Equipment tree generation method and device, computing equipment and storage medium

Abstract

The invention discloses an equipment tree generation method and device, computing equipment and a storage medium, and belongs to the technical field of artificial intelligence. In the method, positions and categories of multiple devices in a site and positions of multiple sub-regions in the site are determined based on a 3D point cloud map of the site, then the sub-regions where the multiple devices are located in the site are determined, and device information of the multiple devices is sorted according to the sub-regions and categories of the multiple devices. And generating a device tree corresponding to the plurality of devices. According to the method, the equipment in the field can be automatically detected, the sub-region where the equipment is located can be automatically determined, then the standardized equipment tree is automatically generated according to the sequence of the sub-regions and the categories, the generation efficiency of the equipment tree is improved, the equipment tree can be updated in time when the layout of the equipment is changed, and the user experience is improved. And the equipment information does not need to be manually rearranged to update the equipment tree, so that the labor cost can be saved.

Description

[0001] This application claims priority to Chinese patent application No. 202310904089.3, filed on July 21, 2023, and entitled “A device tree generation method, device and system”, the entire contents of which are incorporated by reference into this application. Technical Field

[0002] The present application relates to the field of artificial intelligence technology, and in particular to a device tree generation method, apparatus, computing device and storage medium. Background Art

[0003] Before using the equipment that performs the inspection task to replace manual inspection of the equipment in the site, the equipment information needs to be entered into the equipment that performs the inspection task to deploy the inspection of the equipment. Among them, since there are many devices in the site, and the equipment information of each device includes the inspection point and category of the equipment, if the equipment information is not stored in an orderly format, it will lead to unreasonable inspection routes, and then cause the equipment that performs the inspection task to walk repeatedly. Therefore, it is usually necessary to record the equipment information in a tree structure in the order of location-category-task type-number, that is, to generate a device tree.

[0004] In the related art, operators manually generate a device tree. However, in this method, the generation efficiency of the device tree is low, and when the layout of the equipment changes due to re-construction, the generated device tree cannot be updated in time and needs to be manually re-checked and updated, and the update efficiency of the device tree is low. Summary of the invention

[0005] The embodiment of the present application provides a device tree generation method, apparatus, computing device and storage medium, which can improve the generation efficiency of the device tree, and when the device layout in the site changes, the device tree can also be updated in time without manually reorganizing the device information to update the device tree, which can save labor costs. The technical solution is as follows.

[0006] In a first aspect, a method for generating a device tree is provided, wherein the device tree includes a plurality of devices in a site, the method comprising:

[0007] Based on a three-dimensional (3D) point cloud map of the site, locations and categories of multiple devices in the site and locations of multiple sub-areas in the site are determined, and then the sub-areas where the multiple devices are located in the site are determined. According to the order of the sub-areas and categories of the multiple devices, device information of the multiple devices is sorted to generate a device tree corresponding to the multiple devices.

[0008] The device information includes the categories and inspection point information corresponding to multiple devices. The arrangement order of each device in the device tree indicates the detection order of each device. The generated device tree can be entered into the device that performs the inspection task, and then the device that performs the inspection task can detect each device in different sub-areas in turn based on the detection order indicated by the arrangement order of each device in the device tree. In addition, the device that performs the inspection task can also inspect only the target sub-area in the site based on the device tree, wherein the device that performs the inspection task can reach each inspection point in the target sub-area in turn based on the navigation map of the site and the target sub-area of ​​the device that performs the inspection task, according to the detection order of each device in the target sub-area in the entered device tree, and then detect each device in the sub-area.

[0009] In the above method, the equipment in the site can be automatically detected and the sub-area in which it is located can be automatically determined, and then a standardized device tree can be automatically generated in the order of sub-areas and categories, thereby improving the efficiency of device tree generation. When the equipment layout changes, the device tree can also be updated in time without manually reorganizing the equipment information to update the device tree, which can save labor costs.

[0010] Optionally, determining locations of multiple sub-areas of the site based on a 3D point cloud map of the site includes:

[0011] A two-dimensional (2D) grid map (GM) corresponding to the 3D point cloud map is generated, a site layout map of the site is obtained, the site layout map indicates multiple sub-areas in the site, and a grid area corresponding to each sub-area in the site layout map is determined on the 2D grid map.

[0012] The grid area includes at least one grid, and the grid area indicates that the devices are distributed relatively concentratedly within the range of the grid area.

[0013] Optionally, based on the locations of the multiple sub-areas and the locations of the multiple devices, determining the sub-areas in which the multiple devices are located in the venue includes:

[0014] Based on the grid areas where the positions of the multiple devices are located, sub-areas where the multiple devices are located in the venue are determined.

[0015] Optionally, determining a grid area corresponding to each sub-area on the 2D grid map includes:

[0016] identifying a plurality of grid regions on the 2D grid map;

[0017] A number of sub-areas are identified in the site layout;

[0018] The multiple grid regions are matched one by one with the multiple sub-regions to obtain a grid region corresponding to each of the sub-regions.

[0019] Among them, the distribution characteristics of multiple sub-areas in the site layout map (relative position relationship with other sub-areas, etc.) are used as templates, multiple grid areas and their distribution characteristics on the 2D grid map are determined on the 2D grid map, and sub-areas and grid areas with the same or similar distribution characteristics are matched one by one.

[0020] Optionally, matching the multiple grid regions with the multiple sub-regions one by one to obtain a grid region corresponding to each of the sub-regions includes:

[0021] Divide multiple grid areas into at least one group according to a first preset rule, and sort different grid areas in the same group of grid areas according to a second preset rule; divide multiple sub-areas into at least one group according to the first preset rule, and sort different sub-areas in the same group of sub-areas according to the second preset rule; match the sub-areas and grid areas with the same corresponding group number and group sequence number to obtain the grid area corresponding to each sub-area.

[0022] Among them, the first preset rule can be to divide the areas with the same ordinate into a group, and the corresponding second preset rule is to sort the areas in the same group in the order of the horizontal coordinate from small to large. Among them, for example: according to the center point coordinates (Xc, Yc) of each sub-area, the sub-areas with the same Yc are divided into the same group, and the sub-areas of each group are sorted according to the size of Yc, and the sub-areas in each group of sub-areas are sorted according to the size of Xc, so as to obtain the group number and the sequence number within the group of each sub-area; similarly, the group number and the sequence number within the group of each grid area are determined; the corresponding sub-areas with the same group number and the same sequence number are matched with the grid area to obtain the grid area corresponding to each sub-area. The first preset rule can also be to divide the areas with the same shape into a group, and the corresponding second preset rule is to sort the areas in the same group in the order of the ordinate first and the horizontal coordinate later, etc., which is not limited in the embodiment of the present application.

