A method, device and storage medium for quickly generating a low-altitude airspace digital base based on high-precision dense point cloud

Abstract

The present invention relates to the technical field of data acquisition systems, and proposes a method, device and storage medium for quickly generating a low-altitude airspace digital base based on a high-precision dense point cloud. The method comprises: an unmanned aerial vehicle sampling module; a survey area zoning calculation module; a dense point cloud generation and processing module; and a digital base calculation generation and storage module, which effectively solves the bottlenecks of uniqueness, readability, scale and correlation of traditional spatial information in identification and expression, realizes efficient organization, processing and application of multi-source and multi-scale data gridding, and breaks through the technical barriers to cross-industry application of geographic spatial information.

Description

Technical Field

[0001] The present invention proposes a method, a device and a storage medium for quickly generating a low-altitude airspace digital base based on a high-precision dense point cloud, and belongs to the technical field of data acquisition systems. Background Art

[0002] In recent years, we have actively promoted the construction of new smart cities and accelerated the overall layout of the construction of Digital China. The digital base can integrate scattered data resources, break data silos, and provide infrastructure for digital construction.

[0003] Chinese patent publication number CN118351722B discloses a method, terminal and medium for generating a four-dimensional digital base applied to airspace, the method comprising: obtaining airspace data within a specified time and space range, the airspace data including the categories of factors affecting flight within the airspace, and the attribute data corresponding to each of the factor categories; mapping and integrating the airspace data according to the four-dimensional time and space vector to obtain four-dimensional airspace data; performing fusion calculation on the four-dimensional airspace data to generate the four-dimensional digital base. Taking into account the influence of the complex ground environment closely related to the airspace, the four-dimensional fusion of the factor categories affecting flight within the range and the attribute data corresponding to each of the factor categories is achieved, and a four-dimensional digital base is generated, which is conducive to better digital expression of the airspace.

[0004] The present invention proposes a new method for constructing a digital base. Different aerial photography equipment is built through drone aerial photography technology to quickly and accurately extract element information in the natural geographical environment in the survey area, and the information is converted into dense point cloud data. The dense point cloud is constructed into digital base basic data through global grid code technology. At the same time, a storage structure based on vector tiles is designed to store the digital base basic data, and the Elasticsearch engine is used to store these data. Summary of the invention

[0005] The present invention provides a method for quickly generating a low-altitude airspace digital base based on a high-precision dense point cloud, comprising:

[0006] The drone sampling module collects visible light image data of the geographical environment by carrying different aerial photography equipment. It is also equipped with a drone flight safety monitoring module to sense the status of the drone equipment in real time, feedback the progress of task execution, and calculate the risk factor to control the drone to stop or return.

[0007] The measurement area partition calculation module can divide the area to be collected into multiple small areas according to the actual task requirements to improve the collection efficiency;

[0008] Dense point cloud generation and processing module, used to convert visible light image data collected by drones into dense point cloud data, including data information analysis, image ground projection, construction of R-tree spatial index, dense point cloud data generation, point cloud data outlier removal and data coordinate unification;

[0009] The digital base calculation generation and storage module is used to convert dense point cloud data into digital base tiles and use the Elasticsearch engine for storage and retrieval.

[0010] As a further solution of the present invention, the UAV flight safety monitoring module includes identifying the environmental conditions through sensors before flight, identifying the hardware conditions of the UAV equipment to determine whether the mission can be performed, and calculating the risk factor in real time by real-time monitoring of environmental changes during flight. The calculation method is as follows:

[0011]

[0012] Among them, F is the risk factor, V is the wind speed, E is the power, T is the temperature, R is the rainfall, k1, k2, k3, and k4 are weight coefficients. When the risk factor is too high, the drone is notified to interrupt the mission.

[0013] As a further solution of the present invention, the measurement area division calculation module includes but is not limited to two division methods: an equal rectangular measurement area based on a grid division method and a circular measurement area based on an angle division method.

[0014] Furthermore, the method for dividing the measurement area based on the equal rectangle is as follows: it is necessary to set the length and width of the smallest rectangle after division, then obtain the length of the measurement area, divide the length of the measurement area by the length of the smallest rectangle to obtain the long side divisor and the long side remainder, round down the long side divisor to obtain the number of long side divisions, and if the long side remainder is greater than 0, the number of length divisions is increased by 1. Then obtain the width of the measurement area, divide the width of the smallest rectangle by the width of the measurement area to obtain the wide side divisor and the wide side remainder, round down the wide side divisor to obtain the number of wide side divisions, and if the wide side remainder is greater than 0, the number of width divisions is increased by 1; the coordinates of the upper left corner point of the rectangle after the final division and the length and width dimensions are calculated by the length and width quantities, and finally rectangles of the same size are calculated as each small measurement area, and these measurement areas are stored in the computer memory for subsequent use.

