian movement and crowd evacuation modeling in a variety of complex environments. The prerequisite for the effective use of the social force model in actual scenarios is the need to check and verify the social force model according to the application scenario. Therefore, it is necessary to confirm the correct realization of the social force model structure from the perspective of theory and practical application. The expected velocity parameter has an important impact on the movement of the crowd, including evacuation time and flow rate, so the expected velocity also needs to be considered during model validation. In addition, the performance of the social force model is also sensitive to the expected rate, measured by entropy. However, research and analysis on the desired rate setting is still lacking. In this paper, the optimal selection of the expected rate sub-model in the social force model is carried out in a data-driven manner, so that the social force model can more realistically reflect the movement of crowds, so that it can be better used in crowd control based on modeling and simulation.

In order to protect our country's territorial waters from infringement and maintain maritime rights and interests, it is necessary to establish a marine defense system that combines both offshore defense and far sea defense. Coast-to-shore/GEO satellite-ship-to-ship bistatic inverse synthetic aperture radar (inverse synthetic aperture radar, ISAR) has all-day and all-weather characteristics, and can realize long-distance continuous tracking, imaging and identification of ship targets in offshore/far sea areas. In addition, these two bistatic ISAR imaging systems also have the advantages of high concealment, strong anti-strike capability, high flexibility, multiple imaging modes, and rich target information, thus improving the reliability of the marine defense system.

With the booming development of China's ocean shipping and tourism business, the demand for safety and economy when ships are sailing at sea is increasing. Accurate meteorological and hydrographic forecasts can provide meteorological navigation for ships to avoid typhoons and bad weather areas as much as possible and reduce the damage to the ship's hull from wind and waves. To obtain accurate and reliable wind speed prediction results, this paper combines the advantages of convolutional neural network and gated recurrent unit network to form a deep convolutional gated recurrent unit network model (CNN-GRU). For multiple locations, the CNN-GRU algorithm is used to extract the characteristic meteorological elements, and then the convolutional neural network is used to establish the spatial characteristic relationship between each location, and the gated recurrent unit network is used to establish the temporal characteristic relationship between historical time points, and the final wind speed prediction results are obtained based on the spatio-temporal correlation analysis. In this paper, the CNN-GRU model was established using meteorological data from 2019 to 2021, and the prediction results were compared with CNN model and GRU model and the accuracy was verified. The results show that the experimental results obtained by the CNN-GRU model are more accurate and prove the effectiveness of the proposed method.

The Traveling Salesman Problem is a typical combinatorial optimization problem, which has not been well solved until now. In this paper, The Genetic Algorithm is used to solve this problem, and the gene segment is regarded as an urban sequence. The introduction of the crossover rate and the mutation rate not only ensures the current good genes, but also produces better genes. In this way, the optimization of the problem solution is continuously realized. We experimentally validate The Genetic Algorithm with 100 cities, and for this problem, The Ant Colony Algorithm and The Particle Swarm Algorithm are used for experimental comparison. The experimental results show that The Genetic Algorithm has a strong global optimization ability and better overall performance under the premise of ensuring a faster operation speed.

According to the characteristics of ship maneuvering motion control, this paper constructs the membership function and control rules of the input and output language variables of the ship course fuzzy neural network controller, and designs a fuzzy controller. Aiming at the deficiencies of the basic fuzzy controller, a two-stage fuzzy controller with fuzzy PI control and self-adjusting parameters is proposed. When it is applied to the ship heading control system, the system has satisfactory results in terms of immunity and response speed. By establishing the description function of the fuzzy controller, the analysis concludes that the ship course fuzzy control system is stable.

Named entity recognition (NER) is an important research direction in natural language processing (NLP). Traditional machine learning algorithms in NER have problems such as low accuracy, highly dependent feature design, poor domain adaptability, and inability to handle the different contexts of multiple meanings of the term in recognizing Chinese entities. Based on these problems, this paper adopts a method based on the BERT-CRF model in Chinese NER. The BERT preprocessing language model generates word vectors that represent contextual semantic information, automatically extract numerous word-level features and semantic features in text, and decodes through the CRF layer generates entity tag sequences. In this paper, the BERT model has been fine-tuned to make the model perform better on NER tasks, and the experimental verification is carried out on the People’s Daily dataset, and the F1 value reaches 94.5%.