Intelligent inspection method and system of inspection robot

Abstract

The present invention provides an intelligent inspection method and system for inspection robots, comprising: a server and a management end interact to construct a multi-layer twin space of a monitored area, and receive the inspection attributes and inspection positions configured by the management end for each twin module in the multi-layer twin space; the twin modules in the multi-layer twin space are determined based on the identity of the Internet of Things device, and the inspection positions of the twin modules are extracted; the server obtains the current position of each four-legged inspection robot to obtain the corresponding second-layer position information and second-plane position information, and determines the inspection path of the four-legged inspection robot based on the first-layer position information and the first-plane position information; if the twin module is judged to be a necessary inspection point, inspection data is extracted based on the inspection strategy pre-trained for the necessary inspection point; if the twin module is judged to be a dynamic inspection point, inspection data is extracted based on the historical inspection strategy or the newly generated strategy of the dynamic inspection point.

Description

Technical Field

[0001] The present invention relates to data processing technology, and in particular to an intelligent inspection method and system of an inspection robot. Background Art

[0002] With the continuous development of industrial automation and intelligent technology, inspection robots, as a new type of intelligent equipment, have been widely used in various industrial scenarios, such as the safety monitoring and maintenance of important facilities such as power systems, petrochemicals, airports and ports. Inspection robots replace manual inspections in an automated way, and can perform continuous and high-precision inspection tasks in complex and dangerous environments, thereby ensuring the safe operation of equipment and the environment, and reducing labor costs and safety risks. Traditional robots frequently fail, and there is room for improvement in implementation efficiency, attendance rate, failure rate, online rate, and recognition accuracy, and they cannot meet new demands such as off-road obstacle crossing, remote maintenance, and emergency inspections. It is urgent to use emerging technologies such as artificial intelligence and big data, combined with the requirements of autonomous and controllable power industry, to build an intelligent operation and inspection system, improve operation and inspection efficiency and quality, and promote the transformation of operation and maintenance models to digitalization and intelligence.

[0003] In the prior art, the control of the inspection robot still remains at the single-layer inspection control on the ground. For the inspection scene of complex multi-story buildings, human participation in control is still required, and the inspection efficiency is low.

[0004] Therefore, how to automatically control the inspection robot to perform efficient inspections on multi-layer inspection targets has become an urgent problem that needs to be solved. Summary of the invention

[0005] The embodiments of the present invention provide an intelligent inspection method and system for an inspection robot, which can automatically control the inspection robot to perform efficient inspections on multi-layer inspection targets.

[0006] A first aspect of an embodiment of the present invention provides an intelligent inspection method of an inspection robot, comprising:

[0007] S1, the server and the management end interact to build a multi-layer twin space of the monitored area, and receive the inspection attributes and inspection positions configured by the management end for each twin module in the multi-layer twin space. The inspection attributes include necessary inspection points and dynamic inspection points;

[0008] S2, after determining that any IoT device does not meet the preset requirements, the server determines the twin module in the multi-layer twin space based on the identity of the IoT device, and extracts the inspection position of the twin module, where the inspection position includes at least the first layer position information and the first plane position information;

[0009] S3, the server obtains the current position of each quadruped inspection robot to obtain the corresponding second-layer position information and second-plane position information, and determines the inspection path of the quadruped inspection robot based on the first-layer position information and the first-plane position information;

[0010] S4: If the twin module is judged to be a necessary inspection point, inspection data extraction is performed based on the inspection strategy pre-trained for the necessary inspection point; if the twin module is judged to be a dynamic inspection point, inspection data extraction is performed based on the historical inspection strategy or newly generated strategy of the dynamic inspection point.

[0011] Optionally, in a possible implementation manner of the first aspect, the S1 includes:

[0012] S11, the server and the management end interact to construct a twin site corresponding to the monitored area, and divide the twin site into multiple twin areas;

[0013] S12, interact with the management end to build a twin baseboard or multiple twin baseboards in each twin area, and interact with the management end to establish a corresponding twin module at each twin baseboard, each twin module having corresponding power equipment and IoT equipment;

[0014] S13, automatically generate the inspection position of each twin module based on the position of the twin area, the position of the twin baseboard and the position of the twin module, and receive the inspection attributes of each twin module from the management end.

[0015] Optionally, in a possible implementation manner of the first aspect, the S13 includes:

[0016] S131, labeling the twin regions in sequence according to the generation order of the twin regions, and counting the positions of the twin bottom plates in each twin region in sequence to generate corresponding bottom plate layer labels;

[0017] S132, acquiring all twin modules on each twin baseboard, and generating a module number of each twin module based on a planar position relationship between the twin module and the twin baseboard;

[0018] S133, generating the inspection position configured for each twin module based on the area number, bottom plate layer number and module number.

[0019] Optionally, in a possible implementation manner of the first aspect, the S132 includes:

[0020] Obtaining a top view of all twin modules combined with the twin base plate, coordinate processing the top view, and obtaining the center point of each twin module by positioning based on the edge contour points of all twin modules;

[0021] Mark the center points of all the twin modules in the upper view, determine the power and / or information flow directions corresponding to all the twin modules as the label directions, and divide the corresponding twin modules into equal parts based on the label directions to obtain multiple module segments;

[0022] Based on the positional relationship of the module segments, the module segment selection order is obtained according to the preset sorting strategy, the module segments are selected in sequence according to the module segment selection order, and the twin modules in the module segments are selected and labeled in sequence based on the labeling direction to obtain the module label of each twin module.

[0023] Optionally, in a possible implementation manner of the first aspect, the S2 includes:

[0024] S21, after determining that the monitoring information of any IoT device does not meet the preset requirements of the standard monitoring information, the server determines the twin modules in the multi-layer twin space based on the identity of the IoT device, and each IoT device identity has an associated twin module;

[0025] S22, extract the inspection position of the twin module, obtain the first layer position information based on the area label and the bottom plate layer label, and obtain the first plane position information based on the module label.

[0026] Optionally, in a possible implementation manner of the first aspect, S3 includes:

[0027] S31, the server performs primary positioning based on the positioning module of the quadruped inspection robot, and determines the twin area where each quadruped inspection robot is located according to the primary positioning;

[0028] S32, if the twin base plate corresponding to the corresponding twin area is layer 1, directly obtain the second layer position information;

[0029] S33, if the twin bottom plate corresponding to the corresponding twin area is multi-layered, then the layer information identified by each quadruped inspection robot at the previous closest moment is received to obtain the second layer position information, and each layer corresponding to the twin bottom plate has a preset layer QR code;

[0030] S34, based on the current position of the quadruped inspection robot, position superposition is performed on the corresponding twin bottom plate to obtain second plane position information of the quadruped inspection robot.

