Laser point cloud data management method, system and medium based on point cloud pyramid

Abstract

The present invention discloses a laser point cloud data management method, system, and medium based on a point cloud pyramid. The method includes: obtaining location information of a defect to be repaired and target point cloud data corresponding to the location information, the target point cloud data including point cloud data corresponding to each target charged body and each target grounded body within a preset range of the defect to be repaired; storing the target point cloud data based on the target point cloud data, extracting the location information as an index condition to store the target point cloud data in a constructed point cloud pyramid; obtaining a search data packet, obtaining the corresponding index condition based on the search data packet to query the point cloud pyramid, obtaining the target point cloud data corresponding to the current search data packet, and outputting the data. The present invention can improve the efficiency of managing point cloud data corresponding to the defect to be repaired by constructing a point cloud pyramid, and can also improve data retrieval efficiency when a user searches.

Description

Technical Field

[0001] The present invention relates to the technical field of data management, and more specifically, to a laser point cloud data management method, system and medium based on point cloud pyramid. Background Art

[0002] Nowadays, with the increasing demand for electricity in residents' lives and production, power grid companies have also adopted a variety of methods in the daily maintenance of power grid equipment, including manual patrols and inspections, live on-site operations, or the establishment of many automated control systems to ensure the normal operation of power grid equipment.

[0003] With the advancement of science and technology, 3D laser scanning technology, due to its high measurement accuracy, high sampling density, and convenient data acquisition, has been applied to power grid equipment maintenance, becoming one of the primary means of rapidly acquiring 3D spatial information. However, due to the high sampling frequency of laser equipment, it can generate hundreds of thousands, millions, or even tens of millions of 3D spatial points per second. These 3D points contain not only position and time information, but also intensity and echo information. During actual live field operations, a variety of data may be generated, such as power data, equipment parameters, live properties, and other data generated by actual operations, necessitating the orderly management of this data.

[0004] Therefore, a laser point cloud data management method is urgently needed to process massive data. Summary of the Invention

[0005] In order to address the deficiencies in the prior art, the present invention provides a laser point cloud data management method, system, and medium based on a point cloud pyramid. The constructed point cloud pyramid can improve the efficiency of managing point cloud data corresponding to defects to be repaired, and can also improve data retrieval efficiency when users search.

[0006] A first aspect of the present invention provides a laser point cloud data management method based on a point cloud pyramid, comprising the following steps:

[0007] Obtaining location information of the defect to be repaired and target point cloud data corresponding to the location information, wherein the target point cloud data includes point cloud data corresponding to each target charged body within a preset range of the defect to be repaired and point cloud data corresponding to each target grounded body;

[0008] Storing based on the target point cloud data, wherein the position information is extracted as an index condition to store the target point cloud data in the constructed point cloud pyramid;

[0009] A search data packet is obtained, and the corresponding index condition is obtained based on the search data packet to perform a query in the point cloud pyramid, and the target point cloud data corresponding to the current search data packet is obtained and output.

[0010] In this solution, the acquisition of the location information of the defect to be repaired and the target point cloud data corresponding to the location information specifically includes:

[0011] Get the input maintenance data packet;

[0012] Identifying the location information corresponding to the defect to be repaired based on the repair data packet;

[0013] The target point cloud data is acquired based on the position information and a collection device arranged at the defect to be repaired, wherein the collection device includes an image collection device and a scanning measurement device.

[0014] In this solution, the acquisition of the target point cloud data based on the acquisition device provided at the defect to be repaired specifically includes:

[0015] Acquire an operation site image of the defect to be repaired based on the image acquisition device, and perform target image recognition and differentiation based on the operation site image to acquire each of the target charged objects and each of the target grounded objects in the current operation site image;

[0016] Based on the scanning and measuring device, point cloud data of the defect to be repaired is obtained, and based on the recognition result of the image acquisition device, point cloud data corresponding to each target charged body and point cloud data corresponding to each target grounded body are obtained, thereby obtaining the target point cloud data, wherein the scanning and measuring device includes a three-dimensional laser scanning device.

[0017] In this solution, the method further includes constructing the point cloud pyramid, specifically including:

[0018] Clustering is performed based on the point cloud data to obtain point cloud groups corresponding to different position information, wherein the number of point clouds in the point cloud group is positively correlated with the value of the clustering parameter;

[0019] Performing block processing based on the different position information, wherein the Euclidean distance between the standard point and the different position information is calculated, and distance division is performed based on the Euclidean distance to obtain corresponding pyramid levels;

[0020] The point cloud pyramid is constructed based on the pyramid levels and the point cloud number groups corresponding to each level, wherein a data flow direction of each layer in the pyramid level is positively correlated with an ascending arrangement value of the Euclidean distance, and an index reference point in the data flow is the position information.

[0021] In this solution, the storage based on the target point cloud data specifically includes:

[0022] Obtaining location information in the target point cloud data;

[0023] Calculating the target Euclidean distance to the standard point based on the position information;

[0024] Identifying, based on the target Euclidean distance, pyramid level parameters corresponding to the current target point cloud data and corresponding reference point parameters in the data stream;

[0025] The pyramid level corresponding to the current target point cloud data is identified based on the pyramid level parameter, and the index reference point where the current target point cloud data is located in the data stream is identified based on the reference point parameter.

[0026] In this solution, the method further includes:

[0027] Obtaining a reference level corresponding to the target point cloud data currently stored in the point cloud pyramid;

[0028] The current search data level is matched based on the reference level, wherein when the search data level is greater than or equal to the reference level, the target point cloud data corresponding to the current search data packet is output.

[0029] A second aspect of the present invention further provides a laser point cloud data management system based on a point cloud pyramid, comprising a memory and a processor, wherein the memory includes a laser point cloud data management method program based on a point cloud pyramid, and when the laser point cloud data management method program based on a point cloud pyramid is executed by the processor, the following steps are implemented:

[0030] Obtaining location information of the defect to be repaired and target point cloud data corresponding to the location information, wherein the target point cloud data includes point cloud data corresponding to each target charged body within a preset range of the defect to be repaired and point cloud data corresponding to each target grounded body;

[0031] Storing based on the target point cloud data, wherein the position information is extracted as an index condition to store the target point cloud data in the constructed point cloud pyramid;

[0032] A search data packet is obtained, and the corresponding index condition is obtained based on the search data packet to perform a query in the point cloud pyramid, and the target point cloud data corresponding to the current search data packet is obtained and output.

[0033] In this solution, the acquisition of the location information of the defect to be repaired and the target point cloud data corresponding to the location information specifically includes:

[0034] Get the input maintenance data packet;

[0035] Identifying the location information corresponding to the defect to be repaired based on the repair data packet;

[0036] The target point cloud data is acquired based on the position information and a collection device arranged at the defect to be repaired, wherein the collection device includes an image collection device and a scanning measurement device.