[0023] Optionally, the method further comprises:

[0024] Digital twin modeling is performed on the site to obtain a 3D point cloud map of the site.

[0025] In a second aspect, a device tree generation apparatus is provided, which is used to execute the device tree generation method. Specifically, the device tree generation apparatus includes a functional module for executing the device tree generation method provided by the first aspect or any optional manner of the first aspect.

[0026] According to a third aspect, a computing device is provided. The computing device includes a processor, wherein the processor is configured to execute program code so that the computing device performs operations performed by the device tree generation method described above.

[0027] In a fourth aspect, a computing device cluster is provided, the computing device machine comprising at least one computing device, each computing device comprising a processor and a memory, the processor of at least one computing device being used to execute program code stored in the memory of at least one computing device, so that the computing device cluster executes to implement operations performed by the above-mentioned device tree generation method.

[0028] In a fifth aspect, a computer-readable storage medium is provided, wherein at least one program code is stored in the storage medium, and the program code is read by a processor to enable a computing device to perform operations performed by the above-mentioned device tree generation method.

[0029] In a sixth aspect, a computer-readable storage medium is provided, wherein at least one program code is stored in the storage medium, and the program code is read by a processor of a computing device machine to enable a computing device cluster to perform operations performed by the above-mentioned device tree generation method.

[0030] In a seventh aspect, a computer program product or a computer program is provided, the computer program product or the computer program including a program code, the program code being stored in a computer-readable storage medium, the processor of a computing device reading the program code from the computer-readable storage medium, the processor executing the program code, so that the computing device executes the device tree generation method provided in the above-mentioned first aspect or various optional implementations of the first aspect.

[0031] In an eighth aspect, a computer program product or a computer program is provided, which includes a program code, and the program code is stored in a computer-readable storage medium. A processor of a computing device in a computing device cluster reads the program code from the computer-readable storage medium, and the processor executes the program code, so that the computing device cluster executes the device tree generation method provided in the above-mentioned one aspect or various optional implementations of one aspect.

[0032] Based on the implementations provided in the above aspects, this application can also be further combined to provide more implementations. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 It is a schematic diagram of an implementation environment of a device tree generation method provided in an embodiment of the present application;

[0034] Figure 2 is a flow chart of a device tree generation method provided in an embodiment of the present application;

[0035] Figure 3 It is a site layout diagram of a site provided in an embodiment of the present application;

[0036] Figure 4 is a schematic diagram of a 2D grid map provided in an embodiment of the present application;

[0037] Figure 5 A schematic diagram of a recognition result of a grid area in a 2D grid map provided in an embodiment of the present application;

[0038] Figure 6 It is a schematic diagram of a device tree of inspection points provided in an embodiment of the present application;

[0039] Figure 7 It is a flowchart of a method for generating a device tree provided in an embodiment of the present application;

[0040] Figure 8 It is a structural schematic diagram of a patrol point planning device provided in an embodiment of the present application;

[0041] Fig. 9 is a schematic diagram of the structure of a computing device provided in an embodiment of the present application;

[0042] Fig.10 is a schematic diagram of a computing device cluster provided in an embodiment of the present application;

[0043] Fig.11 It is a schematic diagram of a possible implementation method of a computing device cluster provided in an embodiment of the present application. DETAILED DESCRIPTION

[0044] In order to make the objectives, technical solutions and advantages of the present application clearer, the implementation methods of the present application will be further described in detail below with reference to the accompanying drawings.

[0045] In order to facilitate understanding of the technical content of this application, several technical terms involved in this application are explained below.

[0046] Robot inspection: Inspection refers to the inspection and registration of the environment and equipment of the inspection site to promptly discover faults and hidden dangers. Inspection can realize standardized, standardized and process-based error detection, timely discover and solve problems to prevent them from happening. Robot inspection refers to the use of robots to perform inspection tasks to improve inspection efficiency, reduce inspection costs, and reduce the occurrence of casualties. Robot inspection can be applied to many fields, such as power inspection, oil pipeline inspection, highway inspection, fire scene inspection, etc.

[0047] Inspection point planning: Inspection point information includes the location of the inspection point, the direction of the inspection point, the position of the camera carried by the equipment performing the inspection, the camera magnification, and the camera focal length. During the deployment phase, it is necessary to determine the location and direction of the inspection points of the objects to be inspected in the inspection site, that is, to plan the inspection points to guide the execution of the inspection tasks.

[0048] Digital twin technology: Digital twin is an enabling technology that supports the digital transformation of the economy and society. It comprehensively uses information technologies such as perception, computing, and modeling to describe, diagnose, predict, and make decisions on physical space through software definition, thereby realizing the interactive mapping of physical space and cyberspace.

[0049] Equipment tree: Since there are many devices in the site, and each device includes multiple attributes such as the inspection point and category of the device, if the equipment information is not stored in an orderly format, it will lead to unreasonable inspection routes, which will cause the equipment performing the inspection task to walk repeatedly. Therefore, it is usually necessary to record the equipment information in a tree structure in the order of location-category-task type-number, that is, to generate a device tree.

[0050] The implementation environment of the embodiments of the present application is introduced below.

[0051] Figure 1 is a schematic diagram of an implementation environment of a device tree generation method provided in an embodiment of the present application, such as Figure 1 As shown, the implementation environment includes a computing device 101 and a device for performing inspection tasks 102. The computing device 101 and the device for performing inspection tasks 102 are connected to each other in a communication manner via a wired network or a wireless network.

