Guano are an important factor affecting the cleanliness of photovoltaic modules on floating solar power plants at sea. It can lead to a decrease in photoelectric conversion efficiency, power loss, and even the occurrence of “hot spots”, thereby causing damage to the components. Therefore, the segmentation and detection of guano are crucial for visual automation in the cleaning and inspection processes. However, the composition, density, and thickness of guano naturally vary, leading to inconsistent levels of transparency and color. The uneven intensity of guano images greatly reduces the accuracy of segmentation and detection. Addressing this issue, this study proposes a segmentation algorithm based on combining different color channels to segment guano on the surface of photovoltaic modules. The mean shift method is used for adaptive segmentation to facilitate the detection of guano. Furthermore, the segmentation results obtained by this method are introduced into the input space to improve the traditional Mask RCNN. In addition, this study successfully produced a dataset of guano on the surface of photovoltaic modules using on-site data collection and with the help of a platform built in-house in the laboratory. The experimental results on the self-constructed dataset demonstrate that the enhanced Mask R-CNN model has shown an approximate increase of 5.9% and 6.0% in mAP values for object recognition and segmentation compared to the traditional Mask R-CNN model. This indicates the effectiveness of the methodology proposed in this study.

The erosion and sedimentation of bank slopes are important factors affecting the safety of wharf operations. The essence of bank slope monitoring is to identify the water–soil interface. This paper proposes a model for soil-and-water interface identification and monitoring equipment buried on the bank slope of the wharf, based on the difference in thermodynamic heat transfer between water and soil media, and presents the results of multi-condition numerical simulation. The comparison between numerical simulation results and indoor experimental results shows that the overall patterns are consistent, with an error of less than 11.4%, which is lower than the deviation between theoretical calculation results and indoor experiments. Based on the accuracy of the numerical calculation results, the temperature rise and propagation characteristics of linear heat sources made of iron and PVC in eight types of cohesive soils and six types of non-cohesive soils were studied. The results indicate that there are significant differences in the temperature distribution of linear heat sources made of iron and PVC in both water and soil media. The monitoring equipment model based on the difference in heat transfer between water and soil can be applied in practical engineering. This provides a foundation for the design and application of subsequent monitoring equipment.

71% of the Earth's surface is covered by water, of which 97% is the ocean. Due to various complex environmental factors such as ocean climate and ocean current disturbances, underwater exploration and inspection technology in the ocean urgently needs to be replaced by underwater robots. Based on the exploration and inspection needs of underwater oceans, underwater robots have advantages such as small size, low danger factor, and suitability for various complex environments. Especially in long-term operations, underwater robots can still maintain high-quality work efficiency. In response to the issue of long-term homework, this article chooses underwater wireless charging technology as the power supply method for underwater robots, aiming to improve their underwater work endurance. This article analyzes and verifies the feasibility of applying wireless charging to the power supply of underwater robots through theoretical derivation, simulation, and experiments. The experimental results show that the application of wireless charging function in underwater robots can conveniently and quickly supplement electrical energy, effectively improving the underwater operation time of underwater robots.

The erosion of the seabed around offshore structures has emerged as a critical factor impeding the operational safety of offshore engineering facilities. Prompt and precise identification and monitoring of the water–soil interface hold significant importance in mitigating the seabed erosion challenges facing offshore structures. To tackle this issue, a monitoring framework for the water–soil interface is proposed, grounded in heat transport theory. This framework exploits the thermodynamic variances between seawater and the seabed soil to examine the temperature changes in linear heat sources in water and soil under a constant power. In this study, a typical metallic material—iron (Fe)—and non-metallic material—polyvinyl chloride (PVC)—are considered the linear heat sources, and their temperature variations are analyzed within this framework. The findings reveal that the temperature of the linear heat sources rapidly stabilizes, with the ultimate temperature exhibiting a logarithmic correlation with the convective heat transfer coefficient. To further test the practicability of the framework, an indoor test is conducted. The errors between the theoretical calculation results and the experimental results are less than 14% in water and 19% in soil. The results of the framework and the indoor test have a high degree of coincidence. This framework has proved that it can be used in practical engineering.

Offshore floating solar power stations represent a new frontier in energy development. These stations maximize solar energy use, reduce land consumption and promote algae growth. However, moist marine air makes airborne particles like sand adhere more easily to PV panels, reducing photoelectric conversion efficiency and causing overheating and potential damage. Additionally, drones and fixed cameras face challenges due to complex maritime factors and complicating image analysis. To address these issues, this paper proposes an exploratory framework for identifying dust regions on photovoltaic panels specifically for offshore floating solar power stations. The framework aims to address how to accurately and reliably identify dust accumulation on PV panels, even in images with complex background interference. The framework is designed to be compatible with existing image recognition models and incorporates specialized enhancement modules to further improve recognition accuracy and efficiency. In this study, we utilized a dust feature enhancement module based on the HLS color space, combined with Mask R-CNN, to demonstrate the framework's feasibility and effectiveness. Experimental results show that, compared to a single Mask R-CNN, the framework significantly reduces misidentification and omission of dust areas, boosting the confidence level and effectively meeting the operational needs of offshore floating solar power stations.

This article introduces the challenges in maintaining ocean photovoltaic platforms and proposes a solution: using a bionic robot with the shape of a sea turtle to address the issue of ocean current fluctuations during the maintenance of ocean photovoltaic platforms. This method, through bionic design and optimization of structural parameters, achieves a more stable and energy-efficient movement of the robot in the marine environment, thereby improving the operational efficiency of ocean photovoltaic platforms and reducing energy consumption. The article also presents the kinematic and dynamic models of the robot, and conducts fluid simulation experiments and thrust simulation experiments. The innovation of this article lies in the application of bionic design to marine engineering. Through simulated comparisons of different flipper-shaped structures and thrust experiments, optimal structural model parameters are obtained. The experimental results validate the stability and effectiveness of the robot's movement, providing broad prospects for its application in areas such as marine platform maintenance and underwater pipeline inspection.

Reinforced concrete structures are commonly employed in civil engineering due to their low cost and high performance. However, their exposure to the marine environment, which is typically characterized by elevated salt content and humidity levels, heightens the probability of corrosion. Such corrosion can lead to a decrease in the service life of these structures, which is of utmost importance to predict in order to prevent potential safety hazards. To address this issue, this study investigated the corrosion mechanism of reinforced concrete structures in the marine environment and found Cl- concentration to be the most critical parameter for the corrosion of steel. Afterward, a predictive model is proposed based on the Back Propagation Artificial Neural Network (BPANN) to estimate the residual service life of reinforced concrete structures in different marine environments by forecasting the Cl- concentration in reinforced concrete components. The results demonstrate the high accuracy of the proposed BPANN model, with a maximum relative error of 6.292 %. Consequently, the residual service life forecast system of reinforced concrete structures proposed in this study can provide useful guidance based on the simplistic framework for designing concrete structures in the marine environment by predicting their effective service life. Thus, this study provides valuable insights for promoting the safety and economic benefits of reinforced concrete structures in the marine environment.