A three-dimensional point cloud infrared thermography coloring method and system

Abstract

The application provides a three-dimensional point cloud infrared thermal imaging coloring method and system, which comprises the following steps: obtaining three-dimensional laser point cloud data, infrared spectrum data and POS data of a power transmission line channel; reconstructing a first infrared map by adopting a differential solution method on the infrared spectrum data and the POS data; adding temperature information to the first infrared map, and then generating an infrared digital surface model by adding boundary information after image segmentation according to the span of the power transmission line; performing data registration on the POS data corresponding to the three-dimensional laser point cloud data to obtain final registration data; and importing the final registration data and the infrared digital surface model into a three-dimensional modeling software to perform interactive modeling and obtain a three-dimensional laser point cloud model with temperature information. Based on the method, a three-dimensional point cloud infrared thermal imaging coloring system is also provided. The application can detect and calculate the three-dimensional infrared thermal imaging data of a measured device, and provide a basis for further heat judgment for operation and maintenance personnel.

Description

Technical Field

[0001] This invention belongs to the field of navigation and positioning technology, and specifically relates to a three-dimensional point cloud infrared thermal imaging coloring method and system. Background Technology

[0002] With the advancements in UAV navigation and positioning technology, infrared thermal imaging measurement technology, and sensor technology, three-dimensional modeling methods using three-dimensional laser point clouds and infrared spectral information of transmission lines have become possible. Thermal infrared three-dimensional modeling technology has significant application value, at least in the following two aspects: 1. Using an infrared thermal imager mounted on a UAV, the heating points of transmission line targets can be detected at close range and from multiple angles to obtain accurate two-dimensional temperature distribution, generating thermal images. The operating status of equipment can be determined by analyzing the characteristics of these thermal images. This method offers advantages such as high efficiency, safety, and immunity to interference from high-voltage electromagnetic fields; 2. Using a three-dimensional model established by scanning point clouds with lidar to describe and represent the spatial distribution of the transmission line body is a pathway towards three-dimensional digitization of transmission lines. It can more three-dimensionally display the spatial distribution of heating points on the transmission line, avoiding blind spots caused by spatial obstruction in traditional two-dimensional maps.

[0003] However, existing power transmission infrared thermal imaging technology can only detect thermal imaging data on the surface of the equipment under test, and cannot detect thermal imaging data in the space of the equipment under test. The poor spatial perception of the heat-generating parts makes it impossible for maintenance personnel to accurately determine the location of the heat-generating points. Summary of the Invention

[0004] To address the aforementioned technical issues, this invention proposes a three-dimensional point cloud infrared thermal imaging coloring method and system, which can detect and calculate the spatial three-dimensional infrared thermal imaging data of the device under test, providing a basis for maintenance personnel to further determine the heat generation.

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

[0006] A three-dimensional point cloud infrared thermal imaging colorization method includes the following steps:

[0007] Acquire 3D laser point cloud data, infrared spectrum data, and POS data of the power transmission line corridor;

[0008] The infrared spectrum data and POS data are used to perform two-dimensional map reconstruction to obtain the first infrared image. After adding temperature information to the first infrared image, the image is segmented according to the span of the transmission line and boundary information is added to generate an infrared digital surface model.

[0009] Data registration is performed on the POS data corresponding to the three-dimensional laser point cloud data to obtain the final registration data; the final registration data and the infrared digital surface model are combined and imported into the three-dimensional modeling software for interactive modeling to obtain a three-dimensional laser point cloud model with temperature information.

[0010] Furthermore, the process of acquiring the three-dimensional laser point cloud data, infrared spectrum data, and POS data of the power transmission line channel includes: acquiring the three-dimensional laser point cloud data, infrared spectrum data, and image POS data of the power transmission line channel through UAV scanning.

[0011] Furthermore, the process of reconstructing a two-dimensional map from the infrared spectrum data using differential calculation to obtain the first infrared image includes: using dynamic post-processing technology to calculate the first infrared image from the infrared spectrum data; the first infrared image is an infrared spectrum with coordinates, wherein the coordinates are latitude, longitude and altitude.

[0012] Furthermore, the process of adding temperature information to the first infrared image includes: extracting temperature information from the first infrared image using infrared analysis software and displaying it on the first infrared image.

[0013] Furthermore, the method for adding boundary information includes: using the function BORDER_CONSTANT to fill the boundary with a constant for the segmented first infrared image; or using the function BORDER_REPLICATE to copy the nearest row or column of the segmented first infrared image as the boundary.

