469 results about "Robot position" patented technology

The present invention relates to the technical field of bridge structure monitoring, and specifically to a bridge disease monitoring method based on an inspection robot, comprising: obtaining a BIM model of a bridge, collecting GIS data of a location of the bridge and establishing associations with elements in the BIM model, and integrating the BIM model and the GIS data into a BIM+GIS fusion model; configuring a sensor and a communication module in the inspection robot, building a Web platform and establishing a communication connection with the inspection robot; when the inspection robot inspects the bridge, real-time data is collected through sensors, and the collected bridge structure data and position data of the inspection robot are sent to the Web platform; the Web platform receives and parses the position data, searches for a component corresponding to the position data in the BIM+GIS fusion model according to the position data, and maps the real-time position of the inspection robot to the corresponding component, thereby realizing accurate positioning and dynamic visualization of the position of the inspection robot.

The invention discloses an automatic goods warehousing method and system for intelligent warehousing, and belongs to the technical field of intelligent warehousing. According to the method, dynamic event information such as cargo elevator fault signals and robot position conflict detection data is obtained through a real-time data interface, a dynamic path self-adaptive scheduling mechanism is constructed based on a path planning algorithm, and a standby path set is preset; when a dynamic event is detected, the edge calculation unit is triggered to complete a standby path calculation strategy within a system preset time threshold value, and finally the storage robot is controlled to execute storage work according to a planned path. The system comprises a data sensing module, a path planning module, an edge calculation unit and an execution control module, and the modules cooperate to realize real-time path optimization under a dynamic event. According to the method, the problems of insufficient dynamic scene adaptability and low equipment cooperation efficiency in traditional storage are effectively solved, the storage efficiency is improved, and the energy consumption is reduced.

The invention provides a robot positioning prediction method and device, a medium and equipment, and relates to the field of industrial robot motion control. According to the method, on one hand, a physical-data double-branch collaborative network is designed, meanwhile, a DH parameter method is used for predicting and obtaining a theoretical position coordinate of a target robot, an improved Transform network is used for predicting and obtaining a coordinate compensation value of the target robot, so that a theoretical value and the compensation value are decoupled, and the limitation of single model establishment is avoided; and then, the two are fused, and high-precision end point location prediction for the target robot is realized by combining priori knowledge of robot kinematics and nonlinear expression ability of deep learning. And on the other hand, a spatial physical information mixed loss function is provided, and a spatial topological structure output by the DH model is used to guide the distribution of predicted values of the whole position prediction model, so that the geometric rationality of target robot end point location prediction is improved.

The invention discloses a photovoltaic cleaning robot operation scheduling method and system based on environment prediction, and belongs to the technical field of photovoltaic power station intelligent operation and maintenance and robots, and the method comprises the steps: obtaining weather forecast data, photovoltaic panel real-time dust accumulation data and historical power generation efficiency attenuation data, and generating environment prediction parameters; generating a dynamic weight value based on the environment prediction parameters and a preset power generation loss threshold value, and judging the sweeping emergency degree of each photovoltaic panel area; distributing an operation priority according to the cleaning emergency degree, and generating an initial cleaning task sequence; and a multi-robot cooperative path planning result is generated by combining the photovoltaic power station topological structure and the real-time position data of the robots. According to the method, the environment prediction technology and the dynamic scheduling strategy are combined, the cleaning task can be adaptively adjusted according to weather changes and dust accumulation conditions, the robot operation path is optimized, and the power generation efficiency of a photovoltaic power station and the utilization rate of robot resources are remarkably improved.

The invention is suitable for the technical field of indoor robot positioning, and provides a multi-sensor fusion indoor robot positioning method, which comprises the following steps: data acquisition: providing environment data and self state information for an indoor robot; preprocessing sensor data, and providing input for data processing and analysis; constructing a sensor data fusion model, and integrating data of a plurality of sensors; and robot position information is updated and output in real time. The invention also provides a multi-sensor fusion indoor robot positioning system which is used for realizing the multi-sensor fusion indoor robot positioning method. According to the invention, by integrating information collected by different sensors, the positioning accuracy and stability of the indoor robot are significantly improved; the method has the characteristics of high positioning precision, strong anti-interference capability, good environmental adaptability and the like, and is suitable for multiple scenes such as indoor robot navigation systems and intelligent residences.