[0031] Optionally, in a possible implementation manner of the first aspect, the S34 includes:

[0032] S341, constructing a first overlapping layer with a preset length and a preset width with the current position of the quadruped inspection robot as the center point, wherein the preset length is twice the average length of all twin bottom plates, and the preset width is twice the average width of all twin bottom plates;

[0033] S342, obtaining the twin base plate corresponding to the layer information and constructing a second overlapping layer, overlapping the current position of the four-legged inspection robot in the first overlapping layer with the corresponding position in the second overlapping layer, to obtain an overlapping layer after overlapping;

[0034] S343, obtaining the positional relationship between the quadruped inspection robot and each twin module in the overlapping layers, and determining the position of the closest twin module to obtain the second plane position information of the quadruped inspection robot.

[0035] Optionally, in a possible implementation manner of the first aspect, S3 includes:

[0036] The server calculates the difference between the area labels in the first layer position information and the second layer position information to obtain an area difference value, and selects the four-legged inspection robot with the smallest area difference value as the determined four-legged inspection robot;

[0037] If there are multiple quadruped inspection robots with the smallest regional difference, the cross-layer difference of the quadruped inspection robot is obtained based on the bottom plate layer label in the first layer position information and the second layer position information, and the quadruped inspection robot with the smallest cross-layer difference is selected as the determined quadruped inspection robot;

[0038] If there are multiple quadruped inspection robots with the smallest cross-layer difference, the difference of the module numbers of the quadruped inspection robots is calculated to obtain the cross-module difference, and the quadruped inspection robot with the smallest regional cross-module difference is selected as the determined quadruped inspection robot;

[0039] If there are multiple quadruped inspection robots with the smallest cross-module difference, a quadruped inspection robot is randomly selected and the inspection path of the quadruped inspection robot is generated.

[0040] Optionally, in a possible implementation of the first aspect, if there are multiple quadruped inspection robots with the smallest cross-module difference, randomly selecting a quadruped inspection robot and generating an inspection path for the quadruped inspection robot comprises:

[0041] If the determined quadruped inspection robot and the twin module are located on the same twin baseboard, an inspection path is generated based on the module number of the twin module to be inspected and the module number corresponding to the quadruped inspection robot;

[0042] If the determined quadruped inspection robot and the twin module are in the same twin area and different twin base plates, the entrance position and exit position corresponding to each twin base plate are obtained;

[0043] A first subpath is obtained based on the module number corresponding to the quadruped inspection robot and the exit position corresponding to the twin bottom plate where the quadruped inspection robot is located, and a second subpath is obtained based on the entrance position of the twin bottom plate corresponding to the twin module to be inspected and the position of the twin module to be inspected;

[0044] The path between the twin base plate where the quadruped inspection robot is located and the twin base plate corresponding to the twin module to be inspected is determined to obtain a third sub-path, and an inspection path is generated based on the first sub-path, the second sub-path and the third sub-path.

[0045] Optionally, in a possible implementation of the first aspect, the method further includes:

[0046] If the determined quadruped inspection robot and the twin module are in different twin areas, the entrance position and exit position corresponding to each twin module are obtained;

[0047] The fourth subpath is obtained based on the module number corresponding to the quadruped inspection robot and the exit position corresponding to the twin bottom plate where the quadruped inspection robot is located, and the fifth subpath is obtained based on the entrance position of the twin bottom plate corresponding to the twin module to be inspected and the position of the twin module to be inspected;

[0048] Determine the path from the twin base plate where the quadruped inspection robot is located to the exit of the twin area to obtain the sixth sub-path, and determine the path from the twin base plate where the twin module to be inspected is located to the entrance of the twin area to obtain the seventh sub-path;

[0049] Determine the path between the twin area of ​​the quadruped inspection robot and the twin area where the twin module to be inspected is located to obtain the eighth sub-path, and generate the inspection path based on the fourth sub-path, the fifth sub-path, the sixth sub-path, and the seventh sub-path.

[0050] Optionally, in a possible implementation manner of the first aspect, the S4 includes:

[0051] S41, if the twin module is determined to be a necessary inspection point, the quadruped inspection robot is controlled to adjust the preset posture to extract inspection data based on the inspection strategy pre-trained for the necessary inspection point;

[0052] S42, if the twin module is determined to be a dynamic inspection point, the historical inspection strategy of the dynamic inspection point is obtained and the historical inspection strategy is extracted to control the quadruped inspection robot to adjust the preset posture to extract inspection data;

[0053] S43: If it is determined that the twin module is a dynamic inspection point and there is no historical inspection strategy, the relative position of the IoT device in the twin module is obtained, and the quadruped inspection robot is controlled to perform dynamic posture adjustment according to the relative position to extract inspection data.

[0054] A second aspect of an embodiment of the present invention provides an intelligent inspection system of an inspection robot, comprising:

[0055] A construction module is used to enable the server and the management end to interact and build a multi-layer twin space of the monitored area, and receive the inspection attributes and inspection positions configured by the management end for each twin module in the multi-layer twin space, wherein the inspection attributes include necessary inspection points and dynamic inspection points;

[0056] An extraction module is used to enable the server to determine the twin module in the multi-layer twin space based on the identity of the IoT device after determining that any IoT device does not meet the preset requirements, and extract the inspection position of the twin module, wherein the inspection position includes at least the first layer position information and the first plane position information;

[0057] An acquisition module, used to enable the server to acquire the current position of each quadruped inspection robot to obtain the corresponding second-layer position information and second-plane position information, and determine the inspection path of the quadruped inspection robot based on the first-layer position information and the first-plane position information;

[0058] The inspection module is used to extract inspection data based on the pre-trained inspection strategy of the necessary inspection point if the twin module is judged to be a necessary inspection point; if the twin module is judged to be a dynamic inspection point, the inspection data is extracted based on the historical inspection strategy or the newly generated strategy of the dynamic inspection point.

