Method and system for work control of a robot for defect detection and repair

Abstract

The application discloses a work control method and system for a fault detection maintenance robot, and relates to the technical field of photovoltaic power station operation and maintenance. The method comprises the following steps: importing a photovoltaic power station GIS map into a target robot, planning an inspection path, and driving the target robot to move; when the target robot moves to a power station area, the directional deployment of a mechanical arm is controlled in a preset deployment posture, a multi-modal fault detection module integrated at the end of the mechanical arm is triggered, concurrent detection under multi-modal orientation is performed, and a detection signal set is determined; the detection signal set is returned, signal distribution is updated along with the detection process, independent detection of each mode is performed, multi-modal spatial distribution fusion is carried out, fault detection detection is determined, and the detection is sent to an operation and maintenance mobile terminal for alarm. The technical problem of low photovoltaic power station inspection efficiency in the prior art under complex terrain is solved, and the technical effect of improving the photovoltaic power station inspection efficiency is achieved.

Description

Technical Field

[0001] This invention relates to the field of photovoltaic power plant operation and maintenance technology, specifically to an operation control method and system for a flaw detection and maintenance robot. Background Technology

[0002] Photovoltaic power plants are exposed to complex outdoor environments for extended periods, suffering from erosion due to climate and mechanical vibrations. This leads to frequent structural defects such as pile corrosion and truss cracks, seriously threatening the safe operation of the power plants. However, current photovoltaic power plant operation and maintenance face numerous technical bottlenecks, severely restricting the improvement of efficiency and quality. While traditional wheeled or tracked robots are used in some scenarios, their design limitations mean that their ground clearance is typically less than 10 centimeters, and their obstacle-crossing ability is generally less than 15 centimeters. They are inadequate when facing complex terrain such as gaps in photovoltaic panel supports or gravel terrain, resulting in nearly half of the plant area becoming inspection "blind spots," failing to achieve comprehensive and effective coverage. In flaw detection operations, the contradiction between the narrow space under the photovoltaic panels and the fixed installation method of the rigid robotic arm is becoming increasingly prominent. During operation, the robotic arm is prone to colliding with the photovoltaic panels, potentially damaging them and affecting the normal progress of flaw detection work. At the same time, existing flaw detection equipment relies heavily on manual measurement, which is not only inefficient but also greatly affected by human factors, resulting in a high rate of missed detections and making it difficult to meet the power plant's demand for accurate flaw detection. Summary of the Invention

[0003] This application provides an operation control method and system for a flaw detection and maintenance robot, which solves the technical problem of low inspection efficiency of photovoltaic power plants in complex terrain in the prior art.

[0004] The first aspect of this application provides an operation control method for a flaw detection and repair robot, the method comprising:

[0005] The GIS map of the photovoltaic power station is imported into the target robot to plan the inspection path and drive the robot to move. The target robot is equipped with a triangular tracked chassis, and the track tilt angle is adaptively adjusted to the terrain according to the trajectory. When the target robot moves to the power station area, it controls the orientation and deployment of the robotic arm in a preset deployment posture, triggering the multimodal flaw detection module integrated at the end of the robotic arm to perform concurrent detection under multimodal orientation and determine the detection signal set. The detection signal set is identified by a spatiotemporal code. The detection signal set is transmitted back, and the signal distribution is updated as the detection progresses. By performing modal independent detection and multimodal spatial distribution fusion, a flaw detection report is determined and sent to the maintenance mobile terminal for alarm.

[0006] A second aspect of this application provides an operation control system for a flaw detection and repair robot, the system comprising:

[0007] A mobile component is used to import the GIS map of the photovoltaic power station into the target robot, plan the inspection path, and drive the target robot to move. The target robot is equipped with a triangular tracked chassis, and the track tilt angle is adaptively adjusted to a second-order equipotential angle according to the trajectory terrain. A detection component is used to control the orientation and deployment of the robotic arm in a preset deployment posture when the target robot moves to the power station area. This triggers the multimodal flaw detection module integrated at the end of the robotic arm to perform concurrent detection under multimodal orientation and determine the detection signal set. The detection signal set is identified by a spatiotemporal code. An alarm component is used to transmit the detection signal set back, update the signal distribution as the detection process progresses, and determine the flaw detection report by performing modal independent detection and multimodal spatial distribution fusion, and send it to the operation and maintenance mobile terminal for alarm.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0009] The GIS map of the photovoltaic power station is imported into the target robot, an inspection path is planned, and the target robot is driven to move. The target robot is equipped with a triangular tracked chassis, and the track tilt angle is adaptively adjusted to the terrain according to the trajectory. When the target robot moves to the station area, it controls the orientation and deployment of the robotic arm in a preset deployment posture, triggering the multimodal flaw detection module integrated at the end of the robotic arm to perform concurrent detection under multimodal orientation, and determine the detection signal set. The detection signal set is identified by a spatiotemporal code. The detection signal set is transmitted back, and the signal distribution is updated as the detection progresses. By performing independent modal detection and multimodal spatial distribution fusion, the flaw detection report is determined and sent to the maintenance mobile terminal for alarm. This solves the technical problem of low inspection efficiency of photovoltaic power stations in complex terrain in existing technologies, and achieves the technical effect of improving the inspection efficiency of photovoltaic power stations. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments of the present invention, 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 the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 A schematic flowchart of an operation control method for a flaw detection and repair robot provided in an embodiment of this application;