[0015] Furthermore, the circular measurement area segmentation method based on the angle division method is as follows:

[0016] The parameters that need to be set and obtained include the measurement area radius R, the inner concentric circle radius r, the minimum area of ​​measurement area segmentation M, the segmentation angle θ, etc.

[0017] (1) A concentric circle (area 1) is calculated based on the input inner circle radius parameter r.

[0018] (2) Then, the outer ring is divided into a plurality of sector-shaped areas of equal size (area 2, etc.) according to the set division angle θ.

[0019] (3) Traverse each sector area in turn, calculate the side length (the side length of area 3) based on the minimum area M, and then calculate the coordinate position information of the entire area (area 3), and calculate the coordinate information of all faces in turn (area 4 and the remaining coordinates) to complete the calculation of the current sector area.

[0020] (4) All sectors are divided and calculated to obtain all small measurement areas, which are then stored in the computer memory for subsequent use.

[0021] As a further solution of the present invention, the data information analysis is performed by analyzing the visible light image file collected by the drone, and the key information obtained by the analysis is stored in the memory of the computer;

[0022] The image ground projection is used to calculate the ground coverage of each image. The projection range of each image on the ground is accurately calculated through multiple parameters of the camera, including focal length, sensor size, position when shooting, and posture data of the aircraft when shooting.

[0023] The R-tree spatial index is constructed by using the ground projection range of the image to establish a spatial index for this range so as to quickly query and match images.

[0024] As a further solution of the present invention, the process of generating dense point cloud data includes the following steps:

[0025] Image pairing: The system traverses all collected drone image data. For each image, the system uses the previously constructed R-tree spatial index to find other images that intersect with its projection range;

[0026] Feature point extraction and matching: For each pair of paired images, the system extracts their feature points and matches them. These feature points correspond to different perspectives of the same feature in the actual geographical environment;

[0027] Sparse point cloud generation: Using the matched feature points and the camera’s posture and position information, the system can generate a preliminary sparse point cloud;

[0028] Multi-view stereo reconstruction: Based on the sparse point cloud, the system uses multi-view stereo reconstruction technology to increase the density of points.

[0029] As a further solution of the present invention, removing outliers from the point cloud data comprises the following steps:

[0030] Step 1: Set the radius r and traverse each point in the original point cloud data in turn, where each point is denoted as p;

[0031] Step 2: extract all point data within a radius r around point p to obtain the domain point, calculate the distance from point p to its domain point, and calculate the average distance and standard deviation based on these distances;

[0032] Step 3: Calculate the outlier index of point p based on the mean and standard deviation. The outlier index is:

[0033]

[0034] Where d is the average distance from p to other points in the neighborhood, μ is the average distance of points in the neighborhood, and σ is the standard deviation of points in the neighborhood;

[0035] Step 4 sets the index threshold. If the index z of point p is greater than the index threshold, point p is regarded as an outlier and the data of this point is discarded.

[0036] As a further solution of the present invention, the data coordinate unification includes the following steps:

[0037] Step 1: extract all batches of point cloud data, traverse each batch of point cloud data to build an octree spatial index to speed up the query efficiency of point cloud data;

[0038] Step 2: Calculate the outsourcing range of all batches of point cloud data, and use the outsourcing range to build an R-tree spatial index to quickly extract intersecting point cloud batch data;

[0039] Step 3: Randomly select a batch of point cloud data as the target point cloud data, record it as Q, extract the point cloud batch data P that intersects with it through the R-tree spatial index, and calculate the feature points of P and Q data respectively;

[0040] Step 4: Use the feature matching algorithm SURF to pair the feature points in the P and Q data to obtain the feature point matching results. The preliminary rotation and transformation matrix T is calculated through the matching results. The transformation matrix is ​​used to transform the point data in P so that the point cloud in P can be roughly aligned with Q, thereby improving the efficiency and accuracy of the registration for subsequent fine matching.