[0014] Furthermore, the process of registering the POS data corresponding to the three-dimensional laser point cloud data to obtain the registered data includes:

[0015] First, the POS data corresponding to the three-dimensional laser point cloud data is coarsely registered using the sample consistency initial registration algorithm to obtain intermediate registration data.

[0016] After coarse registration is completed, the iterative nearest point algorithm is used to perform fine registration on the intermediate registration data to obtain the final registration data.

[0017] Furthermore, the process of combining the final registration data and the infrared digital surface model and importing them into 3D modeling software for interactive modeling to obtain a 3D laser point cloud model with temperature information includes: fitting the final registration data and the infrared digital surface model to spatial coordinates, obtaining the brightness information of the fitted image, and then performing 3D reconstruction by analyzing the brightness, shadows, focal length, texture and parallax in the image, and adding the constraint relationship of the corresponding features to obtain a 3D laser point cloud model with temperature information.

[0018] The present invention also proposes a three-dimensional point cloud infrared thermal imaging coloring system, including an acquisition module, a first processing module and a second processing module;

[0019] The acquisition module is used to acquire three-dimensional laser point cloud data, infrared spectrum data and POS data of the transmission line channel;

[0020] The first processing module is used to perform two-dimensional map reconstruction on the infrared spectrum data and POS data using differential calculation to obtain a first infrared image. After adding temperature information to the first infrared image, the module performs image segmentation based on the span of the transmission line and adds boundary information to generate an infrared digital surface model.

[0021] The second processing module is used to perform data registration on the POS data corresponding to the three-dimensional laser point cloud data to obtain the final registration data; the final registration data and the infrared digital surface model are combined and imported into the three-dimensional modeling software for interactive modeling to obtain a three-dimensional laser point cloud model with temperature information.

[0022] Furthermore, the process executed by the first processing module includes:

[0023] The infrared spectrum data is processed using dynamic post-processing technology to obtain a first infrared image; the first infrared image is an infrared spectrum with coordinates, wherein the coordinates are latitude, longitude and altitude;

[0024] Temperature information is extracted from the first infrared image using infrared analysis software and displayed on the first infrared image;

[0025] The first infrared image after segmentation is filled with a constant using the function BORDER_CONSTANT; or the nearest row or column in the first infrared image after segmentation is copied as the boundary to form an infrared digital surface model using the function BORDER_REPLICATE.

[0026] Furthermore, the process executed by the second processing module includes:

[0027] First, the POS data corresponding to the 3D laser point cloud data is coarsely registered using the sample consistency initial registration algorithm to obtain intermediate registration data; after the coarse registration is completed, the iterative nearest point algorithm is used to finely register the intermediate registration data to obtain the final registration data.

[0028] The final registration data and the infrared digital surface model are fitted with spatial coordinates. The brightness information of the fitted image is obtained, and then the brightness, shadow, focal length, texture and parallax in the image are analyzed to perform three-dimensional reconstruction. The corresponding feature constraints are added to obtain a three-dimensional laser point cloud model with temperature information.

[0029] The effects described in the invention are merely those of the embodiments, and not all the effects of the invention. One of the above technical solutions has the following advantages or beneficial effects:

[0030] This invention proposes a three-dimensional point cloud infrared thermal imaging colorization method and system. The method includes acquiring three-dimensional laser point cloud data, infrared spectrum data, and POS data of a power transmission line channel; reconstructing a two-dimensional map from the infrared spectrum data and POS data using differential computation to obtain a first infrared image; adding temperature information to the first infrared image; segmenting the image according to the transmission line span and adding boundary information to generate an infrared digital surface model; registering the POS data corresponding to the three-dimensional laser point cloud data to obtain final registered data; and combining the final registered data and the infrared digital surface model into three-dimensional modeling software for interactive modeling to obtain a three-dimensional laser point cloud model with temperature information. Based on this three-dimensional point cloud infrared thermal imaging colorization method, a three-dimensional point cloud infrared thermal imaging colorization system is also proposed. This invention uses an unmanned aerial vehicle (UAV) equipped with an infrared temperature measurement camera to collect infrared orthophotos of power line channels. Based on the aerial triaxial scanning algorithm, it stitches together infrared photos from the entire infrared channel and integrates temperature measurement software algorithms onto the infrared photos to display specific heating temperatures. The heating spectrum and the matched three-dimensional laser point cloud model of the transmission line are fitted to spatial coordinates to generate a three-dimensional point cloud infrared color model. This model can detect and calculate the spatial three-dimensional infrared thermal imaging data of the tested equipment, providing a basis for maintenance personnel to further determine the heating status. Attached Figure Description

