An integrated wave-tidal current power turbine is affected by both wave and tidal current forces, and its energy efficiency is closely related to the velocity and direction of the two forces. To improve the probability of the horizontal axis turbine reaching maximum energy efficiency under real-time changing sea conditions, we performed the following investigations in this study. Based on the actual application scenario of Lianyungang port, a time series prediction model of tidal current (velocity and flow direction) and wave (mean wave direction, mean wave period, and significant wave height) data for the past year was established. The changes in waves and tidal currents within 24 h after the cutoff point of the existing data were predicted. The integrated wave-tidal current mechanism was studied, and the superposition of wave energy and tidal current energy was transformed into the equivalent velocity vector of wave-tidal current integration. The conversion coefficient between waves and equivalent flows was determined by a numerical wave flume simulation. According to the historical wave and tidal current data, the equivalent velocity range of the integrated action of waves and tidal currents in Lianyungang was determined. The influence of different blade motions on the energy harvesting efficiency of the turbine under the corresponding flow conditions was studied using the Computational Fluid Dynamics (CFD) method to determine the blade motion law of the turbine. The blade motion law of the prototype was verified in a sea trial experiment. The experimental results were basically consistent with the simulation results for the blade motion law designed according to the wave and tidal current prediction law. This design scheme can provide a reference for engineering design for the development and utilization of new marine energy.

To address the challenges of low efficiency, high labour intensity, severe fish damage, and insufficient intelligence in adult fish harvesting within industrialised aquaculture systems, this study developed an integrated harvesting and sorting system incorporating bottom-drain fish collection, automatic herding, a modified screw-lifting mechanism, and digital grading. The system employs a bottom-discharge port on the culture tank—controlled by an electric ball valve—to replace manual net-herding, enabling simultaneous fish-and-water drainage. An automatic herding cart with a retractable net (equipped with surface floats and bottom weights) achieves bottom-hugging herding to prevent fish escape. A multi-column convolutional neural network (MCNN) fish-density recognition model was deployed above the collection tank to adaptively adjust the push-board position, thereby avoiding compression injury. The screw blade head was modified by removing the inlet-edge portions to eliminate the fish-cutting zone, reducing the mean damage rate from 1.8% to 1.32% (N = 12; 66.7% of trials recorded zero damage). Twelve repeated full-scale harvesting trials were conducted using largemouth bass (Micropterus salmoides; body length 20–30 cm, body mass 300–500 g) in a 2.5 m diameter PP circular tank. Results showed a mean collection efficiency of 95.94 ± 3.33% (p < 0.001), a mean damage rate of 1.32 ± 1.96%, and a mean sorting accuracy of 96.83 ± 2.50% (p = 0.014). The MCNN model attained a recognition accuracy of 78–85%. The proposed system realises automated, intelligent, and low-damage fish harvesting, providing technical support for fully automated production-line aquaculture.