A method and system for visual analysis of underground pipelines based on drone mapping

Abstract

The present invention discloses an underground pipeline visualization analysis method and system based on unmanned aerial vehicle mapping, relates to the technical field of unmanned aerial vehicle mapping, and effectively improves the accuracy of underground pipeline positioning. The present invention establishes a city outer surface model based on the city outer surface image, and divides the laser reflection signal spectrum into a plurality of laser reflection signal segments, and then establishes a city underground area point cloud distribution model based on each laser reflection signal segment, and connects the city underground area point cloud distribution model with the city outer surface model to obtain a city visualization point cloud distribution model, and divides a plurality of point cloud data areas with different colors in the city visualization point cloud distribution model according to the absorption rate of each underground material to the laser signal of different frequency bands, and then divides the city three-dimensional visualization pipeline line in the point cloud data area.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle (UAV) surveying and mapping, and in particular to an underground pipeline visualization analysis method and system based on UAV surveying and mapping. Background Art

[0002] As urbanization continues, the management and maintenance of urban underground pipelines has become a major technical challenge. Traditional underground pipeline detection methods rely on manual excavation and limited physical detection equipment, which is not only inefficient but also poses a great safety risk. In addition, the complexity and diversity of underground pipeline networks make accurate mapping and real-time monitoring particularly complex.

[0003] In recent years, the rapid development of UAV technology has provided a new solution for the detection of urban underground pipelines. By carrying high-precision cameras and laser scanners, UAVs can collect images of the city's outer surface and underground and the spectrum of laser reflection signals in real time during flight. Using these data, a high-precision model of the city's outer surface and underground point cloud distribution model can be constructed. For this purpose, a method and system for visual analysis of underground pipelines based on UAV mapping is provided. Summary of the invention

[0004] In order to solve the above technical problems, the purpose of the present invention is to provide an underground pipeline visualization analysis method and system based on drone mapping.

[0005] In order to achieve the above object, the present invention provides the following technical solutions:

[0006] A visualization analysis system for underground pipelines based on drone mapping includes a cloud control terminal, wherein the cloud control terminal is communicatively connected to a drone mapping module, a mapping data processing module, and a visualization segmentation module;

[0007] The drone mapping module is used to plan a number of drone flight routes based on the pre-stored urban mapping area map, and then control each drone to fly along the drone flight route, and collect the city surface image and laser reflection signal spectrum of the location passed during the flight through a high-precision camera and a laser scanner;

[0008] The surveying and mapping data processing module is used to establish a city outer surface model based on the city outer surface image, and to divide the laser reflection signal spectrum into a plurality of laser reflection signal segments, and then to establish a city underground area point cloud distribution model based on each laser reflection signal segment, and to connect the city underground area point cloud distribution model with the city outer surface model to obtain a city visualization point cloud distribution model;

[0009] The visualization segmentation module is used to obtain the absorption rate of each underground material to the laser signal of different frequency bands, and divide a number of point cloud data areas with different colors in the urban visualization point cloud distribution model according to the absorption rate of each underground material to the laser signal of different frequency bands, and then divide the city's three-dimensional visualization pipeline lines in the point cloud data area.

[0010] Furthermore, the process of collecting the city outer surface image and the laser reflection signal spectrum includes:

[0011] m drones are provided, each drone is equipped with a camera, a laser scanner and a wireless communication device, and each drone is numbered, where m is a natural number greater than 0;

[0012] The same surveying and mapping data collection range is set for each UAV, wherein the surveying and mapping data collection range includes an image shooting range and a laser signal receiving range, wherein the laser signal receiving range represents the laser reflection signal depth of the maximum receiving depth of the laser scanner;

[0013] According to the image shooting range in the surveying and mapping data collection range, m UAV flight routes are planned on the urban surveying and mapping area map. The width of each UAV flight route is equal to half the length of the image shooting range width, and the combined area of ​​all UAV flight routes is equal to the area of ​​the urban surveying and mapping area map.

