Robot inspection system and method based on multi-level architecture

Abstract

The invention relates to the technical field of robot inspection, in particular to a robot inspection system and method based on a multi-level architecture, and the method comprises the steps: constructing the multi-level architecture based on preset robot inspection demand information, and the multi-level architecture comprises a robot layer, a network layer and an application layer; analyzing a preset inspection task in the preset robot inspection demand information through the robot inspection monitoring platform, and controlling a navigation module to analyze a robot motion posture and an external environment based on the inspection path data; collecting an inspection environment and inspection equipment in the target inspection area; and analyzing the second robot navigation data and the second monitoring data through the robot inspection monitoring platform to obtain an inspection report. The robot navigation data can be verified, the recheck instruction can be generated to supplement the collected data when the navigation data is inconsistent with the standard, the accuracy of the inspection data is improved, and then the accuracy of the inspection result is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of robot inspection, and in particular to a robot inspection system and method based on a multi-level architecture. BACKGROUND

[0002] In the field of industrial automation inspection, robot inspection has gradually become an important means to ensure stable operation of equipment and improve production safety due to its advantages of high efficiency, accuracy, and continuous operation. As a core component of robot inspection, the robot navigation system undertakes the key task of planning the inspection path and guiding the robot to accurately reach the target position, laying the foundation for obtaining comprehensive and accurate inspection data. Currently, robot navigation technology relies on the fusion of multiple sensors, such as laser radar, vision sensors, and inertial measurement units, to realize accurate positioning and navigation by sensing real-time environmental information and combining pre-constructed map data. However, in practical applications, quality control of navigation data faces many challenges. On the one hand, due to the complex and variable inspection environment, there are factors such as dynamic obstacle interference, changes in lighting conditions, and sensor noise, which may cause errors or even errors in the obtained navigation data. However, existing technologies lack effective navigation data verification mechanisms, and cannot accurately determine the reliability and accuracy of navigation data in real time. On the other hand, when the navigation data does not conform to the pre-set standard, the system cannot automatically generate recheck instructions to guide the robot to collect supplementary data in related areas. This makes it possible for the robot to continue inspection according to the wrong path during the inspection process, resulting in missing data in some key areas or biased data that cannot truly reflect the actual operation status of the equipment.

[0003] Chinese Patent Publication No. CN110618688A discloses a kind of inspection robot system and its control method, including control center and mobile robot, mobile robot includes mobile robot body and control processor arranged on mobile robot body, control processor and mobile robot body are electrically connected to control robot body action, mobile robot is connected with control center signal by control processor, to transmit the inspection information of mobile robot to control center;Mobile robot body is also respectively provided with laser radar and information acquisition device, laser radar is used to provide the position information of mobile robot itself and the positioning information of inspection object, and plans preset trajectory;Control processor is electrically connected with laser radar and information acquisition device respectively, controls mobile robot to move according to preset trajectory, and controls information acquisition device to collect the information of inspection object.The scheme can realize the movement of mobile robot according to preset trajectory, collect inspection information and transmit information to control center, but it cannot verify robot navigation data, and cannot generate recheck instructions to supplement data when navigation data does not conform to the standard, resulting in missing or biased inspection data and reducing the accuracy of inspection results. Summary of the Invention

[0004] To address this issue, the present invention provides a robot inspection system and method based on a multi-level architecture, which overcomes the problem in the prior art that it is impossible to verify robot navigation data, nor can it generate re-inspection instructions to supplement the collected data when the navigation data is inconsistent with the standard, resulting in missing or biased inspection data and reduced accuracy of inspection results.

[0005] To achieve the above objectives, this invention provides a robot inspection method based on a multi-level architecture, comprising the following steps: S1. Construct a multi-level architecture based on preset robot inspection requirements information. The multi-level architecture includes a robot layer, a network layer, and an application layer. The robot layer includes a communication module, an ACR central control module, a navigation module, and an external sensor module. The application layer includes a robot inspection and monitoring platform. The network layer is communicatively connected to the robot layer and the application layer, respectively. S2. The robot inspection monitoring platform parses the preset inspection tasks in the preset robot inspection requirements information to obtain inspection instructions, and sends the inspection instructions to the communication module through the network layer. S3. The inspection command is transmitted to the ACR central control module through the communication module. The ACR central control module parses the inspection command, generates inspection path data, transmits the inspection path data to the navigation module, and controls the navigation module to analyze the robot's motion posture and external environment based on the inspection path data to obtain the first robot navigation data. The first robot navigation data is then output to the ACR central control module. S4. The ACR central control module controls the external sensor module to collect the inspection environment and inspection equipment in the target inspection area to obtain the first monitoring data. The first robot navigation data and the first monitoring data are then transmitted to the network layer for data transmission processing according to the communication module to obtain the corresponding second robot navigation data and second monitoring data. S5. Analyze the navigation data and monitoring data of the second robot through the robot inspection and monitoring platform to obtain inspection reports, including: If the second robot navigation data is consistent with the preset robot navigation standard, then the first inspection report is generated based on the first monitoring data, and the first inspection report is output as the inspection report. If the second robot navigation data is inconsistent with the preset robot navigation standard, an abnormal alarm information containing the abnormal inspection area will be generated in the robot inspection monitoring platform. Based on the abnormal alarm information, a re-inspection instruction containing the name of the alarming robot will be generated. According to the re-inspection instruction, the corresponding robot's external sensor module will be controlled to collect the inspection environment and inspection equipment in the abnormal inspection area to obtain supplementary monitoring data. The supplementary monitoring data and the second monitoring data will be analyzed to obtain a second inspection report, which will be output as the inspection report.

[0006] Compared with the prior art, the beneficial effects of this application are as follows: The robot inspection and monitoring platform at the application layer performs a consistency analysis on the second robot navigation data obtained after transmission and processing at the network layer, comparing it with a preset robot navigation standard to effectively verify the robot navigation data. Existing technologies rely solely on LiDAR to obtain position information and planned trajectories, failing to verify whether the navigation data conforms to the standard, which can easily lead to undetected navigation deviations. In this application, the robot inspection and monitoring platform, by comparing the second robot navigation data with the preset robot navigation standard, can promptly identify navigation anomalies, avoiding subsequent inspection data loss due to navigation deviations, and ensuring the accuracy of inspection results from the data source. When the second robot's navigation data deviates from the preset robot navigation standard, the robot inspection monitoring platform first generates an anomaly alarm message containing the abnormal inspection area. Then, based on this alarm message, it generates a re-inspection command containing the name of the alarming robot. Subsequently, according to the re-inspection command, it controls the corresponding robot's external sensor module to re-collect the inspection environment and equipment in the abnormal inspection area, obtaining supplementary monitoring data. Existing technologies cannot supplement data collection when navigation data is abnormal, easily leading to inspection data deviations or omissions. This application, by driving the external sensor module to collect data a second time through the re-inspection command, integrates the supplementary monitoring data with the second monitoring data to generate a second inspection report, effectively overcoming the problem of existing technologies' inability to supplement collected data, correcting data deviations, and improving the accuracy of inspection results. In a multi-layered architecture, the ACR central control module at the robot layer coordinates the navigation module and the external sensor module. The ACR central control module parses inspection commands, generates inspection path data, and transmits it to the navigation module. Simultaneously, it controls the external sensor module to collect first monitoring data. Then, the communication module transmits the first robot navigation data and the first monitoring data to the network layer for processing, generating second robot navigation data and second monitoring data for analysis by the application layer. Existing technologies have incomplete data processing chains, making it difficult to support navigation verification and re-inspection. This application, through the collaborative control of the ACR central control module and the data processing capabilities of the network layer, ensures the reliability of navigation data and monitoring data transmission and processing, providing technical support for the implementation of navigation verification and re-inspection mechanisms. It overcomes the problem of insufficient data processing in existing technologies making verification and re-inspection difficult to implement, further ensuring the accuracy of inspection data.