[0037] In this solution, the acquisition of the target point cloud data based on the acquisition device provided at the defect to be repaired specifically includes:

[0038] Acquire an operation site image of the defect to be repaired based on the image acquisition device, and perform target image recognition and differentiation based on the operation site image to acquire each of the target charged objects and each of the target grounded objects in the current operation site image;

[0039] Based on the scanning and measuring device, point cloud data of the defect to be repaired is obtained, and based on the recognition result of the image acquisition device, point cloud data corresponding to each target charged body and point cloud data corresponding to each target grounded body are obtained, thereby obtaining the target point cloud data, wherein the scanning and measuring device includes a three-dimensional laser scanning device.

[0040] In this solution, the method further includes constructing the point cloud pyramid, specifically including:

[0041] Clustering is performed based on the point cloud data to obtain point cloud groups corresponding to different position information, wherein the number of point clouds in the point cloud group is positively correlated with the value of the clustering parameter;

[0042] Performing block processing based on the different position information, wherein the Euclidean distance between the standard point and the different position information is calculated, and distance division is performed based on the Euclidean distance to obtain corresponding pyramid levels;

[0043] The point cloud pyramid is constructed based on the pyramid levels and the point cloud number groups corresponding to each level, wherein a data flow direction of each layer in the pyramid level is positively correlated with an ascending arrangement value of the Euclidean distance, and an index reference point in the data flow is the position information.

[0044] In this solution, the storage based on the target point cloud data specifically includes:

[0045] Obtaining location information in the target point cloud data;

[0046] Calculating the target Euclidean distance to the standard point based on the position information;

[0047] Identifying, based on the target Euclidean distance, pyramid level parameters corresponding to the current target point cloud data and corresponding reference point parameters in the data stream;

[0048] The pyramid level corresponding to the current target point cloud data is identified based on the pyramid level parameter, and the index reference point where the current target point cloud data is located in the data stream is identified based on the reference point parameter.

[0049] This plan also includes:

[0050] Obtaining a reference level corresponding to the target point cloud data currently stored in the point cloud pyramid;

[0051] The current search data level is matched based on the reference level, wherein when the search data level is greater than or equal to the reference level, the target point cloud data corresponding to the current search data packet is output.

[0052] The third aspect of the present invention provides a computer-readable storage medium, which includes a machine-readable laser point cloud data management method program based on a point cloud pyramid. When the laser point cloud data management method program based on a point cloud pyramid is executed by a processor, the steps of the laser point cloud data management method based on a point cloud pyramid as described in any one of the above items are implemented.

[0053] The beneficial effects of the present invention are that, compared with the prior art, the point cloud pyramid constructed can improve the efficiency of managing point cloud data corresponding to defects to be repaired, and can improve data retrieval efficiency when users search.

[0054] The beneficial effects of the present invention also include: using the position information as an index condition during storage to store the target point cloud data in the constructed point cloud pyramid, which can more effectively distinguish and retrieve the point cloud data;

[0055] The data flow direction of each layer in the pyramid hierarchy is positively correlated with the ascending order of the Euclidean distance, and location information is used as the index reference point in the data flow, which solves the problem of unreasonable distribution in traditional pyramid structures and realizes efficient and reliable data storage and scheduling.

[0056] Setting the retrieval data level and the review level prevents data leakage and maintains the security of transmission line data. The present invention dynamically changes the level of the point cloud pyramid according to the mean calculation results of different location information, solving the problem of a large amount of point cloud data appearing in Euclidean distances with a small difference and very little point cloud data appearing in individual levels, thereby avoiding excessive data concentration. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1A flowchart of a laser point cloud data management method based on a point cloud pyramid is shown in the present invention;

[0058] Figure 2 A pyramid structure diagram of a laser point cloud data management method based on a point cloud pyramid according to the present invention is shown;

[0059] Figure 3 A block diagram of a laser point cloud data management system based on a point cloud pyramid according to the present invention is shown. DETAILED DESCRIPTION

[0060] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. The embodiments described in this application are only part of the embodiments of the present invention, not all of them. Based on the spirit of the present invention, other embodiments obtained by ordinary technicians in this field without making creative efforts are all within the scope of protection of the present invention. Figure 1 A flowchart of a laser point cloud data management method based on a point cloud pyramid is shown in the present application.

[0061] Figure 1 A flowchart of a laser point cloud data management method based on a point cloud pyramid is shown in the present application.

[0062] like Figure 1 As shown, the present application discloses a laser point cloud data management method based on point cloud pyramid, comprising the following steps:

[0063] S102, obtaining location information of the defect to be repaired and target point cloud data corresponding to the location information, wherein the target point cloud data includes point cloud data corresponding to each target charged body within a preset range of the defect to be repaired and point cloud data corresponding to each target grounded body;

[0064] S104, storing based on the target point cloud data, wherein the position information is extracted as an index condition to store the target point cloud data in the constructed point cloud pyramid;

[0065] S106 , obtaining a search data packet, obtaining the corresponding index condition based on the search data packet to perform a search in the point cloud pyramid, obtaining the target point cloud data corresponding to the current search data packet and outputting the result.

[0066] It should be noted that, in this embodiment, the laser point cloud data management method based on the point cloud pyramid is described, so the technical content involved includes constructing the point cloud pyramid and obtaining the laser point cloud data, as well as how to manage the laser point cloud data in an orderly manner. The detailed steps of constructing the point cloud pyramid are described in the subsequent description, and the laser point cloud data can be obtained based on the acquisition device (such as a three-dimensional laser scanning device) set at the defect to be repaired. The target point cloud data obtained includes the point cloud data corresponding to each target charged body within the preset range of the defect to be repaired, and the point cloud data corresponding to each target grounded body. After obtaining the target point cloud data, the corresponding point cloud data is stored in the constructed point cloud pyramid for orderly management. During storage, the position information is extracted as an index condition to store the target point cloud data in the constructed point cloud pyramid. The position information is used as a retrieval condition to more effectively distinguish and retrieve the point cloud data. Therefore, during actual retrieval, when a retrieval data packet is obtained, the corresponding index condition can be obtained based on the retrieval data packet, so that a query can be performed in the point cloud pyramid to obtain the target point cloud data corresponding to the current retrieval data packet, and then output.

[0067] According to an embodiment of the present invention, obtaining the location information of the defect to be repaired and the target point cloud data corresponding to the location information specifically includes:

[0068] Get the input maintenance data packet;

[0069] Identifying the location information corresponding to the defect to be repaired based on the repair data packet;

[0070] The target point cloud data is acquired based on the position information and a collection device arranged at the defect to be repaired, wherein the collection device includes an image collection device and a scanning measurement device.