[0052] Among them, the computing device 101 can be at least one of a desktop computer, a laptop, a wireless terminal and a laptop portable computer. The computing device 101 has a communication function and can access the Internet. The computing device 101 can generally refer to one of multiple computing devices. The embodiment of the present application only uses the computing device 101 as an example. Those skilled in the art can know that the number of the above computing devices can be more or less. The computing device 101 can also be an independent physical server, or a server cluster composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, distribution networks (content delivery networks, CDN), and big data and artificial intelligence platforms. The embodiment of the present application does not limit this. Exemplarily, the computing device 101 provides a device tree generation function, which can determine the locations and categories of multiple devices from the 3D point cloud map of the site, and determine the locations of multiple sub-areas in the site, and then determine the sub-areas where the multiple devices are located. Based on the sub-areas and categories where the devices are located, the device information of the multiple devices is sorted to generate a device tree corresponding to the multiple devices. In some embodiments, the computing device 101 can enter the generated device tree into the device 102 that performs the inspection task, and then the device 102 that performs the inspection task can detect each device in different sub-areas in turn based on the detection order indicated by the device tree.

[0053] In some embodiments, a 3D point cloud map of a site is generated by a terminal, wherein the terminal is associated with a data acquisition device, and an application that provides a digital twin modeling function is running on the terminal. The application can import the data of the site collected by the data acquisition device into the terminal, and then perform digital twin modeling based on the imported data to generate a 3D point cloud (PCD) map of the site. Among them, the data acquisition device can be a backpack mapping device, which includes two 16-line laser radars and four fisheye cameras that are perpendicular to each other. The mapping device can collect a 3D color point cloud of the site with an error of no more than 1 centimeter (cm). The embodiment of the present application does not limit the type of data acquisition device. The 3D point cloud map can also be generated by the computing device 101, and the embodiment of the present application does not limit the generation method of the 3D point cloud map.

[0054] The device 102 for performing the inspection task may be a robot or other device that can be used to perform the inspection task, wherein the robot includes a robot body, a camera, and a pan-tilt head. The robot body is used to control the movement and orientation of the device 102 for performing the inspection task; the camera is used to collect images; and the pan-tilt head is used to control the orientation of the camera's field of view. Exemplarily, the device 102 for performing the inspection task can, based on the navigation map of the site and the target sub-area of ​​the device for performing the inspection task, sequentially reach each inspection point in the target sub-area in the order of detection of each device in the target sub-area in the device tree entered, and then detect the devices corresponding to each inspection point in the sub-area.

[0055] In some embodiments, the wireless network or wired network uses standard communication technology and / or protocols. The network is typically the Internet, but can also be any network, including but not limited to a local area network (LAN), a metropolitan area network (MAN), a wide area network (WAN), a mobile, wired or wireless network, a private network or any combination of a virtual private network. In some embodiments, peer-to-peer (P2P) communication is implemented between the computing device 101 and the device 102 performing the inspection task based on a remote procedure call protocol (RPC). In some embodiments, the computing device 101 and the device 102 performing the inspection task use technologies and / or formats including hypertext markup language (HTML), extensible markup language (XML), etc. to represent data blocks exchanged through the network. In addition, conventional encryption technologies such as secure socket layer (SSL), transport layer security (TLS), virtual private network (VPN), Internet protocol security (IPsec), etc. can be used to encrypt all or some links. In other embodiments, customized and / or dedicated data communication technologies can be used to replace or supplement the above data communication technologies.

[0056] The embodiment of the present application provides a device tree generation method, in which the location and category of multiple devices in the site and the location of multiple sub-areas in the site are determined based on the 3D point cloud map of the site, and then the sub-areas where the multiple devices are located in the site are determined, and the device information of the multiple devices is sorted in the order of the sub-areas and categories of the multiple devices to generate a device tree corresponding to the multiple devices. In this method, the devices in the site can be automatically detected and the sub-areas where they are located can be automatically determined, and then a standardized device tree can be automatically generated in the order of sub-areas and categories, which improves the generation efficiency of the device tree, and when the device layout changes, the device tree can also be updated in time without manually reorganizing the device information to update the device tree, which can save labor costs.

[0057] The above device tree generation method can be applied to substation inspection scenarios, park inspection scenarios, and scenarios that require long-term monitoring of safety status, such as computer rooms, tunnels, and grain depots. The above application scenarios are introduced below.

[0058] (1) Substation inspection scenario: The safety of the power system plays an irreplaceable role in the development of the national economy. Any accident in any link may lead to a chain reaction, causing large-scale power outages, personal injury, damage to main equipment, and even catastrophic accidents such as the collapse of the entire network. As a key node connecting the backbone network and the distribution network, how to ensure its normal operation is directly related to the stability and safety of the entire power system. In order to ensure real-time monitoring of the operating status of major primary equipment such as main transformers, busbars, switches, etc. in the substation, it is necessary to inspect and maintain the substation. Traditional substation monitoring and inspection are mainly carried out manually, through human senses to make simple qualitative judgments on the equipment, mainly through methods such as seeing, touching, listening, and smelling. However, manual inspection has many shortcomings. Traditional manual inspection methods have the shortcomings of high labor intensity, low work efficiency, scattered detection quality, and single means. The data of manual inspection cannot be accurately and timely connected to the management information system. In addition, with the promotion of unattended mode, the inspection workload is increasing, and the inspection rate and timeliness cannot be guaranteed. In addition, under geographical conditions such as plateaus, hypoxia, cold weather or severe weather conditions, manual inspections still have great safety risks and lack effective inspection methods. Inspections cannot be carried out in a timely manner in severe weather such as strong winds, fog, ice and snow, hail, and thunderstorms. Therefore, the traditional substation inspection method urgently needs a new "successor". The power inspection robot inspects outdoor high-voltage equipment in unmanned or undermanned substations in an autonomous or remotely controlled manner, and can promptly detect abnormal phenomena such as defects in power equipment and hanging foreign objects, automatically alarm or perform pre-set fault processing. It runs flexibly and freely, truly plays a role in reducing staff and increasing efficiency, and can promote the unmanned process of substations more quickly. Inspection point planning and point information recording are necessary deployment tasks for power inspection robots. After the inspection point recording is completed, it is also necessary to generate a device tree to determine the inspection route of the inspection robot based on the device tree.

[0059] (2) Park inspection scenario: With the development of the economy, artificial intelligence has gradually come into people's sight. The construction of smart parks and intelligent communities has brought a series of conveniences to residents. Traditional patrol and security work is moving towards "intelligent patrol" and "three-dimensional patrol" based on artificial intelligence technology, using patrol robots to replace their security personnel. Similarly, patrol point planning and point information recording are also necessary deployment tasks for park patrol robots. After the patrol point recording is completed, it is also necessary to generate a device tree to determine the patrol robot's patrol route based on the device tree.