A method and system for navigating a robot 100 are provided herein. In an embodiment, the method comprises: detecting on-site reference features of the robot's location when the robot is traversing in an environment; identifying objects of interest 180 from the detected on-site reference features; deriving a semantic data for each object of interest 180 from the on-site reference features; generating a semantic cost map 166 based on the semantic data of the objects of interest 180, the semantic cost map 166 representing cost of traversing in the environment; and navigating the robot 100 based on the semantic cost map 166.

The invention discloses a Beidou, inertial navigation and vision-coordinated underground pipeline robot positioning method, which comprises the following steps: firstly, an inertial measurement unit is used to obtain the acceleration and angular velocity information of the robot and correct its output, and then an odometer is used to obtain the mileage data of the robot and identify the pipeline connection point so as to correct its output, and then the angular velocity and mileage information are fused to realize the preliminary prediction of the posture state of the pipeline robot; then, the internal and external parameters of the IMU and the visual sensor are calibrated, and SIFT feature extraction and matching are performed on each captured frame of the image; then, the data of the IMU, mileage information and the binocular camera are fused in a tightly coupled manner, and a nonlinear optimization method is used to obtain the robot posture information with higher accuracy; finally, the three-dimensional position information returned by the Beidou positioning sensor is used to perform posture constraints, and the robot posture information with local accuracy and global drift-free is obtained, so as to realize the precise positioning of the pipeline robot.

A robot system for picking randomly shaped and sized object from a continuously moving stream of objects in bulk, e.g. a 3D bulk, and placing the object singulated and aligned on an induction or directly on a sorter. A pick and place robot has a robotic actuator for moving a gripper with a controllable gripping configuration of its gripping members, e.g. four suction cups, to adapt the gripper for various objects. A control system processes a 3D image of objects upstream of a position of the pick and place robot, identifies separate objects in the 3D image, and selects which object to grip, based on parameters of the identified separate objects determined from the 3D image. Based on e.g. size and shape of the selected object to grip, the gripping configuration of the gripper is adjusted to match the surface of the object to grip for optimal gripping. The robotic actuator, e.g. a gantry type robotic actuator, is then controlled to move the gripper to a position for gripping the object, and afterwards move the gripper with the gripped object to a target position and with a target orientation to release grip of the object and thus place the object on an induction or directly on a sorter. An image after placing the object along with properties of the object determined from the 3D image can be used as input to a machine learning for online improving pick and place performance of the robot system, e.g. for online improving the algorithm for selection of which object to pick, and also for selection of the appropriate gripping configuration to match the object.

An adaptive variable-structure duct robot motion control method based on environmental sensing. A duct robot is a dual-swing-arm tracked mechanism, and onboard sensing devices include a depth camera, an IMU, a wheel speed sensor and a TOF module. The method comprises: a depth camera capturing an image of the interior of a duct to perform scenario recognition, segmenting planar and curved surfaces in the image on the basis of scenario recognition results, and extracting edge lines of the duct; using the edge lines to calculate the width of a straight duct, the curvature of a curved duct, the angle of a slope and the height of a step, in order to determine the passability of a robot; designing a path planner and a swing arm planner to generate a reference trajectory and a swing arm angle sequence of the robot, performing smoothing processing, and inputting the smoothed reference trajectory and swing arm angle sequence into an MPC motion controller; using data from the depth camera, an IMU and a wheel speed sensor to estimate the position and state of the robot by means of an ESKF algorithm, and inputting estimation results and a collision warning signal from a TOF module into the MPC motion controller in real time; and finally, outputting control signals for a locomotion electric motor and a swing arm electric motor. Thus, the adaptive variable-structure and autonomous motion of a robot in different scenarios is realized.

A cleaning system for cleaning an electronic device includes an enclosure and at least one robot positioned within the enclosure. Each robot includes a robotic arm and an end effector coupled to the robotic arm. The cleaning system includes at least one nozzle positioned within the enclosure and at least one dry ice blasting machine configured to eject dry ice particles from the at least one nozzle. The robot is configured to position the electronic device proximate to the at least one nozzle to clean the electronic device with the dry ice particles that are ejected from the at least one nozzle.

