Offshore photovoltaic detection method and system

Abstract

The invention relates to an offshore photovoltaic detection method and system, and the method achieves the efficient detection and digital twinborn display of a photovoltaic supporting structure through the motion and perception capability of an underwater robot in combination with underwater mapping and autonomous inspection planning, thereby improving the operation and maintenance efficiency and safety.

Description

Technical Field

[0001] This application relates to the fields of underwater robots and new energy operation and maintenance technology, and in particular to a detection method and system for marine photovoltaic systems. Background Technology

[0002] As an important development direction in the new energy field, offshore photovoltaic (PV) systems are being deployed at an increasingly rapid pace. PV panels and their supporting structures are deployed long-term in the complex marine environment. Affected by multiple environmental factors such as seawater corrosion, marine organism attachment, and continuous impact from wind and waves, the supporting structures of offshore PV systems are prone to deformation and misalignment, and PV modules often experience loosening and damage. If these equipment malfunctions are not detected and addressed in a timely manner, they will directly affect the operational stability and power generation efficiency of the offshore PV system. Therefore, conducting routine and meticulous inspection and maintenance of offshore PV facilities has become an urgent need for the industry's development.

[0003] Currently, the inspection methods for underwater support structures and related facilities of offshore photovoltaic systems mainly rely on on-site inspection by human divers or operations carried out by surface-mounted equipment. These inspection methods have revealed numerous technical defects and application drawbacks in practical applications, specifically the following problems:

[0004] Low detection efficiency: The operation of human divers is limited by the duration and physical strength of underwater operations, and the inspection of shipborne equipment requires frequent planning of routes and deployment of detection devices. Neither of them can achieve rapid and full-area coverage inspection of large-scale offshore photovoltaic facilities. The overall efficiency of the inspection operation is difficult to match the operation and maintenance needs of the large-scale development of offshore photovoltaics. High operational safety risks: The marine environment presents problems such as complex water currents, low underwater visibility, and uncontrollable wind and waves. Underwater inspection by artificial divers is prone to safety hazards such as collisions and drowning. Surface shipboard equipment is also easily affected by sea conditions, resulting in operational deviations. The safety and reliability of the inspection process are difficult to guarantee. Uneven inspection coverage: Manual and shipboard inspection methods are difficult to achieve comprehensive inspection of the support structure of offshore photovoltaics. Inspection of remote areas and complex structures is prone to omissions, resulting in insufficient integrity of inspection data and failure to fully reflect the actual operating status of offshore photovoltaic facilities. Poor data continuity: Existing detection methods are mostly phased and temporary operations, lacking the ability to monitor offshore photovoltaic facilities continuously over a long period of time. They cannot obtain continuous data on changes in equipment status, making it difficult to achieve early warning and trend analysis of facility failures, and operation and maintenance decisions lack effective data support.

[0005] In summary, existing testing methods can no longer meet the needs of the offshore photovoltaic industry for efficient, safe, comprehensive, and continuous testing and maintenance. There is an urgent need to develop an intelligent and automated testing technology suitable for the marine environment to achieve long-term autonomous testing of underwater offshore photovoltaic facilities and provide technical support for the intelligent operation and maintenance of offshore photovoltaic systems. Summary of the Invention

[0006] To address the aforementioned problems in existing technologies, this application provides an autonomous inspection method for marine photovoltaic systems using a permanently stationed underwater robot. By utilizing the underwater robot's motion and perception capabilities, combined with underwater mapping and autonomous inspection planning, the method achieves efficient inspection and digital twin display of photovoltaic support structures, thereby improving operation and maintenance efficiency and safety.

[0007] According to the first aspect of this application, a method for detecting marine photovoltaic power is provided, applied to an underwater robot, characterized in that it includes: The underwater robot collects multi-source sensor data through its detection components; Based on prior information related to the marine photovoltaic system and the multi-source sensor data, map information of the detection area of ​​the marine photovoltaic system is constructed as the current map information; Path planning information is generated based on the current map information and the preset detection scheme; Motion control information is generated based on the current map information and the detection scheme; Based on the path planning information and the motion control information, the underwater robot is used to detect the marine photovoltaic system and obtain detection data; and In response to the fulfillment of preset conditions for returning to charging, the underwater robot is controlled to return to the underwater charging station for charging according to a preset path.