[0007] Furthermore, the navigation module includes an obstacle avoidance subunit, and the external sensor module includes an infrared laser obstacle avoidance sensor, a lidar, and a speed sensor. The obstacle avoidance subunit collects the distance to obstacles around the robot using the infrared laser obstacle avoidance sensor to obtain obstacle distance data; scans the outline of the obstacles using the lidar to obtain obstacle outline data; collects the robot's real-time movement speed using the speed sensor to obtain movement speed data; and adjusts the robot's movement trajectory based on the obstacle distance data, obstacle outline data, and movement speed data to obtain obstacle avoidance adjustment data, which is then added to the first robot navigation data.

[0008] In this solution, an obstacle avoidance subunit is set up in the navigation module, and the external sensor module is equipped with an infrared laser obstacle avoidance sensor, a lidar, and a speed sensor. The obstacle avoidance subunit uses the infrared laser obstacle avoidance sensor to collect obstacle distance data, the lidar to obtain obstacle contour data, and the speed sensor to obtain motion speed data. Based on this data, the robot's motion trajectory is adjusted and calculated to obtain obstacle avoidance adjustment data, which is added to the first robot navigation data terminal. This enables the robot to perceive the surrounding environment more accurately, fully grasp obstacle information, and, combined with its own speed, adjust its motion trajectory in real time and accurately to effectively avoid obstacles and improve navigation accuracy.

[0009] Furthermore, the navigation module also includes a laser navigation unit, an IMU inertial navigation unit, and a SLAM navigation unit. The external sensor module also includes a light intensity sensor, which acquires light intensity and calculates the environmental obstacle density based on obstacle distance data and obstacle contour data. The ACR central control module controls the navigation module to analyze the robot's motion posture and the external environment based on the inspection path data, combined with light intensity and environmental obstacle density, including: When the density of environmental obstacles does not exceed the preset density of environmental obstacles, the environmental contour of the target inspection area is scanned by the laser navigation unit, and environmental contour data is generated by combining the path coordinates in the inspection path data. When the light intensity is less than the preset light intensity, the robot's motion acceleration and angular velocity are collected by the IMU inertial navigation unit, and combined with the motion velocity data in the path coordinates in the inspection path data to generate motion inertial data; When the density of environmental obstacles is greater than the preset environmental obstacle density and the light intensity is greater than or equal to the preset light intensity, the SLAM navigation unit fuses and models the environmental contour data and motion inertial data, and generates environmental map data by combining the path planning parameters of the path coordinates in the inspection path data. The environmental contour data, motion inertia data, and environmental map data are input into the first robot navigation data.

[0010] In this solution, by incorporating a laser navigation unit, an IMU inertial navigation unit, and a SLAM navigation unit into the navigation module, and equipping the external sensor module with a light intensity sensor, the system can acquire light intensity and calculate the density of environmental obstacles. The ACR central control module then combines this data with inspection path data to flexibly analyze the robot's motion posture and the external environment. Under different conditions, the laser navigation unit generates environmental contour data, the IMU inertial navigation unit generates motion inertial data, and the SLAM navigation unit generates environmental map data. Finally, these various data sources are input into the first robot navigation data, enabling the robot to adapt to environments with varying light levels and obstacle densities, accurately acquire environmental information, and improve the accuracy, stability, and adaptability of navigation, thus meeting the inspection needs in complex scenarios.

[0011] Furthermore, the robot layer also includes a gimbal control unit, which comprises a visible light image acquisition subunit, an infrared thermal imaging subunit, a laser spectrum acquisition subunit, and a nighttime lighting auxiliary unit. The visible light image acquisition subunit acquires images of the appearance of the inspection equipment and meters within the target inspection area, obtaining visible light equipment image data. The infrared thermal imaging subunit acquires the temperature field distribution of the inspection equipment, obtaining equipment thermal imaging data. The laser spectrum acquisition subunit performs spectral analysis on the gas composition in the inspection environment, obtaining gas spectral data. The nighttime lighting auxiliary unit acquires environmental image data of the illuminated area. The visible light equipment image data, equipment thermal imaging data, gas spectral data, and illuminated area environmental image data are then transmitted to the first monitoring data.

[0012] In this solution, a gimbal control unit containing multiple sub-units is set up at the robot layer. The visible light image acquisition sub-unit acquires visible light image data of the inspection equipment's appearance and meters, allowing for a direct view of the equipment's appearance. The infrared thermal imaging sub-unit acquires thermal imaging data of the equipment, enabling precise understanding of the equipment's temperature field distribution and timely detection of overheating hazards. The laser spectral acquisition sub-unit analyzes gas composition to obtain gas spectral data, which can detect potential gas leaks and other problems. The nighttime lighting auxiliary illumination sub-unit acquires environmental image data of the illuminated area, ensuring the effectiveness of nighttime inspections. This effectively improves the accuracy, comprehensiveness, and timeliness of equipment inspections and reduces the risk of malfunctions.

[0013] Furthermore, the robot layer also includes a power management unit, which controls the power supply to each functional module according to the robot's operating status, including: When the robot is in standby mode, the power management unit only provides power to the communication module and the ACR central control module, cuts off the power supply to other functional modules, and collects real-time power consumption data of the communication module and the ACR central control module. When the robot is in inspection mode, the power management unit provides power to all functional modules and collects real-time power consumption data of each functional module. Based on the collected real-time power consumption data and the robot's remaining battery power data, the battery life is calculated to obtain power reserve status data, which is then transmitted to the ACR central control module.

[0014] In this solution, a power management unit is installed at the robot level to precisely control the power supply to each functional module based on the robot's operating status. In standby mode, only the communication module and the ACR central control module are powered, while power to other functional modules is cut off, reducing standby power consumption. During inspection, power is supplied to all functional modules to ensure normal operation. Simultaneously, real-time power consumption data of each module under different states is collected, and combined with remaining battery power data to calculate battery life, obtaining power reserve status data and transmitting it to the ACR central control module. This effectively optimizes power distribution, reduces unnecessary energy loss, extends robot endurance, and allows the central control module to monitor power status in real time, rationally plan tasks, and improve the stability and reliability of robot operation.

[0015] Furthermore, the network layer includes a wireless communication unit, a robot application server, a data server, and a wireless automatic charging terminal. Based on the network environment of the target inspection area, a corresponding communication method is selected, and the first robot navigation data and the first monitoring data transmitted from the robot layer are transmitted to the robot application server according to the communication method. The robot application server performs format conversion and protocol adaptation on the first robot navigation data and the first monitoring data to obtain adapted data. The data server stores the adapted data and establishes a data index. When the robot's remaining battery power is lower than a preset remaining battery power, the wireless communication unit receives a charging command sent by the robot inspection monitoring platform, controls the robot to proceed to the wireless automatic charging terminal, and automatically charges the robot according to the wireless automatic charging terminal, obtaining charging status data, which is then transmitted to the data server for storage.

[0016] In this solution, by setting up a wireless communication unit, a robot application server, a data server, and a wireless automatic charging terminal at the network layer, the communication method can be selected according to the network environment of the target inspection area, ensuring stable data transmission. The robot application server performs format conversion and protocol adaptation for the first robot navigation data and the first monitoring data, improving data universality. The data server stores the adapted data and builds an index for easy querying and management. When the battery is low, the wireless communication unit receives a charging command and controls the robot to automatically charge at the wireless automatic charging terminal; the charging status data is stored in the data server. This makes data transmission more reliable, processing more efficient, and also automatically manages charging, ensuring the robot's continuous and stable operation and improving inspection efficiency and reliability.

[0017] Furthermore, the external sensor module also includes a temperature and humidity detection unit, a combustible and toxic gas concentration detection unit, and a sound acquisition unit; the temperature and humidity detection unit collects the air temperature and relative humidity in the target inspection area to obtain temperature and humidity data; the combustible and toxic gas concentration detection unit collects the concentration of combustible and toxic gases in the target inspection area to obtain gas concentration data; the sound acquisition unit collects the operating sound of the equipment in the target inspection area to obtain equipment sound data; and the temperature and humidity data, gas concentration data, and equipment sound data are input into the first monitoring data.

[0018] In this solution, temperature and humidity data of the target inspection area are collected by the temperature and humidity detection unit, which can monitor the environmental temperature and humidity conditions in real time and avoid the impact of abnormal temperature and humidity on equipment operation or the occurrence of safety hazards. Gas concentration data is obtained by the combustible and toxic gas concentration detection unit, which can detect combustible and toxic gas leaks in a timely manner and ensure the safety of personnel and equipment. The sound acquisition unit collects equipment operation sound data, which helps to determine whether the equipment is operating normally. Inputting these data into the first monitoring data enriches the monitoring dimensions and makes the monitoring of the target inspection area more comprehensive and accurate.