The invention provides a visual optimization method, medium and system for a welding robot for a floating ship plate of an oil storage tank, and belongs to the technical field of electric digital data processing.The method comprises the steps that key information of the welding process is comprehensively obtained and deeply analyzed through a multi-dimensional and multi-scale data collection and decomposition strategy, and the welding process is optimized; comprising complex data such as welding seam images, robot poses, current, voltage, oil mist concentration and floating ship plate vibration frequency. By constructing an image interference compensation model and a pose compensation model based on multivariate decomposition, accurate enhancement of weld image quality and dynamic correction of robot poses are achieved, and interference factors and system errors in the welding process are effectively eliminated. Finally, the optimized high-quality image data and accurate pose data serve as input of a machine vision module, the vision guiding precision of the oil storage tank floating ship plate welding robot is remarkably improved, and the technical problem that the detection reliability is seriously reduced due to the fact that a vision system of the welding robot is often affected by many interference factors is solved.

The invention relates to the technical field of multi-sensor fusion positioning, and discloses an outdoor robot multi-sensor fusion method. Comprising the following steps that robot operation data are obtained through multiple sensors in the running process of a robot; taking the preprocessed operation data as measurement input of an unscented Kalman filter state estimation process, and obtaining first position data after fusion processing; learning the noise characteristics of each sensor by using a long-short-term memory network of a sliding window, and compensating the position deviation of the robot to obtain second position data; performing time synchronization processing on the first position data and the second position data, and inputting the first position data and the second position data into an improved double-layer multi-model adaptive estimation framework; the improved double-layer multi-model adaptive estimation framework adopts a double-layer structure; and obtaining final real-time position data. The method can inhibit noise and drift of a single sensor, reduces error accumulation caused by factors such as environment shielding, improves positioning precision, robustness and continuity, and is suitable for scenes such as outdoor driving robots.

The invention relates to the technical field of underwater robot control, in particular to an autonomous line patrol motion control method of an underwater cable detection robot, which comprises the following steps of: establishing a six-degree-of-freedom kinematics equation and a kinetic model of the robot, and collecting position information of the robot and a current heading angle of the robot; the target heading angle of the robot is calculated through an optimal deflection angle fusion algorithm, the current heading angle, collected by a sensor, of the robot is compared with the target heading angle, an error epsi is obtained, and the error epsi is added into a single-ring PID algorithm for adjustment; and controlling the thrust and direction of a propeller in the underwater cable detection robot according to a control signal tau (t) output by the single-loop PID algorithm and robot position information, judging whether a target heading angle of the underwater cable detection robot is consistent with a current heading angle, if so, completing autonomous line patrol, otherwise, continuing to correct, and if not, completing the detection of the underwater cable detection robot. The attitude control precision and the path navigation stability of the robot in the underwater environment are improved.

A motion control method for an adaptive self-reconfigurable pipeline robot based on environmental perception includes: acquiring internal images of the pipeline for scene recognition, segmenting planar surfaces and curved surfaces in the images according to recognition results, and extracting boundary lines of the pipeline; calculating a straight-pipe width, a bent-pipe curvature, a slope angle, and a step height to analyze passability of the robot; designing a path planner and a swing-arm planner to generate a reference trajectory and a swing-arm angle sequence of the robot, performing smoothing, and inputting the reference trajectory and the swing-arm angle sequence into a model predictive control (MPC) motion controller; estimating a position and state of the robot through an Error State Kalman Filter (ESKF) algorithm, and inputting estimation results and collision warning signals into the MPC motion controller; and finally outputting a signal for control of a motor and a swing-arm motor.

The invention relates to the technical field of elevator control, and discloses an elevator task control method and system based on medical logistics robot scheduling. The method comprises the steps that according to a dispatching task of a medical logistics robot, the robot position and electric quantity information of an available medical logistics robot, a dispatching task to be executed, the floor where an elevator of an available elevator is located, the running direction, whether the elevator is fully loaded or not and the floor where the elevator needs to stop are recognized, and a distribution combination is constructed; an evaluation function is constructed according to the robot path length of each scheduling task in the distribution combination and the time of taking the corresponding available elevator, and a target medical logistics robot and a target available elevator are obtained through scheduling optimization based on the evaluation function; constructing a target control signal according to the target medical logistics robot and the target available elevator; and a scheduling path of the target medical logistics robot is generated, and elevator task control is conducted on the target available elevator according to the scheduling path and the target control signal. The accuracy of elevator task control can be improved.