[0041] Step 5: traverse each point in P, and use the octree spatial index query to calculate the nearest point in Q and establish a point pair with the current point;

[0042] Step 6: Use the least squares method to calculate the best transformation matrix for moving the point cloud P to Q through these point pairs, and use the best transformation matrix to transform the point data in P;

[0043] Step 7, traverse the point pair data in step 5, calculate the root mean square error between P and Q, and determine whether the error converges;

[0044] Step 8, repeat steps 5 to 7 until the error reaches a relatively stable minimum value, and then end the calculation. At this time, a P aligned with Q is obtained, and the point data in P is merged into Q to form a new target point cloud data Q;

[0045] Step 9: De-duplicate the point cloud data in Q to avoid data redundancy caused by overlapping areas, and update the octree index of Q;

[0046] Step 10: Repeat steps 3 to 9 until all batches of point cloud data are calculated to obtain a complete survey area and merged point cloud data.

[0047] As a further solution of the present invention, the digital base calculation generation and storage module includes a digital base generation module and a digital base storage module, and the digital base generation module generates the following steps:

[0048] Step 1: traverse the input point cloud data, extract the xyz coordinates of the point cloud data, and determine the calculation level;

[0049] Step 2: Calculate the latitude and longitude span difference corresponding to the current level according to the calculation level, denoted as θ;

[0050] Step 3, obtain a globally unique code based on the calculation level and xy coordinates;

[0051] Step 4: Calculate the number of levels from the ground upward or downward based on the calculated level and the z coordinate, recorded as n, where n is an integer, n ≥ 0 above the ground, and n < 0 below the ground; the calculation method is as follows:

[0052]

[0053] in, is the latitude and longitude span difference corresponding to the basic grid defined in the initial subdivision range; H is the absolute height of the current point, is the semi-major axis of the Earth in meters, =6378137;

[0054] Step 5: Encode the globally unique code, the number of levels corresponding to the height, the current calculation level, etc. into a vector tile format through pbf to form a digital base tile, and finally store the digital base tile in the computer memory to facilitate data sharing between different organizations and systems;

[0055] Step 6: Repeat steps 1 to 5 until all point cloud data are calculated and pushed to the data storage and update module.

[0056] As a further solution of the present invention, the digital base storage module storage comprises the following steps:

[0057] Step 1: Establish an Elasticsearch digital base storage database and configure the grid code as an index;

[0058] Step 2: Receive and obtain digital base tile data, and parse the globally unique grid code in the data;

[0059] Step 3: Use grid coding to query whether digital base tile data already exists in Elasticsearch;

[0060] Step 4: If the data in step 3 does not exist, insert the data into Elasticsearch; if it exists, first obtain and parse the digital base tile from Elasticsearch, and merge the attribute information and coordinate information in the two tiles.

[0061] The present invention also provides a device for quickly generating a low-altitude airspace digital base based on a high-precision dense point cloud. The device includes a memory and a processor. A computer program is stored in the memory. When the processor executes the computer program, the above-mentioned method for quickly generating a low-altitude airspace digital base based on a high-precision dense point cloud is implemented.

[0062] The present invention also provides a storage medium for quickly generating a low-altitude airspace digital base based on a high-precision dense point cloud, wherein the storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed, the method for quickly generating a low-altitude airspace digital base based on a high-precision dense point cloud is implemented.

[0063] The effect of the above technical solution is: the digital base constructed by this system meets the needs of unified organization, integrated sharing, efficient retrieval and application of information in the big data era, and integrates and maps the geographic information data extracted by drones from the natural environment to the earth's spatial grid, effectively solving the bottlenecks of uniqueness, readability, scale and correlation in the identification and expression of traditional spatial information, and realizing efficient organization, processing and application of multi-source and multi-scale data grids, breaking through the technical barriers to cross-industry application of geographic spatial information and promoting the development of earth system science. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 It is a schematic flow chart of a method for rapidly generating a low-altitude airspace digital base based on a high-precision dense point cloud according to the present invention;

[0065] Figure 2 It is a schematic diagram of the flow chart of the UAV flight safety monitoring module of the present invention;

[0066] Figure 3 It is a schematic diagram of the process of the equal rectangular measurement area segmentation method based on the grid division method of the present invention;

[0067] Figure 4 It is a schematic flow chart of a circular measurement area segmentation method based on an angle division method of the present invention;

[0068] Figure 5 It is a schematic diagram of the data information analysis process of the present invention;

[0069] Figure 6 It is a schematic diagram of the data processing steps of the image ground projection of the present invention;