[0019] Furthermore, the robot inspection and monitoring platform includes an inspection mode management unit, an access control unit, and a data display unit. The inspection mode management unit categorizes preset inspection tasks, generates task parameters corresponding to the inspection mode, and parses the preset inspection tasks based on the task parameters to obtain inspection instructions matching the inspection mode. The access control unit verifies the identity of users logging into the platform and assigns corresponding operation permissions based on their identities. The data display unit visualizes the second robot navigation data and the second monitoring data, generating real-time monitoring screens, data trend curves, and device status icons.

[0020] In this solution, the inspection mode management unit categorizes preset inspection tasks and generates matching task parameters and inspection instructions, which can accurately adapt to the needs of different inspection scenarios and improve the flexibility and targeting of inspections. The permission management unit verifies user identity and assigns operation permissions to ensure platform data security and operational standards. The data display unit visualizes the navigation data of the second robot and the second monitoring data, generating intuitive real-time monitoring screens, data trend curves, and equipment status icons, which allows users to quickly grasp the inspection situation and equipment status, improving monitoring efficiency and decision-making accuracy.

[0021] Furthermore, the preset robot navigation standard includes an allowable deviation threshold, an allowable offset threshold, and an allowable matching degree threshold. The navigation accuracy threshold is set according to the accuracy requirements of the inspection equipment, the path deviation threshold is set according to the spatial range of the inspection area, and the environmental adaptation parameter is set according to the terrain features of the inspection area. The robot inspection monitoring platform extracts the motion posture deviation value, path offset, and environmental perception matching degree from the second robot navigation data, and compares the motion posture deviation value with the allowable deviation threshold to obtain a first comparison result. The path offset is compared with the allowable offset threshold to obtain a second comparison result, and the environmental perception matching degree is compared with the allowable matching degree threshold to obtain a third comparison result. If the first comparison result is within the allowable deviation threshold, the second comparison result is within the allowable offset threshold, and the third comparison result is within the allowable matching degree threshold, then the second robot navigation data is determined to be consistent with the preset robot navigation standard. If any comparison result exceeds the corresponding threshold, then it is determined to be inconsistent.

[0022] This solution sets allowable deviation thresholds, allowable offset thresholds, and allowable matching degree thresholds, and determines navigation accuracy, path deviation, and environmental adaptation parameters based on the inspection equipment, area, and terrain features, making the preset standards more targeted and reasonable. By extracting motion posture deviation values, path offsets, and environmental perception matching degrees from the second robot's navigation data through the robot inspection monitoring platform and comparing them with the corresponding thresholds, it can accurately determine whether the navigation data meets the standards, promptly detect robot navigation anomalies, improve the accuracy and reliability of inspections, avoid inspection omissions or errors caused by navigation deviations, and ensure efficient and stable inspection work.

[0023] This invention also provides a robot inspection system based on a multi-level architecture, comprising: A multi-level architecture building module is used to construct a multi-level architecture based on preset robot inspection requirements information. The multi-level architecture includes a robot layer, a network layer, and an application layer. The robot layer includes a communication module, an ACR central control module, a navigation module, and an external sensor module. The application layer includes a robot inspection and monitoring platform. The network layer is communicatively connected to the robot layer and the application layer, respectively. The inspection instruction generation module is used to parse the preset inspection tasks in the preset robot inspection requirement information through the robot inspection monitoring platform, obtain inspection instructions, and send the inspection instructions to the communication module through the network layer. The inspection path data generation module is used to transmit inspection instructions to the ACR central control module through the communication module, and to parse the inspection instructions through the ACR central control module to generate inspection path data. The inspection path data is then transmitted to the navigation module, which controls the navigation module to analyze the robot's motion posture and external environment based on the inspection path data to obtain the first robot navigation data. Finally, the first robot navigation data is output to the ACR central control module. The monitoring data generation module is used to control the external sensor module through the ACR central control module to collect the inspection environment and inspection equipment in the target inspection area, obtain the first monitoring data, and transmit the first robot navigation data and the first monitoring data to the network layer for data transmission processing according to the communication module to obtain the corresponding second robot navigation data and second monitoring data. The inspection report generation module is used to analyze the navigation data and monitoring data of the second robot through the robot inspection and monitoring platform to generate inspection reports, including: If the second robot navigation data is consistent with the preset robot navigation standard, then the first inspection report is generated based on the first monitoring data, and the first inspection report is output as the inspection report. If the second robot navigation data is inconsistent with the preset robot navigation standard, an abnormal alarm information containing the abnormal inspection area will be generated in the robot inspection monitoring platform. Based on the abnormal alarm information, a re-inspection instruction containing the name of the alarming robot will be generated. According to the re-inspection instruction, the corresponding robot's external sensor module will be controlled to collect the inspection environment and inspection equipment in the abnormal inspection area to obtain supplementary monitoring data. The supplementary monitoring data and the second monitoring data will be analyzed to obtain a second inspection report, which will be output as the inspection report. Attached Figure Description

[0024] Figure 1 This is a flowchart illustrating a robot inspection method based on a multi-level architecture according to an embodiment of the present invention. Figure 2 This is a schematic diagram of a robot inspection system based on a multi-level architecture according to an embodiment of the present invention. Detailed Implementation

[0025] The following detailed description illustrates the specific implementation method: like Figure 1 As shown, it is a flowchart illustrating a robot inspection method based on a multi-level architecture according to an embodiment of the present invention, including the following steps: S1. Construct a multi-level architecture based on preset robot inspection requirements information. The multi-level architecture includes a robot layer, a network layer, and an application layer. The robot layer includes a communication module, an ACR central control module, a navigation module, and an external sensor module. The application layer includes a robot inspection and monitoring platform. The network layer is communicatively connected to the robot layer and the application layer, respectively. S2. The robot inspection monitoring platform parses the preset inspection tasks in the preset robot inspection requirements information to obtain inspection instructions, and sends the inspection instructions to the communication module through the network layer. S3. The inspection command is transmitted to the ACR central control module through the communication module. The ACR central control module parses the inspection command, generates inspection path data, transmits the inspection path data to the navigation module, and controls the navigation module to analyze the robot's motion posture and external environment based on the inspection path data to obtain the first robot navigation data. The first robot navigation data is then output to the ACR central control module. S4. The ACR central control module controls the external sensor module to collect the inspection environment and inspection equipment in the target inspection area to obtain the first monitoring data. The first robot navigation data and the first monitoring data are then transmitted to the network layer for data transmission processing according to the communication module to obtain the corresponding second robot navigation data and second monitoring data. S5. Analyze the navigation data and monitoring data of the second robot through the robot inspection and monitoring platform to obtain inspection reports, including: If the second robot navigation data is consistent with the preset robot navigation standard, then the first inspection report is generated based on the first monitoring data, and the first inspection report is output as the inspection report. If the second robot navigation data is inconsistent with the preset robot navigation standard, an abnormal alarm information containing the abnormal inspection area will be generated in the robot inspection monitoring platform. Based on the abnormal alarm information, a re-inspection instruction containing the name of the alarming robot will be generated. According to the re-inspection instruction, the corresponding robot's external sensor module will be controlled to collect the inspection environment and inspection equipment in the abnormal inspection area to obtain supplementary monitoring data. The supplementary monitoring data and the second monitoring data will be analyzed to obtain a second inspection report, which will be output as the inspection report.

[0026] Specifically, the navigation module includes an obstacle avoidance subunit, and the external sensor module includes an infrared laser obstacle avoidance sensor, a lidar, and a speed sensor. The obstacle avoidance subunit collects the distance to obstacles around the robot using the infrared laser obstacle avoidance sensor to obtain obstacle distance data; scans the outline of the obstacles using the lidar to obtain obstacle outline data; collects the robot's real-time movement speed using the speed sensor to obtain movement speed data; and adjusts the robot's movement trajectory based on the obstacle distance data, obstacle outline data, and movement speed data to obtain obstacle avoidance adjustment data, which is then added to the first robot navigation data.

