A distributed-based point cloud data management method, system, device and medium

Abstract

This invention discloses a distributed point cloud data management method, system, device, and medium, relating to the field of electrical digital data processing. The method includes: classifying collected point cloud data, with each type of point cloud data corresponding to a type of equipment in the power grid; storing the point cloud data in distributed storage nodes based on the classification results; retrieving the corresponding storage node from the distributed storage nodes according to a user request, obtaining the point cloud data from the storage node, and reconstructing a three-dimensional model of the power grid using the point cloud data. The system includes a point cloud data classification module, a point cloud data storage module, and a point cloud data reconstruction module. This invention significantly reduces the data volume by splitting point cloud data into geographic information, standard information, and difference information and storing them distributedly. Furthermore, by recombining these three types of information to reconstruct three-dimensional information, it effectively improves the model reconstruction efficiency.

Description

Technical Field

[0001] This invention relates to the field of electronic digital data processing, and specifically to a distributed point cloud data management method, system, device, and medium. Background Technology

[0002] The power grid has a large number of devices and a wide range of applications. After digitization, it requires a lot of storage space. At the same time, due to the large amount of data, the loading speed of 3D models is slow, which affects the actual user experience of related systems. Therefore, a new management system is needed to manage the point cloud data of power grid devices and optimize the storage and use of point cloud data.

[0003] Many point cloud data management systems have been developed. Extensive research revealed existing management and authorization systems, such as the one disclosed in publication number CN114116721A. These systems typically employ the following methods: LOD tiling of the point cloud data to be processed; uniformly assigning RGB values ​​to the category and corresponding color of the point cloud data within each tile; granting corresponding viewing and editing permissions to each tile based on the point cloud data's operation and maintenance management unit information; creating index information for each tile and storing the index information in the form of a database table; and dividing point cloud data of the same version in the same region by tile and storing it in several large files. However, this system does not reduce the storage volume of point cloud data, has an upper limit to the indexing efficiency of point cloud data, and there is still room for improvement in the reconstruction speed of point cloud data. Summary of the Invention

[0004] This invention addresses the shortcomings of existing technologies by providing a distributed point cloud data management method, system, device, and medium.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] A distributed point cloud data management method includes the following steps:

[0007] The collected point cloud data is classified and processed, with each type of point cloud data corresponding to a type of equipment in the power grid;

[0008] The point cloud data is stored in distributed storage nodes based on the classification results;

[0009] Based on the user's request, the corresponding storage node is retrieved from the distributed storage nodes, and point cloud data is obtained from the storage node. The point cloud data is then used to reconstruct the three-dimensional model of the power grid.

[0010] To optimize the above technical solution, the specific measures also include:

[0011] Furthermore, the specific process of classifying the collected point cloud data is as follows:

[0012] Spatial features are obtained by analyzing point cloud data;

[0013] Store the characteristic information of each type of power grid equipment;

[0014] The feature information of power grid equipment and the spatial features of point cloud data are verified to determine the equipment classification of the point cloud data and form actual equipment data packages; each actual equipment data package corresponds to a specific power grid equipment; geographic information is extracted from each actual equipment data package.

[0015] The standard point cloud data for each type of device is constructed as follows:

[0016] The ratio of the number of point clouds at each coordinate position to the number of power grid devices is calculated. When the ratio is greater than a threshold, the corresponding coordinate position is called the standard coordinate. The attribute information in the point cloud data at each standard coordinate is compared, and the attribute information with the most point clouds is selected as the attribute of the standard coordinate. All standard coordinates and corresponding attribute information of a power grid device type constitute the standard point cloud data of a power grid device type.

[0017] Standard point cloud data is packaged into standard equipment data packages; each standard equipment data package corresponds to a type of power grid equipment; equipment type tags are added to the standard equipment data packages.

[0018] The coordinate information of point cloud data in actual device data packets is absolute coordinates; the coordinate information of point cloud data in standard device data packets is relative coordinates; geographic information is used to convert the relative coordinates in standard device data packets into absolute coordinates;

[0019] Based on absolute coordinates, the point cloud data in the standard device data package is compared one by one with the point cloud data in the actual device data package. The difference point cloud data in the actual device data package is extracted and a difference data package is generated. The corresponding device type label is added to the difference data package.

