With the development of marine resources, research on underwater robots has received unprecedented attention. The discovery and application of new smart materials provide new ideas for the research of underwater robots, which can overcome the issues of traditional underwater robots and optimize their design. A macro fiber composite (MFC) is a new type of piezoelectric fiber composite that combines actuators and sensors. The material has excellent deflection, good flexibility, and a high electromechanical coupling coefficient. Bionic mechatronics design is an effective way to innovate mechatronics in the future and can significantly improve mechatronics system performance. As an important issue for the design of bionic mechatronics, it is necessary to make robots as soft as natural organisms to achieve similar biological movement with both higher efficiency and performance. Compared with traditional rigid robots, the design and control of a soft robotic fish are difficult because the coupling between the flexible structure and the surrounding environment should be considered, which is difficult to solve due to the large deformation and coupling dynamics. In this paper, an MFC smart material is applied as an actuator in the design of bionic robotic fish. Combined with the piezoelectric constitutive and elastic constitutive equations of the MFC material, the voltage‐drive signal is converted to a mechanical load applied to the MFC actuator, which makes the MFC material deform and drives the movement of the robotic fish. The characteristics of caudal fin motion during the swimming process of the bionic robotic fish were analyzed by an acoustic‐solid coupling analysis method. The motion control analysis of the bionic robotic fish was carried out by changing the applied driving signal. Through numerical analysis, a new type of soft robotic fish was designed, and the feasibility of using an MFC smart material for underwater bionic robotic fish actuators was verified. The new soft robotic fish was successfully developed to achieve high performance.

This combined with the data processing requirements of large-scale sea land mixed high-resolution remote sensing images, this paper proposes a parallel target detection algorithm based on sea land segmentation results, and uses mpi4py function library to realize the parallel algorithm. The large-scale high-resolution remote sensing images are divided into smaller sub image blocks, and multi process synchronous sea land segmentation processing is realized based on the trained semantic segmentation model, In addition to the whole ocean sub image block, only the whole land sub image block and sea land mixed sub image block are subject to target detection processing, so as to reduce the target detection area of high-resolution remote sensing image, greatly reduce the data processing time of large-scale highresolution remote sensing image, and improve the detection efficiency. The experimental results show that on the basis of ensuring the detection accuracy, the parallel segmentation before detection technology can significantly shorten the image processing time and has better speedup and scalability than the traditional parallel target detection technology.