Power pipe gallery modeling method and system based on multi-data fusion and medium

Abstract

The invention relates to the technical field of digital twinning of electric power pipe galleries, and provides an electric power pipe gallery modeling method and system based on multi-data fusion and a medium. The method comprises the following steps: S1, acquiring point cloud data obtained by scanning a to-be-modeled area of the electric power pipe gallery through a laser radar, image data acquired by a camera and a two-dimensional time profile image obtained by scanning through a ground penetrating radar; s2, performing homologous processing on the point cloud data, the two-dimensional time profile image and the image data, and performing fusion to obtain fused point cloud data under a three-dimensional coordinate system; and S3, performing feature extraction and clustering based on the fused point cloud data, and performing three-dimensional model construction of the electric power pipe gallery. Compared with traditional laser radar surveying and mapping, point cloud color texture information is enriched by combining image data; in addition, image information of the ground penetrating radar is combined, information in the vertical direction is optimized, and information loss of two-dimensional image data in the third axis direction is supplemented.

Description

Technical Field

[0001] The present invention relates to the technical field of digital twin of electric power pipeline corridors, and in particular to a method, system and computer-readable storage medium for modeling digital twin of electric power pipeline corridors based on multi-data fusion. Background Art

[0002] The power pipeline gallery, also known as the cable tunnel or power tunnel, is an important municipal infrastructure in the city. The cable tunnel is located deep underground and extends to every corner of the city. Once a problem occurs, it will have a serious impact on the normal operation of the city.

[0003] Due to the complexity and particularity of underground power pipelines, scientific management is a severe challenge. In addition to the traditional manual inspection method, digital twin technology has been derived to create three-dimensional digital twins of power pipelines.

[0004] Laser scanning and computer 3D point cloud processing technology can digitally record key steps such as cable laying and accurately measure key parameters, assisting construction and acceptance personnel to achieve efficient and accurate analysis of cable laying location information and construction quality verification. The measurement technology based on 3D point cloud can accurately reconstruct the 3D structure and spatial orientation of the object to be measured, and the measurement of geometric parameters is more accurate, so it has gradually been applied in the field of power cable detection.

[0005] Based on laser 3D scanning technology, we can build high-precision 3D visualization scenes of underground cable corridors and tunnels, intuitively present the operating parameters and environmental parameters of the cable corridors, and help realize the visualization functions of remote monitoring and intelligent operation and maintenance of the system.

[0006] However, due to the difference between the structure of the interior of the cable tunnel and the outdoor elevated tower, the cable tunnel has the characteristics of small internal space and many densely packed components, making it still difficult in the existing technology to effectively obtain the digital model of the cable accessories through laser scanning technology and provide reasonable control measures for the cable accessory level. Summary of the invention

[0007] The object of the present invention is to provide a power pipeline corridor modeling method, system and computer-readable storage medium based on multi-data fusion to solve the above-mentioned technical problems.

[0008] The present invention provides a method for modeling a power pipeline corridor based on multi-data fusion, comprising the steps of:

[0009] S1, obtaining point cloud data obtained by laser radar scanning of the area to be modeled for the power corridor, image data collected by the camera, and a two-dimensional time profile image obtained by ground penetrating radar scanning;

[0010] S2, after homologation processing, the point cloud data, the two-dimensional time section image, and the image data are fused to obtain fused point cloud data in a three-dimensional coordinate system, including:

[0011] S21, filtering, denoising and smoothing the point cloud data, and then establishing laser point cloud data based on a three-dimensional coordinate system based on preset sampling mark points as reference points;

[0012] S22, based on the preset sampling mark point as the reference point, the spatial coordinates of each point in the laser point cloud data are projected into two-dimensional plane pixel coordinates using the camera's intrinsic parameter matrix Q, and for each pixel point in the image data, the point closest to the pixel point is found in the two-dimensional plane pixel coordinates, and the coordinate value passing through the point is compared with the camera depth value d and the inverse matrix Q of the camera's intrinsic parameter matrix Q -1 , performing back-projection to obtain the coordinates of each pixel point of the image data in the three-dimensional coordinate system to obtain image point cloud data;