[0031] like Figure 1 This is a flowchart of a three-dimensional point cloud infrared thermal imaging coloring method according to Embodiment 1 of the present invention;

[0032] like Figure 2 This is a schematic diagram of a three-dimensional point cloud infrared thermal imaging coloring system according to Embodiment 2 of the present invention. Detailed Implementation

[0033] To clearly illustrate the technical features of this solution, the invention will be described in detail below through specific embodiments and in conjunction with the accompanying drawings. The following disclosure provides many different embodiments or examples for implementing different structures of the invention. To simplify the disclosure of the invention, components and arrangements of specific examples are described below. Furthermore, reference numerals and / or letters may be repeated in different examples. This repetition is for simplification and clarity and does not in itself indicate a relationship between the various embodiments and / or arrangements discussed. It should be noted that the components illustrated in the drawings are not necessarily drawn to scale. Descriptions of well-known components, processing techniques, and processes are omitted in this invention to avoid unnecessarily limiting the invention.

[0034] Example 1

[0035] Embodiment 1 of this invention proposes a three-dimensional point cloud infrared thermal imaging colorization method to address the technical problem that existing power transmission infrared thermal imaging technology can only detect thermal imaging data of the surface layer of the equipment under test, and cannot detect thermal imaging data of the space under test. This results in poor spatial perception of heat-generating areas, making it difficult for maintenance personnel to accurately determine the location of heat points. This invention integrates three-dimensional laser point cloud and UAV orthophoto infrared thermal imaging detection methods to detect and calculate three-dimensional infrared thermal imaging data of the space under test, providing a basis for maintenance personnel to further determine the location of heat sources.

[0036] like Figure 1 A flowchart of a three-dimensional point cloud infrared thermal imaging colorization method according to Embodiment 1 of the present invention is provided;

[0037] The process involves acquiring three-dimensional laser point cloud data, infrared orthophoto data, and POS data of the power transmission line corridor. Specifically, this includes using drones to scan and obtain three types of data: three-dimensional laser point cloud data, infrared orthophoto maps, and POS data from photographic images of the transmission line corridor.

[0038] A first infrared image is obtained by performing a two-dimensional map reconstruction on the infrared spectrum data and POS data using differential calculation. Temperature information is then added to the first infrared image.

[0039] Post-Processed Kinematic (PPK) is a post-processing differential technology for obtaining centimeter-level positioning accuracy. PPK can record data from both the mobile and base stations separately and perform post-processing differential analysis, thus it is not limited by the communication link and protocol between the base station and the mobile station. PPK requires input of infrared image coordinates (latitude, longitude, and altitude), pitch angle, yaw angle, roll angle, horizontal accuracy, and vertical accuracy. After calculation, it obtains the coordinates (latitude, longitude, and altitude) of the known aerial infrared image, facilitating subsequent photo stitching.

[0040] The first infrared image is a temperature-information spectrum obtained by infrared thermometry when the image is taken directly at the ground.

[0041] The process of adding temperature information to the first infrared image is as follows: using infrared analysis software, the hottest infrared heat spots are extracted and attached to the image; that is, using the infrared images with coordinate information, the convolution back projection method is used to stitch together an image with a larger coverage area and more comprehensive temperature information based on the degree of overlap.

[0042] Image segmentation is based on the span of power transmission lines. The reconstructed 2D map consists of numerous photographs, resulting in a large file size and slow viewing speed. Therefore, it is necessary to segment the image to obtain smaller tiles for overlay with the point cloud model. Without segmentation, the computational burden on the model would increase. The most important purpose of segmentation is to prevent latency and buffering issues when scaling up or down the overlaid point cloud.

[0043] The first infrared image after segmentation is filled with a constant using the function BORDER_CONSTANT; or the infrared digital surface model is generated by copying the nearest row or column of the first infrared image after segmentation as the boundary using the function BORDER_REPLICATE.

[0044] The POS data corresponding to the acquired 3D laser point cloud data is registered to obtain registration data. The specific process includes:

[0045] First, the POS data corresponding to the three-dimensional laser point cloud data is coarsely registered using the sample consistency initial registration algorithm to obtain intermediate registration data.