[0027] In this embodiment, the robot effectively avoids obstacles by integrating multi-angle infrared laser obstacle avoidance sensors, anti-fall sensors, safety edges, and lidar. The obstacle avoidance safety distance is controlled within an adjustable range of 2 meters. The robot is equipped with an anti-collision braking system to prevent damage to equipment or injury to personnel due to insufficient braking at close range. The braking distance is ≤10cm. An emergency stop button is installed on the back of the robot. In the event of a loss of control, pressing the button will cause the robot to shut down and stop. The robot uses infrared obstacle avoidance sensors for forward and backward obstacle avoidance with an accuracy of 0.05 meters and an obstacle avoidance distance of 2 meters. Simultaneously, the optimal route is pre-planned to avoid obstacles during the inspection path planning. The robot's own safety edges prevent collisions that could cause injury to personnel and equipment. Deep ditch anti-fall sensors prevent the robot from entering ditches or steps, thus preventing malfunctions. LiDAR scans the environmental contour structure to form point cloud data. Navigation control performs calculations to determine obstacle characteristics and distances within the robot's operating space. The navigation algorithm logic can automatically determine and perform obstacle avoidance. The infrared laser obstacle avoidance sensor uses the GP2Y0A21YK0F sensor, the lidar uses the RPLLIDARA1 radar, and the speed sensor uses the Hall effect speed sensor AHKC-EKA. When the robot is running, the infrared sensor continuously emits laser light and receives reflected light, acquiring real-time obstacle distance data within a range of 0.1-1m (e.g., an obstacle is detected 0.5m ahead). The lidar performs a 360° environmental scan at a 10Hz scanning frequency, generating point cloud data. After processing by the SLAM algorithm, the obstacle outline is identified as a cube with a side length of 0.3m. The speed sensor outputs the robot's real-time speed v = 0.8m / s and angular velocity ω = 0.2rad / s through the Hall effect. The algorithm first dynamically calculates the safe distance threshold d_safe = 0.5v + 0.2 = 0.6m based on v. Comparing this to the infrared sensor's measured distance of 0.5m, which is less than the safe distance threshold, the obstacle avoidance process is triggered. Subsequently, combining the lidar contour data, a candidate path set is generated using the Dynamic Window Method (DWA). The optimal path that meets the robot's minimum turning radius (0.15m) and avoids obstacles is selected, and the turning angle θ = 15° and the velocity correction Δv = -0.3m / s are calculated. Finally, the obstacle avoidance adjustment data (θ = 15°, Δv = -0.3m / s) is weighted and fused with the original navigation data (such as target point coordinates). After suppressing sensor noise through Kalman filtering, a new local path command is generated to achieve safe obstacle avoidance navigation. The entire process, from data acquisition to path adjustment, takes approximately 100ms, ensuring the robot responds to environmental changes in real time.

[0028] Specifically, the navigation module further includes a laser navigation unit, an IMU inertial navigation unit, and a SLAM navigation unit. The external sensor module also includes a light intensity sensor, which acquires light intensity and calculates environmental obstacle density based on obstacle distance and contour data. The ACR central control module controls the navigation module to analyze the robot's motion posture and the external environment based on the inspection path data, combined with light intensity and environmental obstacle density, including: When the density of environmental obstacles does not exceed the preset density of environmental obstacles, the environmental contour of the target inspection area is scanned by the laser navigation unit, and environmental contour data is generated by combining the path coordinates in the inspection path data. When the light intensity is less than the preset light intensity, the robot's motion acceleration and angular velocity are collected by the IMU inertial navigation unit, and combined with the motion velocity data in the path coordinates in the inspection path data to generate motion inertial data; When the density of environmental obstacles is greater than the preset environmental obstacle density and the light intensity is greater than or equal to the preset light intensity, the SLAM navigation unit fuses and models the environmental contour data and motion inertial data, and generates environmental map data by combining the path planning parameters of the path coordinates in the inspection path data. The environmental contour data, motion inertia data, and environmental map data are input into the first robot navigation data.

[0029] In this embodiment, the preset illumination intensity DE range needs to be determined based on the actual inspection scenario of the robot. In an indoor environment, the value may be between 100-500 lux under normal lighting conditions; outdoors, under strong sunlight during the day, it may reach 10,000-100,000 lux. To determine this, illumination data for normal robot operation in different scenarios is first collected, and its stable operating range is analyzed. Combined with the robot's sensor performance and task requirements, a threshold that ensures the robot accurately perceives the environment and operates stably is comprehensively determined as the preset illumination intensity. For example, in an indoor scenario, it can be set to 300 lux.

[0030] The preset obstacle density range also depends on the scene. In simple, open scenes, the value might be 0-5 obstacles per square meter. Complex and narrow areas, possibly 10-20 / Upon determination, multiple LiDAR scans are performed on the target inspection area to statistically analyze the number of obstacle point clouds and the scanned area in different regions, thereby calculating the actual obstacle density distribution in the environment. Based on the robot navigation algorithm's adaptability to environmental complexity, a density value that can distinguish between simple and complex environments is selected as the preset environmental obstacle density, such as 8 obstacles per [location missing]. .

[0031] The environmental obstacle density ρ = number of obstacle point clouds N / area A of the lidar scanning area. When the environmental obstacle density ρ does not exceed the preset environmental obstacle density, the lidar navigation unit is activated. The lidar emits a laser beam at a certain frequency towards the target inspection area. The beam reflects back after encountering an obstacle, and the obstacle distance is calculated by measuring the round-trip time. Simultaneously, the starting point and direction of the scan are determined by combining the path coordinates from the inspection path data. Following a set scanning mode (such as spiral scan or parallel scan), the surrounding environment is scanned in all directions to acquire the spatial location information of obstacles. The obtained obstacle distance and location information are organized according to a specific format to generate environmental contour data of the target inspection area, providing a basis for subsequent navigation.

[0032] When the light intensity is lower than the preset light intensity, the IMU (Inertial Measurement Unit) begins to operate. The IMU includes accelerometers and gyroscopes. The accelerometers measure the robot's acceleration along the three axes (X, Y, and Z), and the gyroscopes measure the robot's angular velocity around these axes. The IMU collects this data in real time during the robot's movement. Simultaneously, it extracts motion velocity data from the path coordinates in the inspection path data. The acceleration and angular velocity data collected by the IMU are combined with the motion velocity data, and integrated and processed using a built-in algorithm to remove noise interference, yielding the robot's motion state information at different times, such as displacement and attitude changes. Ultimately, this generates motion inertial data reflecting the robot's motion characteristics.

[0033] When the obstacle density in the environment exceeds a preset value and the illumination intensity meets the requirements, the SLAM navigation unit is activated. First, the environmental contour data previously generated by the laser navigation unit and the motion inertial data generated by the IMU inertial navigation unit are used as input. The SLAM algorithm uses the obstacle location information in the environmental contour data to construct an initial feature map of the environment, and simultaneously combines this with the robot's motion information from the motion inertial data to estimate and correct the robot's pose. Then, path planning parameters, such as path turning points and directions, are extracted from the path coordinates in the inspection path data. These path planning parameters are added to the modeling process to further optimize and adjust the environmental features and robot pose. Through continuous iteration and updates, environmental map data containing the distribution of environmental obstacles and feasible robot paths is finally generated.

[0034] Specifically, the robot layer also includes a gimbal control unit, which comprises a visible light image acquisition subunit, an infrared thermal imaging subunit, a laser spectrum acquisition subunit, and a nighttime lighting auxiliary unit. The visible light image acquisition subunit acquires images of the appearance of the inspection equipment and meters within the target inspection area, obtaining visible light equipment image data. The infrared thermal imaging subunit acquires the temperature field distribution of the inspection equipment, obtaining equipment thermal imaging data. The laser spectrum acquisition subunit performs spectral analysis on the gas composition in the inspection environment, obtaining gas spectral data. The nighttime lighting auxiliary unit acquires environmental image data of the illuminated area. The visible light equipment image data, equipment thermal imaging data, gas spectral data, and illuminated area environmental image data are then transmitted to the first monitoring data.