[0013] S23, based on the preset sampling mark point as the reference point, performing time-depth conversion on the two-dimensional time profile image to obtain the coordinates of all sampling points on the ground penetrating radar image in the three-dimensional coordinate system to obtain detection radar point cloud data;

[0014] S24, matching and fusing the laser point cloud data, the image point cloud data and the detection radar point cloud data to obtain fused point cloud data in the three-dimensional coordinate system;

[0015] S3, performing feature extraction and clustering based on the fused point cloud data, and constructing a three-dimensional model of the power pipeline corridor.

[0016] Preferably, in step S21, the step of filtering and denoising the point cloud data includes:

[0017] S211, removing noise points in the point cloud data by a filtering algorithm, retaining valid point cloud data to obtain filtered point cloud data;

[0018] S212, for each point p in the filtered point cloud data, calculate the average value μ and standard deviation σ of the coordinate values ​​of the neighboring points around it;

[0019] S213, based on the distance dist(i) from each neighborhood point i to point p and the weight coefficient w of the neighborhood point i i , the smoothed coordinates f(P) of point p are obtained through the Gaussian smoothing algorithm to reduce the noise and irregularity of the filtered point cloud data.

[0020] Preferably, in step S21,

[0021] The calculation formula of the average value μ is:

[0022] The calculation formula of the standard deviation σ is:

[0023]

[0024] The weight coefficient w i The calculation formula is

[0025] The calculation formula of the smoothed coordinate f(P) is:

[0026] where {x1, x2, ..., x n} represents the set of coordinate values ​​of n neighborhood points, and λ represents the parameter of the Gaussian function.

[0027] Preferably, in the step S213, the smoothed coordinates f(P) of the point p are obtained by using a Gaussian smoothing algorithm, and the following steps are also included:

[0028] Calculate the error distance dis between the smoothed point coordinates f(P) and point p, dis = sqrt((f(P).xp.x) 2 +(f(P).yp.y) 2 +(f(P).zp.z) 2 );

[0029] Calculate the squared error r of all smoothed coordinates f(P), Among them, f(P).x, f(P).y, f(P).z represent the x, y, z coordinates of the smoothed coordinate point f(P), px, py, pz represent the x, y, z coordinates of the point p, respectively; m represents the total number of points;

[0030] If the square difference r is greater than the preset threshold, the parameter λ of the Gaussian function is adjusted to continue the operation; if the square difference r is less than or equal to the preset threshold, the subsequent steps are entered.

[0031] Preferably, in step S22, the calculation formula of the coordinates D(x, y, z) of each pixel point (s, t) of the image data in the three-dimensional coordinate system is D(x, y, z)=Q -1 -1*[s',t',d].

[0032] Preferably, the step S23 includes:

[0033] S231, performing corresponding data post-processing on the two-dimensional time profile image collected by the ground penetrating radar;

[0034] S232, based on the preset sampling mark point as the reference point, coordinate matching is performed on the processed ground penetrating radar image according to the ground penetrating radar and time synchronization file to obtain the position of the reference point in the vertical direction of the ground penetrating radar image;

[0035] S233, starting from the coordinates of the reference point, time-depth conversion is performed on the longitudinal axis of the ground penetrating radar image in both the forward and reverse directions of time to obtain the coordinates of all sampling points on the ground penetrating radar image.

[0036] Preferably, the post-processing includes de-oscillation filtering, automatic gain, background filtering, bandpass filtering and image smoothing.

[0037] Preferably, the coordinates of the reference point are set to (0,0,0).