[0060] (3) Scenarios that require long-term safety monitoring, such as computer rooms, tunnels, and grain warehouses: Inspection scenarios such as computer rooms, tunnels, and grain warehouses all have objects to be inspected. Therefore, inspection point planning and point information recording are also necessary deployment tasks for inspection robots in these inspection scenarios. After the inspection point recording is completed, device tree generation is required to determine the inspection route of the inspection robot based on the device tree.

[0061] It should be noted that the above description of the application scenario is only exemplary. The device tree generation method provided in the embodiment of the present application can be migrated to any robot inspection scenario or manual inspection scenario, and the embodiment of the present application is not limited to this.

[0062] The following is an introduction to a device tree generation method provided in an embodiment of the present application. Figure 2 is a flow chart of a device tree generation method provided in an embodiment of the present application, such as Figure 2 As shown, taking the method executed by a computing device as an example, the method includes the following steps 201 to 206.

[0063] 201. The computing device determines the locations and categories of multiple devices in the venue based on a 3D point cloud map of the venue.

[0064] In some embodiments, a 3D point cloud map of a site is generated by a terminal, wherein the terminal is associated with a data acquisition device, and an application providing a digital twin modeling function is running on the terminal, and the application can import the data of the site collected by the data acquisition device into the terminal, and then perform digital twin modeling based on the imported data to generate a 3D point cloud map of the site. In some embodiments, the data acquisition device can be a backpack-type mapping device, which includes two 16-line laser radars and four fisheye cameras perpendicular to each other. The mapping device can collect 3D color point cloud data of the site with an error of no more than 1 centimeter (cm). The embodiment of the present application does not limit the type of data acquisition device. The 3D point cloud map can also be generated by a computing device, and the embodiment of the present application does not limit the generation method of the 3D point cloud map.

[0065] Among them, taking the substation inspection scene as an example, the site is a substation, and the equipment includes meters, insulators, voltage transformers, current transformers, grounding switches, disconnector tubes, bushings, lightning arrester rings, drainage wire clamps, electrical boxes, ladder locks, insulator connections, etc., which are not limited in the embodiments of this application. The location of the device refers to the absolute coordinates of the device in the 3D point cloud map.

[0066] Among them, the site includes multiple sub-areas, e.g. Figure 3 is a site layout diagram of a site provided in an embodiment of the present application, such as Figure 3As shown, the venue A includes sub-areas A1, A2, A3 and A4. In some embodiments, each sub-area can be further divided into multiple sub-areas, for example, the sub-area A1 is further divided into A11 and A12, etc. The embodiment of the present application does not limit the division method of the sub-areas in the venue.

[0067] Among them, the process of the computing device determining the location and category of multiple devices in the venue based on the 3D point cloud map of the venue includes: based on the inspection target, detecting the location and category of multiple devices in the 3D point cloud map. Among them, the inspection target refers to the category of equipment preset manually or on the venue. In some embodiments, the computing device automatically identifies the location and category of the equipment on the 3D point cloud map based on a 3D point cloud detection algorithm, for example, BirdNet. In the above method, the location and category of the equipment are identified by the point cloud detection algorithm, without the need to manually remotely control the equipment performing the inspection task to go to the vicinity of each device to determine the location and category of the equipment, which can improve efficiency and reduce labor costs, and is not affected by extreme environments.

[0068] 202. The computing device generates a 2D grid map corresponding to the 3D point cloud map.

[0069] In some embodiments, the computing device uses a 3D point cloud projection method to convert a 3D point cloud map into a 2D grid map. The process includes: dividing the 3D point cloud map into n×m grids on a 2D plane corresponding to the ground, where n and m are integers greater than 1; for each grid, p(s=1) is used to represent the probability of being unoccupied, and p(s=0) is used to represent the probability of being occupied, and the sum of the two is 1, where s represents the occupancy state of the grid, s=1 indicates that the grid is unoccupied, and s=0 indicates that the grid is occupied; traversing each grid, and updating the occupancy state of the grid based on the occupancy state of other grids around the grid, and after all grids are traversed, a 2D grid map corresponding to the 3D point cloud map is obtained. The process of updating the occupancy state of the grid based on the occupancy state of other grids around the grid can be expressed by the following formula (1):

[0070]

[0071] In the above formula (1), z represents the occupancy status of other grids around the grid, p(s=1|z) represents the probability that the grid is unoccupied when other grids around it are in the current occupancy status, p(s=0|z) represents the probability that the grid is occupied when other grids around it are in the current occupancy status, Odd(s|z) represents the occupancy status of the grid when other grids around it are in the current occupancy status. In some embodiments, if the value of Odd(s|z) is greater than a preset value, the value of Odd(s|z) is set to 1 to indicate that the occupancy status of the grid is unoccupied; if the value of Odd(s|z) is less than or equal to the preset value, the value of Odd(s|z) is set to 0 to indicate that the occupancy status of the grid is occupied. Figure 4 As shown, Figure 4 is a schematic diagram of a 2D grid map provided in an embodiment of the present application. Figure 4 It includes n×m grids, where the black-filled parts represent occupied grids and the white-filled parts represent unoccupied grids.

[0072] In the above method, a 3D point cloud projection method is used when generating a 2D grid map corresponding to a 3D point cloud map. Each grid in the 2D grid map has an occupancy state, and the occupancy state is associated with the occupancy state of grids around the grid. Therefore, when reducing the 3D information to 2D information, sufficient 3D information can be retained as much as possible on the basis of the original resolution, which is beneficial to improve the accuracy of the generated 2D grid map.

[0073] 203. The computing device obtains a site layout map of the site, where the site layout map indicates multiple sub-areas in the site.

[0074] The multiple sub-areas included in the site layout diagram can be obtained by dividing the area based on any division granularity. Figure 3 For example, site A includes sub-areas A1, A2, A3 and A4. In some embodiments, each sub-area can be further divided into multiple sub-areas, for example, sub-area A1 is further divided into A11 and A12, etc. It should be noted that the above Figure 3 The site layout diagram shown is only exemplary, and the embodiment of the present application does not limit the form of the site layout diagram and the way of dividing the sub-areas.

[0075] 204. The computing device determines, on the 2D grid map, a grid area corresponding to each sub-area in the site layout map.