[0020] Furthermore, the specific process for extracting geographic information from each actual device data packet is as follows:

[0021] Obtain the coordinate information of the point cloud data in the actual device data packet, and calculate the center coordinates (x0, y0, z0) according to the following formula:

[0022]

[0023]

[0024]

[0025] Among them, (x i y i , zi ) represents the coordinate information of the i-th point cloud data in the actual device data packet, and n represents the number of point cloud data in the actual device data packet;

[0026] The planar direction angle α and the vertical direction angle β are calculated using the following formulas:

[0027]

[0028]

[0029] The geographic information of the actual device data packet consists of the center coordinates and orientation angle.

[0030] Furthermore, the specific process of converting relative coordinates in the standard device data packet into absolute coordinates using geographic information is as follows:

[0031] Using the aforementioned geographic information, the relative coordinates (x', y', z') are converted into absolute coordinates (x, y, z) according to the following formula:

[0032] x = x0 + x′·sinα;

[0033] y = y0 + y′·cosα;

[0034] z = z0 + z′·tanβ;

[0035] In the formula, α is the planar direction angle, β is the vertical direction angle, and (x0, y0, z0) are the center coordinates.

[0036] Furthermore, the specific steps of verifying the feature information of power grid equipment and the spatial features of point cloud data to determine the equipment classification of the point cloud data are as follows:

[0037] The matching index Q is calculated using the following formula:

[0038]

[0039] Where nT is the number of feature information of the power grid equipment, k i V represents the weight value of the i-th feature information of the power grid equipment. i V is the measured value of the i-th feature information. i0 The standard value of the i-th feature information;

[0040] The type of power grid equipment with the highest matching index is used as the equipment classification for point cloud data.

[0041] Furthermore, the specific process for extracting the difference point cloud data existing in the actual device data packet is as follows:

[0042] The difference index Y between point cloud data in a standard device data package with the same absolute coordinates and point cloud data in an actual device data package is calculated using the following formula:

[0043]

[0044] Where, ΔS i This represents the difference value of the i-th attribute item, where m is the number of attribute items;

[0045] Point cloud data from standard equipment data packets with a difference index greater than a threshold are extracted as difference point cloud data.

[0046] Furthermore, the specific steps of reconstructing the three-dimensional model of the power grid using point cloud data are as follows:

[0047] A basic 3D model is reconstructed based on standard point cloud data and geographic information, and then the differential point cloud data is overlaid on the basic 3D model to obtain the final 3D model.

[0048] This invention also proposes a distributed point cloud data management system, comprising:

[0049] The point cloud data classification module is used to classify the collected point cloud data, with one type of point cloud data corresponding to one type of equipment in the power grid.

[0050] The point cloud data storage module is used to store point cloud data to distributed storage nodes based on the classification results;

[0051] The point cloud data reconstruction module is used to retrieve the corresponding storage node from the distributed storage nodes according to the user's request, obtain point cloud data from the storage node, and reconstruct the three-dimensional model of the power grid using the point cloud data.

[0052] The present invention also proposes a computer-readable storage medium storing a computer program that enables a computer to execute the distributed point cloud data management method described above.

[0053] The present invention also proposes an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, it implements the distributed point cloud data management method described above.

[0054] The beneficial effects of this invention are:

[0055] This invention effectively reduces the storage volume of point cloud data by dividing the point cloud data of power grid equipment into geographic information, standard information, and difference information and storing them in a distributed manner. It also uses the repetitive nature of power grid equipment to restore the abnormal conditions of power grid equipment. When restoring the three-dimensional model of power grid equipment, it can simultaneously retrieve the three types of information and reconstruct the model, effectively improving the model reconstruction efficiency. Attached Figure Description

[0056] Figure 1 This is a schematic diagram of the overall structural framework of the system proposed in this invention;

[0057] Figure 2 This is a schematic diagram of the point cloud data classification module of the present invention;

[0058] Figure 3 This is a schematic diagram of the point cloud data reconstruction module of the present invention;

[0059] Figure 4 This is a schematic diagram of the equipment classification unit structure of the present invention;

[0060] Figure 5 This is a schematic diagram of the standard building blocks of the present invention. Detailed Implementation