[0059] Technical effects:

[0060] The present invention constructs a multi-layer twin space of the monitored area through the interaction between the server and the management terminal, including detailed models of the twin site, twin area, twin base plate and twin module. Through the construction of this multi-layer twin space, the structure and function of the physical environment can be fully simulated, providing a realistic virtual environment for the inspection robot, and realizing accurate path planning and navigation. The server automatically generates the inspection position according to the position of the twin area, the position of the twin base plate and the position of the twin module, and uniquely identifies each module based on the labeling system. In this way, the inspection robot can flexibly respond to inspection tasks at different levels and positions, and significantly improve the optimization and execution efficiency of the inspection path.

[0061] The present invention proposes a flexible inspection attribute configuration method, including necessary inspection points and dynamic inspection points. Necessary inspection points are key points that are fixed and must be inspected, while dynamic inspection points are dynamically adjusted according to real-time conditions and historical data. After determining that any IoT device does not meet the preset requirements, the server can quickly locate the relevant twin module based on the device identity and extract its inspection position, including the first-layer position information and the first-plane position information. At the same time, the server obtains the current position of the inspection robot, calculates its second-layer position information and the second-plane position information, and determines the optimal inspection path based on this information. This dynamic adjustment mechanism enables the inspection robot to flexibly respond to emergencies and environmental changes, ensuring the real-time and effectiveness of the inspection work.

[0062] The present invention controls the inspection robot to extract and process data through pre-trained inspection strategies and real-time generated strategies. For necessary inspection points, the robot adjusts the posture according to the pre-trained strategy to inspect, ensuring the consistency and accuracy of the inspection; for dynamic inspection points, the robot dynamically adjusts based on historical strategies or real-time generated strategies to flexibly respond to changes and emergencies. In this process, the server can receive and process inspection data in real time, perform intelligent analysis and feedback, and improve the efficiency and quality of inspection work. In particular, when the twin module is judged to be a dynamic inspection point and there is no historical inspection strategy, by obtaining the relative position of the Internet of Things device in the twin module, the robot can dynamically adjust according to the real-time position information to ensure the accuracy and comprehensiveness of the inspection data. In summary, the present invention significantly improves the navigation accuracy, task execution efficiency and data processing capability of the inspection robot through the construction of multi-layer twin space, intelligent identification and adjustment of dynamic inspection points, and intelligent inspection strategies and data processing, and solves the problems of poor flexibility, low recognition accuracy and limited data processing capability in the prior art. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 It is a flow chart of an intelligent inspection method of an inspection robot provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0064] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0065] See also Figure 1, is a flow chart of an intelligent inspection method of an inspection robot provided by an embodiment of the present invention, the method comprising:

[0066] S1, the server and the management end interact to build a multi-layer twin space of the monitored area, and receive the inspection attributes and inspection positions configured by the management end for each twin module in the multi-layer twin space. The inspection attributes include necessary inspection points and dynamic inspection points.

[0067] This step involves building a digital twin space and configuring inspection attributes and inspection locations. In this step, the server interacts with the management end to build a virtual environment that simulates the structure and function of the physical space (the monitored area). This virtual environment is called a multi-layer twin space, which contains a series of "twin modules" corresponding to the physical environment.

[0068] The multi-layer twin space may involve multiple levels, from the overall layout of the building to the detailed model of specific equipment. This space is interactive and allows users (such as engineers or managers) to observe and operate objects in the corresponding physical world in the virtual environment. The management end can set specific inspection attributes for each twin module. These attributes determine the points that the inspection robot should focus on when performing tasks in the physical environment. Inspection attributes include necessary inspection points and dynamic inspection points. Necessary inspection points must be inspected and may represent key equipment or important locations, which are essential for safe operation. Dynamic inspection points may be adjusted according to changes in circumstances, such as the operating status of the equipment. The inspection location is a specific coordinate or area, a specific location that the robot needs to reach in the physical environment. Through these settings, the management end can accurately control the inspection route and tasks of the inspection robot to ensure that important areas are monitored, and at the same time adjust the inspection focus based on real-time data or other variables. Building such a multi-layer twin space can help realize real-time monitoring and intelligent management of the environment, provide accurate navigation and task execution instructions for the inspection robot, and enhance operation and maintenance efficiency and safety.

[0069] In some embodiments, the S1 includes:

[0070] S11, the server and the management terminal interact to construct a twin site corresponding to the monitored area, and divide the twin site into multiple twin areas.

[0071] The server interacts with the management end to first build a virtual twin site that matches the physical monitored area. This twin site is then divided into multiple smaller twin areas, each representing a specific part of the monitored site.

[0072] S12, interact with the management end to build a layer of twin baseboard or multiple layers of twin baseboard in each twin area, interact with the management end to establish a corresponding twin module at each twin baseboard, and each twin module has corresponding power equipment and Internet of Things equipment.

[0073] Inside each twin area, the management end interacts with the server to build one or more layers of twin baseboards, which represent the division of different floors in the physical space. On each twin baseboard, corresponding twin modules are established. These twin modules are virtual mappings of specific equipment or facilities, and each twin module contains corresponding power equipment and IoT devices. IoT devices include sensors, surveillance cameras, etc., which can provide real-time data and status updates.

[0074] S13, automatically generate the inspection position of each twin module based on the position of the twin area, the position of the twin baseboard and the position of the twin module, and receive the inspection attributes of each twin module from the management end.

[0075] Based on the specific locations of the twin area, twin baseboard, and twin modules, the system automatically generates inspection locations for each twin module, which are the specific points that the inspection robot needs to reach during physical operations. At the same time, the server receives input from the management end and sets inspection attributes for each twin module. The inspection attributes determine the monitoring focus of the inspection robot in the physical space, which can be necessary inspection points or dynamic inspection points. The completion of these steps provides the inspection robot with a detailed virtual map and task list to perform intelligent inspections in the actual environment. In this way, inspections can be more accurate and efficient, while achieving real-time monitoring and maintenance of physical equipment.

[0076] Wherein, the S13 includes:

[0077] S131, labeling the twin areas in sequence according to the generation order of the twin areas, and counting the positions of the twin bottom plates in each twin area in sequence to generate corresponding bottom plate layer labels.

[0078] First, each twin region is uniquely labeled in the order in which it is created. This can help distinguish different twin regions during the inspection process, and the distance between adjacent region labels is close. Then, the position of the twin baseboards in each twin region is counted, and the corresponding baseboard layer label is generated for each layer of baseboard. This means that if there are multiple layers of twin baseboards in a twin region, each layer will be assigned a unique identifier.

[0079] S132, obtaining all twin modules on each twin baseboard, and generating a module number for each twin module based on the planar position relationship between the twin module and the twin baseboard.