[0008] According to a second aspect of this application, a detection system for marine photovoltaic systems is provided, characterized in that it comprises: Shore-based monitoring devices; Communication devices; and An underwater robot for performing the method described in the first aspect; The shore-based monitoring device communicates with the underwater robot via the communication device.

[0009] According to a third aspect of this application, an electronic device is provided, comprising: Processor; and A memory storing computer instructions that, when executed by the processor, cause the processor to perform the method described in the first aspect.

[0010] According to a fourth aspect of this application, a non-transitory computer storage medium is provided, which stores a computer program that, when executed by a plurality of processors, causes the processors to perform the method described in the first aspect.

[0011] The detection method and system for offshore photovoltaic systems provided in this application enable robots to be permanently stationed underwater and perform autonomous detection, reducing human intervention; support routine inspections and inspections at designated locations, balancing coverage and specificity; the system has shore-based anomaly identification and shore-based post-processing capabilities, forming a two-level intelligent detection architecture and improving detection efficiency; in addition, the detection results are linked with a digital twin platform to enhance the visualization and intelligence level of offshore photovoltaic system operation and maintenance. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings, without exceeding the scope of protection claimed by this application.

[0013] Figure 1 This is a schematic diagram of a marine photovoltaic detection system according to an embodiment of this application.

[0014] Figure 2 This is a schematic diagram of an underwater robot.

[0015] Figure 3 This is a schematic diagram of the composition of an underwater robot according to an embodiment of this application.

[0016] Figure 4 This is a flowchart of a method for detecting marine photovoltaic systems using an underwater robot, according to one embodiment of this application.

[0017] Figure 5 This is a flowchart of a method for detecting marine photovoltaic systems using an underwater robot, according to another embodiment of this application.

[0018] Figure 6 This is a flowchart of a method for detecting marine photovoltaic systems using an underwater robot, according to yet another embodiment of this application.

[0019] Figure 7 This is a flowchart of a method for detecting marine photovoltaic systems using an underwater robot, according to another embodiment of this application.

[0020] Figure 8 This is a schematic diagram of the structure of an electronic device provided in this application. Detailed Implementation

[0021] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0022] Figure 1 This is a schematic diagram of a marine photovoltaic detection system according to an embodiment of this application. Figure 1 As shown, the system includes an underwater robot, a shore-based monitoring device, an underwater charging station, and a communication device. The underwater robot can be any machine that operates underwater, such as a UUV (Unmanned Underwater Vehicle). The shore-based monitoring device is a land-based device that interacts with the underwater robot, handling data transmitted by the underwater robot, sending commands to the underwater robot, storing data, creating digital twins, and displaying information. The underwater robot and the shore-based monitoring device communicate through the communication device, which can establish wired or wireless communication links, such as 4G or 5G communication. Figure 1 Although it is shown as 4G communication, this is only an example, and any suitable communication method can be used in this application. A high-bandwidth, low-latency data transmission channel can be established. At the charging pile location, the detection data can be transmitted to the shore-based monitoring device via cable or wirelessly. In one specific embodiment, the underwater charging pile can be a horn-shaped guide port equipped with a flashing light to guide the underwater robot into the charging pile. After docking, the underwater robot can connect to the charging pile via a mechanical lock or magnetic interface and simultaneously enter charging mode. In one embodiment, the communication device can be integrated with the underwater charging pile into a single device.

[0023] In one embodiment, such as Figure 2 As shown, the underwater robot can serve as a basic platform for all-attitude underwater robots, equipped with multiple complementary sensors and a high-precision navigation system to form a multimodal perception system, meeting the mapping and detection needs in complex underwater environments. The underwater robot supports six degrees of freedom motion control, enabling full-attitude adjustments such as pitch, roll, and yaw, ensuring flexible maneuverability and precise positioning in complex underwater flow fields and around photovoltaic support structures. Equipped with an underwater dwelling electromagnetic charging device, it provides a foundation for long-term autonomous operation.