[0076] The computing device identifies multiple grid areas on the 2D grid map, identifies multiple sub-areas in the site layout, and matches the multiple grid areas with the multiple sub-areas one by one to obtain the grid area corresponding to each sub-area. The grid area includes at least one grid, and the grid area indicates that the devices are distributed more concentratedly within the range of the grid area. The computing device uses the distribution characteristics of the multiple sub-areas in the site layout (relative position relationship with other sub-areas, etc.) as a template, determines the multiple grid areas and their distribution characteristics on the 2D grid map, and matches the sub-areas and grid areas with the same or similar distribution characteristics one by one.

[0077] In some embodiments, the process of the computing device matching multiple grid regions with multiple sub-regions one by one includes: dividing the multiple grid regions into at least one group according to a first preset rule, and sorting different grid regions in the same group of grid regions according to a second preset rule; dividing the multiple sub-regions into at least one group according to the first preset rule, and sorting different sub-regions in the same group of sub-regions according to the second preset rule; matching the sub-regions with the same group number and the same sequence number in the group with the grid region to obtain the grid region corresponding to each sub-region. Among them, the first preset rule can be to divide the regions with the same ordinate into one group, and the corresponding second preset rule is to sort the regions in the same group in the order of the abscissa from small to large. For example: according to the coordinates (Xc, Yc) of the center point of each sub-region, the sub-regions with the same Yc are divided into the same group, and the sub-regions of each group are sorted according to the size of Yc, and the sub-regions in each group of sub-regions are sorted according to the size of Xc, so as to obtain the group number and the sequence number within the group of each sub-region; similarly, the group number and the sequence number within the group of each grid region are determined; the corresponding sub-regions with the same group number and the same sequence number within the group are matched with the grid region to obtain the grid region corresponding to each sub-region. The first preset rule can also be to divide the regions with the same shape into a group, and the corresponding second preset rule is to sort in the same group in the order of first the ordinate and then the abscissa, etc., which is not limited in the embodiment of the present application.

[0078] Below Figure 3 and Figure 5 Take the above process as an example to illustrate. Figure 5 A schematic diagram of a recognition result of a grid area in a 2D grid map provided in an embodiment of the present application, wherein a computing device Figure 3 In the site layout shown, multiple sub-areas are identified, namely A1, A2, A3 and A4; the computing device Figure 5In the 2D grid map shown, multiple grid areas are identified, namely B1, B2, B3 and B4; the computing device divides the sub-areas into the first group: A1, A2, the second group: A3, A4, and the grid areas are grouped and sorted similarly, and then the computing device matches the first sub-area (A1) of the first group in the sub-areas with the first grid area (B1) of the first group in the grid area. The matching process of other sub-areas and grid areas is similar and will not be repeated. Finally, it is determined that B1 corresponds to A1, B2 corresponds to A2, B3 corresponds to A3, and B4 corresponds to A4.

[0079] In some embodiments, the computing device is configured with a display module, and the computing device displays each grid area in the 2D grid map and each sub-area in the site layout map, and then the object logged in to the computing device can use human-computer interaction to mark the sub-area corresponding to each grid area in the 2D grid map to achieve a one-to-one matching of the grid areas and the sub-areas. The embodiments of the present application do not limit the matching method of the grid areas and the sub-areas.

[0080] It should be noted that the above steps 202 to 204 are an implementation method of determining the positions of multiple sub-areas of the venue based on the 3D point cloud map of the venue. In some embodiments, this step is also implemented based on other methods, which is not limited in the embodiments of the present application.

[0081] It should be noted that the execution order of first executing the device location and category determination process corresponding to step 201 and then executing the sub-area location determination process corresponding to steps 202 to 204 is merely exemplary. In some embodiments, the sub-area location determination process may be executed first, and then the device location and category determination process may be executed, or the above two processes may be executed in parallel. The embodiments of the present application do not limit the execution order of the two processes.

[0082] 205. The computing device determines, based on the grid area where the positions of the multiple devices are located, the sub-areas where the multiple devices are located in the venue.

[0083] For each device, the computing device determines the grid area where the device falls based on the location of the device and the coordinate range corresponding to each grid area; the computing device determines the sub-area corresponding to the grid area where the device falls based on the correspondence between the grid area and the sub-area, so as to obtain the sub-area where the device is located in the venue. Figure 3 and Figure 5For example, if the coordinates of device S1 fall within the coordinate range of grid area B1, and grid area B1 corresponds to sub-area A1, then the sub-area where device S1 is located in the venue is A1. In some embodiments, sub-area A1 is further divided into A11 and A12, and correspondingly, grid area B1 is further identified as B11 and B12. If the coordinates of device S1 fall within grid area B11, then the sub-area where device S is located in the venue is A11 in A1.

[0084] It should be noted that the above step 205 is an implementation method for determining the sub-areas where multiple devices are located in the venue based on the locations of multiple sub-areas and the locations of multiple devices. In some embodiments, this step is also implemented based on other methods, which is not limited in the embodiments of the present application.

[0085] 206. The computing device sorts the device information of the multiple devices according to the sub-areas and categories of the multiple devices to generate a device tree corresponding to the multiple devices, where the device information includes the categories and inspection point information corresponding to the multiple devices.

[0086] Among them, the computing device first sorts multiple devices according to the sub-area where the devices are located, and then sorts the devices in the same sub-area according to the category. In some embodiments, after sorting multiple devices according to the sub-area and category, the devices with the same sub-area and category are also sorted based on the detection tasks and device numbers corresponding to the devices, etc., which is not limited to the embodiments of the present application. Continuing with the example in the above step 203, if the sub-area where device S1 is located is A11 in A1, and the category is Class I, the sub-area where device S2 is located is A11 in A1, and the category is Class I, the sub-area where device S3 is located is A21 in A2, and the category is Class I, the sub-area where device S4 is located is A21 in A2, and the category is Class II, and the devices are sorted in the order of sub-area, category, and device number. If the generated device tree is as follows: Figure 6 As shown, Figure 6 It is a schematic diagram of a device tree of inspection points provided in an embodiment of the present application.