[0080] This solution obtains information about all twin modules on each twin baseboard. Twin modules represent specific equipment or facilities in the physical environment. A unique module label is generated for each twin module based on the planar position relationship between the twin module and the twin baseboard on which it is located. This involves considering the specific position of the module on the baseboard, such as coordinates or reference points. Through these two sub-steps, a labeling system is created to uniquely identify and locate the twin baseboard and twin modules in each twin area. These labels are crucial for subsequent inspection tasks because they allow inspection robots or operators to accurately find and monitor every element in the virtual environment. In this way, when inspection robots perform tasks in the physical environment, they can ensure that they follow the correct path and are able to inspect and maintain specific equipment.

[0081] Wherein, the S132 includes:

[0082] The top view after all twin modules are combined with the twin base plate is obtained, the top view is coordinate-processed, and the center point of each twin module is obtained based on the edge contour points of all twin modules. First, the top view after all twin modules are combined with the twin base plate is obtained. The top view is a perspective from the top, showing the layout of all twin modules on the twin base plate. Then, the top view is coordinate-processed, that is, a coordinate system is established in the top view so that the position of the twin module can be accurately described. The center point position of each twin module is determined by analyzing the edge contour points of all twin modules. The edge contour point refers to the point set of the boundary of the twin module, which can be obtained by edge detection algorithms such as OPCVER. Among them, the way to obtain the center point of each twin module can be to find the maximum horizontal coordinate, the minimum horizontal coordinate, the maximum vertical coordinate and the minimum vertical coordinate of the edge contour point, and use the maximum horizontal coordinate and the minimum horizontal coordinate to obtain a horizontal coordinate, and use the maximum vertical coordinate and the minimum vertical coordinate to obtain a vertical coordinate, and finally use the horizontal coordinate and the vertical coordinate to obtain the center point.

[0083] In the upper view, the center point of all twin modules is marked, the power and / or information flow direction corresponding to all twin modules is determined as the label direction, and the corresponding twin modules are equally divided based on the label direction to obtain multiple module segments. This solution will mark the center point of all twin modules in the upper view, which provides a positioning reference for subsequent steps. Determine the power and / or information flow direction corresponding to all twin modules and use it as the label direction. The label direction may be consistent with the connection direction of the cable or data line, indicating the flow path of energy or data. Based on the label direction, the corresponding twin modules are equally divided, and each twin module is cut into multiple module segments. This helps to perform more refined labeling and management within the module. For example, in one power flow direction, there are 9 twin modules, and the equal division can be divided into 3 segments according to demand, so one module segment has 3 twin modules.

[0084] Based on the positional relationship of the module segments, the module segment selection order is obtained according to the preset sorting strategy, the module segments are selected in sequence according to the module segment selection order, and the twin modules in the module segments are selected and labeled in sequence based on the label direction to obtain the module label of each twin module. This scheme selects each module segment in sequence according to the module segment selection order. Then, based on the label direction, the twin modules in the module segments are selected and labeled in sequence. For example, first select module segment 1, then select module segment 2, and then select module segment 3. Then the labels of the three twin modules in module segment 1 are 11, 12, and 13, respectively, the labels of the three twin modules in module segment 2 are 21, 22, and 23, respectively, and the labels of the three twin modules in module segment 3 are 31, 32, and 33, respectively. Each twin module obtains a module label, which can be a serial number or other form of unique identifier. Through the above steps, an orderly labeling system can be generated for the modules in the digital twin environment, which is helpful for subsequent monitoring, control and maintenance operations. This labeling system ensures the rapid location and identification of modules in the virtual environment, and may also provide the necessary navigation and reference information for automated inspections in the physical environment.

[0085] S133, generating the inspection position configured for each twin module based on the area number, bottom plate layer number and module number.

[0086] First, collect the three different levels of labels assigned to each twin module: area label, floor layer label and module label. Each label represents the location information of the twin module at different levels in the digital twin environment. The area label represents the large-scale area or partition where the twin module is located, for example, corresponding to a building. The floor layer label represents the position of the twin module in the floor hierarchy in a specific area, for example, corresponding to the floor of a building. The module label is generated according to step S132, indicating the specific location of the twin module in a specific floor layer. These three labels are merged to form a multi-level identification system for generating a unique inspection position for each twin module. This inspection position can be a composite identifier, with three labels arranged in a certain format, for example: area label-floor layer label-module label. The generated inspection position can be used for automated or manual inspections in a physical environment, so that inspectors or robots can quickly and accurately locate each twin module according to the label. According to this inspection location, the inspector or robot can navigate to the correct module, perform the inspection task, and associate the inspection results with the specific location of the twin module. In this way, step S133 ensures the close correspondence between the digital twin environment and the physical environment, thereby improving the efficiency and accuracy of inspection and maintenance. This multi-level labeling system helps to quickly identify the problem and facilitates data collection and analysis.

[0087] S2. After determining that any IoT device does not meet the preset requirements, the server determines the twin module in the multi-layer twin space based on the identity of the IoT device, and extracts the inspection position of the twin module, which includes at least the first layer position information and the first plane position information.

[0088] When the server detects that the monitoring information of an IoT device does not meet the preset requirements, it will use the device's identity information to determine the digital twin module associated with it and extract the inspection location of the twin module in the multi-layer twin space. This process ensures that the status of the physical device can be accurately reflected in its digital twin and that the problem can be quickly located for subsequent inspection and maintenance.

[0089] In some embodiments, the S2 includes:

[0090] S21, after the server determines that the monitoring information of any IoT device does not meet the preset requirements of the standard monitoring information, it determines the twin modules in the multi-layer twin space based on the identity of the IoT device, and each IoT device identity has an associated twin module.

[0091] This step starts when the server receives monitoring information from an IoT device and determines that the information does not meet the preset standard requirements. Each IoT device's identity information has a predefined mapping relationship with a specific twin module, and the server can use this mapping relationship to quickly locate the corresponding module in the digital twin space.

[0092] S22, extract the inspection position of the twin module, obtain the first layer position information based on the area label and the bottom plate layer label, and obtain the first plane position information based on the module label.

[0093] Once the twin module is identified, the server extracts the first-level location information based on the module's area label and floor layer label. The first-level location information represents the module's macroscopic location in the physical space, such as the floor it is located on. Next, the server uses the module's module label to get the first-level location information. The first-level location information provides the module's precise location within a specific floor, such as its specific location within a floor plan.