[0061] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0062] In one embodiment, the present invention proposes a distributed point cloud data management method, comprising the following steps:

[0063] The collected point cloud data is classified and processed, with each type of point cloud data corresponding to a type of equipment in the power grid; the specific process is as follows:

[0064] Spatial features are obtained by analyzing point cloud data;

[0065] Store the characteristic information of each type of power grid equipment;

[0066] The feature information of power grid equipment and the spatial features of point cloud data are verified to determine the equipment classification of the point cloud data and form an actual equipment data package; each actual equipment data package corresponds to a specific power grid device; the specific process for determining the equipment classification of point cloud data is as follows:

[0067] The matching index Q is calculated using the following formula:

[0068]

[0069] Where nT is the number of feature information of the power grid equipment, k i V represents the weight value of the i-th feature information of the power grid equipment. i V is the measured value of the i-th feature information. i0 The standard value of the i-th feature information;

[0070] The type of power grid equipment with the highest matching index is used as the equipment classification for point cloud data.

[0071] Geographic information is extracted from each actual device data packet; the specific process for extracting geographic information is as follows:

[0072] Obtain the coordinate information of the point cloud data in the actual device data packet, and calculate the center coordinates (x0, y0, z0) according to the following formula:

[0073]

[0074]

[0075]

[0076] Among them, (x i y i , z i ) represents the coordinate information of the i-th point cloud data in the actual device data packet, and n represents the number of point cloud data in the actual device data packet;

[0077] The planar direction angle α and the vertical direction angle β are calculated using the following formulas:

[0078]

[0079]

[0080] The geographic information of the actual device data packet consists of the center coordinates and orientation angle.

[0081] The standard point cloud data for each type of device is constructed as follows:

[0082] The ratio of the number of point clouds at each coordinate position to the number of power grid devices is calculated. When the ratio is greater than a threshold, the corresponding coordinate position is called the standard coordinate. The attribute information in the point cloud data at each standard coordinate is compared, and the attribute information with the most point clouds is selected as the attribute of the standard coordinate. All standard coordinates and corresponding attribute information of a power grid device type constitute the standard point cloud data of a power grid device type.

[0083] Standard point cloud data is packaged into standard equipment data packages; each standard equipment data package corresponds to a type of power grid equipment; equipment type tags are added to the standard equipment data packages.

[0084] The coordinate information of point cloud data in actual device data packets is absolute coordinates; the coordinate information of point cloud data in standard device data packets is relative coordinates. The process of converting relative coordinates in standard device data packets to absolute coordinates using geographic information is as follows:

[0085] Using the aforementioned geographic information, the relative coordinates (x', y', z') are converted into absolute coordinates (x, y, z) according to the following formula:

[0086] x = x0 + x′·sinα;

[0087] y = y0 + y′·cosα;

[0088] z = z0 + z′·tanβ;

[0089] In the formula, α is the planar direction angle, β is the vertical direction angle, and (x0, y0, z0) are the center coordinates.

[0090] Based on absolute coordinates, the point cloud data in the standard device data package is compared one-to-one with the point cloud data in the actual device data package. The differing point cloud data in the actual device data package is extracted and a difference data package is generated. A label corresponding to the device type is added to the difference data package. The specific process for extracting the differing point cloud data in the actual device data package is as follows:

[0091] The difference index Y between point cloud data in a standard device data package with the same absolute coordinates and point cloud data in an actual device data package is calculated using the following formula:

[0092]

[0093] Where, ΔS i This represents the difference value of the i-th attribute item, where m is the number of attribute items;

[0094] Point cloud data from standard equipment data packets with a difference index greater than a threshold are extracted as difference point cloud data.

[0095] The point cloud data is stored in distributed storage nodes based on the classification results;

[0096] Based on the user's request, the corresponding storage node is retrieved from the distributed storage nodes, and point cloud data is obtained from the storage node. The point cloud data is then used to reconstruct a 3D model of the power grid. A basic 3D model is restored based on standard point cloud data and geographic information. The differential point cloud data is then overlaid on the basic 3D model to obtain the final 3D model.

