In the evaluation problem of fishing vessel fishing capacity, the imperfect evaluation index system and the methods of evaluation indexes are mostly artificial qualitative evaluation methods, which lead to strong subjectivity and fuzziness as well as low accuracy of evaluation results. Therefore, this study introduces cloud model theory on the basis of improving the evaluation index system, converts the artificial qualitative evaluation results into the digital characteristics of clouds, realizes the mutual transformation of qualitative evaluation and quantitative evaluation, and improves the accuracy of evaluation results. Taking the trawler as an example, the cloud model method is used to evaluate the fishing capacity, and the result obtained is (77.1408, 1.6897, 0.0), the result obtained by the fuzzy comprehensive evaluation method is 76.664785, and the result obtained by the cloud center of gravity evaluation method is 0.7919. Compared with the other two methods, the cloud model method uses three numerical characteristics to describe the results, and combining the different numerical characteristics meanings, the evaluation results can be judged to be accurate, and the influence of ambiguity on the results is greatly reduced. Meanwhile, the evaluation results can be presented in the form of pictures, and the results are more intuitive; in addition, the cloud model of the evaluation results is compared with the standard cloud model for similarity, which improves the credibility and authenticity of the results.

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.

To address issues such as insufficient fishing vessel data records, low energy efficiency, and high emissions, this study constructs and validates a predictive model for the maximum power limitation of fishing vessels. Using gillnet fishing vessels as a case study, the Energy Efficiency Existing Ship Index (EEXI) reference line formula for gillnet fishing vessels is fitted using the nonlinear least squares method. Sensitivity analysis reveals power’s impact on fishing vessels’ energy efficiency. The maximum power calculation method for fishing vessels is derived by combining the EEXI calculation formula, the reference line formula, and the power-speed relationship. Three regression prediction models—Decision Tree, Random Forest, and Gradient Boosting—are used to construct prediction models with gross tonnage, length between perpendiculars, and gross tonnage and length between perpendiculars as inputs, respectively. Results show that power significantly impacts the energy efficiency of fishing vessels. The EEXI reference line formula for gillnet fishing vessels has MAE, MSE, MAPE, RMSE, and R^2 values of 13.3518, 369.5200, 18%, 19.2229, and 0.6366, respectively. The Random Forest regression model with gross tonnage as input performs best in predicting the maximum power limitation of gillnet fishing vessels, with MAE, MSE, MAPE, RMSE, and R^2 values of 5.61423, 6152.40982, 1.90888%, 78.43730, and 0.633062393, respectively. This paper provides a reliable calculation method and prediction model for the maximum power limitation of fishing vessels, offering systematic technical support and decision-making references for limiting the maximum power of fishing vessels.

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.