[0035] In this embodiment, visible light image acquisition uses a Hikvision DS-2CD3T46FWDV3-I3 camera (4 megapixels, supports H.265 encoding), and the YOLOv5 target detection algorithm is used to identify meter readings (input: visible light device image data; output: meter reading and position coordinates, e.g., inputting a 220V voltmeter image, outputting "220.5V" and the center coordinates of the meter dial). Infrared thermal imaging uses a DJI M3T thermal imaging module (640×512 resolution), employing a temperature field clustering algorithm (input: device thermal imaging data; output: high-temperature area markings, e.g., marking anomalies when the transformer joint temperature is ≥80℃). Laser spectral acquisition uses an Ocean Optics FLAME-S spectrometer (wavelength range 200-1100nm), and the PCA-SVM model is used to analyze gas spectral data (input: gas spectral data; output: gas composition and concentration, e.g., inputting a methane spectrum, outputting "CH4 500ppm"). The nighttime lighting utilizes an LED dimming module (brightness adjustable from 0-100%), combined with an adaptive ambient brightness algorithm (input: ambient image data of the illuminated area; output: optimal lighting brightness value). All data is transmitted to the first monitoring data unit via an RS485 bus, encapsulated in JSON format, and includes timestamps, device IDs, data types, and numerical fields.

[0036] Specifically, the robot layer also includes a power management unit, which controls the power supply to each functional module according to the robot's operating status, including: When the robot is in standby mode, the power management unit only provides power to the communication module and the ACR central control module, cuts off the power supply to other functional modules, and collects real-time power consumption data of the communication module and the ACR central control module. When the robot is in inspection mode, the power management unit provides power to all functional modules and collects real-time power consumption data of each functional module. Based on the collected real-time power consumption data and the robot's remaining battery power data, the battery life is calculated to obtain power reserve status data, which is then transmitted to the ACR central control module.

[0037] In this embodiment, the implementation of the robot power management unit requires a combination of hardware circuit design and software algorithm control. Specifically, it can be constructed from the following dimensions: In terms of hardware architecture, a hierarchical power control topology is adopted. The main power supply outputs multiple regulated power supplies through a DC-DC converter, with each channel configured with an electronic switch (such as a MOSFET) to achieve module-level power supply control. In standby mode, only the power supply paths of the communication module (such as the 4G / 5G module) and the ACR central control module are turned on; in inspection mode, the power switches of all functional modules are activated through GPIO signals or I2C bus instructions. Current detection uses a high-precision Hall sensor or shunt resistor in conjunction with an ADC to collect the operating current of each module in real time; voltage monitoring is connected to the ADC pin of the main control MCU through a voltage divider circuit, combined with a coulomb counter chip to accurately measure the remaining battery power. At the software logic level, state machine management is the core. Standby / inspection states are identified through sensor data (such as accelerometers to determine motion state) or task scheduler signals. In standby mode, a low-power strategy is executed: power is turned off for unnecessary modules, and a timed wake-up mechanism is started to check for state changes; during inspection, power is turned on for all modules, and multiple ADC data are collected synchronously. Power consumption calculation employs a sliding window algorithm, multiplying the real-time current by the voltage after mean filtering to obtain instantaneous power consumption, and then fitting a power consumption curve using historical data. Battery life calculation is based on the ratio of remaining battery power to the current average power consumption, incorporating a battery safety threshold correction (e.g., reserving 15% battery power to prevent over-discharge). The result is transmitted to the ACR module via SPI or UART bus. For system optimization, the power management unit integrates a watchdog timer to prevent deadlock, and CRC checksums ensure reliable data transmission. The hardware design reserves I / O expansion interfaces, and the software architecture supports OTA updates of algorithm parameters (such as power consumption model coefficients) to adapt to changes in battery characteristics or task requirements. Through hardware current sampling accuracy calibration and software filtering algorithm optimization, the power consumption data error is ensured to be less than 5%, and the battery life calculation error is controlled within 10%, meeting the stringent power management requirements of industrial inspection robots.

[0038] Specifically, the network layer includes a wireless communication unit, a robot application server, a data server, and a wireless automatic charging terminal. Based on the network environment of the target inspection area, a corresponding communication method is selected, and the first robot navigation data and the first monitoring data transmitted from the robot layer are transmitted to the robot application server according to the communication method. The robot application server performs format conversion and protocol adaptation on the first robot navigation data and the first monitoring data to obtain adapted data. The data server stores the adapted data and establishes a data index. When the robot's remaining battery power is lower than a preset remaining battery power, the wireless communication unit receives a charging command sent by the robot inspection monitoring platform, controls the robot to go to the wireless automatic charging terminal, and automatically charges the robot according to the wireless automatic charging terminal, obtaining charging status data, which is then transmitted to the data server for storage.

[0039] In this embodiment, the wireless communication unit integrates 4G / 5G / Wi-Fi modules and automatically selects the optimal communication link through RSSI signal strength detection and network latency testing, supporting dynamic switching strategies. Navigation and monitoring data transmitted from the robot layer are encrypted and compressed before being transmitted to the robot application server via MQTT / HTTP protocols. The robot application server deploys data adaptation middleware, employing a JSON / XML standard format conversion engine and a protocol parser to convert industrial protocols such as CAN / Modbus to TCP / IP. Field alignment and unit unification are achieved through a data mapping table. The adapted data is buffered via a Kafka message queue and then written to the distributed database cluster of the data server, using a combination of MySQL and Elasticsearch for storage. MySQL stores structured data, while Elasticsearch establishes full-text and geofencing indexes, supporting multi-dimensional queries by time, location, type, and other dimensions. When the battery management unit reports that the battery level is lower than the preset remaining battery level, the wireless communication unit receives a charging command from the inspection platform and generates the optimal path to the wireless charging terminal using a path planning algorithm. The charging terminal employs electromagnetic coupling wireless charging technology, incorporating a built-in power sensor and status monitoring module. During charging, it collects voltage, current, and temperature data in real time. After preprocessing by an edge computing module, the data is transmitted back to the data server via a LoRa communication unit. The data server writes the charging status data into the time-series database InfluxDB and triggers an alarm notification mechanism. Simultaneously, a visualization platform displays the charging progress and historical data curves. High availability is achieved through containerized deployment, using Kubernetes for service orchestration and automatic scaling, combined with Prometheus to monitor the operational status of each module, ensuring collaborative work among network layer components and achieving a closed-loop management system for efficient data transmission, intelligent processing, and secure storage. The robot layer also includes an offline storage module, and the multi-level architecture also includes a charging station module. The network layer is communicatively connected to both the offline storage module and the charging station module. The offline storage module, based on the communication status of the network layer, stores the first robot navigation data to obtain offline navigation data and the first monitoring data to obtain offline monitoring data when the network is interrupted. When the network is restored, it transmits the offline navigation data and offline monitoring data to the communication module. The charging station module generates a charging control signal based on the robot's battery level data and transmits it to the communication module through the network layer. The communication module then transmits the charging control signal to the ACR central control module, which controls the robot to move to the charging station module for charging. The offline navigation data, offline monitoring data, and charging control signal are all included in the analysis scope of the second robot navigation data or the second monitoring data.The determination of the preset remaining battery power data needs to be based on the duration and complexity of the inspection task performed by the robot, as well as the power consumption of each functional module during the task execution. By actually testing the power consumption of the robot in different task scenarios, the minimum power required to complete the task is estimated. Generally speaking, in order to ensure that the robot can return to the charging point smoothly and avoid damage to the battery due to low power, the preset remaining battery power data is usually taken as 15%-25% of the total battery capacity. For example, if the total battery capacity is 100Ah, the preset value can be set to 15Ah-25Ah.

[0040] Specifically, the external sensor module further includes a temperature and humidity detection unit, a combustible and toxic gas concentration detection unit, and a sound acquisition unit; the temperature and humidity detection unit collects the air temperature and relative humidity in the target inspection area to obtain temperature and humidity data; the combustible and toxic gas concentration detection unit collects the concentration of combustible and toxic gases in the target inspection area to obtain gas concentration data; the sound acquisition unit collects the operating sound of the equipment in the target inspection area to obtain equipment sound data; and the temperature and humidity data, gas concentration data, and equipment sound data are input into the first monitoring data.