[0094] S3, the server obtains the current position of each quadruped inspection robot to obtain the corresponding second-layer position information and second-plane position information, and determines the inspection path of the quadruped inspection robot based on the first-layer position information and the first-plane position information.

[0095] This step is used to determine the position positioning and path planning process of the quadruped inspection robot. This process includes the server obtaining and processing the robot's position to ensure that they inspect according to the predetermined path.

[0096] In some embodiments, the S3 includes:

[0097] S31, the server performs primary positioning based on the positioning module of the quadruped inspection robot, and determines the twin area where each quadruped inspection robot is located according to the primary positioning.

[0098] The server uses the positioning module of the quadruped inspection robot to perform primary positioning, thereby determining the twin area that each robot is currently in. The twin area may refer to a building in the digital twin system that corresponds to the real world.

[0099] S32: If the twin base plate corresponding to the corresponding twin area is layer 1, the second layer position information is directly obtained.

[0100] If the twin baseboard corresponding to the twin area is a single-layer structure, the server can directly obtain the second-layer position information. It can be understood that the second-layer position information is the first layer.

[0101] S33, if the twin base plate corresponding to the corresponding twin area is multi-layer, then the layer information identified by each quadruped inspection robot at the previous closest moment is received to obtain the second layer position information, and the layer corresponding to each twin base plate has a preset layer QR code.

[0102] If the twin base corresponding to the twin area is a multi-layer structure, the server will determine the second layer position information based on the layer information recognized by the robot at the most recent moment. This is achieved by reading the preset layer QR code of each layer.

[0103] S34, based on the current position of the quadruped inspection robot, position superposition is performed on the corresponding twin bottom plate to obtain second plane position information of the quadruped inspection robot.

[0104] The server will superimpose the current position of the quadruped inspection robot with the position information on the corresponding twin base plate to obtain the second plane position information of the robot. Through the above steps, the server can obtain the accurate position information of each quadruped inspection robot in the digital twin system and determine their inspection routes based on this information. This process can increase the degree of automation of the inspection process and improve the operating efficiency and safety of the quadruped robot.

[0105] Wherein, the S34 includes:

[0106] S341, constructing a first overlapping layer with the current position of the quadruped inspection robot as the center point according to a preset length and a preset width, wherein the preset length is twice the average length of all twin base plates, and the preset width is twice the average width of all twin base plates.

[0107] The server uses the current position of the quadruped inspection robot as the center point to construct a layer of preset length and width. The size of this layer is calculated based on the average length and width of all twin bases, and the length and width of the layer are twice the corresponding size of the twin base. This is done to ensure that the layer covers a larger range of twin bases.

[0108] S342, obtaining the twin base plate corresponding to the layer information and constructing a second overlapping layer, overlapping the current position of the four-legged inspection robot in the first overlapping layer with the corresponding position in the second overlapping layer to obtain the overlapped overlapping layer.

[0109] The server obtains the information of the current layer of the quadruped inspection robot and the corresponding twin base plate, and constructs a second overlapping layer. Then, the server aligns and overlaps the current position of the quadruped inspection robot in the first overlapping layer with the corresponding position in the second overlapping layer, thereby obtaining an overlapped layer. The current position of the quadruped inspection robot can be longitude and latitude information.

[0110] S343, obtaining the positional relationship between the quadruped inspection robot and each twin module in the overlapping layers, and determining the position of the closest twin module to obtain the second plane position information of the quadruped inspection robot.

[0111] The server analyzes the overlapped layers to determine the specific positional relationship between the quadruped inspection robot and each twin module. The server will find the twin module closest to the robot's position and use it to determine the robot's second plane position information. This step can help the server accurately guide the robot to a specific inspection point or equipment. Through these detailed steps, the server can generate accurate position information for each quadruped inspection robot and its corresponding position in the digital twin system. Such information is the key to developing an effective inspection path and can also ensure that the robot can accurately reach the designated inspection point when performing a task. This process is crucial to improving inspection efficiency and accuracy.

[0112] In some embodiments, the S3 includes:

[0113] The server calculates the difference between the area labels in the first layer position information and the second layer position information to obtain the area difference, and selects the quadruped inspection robot with the smallest area difference as the determined quadruped inspection robot. The server of this solution first calculates the area label difference between the first layer position information and the second layer position information of each quadruped inspection robot. The area label difference reflects the relative distance between the current position of the quadruped inspection robot and the target position. Generally speaking, the larger the area label difference, the farther the distance. Then, the server selects the robot with the smallest area difference, because this means that the robot is already in the target area or closest to the target area, thereby reducing movement time and improving efficiency.

[0114] If there are multiple quadruped inspection robots with the smallest area difference, the cross-layer difference of the quadruped inspection robot is obtained based on the bottom layer label in the first layer position information and the second layer position information, and the quadruped inspection robot with the smallest cross-layer difference is selected as the determined quadruped inspection robot. If there are multiple quadruped inspection robots with the same minimum area difference, the server will calculate the cross-layer difference based on the bottom layer label in the first layer and the second layer position information. The bottom layer label may refer to information such as the floor where the robot is located. The server selects the robot with the smallest cross-layer difference as the final inspection robot because it is closer to the target layer.

[0115] If there are multiple quadruped inspection robots with the smallest cross-layer difference, the difference between the module labels of the quadruped inspection robots is calculated to obtain the cross-module difference, and the quadruped inspection robot with the smallest regional cross-module difference is selected as the determined quadruped inspection robot. If there are multiple quadruped inspection robots with the same minimum cross-layer difference, the server will calculate the difference between their module labels to obtain the cross-module difference. The server selects the robot with the smallest cross-module difference because it is closest to the target module and can start executing the task faster.

[0116] If there are multiple quadruped inspection robots with the smallest cross-module difference, a quadruped inspection robot is randomly selected and the inspection path of the quadruped inspection robot is generated. If after all the above comparisons, there are still multiple quadruped inspection robots with the same difference, the server will randomly select a robot and generate an inspection path for it. This process ensures that the most suitable robot for the current task can be selected from multiple available quadruped inspection robots. By minimizing the regional difference, cross-layer difference, and cross-module difference, the server can optimize inspection efficiency and reduce time consumption. If multiple candidates appear in this process, the server will determine the most suitable robot through further comparison or final random selection. This method is particularly useful in multi-robot systems, which can ensure that each inspection task is performed by the robot closest to the target, thereby improving the efficiency and effectiveness of the entire inspection system.