[0014] The UAV mapping module assigns all UAV flight routes to each UAV in turn, and then each UAV flies twice over the city along the assigned UAV flight route. During the flight, the camera captures the image of the outer surface of the city within its image shooting range, and the laser scanner simultaneously emits laser signals of n frequency bands vertically to the ground, thereby continuously generating a laser reflection signal spectrum within the laser signal receiving range, and marking the frequency band of each laser reflection signal spectrum, where n is a natural number greater than 0.

[0015] Furthermore, the process of establishing the city outer surface model includes:

[0016] The city surface images collected by each drone are stitched together in chronological order to obtain two sets of image sequences with opposite orders;

[0017] Then, according to the spatial position relationship between the flight routes of each drone, the image sequences of the same order are spliced ​​and overlapped to obtain two total images of the city's outer surface, and the two total images of the city's outer surface are divided into several image areas of the same size, and the image areas in the two total images of the city's outer surface are matched in sequence;

[0018] The pixel value of each pixel in each image area is obtained, and then the average pixel value between pixels at the same position in the image area from different city outer surface total images is obtained, and the image area is reassigned to the corresponding two pixels, the two city outer surface total images are overlapped and mapped, and the city outer surface model is established based on the city outer surface total images after overlapping mapping.

[0019] Furthermore, the process of dividing the laser reflection signal segments includes:

[0020] A two-dimensional coordinate system is established, and the spectra of laser reflection signals corresponding to the same UAV number, the same generation time but different frequencies are mapped onto the same two-dimensional coordinate system;

[0021] Set the fluctuation amplitude threshold and feature number threshold, and set several time nodes to divide each laser reflection signal spectrum into several amplitude coordinate points, and then obtain the fluctuation amplitude difference between the amplitude coordinate points of each adjacent time node, and judge whether the fluctuation amplitude difference is greater than or equal to the fluctuation amplitude threshold. According to the judgment result, record the number of feature points once at the coordinate axis position between the time nodes.

[0022] The number of feature points at each position on the coordinate axis is counted, and it is determined whether the number of feature points is greater than or equal to the feature quantity threshold. If the number of feature points is greater than or equal to the feature quantity threshold, the corresponding position is recorded as the starting point of the feature fluctuation, otherwise no operation is performed. According to the position distribution of the starting point of the feature fluctuation on the coordinate axis, each laser reflection signal spectrum is divided into several laser reflection signal segments.

[0023] Furthermore, the process of establishing the city visualization point cloud distribution model includes:

[0024] According to each laser reflection signal fragment, the corresponding regional point cloud data is generated, and then according to the distribution of the drone flight routes of each drone, the point cloud data of each region are spliced ​​in sequence to obtain the urban underground area point cloud distribution model. Then, according to the overlapping relationship of the data acquisition range between the high-precision camera and the laser scanner, the urban outer surface model and the urban underground area point cloud distribution model are spliced ​​up and down to obtain the urban visualization point cloud distribution model.

[0025] Furthermore, the process of dividing the point cloud data areas of multiple colors includes:

[0026] Establish a three-dimensional coordinate system, and map the urban visualization point cloud distribution model into the three-dimensional coordinate system, and then divide the point cloud data of each area in the urban visualization point cloud distribution model into several point cloud sub-areas;

[0027] Obtain the laser reflection signal fragments corresponding to the point cloud data of each region, obtain the absorption conditions of laser signals of different frequency bands by each material through the Internet, and then annotate the corresponding regional physical properties in the point cloud sub-regions within the point cloud data of each region according to the conditions of the peaks and troughs in the same pair of time nodes but different laser reflection signal fragments;

[0028] The point cloud sub-regions at adjacent positions in the urban visualization point cloud distribution model are matched with each other. If two point cloud sub-regions have the same regional physical property labels, the same color labels are set for the two point cloud sub-regions. Otherwise, no operation is performed and the colored point cloud sub-regions are connected, thereby planning several colored point cloud data regions in the urban visualization point cloud distribution model.