[0097] In another embodiment, the present invention proposes a distributed point cloud data management system corresponding to the method of Embodiment 1, the overall system structure framework of which is as follows: Figure 1 As shown, it includes:

[0098] The point cloud data classification module is used to classify the collected point cloud data, with one type of point cloud data corresponding to one type of equipment in the power grid.

[0099] The point cloud data storage module is used to store point cloud data to distributed storage nodes based on the classification results;

[0100] The point cloud data reconstruction module is used to retrieve the corresponding storage node from the distributed storage nodes according to the user's request, obtain point cloud data from the storage node, and reconstruct the three-dimensional model of the power grid using the point cloud data.

[0101] Combination Figure 2 The point cloud data classification module includes a device classification unit, a standard construction unit, and a difference extraction unit. The device classification unit is used to classify and analyze point cloud data. The standard construction unit is used to construct standard point cloud data for each classified device. The difference extraction unit is used to extract point cloud data with differences from the collected point cloud data.

[0102] The point cloud data storage module includes a geographic information storage unit, a standard information storage unit, and a difference information storage unit. The geographic information storage unit is used to store the geographic information of the power grid equipment. The standard information storage unit is used to store the standard point cloud data of each type of power grid equipment. The difference information storage unit is used to store the difference point cloud data of each power grid equipment.

[0103] Combination Figure 3 The point cloud data reconstruction module includes an application processing unit, a point cloud retrieval unit, and a model reconstruction unit. The application processing unit is used to receive and process application applications. The point cloud retrieval unit retrieves the corresponding node based on the processing result of the application application and obtains point cloud data from the node. The model reconstruction unit is used to reconstruct the obtained point cloud data to form three-dimensional model information.

[0104] Combination Figure 4 The equipment classification unit includes an equipment feature register, a spatial analysis processor, and a feature verification processor. The equipment feature register is used to store the feature information of each type of power grid equipment. The spatial analysis processor is used to analyze point cloud data to obtain spatial features. The feature verification processor is used to verify the feature information and spatial features to determine the equipment classification of the point cloud data.

[0105] The feature verification processor packages the point cloud data that has been classified into actual device data packages, and adds a device type label to the actual device data packages;

[0106] The device classification unit further includes a geographic information extraction processor, which is used to extract geographic information from each actual device data packet;

[0107] Combination Figure 5 The standard construction unit includes a similar data cache, a relative coordinate processor, and a standard point cloud filtering processor. The similar data cache is used to temporarily store actual device data packets with the same device classification. The relative coordinate processor is used to convert the absolute coordinate information of the point cloud data in the actual device data packet into relative coordinate information. The standard point cloud filtering processor is used to filter out the standard point cloud data at each coordinate position.

[0108] The relative coordinate processor converts absolute coordinates (x, y, z) into relative coordinates (x′, y′, z′) according to the following formula:

[0109]

[0110]

[0111]

[0112] Among them, (x min y min , z min ) and (x max y max , z max ) are the absolute coordinates of the minimum and maximum reference points in the equipment, respectively; Δx is the horizontal distance, Δy is the vertical distance, and Δz is the vertical distance.

[0113] The relative coordinate processor calculates the reference value P of the absolute coordinates of each point cloud according to the following formula:

[0114] P = x + y + z;

[0115] The point cloud with the smallest reference value is taken as the minimum reference point, and the point cloud with the largest reference value is taken as the maximum reference point.

[0116] The standard point cloud filtering processor calculates the ratio of the number of point clouds to the number of devices at each coordinate position. When the ratio is greater than a threshold, the corresponding coordinate position is called the standard coordinate. The standard point cloud filtering processor compares the attribute information in the point cloud data at each standard coordinate and selects the attribute information with the most point clouds as the attribute of the standard coordinate. All standard coordinates and corresponding attribute information of a power grid device type constitute the standard point cloud data of a power grid device type. They are packaged into a standard device data package, and a device type tag is added to the standard device data package.

[0117] The actual equipment data packet corresponds to a specific power grid device, while the standard equipment data packet corresponds to a type of power grid device. The coordinate information of the point cloud data in the actual equipment data packet is absolute coordinates, while the coordinate information of the point cloud data in the standard equipment data packet is relative coordinates.