[0012] Figure 2 This is a schematic diagram of the operation control system for a flaw detection and repair robot provided in an embodiment of this application.

[0013] Explanation of reference numerals in the attached diagram: 11 moving component, 12 detection component, 13 alarm component. Detailed Implementation

[0014] This application solves the technical problem of low inspection efficiency of photovoltaic power plants in complex terrain by providing an operation control method and system for flaw detection and maintenance robots.

[0015] 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 a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0016] It should be noted that the terms "comprising" and "having" are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or modules that are not explicitly listed or that are inherent to these processes, methods, products, or devices.

[0017] Example 1, as Figure 1 As shown, this application provides an operation control method for a flaw detection and repair robot, wherein the method includes:

[0018] The GIS map of the photovoltaic power station is imported into the target robot, the inspection path is planned, and the target robot is driven to move. The target robot is equipped with a triangular track chassis, and the track tilt angle is adaptively adjusted in a second-order equipotential manner according to the trajectory terrain.

[0019] By importing the GIS map of the photovoltaic power station into the target robot, the system can plan the inspection path based on the geographical information of the station and drive the target robot to move along the preset path.

[0020] The target robot is equipped with a triangular track chassis. The chassis design improves the robot's mobility and stability, making it particularly suitable for complex terrain and uneven environments. The target robot utilizes track tilt angle adaptive adjustment technology to adjust the track tilt angle in real time according to the terrain of the inspection path, thus adapting to different terrain changes.

[0021] The track tilt angle is adjusted using a second-order isotropic adjustment method. When a change in the terrain is detected, the system adaptively adjusts the track tilt angle. Specifically, the system first determines the slope and terrain undulation of the path, performs an initial tilt angle adjustment using first-order adjustment, and then performs a fine adjustment using second-order adjustment to ensure the robot remains stable during movement, avoids tilting or slipping, and improves the efficiency and safety of inspection tasks.

[0022] When the robot travels on uneven terrain, the system independently adjusts the tilt angle of each track, allowing each track to adaptively adjust to the ground undulations at its location. Through these adjustments, the robot as a whole maintains a stable horizontal plane, ensuring balance and stability during movement. The tilt angle of a single track is adjustable (0-30°), and combined with a differential steering algorithm, it can adapt to gravel and muddy surfaces with a slope ≤40°, with a minimum turning radius ≤0.5m.

[0023] Furthermore, the robotic arm is a multi-degree-of-freedom folding arm that can be stored inside the robot's internal structure. The end of the robotic arm integrates a multi-modal flaw detection module, wherein the front-end components of the multi-modal flaw detection module include at least an ultrasonic flaw detector, an electromagnetic eddy current sensor, and an infrared thermal imager.

[0024] The robotic arm is a multi-degree-of-freedom folding arm, possessing high flexibility and adjustability, capable of performing complex tasks. Designed with a retractable structure, the robotic arm can be stored internally when the robot is not in operation, optimizing space utilization and protecting the arm from external damage. When the robot needs to perform inspection tasks, the robotic arm can be deployed from its retracted state and positioned at the appropriate work location.

[0025] The robotic arm's end effector integrates a multimodal flaw detection module. This module is designed to perform multiple flaw detection techniques within the same work cycle, increasing the comprehensiveness and accuracy of the inspection. Specifically, the front-end components of the multimodal flaw detection module include at least an ultrasonic flaw detector, an electromagnetic eddy current sensor, and an infrared thermal imager. The ultrasonic flaw detector is primarily used to detect internal cracks in the foundation piles (accuracy ±0.1mm), and the electromagnetic eddy current sensor is used to identify surface corrosion on the truss (resolution 0.5mm). 2 Infrared thermal imagers can provide temperature distribution maps and monitor overheating of electrical connection points (temperature sensitivity 0.1℃).