[0046] After coarse registration is completed, the iterative nearest point algorithm is used to perform fine registration on the intermediate registration data to obtain the final registration data.

[0047] The Sample Consensus Initial Aligment (SAC-IA) algorithm performs coarse registration to obtain intermediate registration data. This algorithm relies on point feature histograms, so the FPFH of the point cloud should be calculated before executing this algorithm. The general idea of ​​the algorithm is as follows:

[0048] Select n sampling points from the point cloud P to be registered. In order to ensure that the sampled points have different FPFH features as much as possible, the distance between each pair of sampling points should be greater than the pre-given minimum distance threshold d.

[0049] Find one or more points in the target point cloud Q that have similar FPFH features to the sampled points in point cloud P, and randomly select one of these similar points as the one-to-one correspondence between point cloud P and target point cloud Q.

[0050] The rigid body transformation matrix between corresponding points is calculated, and then the performance of the current registration transformation is judged by solving the "sum of distance errors" function after the transformation of corresponding points. The Huber penalty is often used in this distance error sum function.

[0051] In the formula: mi is a pre-given value, l i Let be the distance difference between the corresponding points in the i-th group after transformation. The ultimate goal of the coarse registration is to find an optimal set of transformations among all transformations that minimizes the error function. This transformation is the final registration transformation matrix, from which the registration result can be obtained.

[0052] The transformation matrix obtained by SAC-IA is inaccurate, so it can only be used for coarse registration. The SAC-IA algorithm can be implemented in the registration module of the PCL library. When the number of points is large, calculating FPFH features is slow, making the SAC-IA algorithm inefficient. In this case, it is necessary to downsample the point cloud to reduce the number of points, but this will cause some feature points to be lost, thus reducing the registration accuracy.

[0053] The Iterative Closest Point (ICP) algorithm, based on SVD, has the following general idea:

[0054] The two point clouds P′ (the source point cloud after coordinate transformation) and Q after initial registration are used as the initial point set for fine registration;

[0055] For each point pi in the source point cloud P', find the nearest corresponding point qi in the target point cloud Q, and use it as the corresponding point in the target point cloud to form an initial pair of corresponding points.

[0056] The correspondences in the initial set of corresponding points are not all correct. Incorrect correspondences will affect the final registration result. Incorrect corresponding point pairs are removed by using a direction vector threshold.

[0057] Calculate the rotation matrix R and translation vector T to minimize them, i.e., minimize the mean square error between the corresponding point sets. Set a threshold ε = dk - dk-1 and a maximum number of iterations Nmax. Apply the rigid body transformation obtained in the previous step to the source point cloud P′ to obtain the new point cloud P″. Calculate the distance error between P″ and Q. If the error between two iterations is less than the threshold ε or the current number of iterations is greater than Nmax, the iteration ends. Otherwise, update the initially registered point sets to P″ and Q, and continue to repeat the above steps until the convergence condition is met.

[0058] The ICP algorithm is sensitive to parameters, and the following parameters need to be set before use:

[0059] `setMaximumIterations` sets the maximum number of iterations; `icp` is an iterative method, and this is the maximum number of iterations allowed. `setEuclideanFitnessEpsilon` sets the convergence condition: the sum of the mean squared errors must be less than a threshold to stop iteration. `setTransformationEpsilon` sets the difference between two transformation matrices (generally set to 1e-10). `setMaxCorrespondenaceDistance` sets the maximum distance between corresponding point pairs (this value has a significant impact on the registration results).

[0060] When two point clouds differ significantly, the ICP algorithm is prone to getting trapped in local optima, thus failing to achieve satisfactory matching results. Therefore, an initial transformation matrix is ​​required. The ICP algorithm can be implemented in the registration module of the PCL library.

[0061] The process of combining the final registration data and the infrared digital surface model into 3D modeling software for interactive modeling to obtain a 3D laser point cloud model with temperature information includes: fitting the final registration data and the infrared digital surface model to spatial coordinates; obtaining the brightness information of the fitted image; and then performing 3D reconstruction by analyzing the brightness, shadows, focal length, texture and parallax in the image, and adding the corresponding feature constraints to obtain a 3D laser point cloud model with temperature information.