[0038] The present invention also provides a power pipeline corridor modeling system based on multi-data fusion, including a laser radar scanning device, a camera device, a ground penetrating radar scanning device and a computer background server;

[0039] A laser radar scanning device is used to obtain point cloud data of the area to be modeled for the power pipeline corridor;

[0040] A camera device, used to collect image data of the area to be modeled for the power pipeline corridor;

[0041] A ground penetrating radar scanning device is used to scan the area to be modeled for the power pipeline corridor to obtain a two-dimensional time profile image;

[0042] The computer backend server includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the power pipeline corridor modeling method based on multi-data fusion as described in any one of the above items are implemented.

[0043] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the power pipeline corridor modeling method based on multi-data fusion as described in any of the above items.

[0044] The power corridor modeling method based on multi-data fusion provided by the present invention includes the following steps: S1, obtaining point cloud data obtained by laser radar scanning of the area to be modeled of the power corridor, image data collected by a camera, and a two-dimensional time profile image obtained by ground penetrating radar scanning; S2, fusing the point cloud data, the two-dimensional time profile image, and the image data to obtain fused point cloud data in a three-dimensional coordinate system after homologation processing; S3, performing feature extraction and clustering based on the fused point cloud data, and constructing a three-dimensional model of the power corridor. Compared with the point cloud three-dimensional model obtained by traditional laser radar mapping, the point cloud color and texture information is enriched by combining image data; in addition, the image information of the ground penetrating radar is combined to optimize the information in the vertical direction, making up for the missing information in the third axis direction of the two-dimensional image data; the fused fused point cloud data has both high precision and realism, giving full play to the advantages of different data sources and overcoming the defects of a single data source. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 It is a flowchart of a method for modeling a power pipeline corridor based on multi-data fusion in one embodiment of the present invention.

[0046] Figure 2 for Figure 1 Sub-process step diagram of step S2 in .

[0047] Figure 3 It is a schematic diagram of the hardware structure of a system running a power pipeline corridor modeling method based on multi-data fusion in one embodiment of the present invention.

[0048] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings in conjunction with the embodiments. DETAILED DESCRIPTION

[0049] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0050] In the subsequent description, the suffixes such as "module", "component" or "unit" used to represent elements are only used to facilitate the description of the present invention, and have no specific meanings. Therefore, "module", "component" or "unit" can be used in a mixed manner.

[0051] The present invention will now be described in further detail with reference to the accompanying drawings.

[0052] See also Figure 1 , which is a process step diagram of a power pipeline corridor modeling method based on multi-data fusion in one embodiment of the present invention.

[0053] A method for modeling a power pipeline corridor based on multi-data fusion, comprising the steps of:

[0054] S1, obtaining point cloud data obtained by laser radar scanning of the area to be modeled for the power corridor, image data collected by the camera, and a two-dimensional time profile image obtained by ground penetrating radar scanning.

[0055] LiDAR scanning is generally performed by moving around the object being measured or moving at a constant speed along the object being measured. The point cloud data obtained by LiDAR scanning is a discrete point cloud with a topological relationship. Currently, the relevant technology for generating three-dimensional digital models by laser scanning is very mature and will not be elaborated in this article.

[0056] The image data collected by the camera is generally obtained by moving around the object being measured or moving at a constant speed along the object being measured.

[0057] Ground penetrating radar can penetrate objects or the ground to obtain internal abnormal shapes. The data obtained by ground penetrating radar is a two-dimensional time profile composed of continuous single-channel waves with a certain proportion in the horizontal and vertical directions.

[0058] S2, after homologation processing, the point cloud data, the two-dimensional time section image, and the image data are fused to obtain fused point cloud data in a three-dimensional coordinate system, see Figure 1 , S2 specifically includes:

[0059] S21, filtering, denoising and smoothing the point cloud data, and then establishing laser point cloud data based on a three-dimensional coordinate system based on a preset sampling mark point as a reference point; the coordinates of the reference point can be set to (0,0,0);