[0071] It should be noted that, in this embodiment, to obtain the target point cloud data, it is necessary to first obtain the input maintenance data packet, and then perform data analysis based on the maintenance data packet to identify the position information corresponding to the defect to be repaired, so that the point cloud data can be obtained based on the acquisition device set at the defect to be repaired, wherein the acquisition device includes an image acquisition device and a scanning measurement device. For example, the maintenance data packet input by the user describes that the defect at position A needs to be repaired. Therefore, after obtaining the current maintenance data packet, the identified defect to be repaired is position A, so that the target point cloud data can be obtained based on the acquisition device set at position A. The specific acquisition steps are described in the subsequent instructions.

[0072] According to an embodiment of the present invention, acquiring the target point cloud data based on a collection device provided at the defect to be repaired specifically includes:

[0073] Acquire an operation site image of the defect to be repaired based on the image acquisition device, and perform target image recognition and differentiation based on the operation site image to acquire each of the target charged objects and each of the target grounded objects in the current operation site image;

[0074] Based on the scanning and measuring device, point cloud data of the defect to be repaired is obtained, and based on the recognition result of the image acquisition device, point cloud data corresponding to each target charged body and point cloud data corresponding to each target grounded body are obtained, thereby obtaining the target point cloud data, wherein the scanning and measuring device includes a three-dimensional laser scanning device.

[0075] It should be noted that, in this embodiment, the above embodiment illustrates that the target point cloud data can be obtained based on the acquisition device, wherein the acquisition device includes an image acquisition device and a scanning measurement device. Specifically, based on the image acquisition device, an image of the working site at the defect to be repaired is acquired, and based on the working site image, target image recognition and differentiation are performed to obtain each of the target charged bodies and each of the target grounded bodies in the current working site image. wherein, object analysis based on the image is a technical means that can be implemented by those skilled in the art, and will not be elaborated here. further, based on the scanning measurement device, point cloud data of the defect to be repaired is acquired, and based on the recognition result of the image acquisition device, each target charged body and each target grounded body are acquired. The point cloud data corresponding to the target charged body and the point cloud data corresponding to each target grounded body are obtained, thereby obtaining the target point cloud data, wherein the scanning and measuring device includes a three-dimensional laser scanning device. Specifically, since the image acquisition device can identify the corresponding target charged body and the target grounded body, the point cloud data of the corresponding target charged body and the point cloud data of the target grounded body can be obtained according to the three-dimensional laser scanning device. Specifically, during implementation, it is only necessary to perform coordinate matching between the work site image acquired by the image acquisition and the point cloud data scanned by the three-dimensional laser scanning device, so that the position of the target charged body identified by the image acquisition device in the scanned point cloud data can be obtained, thereby obtaining the point cloud data corresponding to the target charged body.

[0076] According to an embodiment of the present invention, the method further includes constructing the point cloud pyramid, specifically comprising:

[0077] Clustering is performed based on the point cloud data to obtain point cloud groups corresponding to different position information, wherein the number of point clouds in the point cloud group is positively correlated with the value of the clustering parameter;

[0078] Performing block processing based on the different position information, wherein the Euclidean distance between the standard point and the different position information is calculated, and distance division is performed based on the Euclidean distance to obtain corresponding pyramid levels;

[0079] The point cloud pyramid is constructed based on the pyramid levels and the point cloud number groups corresponding to each level, wherein a data flow direction of each layer in the pyramid level is positively correlated with an ascending arrangement value of the Euclidean distance, and an index reference point in the data flow is the position information.

[0080] It should be noted that, in this embodiment, the steps of constructing the point cloud pyramid are described, wherein the constructed point cloud pyramid is divided into blocks based on different said position information, thereby obtaining corresponding pyramid levels, wherein the Euclidean distance between different position information is calculated based on the standard point, and the distance is divided by the Euclidean distance. Preferably, the standard point is a point cloud group corresponding to the preset position information, which can be input by the user. For example, "3m" is a level, and the point cloud data corresponding to each position information is obtained by clustering to obtain a different point cloud group. Accordingly, the number of point clouds in the point cloud group of each position information in each level is positively correlated with the size value of the clustering parameter. When the distance is used as the clustering parameter, the greater the distance, the greater the number of point clouds obtained by clustering. After the point cloud group and pyramid levels are constructed, the point cloud pyramid can be constructed based on the pyramid levels and the point cloud group corresponding to each level, wherein the data flow direction of each layer in the pyramid level is positively correlated with the ascending value of the Euclidean distance, and the index reference point in the data flow is the said position information. Specifically, as Figure 2 As shown, with the standard point O as the starting point, the higher the pyramid level, the greater the Euclidean distance between the point cloud group in the corresponding level and the standard point. Correspondingly, in the same level, the direction of the data flow is positively correlated with the ascending order of the Euclidean distance, that is, in the same level, the later the point cloud group in the data flow, the greater the Euclidean distance between its corresponding position information and the standard point. For example, in the first level, there are three point cloud groups α, β, and μ, and the Euclidean distance relationship between their corresponding position relationship and the standard point is D α <D β <D μ , where D α is the Euclidean distance between the position relationship of the point cloud group α and the standard point, D β is the Euclidean distance between the position relationship of the point cloud group β and the standard point, D μ is the Euclidean distance between the positional relationship of the point cloud group μ and the standard point.

[0081] According to an embodiment of the present invention, the storing based on the target point cloud data specifically includes:

[0082] Obtaining location information in the target point cloud data;

[0083] Calculating the target Euclidean distance to the standard point based on the position information;

[0084] Identifying, based on the target Euclidean distance, pyramid level parameters corresponding to the current target point cloud data and corresponding reference point parameters in the data stream;

[0085] The pyramid level corresponding to the current target point cloud data is identified based on the pyramid level parameter, and the index reference point where the current target point cloud data is located in the data stream is identified based on the reference point parameter.

[0086] The process of determining the reference point parameters includes: comparing the Euclidean distances corresponding to the point cloud data stored in the pyramid level to be stored, and if the current target Euclidean distance is the Nth in the ascending sequence of the Euclidean distances of the positional relationship corresponding to the point cloud data stored in the level, then determining the Nth reference point of the data stream to store the current target point cloud data.