[0029] Furthermore, the process of dividing the urban three-dimensional visualization pipeline line includes:

[0030] According to the absorption conditions of laser signals of different frequency bands by the materials used for pipelines or the external materials of optical fibers, the point cloud data area corresponding to the pipeline or the outside of the optical fiber is separated in the urban visualization point cloud distribution model, and point cloud sub-areas surrounding the point cloud data area in the pipeline state are cut out around the point cloud data area in the pipeline state, and the divided point cloud sub-areas are associated with the corresponding point cloud data area in the pipeline state, thereby dividing a number of urban three-dimensional visualization pipeline lines in the urban visualization point cloud distribution model.

[0031] A method for visualizing and analyzing underground pipelines based on drone mapping includes the following steps:

[0032] Step S1: plan several drone flight routes according to the pre-stored urban surveying and mapping area map, and then control each drone to fly along the drone flight route, and collect the city surface image and laser reflection signal spectrum of the passing location during the flight through a high-precision camera and a laser scanner;

[0033] Step S2, establishing a city outer surface model according to the city outer surface image, and dividing the laser reflection signal spectrum into a plurality of laser reflection signal segments, and then establishing a city underground area point cloud distribution model according to each laser reflection signal segment, and connecting the city underground area point cloud distribution model with the city outer surface model to obtain a city visualization point cloud distribution model;

[0034] Step S3, obtaining the absorption rate of each underground material to the laser signal of different frequency bands, and dividing a number of point cloud data areas with different colors in the urban visualization point cloud distribution model according to the absorption rate of each underground material to the laser signal of different frequency bands;

[0035] Step S4, dividing a number of point cloud data areas in pipeline state within the point cloud data area, and cutting out point cloud sub-areas surrounding the point cloud data area in pipeline state around the point cloud data area in pipeline state, and then dividing a number of urban three-dimensional visualization pipeline lines within the urban visualization point cloud distribution model.

[0036] Compared with the prior art, the present invention has the following beneficial effects:

[0037] 1. The present invention uses data collected by laser scanners and high-precision cameras to construct an accurate urban surface model and underground area point cloud distribution model. By combining the absorption rate of underground materials to laser signals, the accuracy of underground pipeline positioning is further improved.

[0038] 2. The present invention divides the point cloud data area into pipeline states within the point cloud data area and cuts out the surrounding point cloud sub-areas to generate a three-dimensional visualization of the city's pipeline lines. This not only helps professionals to conduct detailed analysis and planning, but also provides non-professionals with an intuitive underground pipeline distribution map, facilitating decision-making and execution of urban management. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 It is the principle diagram of the present invention;

[0040] Figure 2 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0041] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the specific implementation method, structure, characteristics and effects of the present invention are described in detail below in combination with the accompanying drawings and preferred embodiments.

[0042] like Figure 1 As shown, an underground pipeline visualization analysis system based on drone mapping includes a cloud control terminal, and the cloud control terminal is communicatively connected to a drone mapping module, a mapping data processing module, and a visualization segmentation module;

[0043] The UAV mapping module is used to plan several UAV flight routes according to the pre-stored urban mapping area map, and then control each UAV to fly along the UAV flight route, and collect the city surface image and laser reflection signal spectrum of the passing position during the flight through a high-precision camera and a laser scanner. The specific process includes:

[0044] m drones are set up, each of which is equipped with a camera, a laser scanner and a wireless communication device, and then each drone is connected to the drone mapping module for communication. According to the communication connection result, the drone mapping module sets a number a1, a2, a3, ..., a for each drone. m , where m is a natural number greater than 0;

[0045] The same surveying and mapping data collection range is set for each drone, and the surveying and mapping data collection range includes an image shooting range and a laser signal receiving range, wherein the laser signal receiving range indicates the depth of the laser reflection signal at the maximum receiving depth of the laser scanner. For example, if the laser signal receiving range is 100m, it means that the laser scanner only receives the laser reflection signal within 100m underground.