[0041] In this embodiment, the external sensor module achieves environmental monitoring through multi-parameter collaborative acquisition and data fusion. The temperature and humidity detection unit adopts a digital sensor integrated with a temperature compensation algorithm, acquiring air temperature and humidity data in real time via an I2C bus. After Kalman filtering and smoothing, it outputs stable values, ensuring a temperature error ≤0.3℃ and a humidity error ≤3%RH. The combustible and toxic gas concentration detection unit is based on a multi-sensor array design. A catalytic combustion sensor detects combustible gases, while an electrochemical sensor monitors toxic gases. The analog signal is converted to digital signal with high precision, and then combined with a nonlinear calibration curve and a temperature and humidity compensation model to correct for environmental interference, achieving a concentration measurement error ≤5%FS. The combustible and toxic gas concentration sensor monitors the area under inspection. Concentration was collected Concentration data, for Concentration was collected Concentration data, for Concentration was collected Concentration data is collected for CO concentration; methane leakage data is collected using a laser methane telemetry device to detect methane leaks in the inspection area. The sound acquisition unit uses a MEMS microphone array to capture the sound waves from the equipment, analyzes the spectral characteristics using Fast Fourier Transform, filters out background interference using an adaptive noise suppression algorithm, and extracts the characteristic frequencies of the equipment operation to generate sound feature vectors. All sensor data is aggregated to the main control module via the SPI bus, and a timestamp synchronization mechanism is used to align multi-source data. After data compression and encryption, it is encapsulated into standard JSON format as the first monitoring data. This first monitoring data is transmitted to the robot layer via industrial Ethernet, where it is fused with navigation data to form a complete monitoring information stream. This supports real-time monitoring of environmental parameters and anomaly warnings during inspection tasks, ensuring that data acquisition accuracy and transmission efficiency meet the requirements of industrial inspection scenarios. Specifically, the robot inspection and monitoring platform includes an inspection mode management unit, an access control unit, and a data display unit. The inspection mode management unit categorizes preset inspection tasks, generates task parameters corresponding to the inspection mode, and parses the preset inspection tasks according to the task parameters to obtain inspection instructions matching the inspection mode. The access control unit verifies the identity of users logging into the platform and assigns corresponding operation permissions based on the user's identity. The data display unit visualizes the second robot navigation data and the second monitoring data to generate real-time monitoring screens, data trend curves, and device status icons.

[0042] In this embodiment, the preset inspection tasks cover comprehensive, routine, special, unique, and custom tasks. The inspection mode management unit first classifies these tasks, for example, classifying comprehensive tasks as a broad category covering all equipment in the entire inspection area. Then, it generates corresponding task parameters based on the inspection mode (automatic inspection, abnormal inspection, fixed-point inspection, manual inspection). For automatic inspection mode, task parameters include inspection time intervals and inspection routes; for abnormal inspection mode, parameters such as abnormal thresholds need to be set. Next, the preset tasks are parsed based on these parameters. For example, in automatic inspection mode, a comprehensive task is parsed as an inspection instruction to check all equipment sequentially along a predetermined route at fixed time intervals. When a user logs into the platform, the permission management unit verifies the user's identity by comparing it with pre-stored user identity information. For example, it matches the user's entered account and password with records in the database. After successful verification, operation permissions are assigned according to the user's identity. Administrators have the highest permissions and can perform operations such as task setting and user management; ordinary operators can only perform basic operations such as inspection tasks and viewing data, thereby ensuring platform security and data confidentiality. After receiving the navigation data and monitoring data from the second robot, the data display unit processes them using visualization technology. The navigation data is converted into real-time position information of the robot within the inspection area, generating a real-time monitoring screen that intuitively displays the robot's position and movement trajectory. For the monitoring data, algorithms are used to analyze and generate data trend curves, reflecting the changes in equipment operating parameters over time. Simultaneously, equipment status icons are generated based on equipment status data, such as green indicating normal operation and red indicating a fault, allowing users to quickly understand the equipment status.

[0043] Specifically, the preset robot navigation standard includes an allowable deviation threshold, an allowable offset threshold, and an allowable matching degree threshold. The navigation accuracy threshold is set according to the accuracy requirements of the inspection equipment, the path deviation threshold is set according to the spatial range of the inspection area, and the environmental adaptation parameter is set according to the terrain features of the inspection area. The robot inspection monitoring platform extracts the motion posture deviation value, path offset, and environmental perception matching degree from the second robot navigation data. The robot inspection monitoring platform compares the motion posture deviation value with the allowable deviation threshold to obtain a first comparison result, compares the path offset with the allowable offset threshold to obtain a second comparison result, and compares the environmental perception matching degree with the allowable matching degree threshold to obtain a third comparison result. If the first comparison result is within the allowable deviation threshold, the second comparison result is within the allowable offset threshold, and the third comparison result is within the allowable matching degree threshold, then the second robot navigation data is determined to be consistent with the preset robot navigation standard. If any comparison result exceeds the corresponding threshold, then it is determined to be inconsistent.

[0044] In this embodiment, a preset navigation standard parameter library is configured in the robot inspection and monitoring platform, storing allowable deviation thresholds, allowable offset thresholds, and allowable matching degree thresholds. The navigation accuracy threshold needs to be set with specific values ​​based on the accuracy specification of the inspection equipment. The path deviation threshold needs to be calculated based on the spatial range boundary value of the 3D modeling data of the inspection area. The environmental adaptation parameter needs to be generated by the terrain feature analysis module to generate adaptation coefficients. The robot inspection and monitoring platform needs to deploy a data acquisition module to acquire the second robot navigation data in real time through the ROS2 communication protocol and analyze the three indicators: motion posture deviation value, path offset, and environmental perception matching degree. The comparison engine adopts a triple threshold verification mechanism: the difference between the motion posture deviation value and the allowable deviation threshold is calculated to determine whether it is within the ± threshold range to generate the first comparison result; the path offset is calculated using Euclidean distance and compared with the allowable offset threshold to generate the second comparison result; the environmental perception matching degree adopts a cosine similarity algorithm to calculate the feature matching degree between the real-time perception data and the preset environment model and compare it with the allowable matching degree threshold to generate the third comparison result. Ultimately, a logical AND operation is used. A consistency judgment is output only when all three comparison results meet the corresponding threshold conditions. If any indicator exceeds the limit, an inconsistency alarm is triggered, and a diagnostic report containing the exceeding indicator, timestamp, and location information is automatically generated for maintenance personnel to quickly locate the problem. The entire process must achieve millisecond-level response to ensure the real-time performance and security of the inspection operation.

[0045] The allowable deviation threshold typically ranges from 0.1° to 3° (angle deviation), depending on the accuracy of the inspection equipment. For industrial-grade high-precision inspection robots (such as power line inspection and chemical equipment inspection), the equipment's sensors (gyroscopes, IMUs) are highly accurate, and the allowable deviation threshold is set at 0.1° to 1°. In these scenarios, excessive posture deviation can cause the detection components (such as infrared cameras and ultrasonic probes) to fail to accurately align with the target, affecting the effectiveness of data acquisition. For consumer-grade or simple indoor inspection robots (such as shopping mall security inspection), the equipment accuracy is lower, and the alignment requirements of the inspection target are not high, so the threshold can be relaxed to 1° to 3°. This can meet basic navigation needs and avoid frequent inconsistencies in judgment due to equipment performance limitations. The allowable offset threshold typically ranges from 0.1m to 2m (distance deviation), based on the spatial range of the inspection area. When inspecting in narrow indoor spaces (such as server room cabinets or warehouse aisle), the space is narrow and there are many obstacles. To avoid robot collisions with equipment, the allowable offset threshold must be strictly controlled within 0.1m to 0.3m. In open outdoor areas (such as factory perimeters and park roads), the space has high redundancy, and slight path deviations will not affect the inspection coverage. The threshold can be relaxed to 0.5m-2m. For example, when inspecting park roads, even if the robot deviates 0.8m from the planned path, it can still observe the fire-fighting facilities on both sides of the road normally, without excessive restriction on deviation. The allowable matching degree threshold is usually set between 75% and 98% (percentage), mainly related to the terrain features of the inspection area. In scenarios with simple terrain and stable environment (such as an open indoor hall or a flat outdoor concrete ground), the matching difficulty of environmental perception data (such as LiDAR point clouds and visual images) is low. To ensure navigation accuracy, the allowable matching degree threshold needs to be set at 90%-98%. For example, when inspecting an indoor warehouse, the shelf layout is fixed, and the matching degree between the perception data and the preset map needs to reach more than 95% to prevent the robot from getting lost due to matching errors. However, in scenarios with complex terrain (such as outdoor mountain inspection or forest inspection), the terrain is rugged and there is a lot of vegetation obstruction, and the perception data is easily interfered with, making it difficult to achieve a high matching degree. The threshold can be reduced to 75%-90%. If a threshold of 90% or higher is forcibly set, frequent inconsistencies in judgments will occur due to environmental interference, interrupting the inspection process.