[0087] It should be noted that, in this embodiment, the above embodiment illustrates that in the point cloud pyramid, the level is related to the Euclidean distance between the position information and the standard point, and in the same level, the position of the point cloud group is related to the ascending order of the Euclidean distance, wherein, first, the position information in the current target point cloud data is obtained, so that the target Euclidean distance between the point and the standard point can be calculated based on the position information, so that the level and the final position (index reference point) in the corresponding pyramid level can be divided based on the target Euclidean distance. Specifically, the pyramid level parameters corresponding to the current target point cloud data and the corresponding reference point parameters in the data stream are identified based on the target Euclidean distance. In the above embodiment, "3m" is described as a level, so it can be based on the target Euclidean distance. The pyramid level parameter corresponding to the current target point cloud data is obtained according to the Euclidean distance. For example, if the current target Euclidean distance is "5m", the corresponding pyramid level parameter is "2". Therefore, the current target point cloud data is located in the second level of the pyramid level. The corresponding reference point parameter is compared according to the Euclidean distance corresponding to the point cloud data stored in the second level of the pyramid level to be stored. For example, the Euclidean distances corresponding to the positional relationships of the point cloud data stored in the second level of the pyramid level to be stored are "3.6m", "4.2m", "4.5m", "4.6m" and "5.8m" respectively. Then, when the current target Euclidean distance is "5m", the corresponding reference point parameter is "5", that is, the fifth reference point of the data stream is used to store the current target point cloud data.

[0088] According to an embodiment of the present invention, the method further includes:

[0089] Obtaining a reference level corresponding to the target point cloud data currently stored in the point cloud pyramid;

[0090] The current search data level is matched based on the reference level, wherein when the search data level is greater than or equal to the reference level, the target point cloud data corresponding to the current search data packet is output.

[0091] It should be noted that, in this embodiment, when the search data packet is obtained, the corresponding point cloud data will be queried according to the index condition (position information) in the data packet. In actual application, it is necessary to obtain the reference level corresponding to the target point cloud data currently stored in the point cloud pyramid, which means that the point cloud data that the current search data packet wants to query has a reference level set in advance when it is stored in the point cloud pyramid. Therefore, the current search data level can be matched based on the reference level. Only when the search data level is greater than or equal to the reference level, the query is allowed, and the target point cloud data corresponding to the current search data packet is output. Otherwise, the query is not performed, thereby avoiding data leakage.

[0092] In a preferred but non-limiting embodiment of the present invention, when certain point cloud data is stored in the point cloud pyramid, its reference level is set to level three. After obtaining a certain search data packet, it is identified that the search data level of the search data packet is level four, and the search data level is greater than or equal to the reference level. The position information in the data packet is used to query the corresponding point cloud data.

[0093] It is worth mentioning that the method further includes:

[0094] Obtaining the stored data of the point cloud pyramid;

[0095] Distance recognition is performed based on the stored data, thereby dynamically deforming the point cloud pyramid based on the distance recognition result, wherein the level of the point cloud pyramid is dynamically changed based on the distance recognition result.

[0096] It should be noted that, in this embodiment, the above embodiment illustrates that the level of the point cloud pyramid can be "3m" per level, but in application, it may happen that all point cloud data are located in the first level or the first two levels. Therefore, in order to make data storage more balanced and query more efficient, it is necessary to identify the distance between the position information currently stored in the point cloud pyramid and the standard point to re-divide the levels, and perform dynamic deformation based on the distance recognition result to change the Euclidean distance between the corresponding levels of the point cloud pyramid, thereby dynamically changing the levels of the point cloud pyramid.

[0097] It is worth mentioning that the level of the point cloud pyramid is dynamically changed based on the distance recognition result, specifically including:

[0098] Obtaining different position information stored in the current point cloud pyramid;

[0099] Performing mean calculation based on the position information, and using the result of the mean calculation as the intermediate level;

[0100] The upper half mean and the lower half mean are calculated based on the mean result, so that the upper half mean result is used as the standard point and the middle level stratification, and the lower half mean result is used as the stratification between the middle level and the last level.

[0101] It should be noted that, in this embodiment, the position information is used as a parameter for dynamically changing the level, wherein different position information corresponds to different levels, and the Euclidean distance between all position information and the standard point is taken to calculate the average, so that the current average result is used as the dividing line of the middle level. If the distance between the position information and the standard point is less than the average result, it is located between the standard point and the middle level; if the distance between the position information and the standard point is greater than or equal to the average result, it is located between the middle level and the last level. Specifically, for the point cloud data between the standard point and the middle level, it is also necessary to calculate the upper half average between the standard point and the middle level, from The point cloud data within this level are further differentiated, wherein the distance between the position information and the standard point is less than the upper half mean and is located in the first level, and the distance between the position information and the standard point is greater than or equal to the upper half mean and is located in the second level; and for the point cloud data between the middle level and the last level, it is necessary to calculate the lower half mean, and the middle level and the last level are divided based on the lower half mean result. Specifically, the distance between the position information and the standard point is less than the lower half mean and is located in the third level, and the distance between the position information and the standard point is greater than or equal to the lower half mean and is located in the fourth level.

[0102] It is worth mentioning that the method of dynamically changing the level of the point cloud pyramid based on the distance recognition result further includes:

[0103] Obtain information on the number of different Euclidean distances stored in the current point cloud pyramid;

[0104] Performing mean calculation based on the quantity information, and using the result of the mean calculation as the intermediate level;

[0105] Based on the mean result, the upper half mean and the lower half mean are calculated, and the upper half mean result is used as the standard point and the middle level classification, and the lower half mean result is used as the middle level classification with the last level. On the basis of dividing the levels by distance, the quantity information corresponding to the Euclidean distance is used as the parameter for dynamically changing the level, wherein different Euclidean distance quantity information corresponds to different levels, and the average of all Euclidean distance quantities and the number of levels in the level is calculated. The current average result is used as the dividing line for whether the level is divided again. If the number of point cloud data in a certain level is lower than the mean, no further classification is performed. If the number of point cloud data in the level is greater than the mean, further classification is performed. For example, the pyramid currently has three layers. The first layer is divided by a Euclidean distance of 1 meter, and the layer contains 30 Euclidean distance values; the second layer is divided by a Euclidean distance of 3 meters, and the layer contains 100 Euclidean distance values; the first layer is divided by a Euclidean distance of 5 meters, and the layer contains 20 Euclidean distance values; the calculated mean result is (30+100+20) / 3=50. If the number of Euclidean distance information in the second layer is greater than the mean result, the layer is divided into two layers: 2 meters and 3 meters.

[0106] It should be noted that, in this embodiment, the above embodiment describes the definition of the level of the point cloud pyramid based on distance as a reference. In actual application, a large amount of point cloud data will appear in Euclidean distances with a small difference, while very little point cloud data will appear in the levels in the second half that span a large distance. At this time, simply using distance as a reference will cause the data to be too concentrated. Therefore, it is necessary to distinguish based on the number of Euclidean distances between the location information and the standard point. Among them, the step of distinguishing by quantity is consistent with the distance distinction, that is, calculating the mean of the quantity, and then making a secondary distinction on the basis of the distance division, thereby further refining the storage form.