[0117] Wherein, if there are multiple quadruped inspection robots with the smallest cross-module difference, a quadruped inspection robot is randomly selected and an inspection path of the quadruped inspection robot is generated, including:

[0118] If the determined quadruped inspection robot and the twin module are located on the same twin baseboard, the inspection path is generated based on the module number of the twin module to be inspected and the module number corresponding to the quadruped inspection robot. If the selected quadruped inspection robot and the twin module to be inspected are located on the same twin baseboard, the inspection path will be generated directly based on the module number of the twin module and the module number corresponding to the quadruped inspection robot. In this case, path planning is relatively simple because the robot and the target module are located on the same plane or floor.

[0119] If the determined quadruped inspection robot and the twin module are in the same twin area but different twin baseplates, the entry and exit positions corresponding to each twin baseplate are obtained. If the determined quadruped inspection robot and the twin module are in the same twin area but different twin baseplates, additional steps are required to plan the path from the current baseplate to the target baseplate. The server first needs to obtain the entry and exit positions corresponding to each twin baseplate.

[0120] The first sub-path is obtained based on the module number corresponding to the quadruped inspection robot and the exit position corresponding to the twin baseboard where the quadruped inspection robot is located, and the second sub-path is obtained based on the entrance position of the twin baseboard of the twin module to be inspected and the position of the twin module to be inspected. The server will determine the first sub-path (the path from the current position of the robot to the exit of the baseboard) based on the current module number of the quadruped inspection robot and the exit position corresponding to the twin baseboard where it is located. Then, the server will determine the second sub-path (the path from the baseboard entrance to the target module) based on the entrance position of the twin baseboard corresponding to the twin module to be inspected and the specific position of the twin module.

[0121] The path between the twin base plate where the quadruped inspection robot is located and the twin base plate corresponding to the twin module to be inspected is determined to obtain a third sub-path, and an inspection path is generated based on the first sub-path, the second sub-path and the third sub-path.

[0122] Finally, the server needs to determine the third sub-path (the path from the current base plate to the target base plate) between the twin base plate where the quadruped inspection robot is located and the twin base plate corresponding to the twin module to be inspected. The server will generate a complete inspection path based on the first sub-path, the second sub-path, and the third sub-path. This inspection path planning method takes into account the spatial relationship between the quadruped inspection robot and the target twin module, and proposes different path planning strategies for different spatial position relationships. It ensures that the inspection robot can efficiently reach the target module according to the optimal path to perform the inspection task, regardless of whether they are located on the same base plate or between different base plates. In this way, the execution efficiency and effect of the inspection task can be guaranteed.

[0123] Based on the above embodiment, it also includes:

[0124] If the determined quadruped inspection robot and the twin module are in different twin areas, the entry and exit positions corresponding to each twin module are obtained. The server needs to obtain the entry and exit positions corresponding to each twin module to facilitate planning of how the robot enters and leaves different twin bases.

[0125] The fourth sub-path is obtained based on the module number corresponding to the quadruped inspection robot and the exit position corresponding to the twin base plate where the quadruped inspection robot is located, and the fifth sub-path is obtained based on the entrance position of the twin base plate corresponding to the twin module to be inspected and the position of the twin module to be inspected. Generate the fourth sub-path from the current position of the quadruped inspection robot to the exit of its twin base plate. This fourth sub-path is based on the current module number of the quadruped inspection robot and the exit position corresponding to the twin base plate where it is located. At the same time, generate the fifth sub-path from the entrance of the twin base plate of the twin module to be inspected to the target module position. This fifth sub-path is based on the entrance position of the twin base plate corresponding to the twin module to be inspected and the specific position of the twin module.

[0126] Determine the path from the twin base plate where the quadruped inspection robot is located to the exit of the twin area to obtain the sixth sub-path, and determine the path from the twin base plate where the twin module to be inspected is located to the entrance of the twin area to obtain the seventh sub-path. Generate the sixth sub-path from the twin base plate where the robot is located to the exit of its twin area. This sub-path plans how the robot reaches the exit of its twin area from the current base plate. Generate the seventh sub-path from the twin base plate of the twin module to be inspected to the entrance of its twin area. This sub-path plans the path from the base plate where the target module is located to the entrance of its twin area.

[0127] Determine the path between the twin area of ​​the quadruped inspection robot and the twin area where the twin module to be inspected is located to obtain the eighth sub-path, and generate the inspection path based on the fourth sub-path, the fifth sub-path, the sixth sub-path, and the seventh sub-path. Determine the eighth sub-path between the two twin areas. This sub-path connects the twin area where the robot is currently located and the twin area where the target twin module is located, which is a key part of cross-regional movement. The server will generate a complete inspection path based on the fourth sub-path, the fifth sub-path, the sixth sub-path, the seventh sub-path, and the eighth sub-path when necessary. This cross-regional path planning needs to consider more nodes and transition points to ensure that the quadruped inspection robot can efficiently and safely move from one area to another and complete the inspection task. In this way, it can be ensured that the inspection robot can also effectively perform inspection tasks in a twin system over a larger range.

[0128] S4: If the twin module is judged to be a necessary inspection point, inspection data extraction is performed based on the inspection strategy pre-trained for the necessary inspection point; if the twin module is judged to be a dynamic inspection point, inspection data extraction is performed based on the historical inspection strategy or newly generated strategy of the dynamic inspection point.

[0129] This step is further refined into the inspection of specific twin modules in combination with the inspection task of the quadruped inspection robot, where the twin module can be a necessary inspection point or a dynamic inspection point. Different inspection strategies are adopted according to the type of module.

[0130] In some embodiments, the S4 includes:

[0131] S41, if the twin module is judged to be a necessary inspection point, the quadruped inspection robot is controlled to adjust the preset posture to extract inspection data based on the inspection strategy pre-trained for the necessary inspection point.

[0132] For necessary inspection points, the robot uses pre-trained inspection strategies. These strategies are usually preset for key equipment or locations that need to be inspected regularly. The robot adjusts its posture according to these strategies to ensure that data can be collected from the correct angle and position.

[0133] S42, if the twin module is determined to be a dynamic inspection point, the historical inspection strategy of the dynamic inspection point is obtained and the historical inspection strategy is extracted to control the quadruped inspection robot to adjust the preset posture to extract inspection data.