[0087] It should be noted that the arrangement order of each device in the device tree indicates the detection order of each device. The generated device tree can be entered into the device that performs the inspection task, and then the device that performs the inspection task can detect each device in different sub-areas in turn based on the detection order indicated by the arrangement order of each device in the device tree. In addition, the device that performs the inspection task can also inspect only the target sub-area in the site based on the device tree, wherein the device that performs the inspection task can reach each inspection point in the target sub-area in turn based on the navigation map of the site and the target sub-area of ​​the device that performs the inspection task, according to the detection order of each device in the target sub-area in the entered device tree, and then detect each device in the sub-area.

[0088] Below Figure 7 As an example, the above steps 201 to 206 are described by way of example. Figure 7 is a flow chart of a device tree generation method provided in an embodiment of the present application, such as Figure 7 As shown, taking the substation as an example, the computing device obtains the 3D color laser point cloud map obtained after digital twin modeling of the substation; the computing device uses the 3D point cloud detection algorithm to perform equipment detection on the 3D point cloud map to obtain the coordinate information (that is, the position) and equipment category information (that is, the category) of the equipment in the substation; the computing device rasterizes the 3D point cloud map through the grid map generation algorithm to obtain a grid map; the computing device aligns the grid map with the site layout diagram of the substation to obtain the location information of the equipment (that is, the sub-area where it is located); the computing device generates a device tree based on the location information and device category information of the equipment to obtain the device tree of the inspection point.

[0089] It should be noted that the above steps 201 to 206 are described by taking a computing device as the execution subject as an example. In some embodiments, the above steps 201 to 206 can be executed by a device (such as a robot) that performs inspection tasks, and the embodiments of the present application do not limit this.

[0090] In the above method, the equipment in the site can be automatically detected and the sub-area in which it is located can be automatically determined, and then a standardized equipment tree can be automatically generated in the order of sub-areas and categories, thereby improving the efficiency of equipment tree generation. When the equipment layout changes, the equipment tree can also be updated in time without manually reorganizing the information of the inspection points to update the equipment tree, thereby saving labor costs. Furthermore, when generating a 2D grid map corresponding to the 3D point cloud map, a 3D point cloud projection method is adopted. Each grid in the 2D grid map has an occupancy state, and the occupancy state is associated with the occupancy state of the grids around the grid. Therefore, when reducing the 3D information to 2D information, sufficient 3D information can be retained as much as possible on the basis of the original resolution, thereby facilitating improving the accuracy of the generated 2D grid map.

[0091] Figure 8 is a schematic diagram of the structure of a device tree generation apparatus provided in an embodiment of the present application, such as Figure 8 As shown, the device includes a first determination module 801 , a second determination module 802 , a third determination module 803 and a sorting module 804 .

[0092] The first determination module 801 is used to determine the locations and categories of multiple devices in the venue based on the 3D point cloud map of the venue;

[0093] The second determination module 802 is used to determine the positions of multiple sub-areas of the venue based on the 3D point cloud map of the venue;

[0094] The third determination module 803 is used to determine the sub-areas where the multiple devices are located in the venue based on the locations of the multiple sub-areas and the locations of the multiple devices;

[0095] The sorting module 804 is used to sort the device information of the multiple devices according to the sub-areas and categories of the multiple devices to generate a device tree corresponding to the multiple devices, and the device information includes the categories and inspection point information corresponding to the multiple devices.

[0096] Optionally, the second determining module 802 includes:

[0097] A generating unit, used for generating a 2D grid map corresponding to the 3D point cloud map;

[0098] An acquisition unit, configured to acquire a site layout map of the site, wherein the site layout map indicates a plurality of sub-areas in the site;

[0099] The determination unit is used to determine the grid area corresponding to each sub-area in the site layout diagram on the 2D grid map.

[0100] Optionally, the third determining module 804 is configured to:

[0101] Based on the grid areas where the positions of the multiple devices are located, sub-areas where the multiple devices are located in the venue are determined.

[0102] Optionally, the determining unit includes:

[0103] A first identification subunit is used to identify a plurality of grid areas on a 2D grid map;

[0104] A second identification subunit is used to identify a plurality of sub-areas in the site layout diagram;

[0105] The matching subunit is used to match the multiple grid regions with the multiple sub-regions one by one to obtain the grid region corresponding to each sub-region.

[0106] Optionally, the matching subunit is used to:

[0107] Dividing the plurality of grid regions into at least one group according to a first preset rule, and sorting different grid regions in the same group of grid regions according to a second preset rule;

[0108] Divide the multiple sub-regions into at least one group according to a first preset rule, and sort different sub-regions in the same group of sub-regions according to a second preset rule;

[0109] The sub-regions and grid regions with the same corresponding group number and sequence number within the group are matched to obtain the grid region corresponding to each sub-region.

[0110] Optionally, the device further comprises:

[0111] The modeling module is used to perform digital twin modeling of the site and obtain a 3D point cloud map of the site.

[0112] Among them, the first determination module 801, the second determination module 802, the third determination module 803 and the sorting module 804 can all be implemented by software, or can be implemented by hardware. Exemplarily, the implementation of the first determination module 801 is introduced below by taking the first determination module 801 as an example. Similarly, the implementation of the second determination module 802, the third determination module 803 and the sorting module 804 can refer to the implementation of the first determination module 801.

[0113] As an example of a software functional unit, the first determination module 801 may include code running on a computing instance. Among them, the computing instance may include at least one of a physical host (computing device), a virtual machine, and a container. Further, the above-mentioned computing instance may be one or more. For example, the first determination module 801 may include code running on multiple hosts / virtual machines / containers. It should be noted that the multiple hosts / virtual machines / containers used to run the code can be distributed in the same region (region) or in different regions. Furthermore, the multiple hosts / virtual machines / containers used to run the code can be distributed in the same availability zone (AZ) or in different AZs, each AZ including a data center or multiple data centers with similar geographical locations. Among them, usually a region can include multiple AZs.

[0114] Similarly, multiple hosts / virtual machines / containers used to run the code can be distributed in the same virtual private cloud (VPC) or in multiple VPCs. Usually, a VPC is set up in a region. For cross-region communication between two VPCs in the same region and between VPCs in different regions, a communication gateway needs to be set up in each VPC to achieve interconnection between VPCs through the communication gateway.

[0115] As an example of a hardware functional unit, the first determination module 801 may include at least one computing device, such as a server, etc. Alternatively, the first determination module 801 may also be a device implemented using an application-specific integrated circuit (ASIC) or a programmable logic device (PLD). The PLD may be a complex programmable logical device (CPLD), a field programmable gate array (FPGA), a generic array logic (GAL), or any combination thereof.