[0024] In one specific embodiment, the payload components of the underwater robot may include detection components, such as camera components (e.g., optical binocular cameras) and sonar components (e.g., forward-looking sonar and side-scan sonar), a navigation system, etc. The camera components can acquire underwater optical images and generate near-field 3D point cloud data. Under conditions of good water visibility, they can acquire images of the photovoltaic support column surface to identify corrosion, cracks, and attachments. Combined with binocular vision measurements, they generate local 3D point clouds, improving the accuracy and resolution of near-field detection and providing data support for digital twins. The side-scan sonar can acquire large-area 2D acoustic images, forming lateral coverage of underwater structural contour information, providing large-scale environmental structural constraints for SLAM (Simultaneous Localization and Mapping) mapping, and supporting global identification and map initialization of the photovoltaic support column distribution. The forward-looking sonar can acquire distance and contour information of targets ahead, providing target guidance information during inspection tasks, ensuring that the underwater robot can accurately align with the photovoltaic support column or designated area, and can also avoid obstacles in real time, avoiding collisions with the photovoltaic support structure or other underwater obstacles. The navigation system may include an IMU (Inertial Measurement Unit), a DVL (Doppler Velocimetry), a depth gauge, and an acoustic positioning system (USBL / LBL) for navigation and positioning. The payload may also include actuators such as robotic arms, thrusters, and lights. The payload can acquire detection information from the aquatic environment (e.g., based on sonar components and image information obtained from sonar components) and export this information; it can also perform corresponding movements in the aquatic environment, such as controlling actuators to perform target actions. The navigation system provides real-time pose estimation, motion prediction, and prior information for the SLAM front-end; it suppresses drift during long-term operation and, combined with SLAM back-end optimization, achieves high-precision global positioning.

[0025] Figure 3 This is a schematic diagram illustrating the composition of an underwater robot according to an embodiment of this application. Figure 3 As shown, the underwater robot comprises modules including a task management module, an intelligent decision-making module, a path planning and execution module, a motion control module, a target recognition module, a dynamic mapping module, a multi-source data fusion module, and a multi-source data processing module, such as... Figure 3 As shown, the multi-source data processing module includes a binocular camera ranging and point cloud processing module, a side-scan sonar image processing module, a navigation system data processing module, and a forward-looking sonar data processing module.

[0026] In one embodiment, the underwater robot collects multi-source sensor data through detection components. For example, it can acquire optical images, two-dimensional acoustic images, distance and contour information of targets ahead, and navigation and positioning information through binocular cameras, side-scan sonar, forward-looking sonar, and navigation systems. A multi-source data fusion module can preprocess and align the multi-source data. The multi-source sensor data can come from camera components, sonar components, and navigation systems. The camera components can provide ranging, point clouds, and images; the forward-looking sonar can provide near-field obstacles and target contours; the side-scan sonar can provide large-scale terrain and structural strips; and the navigation system can provide IMU, DVL, depth, and positioning data. This data is then incorporated into the multi-source data fusion to form a unified spatiotemporal state and observation. The multi-source data fusion module fuses prior information related to marine photovoltaic systems with multi-source sensor data. Based on this prior information and multi-source sensor data, a dynamic mapping module constructs a map of the area where the marine photovoltaic system is located. A target recognition module can perform target recognition based on the multi-source sensor data collected by the detection components, including identifying the location, tilt, cracks, corrosion, and attachments of the photovoltaic pillars. The task management module is used for task lifecycle management (e.g., start, pause, retry, end), priority and constraint management, and can break down tasks and distribute them to the execution layer. The task management module instructs the intelligent decision-making module to determine the task or detection plan (e.g., inspection) to be performed by the underwater robot based on the constructed map information and recognition results. The intelligent decision-making module can perform status assessment and strategy selection based on task status feedback and environmental / target updates, including continuing inspection, replanning obstacle avoidance, supplementary cleaning, return to docking, termination, etc. The path planning and execution module generates path planning information based on the detection plan to be performed, and can perform path planning (e.g., coverage, obstacle avoidance, return to docking) based on the task objective and environmental map, and monitor and adjust the execution process. The motion control module can convert "behavioral decisions / paths" into thruster and attitude control commands, driving the underwater robot to move along a trajectory and maintain a stable attitude. The motion control module generates motion control information based on the detection scheme to be executed, and controls the movement of the underwater robot. For example, it guides the robot to approach the target column using sonar and optical images, reduces positioning errors caused by wave and current interference with the equipment's navigation system, and reaches the target column surface. The navigation information can be corrected as needed.