[0134] For dynamic inspection points, the robot extracts data based on historical inspection strategies or newly generated strategies. Dynamic inspection points may be inspection locations determined based on real-time data or specific events, which are more flexible than fixed necessary inspection points.

[0135] S43: If it is determined that the twin module is a dynamic inspection point and there is no historical inspection strategy, the relative position of the IoT device in the twin module is obtained, and the quadruped inspection robot is controlled to perform dynamic posture adjustment according to the relative position to extract inspection data.

[0136] If the twin module is a dynamic inspection point but there is no historical inspection strategy, the robot will obtain the relative position of the IoT device at that point on the twin module. The robot then makes dynamic posture adjustments based on this position information to more efficiently collect the required inspection data. In this way, inspection tasks can be performed more accurately and effectively based on the characteristics and requirements of each twin module. The pre-trained strategy for necessary inspection points ensures the consistency and accuracy of inspections, while the flexible strategy for dynamic inspection points allows the robot to adapt to changes and unexpected situations, improving the adaptability and efficiency of the entire inspection process.

[0137] An embodiment of the present invention provides an intelligent inspection system of an inspection robot, the system comprising:

[0138] A construction module is used to enable the server and the management end to interact and build a multi-layer twin space of the monitored area, and receive the inspection attributes and inspection positions configured by the management end for each twin module in the multi-layer twin space, wherein the inspection attributes include necessary inspection points and dynamic inspection points;

[0139] An extraction module is used to enable the server to determine the twin module in the multi-layer twin space based on the identity of the IoT device after determining that any IoT device does not meet the preset requirements, and extract the inspection position of the twin module, wherein the inspection position includes at least the first layer position information and the first plane position information;

[0140] An acquisition module, used to enable the server to acquire the current position of each quadruped inspection robot to obtain the corresponding second-layer position information and second-plane position information, and determine the inspection path of the quadruped inspection robot based on the first-layer position information and the first-plane position information;

[0141] The inspection module is used to extract inspection data based on the pre-trained inspection strategy of the necessary inspection point if the twin module is judged to be a necessary inspection point; if the twin module is judged to be a dynamic inspection point, the inspection data is extracted based on the historical inspection strategy or the newly generated strategy of the dynamic inspection point.

[0142] The present invention also provides a storage medium, in which a computer program is stored. When the computer program is executed by a processor, it is used to implement the methods provided by the various embodiments described above.

[0143] Among them, the storage medium can be a computer storage medium or a communication medium. The communication medium includes any medium that facilitates the transmission of a computer program from one place to another. The computer storage medium can be any available medium that can be accessed by a general-purpose or special-purpose computer. For example, the storage medium is coupled to the processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can be located in an application-specific integrated circuit (Application Specific Integrated Circuits, referred to as: ASIC). In addition, the ASIC can be located in a user device. Of course, the processor and the storage medium can also exist in a communication device as discrete components. The storage medium can be a read-only memory (ROM), a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, and an optical data storage device, etc.

[0144] The present invention also provides a program product, which includes an execution instruction, which is stored in a storage medium. At least one processor of a device can read the execution instruction from the storage medium, and at least one processor executes the execution instruction so that the device implements the methods provided in the above various embodiments.

[0145] In the above-mentioned terminal or server embodiments, it should be understood that the processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), etc. A general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in the present invention may be directly implemented as being executed by a hardware processor, or may be executed by a combination of hardware and software modules in the processor.

[0146] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. An intelligent inspection method using an inspection robot, characterized in that: include: S1, the server and the management end interact to build a multi-layer twin space of the monitored area, and receive the inspection attributes and inspection positions configured by the management end for each twin module in the multi-layer twin space. The inspection attributes include necessary inspection points and dynamic inspection points; S2, after determining that any IoT device does not meet the preset requirements, the server determines the twin module in the multi-layer twin space based on the identity of the IoT device, and extracts the inspection position of the twin module, where the inspection position includes at least the first layer position information and the first plane position information; S3, the server obtains the current position of each quadruped inspection robot to obtain the corresponding second-layer position information and second-plane position information, and determines the inspection path of the quadruped inspection robot based on the first-layer position information and the first-plane position information; S4: If the twin module is judged to be a necessary inspection point, the inspection data is extracted based on the inspection strategy pre-trained for the necessary inspection point; if the twin module is judged to be a dynamic inspection point, the inspection data is extracted based on the historical inspection strategy or the newly generated strategy of the dynamic inspection point; The S1 includes: S11, the server and the management end interact to construct a twin site corresponding to the monitored area, and divide the twin site into multiple twin areas; S12, interact with the management end to build a twin baseboard or multiple twin baseboards in each twin area, and interact with the management end to establish a corresponding twin module at each twin baseboard, each twin module having corresponding power equipment and IoT equipment; S13, automatically generating the inspection position of each twin module based on the position of the twin area, the position of the twin baseboard and the position of the twin module, and receiving the inspection attributes of each twin module from the management end; The S13 includes: S131, labeling the twin regions in sequence according to the generation order of the twin regions, and counting the positions of the twin bottom plates in each twin region in sequence to generate corresponding bottom plate layer labels; S132, acquiring all twin modules on each twin baseboard, and generating a module number of each twin module based on a planar position relationship between the twin module and the twin baseboard; S133, generating an inspection position configured for each twin module based on the area number, the bottom plate layer number and the module number; The S132 includes: Obtaining a top view of all twin modules combined with the twin base plate, coordinate processing the top view, and obtaining the center point of each twin module by positioning based on the edge contour points of all twin modules; Mark the center points of all the twin modules in the upper view, determine the power and / or information flow directions corresponding to all the twin modules as the label directions, and divide the corresponding twin modules into equal parts based on the label directions to obtain multiple module segments; Based on the positional relationship of the module segments, the module segment selection order is obtained according to the preset sorting strategy, the module segments are selected in sequence according to the module segment selection order, and the twin modules in the module segments are selected and labeled in sequence based on the labeling direction to obtain the module label of each twin module.

2. The intelligent inspection method of the inspection robot according to claim 1, characterized in that: The S2 includes: S21, after determining that the monitoring information of any IoT device does not meet the preset requirements of the standard monitoring information, the server determines the twin modules in the multi-layer twin space based on the identity of the IoT device, and each IoT device identity has an associated twin module; S22, extract the inspection position of the twin module, obtain the first layer position information based on the area label and the bottom plate layer label, and obtain the first plane position information based on the module label.