[0118] Each point cloud data includes a coordinate point and attribute information, wherein the attribute information is used to describe the image information such as color, brightness, and saturation of the corresponding coordinate point;

[0119] The difference extraction unit includes a coordinate inverse transformation processor and a difference comparison processor. The coordinate inverse transformation processor is used to convert the relative coordinates in the standard device data packet into absolute coordinates. The difference comparison processor compares the point cloud data in the standard device data packet with the point cloud data in the actual device data packet based on the converted coordinates, and extracts the point cloud data that differs from the actual device data packet.

[0120] The difference point cloud data extracted by the difference extraction unit is packaged into a difference data package, and a tag with the corresponding device type is added to the difference data package;

[0121] The geographic information storage unit includes a geographic information memory and a geographic information retrieval processor. The geographic information memory is used to store geographic information, and the geographic information retrieval processor is used to retrieve the storage nodes of geographic information. After retrieval, the communication information of the storage node where the specific geographic information is located can be obtained.

[0122] The standard information storage unit includes a standard information memory and a standard information retrieval processor. The standard information memory is used to store point cloud data in the standard device data packet, and the standard information retrieval processor is used to retrieve the storage node of the standard device data packet. After retrieval, the communication information of the storage node where the specific standard device data packet is located can be obtained.

[0123] The difference information storage unit includes a difference information memory and a difference information retrieval processor. The difference information memory is used to store point cloud data in the difference data packets, and the difference information retrieval processor is used to retrieve the storage nodes of the difference data packets. After retrieval, the communication information of the storage node where the specific difference data packet is located can be obtained.

[0124] The application processing unit includes an application receiving register and an application parsing processor. The application receiving register is used to receive application applications sent by users, and the application parsing processor is used to parse the regional information in the application application to obtain the types of power grid equipment and spatial information contained in the region.

[0125] The point cloud retrieval unit includes a node connection processor and a retrieval call processor. The node connection processor is used to establish a connection with the storage node. The retrieval call processor is used to call the geographic information retrieval processor, standard information retrieval processor and difference information retrieval processor in the node to obtain the storage node communication information of the required data. Based on the communication information, it can establish a connection with the correct storage node to obtain data.

[0126] The node connection processor establishes a connection with the nearest node, and then establishes a connection with the corresponding node based on the feedback results of the geographic information retrieval processor, the standard information retrieval processor and the difference information retrieval processor, and obtains the required geographic information and point cloud data.

[0127] The model reconstruction unit includes a basic reconstruction processor and an overlay reconstruction processor. The basic reconstruction processor restores a basic 3D model based on standard point cloud data and geographic information. The overlay reconstruction processor is used to overlay the differential point cloud data onto the basic 3D model to obtain the final 3D model.

[0128] The geographic information extraction processor obtains the coordinate information of the point cloud data in the actual device data packet, and calculates the center coordinates (x0, y0, z0) according to the following formula:

[0129]

[0130]

[0131]

[0132] Among them, (x i y i , z i ) represents the coordinate information of the i-th point cloud data in the actual device data packet, and n represents the number of point cloud data in the actual device data packet;

[0133] The geographic information extraction processor calculates the orientation angles α and β according to the following formula:

[0134]

[0135]

[0136] The geographic information of the actual device data packet is composed of the center coordinates and orientation angle;

[0137] The coordinate inverse transformation processor converts the relative coordinates (x', y', z') into absolute coordinates (x, y, z) according to the following formula:

[0138] x = x0 + x′·sinα;

[0139] y = y0 + y′·cosα;

[0140] z = z0 + z′·tanβ;

[0141] The 'i' mentioned above is an ordinal number used to represent the sequence number.

[0142] The system manages point cloud data in two ways: acquisition response and application response. Acquisition response is further divided into initialization response and update response. Initialization response is executed only once, update response is executed once for each maintenance acquisition, and application response is executed once for each application request.

[0143] The initialization response process of this system includes the following steps:

[0144] S1. The device classification unit classifies the collected point cloud data;

[0145] S2. The standard construction unit analyzes and processes the point cloud data of the same type to obtain standard point cloud data for each type of power grid equipment, and sends the standard point cloud data to the standard information storage unit.