[0060] S22, based on the preset sampling mark point as the reference point, construct a two-dimensional plane pixel coordinate for the image data, use the camera's intrinsic parameter matrix Q to project the spatial coordinates of each point in the laser point cloud data into a two-dimensional plane pixel coordinate, for each pixel point D (s, t) in the image data, find the point (s', t') closest to the pixel point in the two-dimensional plane pixel coordinate, and compare the coordinate value D (s', t') passing through the point with the camera depth value d and the inverse matrix Q of the camera's intrinsic parameter matrix Q -1 , performing back-projection to obtain the coordinates of each pixel point of the image data in the three-dimensional coordinate system to obtain image point cloud data;

[0061] S23, based on the preset sampling mark point as the reference point, performing time-depth conversion on the two-dimensional time profile image to obtain the coordinates of all sampling points on the ground penetrating radar image in the three-dimensional coordinate system to obtain detection radar point cloud data;

[0062] S24, matching and fusing the laser point cloud data, the image point cloud data and the detection radar point cloud data to obtain fused point cloud data in the three-dimensional coordinate system.

[0063] It is understandable that there are multiple reference points, and generally multiple points are marked in the power pipeline corridor, such as specially colored and easily detectable marks, to ensure that the nearest reference point can be found for calculation.

[0064] Preferably, the calculation of an image or a minimum segmentation unit may also include multiple reference points for calculation.

[0065] Specifically, in step S21, the step of filtering and denoising the point cloud data includes:

[0066] S211, removing noise points in the point cloud data by a filtering algorithm, retaining valid point cloud data to obtain filtered point cloud data;

[0067] S212, for each point p in the filtered point cloud data, calculate the average value μ and standard deviation σ of the coordinate values ​​of the neighboring points around it;

[0068] Among them, the calculation formula of the average value μ is:

[0069] The calculation formula of the standard deviation σ is:

[0070]

[0071] S213, according to the distance from each neighborhood point to point p and the weight coefficient, the smoothed coordinates f(P) of point p are obtained by using a Gaussian smoothing algorithm to reduce the noise and irregularity of the filtered point cloud data; wherein,

[0072] The weight coefficient w i The calculation formula is

[0073] The calculation formula of the smoothed coordinate f(P) is:

[0074] where {x1, x2, ..., x n} represents the set of coordinate values ​​of n neighborhood points, and λ represents the parameter of the Gaussian function.

[0075] Furthermore, in a preferred embodiment, in the step S213, the smoothed coordinates f(P) of the point p are obtained by the Gaussian smoothing algorithm, and the following steps are also included:

[0076] Calculate the error distance dis between the smoothed point coordinates f(P) and point p, dis = sqrt((f(P).xp.x) 2 +(f(P).yp.y) 2 +(f(P).zp.z) 2 );

[0077] Calculate the squared error r of all smoothed coordinates f(P), Among them, f(P).x, f(P).y, f(P).z represent the x, y, z coordinates of the smoothed coordinate point f(P), px, py, pz represent the x, y, z coordinates of the point p, respectively;

[0078] If the square difference r is greater than the preset threshold, the parameter λ of the Gaussian function is adjusted to continue the operation; if the square difference r is less than or equal to the preset threshold, the subsequent steps are entered.

[0079] Specifically, in step S22, the calculation formula of the coordinate D(x, y, z) of the image data pixel point (s, t) in the three-dimensional coordinate system is D(x, y, z)=Q -1 -1*[s',t',d].

[0080] Preferably, the step S23 includes:

[0081] S231, performing corresponding data post-processing on the two-dimensional time profile image collected by the ground penetrating radar; the post-processing includes de-oscillation filtering, automatic gain, background filtering, band-pass filtering and image smoothing;

[0082] S232, based on the preset sampling mark point as the reference point, coordinate matching is performed on the processed ground penetrating radar image according to the ground penetrating radar and time synchronization file to obtain the position of the reference point in the vertical direction of the ground penetrating radar image;

[0083] S233, starting from the coordinates of the reference point, time-depth conversion is performed on the longitudinal axis of the ground penetrating radar image in both the forward and reverse directions of time to obtain the coordinates of all sampling points on the ground penetrating radar image.