[0026] Furthermore, the ultrasonic flaw detector uses the foundation pile as the scanning target and scans along the axial direction of the foundation pile; the electromagnetic eddy current sensor uses the truss surface as the scanning target and scans using a gridded detection method with a preset grid spacing; the infrared thermal imager uses the electrical junction box as the scanning target and scans the entire wiring layout.

[0027] Ultrasonic flaw detectors, electromagnetic eddy current sensors, and infrared thermal imagers each employ different scanning methods to achieve comprehensive flaw detection of photovoltaic power station equipment. Specifically, the ultrasonic flaw detector scans the foundation piles, probing along the pile's axial direction with a set frequency of 2.5MHz and a step accuracy of 1mm. By emitting high-frequency sound waves and receiving the reflected signals, the ultrasonic flaw detector can detect internal defects such as cracks or voids in the foundation piles, ensuring the structural integrity of the piles. The electromagnetic eddy current sensor scans the truss surface, using a gridded detection method with a preset grid spacing (5mm). The electromagnetic eddy current sensor detects surface and near-surface defects in conductive materials through electromagnetic induction. Its gridded scanning method can cover all locations on the truss surface, ensuring thorough detection of potential cracks, corrosion, or other surface defects. The infrared thermal imager scans the electrical junction boxes, using a panoramic scanning method to perform panoramic thermal imaging (30Hz frame rate) of the electrical junction boxes, identifying overheating anomalies. Infrared thermal imagers identify overheating problems or faults by detecting the thermal radiation of electrical junction boxes. Through thermal imaging technology, they can display the temperature distribution of the junction box in real time and promptly detect hot spots caused by short circuits, poor contact, or overload. The panoramic scanning method of the junction box ensures that the entire surface area of ​​the junction box can be efficiently detected by thermal imaging, accurately identifying potential temperature anomalies.

[0028] Using three different flaw detection technologies, the robot can conduct a comprehensive and efficient inspection of the foundation piles, truss surfaces, and electrical junction boxes in photovoltaic power plants, promptly identifying potential structural problems or equipment failures and improving the safety and reliability of operation and maintenance work.

[0029] Furthermore, before the track tilt angle undergoes second-order equipotential adjustment based on the terrain, an equipotential adjustment module is constructed, including:

[0030] First-order nodes are deployed using the first undulation angle based on road condition characteristics as the first-order control target; second-order nodes are deployed using the inclination variables of the upper-level control node and the lower-level control node as the second-order control target; and the first-order nodes and the second-order nodes are sequentially cascaded to determine the isostatic adjustment module.

[0031] Before the track tilt angle is adaptively adjusted to the second-order equipotential adjustment based on the trajectory terrain, an equipotential adjustment module is constructed to precisely adjust the track tilt angle according to the terrain characteristics of the photovoltaic power station, thereby ensuring the stable movement and efficient inspection of the target robot in complex terrain.

[0032] Specifically, based on the road conditions of the photovoltaic power station, a first undulation tilt angle is set as the first-order control target. The first undulation tilt angle represents the initial tilt angle that the track needs to adjust on uneven terrain to adapt to the undulation changes of the terrain. The first-order control target calculates a suitable initial tilt angle adjustment value by analyzing factors such as the slope of the path and the undulation of the terrain to ensure that the track can move smoothly and remain stable. According to the set first-order control target, the system deploys first-order nodes to control the tilt angle of the track.

[0033] Based on the first-order adjustment, the tilt angle variables of the upper and lower control nodes serve as the second-order control targets. These second-order targets are used to eliminate tilting problems caused by minor errors or uneven ground conditions generated during the first-order adjustment process. According to the second-order control targets, the system deploys second-order nodes to adjust the tracks with higher precision. These second-order nodes can precisely control the fine-tuning of the tracks, ensuring the robot's stability and mobility in complex terrain and preventing tilting or slippage caused by minor imbalances.

[0034] By cascading first-order and second-order nodes sequentially, an equipotential adjustment module is formed. Based on this module, the system can dynamically adapt to changes in the terrain along the path and adjust the tilt angle of the tracks in real time during travel, ensuring that the robot maintains a stable operating state at all times.

[0035] Furthermore, the flaw detection and repair robot includes independently driven first and second tracks; the equipotential adjustment module is deployed at the first control end of the first track and the second control end of the second track, and a synchronization timestamp constraint is established between the first control end and the second control end. The flaw detection and repair robot includes independently driven first and second tracks, and the two tracks can be independently controlled and driven to adapt to different terrains and movement requirements.