[0146] S3. The equipment classification unit obtains the geographic information of each power grid device from the point cloud data based on the classification results, and sends the geographic information to the geographic information storage unit.

[0147] S4. The difference information storage unit compares the point cloud data of each power grid device with the corresponding standard point cloud data, extracts the difference point cloud data, and sends the difference point cloud data to the difference information storage unit.

[0148] The maintenance response process of this system includes the following steps:

[0149] S21. The device classification unit classifies the collected point cloud data;

[0150] S22. The difference information storage unit compares the point cloud data with the corresponding standard point cloud data, extracts the difference point cloud data, and sends the difference point cloud data to the difference information storage unit.

[0151] S23. The difference information storage unit searches for the target device. If the difference information of the target device exists, the difference information is overwritten and updated. If the difference information of the target device does not exist, new difference information of the target device is generated.

[0152] The target equipment refers to specific power grid equipment that differs from standard point cloud data.

[0153] The application response process of this system includes the following steps:

[0154] S31, The application processing unit receives the application application;

[0155] S32. The application processing unit parses the area information in the application application to obtain the retrieval address information;

[0156] S33. The point cloud retrieval unit obtains geographic information, standard point cloud data, and differential point cloud data from the point cloud data storage module based on the retrieval address information;

[0157] S34. The model reconstruction unit processes standard point cloud data and geographic information to obtain a basic three-dimensional model.

[0158] S35. The model reconstruction unit overlays the difference point cloud data onto the basic three-dimensional model to obtain the final three-dimensional model.

[0159] In another embodiment, the present invention provides a computer-readable storage medium storing a computer program that causes a computer to execute the distributed point cloud data management method as described in Embodiment 1.

[0160] In another embodiment, the present invention proposes an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, it implements the distributed point cloud data management method as described in Embodiment 1.

[0161] In the embodiments disclosed in this application, a computer storage medium may be a tangible medium that may contain or store programs for use by or in conjunction with an instruction execution system, apparatus, or device. The computer storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of computer storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0162] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this application can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0163] The above are merely preferred embodiments of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principles of the present invention should be considered within the scope of protection of the present invention.

Claims

1. A distributed point cloud data management method, characterized in that, Includes the following steps: The collected point cloud data is classified and processed, with each type of point cloud data corresponding to a type of equipment in the power grid; Spatial features are obtained by analyzing point cloud data; Store the characteristic information of each type of power grid equipment; The feature information of power grid equipment and the spatial features of point cloud data are verified to determine the equipment classification of the point cloud data and form actual equipment data packages; each actual equipment data package corresponds to a specific power grid equipment; geographic information is extracted from each actual equipment data package. The standard point cloud data for each type of device is constructed as follows: The ratio of the number of point clouds at each coordinate position to the number of power grid devices is calculated. When the ratio is greater than a threshold, the corresponding coordinate position is called the standard coordinate. The attribute information in the point cloud data at each standard coordinate is compared, and the attribute information with the most point clouds is selected as the attribute of the standard coordinate. All standard coordinates and corresponding attribute information of a power grid device type constitute the standard point cloud data of a power grid device type. Standard point cloud data is packaged into standard equipment data packages; each standard equipment data package corresponds to a type of power grid equipment; equipment type tags are added to the standard equipment data packages. The coordinate information of point cloud data in actual device data packets is absolute coordinates; the coordinate information of point cloud data in standard device data packets is relative coordinates; geographic information is used to convert the relative coordinates in standard device data packets into absolute coordinates; Based on absolute coordinates, the point cloud data in the standard device data package is compared one by one with the point cloud data in the actual device data package. The difference point cloud data in the actual device data package is extracted and a difference data package is generated. The corresponding device type label is added to the difference data package. The point cloud data is stored in distributed storage nodes based on the classification results; Based on the user's request, the corresponding storage node is retrieved from the distributed storage nodes, and point cloud data is obtained from the storage node. The point cloud data is then used to reconstruct the three-dimensional model of the power grid. A basic 3D model is reconstructed based on standard point cloud data and geographic information, and then the differential point cloud data is overlaid on the basic 3D model to obtain the final 3D model.