[0107] It is worth mentioning that the method further includes nesting another point cloud pyramid in the point cloud pyramid, specifically including:

[0108] Get the layer distance of the current point cloud pyramid;

[0109] The layered distance is used as the total distance of another nested point cloud pyramid; and secondary layering is performed based on the total distance to complete the nesting operation of the point cloud pyramid.

[0110] It should be noted that, in this embodiment, the above embodiment describes the optimization of storage and retrieval queries by secondary division of distance or quantity, and this embodiment describes the optimization of data storage and query by continuous nesting of point cloud pyramids. Specifically, first obtain the layer distance of the current point cloud pyramid. The above embodiment describes that it can be "3m", and the total distance corresponding to the four-layer pyramid is "12m". Since there may be too much data concentrated in the same level, for example, the data is concentrated in the second layer, the layer distance of the second layer can be used as the total distance in the nested point cloud pyramid for secondary layering, that is, the layer distance of the second layer is "6m", and "6m" is used as the sum of the distances of another nested point cloud pyramid for layering. Then, each layer distance in another nested point cloud pyramid corresponds to "1.5m", thereby optimizing the storage and retrieval queries of point cloud data.

[0111] The process of determining the corresponding reference point parameters includes: comparing the Euclidean distances corresponding to the point cloud data stored in the pyramid level to be stored, and if the current target Euclidean distance is the Nth in the ascending sequence of the Euclidean distances of the positional relationship corresponding to the point cloud data stored in the level, then determining the Nth reference point of the data stream to store the current target point cloud data.

[0112] Figure 3 A block diagram of a laser point cloud data management system based on a point cloud pyramid according to the present invention is shown.

[0113] like Figure 3 As shown, the second aspect of the present invention discloses a laser point cloud data management system based on point cloud pyramid, the system also includes a memory and a processor, the processor

[0114] The laser point cloud data management method program based on the point cloud pyramid is included, and when the laser point cloud data management method program based on the point cloud pyramid is executed by the processor, the following steps are implemented:

[0115] Obtaining location information of the defect to be repaired and target point cloud data corresponding to the location information, wherein the target point cloud data includes point cloud data corresponding to each target charged body within a preset range of the defect to be repaired and point cloud data corresponding to each target grounded body;

[0116] Storing based on the target point cloud data, wherein the position information is extracted as an index condition to store the target point cloud data in the constructed point cloud pyramid;

[0117] A search data packet is obtained, and the corresponding index condition is obtained based on the search data packet to perform a query in the point cloud pyramid, and the target point cloud data corresponding to the current search data packet is obtained and output.

[0118] It should be noted that, in this embodiment, the laser point cloud data management method based on the point cloud pyramid is described, so the technical content involved includes constructing the point cloud pyramid and obtaining the laser point cloud data, as well as how to manage the laser point cloud data in an orderly manner. The detailed steps of constructing the point cloud pyramid are described in the subsequent description, and the laser point cloud data can be obtained based on the acquisition device (such as a three-dimensional laser scanning device) set at the defect to be repaired. The target point cloud data obtained includes the point cloud data corresponding to each target charged body within the preset range of the defect to be repaired, and the point cloud data corresponding to each target grounded body. After obtaining the target point cloud data, the corresponding point cloud data is stored in the constructed point cloud pyramid for orderly management. During storage, the position information is extracted as an index condition to store the target point cloud data in the constructed point cloud pyramid. The position information is used as a retrieval condition to more effectively distinguish and retrieve the point cloud data. Therefore, during actual retrieval, when a retrieval data packet is obtained, the corresponding index condition can be obtained based on the retrieval data packet, so that a query can be performed in the point cloud pyramid to obtain the target point cloud data corresponding to the current retrieval data packet, and then output.

[0119] According to an embodiment of the present invention, obtaining the location information of the defect to be repaired and the target point cloud data corresponding to the location information specifically includes:

[0120] Get the input maintenance data packet;

[0121] Identifying the location information corresponding to the defect to be repaired based on the repair data packet;

[0122] The target point cloud data is acquired based on the position information and a collection device arranged at the defect to be repaired, wherein the collection device includes an image collection device and a scanning measurement device.

[0123] It should be noted that, in this embodiment, to obtain the target point cloud data, it is necessary to first obtain the input maintenance data packet, and then perform data analysis based on the maintenance data packet to identify the position information corresponding to the defect to be repaired, so that the point cloud data can be obtained based on the acquisition device set at the defect to be repaired, wherein the acquisition device includes an image acquisition device and a scanning measurement device. For example, the maintenance data packet input by the user describes that the defect at position A needs to be repaired. Therefore, after obtaining the current maintenance data packet, the identified defect to be repaired is position A, so that the target point cloud data can be obtained based on the acquisition device set at position A. The specific acquisition steps are described in the subsequent instructions.

[0124] According to an embodiment of the present invention, acquiring the target point cloud data based on a collection device provided at the defect to be repaired specifically includes:

[0125] Acquire an operation site image of the defect to be repaired based on the image acquisition device, and perform target image recognition and differentiation based on the operation site image to acquire each of the target charged objects and each of the target grounded objects in the current operation site image;

[0126] Based on the scanning and measuring device, point cloud data of the defect to be repaired is obtained, and based on the recognition result of the image acquisition device, point cloud data corresponding to each target charged body and point cloud data corresponding to each target grounded body are obtained, thereby obtaining the target point cloud data, wherein the scanning and measuring device includes a three-dimensional laser scanning device.

[0127] It should be noted that, in this embodiment, the above embodiment illustrates that the target point cloud data can be obtained based on the acquisition device, wherein the acquisition device includes an image acquisition device and a scanning measurement device. Specifically, based on the image acquisition device, an image of the working site at the defect to be repaired is acquired, and based on the working site image, target image recognition and differentiation are performed to obtain each of the target charged bodies and each of the target grounded bodies in the current working site image. wherein, object analysis based on the image is a technical means that can be implemented by those skilled in the art, and will not be elaborated here. further, based on the scanning measurement device, point cloud data of the defect to be repaired is acquired, and based on the recognition result of the image acquisition device, each target charged body and each target grounded body are acquired. The point cloud data corresponding to the target charged body and the point cloud data corresponding to each target grounded body are obtained, thereby obtaining the target point cloud data, wherein the scanning and measuring device includes a three-dimensional laser scanning device. Specifically, since the image acquisition device can identify the corresponding target charged body and the target grounded body, the point cloud data of the corresponding target charged body and the point cloud data of the target grounded body can be obtained according to the three-dimensional laser scanning device. Specifically, during implementation, it is only necessary to perform coordinate matching between the work site image acquired by the image acquisition and the point cloud data scanned by the three-dimensional laser scanning device, so that the position of the target charged body identified by the image acquisition device in the scanned point cloud data can be obtained, thereby obtaining the point cloud data corresponding to the target charged body.