3. The intelligent inspection method of the inspection robot according to claim 1, characterized in that: The S3 includes: S31, the server performs primary positioning based on the positioning module of the quadruped inspection robot, and determines the twin area where each quadruped inspection robot is located according to the primary positioning; S32, if the twin base plate corresponding to the corresponding twin area is layer 1, directly obtain the second layer position information; S33, if the twin bottom plate corresponding to the corresponding twin area is multi-layered, then the layer information identified by each quadruped inspection robot at the previous closest moment is received to obtain the second layer position information, and each layer corresponding to the twin bottom plate has a preset layer QR code; S34, based on the current position of the quadruped inspection robot, position superposition is performed on the corresponding twin bottom plate to obtain second plane position information of the quadruped inspection robot.

4. The intelligent inspection method of the inspection robot according to claim 3, characterized in that: The S34 includes: S341, constructing a first overlapping layer with a preset length and a preset width with the current position of the quadruped inspection robot as the center point, wherein the preset length is twice the average length of all twin bottom plates, and the preset width is twice the average width of all twin bottom plates; S342, obtaining the twin base plate corresponding to the layer information and constructing a second overlapping layer, overlapping the current position of the four-legged inspection robot in the first overlapping layer with the corresponding position in the second overlapping layer, to obtain an overlapping layer after overlapping; S343, obtaining the positional relationship between the quadruped inspection robot and each twin module in the overlapping layers, and determining the position of the closest twin module to obtain the second plane position information of the quadruped inspection robot.

5. The intelligent inspection method of the inspection robot according to claim 3, characterized in that: The S3 includes: The server calculates the difference between the area labels in the first layer position information and the second layer position information to obtain an area difference value, and selects the four-legged inspection robot with the smallest area difference value as the determined four-legged inspection robot; If there are multiple quadruped inspection robots with the smallest regional difference, the cross-layer difference of the quadruped inspection robot is obtained based on the bottom plate layer label in the first layer position information and the second layer position information, and the quadruped inspection robot with the smallest cross-layer difference is selected as the determined quadruped inspection robot; If there are multiple quadruped inspection robots with the smallest cross-layer difference, the difference of the module numbers of the quadruped inspection robots is calculated to obtain the cross-module difference, and the quadruped inspection robot with the smallest regional cross-module difference is selected as the determined quadruped inspection robot; If there are multiple quadruped inspection robots with the smallest cross-module difference, a quadruped inspection robot is randomly selected and an inspection path for the quadruped inspection robot is generated.

6. The intelligent inspection method of the inspection robot according to claim 5, characterized in that: If there are multiple quadruped inspection robots with the smallest cross-module difference, a quadruped inspection robot is randomly selected and an inspection path of the quadruped inspection robot is generated, including: If the determined quadruped inspection robot and the twin module are located on the same twin baseboard, an inspection path is generated based on the module number of the twin module to be inspected and the module number corresponding to the quadruped inspection robot; If the determined quadruped inspection robot and the twin module are in the same twin area and different twin base plates, the entrance position and exit position corresponding to each twin base plate are obtained; A first subpath is obtained based on the module number corresponding to the quadruped inspection robot and the exit position corresponding to the twin bottom plate where the quadruped inspection robot is located, and a second subpath is obtained based on the entrance position of the twin bottom plate corresponding to the twin module to be inspected and the position of the twin module to be inspected; The path between the twin base plate where the quadruped inspection robot is located and the twin base plate corresponding to the twin module to be inspected is determined to obtain a third sub-path, and an inspection path is generated based on the first sub-path, the second sub-path and the third sub-path.

7. The intelligent inspection method of the inspection robot according to claim 5, characterized in that: Also includes: If the determined quadruped inspection robot and the twin module are in different twin areas, the entrance position and exit position corresponding to each twin module are obtained; The fourth subpath is obtained based on the module number corresponding to the quadruped inspection robot and the exit position corresponding to the twin bottom plate where the quadruped inspection robot is located, and the fifth subpath is obtained based on the entrance position of the twin bottom plate corresponding to the twin module to be inspected and the position of the twin module to be inspected; Determine the path from the twin base plate where the quadruped inspection robot is located to the exit of the twin area to obtain the sixth sub-path, and determine the path from the twin base plate where the twin module to be inspected is located to the entrance of the twin area to obtain the seventh sub-path; Determine the path between the twin area of ​​the quadruped inspection robot and the twin area where the twin module to be inspected is located to obtain the eighth sub-path, and generate an inspection path based on the fourth sub-path, the fifth sub-path, the sixth sub-path, the seventh sub-path and the eighth sub-path.

8. The intelligent inspection method of the inspection robot according to claim 1, characterized in that: The S4 includes: S41, if the twin module is determined to be a necessary inspection point, the quadruped inspection robot is controlled to adjust the preset posture to extract inspection data based on the inspection strategy pre-trained for the necessary inspection point; S42, if the twin module is determined to be a dynamic inspection point, the historical inspection strategy of the dynamic inspection point is obtained and the historical inspection strategy is extracted to control the quadruped inspection robot to adjust the preset posture to extract inspection data; S43: If it is determined that the twin module is a dynamic inspection point and there is no historical inspection strategy, the relative position of the IoT device in the twin module is obtained, and the quadruped inspection robot is controlled to perform dynamic posture adjustment according to the relative position to extract inspection data.

9. An intelligent inspection system for an inspection robot based on the intelligent inspection method for an inspection robot according to any one of claims 1 to 8, characterized in that: include: A construction module is used to enable the server and the management end to interact and build a multi-layer twin space of the monitored area, and receive the inspection attributes and inspection positions configured by the management end for each twin module in the multi-layer twin space, wherein the inspection attributes include necessary inspection points and dynamic inspection points; An extraction module is used to enable the server to determine the twin module in the multi-layer twin space based on the identity of the IoT device after determining that any IoT device does not meet the preset requirements, and extract the inspection position of the twin module, wherein the inspection position includes at least the first layer position information and the first plane position information; An acquisition module, used to enable the server to acquire the current position of each quadruped inspection robot to obtain the corresponding second-layer position information and second-plane position information, and determine the inspection path of the quadruped inspection robot based on the first-layer position information and the first-plane position information; The inspection module is used to extract inspection data based on the pre-trained inspection strategy of the necessary inspection point if the twin module is judged to be a necessary inspection point; if the twin module is judged to be a dynamic inspection point, the inspection data is extracted based on the historical inspection strategy or the newly generated strategy of the dynamic inspection point.

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