[0062] The present invention, in embodiment 1, proposes a three-dimensional point cloud infrared thermal imaging colorization method. This method uses an UAV equipped with an infrared temperature measurement camera to acquire infrared orthophotos of the power line channel. Based on the aerial triaxial ray algorithm, it stitches together the full-channel aerial infrared photos and integrates the temperature measurement software algorithm onto the infrared photos to display the specific heating temperature. The heating spectrum and the matched three-dimensional laser point cloud model of the power transmission line are fitted to spatial coordinates to generate a three-dimensional point cloud infrared colorization model. This model can detect and calculate the spatial three-dimensional infrared thermal imaging data of the tested equipment, providing a basis for maintenance personnel to further determine the heating temperature.

[0063] Example 2

[0064] Based on the three-dimensional point cloud infrared thermal imaging coloring method proposed in Embodiment 1 of this invention, Embodiment 2 of this invention also proposes a three-dimensional point cloud infrared thermal imaging coloring system, such as... Figure 2 This is a schematic diagram of a three-dimensional point cloud infrared thermal imaging coloring system according to Embodiment 2 of the present invention. The system includes an acquisition module, a first processing module, and a second processing module.

[0065] The acquisition module is used to acquire three-dimensional laser point cloud data, infrared spectrum data, and POS data of the transmission line channel;

[0066] The first processing module is used to perform two-dimensional map reconstruction on the infrared spectrum data and POS data using differential calculation to obtain a first infrared image. After adding temperature information to the first infrared image, the module performs image segmentation based on the span of the transmission line and adds boundary information to generate an infrared digital surface model.

[0067] The second processing module is used to perform data registration on the POS data corresponding to the 3D laser point cloud data to obtain the final registration data; the final registration data and the infrared digital surface model are combined and imported into the 3D modeling software for interactive modeling to obtain a 3D laser point cloud model with temperature information.

[0068] The acquisition module's execution process includes: acquiring three types of data through drone scanning: 3D laser point cloud of the line channel, infrared orthophoto map, and POS data of image photos.

[0069] The process executed by the first processing module includes:

[0070] The first infrared image is obtained by using dynamic post-processing technology to solve the infrared spectrum data; the first infrared image is an infrared spectrum with coordinates, wherein the coordinates are latitude, longitude and altitude;

[0071] Temperature information is extracted from the first infrared image using infrared analysis software and displayed on the first infrared image;

[0072] The first infrared image after segmentation is filled with a constant using the function BORDER_CONSTANT; or the nearest row or column in the first infrared image after segmentation is copied as the boundary to form an infrared digital surface model using the function BORDER_REPLICATE.

[0073] The process executed by the second processing module includes:

[0074] First, the POS data corresponding to the 3D laser point cloud data is coarsely registered using the sample consistency initial registration algorithm to obtain intermediate registration data; after the coarse registration is completed, the iterative nearest point algorithm is used to finely register the intermediate registration data to obtain the final registration data.

[0075] The final registration data and the infrared digital surface model are fitted with spatial coordinates. The brightness information of the fitted image is obtained, and then the brightness, shadow, focal length, texture and parallax in the image are analyzed to perform three-dimensional reconstruction. The corresponding feature constraints are added to obtain a three-dimensional laser point cloud model with temperature information.

[0076] The present invention, in embodiment 2, proposes a three-dimensional point cloud infrared thermal imaging colorization system. This system uses an UAV equipped with an infrared temperature measurement camera to acquire infrared orthophotos of the power line channel. Based on the aerial triaxial algorithm, it stitches together the full-channel aerial infrared photos and integrates the temperature measurement software algorithm onto the infrared photos to display the specific heating temperature. The heating spectrum and the matched three-dimensional laser point cloud model of the power transmission line are fitted to spatial coordinates to generate a three-dimensional point cloud infrared colorization model. This model can detect and calculate the spatial three-dimensional infrared thermal imaging data of the tested equipment, providing a basis for maintenance personnel to further determine the heating temperature.

[0077] For a description of the relevant parts of the three-dimensional point cloud infrared thermal imaging coloring system provided in this application embodiment, please refer to the detailed description of the corresponding parts in the three-dimensional point cloud infrared thermal imaging coloring method provided in Embodiment 1 of this application, which will not be repeated here.

[0078] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that the elements inherent in a process, method, article, or apparatus that includes a list of elements are included. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element. Additionally, portions of the technical solutions provided in the embodiments of this application that are consistent with the implementation principles of corresponding technical solutions in the prior art have not been described in detail to avoid excessive elaboration.