[0036] During robot movement, the tilt angle adjustment of the tracks needs to be precisely controlled according to ground undulations. Therefore, the equipotential adjustment module is deployed at the first control end of the first track and the second control end of the second track. In this way, the tilt angles of the first and second tracks can be adjusted independently according to their respective terrain conditions, ensuring that the robot remains stable during movement and adapts to complex terrain.

[0037] To ensure the coordination and synchronization of the two tracks, a synchronization timestamp constraint is established between the first and second control terminals. This constraint ensures that the tilt angle adjustments of the two tracks are performed synchronously at the same time step, preventing the robot from becoming unbalanced or skewed due to asynchronous adjustments during movement. Through this timestamp constraint, the system can precisely control the movement of the two tracks, ensuring the robot's stability and efficiency in complex terrain, and improving the operational performance and accuracy of the flaw detection and repair robot.

[0038] Furthermore, the track tilt angle adaptively performs second-order equipotential adjustment based on the track terrain, including:

[0039] As the patrol path is moved, the real-time path terrain is determined. Based on the real-time path terrain, the isostatic adjustment module deployed at the first control terminal performs a first-order tilt angle decision based on the road condition undulation characteristics to determine the first-order road condition tilt angle. The upper-level track tilt angle of the upper-level control node is retrieved, and the difference between the first-order road condition tilt angle and the upper-level track tilt angle is calculated as the second-order adjustment tilt angle. According to the second-order adjustment tilt angle, the first track is driven to perform track tilt angle adjustment.

[0040] Preferably, as the robot moves along the inspection path, the system determines the path conditions in real time based on data fed back from sensors, analyzing the terrain undulations and slope changes. Based on the real-time path conditions, the isostatic adjustment module deployed at the first control terminal performs a first-order tilt angle decision based on the road condition undulation characteristics, determining a first-order road condition tilt angle for initial matching of terrain undulations. That is, based on the path ground features, such as slope and elevation difference, a suitable initial tilt angle adjustment value is calculated. After the first-order tilt angle decision is completed, the system retrieves the upper-level track tilt angle from the upper-level control node, calculates the difference between the first-order road condition tilt angle and the upper-level track tilt angle to obtain a second-order adjustment tilt angle for further fine adjustment. Finally, based on the second-order adjustment tilt angle, the first track is driven to perform tilt angle control, achieving precise adjustment of the track posture, thereby ensuring that the robot can maintain overall balance and stability in complex terrain, improving the continuity and safety of inspection operations.

[0041] When the target robot moves to the site area, it controls the directional deployment of the robotic arm in a preset deployment posture, triggering the multimodal flaw detection module integrated at the end of the robotic arm to perform concurrent detection under multimodal orientation and determine the detection signal set, wherein the detection signal set is identified by a time-space code.

[0042] Once the target robot moves to the designated inspection area of ​​the photovoltaic power station, the system controls the robot to prepare for operation in a preset deployment posture. The preset deployment posture consists of initial parameters set according to the station's inspection task requirements, ensuring rapid deployment of the robotic arm in the optimal position. Subsequently, the control system drives the robotic arm to deploy in an oriented manner. The robotic arm performs multi-degree-of-freedom adjustments based on the target inspection position and posture commands, enabling the end-effector flaw detection module to accurately position itself towards the inspection target. The multimodal flaw detection module integrated at the end of the robotic arm is triggered and activated after deployment. Various built-in flaw detection sensors (such as ultrasonic flaw detectors, electromagnetic eddy current sensors, and infrared thermal imagers) are simultaneously activated, performing concurrent detection operations according to multimodal orientation requirements. Concurrent detection under multimodal orientation enables the parallel acquisition of multi-dimensional detection information across different sensors, improving detection efficiency and comprehensiveness. During detection, various detection signals are collected to form a detection signal set. Each data point in the signal set is bound to a unique spatiotemporal code, which identifies the spatial location and timestamp of the data acquisition, ensuring the accuracy of subsequent data processing, positioning, and fault analysis.

[0043] Furthermore, by controlling the directional deployment of the robotic arm in a preset deployment posture, the multimodal flaw detection module integrated at the end effector of the robotic arm is triggered to perform concurrent detection under multimodal orientation, including:

[0044] Using the storage structure interface of the robotic arm as a reference point, a detection target is determined as it moves along a trajectory. The detection target includes spatial distance and spatial azimuth. The detection target is identified, and using the spatial distance as an arm length constraint, multi-degree-of-freedom adjustment based on the robotic arm is performed to determine a first adjustment scheme, wherein the first adjustment scheme involves the directional deployment of the robotic arm. A multimodal orientation view is determined using the spatial azimuth. Based on the multimodal orientation view, the front-end component is oriented and adjusted to determine a second adjustment scheme. The first adjustment scheme and the second adjustment scheme are combined as a detection scheme, and concurrent detection under multimodal orientation is performed.