2. The distributed point cloud data management method as described in claim 1, characterized in that, The specific process for extracting geographic information from each actual device data packet is as follows: Obtain the coordinate information of the point cloud data in the actual device data packet, and calculate the center coordinates (x0, y0, z0) according to the following formula: ； ； ； Among them, (x i y i , z i ) represents the coordinate information of the i-th point cloud data in the actual device data packet, and n represents the number of point cloud data in the actual device data packet; The plane orientation angle is calculated using the following formula. and vertical direction angle : ； ； The geographic information of the actual device data packet consists of the center coordinates and orientation angle.

3. The distributed point cloud data management method as described in claim 1, characterized in that, The specific process of converting relative coordinates in standard device data packets into absolute coordinates using geographic information is as follows: Using the aforementioned geographic information, the relative coordinates (x', y', z') are converted into absolute coordinates (x, y, z) according to the following formula: ； ； ； In the formula, It is the plane direction angle. Let (x0, y0, z0) be the vertical direction angle, and (x0, y0, z0) be the center coordinates.

4. The distributed point cloud data management method as described in claim 1, characterized in that, The specific steps for verifying the feature information of power grid equipment and the spatial features of point cloud data to determine the equipment classification of point cloud data are as follows: The matching index Q is calculated using the following formula: ； Where nT is the number of feature information of the power grid equipment, k i V represents the weight value of the i-th feature information of the power grid equipment. i V is the measured value of the i-th feature information. i0 The standard value of the i-th feature information; The type of power grid equipment with the highest matching index is used as the equipment classification for point cloud data.

5. The distributed point cloud data management method as described in claim 1, characterized in that, The specific process for extracting the differential point cloud data from the actual device data packet is as follows: The difference index Y between point cloud data in a standard device data package with the same absolute coordinates and point cloud data in an actual device data package is calculated using the following formula: ； in, This represents the difference value of the i-th attribute item, where m is the number of attribute items; Point cloud data from standard equipment data packets with a difference index greater than a threshold are extracted as difference point cloud data.

6. A distributed point cloud data management system, characterized in that, include: The point cloud data classification module is used to classify the collected point cloud data, with one type of point cloud data corresponding to one type of equipment in the power grid. Spatial features are obtained by analyzing point cloud data; Store the characteristic information of each type of power grid equipment; The feature information of power grid equipment and the spatial features of point cloud data are verified to determine the equipment classification of the point cloud data and form actual equipment data packages; each actual equipment data package corresponds to a specific power grid equipment; geographic information is extracted from each actual equipment data package. The standard point cloud data for each type of device is constructed as follows: The ratio of the number of point clouds at each coordinate position to the number of power grid devices is calculated. When the ratio is greater than a threshold, the corresponding coordinate position is called the standard coordinate. The attribute information in the point cloud data at each standard coordinate is compared, and the attribute information with the most point clouds is selected as the attribute of the standard coordinate. All standard coordinates and corresponding attribute information of a power grid device type constitute the standard point cloud data of a power grid device type. Standard point cloud data is packaged into standard equipment data packages; each standard equipment data package corresponds to a type of power grid equipment; equipment type tags are added to the standard equipment data packages. The coordinate information of point cloud data in actual device data packets is absolute coordinates; the coordinate information of point cloud data in standard device data packets is relative coordinates; geographic information is used to convert the relative coordinates in standard device data packets into absolute coordinates; Based on absolute coordinates, the point cloud data in the standard device data package is compared one by one with the point cloud data in the actual device data package. The difference point cloud data in the actual device data package is extracted and a difference data package is generated. The corresponding device type label is added to the difference data package. The point cloud data storage module is used to store point cloud data to distributed storage nodes based on the classification results; The point cloud data reconstruction module is used to retrieve the corresponding storage node from the distributed storage nodes according to the user's application, obtain point cloud data from the storage node, and reconstruct the three-dimensional model of the power grid using the point cloud data. A basic 3D model is reconstructed based on standard point cloud data and geographic information, and then the differential point cloud data is overlaid on the basic 3D model to obtain the final 3D model.

7. A computer-readable storage medium storing a computer program, characterized in that, The computer program causes the computer to execute the distributed point cloud data management method as described in any one of claims 1-5.

8. An electronic device, characterized in that, include: The device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, it implements the distributed point cloud data management method as described in any one of claims 1-5.

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