[0070] Figure 7 It is a schematic diagram of the steps of constructing an R-tree spatial index of the present invention;

[0071] Figure 8 It is a schematic diagram of the steps of generating dense point cloud data of the present invention;

[0072] Fig. 9 It is a schematic diagram of the process of removing outliers from point cloud data of the present invention;

[0073] Fig.10 It is a schematic diagram of the steps of data coordinate unification of the present invention;

[0074] Fig.11 It is a schematic diagram of the steps of generating a digital base of the present invention;

[0075] Fig.12 It is a schematic diagram of the storage steps of the digital base of the present invention. DETAILED DESCRIPTION

[0076] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0077] In one embodiment of the present invention, a method for rapidly generating a low-altitude airspace digital base based on a high-precision dense point cloud includes: a drone sampling module, which collects visible light image data of a geographical environment by carrying different aerial photography equipment, and is provided with a drone flight safety monitoring module, which senses the drone equipment status in real time, feeds back the progress of task execution, and calculates a risk factor to control the drone to brake or return;

[0078] The measurement area partition calculation module can divide the area to be collected into multiple small areas according to the actual task requirements to improve the collection efficiency;

[0079] Dense point cloud generation and processing module, used to convert visible light image data collected by drones into dense point cloud data, including data information analysis, image ground projection, construction of R-tree spatial index, dense point cloud data generation, point cloud data outlier removal and data coordinate unification;

[0080] The digital base calculation generation and storage module is used to convert dense point cloud data into digital base tiles and use the Elasticsearch engine for storage and retrieval.

[0081] Specifically, in order to make the drone's mission highly automated, the system places the drone in a fixed position, and equips the installation position with many practical devices such as power supply, network, rain sensor, temperature sensor, wind speed sensor, etc., to sense the drone's equipment status in real time and provide timely feedback on the progress of the mission. It is inevitable that drones will encounter emergencies during flight. If these emergencies are not properly handled in a timely manner, it is very likely to cause safety accidents and loss of life and property. Therefore, the system is equipped with a drone mission safety monitoring system, which calculates the risk factor of the current mission in real time through the weather conditions and equipment operation status fed back by the drone. If there is an abnormality, the drone will be controlled to stop or return in time to improve flight safety.

[0082] Furthermore, the UAV flight safety monitoring module includes identifying the environmental conditions through sensors before flight, identifying the hardware conditions of the UAV equipment to determine whether the mission can be performed, and calculating the risk factor in real time by real-time monitoring of environmental changes during flight. The calculation method is as follows:

[0083]

[0084] Among them, F is the risk factor, V is the wind speed, E is the power, T is the temperature, R is the rainfall, k1, k2, k3, and k4 are weight coefficients. When the risk factor is too high, the drone is notified to interrupt the mission.

[0085] Furthermore, the measurement area division calculation module includes but is not limited to two division methods: an equal rectangular measurement area based on a grid division method and a circular measurement area based on an angle division method.

[0086] Furthermore, the method for dividing the measurement area based on the equal rectangle is as follows: it is necessary to set the length and width of the smallest rectangle after division, then obtain the length of the measurement area, divide the length of the measurement area by the length of the smallest rectangle to obtain the long side divisor and the long side remainder, round down the long side divisor to obtain the number of long side divisions, and if the long side remainder is greater than 0, the number of length divisions is increased by 1. Then obtain the width of the measurement area, divide the width of the smallest rectangle by the width of the measurement area to obtain the wide side divisor and the wide side remainder, round down the wide side divisor to obtain the number of wide side divisions, and if the wide side remainder is greater than 0, the number of width divisions is increased by 1; the coordinates of the upper left corner point of the rectangle after the final division and the length and width dimensions are calculated by the length and width quantities, and finally rectangles of the same size are calculated as each small measurement area, and these measurement areas are stored in the computer memory for subsequent use.

[0087] Furthermore, the circular measurement area segmentation method based on the angle division method is as follows:

[0088] The parameters that need to be set and obtained include the measurement area radius R, the inner concentric circle radius r, the minimum area of ​​measurement area segmentation M, the segmentation angle θ, etc.

[0089] (1) A concentric circle (area 1) is calculated based on the input inner circle radius parameter r.

[0090] (2) Then, the outer ring is divided into a plurality of sector-shaped areas of equal size (area 2, etc.) according to the set division angle θ.