[0027] In one embodiment, the underwater robot can construct a map of the detection area of ​​the offshore photovoltaic system based on prior information and collected multi-source sensor data, serving as the current map information. The prior information may include structural diagrams of the offshore photovoltaic system, such as the location and dimensions of support columns, typically derived from design drawings or installation measurements. In a specific embodiment, the underwater robot can construct a 3D map of the detection area based on SLAM technology, where the detection area can be a manually defined region.

[0028] The map construction process can include building local and global maps. In one embodiment, the camera and sonar can extract feature points (e.g., the attachment of shellfish to the supporting column, the degree of surface corrosion, and the tilt of the column), detect the column profile, and generate relative pose and local point cloud. The feature points can be converted into a local point cloud, and the column features (sectioned cylinder, column shadow) can be extracted as landmarks to construct a local map. Then, the camera, sonar, odometry, prior column position (prior column position) constraints can be fused with the reference anchor points to perform loop closure detection and optimization (loop closure detection, loop closure correction) and eliminate accumulated drift to form a global map.

[0029] In one embodiment, the underwater robot can perform baseline alignment and correction on the constructed map information, and can use underwater charging piles, reference pillars, surface GPS, or USBL acoustic base stations for global constraints to reduce drift. In another embodiment, the underwater robot can also verify the constructed map information and automatically evaluate the location. Figure 1 The system performs consistency checks, compares with prior column positions, outputs accuracy indicators and confidence levels, triggers retesting or manual correction when necessary, detects randomly selected columns through control equipment, and determines the consistency between the map and the actual underwater environment based on the distance between the equipment reaching the target point and the column.

[0030] In one embodiment, the underwater robot can transmit map information to a shore-based monitoring device via a communication device, such as via cable or Wi-Fi / 4G after surfacing. The underwater robot can also transmit multi-source sensor data collected by the underwater robot to the shore-based monitoring device via the communication device. The shore-based monitoring device optimizes the map information to obtain optimized map data. In a specific embodiment, the shore-based monitoring device can combine the map information transmitted by the underwater robot with shore-based computing power to perform fine-grained optimization and error correction on the global map. Then, the shore-based monitoring device transmits the optimized map information to the underwater robot.

[0031] In one embodiment, the underwater robot generates path planning information and motion control information based on current map information and a detection scheme. In another embodiment, the underwater robot can generate path planning information and motion control information based on existing algorithms, or it can determine path planning information and motion control information based on a neural network model; this application does not impose any limitations on this. The detection scheme may include patrol inspection, routine inspection, continued patrol inspection, (re)planning obstacle avoidance, supplementary sweeping, and return docking. The detection scheme can be generated by the underwater robot, which can autonomously detect offshore photovoltaic systems. In one embodiment, the underwater robot can determine a routine patrol path based on current map information, the detection scheme, and the underwater robot's energy consumption. The detection scheme can also be generated by a shore-based monitoring device to guide the underwater robot's detection behavior. For example, the shore-based monitoring device can instruct localized focused detection of target support columns or photovoltaic modules. In another embodiment, the underwater robot can determine a detection path for a specified location based on current map information and the inspection task for a specified location of the offshore photovoltaic system in the detection scheme. In one embodiment, the underwater robot can determine motion control information during the detection of offshore photovoltaic systems based on current map information, the detection scheme, and multi-source sensor data.

[0032] In one embodiment, the underwater robot detects the marine photovoltaic system according to path planning information and motion control information to obtain detection data. In a specific embodiment, the underwater robot acquires image, sonar, and navigation position information, has real-time data processing capabilities, and can autonomously identify anomalies (such as support column displacement, surface damage, and abnormal attachments) and manage the identified data to obtain detection data. In a specific embodiment, the underwater robot can identify marine photovoltaic systems using corresponding identification models or algorithms, including column position, tilt, cracks, corrosion, and attachments. The identification results can be bound to the original data (including collected multi-source sensor data) and stored as a task data package.