[0046] like Figure 2 As shown, it is a structural diagram of a robot inspection system based on a multi-level architecture according to an embodiment of the present invention, including: A multi-level architecture building module is used to construct a multi-level architecture based on preset robot inspection requirements information. The multi-level architecture includes a robot layer, a network layer, and an application layer. The robot layer includes a communication module, an ACR central control module, a navigation module, and an external sensor module. The application layer includes a robot inspection and monitoring platform. The network layer is communicatively connected to the robot layer and the application layer, respectively. The inspection instruction generation module is used to parse the preset inspection tasks in the preset robot inspection requirement information through the robot inspection monitoring platform, obtain inspection instructions, and send the inspection instructions to the communication module through the network layer. The inspection path data generation module is used to transmit inspection instructions to the ACR central control module through the communication module, and to parse the inspection instructions through the ACR central control module to generate inspection path data. The inspection path data is then transmitted to the navigation module, which controls the navigation module to analyze the robot's motion posture and external environment based on the inspection path data to obtain the first robot navigation data. Finally, the first robot navigation data is output to the ACR central control module. The monitoring data generation module is used to control the external sensor module through the ACR central control module to collect the inspection environment and inspection equipment in the target inspection area, obtain the first monitoring data, and transmit the first robot navigation data and the first monitoring data to the network layer for data transmission processing according to the communication module to obtain the corresponding second robot navigation data and second monitoring data. The inspection report generation module is used to analyze the navigation data and monitoring data of the second robot through the robot inspection and monitoring platform to generate inspection reports, including: If the second robot navigation data is consistent with the preset robot navigation standard, then the first inspection report is generated based on the first monitoring data, and the first inspection report is output as the inspection report. If the second robot navigation data is inconsistent with the preset robot navigation standard, an abnormal alarm information containing the abnormal inspection area will be generated in the robot inspection monitoring platform. Based on the abnormal alarm information, a re-inspection instruction containing the name of the alarming robot will be generated. According to the re-inspection instruction, the corresponding robot's external sensor module will be controlled to collect the inspection environment and inspection equipment in the abnormal inspection area to obtain supplementary monitoring data. The supplementary monitoring data and the second monitoring data will be analyzed to obtain a second inspection report, which will be output as the inspection report.

[0047] In this embodiment, the multi-level architecture establishment module first constructs a multi-level architecture based on preset robot inspection requirements information. This architecture includes a robot layer, a network layer, and an application layer. The robot layer integrates a communication module, an ACR central control module, a navigation module, and an external sensor module. The application layer has a robot inspection monitoring platform. The network layer establishes communication connections with both the robot layer and the application layer. The inspection command generation module parses the preset inspection tasks in the preset robot inspection requirements information through the robot inspection monitoring platform, generates inspection commands, and then sends the inspection commands to the communication module of the robot layer via the network layer. The inspection path data generation module transmits the inspection commands to the ACR central control module through the communication module. The ACR central control module parses the inspection commands, generates inspection path data, and transmits it to the navigation module. The navigation module analyzes the robot's motion posture and the external environment based on the inspection path data, obtains the first robot navigation data, and outputs it to the ACR central control module. In the monitoring data generation module, the ACR central control module controls the external sensor module to collect data on the inspection environment and equipment of the target inspection area, obtaining the first monitoring data. Then, the first robot navigation data and the first monitoring data are transmitted to the network layer via the communication module. After data transmission processing at the network layer, the second robot navigation data and the second monitoring data are obtained. The inspection report generation module analyzes the second robot navigation data and the second monitoring data through the robot inspection monitoring platform. If the second robot navigation data is consistent with the preset robot navigation standard, the first inspection report is generated and output based on the first monitoring data. If they are inconsistent, the robot inspection monitoring platform generates an abnormal alarm information containing the abnormal inspection area. Based on the abnormal alarm information, a re-inspection command containing the name of the alarming robot is generated, controlling the external sensor module of the corresponding robot to collect data on the inspection environment and equipment of the abnormal inspection area to obtain supplementary monitoring data. The supplementary monitoring data and the second monitoring data are analyzed to obtain the second inspection report and output.

[0048] The above are merely embodiments of the present invention. Commonly known structures and characteristics are not described in detail here. Those skilled in the art are aware of all common technical knowledge in the field prior to the application date or priority date, are aware of all existing technologies in that field, and have the ability to apply conventional experimental methods prior to that date. Those skilled in the art can, under the guidance of this application, improve and implement this solution in combination with their own capabilities. Some typical known structures or methods should not be obstacles for those skilled in the art to implement this application. It should be noted that those skilled in the art can make several modifications and improvements without departing from the structure of the present invention. These should also be considered within the scope of protection of the present invention, and will not affect the effectiveness of the implementation of the present invention or the practicality of the patent. The scope of protection claimed in this application should be determined by the content of its claims, and the specific embodiments described in the specification can be used to interpret the content of the claims.

Claims

1. A robot inspection method based on a multi-level architecture, characterized in that: Includes the following steps: S1. Construct a multi-level architecture based on preset robot inspection requirements information. The multi-level architecture includes a robot layer, a network layer, and an application layer. The robot layer includes a communication module, an ACR central control module, a navigation module, and an external sensor module. The application layer includes a robot inspection and monitoring platform. The network layer is communicatively connected to the robot layer and the application layer, respectively. S2. The robot inspection monitoring platform parses the preset inspection tasks in the preset robot inspection requirements information to obtain inspection instructions, and sends the inspection instructions to the communication module through the network layer. S3. The inspection command is transmitted to the ACR central control module through the communication module. The ACR central control module parses the inspection command, generates inspection path data, transmits the inspection path data to the navigation module, and controls the navigation module to analyze the robot's motion posture and external environment based on the inspection path data to obtain the first robot navigation data. The first robot navigation data is then output to the ACR central control module. S4. The ACR central control module controls the external sensor module to collect the inspection environment and inspection equipment in the target inspection area to obtain the first monitoring data. The first robot navigation data and the first monitoring data are then transmitted to the network layer for data transmission processing according to the communication module to obtain the corresponding second robot navigation data and second monitoring data. S5. Analyze the navigation data and monitoring data of the second robot through the robot inspection and monitoring platform to obtain inspection reports, including: If the second robot navigation data is consistent with the preset robot navigation standard, then the first inspection report is generated based on the first monitoring data, and the first inspection report is output as the inspection report. If the second robot navigation data is inconsistent with the preset robot navigation standard, an abnormal alarm information containing the abnormal inspection area will be generated in the robot inspection monitoring platform. Based on the abnormal alarm information, a re-inspection instruction containing the name of the alarming robot will be generated. According to the re-inspection instruction, the corresponding robot's external sensor module will be controlled to collect the inspection environment and inspection equipment in the abnormal inspection area to obtain supplementary monitoring data. The supplementary monitoring data and the second monitoring data will be analyzed to obtain a second inspection report, which will be output as the inspection report.

2. The robot inspection method based on a multi-level architecture according to claim 1, characterized in that: The navigation module includes an obstacle avoidance subunit, and the external sensor module includes an infrared laser obstacle avoidance sensor, a lidar, and a speed sensor. The obstacle avoidance subunit collects the distance to obstacles around the robot using the infrared laser obstacle avoidance sensor to obtain obstacle distance data; it scans the outline of the obstacles using the lidar to obtain obstacle outline data; it collects the robot's real-time movement speed using the speed sensor to obtain movement speed data; and it adjusts the robot's trajectory based on the obstacle distance data, obstacle outline data, and movement speed data to obtain obstacle avoidance adjustment data, which is then added to the first robot navigation data.