[0045] Specifically, using the robotic arm's storage structure interface as a reference point and considering the robot's positional changes along the inspection trajectory, the system dynamically determines the detection target. The target includes two parameters: spatial distance and spatial azimuth, describing its relative position in three-dimensional space. After identifying the target, the system uses the target's spatial distance as an arm length constraint and, combined with the robotic arm's multi-degree-of-freedom motion capabilities, performs attitude adjustment to generate a first adjustment scheme. This first scheme controls the robotic arm to unfold along a suitable direction and length, achieving directional deployment and positioning. Subsequently, based on the target's spatial azimuth, the system determines the multimodal orientation viewpoint required for flaw detection and optimizes sensor orientation for different flaw detection modes (such as ultrasonic, electromagnetic eddy current, and infrared thermal imaging). Based on the multimodal orientation viewpoint, the system further precisely adjusts the angle and direction of the multimodal end-effector of the robotic arm, forming a second adjustment scheme. Finally, the system combines the first and second adjustment schemes to form a complete detection scheme and synchronously triggers each flaw detection sensor according to this scheme, performing concurrent detection under multimodal orientation to ensure the spatial accuracy of the detection task and the completeness of the detection coverage.

[0046] Furthermore, the multimodal flaw detection module includes an arm span unit and a multimodal control unit; the first adjustment scheme is determined based on the arm span unit; the second adjustment scheme is determined based on the multimodal control unit, wherein the multimodal control unit includes multimodal branches corresponding to each front-end component, and the underlying logic drives training with a scanning target-scanning method.

[0047] The multimodal flaw detection module includes an arm extension unit and a multimodal control unit. The system determines the first adjustment scheme through the arm extension unit, which is responsible for the extension, retraction, and positioning of the robotic arm, ensuring that the flaw detection equipment at the end of the robotic arm can accurately reach the detection target and maintain a stable working posture. The adjustment of the arm extension unit is based on the spatial distance constraints and azimuth angle of the target, enabling the robotic arm to provide the optimal detection angle and detection distance when performing flaw detection tasks.

[0048] Based on this, the system determines the second adjustment scheme through the multi-mode control unit. The multi-mode control unit is responsible for controlling and coordinating the working modes of multiple flaw detection sensors at the end of the robotic arm, including ultrasonic flaw detectors, electromagnetic eddy current sensors, and infrared thermal imagers. Each sensor is configured with a corresponding adjustment scheme according to its characteristics and scanning requirements.

[0049] The multi-mode control unit contains multi-modal branches corresponding to each front-end component. Each branch is responsible for controlling the operation of its corresponding flaw detection module (such as an ultrasonic flaw detector, an electromagnetic eddy current sensor, an infrared thermal imager, etc.). Each flaw detection module has its unique scanning target and scanning mode. Therefore, the multi-mode control unit uses the scanning target-scanning mode as its underlying logic to drive the scanning mode of each flaw detection module and performs corresponding training and optimization. Specifically, the scanning target refers to the object that the flaw detection module needs to detect, such as foundation piles, truss surfaces, or electrical junction boxes, while the scanning mode refers to the specific operating mode of the flaw detection module when performing the detection task. For example, an ultrasonic flaw detector may need to scan along a specific axis, while an infrared thermal imager needs to scan from a panoramic view. The multi-mode control unit automatically adjusts the scanning parameters and operating mode of each flaw detection module according to its characteristics, ensuring that each sensor can perform its task in its optimal working state. Through this target- and mode-based underlying logic drive, the system can effectively coordinate the work of multiple flaw detection modules, achieve efficient multi-modal concurrent detection, and optimize the comprehensiveness and accuracy of the flaw detection task.

[0050] Through the coordinated operation of the arm extension unit and the multi-mode control unit, the system can precisely adjust the working posture of the robotic arm and the scanning mode of the sensors according to different flaw detection requirements, thereby achieving efficient concurrent detection of the multi-mode flaw detection module in complex environments.

[0051] The detection signal set is returned, and the signal distribution is updated as the detection process progresses. By performing modal independent detection and multimodal spatial distribution fusion, the flaw detection unit is determined and sent to the operation and maintenance mobile terminal for alarm.