[0046] When the staff uploads the urban surveying and mapping area map to the UAV surveying and mapping module, the UAV surveying and mapping module plans m UAV flight routes on the urban surveying and mapping area map according to the image shooting range in the surveying and mapping data collection range. The width of each UAV flight route is equal to half the length of the image shooting range width, and the combined area of ​​all UAV flight routes is equal to the area of ​​the urban surveying and mapping area map;

[0047] The UAV mapping module assigns all UAV flight routes to each UAV in turn, and then each UAV flies twice over the city along the assigned UAV flight route. During the flight, the camera captures the image of the outer surface of the city within its image capturing range, and the laser scanner simultaneously transmits laser signals of n frequency bands vertically to the ground, thereby continuously generating the laser reflection signal spectrum within the laser signal receiving range, and marking the frequency band of each laser reflection signal spectrum, where n is a natural number greater than 0;

[0048] After all drones have flown back and forth twice along the assigned drone flight route, each drone sends the city's outer surface images and laser reflection signal spectrum it has collected to the drone mapping module via wireless communication devices;

[0049] The UAV mapping module integrates the city surface images and laser reflection signal spectra uploaded by the same UAV in sequence according to the acquisition time to obtain the corresponding city mapping data set, and after marking the corresponding UAV number, sends all the city mapping data sets to the mapping data processing module and the visualization segmentation model.

[0050] The surveying and mapping data processing module is used to establish a city outer surface model based on the city outer surface image, and to divide the laser reflection signal spectrum into a plurality of laser reflection signal segments, and then to establish a city underground area point cloud distribution model based on each laser reflection signal segment, and to connect the city underground area point cloud distribution model with the city outer surface model to obtain a city visualization point cloud distribution model. The specific process includes:

[0051] Since the width of each drone's flight path is equal to half the length of the image capture range width, there are common parts between the city's outer surface images collected by drones in adjacent drone flight paths;

[0052] Then, firstly, the city outer surface images collected by each drone are stitched together in chronological order to obtain two sets of image sequences with opposite orders;

[0053] Then, according to the spatial position relationship between the flight routes of each drone, the image sequences of the same order are spliced ​​and overlapped to obtain two total images of the city's outer surface, and the two total images of the city's outer surface are divided into several image areas of the same size, and the image areas in the two total images of the city's outer surface are matched in sequence;

[0054] Obtain the pixel value of each pixel in each image area, and then obtain the average pixel value between pixels at the same position in the image area from the total image of the outer surface of different cities, and reassign the image area to the corresponding two pixels;

[0055] The above pixel averaging process is repeated, and then the two city outer surface total images are overlapped and mapped, and the city outer surface model is established based on the city outer surface total images after overlap mapping.

[0056] Furthermore, when the laser signal passes through different objects such as soil, pipelines, and materials transported by the pipelines, different materials have different absorption rates for laser signals of different frequency bands, so the intensity of the laser reflection signal generated by the laser signals of different frequency bands passing through the same object is different, and when the laser signal passes through the material continuously, the generated laser reflection signal will mutate;

[0057] Then, the surveying and mapping data processing module establishes a two-dimensional coordinate system, and maps the spectrum of the laser reflection signal with the same UAV number, the same generation time but different frequencies onto the same two-dimensional coordinate system;

[0058] It should be noted that, since the reflection time and emission position of the laser signal corresponding to the laser reflection signal spectrum generated by the same UAV are the same, when the laser signals of different frequency bands pass through different underground materials, the corresponding laser reflection signal spectrum generated has different degrees of characteristic fluctuation starting points at the same time node;

[0059] Setting a fluctuation amplitude threshold and a feature quantity threshold, and setting a number of time nodes to divide each laser reflection signal spectrum into a number of amplitude coordinate points, thereby obtaining the fluctuation amplitude difference between the amplitude coordinate points of each adjacent time node, and determining whether the fluctuation amplitude difference is greater than or equal to the fluctuation amplitude threshold;

[0060] If the fluctuation amplitude difference is greater than or equal to the fluctuation amplitude threshold, the number of feature points is recorded once at the coordinate axis position between the time nodes;

[0061] If the fluctuation amplitude difference is less than the fluctuation amplitude threshold, no action will be taken;

[0062] Count the number of feature points at each position on the coordinate axis, and determine whether the number of feature points is greater than or equal to the feature quantity threshold. If the number of feature points is greater than or equal to the feature quantity threshold, the corresponding position is recorded as the starting point of the feature fluctuation, otherwise no operation is performed;