The invention discloses a substation primary and secondary equipment communication prefabricated optical cable online monitoring method and system, and belongs to the technical field of communication prefabricated optical cable monitoring. The system comprises an optical cable parameter acquisition module, a communication quality evaluation module, a physical parameter analysis module, a first-aid repair path planning module and a fault database. The method comprises the following steps: optical cable parameter acquisition: acquiring data in real time, and converting high-dimensional data into low-dimensional data through a principal component analysis algorithm; communication quality evaluation: identifying communication characteristics in the low-dimensional signal parameter matrix based on a fuzzy comprehensive evaluation algorithm, and outputting a communication quality score; the physical parameter analysis module extracts physical deep features of the optical cable by using a deep belief network DBN, and inputs an improved Bayesian classifier to output a state classification result; according to the first-aid repair path planning, OTDR detection parameters are dynamically adjusted, and an optimal first-aid repair path is planned in combination with the position of the inspection robot; according to the invention, optical cable communication quality evaluation, accurate physical state classification and efficient fault repair planning are realized.

The invention provides an intelligent robot path scheduling method and system for scenic spot tourists, and relates to the technical field of path scheduling, and the method comprises the steps: obtaining robot position and state information and tourist service request information, and building a scenic spot electronic map; constructing a robot service capability evaluation model; path planning and scoring are carried out, and an optimal service path is selected; selecting the most suitable target service robot; sending the optimal service path to the target robot and loading the optimal service path; the motion trail of the robot is monitored in real time, and the path is dynamically adjusted if necessary. According to the method, the scenic spot service efficiency and tourist experience are improved.

An autonomous warehouse robotics system integrates a multi-sensor platform, adaptive payload handling, dynamic task reallocation, advanced navigation, energy management, and comprehensive safety features into one mobile robot chassis. The system utilizes LiDAR, stereo vision, ultrasonic sensors, mmWave radar, thermal cameras, and event cameras to perform complete environmental sensing and obstacle detection. Sensor fusion combines adaptive weighting, multi-modal data integration, and statistical filtering to create high-confidence maps for reactive path planning and collision avoidance. The robot's payload system features machine vision for item recognition, telescopic lifts, variable-width grippers, and real-time toolhead verification to handle a variety of goods. Fleet management is achieved through dynamic task reallocation that considers robot location, battery level, and operational delays, all coordinated by a cross-platform middleware architecture that ensures standardized communication, remote monitoring, and over-the-air updates among diverse robot brands. Energy management optimizes power usage via predictive routing, autonomous return-to-charge, and auction-based scheduling. Safety is maintained through proximity detection, behavior-based intervention, and human-robot cohabitation protocols while advanced localization is enhanced by fusing ultra-wideband positioning with visual landmark alignment, inertial sensing, and machine learning to deliver high accuracy in non-line-of-sight conditions. An onboard edge AI module further refines navigation and task prioritization through neural network inference, ensuring robust, adaptive operation in dynamic, unstructured warehouse environments.

The invention discloses a local control method and system of a photovoltaic cleaning robot based on vision, belongs to the technical field of photovoltaic cleaning robot control, and solves the problem that an existing photovoltaic cleaning robot is low in operation efficiency. The photovoltaic panel surface state information and the position state information of the photovoltaic cleaning robot are obtained, so that the photovoltaic panel surface area needing to be cleaned by the photovoltaic cleaning robot is determined, path planning of the photovoltaic cleaning robot is conducted, and overall effective cleaning of the photovoltaic panel is achieved; local effective cleaning of a photovoltaic panel is realized by acquiring real-time positioning state information of a photovoltaic cleaning robot, planning an optimal path of a to-be-cleaned area, acquiring pollution degree information of the surface of the photovoltaic panel and determining cleaning frequency; local path planning of the photovoltaic cleaning robot is achieved, path planning is conducted autonomously according to the range where stains are located, local powerful cleaning is achieved, and the operation efficiency of the photovoltaic cleaning robot is improved.

The invention provides an electromagnetic driving system. In one embodiment, the electromagnetic drive system comprises: a) one or more magnetic robots located within a workspace; b) three independent mechanical arms, wherein each independent mechanical arm comprises a movable electromagnetic coil; c) a positioning system for tracking the one or more magnetic robots; the three independent mechanical arms are controlled through multi-objective optimization to ensure a coil configuration space capable of providing a magnetic field for manipulating the one or more magnetic robots in the working space.