[0052] When the flaw detection module performs multimodal concurrent detection, the system collects and transmits a set of detection signals in real time. This set includes data collected by various flaw detection modules (such as ultrasonic flaw detectors, electromagnetic eddy current sensors, and infrared thermal imagers), and is identified according to spatiotemporal codes. These signals are continuously updated as the detection process progresses; through signal distribution updates, the system ensures that it can acquire the latest detection data in real time and perform subsequent processing.

[0053] The system processes the returned detection signals by performing modal independent detection and multimodal spatial distribution fusion. Modal independent detection refers to each flaw detection module performing independent signal analysis according to its own operating principle, while multimodal spatial distribution fusion merges the detection results of each mode to form a complete and comprehensive flaw detection signal set, providing more comprehensive detection information. Based on the fused flaw detection results, the system determines the flaw detection order and sends it to the maintenance mobile terminal for alarm. Upon receiving the alarm, maintenance personnel can respond quickly and further analyze and repair the detected fault.

[0054] Furthermore, it also includes:

[0055] A low battery threshold is set. When the battery level of the target robot falls below the low battery threshold, a battery swapping command is generated. Based on the battery swapping command, the target robot is driven to autonomously navigate to the battery swapping station. The auxiliary arm unit drives the robotic arm to perform autonomous battery swapping control from the battery pack placement position to the battery pack mounting position.

[0056] The system sets a low battery threshold. When the target robot's battery level falls below this threshold, a battery swapping command is automatically generated. The low battery threshold is a preset standard (e.g., below 15%), ensuring the robot can be recharged or have its battery replaced promptly when its power is insufficient to continue performing tasks. Based on this command, the system drives the target robot to autonomously navigate to the battery swapping station. Autonomous navigation refers to the robot's real-time path planning in the environment, avoiding obstacles, and successfully reaching the station using its built-in navigation system and sensors. Upon arrival at the station, the auxiliary arm unit drives the robotic arm to perform the battery swapping operation. The robotic arm's end effector controls the removal and replacement of the battery pack. Precise movements of the robotic arm ensure the battery pack is safely removed from its placement location and accurately placed into the battery pack mounting slot. The entire battery swapping process requires no human intervention; the robot can automatically complete the battery replacement task, ensuring continuous operation during maintenance.

[0057] In summary, the embodiments of this application have at least the following technical effects:

[0058] The GIS map of the photovoltaic power station is imported into the target robot, an inspection path is planned, and the target robot is driven to move. The target robot is equipped with a triangular tracked chassis, and the track tilt angle is adaptively adjusted to the terrain according to the trajectory. When the target robot moves to the station area, it controls the orientation and deployment of the robotic arm in a preset deployment posture, triggering the multimodal flaw detection module integrated at the end of the robotic arm to perform concurrent detection under multimodal orientation, and determine the detection signal set. The detection signal set is identified by a spatiotemporal code. The detection signal set is transmitted back, and the signal distribution is updated as the detection progresses. By performing independent modal detection and multimodal spatial distribution fusion, the flaw detection report is determined and sent to the maintenance mobile terminal for alarm. This solves the technical problem of low inspection efficiency of photovoltaic power stations in complex terrain in existing technologies, and achieves the technical effect of improving the inspection efficiency of photovoltaic power stations.

[0059] Example 2, based on the same inventive concept as the operation control method for the flaw detection and repair robot in the foregoing examples, such as... Figure 2 As shown, this application provides an operation control system for a flaw detection and repair robot, wherein the system includes:

[0060] The mobile component 11 is used to import the GIS map of the photovoltaic power station into the target robot, plan the inspection path, and drive the target robot to move. The target robot is equipped with a triangular tracked chassis, and the track tilt angle is adaptively adjusted to a second-order equipotential angle according to the trajectory terrain. The detection component 12 is used to control the orientation and deployment of the robotic arm in a preset deployment posture when the target robot moves to the power station area, trigger the multimodal flaw detection module integrated at the end of the robotic arm, perform concurrent detection under multimodal orientation, and determine the detection signal set. The detection signal set is identified by a spatiotemporal code. The alarm component 13 is used to transmit the detection signal set back, update the signal distribution as the detection process progresses, and determine the flaw detection order by performing modal independent detection and multimodal spatial distribution fusion, and send it to the operation and maintenance mobile terminal for alarm.

[0061] Furthermore, the detection component 12 is used to perform the following method:

[0062] The robotic arm is a multi-degree-of-freedom folding arm that can be stored inside the robot's internal structure. The end of the robotic arm integrates a multi-modal flaw detection module, wherein the front-end components of the multi-modal flaw detection module include at least an ultrasonic flaw detector, an electromagnetic eddy current sensor, and an infrared thermal imager.