[0116] The multiple computing devices included in the first determination module 801 can be distributed in the same region or in different regions. The multiple computing devices included in the first determination module 801 can be distributed in the same AZ or in different AZs. Similarly, the multiple computing devices included in the first determination module 801 can be distributed in the same VPC or in multiple VPCs. The multiple computing devices can be any combination of computing devices such as servers, ASICs, PLDs, CPLDs, FPGAs, and GALs.

[0117] It should be noted that, in other embodiments, the steps that the above modules are responsible for implementing can be specified as needed, and all the functions of the above device can be realized by respectively implementing different steps in the above device tree generation method through the above modules. That is, the device tree generation device provided in the above embodiment only uses the division of the above functional modules as an example when implementing the device tree generation method. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the device provided in the above embodiment belongs to the same concept as the corresponding method embodiment. The specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0118] The present application also provides a computing device 900 . Fig. 9 is a schematic diagram of a computing device provided in an embodiment of the present application, such as Fig. 9 As shown, the computing device 900 includes: a bus 901, a processor 902, a memory 903 and a communication interface 904. The processor 902, the memory 903 and the communication interface 904 communicate with each other through the bus 901. The computing device 900 can be a server or a terminal device. It should be understood that the present application does not limit the number of processors and memories in the computing device 900.

[0119] The bus 901 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Fig. 9 The bus 901 is represented by only one line, but it does not mean that there is only one bus or one type of bus. The bus 901 may include a path for transmitting information between various components of the computing device 900 (for example, the memory 903, the processor 902, and the communication interface 904).

[0120] The processor 902 may include any one or more of a central processing unit (CPU), a graphics processing unit (GPU), a microprocessor (MP), or a digital signal processor (DSP).

[0121] The memory 903 may include a volatile memory, such as a random access memory (RAM). The memory 903 may also include a non-volatile memory, such as a read-only memory (ROM), a flash memory, a hard disk drive (HDD), or a solid state drive (SSD).

[0122] The memory 903 stores executable program codes, and the processor 902 executes the executable program codes to respectively implement the functions of the aforementioned determination module 501 and display module 502, thereby implementing the device tree generation method. That is, the memory 903 stores program codes for executing the device tree generation method.

[0123] The communication interface 904 uses a transceiver module such as, but not limited to, a network interface card or a transceiver to implement communication between the computing device 900 and other devices or a communication network.

[0124] The embodiment of the present application also provides a computing device cluster. Fig.10 is a schematic diagram of a computing device cluster provided in an embodiment of the present application, such as Fig.10 As shown, the computing device cluster includes at least one computing device 900. The memory 903 in one or more computing devices 900 in the computing device cluster may store the same program code for executing the device tree generation method.

[0125] In some possible implementations, the memory 903 of one or more computing devices 900 in the computing device cluster may also store a portion of the program code for executing the device tree generation method. In other words, the combination of one or more computing devices 900 may jointly execute the program code for executing the device tree generation method.

[0126] It should be noted that the memory 903 in different computing devices 900 in the computing device cluster can store different program codes, which are respectively used to execute part of the functions of the device tree generation apparatus. That is, the program codes stored in the memory 903 in different computing devices 900 can implement the functions of one or more modules in the first determination module 801, the determination module 802 and the sorting module 803.

[0127] It should be understood that Fig.10 The functionality of the computing device 900 shown in FIG. 9 may also be performed by multiple computing devices 900 .

[0128] In some possible implementations, one or more computing devices in the computing device cluster may be connected via a network, which may be a wide area network or a local area network. Fig.11 A possible implementation is shown. Fig.11 is a schematic diagram of a possible implementation of a computing device cluster provided in an embodiment of the present application, such as Fig.11 As shown, two computing devices 900 are connected via a network. Specifically, they are connected to the network via a communication interface in each computing device.

[0129] The present application implements a computer program product including program code, which may be software or a program product including program code that can be run on a computing device or stored in any available medium. When the computer program product is run on at least one computing device, the at least one computing device is enabled to perform the device tree generation method provided in the above embodiment; when the computer program product is run on a computing device, the computing device is enabled to perform the device tree generation method provided in the above embodiment.

[0130] The embodiment of the present application provides a computer-readable storage medium, which can be any available medium that can be stored by a computing device or a data storage device such as a data center containing one or more available media. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state hard disk), etc. The computer-readable storage medium includes a program code, and when the program code is executed by a computing device, the computing device executes the device tree generation method provided in the above embodiment; when the program code is executed by a computing device or a computing device cluster, the computing device or the computing device cluster executes the device tree generation method provided in the above embodiment.

[0131] It should be noted that the information (including but not limited to user device information, user personal information, etc.), data (including but not limited to data used for analysis, stored data, displayed data, etc.) and signals involved in this application are all authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with the relevant laws, regulations and standards of relevant countries and regions. For example, the 3D point cloud maps involved in this application are all obtained with full authorization.

[0132] Those of ordinary skill in the art will appreciate that the various method steps and units described in the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the steps and components of each embodiment have been generally described in the above description according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Those of ordinary skill in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0133] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0134] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the unit is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, or it can be an electrical, mechanical or other form of connection.

[0135] The unit described as a separate component may or may not be physically separated, and the component displayed as a unit may or may not be a physical unit, that is, it may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiment of the present application.

[0136] In addition, each unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or software unit.

[0137] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of program codes to enable a computing device (which can be a personal computer, a server, or a computing device, etc.) to perform all or part of the steps of the method in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk and other media that can store program codes.

[0138] In this application, the terms "first", "second", etc. are used to distinguish between identical or similar items with substantially the same effects and functions. It should be understood that there is no logical or temporal dependency between "first", "second", and "nth", nor is there a limitation on the quantity and execution order. It should also be understood that although the following description uses the terms first, second, etc. to describe various elements, these elements should not be limited by the terms. These terms are only used to distinguish one element from another element.

[0139] In this application, the term "at least one" means one or more, and the term "multiple" means two or more. For example, multiple inspection points means two or more inspection points. In this document, the terms "system" and "network" are often used interchangeably.