The invention belongs to the technical field of exploration mapping in unknown fields, and particularly relates to a multi-robot collaborative exploration mapping method based on boundaries. The method comprises the following steps: setting a sub-mapping system of any robot, and setting a mother mapping system of a plurality of robots; establishing an environment local grid map by using laser radar detection data in the sub-mapping system; defining the centroid of the junction of the known free area and the unknown area of the grid map as a boundary point; obtaining a candidate target point of each robot; obtaining a collision-free shortest path from the position of the robot to the corresponding target point; in the mobile exploration process, each robot independently establishes and updates a respective local map; and performing map fusion through a map fusion module to obtain a global map of the environment. According to the method, all the robots do not need to carry out global planning, repeated search points are greatly reduced, and the problems that in the prior art, multiple robots can carry out serious repeated search, exploration time is long, and energy consumption is large are solved.

The invention relates to the technical field of robots, and discloses a method and a system for determining the position of a mobile robot, which are used for correcting indoor robot position judgment model parameters based on a wireless signal intensity change mode so as to compensate signal interference errors caused by wall blocking. Correcting the current coordinate of the robot according to the corrected model parameters by fusing the historical track and the real-time sensor information; the corrected current coordinates are combined with known map information and instantly updated map details to regulate and control path plan variables, and an uncertainty estimation matrix is updated through a result output by multi-sensor fusion to improve robustness positioning in a high dynamic environment; through the scheme of the embodiment of the invention, the problem of how to optimize the pose parameters of the mobile robot according to the environment sensing data acquired by the mobile robot so as to solve the problem of positioning error accumulation can be solved, and the robustness of the system is further enhanced.

The invention discloses a robot inverse solution prediction method based on a POA-Transform-BiGRU-Attention integrated optimization algorithm, and the method comprises the steps: carrying out the initialization of the POA algorithm, and carrying out the random initialization of the individual position # imgabs0 # of a robot; the robot individual position # imgabs1 # is evaluated by means of a fitness function, the robot individual position # imgabs2 # is updated, and the updated robot position # imgabs3 # is obtained; enabling the input data Xi to be equal to # imgabs4 #, and performing Transform model training on the input data Xi to generate an output feature Z '; a BiGRU algorithm is utilized to process the input data Xi which completes Transform model training to obtain a final state, and a weighted summation result is solved in combination with an attention mechanism; and generating a predicted inverse solution result according to a weighted summation result, and measuring the accuracy of the predicted inverse solution result by using a target function to obtain a globally optimal solution. The algorithm provided by the invention shows high robustness and excellent prediction precision under various working conditions and noise interference, and the inverse solution precision is higher.

The invention provides a robot autonomous positioning system and method based on a three-dimensional model technology, relates to the field of intelligent autonomous positioning, and realizes rough positioning of a robot by constructing a global map feature descriptor search library and a block index and combining image information. And the point cloud feature information is accurately matched in the corresponding blocks to calculate the position and posture of the robot. According to the method, feature coding is carried out on robot image information, retrieval correspondence is carried out on the robot image information and pre-constructed block indexes, and matching is carried out after related block indexes are extracted, so that the semantic matching degree is intelligently estimated and compared with a preset threshold value, and whether the robot image information is included into one of the block indexes which are roughly positioned globally or not is determined; therefore, the high accuracy of the preliminary positioning stage is ensured, the error accumulation in the subsequent steps is reduced, and the stability of the whole positioning process is enhanced.

The invention discloses a multi-robot control system, which comprises a master robot control system and at least one slave robot control system, and each of the master robot control system and the slave robot control system comprises a demonstrator, a multi-robot control module, a communication module, a servo control module and a robot control module; the demonstrator is used for carrying out man-machine interaction, and parameter setting and operation are carried out on the robot through the robot remote teaching device; the multi-robot control module is used for performing forward and inverse solution operation calculation according to the robot feedback information acquired by the robot control module and generating a control instruction for the robot; the communication module comprises a field bus Ethercat, a master station and a slave station and is used for being responsible for real-time field bus management; the servo control module is used for controlling the action of each servo motor based on the position instruction of the robot control module; the robot control module is used for acquiring feedback information of the robot from the servo control module and executing a robot control instruction, and the control instruction comprises a robot position instruction and a system variable instruction; the invention further discloses a multi-robot control method, a computer readable storage medium and electronic equipment. The multi-robot control method does not need an additional upper computer or PLC assistance.

