We aimed at elevating catalytic performances of cobalt tetrakis(pentafluorophenyl)porphyrin (Co TPFPP) through axial coordination, nanocavities, and covalently grafting action. The Co TPFPP was immobilized onto nanoporous and nonporous chitosan, forming Co TPFPP/np‐ and nonp‐CTS catalysts, respectively. The catalysts were characterized by various spectroscopic techniques. The catalytic performances of these catalysts for cyclohexane oxidation under response‐surface‐methodology‐optimized oxidation reaction conditions were estimated and compared. Co TPFPP/np‐CTS was an excellent catalyst at aspect of catalytic activity, exhibiting the considerable potential reusability, 24.2 mol % yields (KA oil : cyclohexanone and cyclohexanol) in average, and total turnover frequencies (TOFs) of 3.25×10⁶ h⁻¹. This is attributed to the structural characteristics of the Co TPFPP/np‐CTS catalyst: the cobalt porphyrin molecules could be highly scattered on CTS, forming the independent active sites, and were not leached. The axial coordination exerted the most important effect on the catalytic activity, and the covalent grafting action had a decisive effect on the increase of the total TOFs and on the reusability of the catalyst.

This study proposes a novel and comprehensive boat-assisted drone inspection scheme for offshore wind farms. Specifically, we develop a unique wireless communication model, a detailed wind turbine blade detection model, and a route planning scheme for the drone and boat to formulate the minimum energy consumption problem for drone inspection. Then, we demonstrate that this problem is a combinatorial optimization mixed-integer nonlinear programming problem, which we solve by decomposing the optimization problem into four subproblems. Moreover, an improved heuristic algorithm is constructed to solve the optimum energy consumption problem, exploiting these subproblems. The simulation results indicate that the proposed scheme achieves optimal energy consumption for boat-assisted drone inspection. Furthermore, compared with the existing methods, the proposed algorithm obtains the lowest total energy consumption and provides a higher route planning performance for the unmanned aerial vehicle and boat when utilized for offshore wind farm inspection.

Facing the method’s limitations of the existing drone inspection on offshore wind farms, we adopt a new comprehensive‐assisted drone automated inspection scheme under the comprehensive assistance. Our objectives are saving energy and high‐efficient inspection. The such inspection is used to formulating the two mixed‐integer nonlinear programming problems based on two new drone basic models: the mobile edge computing driven drone computation system model and the drone flight model. To solve the problems, we split them into four subproblems, and a new improved heuristic algorithm is created to address. In turns, the waypoints, total inspection time, inspection energy consumption, and traveling distance of unmanned aircraft vehicle (UAV) and the traveling distance of boat are obtained by K‐means algorithm and the smallest enclosing circle (SEC) algorithm, the Lin‐Kernighan Heuristic 3 (LKH‐3) algorithm, and the LKH. Finally, conducting the comprehensive optimization and simulation, the simulation numeric results are gotten. The simulation results demonstrate that as for the two aspects of the total energy consumption and inspection efficiency under different data amount and average wind speed, the scheme improves at most 44% and 23.5% than the current other three; the scheme achieves the objective of saving energy and high‐efficient inspection.

To handle ocean health problems associated with human activities (wind turbine deployment, nuclear wastewater discharge, etc.), collecting broad ocean region data to leverage large-scale sensors is a valid method. When using the novel collection scheme, reducing the total energy consumption (TEC) of boat-assisted drones to collect sensor data is challenging. The objective of this study was to minimize the TEC of boats and drones (owing to the limited battery capacity) during marine environment sensor data collection. To achieve this, new models for drone hovering in wind, data collection, and related wireless communication have been developed, and the TEC minimization problem has been formulated as a new specialized distance-constrained capacitated vehicle routing problem. The problem is divided into four sub-problems to reduce complexity. Based on these four subproblems, an improved heuristic algorithm is proposed. In the algorithm, the drone hovering position and boat waypoint are found using the K-means clustering algorithm and the smallest enclosing circle algorithm. Based on the position and waypoint, the routes of drones and boats are optimized using the Lin-Kernighan heuristic 3 algorithm, thus minimizing the TEC. The simulation results demonstrate that when the boat waypoint is 3 and the sensor number is 2000, owing to the strong local and global search ability, the TEC in the scheme is 0.03×10 ⁸ J less than that of the graph attention neural network method (GANN), while the scheme also provides time saving, scalability, and flexibility.