[0079] While specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art can make other modifications or variations based on the above description. It is neither necessary nor possible to exhaustively describe all embodiments here. Various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A three-dimensional point cloud infrared thermal imaging colorization method, characterized in that, Includes the following steps: Acquire 3D laser point cloud data, infrared spectrum data, and POS data of the power transmission line corridor; The infrared spectrum data and POS data are used to perform two-dimensional map reconstruction to obtain the first infrared image. After adding temperature information to the first infrared image, the image is segmented according to the span of the transmission line and boundary information is added to generate an infrared digital surface model. Data registration is performed on the POS data corresponding to the three-dimensional laser point cloud data to obtain the final registration data; the final registration data and the infrared digital surface model are combined and imported into three-dimensional modeling software for interactive modeling to obtain a three-dimensional laser point cloud model with temperature information; The process of registering the POS data corresponding to the 3D laser point cloud data to obtain the registered data includes: firstly, using the sample consistency initial registration algorithm to perform coarse registration on the POS data corresponding to the 3D laser point cloud data to obtain intermediate registered data; after the coarse registration is completed, using the iterative nearest point algorithm to perform fine registration on the intermediate registered data to obtain the final registered data; The process of combining the final registration data and the infrared digital surface model and importing them into 3D modeling software for interactive modeling to obtain a 3D laser point cloud model with temperature information includes: fitting the final registration data and the infrared digital surface model to spatial coordinates; obtaining the brightness information of the fitted image; then performing 3D reconstruction by analyzing the brightness, shadows, focal length, texture and parallax in the image; and adding the constraint relationship of the corresponding features to obtain a 3D laser point cloud model with temperature information.

2. The three-dimensional point cloud infrared thermal imaging colorization method according to claim 1, characterized in that, The process of acquiring three-dimensional laser point cloud data, infrared spectrum data, and POS data of the power transmission line channel includes: acquiring three-dimensional laser point cloud data, infrared spectrum data, and image POS data of the power transmission line channel through drone scanning.

3. The three-dimensional point cloud infrared thermal imaging colorization method according to claim 1, characterized in that, The process of reconstructing a two-dimensional map from the infrared spectrum data using differential calculation to obtain the first infrared map includes: using dynamic post-processing technology to calculate the first infrared map from the infrared spectrum data; the first infrared map is an infrared spectrum with coordinates, wherein the coordinates are latitude, longitude and altitude.

4. The three-dimensional point cloud infrared thermal imaging colorization method according to claim 1, characterized in that, The process of adding temperature information to the first infrared image includes: extracting temperature information from the first infrared image using infrared analysis software and displaying it on the first infrared image.

5. The three-dimensional point cloud infrared thermal imaging colorization method according to claim 4, characterized in that, The method for adding boundary information includes: using the function BORDER_CONSTANT to fill the boundary with a constant for the segmented first infrared image; or using the function BORDER_REPLICATE to copy the nearest row or column of the segmented first infrared image as the boundary.

6. A three-dimensional point cloud infrared thermal imaging coloring system, used to execute the three-dimensional point cloud infrared thermal imaging coloring method according to any one of claims 1 to 5, characterized in that, It includes an acquisition module, a first processing module, and a second processing module; The acquisition module is used to acquire three-dimensional laser point cloud data, infrared spectrum data and POS data of the transmission line channel; The first processing module is used to perform two-dimensional map reconstruction on the infrared spectrum data and POS data using differential calculation to obtain a first infrared image. After adding temperature information to the first infrared image, the module performs image segmentation based on the span of the transmission line and adds boundary information to generate an infrared digital surface model. The second processing module is used to perform data registration on the POS data corresponding to the three-dimensional laser point cloud data to obtain the final registration data; the final registration data and the infrared digital surface model are combined and imported into the three-dimensional modeling software for interactive modeling to obtain a three-dimensional laser point cloud model with temperature information.

7. A three-dimensional point cloud infrared thermal imaging colorization system according to claim 6, characterized in that, The process executed by the first processing module includes: The infrared spectrum data is processed using dynamic post-processing technology to obtain a first infrared image; the first infrared image is an infrared spectrum with coordinates, wherein the coordinates are latitude, longitude and altitude; Temperature information is extracted from the first infrared image using infrared analysis software and displayed on the first infrared image; The first infrared image after segmentation is filled with a constant using the function BORDER_CONSTANT; or the nearest row or column in the first infrared image after segmentation is copied as the boundary to form an infrared digital surface model using the function BORDER_REPLICATE.

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