[0033] In one embodiment, during the process of underwater robot inspecting marine photovoltaics according to the inspection plan, it obtains identification results (including the positioning of photovoltaic support columns and target identification) through detection components. Based on the identification results, it can combine prior information and multi-source sensor data to construct new map information, update the current map information, and then execute the subsequent inspection plan based on the updated map information.

[0034] In one embodiment, the underwater robot can determine whether to return to the underwater charging station for charging based on preset conditions. In one specific embodiment, the preset conditions may be a comparison between the underwater robot's remaining battery power and a preset value. If the remaining battery power is less than the preset value, the preset conditions are considered met. If the preset conditions for returning to charging are met, the underwater robot returns to the underwater charging station for charging according to a preset path.

[0035] In one embodiment, during the underwater robot's charging process, the constructed map information, or the constructed map information and collected multi-source sensor data, can be sent to the shore-based monitoring device via a communication device. The underwater robot can also transmit detection data to the shore-based monitoring device via the communication device. In one embodiment, the underwater robot can transmit stored detection data (including sonar images, optical images, point cloud data, pose trajectory, anomaly identification results, etc.) in batches to the charging pile cache module, which then uploads it uniformly to the shore-based monitoring device, ensuring that interrupted transmissions can be resumed and data integrity is verified during the transmission process.

[0036] In one embodiment, the shore-based monitoring device combines map information from the underwater robot with shore-based computing power to refine and correct errors in the map information, obtain optimized map information, and transmit the optimized map information to the underwater robot through a communication device.

[0037] In one embodiment, the shore-based monitoring device, combined with shore-based computing power, runs a high-precision image / point cloud recognition model on the detection data from the underwater robot to identify defects such as corrosion, cracks, tilting, and attachments on the support columns, thus determining the identification results for the marine photovoltaic system. In another embodiment, the shore-based monitoring device aligns the identification results with a preset historical database, allowing comparison of the geometric shape and surface condition changes of the same support column of the marine photovoltaic system at different times. The shore-based monitoring device maps the marine photovoltaic identification results to a digital twin model of the marine photovoltaic system. Based on the marine photovoltaic identification results, the device displays the corresponding defect location, type, and historical data comparison on a visualization platform, forming a complete closed loop of operation and maintenance records. The lightweight airborne identification model of the underwater robot in this application enables real-time preliminary anomaly identification, while the shore-based monitoring device performs in-depth analysis and verification. This two-level identification architecture forms an efficient hierarchical detection system.

[0038] Based on the above description, according to one aspect of this application, a method for detecting marine photovoltaic systems is provided. Figure 4 This is a flowchart of a method for detecting marine photovoltaic systems using an underwater robot, according to one embodiment of this application. Figure 4 As shown, the method includes the following steps: Step S401: Collect multi-source sensor data through the detection components of the underwater robot; Step S402: Based on prior information related to the marine photovoltaic system and the multi-source sensor data, construct map information of the detection area of ​​the marine photovoltaic system as the current map information.

[0039] In one embodiment, the underwater robot can construct a map of the marine photovoltaic (PV) inspection area based on prior information and collected multi-source sensor data, serving as the current map information. The prior information may include structural diagrams of the marine PV system, such as the location and dimensions of support columns, typically derived from design drawings or installation measurements. In a specific embodiment, the underwater robot can construct a 3D map of the inspection area based on SLAM technology, where the inspection area can be a manually defined region.

[0040] Step S403: Generate path planning information based on the current map information and the preset detection scheme; Step S404: Generate motion control information based on the current map information and the detection scheme.

[0041] In one embodiment, the underwater robot generates path planning information and motion control information based on current map information and a detection scheme. In another embodiment, the underwater robot can generate path planning information and motion control information based on existing algorithms, or it can determine path planning information and motion control information based on a neural network model; this application does not impose any limitations on this. The detection scheme may include patrol inspection, routine inspection, continued patrol inspection, (re)planning obstacle avoidance, supplementary sweeping, and return docking. The detection scheme can be generated by the underwater robot, which can autonomously detect offshore photovoltaic systems. In one embodiment, the underwater robot can determine a routine patrol path based on current map information, the detection scheme, and the underwater robot's energy consumption. The detection scheme can also be generated by a shore-based monitoring device to guide the underwater robot's detection behavior. For example, the shore-based monitoring device can instruct localized focused detection of target support columns or photovoltaic modules. In another embodiment, the underwater robot can determine a detection path for a specified location based on current map information and the inspection task for a specified location of the offshore photovoltaic system in the detection scheme. In one embodiment, the underwater robot can determine motion control information during the detection of offshore photovoltaic systems based on current map information, the detection scheme, and multi-source sensor data.