3. A robot inspection method based on a multi-level architecture according to claim 2, characterized in that: The navigation module further includes a laser navigation unit, an IMU inertial navigation unit, and a SLAM navigation unit. The external sensor module also includes a light intensity sensor, which acquires light intensity and calculates environmental obstacle density based on obstacle distance and contour data. The ACR central control module controls the navigation module to analyze the robot's motion posture and the external environment based on the inspection path data, combined with light intensity and environmental obstacle density, including: When the density of environmental obstacles does not exceed the preset density of environmental obstacles, the environmental contour of the target inspection area is scanned by the laser navigation unit, and environmental contour data is generated by combining the path coordinates in the inspection path data. When the light intensity is less than the preset light intensity, the robot's motion acceleration and angular velocity are collected by the IMU inertial navigation unit, and combined with the motion velocity data in the path coordinates in the inspection path data to generate motion inertial data; When the density of environmental obstacles is greater than the preset environmental obstacle density and the light intensity is greater than or equal to the preset light intensity, the SLAM navigation unit fuses and models the environmental contour data and motion inertial data, and generates environmental map data by combining the path planning parameters of the path coordinates in the inspection path data. The environmental contour data, motion inertia data, and environmental map data are input into the first robot navigation data.

4. The robot inspection method based on a multi-level architecture according to claim 1, characterized in that: The robot layer also includes a gimbal control unit, which comprises a visible light image acquisition subunit, an infrared thermal imaging subunit, a laser spectrum acquisition subunit, and a nighttime lighting auxiliary unit. The visible light image acquisition subunit acquires images of the appearance of the inspection equipment and meters within the target inspection area, obtaining visible light equipment image data. The infrared thermal imaging subunit acquires the temperature field distribution of the inspection equipment, obtaining equipment thermal imaging data. The laser spectrum acquisition subunit performs spectral analysis on the gas composition in the inspection environment, obtaining gas spectral data. The nighttime lighting auxiliary unit acquires environmental image data of the illuminated area. The visible light equipment image data, equipment thermal imaging data, gas spectral data, and illuminated area environmental image data are then transmitted to the first monitoring data.

5. The robot inspection method based on a multi-level architecture according to claim 1, characterized in that: The robot layer also includes a power management unit, which controls the power supply to each functional module according to the robot's operating status, including: When the robot is in standby mode, the power management unit only provides power to the communication module and the ACR central control module, cuts off the power supply to other functional modules, and collects real-time power consumption data of the communication module and the ACR central control module. When the robot is in inspection mode, the power management unit provides power to all functional modules and collects real-time power consumption data of each functional module. Based on the collected real-time power consumption data and the robot's remaining battery power data, the battery life is calculated to obtain power reserve status data, which is then transmitted to the ACR central control module.

6. The robot inspection method based on a multi-level architecture according to claim 5, characterized in that: The network layer includes a wireless communication unit, a robot application server, a data server, and a wireless automatic charging terminal. Based on the network environment of the target inspection area, a corresponding communication method is selected. The first robot navigation data and the first monitoring data transmitted from the robot layer are then transmitted to the robot application server according to this communication method. The robot application server performs format conversion and protocol adaptation on the first robot navigation data and the first monitoring data to obtain adapted data. The data server stores the adapted data and establishes a data index. When the robot's remaining battery power is lower than a preset remaining battery power, the wireless communication unit receives a charging command from the robot inspection monitoring platform, controls the robot to proceed to the wireless automatic charging terminal, and automatically charges the robot according to the wireless automatic charging terminal, obtaining charging status data, which is then transmitted to the data server for storage.

7. The robot inspection method based on a multi-level architecture according to claim 1, characterized in that: The external sensor module also includes a temperature and humidity detection unit, a combustible and toxic gas concentration detection unit, and a sound acquisition unit. The temperature and humidity detection unit collects the air temperature and relative humidity in the target inspection area to obtain temperature and humidity data. The combustible and toxic gas concentration detection unit collects the concentration of combustible and toxic gases in the target inspection area to obtain gas concentration data. The sound acquisition unit collects the operating sounds of the equipment in the target inspection area to obtain equipment sound data. The temperature and humidity data, gas concentration data, and equipment sound data are input into the first monitoring data.

8. The robot inspection method based on a multi-level architecture according to claim 1, characterized in that: The robot inspection and monitoring platform includes an inspection mode management unit, an access control unit, and a data display unit. The inspection mode management unit classifies the preset inspection tasks, generates task parameters corresponding to the inspection mode, and parses the preset inspection tasks according to the task parameters to obtain inspection instructions that match the inspection mode. The access control unit verifies the identity of users logging into the platform and assigns corresponding operation permissions based on their identities. The data display unit visualizes the navigation data and monitoring data of the second robot, generating real-time monitoring screens, data trend curves, and device status icons.

9. The robot inspection method based on a multi-level architecture according to claim 1, characterized in that: The preset robot navigation standard includes an allowable deviation threshold, an allowable offset threshold, and an allowable matching degree threshold. The navigation accuracy threshold is set according to the accuracy requirements of the inspection equipment, the path deviation threshold is set according to the spatial range of the inspection area, and the environmental adaptation parameter is set according to the terrain features of the inspection area. The robot inspection monitoring platform extracts the motion posture deviation value, path offset, and environmental perception matching degree from the second robot navigation data. The robot inspection monitoring platform compares the motion posture deviation value with the allowable deviation threshold to obtain a first comparison result, compares the path offset with the allowable offset threshold to obtain a second comparison result, and compares the environmental perception matching degree with the allowable matching degree threshold to obtain a third comparison result. If the first comparison result is within the allowable deviation threshold, the second comparison result is within the allowable offset threshold, and the third comparison result is within the allowable matching degree threshold, then the second robot navigation data is determined to be consistent with the preset robot navigation standard. If any comparison result exceeds the corresponding threshold, then an inconsistency is determined.

10. A robot inspection system based on a multi-level architecture, applied to the robot inspection method based on a multi-level architecture as described in any one of claims 1-9, characterized in that: include: A multi-level architecture building module is used to construct a multi-level architecture based on preset robot inspection requirements information. The multi-level architecture includes a robot layer, a network layer, and an application layer. The robot layer includes a communication module, an ACR central control module, a navigation module, and an external sensor module. The application layer includes a robot inspection and monitoring platform. The network layer is communicatively connected to the robot layer and the application layer, respectively. The inspection instruction generation module is used to parse the preset inspection tasks in the preset robot inspection requirement information through the robot inspection monitoring platform, obtain inspection instructions, and send the inspection instructions to the communication module through the network layer. The inspection path data generation module is used to transmit inspection instructions to the ACR central control module through the communication module, and to parse the inspection instructions through the ACR central control module to generate inspection path data. The inspection path data is then transmitted to the navigation module, which controls the navigation module to analyze the robot's motion posture and external environment based on the inspection path data to obtain the first robot navigation data. Finally, the first robot navigation data is output to the ACR central control module. The monitoring data generation module is used to control the external sensor module through the ACR central control module to collect the inspection environment and inspection equipment in the target inspection area, obtain the first monitoring data, and transmit the first robot navigation data and the first monitoring data to the network layer for data transmission processing according to the communication module to obtain the corresponding second robot navigation data and second monitoring data. The inspection report generation module is used to analyze the navigation data and monitoring data of the second robot through the robot inspection and monitoring platform to generate inspection reports, including: If the second robot navigation data is consistent with the preset robot navigation standard, then the first inspection report is generated based on the first monitoring data, and the first inspection report is output as the inspection report. If the second robot navigation data is inconsistent with the preset robot navigation standard, an abnormal alarm information containing the abnormal inspection area will be generated in the robot inspection monitoring platform. Based on the abnormal alarm information, a re-inspection instruction containing the name of the alarming robot will be generated. According to the re-inspection instruction, the corresponding robot's external sensor module will be controlled to collect the inspection environment and inspection equipment in the abnormal inspection area to obtain supplementary monitoring data. The supplementary monitoring data and the second monitoring data will be analyzed to obtain a second inspection report, which will be output as the inspection report.

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