The present invention discloses a master-slave control method and system for a master-slave heterogeneous teleoperation system, including: an operator inputs the link parameters of the slave robot for forward kinematics and inverse kinematics analysis to obtain forward kinematics and inverse kinematics models; the operator modifies the master-slave mapping scale coefficient to achieve the movement of the slave robot with different efficiencies and accuracies; a variable-scale incremental master-slave mapping method is designed to realize the mapping from the position increment of the master robot to the position increment of the slave robot, and a BP neural network is designed to predict and compensate the master-slave following error, further reducing the master-slave following error. The present invention can not only realize the large-range movement of the slave robot in free space, but also ensure the movement positioning accuracy in the fine operation space, with strong versatility and convenient implementation.

The invention provides a robot positioning and grabbing system and method based on laser visual guidance, and relates to the technical field of robots, and the method comprises the steps that an intelligent processing module receives information output by a multi-mode sensing module and carries out intelligent processing operation; the intelligent processing operation comprises an image enhancement operation, a dynamic attention modeling operation and an online learning operation; the self-adaptive control module comprises a mixed pose control unit and a predictive maintenance unit; and the mixed pose control unit adopts a robust inverse kinematics solution based on Lie group and a force-position mixed control mode automatic switching algorithm to calculate the joint angle of the robot and the pose of an end effector. Rich environment and object information is acquired through the multi-modal sensing module, and image enhancement, dynamic attention modeling and online learning operation of the intelligent processing module are combined, so that the recognition precision of a target object is improved, and the influence of illumination and object surface characteristics on recognition is effectively reduced.

The invention provides an online dead reckoning error prediction method based on robot motion and earth surface perception. The method comprises the following steps: constructing an online dead reckoning error neural network predictor model of a robot, wherein the online dead reckoning error neural network predictor model comprises a body convolutional neural network CNN, a visual CNN and a prediction MLP; parameters of the online dead reckoning error neural network predictor are trained, and a trained online dead reckoning error neural network predictor model is obtained; and performing online dead reckoning error prediction on the robot by using a machine body CNN, a visual CNN and a prediction MLP in the trained online dead reckoning error neural network predictor model. According to the method, the neural network parameters can be incrementally optimized on line by using the robot data, and the method has continuous learning ability, so that the adaptability of a robot positioning system to complex motion and varied surface conditions is improved, the method has very high application value, and the blank in the aspect of robot dead reckoning error prediction is filled up.

The invention relates to a system for multi-robot collaborative operation, intelligent task allocation and path planning. The system comprises a task receiving module, a task decomposition module, an execution module, a path planning module and a path calculation module, the task receiving module is used for receiving task information and transmitting the task information to the task decomposition module; the task decomposition module is used for generating sub-task information according to the received task information and distributing the sub-task information to the corresponding robots through the task distribution module of the execution module; the execution module is used for distributing tasks according to the subtask difficulty, the duration and the current load of the robot; and the path planning module is used for sending planning path information and controlling collision-free operation based on the position state of the robot. Through a multi-module collaborative task intelligent allocation mechanism, dynamic obstacle avoidance path planning based on an artificial potential field method and a multi-end-point adaptive adjustment strategy, multi-robot operation load balancing, path collision-free and environment dynamic adaptation are realized, and the collaborative efficiency and safety are remarkably improved.

The invention relates to the technical field of pipe network leakage point positioning, and discloses a GIS-based pipe network leakage point high-precision positioning method and system, and the method comprises the steps: collecting a first camera image, a first infrared thermogram, first laser ranging data and first inertial measurement data through a detection robot; calculating a robot positioning track and constructing a pipeline structure model; detecting local coordinates of leakage points and creating a coded data packet; based on the first RTK-GPS coordinate of the initial inspection well, rotation transformation and ENU coordinate transformation are carried out on the local coordinate of the leakage point, drift correction is carried out when the leakage point arrives at the next inspection well, and the GIS coordinate of the leakage point is obtained; according to the method, the GIS coordinates and the coded data packets of the leakage points are used for generating a defect distribution visualization graph and a maintenance work order, the positioning error is reduced from the meter level to the centimeter level, accurate positioning of defects and automatic distribution of the maintenance work order are achieved, and the inspection cycle and the maintenance response time are shortened.