[0042] Step S405: According to the path planning information and the motion control information, the underwater robot is used to detect the marine photovoltaic system and obtain detection data.

[0043] In one embodiment, the underwater robot detects the marine photovoltaic system according to path planning information and motion control information to obtain detection data. In a specific embodiment, the underwater robot acquires image, sonar, and navigation position information, has real-time data processing capabilities, and can autonomously identify anomalies (such as support column displacement, surface damage, and abnormal attachments) and manage the identified data to obtain detection data. In a specific embodiment, the underwater robot can identify marine photovoltaic systems using corresponding identification models or algorithms, including column position, tilt, cracks, corrosion, and attachments. The identification results can be bound to the original data (including collected multi-source sensor data) and stored as a task data package.

[0044] Step S406: In response to meeting the preset conditions for returning to charging, the underwater robot is controlled to return to the underwater charging station for charging according to the preset path.

[0045] In one embodiment, the underwater robot can determine whether to return to the underwater charging station for charging based on preset conditions. In one specific embodiment, the preset conditions may be a comparison between the underwater robot's remaining battery power and a preset value. If the remaining battery power is less than the preset value, the preset conditions are considered met. If the preset conditions for returning to charging are met, the underwater robot returns to the underwater charging station for charging according to a preset path.

[0046] Figure 5 This is a flowchart of a method for detecting marine photovoltaic systems using an underwater robot, according to another embodiment of this application. Figure 4 compared to, Figure 5 The method shown includes steps S501 to S506 and... Figure 4 Steps S401 to S406 of the method shown are the same, except that, Figure 5 The method shown also includes: Step S507: The map information is transmitted to the shore-based monitoring device via a communication device, so that the shore-based monitoring device optimizes the map information; Step S508: Receive optimized map information from the shore-based monitoring device, use the optimized map information as the current map information, and return to step S503.

[0047] In one embodiment, during the underwater robot's charging process, the constructed map information, or the constructed map information and collected multi-source sensor data, can be sent to the shore-based monitoring device via a communication device. The underwater robot can also transmit detection data to the shore-based monitoring device via the communication device. In one embodiment, the underwater robot can transmit stored detection data (including sonar images, optical images, point cloud data, pose trajectory, anomaly identification results, etc.) in batches to the charging pile cache module, which then uploads it uniformly to the shore-based monitoring device, ensuring that interrupted transmissions can be resumed and data integrity is verified during the transmission process.

[0048] In one embodiment, the shore-based monitoring device combines map information from the underwater robot with shore-based computing power to refine and correct errors in the map information, obtain optimized map information, and transmit the optimized map information to the underwater robot through a communication device.

[0049] Figure 6 This is a flowchart of a method for detecting marine photovoltaic systems using an underwater robot, according to yet another embodiment of this application. Figure 4 compared to, Figure 6 The method shown includes steps S601 to S605 and step S608. Figure 4 Steps S401 to S406 of the method shown are the same, except that, Figure 6 The method shown also includes: Step S606: Determine the location and target identification of the support column of the offshore photovoltaic system using the detection data; and Step S607: Update the current map information based on the positioning of the support column and the target identification, and return to S63.

[0050] In one embodiment, during the process of underwater robot inspecting marine photovoltaics according to the inspection plan, it obtains identification results (including the positioning of photovoltaic support columns and target identification) through detection components. Based on the identification results, it can combine prior information and multi-source sensor data to construct new map information, update the current map information, and then execute the subsequent inspection plan based on the updated map information.

[0051] Figure 7 This is a flowchart illustrating a method for detecting marine photovoltaic systems using an underwater robot, according to another embodiment of this application. Figure 4 compared to, Figure 7 The method shown includes steps S701 to S702 and steps S705 to S708. Figure 4 Steps S401 to S406 of the method shown are the same, except that, Figure 7 The method shown also includes: Step S703: Perform a baseline alignment operation on the current map information to obtain baseline-aligned map information; and Step S704: Verify the map information after benchmark alignment.