[0063] Furthermore, the detection component 12 is used to perform the following method:

[0064] The ultrasonic flaw detector uses the foundation pile as the scanning target and scans along the axial direction of the foundation pile; the electromagnetic eddy current sensor uses the truss surface as the scanning target and scans using a gridded detection method with a preset grid spacing; the infrared thermal imager uses the electrical junction box as the scanning target and scans the entire wiring layout.

[0065] Furthermore, the moving component 11 is used to perform the following methods:

[0066] First-order nodes are deployed using the first undulation angle based on road condition characteristics as the first-order control target; second-order nodes are deployed using the inclination variables of the upper-level control node and the lower-level control node as the second-order control target; and the first-order nodes and the second-order nodes are sequentially cascaded to determine the isostatic adjustment module.

[0067] Furthermore, the moving component 11 is used to perform the following methods:

[0068] The flaw detection and repair robot includes a first track and a second track that are driven independently; the equal position adjustment module is deployed at the first control end of the first track and the second control end of the second track, and a synchronization timestamp constraint is established between the first control end and the second control end.

[0069] Furthermore, the moving component 11 is used to perform the following methods:

[0070] As the patrol path is moved, the real-time path terrain is determined. Based on the real-time path terrain, the isostatic adjustment module deployed at the first control terminal performs a first-order tilt angle decision based on the road condition undulation characteristics to determine the first-order road condition tilt angle. The upper-level track tilt angle of the upper-level control node is retrieved, and the difference between the first-order road condition tilt angle and the upper-level track tilt angle is calculated as the second-order adjustment tilt angle. According to the second-order adjustment tilt angle, the first track is driven to perform track tilt angle adjustment.

[0071] Furthermore, the detection component 12 is used to perform the following method:

[0072] Using the storage structure interface of the robotic arm as a reference point, a detection target is determined as it moves along a trajectory. The detection target includes spatial distance and spatial azimuth. The detection target is identified, and using the spatial distance as an arm length constraint, multi-degree-of-freedom adjustment based on the robotic arm is performed to determine a first adjustment scheme, wherein the first adjustment scheme involves the directional deployment of the robotic arm. A multimodal orientation view is determined using the spatial azimuth. Based on the multimodal orientation view, the front-end component is oriented and adjusted to determine a second adjustment scheme. The first adjustment scheme and the second adjustment scheme are combined as a detection scheme, and concurrent detection under multimodal orientation is performed.

[0073] Furthermore, the detection component 12 is used to perform the following method:

[0074] The multimodal flaw detection module includes an arm span unit and a multimodal control unit; the first adjustment scheme is determined based on the arm span unit; the second adjustment scheme is determined based on the multimodal control unit, wherein the multimodal control unit includes multimodal branches corresponding to each front-end component, and the underlying logic drives training with a scanning target-scanning method.

[0075] Furthermore, the moving component 11 is used to perform the following methods:

[0076] A low battery threshold is set. When the battery level of the target robot falls below the low battery threshold, a battery swapping command is generated. Based on the battery swapping command, the target robot is driven to autonomously navigate to the battery swapping station. The auxiliary arm unit drives the robotic arm to perform autonomous battery swapping control from the battery pack placement position to the battery pack mounting position.

[0077] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0078] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0079] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and modifications fall within the scope of this application and its equivalents, this application intends to include such modifications and modifications.

Claims

1. A method for controlling the operation of a flaw detection and repair robot, characterized in that, The method includes: The GIS map of the photovoltaic power station is imported into the target robot, the inspection path is planned, and the target robot is driven to move. The target robot is equipped with a triangular track chassis, and the track tilt angle is adaptively adjusted in a second-order equipotential manner according to the trajectory terrain. When the target robot moves to the site area, it controls the directional deployment of the robotic arm in a preset deployment posture, triggers the multimodal flaw detection module integrated at the end of the robotic arm, performs concurrent detection under multimodal orientation, and determines the detection signal set, wherein the detection signal set is identified by a time-space code; The detection signal set is returned, and the signal distribution is updated as the detection process progresses. By performing modal independent detection and multimodal spatial distribution fusion, the flaw detection order is determined and sent to the operation and maintenance mobile terminal for alarm. Before the track tilt angle is adaptively adjusted to the second-order equipotential bonding based on the track terrain, an equipotential bonding module is constructed, including: First-order nodes are deployed with the first undulation angle based on road condition characteristics as the first-order control target. Using the tilt angle variables of the upper and lower control nodes as the second-order control targets, second-order nodes are deployed. By sequentially cascading the first-order node and the second-order node, an equal-position adjustment module is determined; The flaw detection and repair robot includes independently driven first and second tracks; The equal position adjustment module is deployed at the first control end of the first track and the second control end of the second track, and a synchronization timestamp constraint between the first control end and the second control end is established. The track tilt angle adaptively adjusts to the terrain using a second-order equipotential method, including: As the inspection path is moved, the real-time path conditions are determined; Based on the real-time road conditions, the isostatic adjustment module deployed at the first control terminal performs a first-order inclination decision based on the road condition undulation characteristics to determine the first-order road condition inclination angle. The upper track tilt angle of the upper control node is retrieved, and the difference between the first-order road condition tilt angle and the upper track tilt angle is calculated as the second-order adjustment tilt angle. Based on the second-order tilt angle adjustment, the first track is driven to perform track tilt angle control.