[0091] (3) Traverse each sector area in turn, calculate the side length (the side length of area 3) based on the minimum area M, and then calculate the coordinate position information of the entire area (area 3), and calculate the coordinate information of all faces in turn (area 4 and the remaining coordinates) to complete the calculation of the current sector area.

[0092] (4) All sectors are divided and calculated to obtain all small measurement areas, which are then stored in the computer memory for subsequent use.

[0093] The effect of the above scheme is that for a complete area to be collected, the spatial span is generally large and the collection time required is long. However, the power of the drone itself is limited and it is impossible to collect the data of the entire area at one time. Therefore, the system divides the area into multiple small areas that can be completed by multiple drones at one time according to the specific situation of the actual task. The drone has different measurement areas for different tasks. In order to reduce the power consumption of the drone and improve the efficiency of task execution, the system designs the measurement area into a rectangular measurement area or a circular measurement area. The different measurement area division methods are as follows.

[0094] Specifically, drones can carry different loads when collecting data, and different loads can collect different result data. The system provides supported data including visible light image data and point cloud data. Among them, visible light image data needs to be converted into dense point cloud data, and dense point cloud data of the complete survey area is generated by removing outliers, merging survey area data, and unifying coordinates. The complete point cloud data is used to build a digital base for the survey area.

[0095] Furthermore, the data information analysis is performed by analyzing the visible light image file collected by the drone, and the key information obtained by the analysis is stored in the memory of the computer;

[0096] The visible light image files collected by drones contain key information for point cloud data extraction, including GPS location information, aircraft information, shooting time, image size, etc. The system will reuse this information in subsequent processing. For ease of use, the system needs to parse the file and store this information in computer memory.

[0097] The image ground projection is used to calculate the ground coverage of each image. The projection range of each image on the ground is accurately calculated through multiple parameters of the camera, including focal length, sensor size, position when shooting, and posture data of the aircraft when shooting.

[0098] With the image range, a spatial index can be established for the image. R-tree is a commonly used spatial index for managing spatial data. It stores different elements in different nodes by dividing the spatial range. When using it, the data can be quickly and effectively queried by passing in the spatial range. It is one of the important ways to speed up queries. Building an R-tree spatial index can quickly query the required images. It only needs to determine that the image spatial ranges intersect to determine that the two images are a pair. This can effectively speed up the matching rate and save a lot of invalid matching time.

[0099] The R-tree spatial index is constructed by using the ground projection range of the image to establish a spatial index for this range so as to quickly query and match images.

[0100] Specifically, point cloud data generation first requires pairing the collected drone image data in pairs. The paired images must have repeated areas when taken. The intersecting image pairs can be quickly screened out through the constructed R-tree spatial index. Sparse point clouds can be preliminarily calculated by performing feature point extraction, feature point matching and other calculation operations on these image pairs. Based on the sparse point cloud, reconstruction is performed to obtain the final dense point cloud data.

[0101] Furthermore, the process of generating dense point cloud data includes the following steps:

[0102] Image pairing: The system traverses all collected drone image data. For each image, the system uses the previously constructed R-tree spatial index to find other images that intersect with its projection range;

[0103] Feature point extraction and matching: For each pair of paired images, the system extracts their feature points and matches them. These feature points correspond to different perspectives of the same feature in the actual geographical environment;

[0104] Sparse point cloud generation: Using the matched feature points and the camera’s posture and position information, the system can generate a preliminary sparse point cloud;

[0105] Multi-view stereo reconstruction: Based on the sparse point cloud, the system uses multi-view stereo reconstruction technology to increase the density of points.

[0106] During the data collection process, some outliers will be generated due to various reasons. The system removes these points through some processing steps to reduce the interference of erroneous data on the digital base results.

[0107] Furthermore, removing outliers from the point cloud data comprises the following steps:

[0108] Step 1: Set the radius r and traverse each point in the original point cloud data in turn, where each point is denoted as p;

[0109] Step 2: extract all point data within a radius r around point p to obtain the domain point, calculate the distance from point p to its domain point, and calculate the average distance and standard deviation based on these distances;

[0110] Step 3: Calculate the outlier index of point p based on the mean and standard deviation. The outlier index is:

[0111]

[0112] Where d is the average distance from p to other points in the neighborhood, μ is the average distance of points in the neighborhood, and σ is the standard deviation of points in the neighborhood;

[0113] Step 4 sets the index threshold. If the index z of point p is greater than the index threshold, point p is regarded as an outlier and the data of this point is discarded.