[0128] According to an embodiment of the present invention, the method further includes constructing the point cloud pyramid, specifically comprising:

[0129] Clustering is performed based on the point cloud data to obtain point cloud groups corresponding to different position information, wherein the number of point clouds in the point cloud group is positively correlated with the value of the clustering parameter;

[0130] Performing block processing based on the different position information, wherein the Euclidean distance between the standard point and the different position information is calculated, and distance division is performed based on the Euclidean distance to obtain corresponding pyramid levels;

[0131] The point cloud pyramid is constructed based on the pyramid levels and the point cloud number groups corresponding to each level, wherein a data flow direction of each layer in the pyramid level is positively correlated with an ascending arrangement value of the Euclidean distance, and an index reference point in the data flow is the position information.

[0132] It should be noted that, in this embodiment, the steps of constructing the point cloud pyramid are described, wherein the constructed point cloud pyramid is divided into blocks based on different said position information, thereby obtaining corresponding pyramid levels, wherein the Euclidean distance between different position information is calculated based on the standard point, and the distance is divided by the Euclidean distance. Preferably, the standard point is a point cloud group corresponding to the preset position information, which can be input by the user. For example, "3m" is a level, and the point cloud data corresponding to each position information is obtained by clustering to obtain a different point cloud group. Accordingly, the number of point clouds in the point cloud group of each position information in each level is positively correlated with the size value of the clustering parameter. When the distance is used as the clustering parameter, the greater the distance, the greater the number of point clouds obtained by clustering. After the point cloud group and pyramid levels are constructed, the point cloud pyramid can be constructed based on the pyramid levels and the point cloud group corresponding to each level, wherein the data flow direction of each layer in the pyramid level is positively correlated with the ascending value of the Euclidean distance, and the index reference point in the data flow is the said position information. Specifically, as Figure 2 As shown, with the standard point O as the starting point, the higher the pyramid level, the greater the Euclidean distance between the point cloud group in the corresponding level and the standard point. Correspondingly, in the same level, the direction of the data flow is positively correlated with the ascending order of the Euclidean distance, that is, in the same level, the later the point cloud group in the data flow, the greater the Euclidean distance between its corresponding position information and the standard point. For example, in the first level, there are three point cloud groups α, β, and μ, and the Euclidean distance relationship between their corresponding position relationship and the standard point is D a <D β <D μ , where D α is the Euclidean distance between the position relationship of the point cloud group α and the standard point, D β is the Euclidean distance between the position relationship of the point cloud group β and the standard point, D μ is the Euclidean distance between the positional relationship of the point cloud group μ and the standard point.

[0133] According to an embodiment of the present invention, the storing based on the target point cloud data specifically includes:

[0134] Obtaining location information in the target point cloud data;

[0135] Calculating the target Euclidean distance to the standard point based on the position information;

[0136] Identifying, based on the target Euclidean distance, pyramid level parameters corresponding to the current target point cloud data and corresponding reference point parameters in the data stream;

[0137] The pyramid level corresponding to the current target point cloud data is identified based on the pyramid level parameter, and the index reference point where the current target point cloud data is located in the data stream is identified based on the reference point parameter.

[0138] The process of determining the reference point parameters includes: comparing the Euclidean distances corresponding to the point cloud data stored in the pyramid level to be stored, and if the current target Euclidean distance is the Nth in the ascending sequence of the Euclidean distances of the positional relationship corresponding to the point cloud data stored in the level, then determining the Nth reference point of the data stream to store the current target point cloud data.

[0139] It should be noted that, in this embodiment, the above embodiment illustrates that in the point cloud pyramid, the level is related to the Euclidean distance between the position information and the standard point, and in the same level, the position of the point cloud group is related to the ascending order of the Euclidean distance, wherein, first, the position information in the current target point cloud data is obtained, so that the target Euclidean distance between the point and the standard point can be calculated based on the position information, so that the level and the final position (index reference point) in the corresponding pyramid level can be divided based on the target Euclidean distance. Specifically, the pyramid level parameters corresponding to the current target point cloud data and the corresponding reference point parameters in the data stream are identified based on the target Euclidean distance. In the above embodiment, "3m" is described as a level, so it can be based on the target Euclidean distance. The pyramid level parameter corresponding to the current target point cloud data is obtained according to the Euclidean distance. For example, if the current target Euclidean distance is "5m", the corresponding pyramid level parameter is "2". Therefore, the current target point cloud data is located in the second level of the pyramid level. The corresponding reference point parameter is compared according to the Euclidean distance corresponding to the point cloud data stored in the second level of the pyramid level to be stored. For example, the Euclidean distances corresponding to the positional relationships of the point cloud data stored in the second level of the pyramid level to be stored are "3.6m", "4.2m", "4.5m", "4.6m" and "5.8m" respectively. Then, when the current target Euclidean distance is "5m", the corresponding reference point parameter is "5", that is, the fifth reference point of the data stream is used to store the current target point cloud data.

[0140] According to an embodiment of the present invention, the method further includes:

[0141] Obtaining a reference level corresponding to the target point cloud data currently stored in the point cloud pyramid;

[0142] The current search data level is matched based on the reference level, wherein when the search data level is greater than or equal to the reference level, the target point cloud data corresponding to the current search data packet is output.

[0143] It should be noted that, in this embodiment, when the search data packet is obtained, the corresponding point cloud data will be queried according to the index condition (position information) in the data packet. In actual application, it is necessary to obtain the reference level corresponding to the target point cloud data currently stored in the point cloud pyramid, which means that the point cloud data that the current search data packet wants to query has a reference level set in advance when it is stored in the point cloud pyramid. Therefore, the current search data level can be matched based on the reference level. Only when the search data level is greater than or equal to the reference level, the query is allowed, and the target point cloud data corresponding to the current search data packet is output. Otherwise, the query is not performed, thereby avoiding data leakage.

[0144] In a preferred but non-limiting embodiment of the present invention, when certain point cloud data is stored in the point cloud pyramid, its reference level is set to level three. After obtaining a certain search data packet, it is identified that the search data level of the search data packet is level four, and the search data level is greater than or equal to the reference level. The position information in the data packet is used to query the corresponding point cloud data.

[0145] It is worth mentioning that the method further includes:

[0146] Obtaining the stored data of the point cloud pyramid;

[0147] Distance recognition is performed based on the stored data, thereby dynamically deforming the point cloud pyramid based on the distance recognition result, wherein the level of the point cloud pyramid is dynamically changed based on the distance recognition result.