[0052] In one embodiment, the underwater robot can perform baseline alignment and correction on the constructed map information, and can use underwater charging piles, reference pillars, surface GPS, or USBL acoustic base stations for global constraints to reduce drift. In another embodiment, the underwater robot can also verify the constructed map information and automatically evaluate the location. Figure 1 The system performs consistency checks, compares with prior column positions, outputs accuracy indicators and confidence levels, triggers retesting or manual correction when necessary, detects randomly selected columns through control equipment, and determines the consistency between the map and the actual underwater environment based on the distance between the equipment reaching the target point and the column.

[0053] According to the detection method and system for offshore photovoltaic systems provided in this application, a permanently stationed underwater robot combined with an underwater charging pile achieves unmanned, long-term autonomous detection cycles, eliminating reliance on frequent manual deployment and retrieval, and significantly improving detection efficiency and coverage. The system integrates routine inspections and designated location checks, supporting a combination of comprehensive and targeted inspections, ensuring both thoroughness and flexibility. A lightweight underwater robot identification model enables real-time preliminary anomaly identification, while a shore-based post-processing system performs in-depth analysis and verification. This two-tiered anomaly identification architecture forms an efficient hierarchical detection system. Linking detection data with a digital twin platform enables spatial visualization and historical comparison of defects in the photovoltaic support structure, supporting intelligent operation and maintenance decisions. Furthermore, the closed-loop system of autonomous detection, data feedback, and digital twin display constructs a complete autonomous detection and operation and maintenance loop, forming a quantifiable, traceable, and evolvable detection method, distinct from existing single-collection or manual inspection methods.

[0054] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0055] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to this application.

[0056] In the several embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be an electrical connection or other forms.

[0057] See Figure 8 , Figure 8 An electronic device is provided, including a processor and a memory. The memory stores computer instructions or one or more programs, which, when executed by the processor, cause the processor to execute the computer instructions to achieve the following: Figures 4 to 7 The method and its detailed scheme are shown.

[0058] It should be understood that the above-described device embodiments are merely illustrative, and the device disclosed in this invention can be implemented in other ways. For example, the division of units / modules described in the above embodiments is only a logical functional division, and there may be other division methods in actual implementation. For example, multiple units, modules, or components may be combined, integrated into another system, or some features may be ignored or not executed.

[0059] Furthermore, unless otherwise specified, the functional units / modules in the various embodiments of the present invention can be integrated into one unit / module, or each unit / module can exist physically separately, or two or more units / modules can be integrated together. The integrated units / modules described above can be implemented in hardware or as software program modules.

[0060] When the integrated unit / module is implemented in hardware, the hardware can be digital circuits, analog circuits, etc. The physical implementation of the hardware structure includes, but is not limited to, transistors, memristors, etc. Unless otherwise specified, the processor or chip can be any suitable hardware processor, such as a CPU, GPU, FPGA, DSP, and ASIC, etc. Unless otherwise specified, the on-chip cache, off-chip memory, and storage can be any suitable magnetic or magneto-optical storage medium, such as resistive random access memory (RRAM), dynamic random access memory (DRAM), static random access memory (SRAM), enhanced dynamic random access memory (EDRAM), high-bandwidth memory (HBM), hybrid memory cube (HMC), etc.

[0061] If the integrated unit / module is implemented as a software program module and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer electronic device (which may be a personal computer, server, or network electronic device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this disclosure. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0062] This application also provides a computer-readable storage medium storing one or more computer programs, which, when executed by multiple processors, cause the processors to perform the following actions: Figures 4 to 7 The method and its detailed scheme are shown.

[0063] This application also provides a computer program product, which includes a computer program that, when run on a computer, causes the computer to perform the methods of any of the above embodiments.

[0064] References to features, advantages, or similar language in this specification do not imply that all features and advantages achievable with this solution should be included or included in any single implementation thereof. Rather, references to features and advantages are understood to mean that a particular feature, advantage, or characteristic described in connection with an embodiment is included in at least one embodiment of this solution. Therefore, discussions of features, advantages, and similar language throughout this specification may, but do not necessarily, refer to the same embodiments.