[0114] Specifically, the data collection process is completed in multiple batches of collection tasks. Therefore, the data coordinate system of each data collection is different. The system needs to convert these data into geographic coordinates under the world coordinate system, unify all batches of point cloud data, and provide a basis for subsequent digital base calculations.

[0115] Furthermore, the data coordinate unification includes the following steps:

[0116] Step 1: extract all batches of point cloud data, traverse each batch of point cloud data to build an octree spatial index to speed up the query efficiency of point cloud data;

[0117] Step 2: Calculate the outsourcing range of all batches of point cloud data, and use the outsourcing range to build an R-tree spatial index to quickly extract intersecting point cloud batch data;

[0118] Step 3: Randomly select a batch of point cloud data as the target point cloud data, record it as Q, extract the point cloud batch data P that intersects with it through the R-tree spatial index, and calculate the feature points of P and Q data respectively;

[0119] Step 4: Use the feature matching algorithm SURF to pair the feature points in the P and Q data to obtain the feature point matching results. The preliminary rotation and transformation matrix T is calculated through the matching results. The transformation matrix is ​​used to transform the point data in P so that the point cloud in P can be roughly aligned with Q, thereby improving the efficiency and accuracy of the registration for subsequent fine matching.

[0120] Step 5: traverse each point in P, and use the octree spatial index query to calculate the nearest point in Q and establish a point pair with the current point;

[0121] Step 6: Use the least squares method to calculate the best transformation matrix for moving the point cloud P to Q through these point pairs, and use the best transformation matrix to transform the point data in P;

[0122] Step 7, traverse the point pair data in step 5 (the P data has been converted), calculate the root mean square error between P and Q, and determine whether the error converges;

[0123] Step 8, repeat steps 5 to 7 until the error reaches a relatively stable minimum value, and then end the calculation. At this time, a P aligned with Q is obtained, and the point data in P is merged into Q to form a new target point cloud data Q;

[0124] Step 9: De-duplicate the point cloud data in Q to avoid data redundancy caused by overlapping areas, and update the octree index of Q;

[0125] Step 10: Repeat steps 3 to 9 until all batches of point cloud data are calculated to obtain a complete survey area and merged point cloud data.

[0126] Specifically, the geospatial grid is based on the GeoSOT (Geographical coordinate globalSubdivision based on One-dimensioninteger and Two ton"power) earth partitioning model, which uniformly divides the earth space into grid units of different scales, and identifies and expresses them according to unified coding rules, thus constructing a gridded earth space data organization reference framework. This framework supports the aggregation of earth surface space, earth stereo space and geographic space information.

[0127] Furthermore, the digital base calculation generation and storage module includes a digital base generation module and a digital base storage module, and the digital base generation module generation includes the following steps:

[0128] Step 1: traverse the input point cloud data, extract the xyz coordinates of the point cloud data, and determine the calculation level;

[0129] Step 2: Calculate the latitude and longitude span difference corresponding to the current level according to the calculation level, denoted as θ;

[0130] Step 3, obtain a globally unique code based on the calculation level and xy coordinates;

[0131] Step 4: Calculate the number of levels from the ground upward or downward based on the calculated level and the z coordinate, recorded as n, where n is an integer, n ≥ 0 above the ground, and n < 0 below the ground; the calculation method is as follows:

[0132]

[0133] in, is the latitude and longitude span difference corresponding to the basic grid defined in the initial subdivision range; H is the absolute height of the current point, is the semi-major axis of the Earth in meters, =6378137;

[0134] Step 5: Encode the globally unique code, the number of levels corresponding to the height, the current calculation level, etc. into a vector tile format through pbf to form a digital base tile, and finally store the digital base tile in the computer memory to facilitate data sharing between different organizations and systems;

[0135] Step 6: Repeat steps 1 to 5 until all point cloud data are calculated and pushed to the data storage and update module.

[0136] Specifically, Elasticsearch (ES) is a distributed full-text search and analysis engine built on Lucene, suitable for real-time processing of large-scale data. It is often used in scenarios such as data search, log analysis, monitoring, data mining and business analysis, and can quickly store, search and analyze massive structured or unstructured data.

[0137] The system uses Elasticsearch as the storage engine for the digital base, uses the calculated global unique code as the index, and formats and stores the encoded pbf digital base tile data. Among them, the ES database may contain digital base tiles built in different tasks at different times. In order to prevent data conflicts, if the data already exists in ES, the system needs to merge and update the data.