[0148] It should be noted that, in this embodiment, the above embodiment illustrates that the level of the point cloud pyramid can be "3m" per level, but in application, it may happen that all point cloud data are located in the first level or the first two levels. Therefore, in order to make data storage more balanced and query more efficient, it is necessary to identify the distance between the position information currently stored in the point cloud pyramid and the standard point to re-divide the levels, and perform dynamic deformation based on the distance recognition result to change the Euclidean distance between the corresponding levels of the point cloud pyramid, thereby dynamically changing the levels of the point cloud pyramid.

[0149] It is worth mentioning that the level of the point cloud pyramid is dynamically changed based on the distance recognition result, specifically including:

[0150] Obtaining different position information stored in the current point cloud pyramid;

[0151] Performing mean calculation based on the position information, and using the result of the mean calculation as the intermediate level;

[0152] The upper half mean and the lower half mean are calculated based on the mean result, so that the upper half mean result is used as the standard point and the middle level stratification, and the lower half mean result is used as the stratification between the middle level and the last level.

[0153] It should be noted that, in this embodiment, the position information is used as a parameter for dynamically changing the level, wherein different position information corresponds to different levels, and the Euclidean distance between all position information and the standard point is taken to calculate the average, so that the current average result is used as the dividing line of the middle level. If the distance between the position information and the standard point is less than the average result, it is located between the standard point and the middle level; if the distance between the position information and the standard point is greater than or equal to the average result, it is located between the middle level and the last level. Specifically, for the point cloud data between the standard point and the middle level, it is also necessary to calculate the upper half average between the standard point and the middle level, from The point cloud data within this level are further differentiated, wherein the distance between the position information and the standard point is less than the upper half mean and is located in the first level, and the distance between the position information and the standard point is greater than or equal to the upper half mean and is located in the second level; and for the point cloud data between the middle level and the last level, it is necessary to calculate the lower half mean, and the middle level and the last level are divided based on the lower half mean result. Specifically, the distance between the position information and the standard point is less than the lower half mean and is located in the third level, and the distance between the position information and the standard point is greater than or equal to the lower half mean and is located in the fourth level.

[0154] It is worth mentioning that the method of dynamically changing the level of the point cloud pyramid based on the distance recognition result further includes:

[0155] Obtain information on the number of different Euclidean distances stored in the current point cloud pyramid;

[0156] Performing mean calculation based on the quantity information, and using the result of the mean calculation as the intermediate level;

[0157] Based on the mean result, the upper half mean and the lower half mean are calculated, and the upper half mean result is used as the standard point and the middle level classification, and the lower half mean result is used as the middle level classification with the last level. On the basis of dividing the levels by distance, the quantity information corresponding to the Euclidean distance is used as the parameter for dynamically changing the level, wherein different Euclidean distance quantity information corresponds to different levels, and the average of all Euclidean distance quantities and the number of levels in the level is calculated. The current average result is used as the dividing line for whether the level is divided again. If the number of point cloud data in a certain level is lower than the mean, no further classification is performed. If the number of point cloud data in the level is greater than the mean, further classification is performed. For example, the pyramid currently has three layers. The first layer is divided by a Euclidean distance of 1 meter, and the layer contains 30 Euclidean distance values; the second layer is divided by a Euclidean distance of 3 meters, and the layer contains 100 Euclidean distance values; the first layer is divided by a Euclidean distance of 5 meters, and the layer contains 20 Euclidean distance values; the calculated mean result is (30+100+20) / 3=50. If the number of Euclidean distance information in the second layer is greater than the mean result, the layer is divided into two layers: 2 meters and 3 meters.

[0158] It should be noted that, in this embodiment, the above embodiment describes the definition of the level of the point cloud pyramid based on distance as a reference. In actual application, a large amount of point cloud data will appear in Euclidean distances with a small difference, while very little point cloud data will appear in the levels in the second half that span a large distance. At this time, simply using distance as a reference will cause the data to be too concentrated. Therefore, it is necessary to distinguish based on the number of Euclidean distances between the location information and the standard point. Among them, the step of distinguishing by quantity is consistent with the distance distinction, that is, calculating the mean of the quantity, and then making a secondary distinction on the basis of the distance division, thereby further refining the storage form.

[0159] It is worth mentioning that the method further includes nesting another point cloud pyramid in the point cloud pyramid, specifically including:

[0160] Get the layer distance of the current point cloud pyramid;

[0161] The layered distance is used as the total distance of another nested point cloud pyramid; and secondary layering is performed based on the total distance to complete the nesting operation of the point cloud pyramid.

[0162] It should be noted that, in this embodiment, the above embodiment describes the optimization of storage and retrieval queries by secondary division of distance or quantity, and this embodiment describes the optimization of data storage and query by continuous nesting of point cloud pyramids. Specifically, first obtain the layer distance of the current point cloud pyramid. The above embodiment describes that it can be "3m", and the total distance corresponding to the four-layer pyramid is "12m". Since there may be too much data concentrated in the same level, for example, the data is concentrated in the second layer, the layer distance of the second layer can be used as the total distance in the nested point cloud pyramid for secondary layering, that is, the layer distance of the second layer is "6m", and "6m" is used as the sum of the distances of another nested point cloud pyramid for layering. Then, each layer distance in another nested point cloud pyramid corresponds to "1.5m", thereby optimizing the storage and retrieval queries of point cloud data.

[0163] The beneficial effect of the present invention is that, compared with the prior art, the laser point cloud data management system based on the point cloud pyramid disclosed in the present application can improve the efficiency of managing the point cloud data corresponding to the defects to be repaired through the constructed point cloud pyramid, and can improve the data retrieval efficiency when the user searches.

[0164] The beneficial effects of the present invention also include: using the position information as an index condition during storage to store the target point cloud data in the constructed point cloud pyramid, which can more effectively distinguish and retrieve the point cloud data;

[0165] The data flow direction of each layer in the pyramid hierarchy is positively correlated with the ascending order of the Euclidean distance, and location information is used as the index reference point in the data flow, which solves the problem of unreasonable distribution in traditional pyramid structures and realizes efficient and reliable data storage and scheduling.

[0166] Setting the retrieval data level and the review level prevents data leakage and maintains the security of transmission line data. The present invention dynamically changes the level of the point cloud pyramid according to the mean calculation results of different location information, solving the problem of a large amount of point cloud data appearing in Euclidean distances with a small difference and very little point cloud data appearing in individual levels, thereby avoiding excessive data concentration.

[0167] On the other hand, the present application also discloses a terminal, including a processor and a storage medium; wherein:

[0168] The storage medium is used to store instructions;

[0169] The processor is configured to operate according to the instructions to execute the steps disclosed in any of the aforementioned method embodiments.