[0084] Specifically, the time-depth conversion step of the ground-penetrating radar image in the vertical direction can be achieved by combining the propagation speed of electromagnetic waves in the medium. The ground-penetrating radar image is a two-dimensional time profile with the acquisition starting point as the coordinate origin. The horizontal axis represents the distance traveled by the ground-penetrating radar antenna on the ground, and the vertical axis represents the round-trip time for the electromagnetic wave to propagate in the medium, encounter an abnormal body, and be reflected back. The specific time-depth conversion method and the selection of the electromagnetic wave propagation path model can be selected by those skilled in the art as needed. According to the privacy of the underground depth and width of the power corridor, the distance between the ground-penetrating radar transmitting antenna and the receiving antenna and the influence of the direct wave can be considered to establish different electromagnetic wave propagation path models.

[0085] Please refer to Figure 3 ,In one embodiment of the present invention, a power pipeline corridor modeling system based on multi-data fusion is also provided, including a laser radar scanning device, a camera device, a ground penetrating radar scanning device and a computer background server;

[0086] A laser radar scanning device is used to obtain point cloud data of the area to be modeled for the power pipeline corridor;

[0087] A camera device, used to collect image data of the area to be modeled for the power pipeline corridor;

[0088] A ground penetrating radar scanning device is used to scan the area to be modeled for the power pipeline corridor to obtain a two-dimensional time profile image;

[0089] The computer backend server includes a memory 61, a processor 62, and a computer program 63 stored in the memory 61 and executable on the processor 62. When the processor 62 executes the computer program 63, the steps of the power pipeline corridor modeling method based on multi-data fusion as described in any one of the above items are implemented.

[0090] This embodiment also provides a computer-readable storage medium storing a computer program, characterized in that when the computer program is executed by a processor, the steps of the power pipeline corridor modeling method based on multi-data fusion as described in any one of the above items are implemented.

[0091] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0092] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0093] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0094] In the embodiments provided by the present invention, it should be understood that the disclosed devices / terminal equipment and methods can be implemented in other ways. For example, the device / terminal equipment embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0095] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0096] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0097] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0098] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0099] In the description of this specification, the description with reference to the terms "an embodiment", "another embodiment", "other embodiments", or "first embodiment to Xth embodiment" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, method steps or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0100] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0101] The above are only preferred embodiments of the present invention, and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A power pipeline corridor modeling method based on multi-data fusion, characterized in that: Includes steps: S1, obtaining point cloud data obtained by laser radar scanning, image data collected by the camera, and two-dimensional time profile images obtained by ground penetrating radar scanning for the area to be modeled of the power corridor; S2, after homologation processing is performed on the point cloud data, the image data and the two-dimensional time section image, fusion is performed to obtain fused point cloud data in a three-dimensional coordinate system, S2 specifically includes: S21, filtering, denoising and smoothing the point cloud data, and then establishing laser point cloud data based on a three-dimensional coordinate system based on preset sampling mark points as reference points; S22, based on the preset sampling mark point as the reference point, the spatial coordinates of each point in the laser point cloud data are projected into two-dimensional plane pixel coordinates using the camera's intrinsic parameter matrix Q, and for each pixel point in the image data, the point closest to the pixel point is found in the two-dimensional plane pixel coordinates, and the coordinate value passing through the point is compared with the camera depth value d and the inverse matrix Q of the camera's intrinsic parameter matrix Q -1 , performing back-projection to obtain the coordinates of each pixel point of the image data in the three-dimensional coordinate system to obtain image point cloud data; S23, based on the preset sampling mark point as the reference point, performing time-depth conversion on the two-dimensional time profile image to obtain the coordinates of all sampling points on the ground penetrating radar image in the three-dimensional coordinate system to obtain detection radar point cloud data; S24, matching and fusing the laser point cloud data, the image point cloud data and the detection radar point cloud data to obtain fused point cloud data in the three-dimensional coordinate system; S3, performing feature extraction and clustering based on the fused point cloud data, and constructing a three-dimensional model of the power pipeline corridor.