[0138] Further, the digital base storage module storage includes the following steps:

[0139] Step 1: Establish an Elasticsearch digital base storage database and configure the grid code as an index;

[0140] Step 2: Receive and obtain digital base tile data, and parse the globally unique grid code in the data;

[0141] Step 3: Use grid coding to query whether digital base tile data already exists in Elasticsearch;

[0142] Step 4: If the data in step 3 does not exist, insert the data into Elasticsearch; if it exists, first obtain and parse the digital base tile from Elasticsearch, and merge the attribute information and coordinate information in the two tiles.

[0143] A device for rapidly generating a low-altitude airspace digital base based on a high-precision dense point cloud, the device comprising a memory and a processor, the memory storing a computer program, and the processor implementing the above-mentioned method for rapidly generating a low-altitude airspace digital base based on a high-precision dense point cloud when executing the computer program.

[0144] A storage medium for quickly generating a low-altitude airspace digital base based on a high-precision dense point cloud, wherein the storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed, the method for quickly generating a low-altitude airspace digital base based on a high-precision dense point cloud is implemented.

[0145] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for rapidly generating a low-altitude airspace digital base based on high-precision dense point cloud, characterized in that: include; The drone sampling module collects visible light image data of the geographical environment by carrying different aerial photography equipment. It is also equipped with a drone flight safety monitoring module to sense the status of the drone equipment in real time, feedback the progress of task execution, and calculate the risk factor to control the drone to stop or return. The measurement area partition calculation module can divide the area to be collected into multiple small areas according to the actual task requirements to improve the collection efficiency; Dense point cloud generation and processing module, used to convert visible light image data collected by drones into dense point cloud data, including data information analysis, image ground projection, construction of R-tree spatial index, dense point cloud data generation, point cloud data outlier removal and data coordinate unification; The digital base calculation generation and storage module is used to convert dense point cloud data into digital base tiles and use the Elasticsearch engine for storage and retrieval; The digital base calculation generation and storage module includes a digital base generation module and a digital base storage module. The digital base generation module generates the following steps: Step 1: traverse the input point cloud data, extract the xyz coordinates of the point cloud data, and determine the calculation level; Step 2: Calculate the latitude and longitude span difference corresponding to the current level according to the calculation level, denoted as θ; Step 3, obtain a globally unique code based on the calculation level and xy coordinates; Step 4: Calculate the number of levels from the ground upward or downward based on the calculated level and the z coordinate, recorded as n, where n is an integer, n ≥ 0 above the ground, and n < 0 below the ground; the calculation method is as follows: in, is the latitude and longitude span difference corresponding to the basic grid defined in the initial subdivision range; H is the absolute height of the current point, is the semi-major axis of the Earth in meters, =6378137; Step 5: Encode the globally unique code, the number of levels corresponding to the height, the current calculation level, etc. into a vector tile format through pbf to form a digital base tile, and finally store the digital base tile in the computer memory to facilitate data sharing between different organizations and systems; Step 6: Repeat steps 1 to 5 until all point cloud data are calculated and pushed to the data storage and update module; The digital base storage module storage comprises the following steps: Step 1: Establish an Elasticsearch digital base storage database and configure the grid code as an index; Step 2: Receive and obtain digital base tile data, and parse the globally unique grid code in the data; Step 3: Use grid coding to query whether digital base tile data already exists in Elasticsearch; Step 4: If the data in step 3 does not exist, insert the data into Elasticsearch; if it exists, first obtain and parse the digital base tile from Elasticsearch, and merge the attribute information and coordinate information in the two tiles.

2. According to the method of claim 1, the method is characterized in that: The UAV flight safety monitoring module includes identifying the environmental conditions through sensors before flight, identifying the hardware conditions of the UAV equipment to determine whether the mission can be performed, and calculating the risk factor in real time by real-time monitoring of environmental changes during flight. The calculation method is as follows: Among them, F is the risk factor, V is the wind speed, E is the power, T is the temperature, R is the rainfall, k1, k2, k3, and k4 are weight coefficients. When the risk factor is too high, the drone is notified to interrupt the mission.

3. According to claim 1, a method for rapidly generating a low-altitude airspace digital base based on a high-precision dense point cloud, characterized in that: The measurement area division calculation module includes but is not limited to two division methods: an equal rectangular measurement area based on a grid division method and a circular measurement area based on an angle division method.