2. The operation control method for a flaw detection and repair robot as described in claim 1, characterized in that, The robotic arm is a multi-degree-of-freedom folding arm that can be stored inside the robot's internal structure. The end of the robotic arm integrates a multi-modal flaw detection module, wherein the front-end components of the multi-modal flaw detection module include at least an ultrasonic flaw detector, an electromagnetic eddy current sensor, and an infrared thermal imager.

3. The operation control method for a flaw detection and repair robot as described in claim 2, characterized in that, The ultrasonic flaw detector uses the foundation pile as the scanning target and scans along the axial direction of the foundation pile; the electromagnetic eddy current sensor uses the truss surface as the scanning target and scans using a gridded detection method with a preset grid spacing; the infrared thermal imager uses the electrical junction box as the scanning target and scans the entire wiring layout.

4. The operation control method for a flaw detection and repair robot as described in claim 3, characterized in that, With a preset deployment posture, the robotic arm is controlled to deploy in an oriented manner, triggering the multimodal flaw detection module integrated at the end effector of the robotic arm to perform concurrent detection under multimodal orientation, including: Using the storage structure interface of the robotic arm as a reference point, the detection target is determined as it moves along the trajectory, wherein the detection target includes spatial distance and spatial azimuth angle; Identify the target to be detected, use the spatial distance as the arm length constraint, perform multi-degree-of-freedom adjustment based on the robotic arm, and determine a first adjustment scheme, wherein the first adjustment scheme performs directional deployment of the robotic arm; The multimodal orientation viewpoint is determined using the aforementioned spatial azimuth angle; Based on the multimodal orientation view, the front-end component is oriented and adjusted to determine a second adjustment scheme; The first adjustment scheme and the second adjustment scheme are combined as a detection scheme to perform concurrent detection under multimodal orientation.

5. The operation control method for a flaw detection and repair robot as described in claim 4, characterized in that, The multimodal flaw detection module includes an arm-extending unit and a multimodal control unit; The first adjustment scheme is determined based on the arm span unit; Based on the multi-mode control unit, the second adjustment scheme is determined, wherein the multi-mode control unit includes multi-modal branches corresponding to each front-end component, and the underlying logic drives the training in a target-scanning manner.

6. The operation control method for a flaw detection and repair robot as described in claim 5, characterized in that, The method further includes: A low battery threshold is set, and when the target robot's battery level falls below the low battery threshold, a battery swapping command is generated. According to the battery swapping command, the target robot is driven to autonomously navigate to the battery swapping station, and the auxiliary arm extension unit drives the robotic arm to perform autonomous battery swapping control from the battery pack placement position to the battery pack mounting card position.

7. An operation control system for a flaw detection and repair robot, characterized in that, The system is used to implement the operation control method for a flaw detection and repair robot according to any one of claims 1-6, the system comprising: A mobile component is used to import a GIS map of a photovoltaic power station into a target robot, plan an inspection path, and drive the target robot to move. The target robot is equipped with a triangular track chassis, and the track tilt angle is adaptively adjusted in a second-order equipotential manner according to the trajectory terrain. The detection component is used to control the directional deployment of the robotic arm in a preset deployment posture when the target robot moves to the site area, trigger the multimodal flaw detection module integrated at the end of the robotic arm, perform concurrent detection under multimodal orientation, and determine the detection signal set, wherein the detection signal set is identified by a time-space code; The alarm component is used to transmit the detection signal set back, update the signal distribution as the detection process progresses, determine the flaw detection order by performing modal independent detection and multimodal spatial distribution fusion, and send it to the operation and maintenance mobile terminal for alarm.

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