The analysis of offshore fishing capacity is of great significance and practical value to the sustainable utilization and conservation of marine fishery resources. Based on the 2004–2020 China Fishery Statistical Yearbook, data envelopment analysis (DEA) was applied for measuring fishing capacity using a number of fishing vessels, total power, total tonnage, and the number of professional fishermen as the input measures and the annual catch as the output measure. Capacity utilization had a calculated range from 80.7 to 100%, and its average is 93.5%. In the first four years of 2003–2007, the excess investment rate of fishing vessels, total tonnage, total power, and fishermen was low (<5%). There was a consistent sharp upward trend in 2007, a gradual downward trend from 2007 to 2015, and an upward trend after reaching a low point in 2015, with the highest gross tonnage of fishing vessels reaching 25.5%. Four regression models that incorporate machine learning algorithms are used, including Lasso, Ridge, KNN, and Polynomial Features. The goodness of fit for the four models was used as the evaluation index, and the offshore annual catch based on the evaluation index was proposed. The forecasting annual catch of the polynomial model can reach 0.98. Furthermore, a comparative simulation of the DEA incorporating the polynomial model was carried out. The results show that DEA can evaluate input factors under the conditions of a given range, and the polynomial model has more advantages in forecasting annual catches. Furthermore, the combined application of DEA and polynomial model was used to analyze and discuss the management policies of China’s offshore fishery, which can provide help and reference for future management.

In high-voltage cables, because of the close fit of their internal structures, interface pressure is generated between conductor and insulator, which affects the performance of the cable. Studies on the calculation and testing of the interfacial pressure of cable conductors are scarce because of the lack of a unified formula and the difficulty of direct measurement. As such, in this study, we devised two methods for calculating and measuring the interface pressure of cable conductors. In the first, we used two physical experimental methods. We used the friction between cable components to perform the calculation and create an experimental method for determining cable conductor interface pressure; on the basis of the equation of the pressure inside and outside a thick-walled cylinder using elasticity mechanics, we calculated the interface pressure on the basis of the measurement of the strain state of the inner and outer diameters of each layer of the cable under different assembly and stripping conditions. We verified the effectiveness of the methods through physical tests and simulations using a YJLW03 1 × 1200 high-voltage cable. Then, we used simulation software ANSYS and SolidWorks to calculate the interface pressure. With different simulation settings, we obtained results regarding interface pressure. Lastly, these simulated values were individually compared with two physical tests, and the error was calculated. Results obtained in the ANSYS environment showed that interface pressure values determined by the geometric interference normal stress, geometric interference pressure, contact interference normal stress, and contact interference pressure methods were 39.75, 36.84, 5.76, and 36.57 MPa, respectively. In SolidWorks software, we used the contact-stress and X-axis normal stress methods. Results were all 37.36 MPa. Then, simulation results and experimental results were compared, and error was calculated. The comparison showed that the X-axis orthogonal stress method was the most accurate. Errors between the X-axis orthogonal stress method and the two physical experiments were 1.5% and 0.48%. Through the above simulation and physical experiments, we determined the interface pressure between conductors and insulators in a high-voltage power cable. We obtained the cable interface pressure value through two kinds of physical experiments, and these two methods were clearly reliable. Simulation experiments showed that using SolidWorks software to simulate this problem obtained better results. Research results provide technical support and reference for the calculation and measurement of cable interface pressure and the optimization of cable performance.

This study addresses the limitations of traditional methods used to analyze factors influencing submarine cable burial depth and emphasizes the underutilization of cable construction data. To overcome these limitations, a machine learning-based model is proposed. The model utilizes cable construction data from the East China Sea to predict the weight of factors influencing cable burial depth. Pearson correlation analysis and principal component analysis are initially employed to eliminate feature correlations. The random forest method is then used to determine the weights of factors, followed by the construction of an optimized backpropagation (BP) neural network using the ISOA-BP hybrid optimization algorithm. The model’s performance is compared with other machine learning algorithms, including support vector regression, decision tree, gradient decision tree, and the BP network before optimization. The results show that the random forest method effectively quantifies the impact of each factor, with water depth, cable length, deviation, geographic coordinates, and cable laying tension as the significant factors. The constructed ISOA-BP model achieves higher prediction accuracy than traditional algorithms, demonstrating its potential for quality control in cable laying construction and data-driven prediction of cable burial depth. This research provides valuable theoretical and practical implications in the field.