4. The method for rapidly generating a low-altitude airspace digital base based on a high-precision dense point cloud according to claim 1 is characterized in that: The data information analysis is performed by analyzing the visible light image files collected by the drone, and the key information obtained by the analysis is stored in the memory of the computer; The image ground projection is used to calculate the ground coverage of each image. The projection range of each image on the ground is accurately calculated through multiple parameters of the camera, including focal length, sensor size, position when shooting, and posture data of the aircraft when shooting. The R-tree spatial index is constructed by using the ground projection range of the image to establish a spatial index for this range so as to quickly query and match images.

5. The method for rapidly generating a low-altitude airspace digital base based on high-precision dense point cloud according to claim 1 is characterized in that: The process of generating dense point cloud data includes the following steps: Image pairing: The system traverses all collected drone image data. For each image, the system uses the previously constructed R-tree spatial index to find other images that intersect with its projection range; Feature point extraction and matching: For each pair of paired images, the system extracts their feature points and matches them. These feature points correspond to different perspectives of the same feature in the actual geographical environment. Sparse point cloud generation: Using the matched feature points and the camera’s posture and position information, the system can generate a preliminary sparse point cloud; Multi-view stereo reconstruction: Based on the sparse point cloud, the system uses multi-view stereo reconstruction technology to increase the density of points.

6. The method for rapidly generating a low-altitude airspace digital base based on a high-precision dense point cloud according to claim 1 is characterized in that: The step of removing outliers from the point cloud data comprises the following steps: Step 1: Set the radius r and traverse each point in the original point cloud data in turn, where each point is denoted as p; Step 2: extract all point data within a radius r around point p to obtain the domain point, calculate the distance from point p to its domain point, and calculate the average distance and standard deviation based on these distances; Step 3: Calculate the outlier index of point p based on the mean and standard deviation. The outlier index is: Where d is the average distance from p to other points in the neighborhood, μ is the average distance of points in the neighborhood, and σ is the standard deviation of points in the neighborhood; Step 4 sets the index threshold. If the index z of point p is greater than the index threshold, point p is regarded as an outlier and the data of this point is discarded.

7. The method for rapidly generating a low-altitude airspace digital base based on a high-precision dense point cloud according to claim 1 is characterized in that: The data coordinate unification comprises the following steps: Step 1: extract all batches of point cloud data, traverse each batch of point cloud data to build an octree spatial index to speed up the query efficiency of point cloud data; Step 2: Calculate the outsourcing range of all batches of point cloud data, and use the outsourcing range to build an R-tree spatial index to quickly extract intersecting point cloud batch data; Step 3: Randomly select a batch of point cloud data as the target point cloud data, record it as Q, extract the point cloud batch data P that intersects with it through the R-tree spatial index, and calculate the feature points of P and Q data respectively; Step 4: Use the feature matching algorithm SURF to pair the feature points in the P and Q data to obtain the feature point matching results. The preliminary rotation and transformation matrix T is calculated through the matching results. The transformation matrix is ​​used to transform the point data in P so that the point cloud in P can be aligned with Q, thereby improving the efficiency and accuracy of the registration for subsequent fine matching. Step 5: traverse each point in P, and use the octree spatial index query to calculate the nearest point in Q and establish a point pair with the current point; Step 6: Use the least squares method to calculate the best transformation matrix for moving the point cloud P to Q through these point pairs, and use the best transformation matrix to transform the point data in P; Step 7, traverse the point pair data in step 5, calculate the root mean square error between P and Q, and determine whether the error converges; Step 8: Repeat steps 5 to 7 until the error reaches a stable minimum value, and then end the calculation. At this time, a P aligned with Q is obtained, and the point data in P is merged into Q to form a new target point cloud data Q. Step 9: De-duplicate the point cloud data in Q to avoid data redundancy caused by overlapping areas, and update the octree index of Q; Step 10: Repeat steps 3 to 9 until all batches of point cloud data are calculated to obtain a complete survey area and merged point cloud data.

8. A device for rapidly generating a low-altitude airspace digital base based on high-precision dense point cloud, characterized in that: The device includes a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, a method for rapidly generating a low-altitude airspace digital base based on a high-precision dense point cloud as described in any one of claims 1 to 7 is implemented.

9. A storage medium for rapid generation of low-altitude airspace digital base based on high-precision dense point cloud, characterized by: The storage medium stores a computer program, which includes program instructions. When the program instructions are executed, the method for quickly generating a low-altitude airspace digital base based on a high-precision dense point cloud as described in any one of claims 1 to 7 is implemented.

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