[0063] According to the position distribution of the characteristic fluctuation starting point on the coordinate axis, each laser reflection signal spectrum is divided into a number of laser reflection signal segments, and then the corresponding regional point cloud data is generated according to each laser reflection signal segment;

[0064] According to the flight route distribution of each UAV, the point cloud data of each area are spliced ​​together in sequence to obtain the point cloud distribution model of the urban underground area. Then, according to the overlapping relationship of the data acquisition range between the high-precision camera and the laser scanner, the urban outer surface model and the urban underground area point cloud distribution model are spliced ​​together to obtain a visualized point cloud distribution model of the city.

[0065] The visualization segmentation module is used to obtain the absorption rate of each underground material to the laser signal of different frequency bands, and divide a number of point cloud data areas with different colors in the urban visualization point cloud distribution model according to the absorption rate of each underground material to the laser signal of different frequency bands, and then divide the urban three-dimensional visualization pipeline line in the point cloud data area. The specific process includes:

[0066] The visualization segmentation module obtains the city visualization point cloud distribution model from the surveying and mapping data processing module;

[0067] Establish a three-dimensional coordinate system, and map the urban visualization point cloud distribution model into the three-dimensional coordinate system, and then divide the point cloud data of each area in the urban visualization point cloud distribution model into several point cloud sub-areas;

[0068] Obtain the laser reflection signal fragments corresponding to the point cloud data of each region, obtain the absorption conditions of laser signals of different frequency bands by each material through the Internet, and then annotate the corresponding regional physical properties in the point cloud sub-regions within the point cloud data of each region according to the conditions of the peaks and troughs in the same pair of time nodes but different laser reflection signal fragments;

[0069] The physical properties of the area include soil name, metal material name, gas name, liquid name, etc.;

[0070] Since urban underground pipelines are used to transport wastewater, natural gas, optical fiber, etc., there must be regional point cloud data of corresponding pipelines around the corresponding regional point cloud data of wastewater, natural gas, optical fiber, etc.

[0071] Therefore, the point cloud sub-regions at adjacent positions in the urban visualization point cloud distribution model are matched with each other. If two point cloud sub-regions have the same regional physical property labels, the same color labels are set for the two point cloud sub-regions, otherwise no operation is performed;

[0072] Repeat the above point cloud sub-region matching process to connect the colored point cloud sub-regions, and then plan several colored point cloud data regions in the urban visualization point cloud distribution model;

[0073] Since the outer materials of the pipes or optical fibers in the city's underground pipelines may be the same, but the substances or cable molecules transported inside are different, there are multiple pipes or optical fibers overlapping in the same point cloud data area in the city's visualization point cloud distribution model;

[0074] Therefore, according to the absorption of laser signals of different frequency bands by the materials used in the pipeline or the external materials of the optical fiber, the point cloud data area corresponding to the pipeline or the outside of the optical fiber is separated in the urban visualization point cloud distribution model. At the same time, since the materials or cables transported by the pipeline flow underground with the pipeline or optical fiber, the corresponding point cloud data area is distributed in the urban visualization point cloud distribution model in the form of pipelines;

[0075] Therefore, a point cloud sub-region surrounding the point cloud data area in the pipeline state is cut out around the point cloud data area in the pipeline state, and the divided point cloud sub-region is associated with the corresponding point cloud data area in the pipeline state, thereby dividing a number of urban three-dimensional visualization pipeline lines in the urban visualization point cloud distribution model.

[0076] like Figure 2 As shown, the present invention also discloses a method for visualizing and analyzing underground pipelines based on drone mapping, comprising the following steps:

[0077] Step S1: plan several drone flight routes according to the pre-stored urban surveying and mapping area map, and then control each drone to fly along the drone flight route, and collect the city surface image and laser reflection signal spectrum of the passing location during the flight through a high-precision camera and a laser scanner;

[0078] Step S2, establishing a city outer surface model according to the city outer surface image, and dividing the laser reflection signal spectrum into a plurality of laser reflection signal segments, and then establishing a city underground area point cloud distribution model according to each laser reflection signal segment, and connecting the city underground area point cloud distribution model with the city outer surface model to obtain a city visualization point cloud distribution model;