2. The power pipeline corridor modeling method based on multi-data fusion according to claim 1 is characterized in that: In step S21, the step of filtering and denoising the point cloud data includes: S211, removing noise points in the point cloud data by a filtering algorithm, retaining valid point cloud data to obtain filtered point cloud data; S212, for each point p in the filtered point cloud data, calculate the average value μ and standard deviation σ of the coordinate values ​​of the neighboring points around it; S213, based on the distance dist(i) from each neighborhood point i to point p and the weight coefficient w of the neighborhood point i i , the smoothed coordinates f(P) of point p are obtained through the Gaussian smoothing algorithm to reduce the noise and irregularity of the filtered point cloud data.

3. The power pipeline corridor modeling method based on multi-data fusion according to claim 2 is characterized in that: In the step S21, The calculation formula of the average value μ is: The calculation formula of the standard deviation σ is: The weight coefficient w i The calculation formula is The calculation formula of the smoothed coordinate f(P) is: where {x1, x2, ..., x n } represents the set of coordinate values ​​of n neighborhood points, and λ represents the parameter of the Gaussian function.

4. The power pipeline corridor modeling method based on multi-data fusion according to claim 3 is characterized in that: In the step S213, the smoothed coordinates f(P) of the point p are obtained by the Gaussian smoothing algorithm, and the steps of: Calculate the error distance dis between the smoothed point coordinates f(P) and point p, dis = sqrt((f(P).xp.x) 2 +(f(P).yp.y) 2 +(f(P).zp.z) 2 ); Calculate the squared error r of all smoothed coordinates f(P), Among them, f(P).x, f(P).y, f(P).z represent the x, y, z coordinates of the smoothed coordinate point f(P), px, py, pz represent the x, y, z coordinates of the point p, respectively; m represents the total number of points; If the square difference r is greater than the preset threshold, the parameter λ of the Gaussian function is adjusted to continue the operation; if the square difference r is less than or equal to the preset threshold, the subsequent steps are entered.

5. The power pipeline corridor modeling method based on multi-data fusion according to claim 1 is characterized in that: In step S22, the coordinates D(x, y, z) of each pixel point (s, t) of the image data in the three-dimensional coordinate system are calculated as follows: D(x, y, z)=Q -1 -1*[s',t',d].

6. The power pipeline corridor modeling method based on multi-data fusion according to claim 1 is characterized in that: The step S23 comprises: S231, performing corresponding data post-processing on the two-dimensional time profile image collected by the ground penetrating radar; S232, based on the preset sampling mark point as the reference point, coordinate matching is performed on the processed ground penetrating radar image according to the ground penetrating radar and time synchronization file to obtain the position of the reference point in the vertical direction of the ground penetrating radar image; S233, starting from the coordinates of the reference point, time-depth conversion is performed on the longitudinal axis of the ground penetrating radar image in both the forward and reverse directions of time to obtain the coordinates of all sampling points on the ground penetrating radar image.

7. The power pipeline corridor modeling method based on multi-data fusion according to claim 6 is characterized in that: The post-processing includes de-oscillation filtering, automatic gain, background filtering, band-pass filtering and image smoothing.

8. The power pipeline corridor modeling method based on multi-data fusion according to any one of claims 1 to 7, characterized in that: The coordinates of the reference point are set to (0,0,0).

9. A power pipeline corridor modeling system based on multi-data fusion, comprising a laser radar scanning device, a camera device, a ground penetrating radar scanning device and a computer backend server; characterized in that: A laser radar scanning device is used to obtain point cloud data of the area to be modeled for the power pipeline corridor; A camera device, used to collect image data of the area to be modeled for the power pipeline corridor; A ground penetrating radar scanning device is used to scan the area to be modeled for the power pipeline corridor to obtain a two-dimensional time profile image; The computer backend service end includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the power pipeline corridor modeling method based on multi-data fusion as described in any one of claims 1 to 8 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the power pipeline corridor modeling method based on multi-data fusion as described in any one of claims 1 to 8 are implemented.

Images