[0065] Furthermore, the features, advantages, and characteristics described herein can be combined in any suitable manner in one or more embodiments. Based on the description herein, those skilled in the art will recognize that this solution can be implemented without one or more specific features or advantages of a particular embodiment. In other instances, additional features and advantages can be appreciated in specific embodiments not presented in all embodiments of this solution.

[0066] The embodiments of this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the embodiments above are only for the purpose of helping to understand the method and core ideas of this application. Furthermore, any changes or modifications made by those skilled in the art based on the ideas of this application, and on the specific implementation methods and application scope of this application, are all within the scope of protection of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for detecting marine photovoltaic power, applied to an underwater robot, characterized in that, include: (a) Acquire multi-source sensor data through the detection components of the underwater robot; (b) Based on prior information related to the marine photovoltaic system and the multi-source sensor data, construct map information of the detection area of ​​the marine photovoltaic system as the current map information; (c) Generate path planning information based on the current map information and the preset detection scheme; (d) Generate motion control information based on the current map information and the detection scheme; (e) Using the underwater robot to detect the marine photovoltaic system according to the path planning information and the motion control information, and obtain detection data; as well as (f) In response to the fulfillment of the preset conditions for returning to charging, the underwater robot is controlled to return to the underwater charging station for charging according to the preset path.

2. The method as described in claim 1, characterized in that, After step (f), the method further includes: The map information is transmitted to the shore-based monitoring device via a communication device, so that the shore-based monitoring device can optimize the map information. Receive optimized map information from the shore-based monitoring device, use the optimized map information as the current map information, and return to step (c).

3. The method as described in claim 1, characterized in that, After step (e) and before step (f), the method further includes: The location of the support pillars and target identification of the offshore photovoltaic system are determined using the detection data; and Based on the positioning of the support column and the target identification, the current map information is updated, and the process returns to step (c).

4. The method according to any one of claims 1 to 3, characterized in that, Prior to step (c), the method further includes: Perform a baseline alignment operation on the current map information to obtain baseline-aligned map information; and The map information after alignment with the reference is verified.

5. The method according to any one of claims 1 to 3, characterized in that, Step (c) includes: Based on the current map information, the detection plan, and the energy consumption of the underwater robot, a routine inspection path is determined; and / or Based on the current map information and the inspection task for the designated location of the offshore photovoltaic in the detection scheme, a detection path for the designated location is determined.

6. The method according to any one of claims 1 to 3, characterized in that, Step (d) includes: Based on the current map information, the detection scheme, and the multi-source sensor data, the motion control information of the underwater robot during the detection of the marine photovoltaic process is determined.

7. The method according to any one of claims 1 to 3, characterized in that, The prior information includes the construction structure diagram of the offshore photovoltaic system.

8. A detection system for marine photovoltaic systems, characterized in that, include: Shore-based monitoring devices; communication devices; as well as An underwater robot for performing the method as described in any one of claims 1 to 7; The shore-based monitoring device communicates with the underwater robot through the communication device.

9. The system as described in claim 8, characterized in that, The shore-based monitoring device is used for: Receive multi-source sensor data and constructed map information collected by the underwater robot; The map information is optimized based on the multi-source sensor data to obtain optimized map information; The optimized map information is sent to the underwater robot; Receive detection data from the underwater robot; The detection data is identified to determine the marine photovoltaic identification result; as well as The marine photovoltaic identification results are mapped to a marine photovoltaic digital twin model for digital twin display.

10. The system as described in claim 9, characterized in that, The shore-based monitoring device is also used for: The identification results of the marine photovoltaic system are aligned with a preset historical database to show the changes in the geometric shape and surface condition of the same support column of the marine photovoltaic system at different times. Mapping the marine photovoltaic identification results to a marine photovoltaic digital twin model; and Based on the identification results of the marine photovoltaic system, the location, type, and historical data comparison of the corresponding defects of the marine photovoltaic system are displayed through a visualization platform.

11. An electronic device, characterized in that, The device includes a memory and a processor, wherein the memory stores a computer program, and the processor, when executing the computer program in the memory, implements the method of any one of claims 1 to 7.

12. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method of any one of claims 1 to 7.

Images