[0079] Step S3, obtaining the absorption rate of each underground material to the laser signal of different frequency bands, and dividing a number of point cloud data areas with different colors in the urban visualization point cloud distribution model according to the absorption rate of each underground material to the laser signal of different frequency bands;

[0080] Step S4, dividing a number of point cloud data areas in pipeline state within the point cloud data area, and cutting out point cloud sub-areas surrounding the point cloud data area in pipeline state around the point cloud data area in pipeline state, and then dividing a number of urban three-dimensional visualization pipeline lines within the urban visualization point cloud distribution model.

[0081] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Although the present invention has been disclosed as a preferred embodiment as above, it is not used to limit the present invention. Any technical personnel in this field can make some changes or modify the technical contents disclosed above into equivalent embodiments without departing from the scope of the technical solution of the present invention. However, any brief modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention are still within the scope of the technical solution of the present invention.

Claims

1. A visual analysis system for underground pipelines based on drone mapping, including a cloud control terminal, characterized in that: The cloud control terminal is communicatively connected to a UAV surveying and mapping module, a surveying and mapping data processing module, and a visualization segmentation module; The drone mapping module is used to plan a number of drone flight routes based on the pre-stored urban mapping area map, and then control each drone to fly along the drone flight route, and collect the city surface image and laser reflection signal spectrum of the location passed during the flight through a high-precision camera and a laser scanner; The surveying and mapping data processing module is used to establish a city outer surface model based on the city outer surface image, and to divide the laser reflection signal spectrum into a plurality of laser reflection signal segments, and then to establish a city underground area point cloud distribution model based on each laser reflection signal segment, and to connect the city underground area point cloud distribution model with the city outer surface model to obtain a city visualization point cloud distribution model; The process of establishing the city outer surface model includes: The city outer surface images collected by each drone are stitched in time order to obtain two sets of image sequences with opposite orders, and then according to the spatial position relationship between the flight routes of each drone, the image sequences with the same order are stitched and overlapped to obtain two city outer surface total images, and the two city outer surface total images are respectively divided into a number of image areas of the same size, and the image areas in the two city outer surface total images are matched in order, and the two city outer surface total images are overlapped and mapped according to the matching results, and the city outer surface model is established according to the city outer surface total images after overlapping mapping; The visualization segmentation module is used to obtain the absorption rate of each underground material to the laser signal of different frequency bands, and divide a number of point cloud data areas with different colors in the urban visualization point cloud distribution model according to the absorption rate of each underground material to the laser signal of different frequency bands, and then divide the urban three-dimensional visualization pipeline line in the point cloud data area; The process of dividing the plurality of point cloud data regions with different colors includes: Establish a three-dimensional coordinate system, and map the urban visualization point cloud distribution model into the three-dimensional coordinate system, and then divide the point cloud data of each area in the urban visualization point cloud distribution model into several point cloud sub-areas; Obtain the laser reflection signal segments corresponding to the point cloud data of each region, and mark the corresponding regional physical properties in the point cloud sub-regions within the point cloud data of each region according to the conditions of the peaks and troughs in the same pair of time nodes but different laser reflection signal segments; The point cloud sub-regions at adjacent positions in the urban visualization point cloud distribution model are matched with each other, and point cloud data regions of several colors are planned in the urban visualization point cloud distribution model according to the matching results.

2. According to claim 1, a visualization analysis system for underground pipelines based on drone mapping is characterized in that: The process of collecting the city outer surface image and the laser reflection signal spectrum includes: Set m drones, each of which is equipped with a camera and a laser scanner, and set a number for each drone, where m is a natural number greater than 0; The same surveying and mapping data collection range is set for each drone, wherein the surveying and mapping data collection range includes an image shooting range and a laser signal receiving range, and m drone flight routes are planned on the urban surveying and mapping area map according to the image shooting range in the surveying and mapping data collection range; The drone mapping module allocates all drone flight routes to each drone in turn, and then each drone flies over the city twice along the drone flight route. During the flight, the camera captures the image of the city's outer surface within its image shooting range, and the laser scanner simultaneously emits laser signals of multiple frequency bands vertically to the ground, thereby continuously generating a laser reflection signal spectrum within the laser signal receiving range, and marking the frequency band of each laser reflection signal spectrum.