[0170] Compared with the prior art, the terminal disclosed in the present invention can improve the efficiency of managing point cloud data corresponding to defects to be repaired by constructing a point cloud pyramid, and can also improve data retrieval efficiency when users search. When storing, the target point cloud data is stored in the constructed point cloud pyramid using location information as an index condition, which can more effectively distinguish and retrieve and output the point cloud data.

[0171] The data flow direction of each layer in the pyramid hierarchy is positively correlated with the ascending order of the Euclidean distance, and location information is used as the index reference point in the data flow, which solves the problem of unreasonable distribution in traditional pyramid structures and realizes efficient and reliable data storage and scheduling.

[0172] Setting the retrieval data level and the review level prevents data leakage and maintains the security of transmission line data. The present invention dynamically changes the level of the point cloud pyramid according to the mean calculation results of different location information, solving the problem of a large amount of point cloud data appearing in Euclidean distances with a small difference and very little point cloud data appearing in individual levels, thereby avoiding excessive data concentration.

[0173] Finally, the present application also discloses a computer-readable storage medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the steps disclosed in any of the aforementioned method embodiments.

[0174] The present invention discloses a computer-readable storage medium. Compared with the prior art, the point cloud pyramid constructed can improve the efficiency of managing point cloud data corresponding to defects to be repaired, and can also improve data retrieval efficiency when users search. When storing, the target point cloud data is stored in the constructed point cloud pyramid using location information as an index condition, which can more effectively distinguish and retrieve and output the point cloud data.

[0175] The data flow direction of each layer in the pyramid hierarchy is positively correlated with the ascending order value of the Euclidean distance, and the location information is used as the index reference point in the data flow, which solves the problem of unreasonable distribution in the traditional pyramid structure and realizes efficient and reliable data storage and scheduling. The retrieval data level and the reference level are set to avoid data leakage and maintain the security of transmission line data. The present invention dynamically changes the level of the point cloud pyramid according to the mean calculation results of different location information, solves the problem of a large amount of point cloud data appearing in Euclidean distances with similar differences and very little point cloud data appearing in individual levels, and avoids excessive concentration of data.

[0176] The present disclosure may be a system, method and / or computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0177] A computer-readable storage medium can be a tangible device that can hold and store instructions for use by an instruction execution device. A computer-readable storage medium can be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination thereof. As used herein, a computer-readable storage medium is not to be construed as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through an electrical wire.

[0178] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions to be stored in the computer-readable storage medium in each computing / processing device.

[0179] The computer program instructions for performing the operations of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, and conventional procedural programming languages ​​such as "C" language or similar programming languages. Computer-readable program instructions may be executed entirely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., utilizing an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may be personalized by utilizing the state information of the computer-readable program instructions. The electronic circuit may execute the computer-readable program instructions, thereby realizing various aspects of the present disclosure.

[0180] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

Claims

1. A laser point cloud data management method based on point cloud pyramid, characterized in that: The following steps are involved: Obtaining location information of the defect to be repaired and target point cloud data corresponding to the location information, wherein the target point cloud data includes point cloud data corresponding to each target charged body within a preset range of the defect to be repaired and point cloud data corresponding to each target grounded body; Storing based on the target point cloud data, wherein the position information is extracted as an index condition to store the target point cloud data in the constructed point cloud pyramid; Obtaining a search data packet, obtaining the corresponding index condition based on the search data packet to perform a query in the point cloud pyramid, obtaining the target point cloud data corresponding to the current search data packet and outputting the result; The obtaining of the location information of the defect to be repaired and the target point cloud data corresponding to the location information specifically includes: Get the input maintenance data packet; Identifying the location information corresponding to the defect to be repaired based on the repair data packet; Acquire the target point cloud data based on the position information and a collection device provided at the defect to be repaired, wherein the collection device includes an image collection device and a scanning measurement device; The step of acquiring the target point cloud data based on a collection device provided at the defect to be repaired specifically includes: Acquire an operation site image of the defect to be repaired based on the image acquisition device, and perform target image recognition and differentiation based on the operation site image to acquire each of the target charged objects and each of the target grounded objects in the current operation site image; Based on the scanning and measuring device, point cloud data of the defect to be repaired is obtained, and based on the recognition result of the image acquisition device, point cloud data corresponding to each target charged body and point cloud data corresponding to each target grounded body are obtained, thereby obtaining the target point cloud data, wherein the scanning and measuring device includes a three-dimensional laser scanning device; The method further includes constructing the point cloud pyramid, specifically comprising: Clustering is performed based on the point cloud data to obtain point cloud groups corresponding to different position information, wherein the number of point clouds in the point cloud group is positively correlated with the value of the clustering parameter; Performing block processing based on the different position information, wherein the Euclidean distance between the standard point and the different position information is calculated, and distance division is performed based on the Euclidean distance to obtain corresponding pyramid levels; The point cloud pyramid is constructed based on the pyramid levels and the point cloud number groups corresponding to each level, wherein a data flow direction of each layer in the pyramid level is positively correlated with an ascending arrangement value of the Euclidean distance, and an index reference point in the data flow is the position information.

2. The laser point cloud data management method based on point cloud pyramid according to claim 1, characterized in that: The storing based on the target point cloud data specifically includes: Obtaining location information in the target point cloud data; Calculating the target Euclidean distance to the standard point based on the position information; Identifying, based on the target Euclidean distance, pyramid level parameters corresponding to the current target point cloud data and corresponding reference point parameters in the data stream; The pyramid level corresponding to the current target point cloud data is identified based on the pyramid level parameter, and the index reference point where the current target point cloud data is located in the data stream is identified based on the reference point parameter.

3. The laser point cloud data management method based on point cloud pyramid according to claim 2, characterized in that: The method further comprises: Obtaining a reference level corresponding to the target point cloud data currently stored in the point cloud pyramid; The current search data level is matched based on the reference level, wherein when the search data level is greater than or equal to the reference level, the target point cloud data corresponding to the current search data packet is output.

4. A laser point cloud data management system based on point cloud pyramid, characterized in that: The invention comprises a memory and a processor, wherein the memory comprises a laser point cloud data management method program based on a point cloud pyramid, and when the laser point cloud data management method program based on a point cloud pyramid is executed by the processor, the steps of a laser point cloud data management method based on a point cloud pyramid as claimed in any one of claims 1 to 3 are implemented.

5. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a laser point cloud data management method program based on a point cloud pyramid. When the laser point cloud data management method program based on a point cloud pyramid is executed by a processor, the steps of a laser point cloud data management method based on a point cloud pyramid as described in any one of claims 1 to 3 are implemented.

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