[0140] It should also be understood that the term "if" may be interpreted to mean "when" or "upon" or "in response to determining" or "in response to detecting." Similarly, the phrase "if it is determined that ..." or "if [a stated condition or event] is detected" may be interpreted to mean "upon determining that ..." or "in response to determining that ..." or "upon detecting [a stated condition or event]" or "in response to detecting [a stated condition or event]," depending on the context.

[0141] The above description is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in the present application, and these modifications or replacements should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0142] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer program codes. When the computer program code is loaded and executed on a computer, the process or function according to the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices.

[0143] The computer program code may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer program code may be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired or wireless means. The computer-readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server or data center that includes one or more available media integrated therein. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a magnetic tape), an optical medium (e.g., a digital video disc (DVD), or a semiconductor medium (e.g., a solid-state drive)), etc.

[0144] A person skilled in the art will understand that all or part of the steps to implement the above embodiments may be accomplished by hardware or by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a disk or an optical disk, etc.

[0145] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A device tree generation method, characterized in that: The device tree includes a plurality of devices of the site, and the method includes: Determine the locations and categories of a plurality of devices in the venue based on the 3D point cloud map of the venue; Determining locations of a plurality of sub-areas of the site based on a 3D point cloud map of the site; Based on the locations of the multiple sub-areas and the locations of the multiple devices, determining the sub-areas where the multiple devices are located in the venue; According to the sub-areas and categories of the multiple devices, the device information of the multiple devices is sorted to generate a device tree corresponding to the multiple devices, wherein the device information includes the categories and inspection point information corresponding to the multiple devices.

2. The method according to claim 1, characterized in that The determining the positions of the plurality of sub-areas of the site based on the 3D point cloud map of the site comprises: Generate a 2D grid map corresponding to the 3D point cloud map; Acquire a site layout map of the site, wherein the site layout map indicates a plurality of sub-areas in the site; A grid area corresponding to each of the sub-areas in the site layout map is determined on the 2D grid map.

3. The method according to claim 2, characterized in that The determining, based on the locations of the multiple sub-areas and the locations of the multiple devices, the sub-areas where the multiple devices are located in the venue includes: Based on the grid areas where the positions of the multiple devices are located, sub-areas where the multiple devices are located in the venue are determined.

4. The method according to claim 2, characterized in that: Determining the grid area corresponding to each of the sub-areas on the 2D grid map comprises: Identifying a plurality of grid areas on the 2D grid map; identifying a plurality of sub-areas in the site layout; The multiple grid regions are matched one by one with the multiple sub-regions to obtain a grid region corresponding to each of the sub-regions.

5. The method according to claim 4, characterized in that The step of matching the plurality of grid regions with the plurality of sub-regions one by one to obtain a grid region corresponding to each of the sub-regions includes: Dividing the plurality of grid regions into at least one group according to a first preset rule, and sorting different grid regions in the same group of grid regions according to a second preset rule; Divide the multiple sub-areas into at least one group according to the first preset rule, and sort different sub-areas in the same group of sub-areas according to the second preset rule; Sub-regions and grid regions with the same corresponding group numbers and serial numbers within the group are matched to obtain the grid region corresponding to each of the sub-regions.

6. The method according to any one of claims 1 to 5, characterized in that The method further comprises: Digital twin modeling is performed on the site to obtain a 3D point cloud map of the site.

7. A device tree generation device, characterized in that: The device tree includes a plurality of devices of the site, and the apparatus includes: A first determination module, configured to determine locations and categories of a plurality of devices in the site based on a 3D point cloud map of the site; A second determination module, configured to determine locations of a plurality of sub-areas of the site based on a 3D point cloud map of the site; A third determining module, configured to determine the sub-areas where the multiple devices are located in the venue based on the locations of the multiple sub-areas and the locations of the multiple devices; The sorting module is used to sort the device information of the multiple devices according to the sub-areas and categories of the multiple devices to generate a device tree corresponding to the multiple devices, wherein the device information includes the categories and inspection point information corresponding to the multiple devices.

8. The device according to claim 7, characterized in that The second determining module includes: A generating unit, used to generate a 2D grid map corresponding to the 3D point cloud map; An acquisition unit, configured to acquire a site layout map of the site, wherein the site layout map indicates a plurality of sub-areas in the site; A determination unit is used to determine, on the 2D grid map, a grid area corresponding to each of the sub-areas in the site layout map.

9. The device according to claim 8, characterized in that The third determining module is used to: Based on the grid areas where the positions of the multiple devices are located, sub-areas where the multiple devices are located in the venue are determined.

10. The device according to claim 8, characterized in that The determining unit comprises: A first identification subunit, used for identifying a plurality of grid areas on the 2D grid map; A second identification subunit is used to identify a plurality of sub-areas in the site layout map; The matching subunit is used to match the multiple grid regions with the multiple sub-regions one by one to obtain a grid region corresponding to each of the sub-regions.

11. The device according to claim 10, characterized in that The matching subunit is used for: Dividing the plurality of grid regions into at least one group according to a first preset rule, and sorting different grid regions in the same group of grid regions according to a second preset rule; Divide the multiple sub-areas into at least one group according to the first preset rule, and sort different sub-areas in the same group of sub-areas according to the second preset rule; Sub-regions and grid regions with the same corresponding group numbers and serial numbers within the group are matched to obtain the grid region corresponding to each of the sub-regions.

12. The device according to any one of claims 7 to 11, characterized in that The device also includes: The modeling module is used to perform digital twin modeling of the site to obtain a 3D point cloud map of the site.

13. A computing device, characterized in that: The computing device comprises a processor, and the processor is configured to execute a program code so that the computing device performs the device tree generation method according to any one of claims 1 to 6.

14. A computing device cluster, characterized in that: comprising at least one computing device, each of the computing devices comprising a processor and a memory; The processor of the at least one computing device is configured to execute a program code stored in a memory of the at least one computing device, so that the computing device cluster executes the device tree generation method according to any one of claims 1 to 6.

15. A computer-readable storage medium, characterized in that: At least one program code is stored in the storage medium, and the at least one program code is read by a processor of a computing device to enable the computing device to execute the device tree generation method according to any one of claims 1 to 6.

16. A computer-readable storage medium, characterized in that: At least one program code is stored in the storage medium, and the at least one program code is read by a processor of the computing device cluster to enable the computing device cluster to execute the device tree generation method according to any one of claims 1 to 6.

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