3. The underground pipeline visualization analysis system based on drone mapping according to claim 2 is characterized in that: The process of dividing the laser reflection signal segments includes: A two-dimensional coordinate system is established, and the spectra of laser reflection signals corresponding to the same UAV number, the same generation time but different frequencies are mapped onto the same two-dimensional coordinate system; Setting a fluctuation amplitude threshold and a feature number threshold, and setting a number of time nodes to divide each laser reflection signal spectrum into a number of amplitude coordinate points, thereby obtaining the fluctuation amplitude difference between the amplitude coordinate points of each adjacent time node, and judging whether the fluctuation amplitude difference is greater than or equal to the fluctuation amplitude threshold, and recording the number of feature points at the coordinate axis position between the time nodes according to the judgment result; The number of feature points at each position on the coordinate axis is counted, and it is determined whether the number of feature points is greater than or equal to the feature quantity threshold. If the number of feature points is greater than or equal to the feature quantity threshold, the corresponding position is recorded as the starting point of the feature fluctuation, otherwise no operation is performed. According to the position distribution of the starting point of the feature fluctuation on the coordinate axis, each laser reflection signal spectrum is divided into several laser reflection signal segments.

4. The underground pipeline visualization analysis system based on drone mapping according to claim 3 is characterized in that: The process of establishing the city visualization point cloud distribution model includes: According to each laser reflection signal fragment, the corresponding regional point cloud data is generated, and then according to the distribution of the drone flight routes of each drone, the point cloud data of each region are spliced ​​in sequence to obtain the urban underground area point cloud distribution model. Then, according to the overlapping relationship of the data acquisition range between the high-precision camera and the laser scanner, the urban outer surface model and the urban underground area point cloud distribution model are spliced ​​up and down to obtain the urban visualization point cloud distribution model.

5. The underground pipeline visualization analysis system based on drone mapping according to claim 4 is characterized in that: The process of dividing the urban three-dimensional visualization pipeline line includes: According to the absorption of laser signals of different frequency bands by the materials used for the pipeline or the external materials of the optical fiber, the point cloud data area corresponding to the pipeline or the outside of the optical fiber is separated in the urban visualization point cloud distribution model, and then, around the point cloud data area in the pipeline state, a point cloud sub-area surrounding the point cloud data area in the pipeline state is cut out, and the divided point cloud sub-area is associated with the corresponding point cloud data area in the pipeline state, and then a number of urban three-dimensional visualization pipeline lines are divided in the urban visualization point cloud distribution model.

6. A method for visual analysis of underground pipelines based on drone mapping, used to implement a system for visual analysis of underground pipelines based on drone mapping as claimed in any one of claims 1 to 5, characterized in that: The following steps are involved: Step S1: plan several drone flight routes according to the pre-stored urban surveying and mapping area map, and then control each drone to fly along the drone flight route, and collect the city surface image and laser reflection signal spectrum of the passing location during the flight through a high-precision camera and a laser scanner; Step S2, establishing a city outer surface model according to the city outer surface image, and dividing the laser reflection signal spectrum into a plurality of laser reflection signal segments, and then establishing a city underground area point cloud distribution model according to each laser reflection signal segment, and connecting the city underground area point cloud distribution model with the city outer surface model to obtain a city visualization point cloud distribution model; Step S3, obtaining the absorption rate of each underground material to the laser signal of different frequency bands, and dividing a number of point cloud data areas with different colors in the urban visualization point cloud distribution model according to the absorption rate of each underground material to the laser signal of different frequency bands; Step S4, dividing a number of point cloud data areas in pipeline state within the point cloud data area, and cutting out point cloud sub-areas surrounding the point cloud data area in pipeline state around the point cloud data area in pipeline state, and then dividing a number of urban three-dimensional visualization pipeline lines within the urban visualization point cloud distribution model.

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