Power transmission line detection imaging method and system

Abstract

The invention relates to a power transmission line detection imaging method and system, and the method comprises the steps: shooting a power transmission line through a single-photon laser radar in a non-interference environment, and obtaining a first photon distribution histogram; calculating peak time of each pixel point in the first photon distribution histogram; shooting the power transmission line through the single-photon laser radar in an interference environment to obtain a second photon distribution histogram; according to the peak time of each pixel point in the first photon distribution histogram, calibrating the expected time position of a target reflection peak in the second photon distribution histogram; calculating the depth distance of each pixel point in the calibrated second photon distribution histogram; and synthesizing the depth distances of the second photon distribution histograms of all the pixels in the interference state to obtain a depth distance map of the power transmission line. According to the invention, high-precision transmission line imaging can be synthesized.

Description

Technical Field

[0001] This invention relates to the field of power transmission line inspection technology, and in particular to an imaging method and system for power transmission line inspection. Background Technology

[0002] The inspection and monitoring of power transmission lines are critical tasks in the operation and maintenance of power systems. Traditional manual inspection methods suffer from low efficiency, high cost, and high risk; therefore, automated power transmission line inspection algorithms have become a research hotspot in recent years. LiDAR technology, especially single-photon lidar based on single-photon avalanche diodes, has become an effective long-range target detection technology due to its high sensitivity and high temporal resolution. However, existing single-photon lidar faces several challenges in practical applications, especially in complex environments.

[0003] In traditional single-photon lidar imaging, the system determines the target's distance and shape by emitting and receiving the echo signal of a laser. Because single-photon lidar can operate in low-light and harsh environments, it has a natural advantage in detecting power transmission lines. However, the challenges in power transmission line detection lie in the weakening of the target signal and noise interference. Especially in complex environments, the laser signal may be affected by background noise from multiple scatterings, leading to weakening of the target signal or complete submersion in the noise. Traditional target detection methods, such as peak detection and two-parameter estimation, typically use fixed gating distances or static parameter models for imaging. However, under varying environmental conditions, these methods often cannot effectively address noise interference.

[0004] Peak detection is a classic image imaging method that determines target distance information by extracting peak values ​​from a histogram. However, in dense fog or atmospherically unstable environments, scattering noise can cause the peak values ​​of the target signal to be close to the noise peak values, resulting in severe image distortion and making the target difficult to distinguish. Two-parameter estimation models noise by estimating the attenuation coefficient and the number of scattering events, thereby achieving target signal separation. Although this method can handle noise problems to some extent, its accuracy remains low, especially in noisy environments, due to the complexity of the model and the uncertainty in parameter estimation. Furthermore, two-parameter estimation typically relies on static fog models or fixed parameter configurations, lacking adaptability to dynamic environmental changes, leading to poor imaging results under interference from factors such as fog, haze, and wind speed variations.

[0005] Therefore, how to improve the accuracy of power transmission line detection in dynamic and complex environments through more flexible and intelligent algorithms has become a key issue that current lidar imaging technology urgently needs to address. Summary of the Invention

[0006] Therefore, the technical problem to be solved by the present invention is to overcome the problem that the imaging of power transmission line detection is easily affected by noise in the prior art, resulting in low detection accuracy.

[0007] To solve the above-mentioned technical problems, the present invention provides an imaging method for detecting power transmission lines, comprising:

[0008] Step S1: In an interference-free environment, the power transmission line is photographed using a single-photon lidar to obtain a first photon distribution histogram of 256*256 pixels;

[0009] Step S2: Calculate the peak time of each pixel in the first photon distribution histogram;

[0010] Step S3: Under interference conditions, the transmission line is photographed using a single-photon lidar to obtain a second photon distribution histogram of 256*256 pixels;

[0011] Step S4: Based on the peak time of each pixel in the first photon distribution histogram, the expected time position of the target reflection peak in the second photon distribution histogram is determined;

[0012] Step S5: Calculate the depth distance of each pixel in the calibrated second photon distribution histogram;

[0013] Step S6: Synthesize the depth distance of the second photon distribution histogram of 256*256 pixels under interference conditions to obtain the depth distance map of the transmission line.

[0014] In one embodiment of the present invention, the method for obtaining a 256*256 pixel first photon distribution histogram in step S1 and a 256*256 pixel second photon distribution histogram in step S3 includes:

[0015] A single-photon lidar is used to perform time-correlated single-photon counting on the power line under both interference-free and interference-affected conditions. The photon distribution of 256*256 pixels within one frame is obtained. A total of 100 frames of data are summed to obtain the first or second photon distribution histogram for each pixel.

[0016] In one embodiment of the present invention, the method for calibrating the expected time position of the target reflection peak in the second photon distribution histogram based on the peak time of each pixel in the first photon distribution histogram includes:

[0017] The scattered echo photons in the second photon distribution histogram are modeled using maximum likelihood estimation;

[0018] The peak time of each pixel in the first photon distribution histogram is used to model the reflected photons in the second photon distribution histogram using maximum likelihood estimation.

[0019] The total echo photons in the second photon distribution histogram are superimposed and modeled based on the scattered echo photons modeled by maximum likelihood estimation and the reflected echo photons modeled by maximum likelihood estimation.

[0020] The total distribution is obtained by modeling the superposition of total echo photons. The total distribution is then solved to obtain the expected time position of the target reflection peak in the second photon distribution histogram, and the peak is then calibrated.

[0021] In one embodiment of the present invention, the scattered echo photons in the second photon distribution histogram are modeled using maximum likelihood estimation. Specifically, the time distribution of the scattered echo photons follows a Gamma distribution, and the probability density function is: ,in, The attenuation coefficient of the Gamma distribution. is the Gamma function, used to represent the normalization factor of the Gamma distribution; The number of scattering events is the Gamma distribution. It is a time variable, representing the time it takes for a photon to travel from emission to reflection.

[0022] The reflected photons in the second photon distribution histogram are modeled using maximum likelihood estimation, based on the peak time of each pixel in the first photon distribution histogram. Specifically, the time distribution of the target reflected photons follows a Gaussian distribution, and the probability density function is: Its peak time is the actual propagation time of the target: , , The peak time of the first photon distribution histogram for each pixel. It is the first photon in the histogram of the first photon distribution. Peak time of the first photon distribution histogram corresponding to each pixel The set of, where, Let be the population standard deviation of the Guass distribution. The mean of the Guass distribution represents the peak time of the target's reflected echo;

[0023] Modeling the total echo photon superposition in the second photon distribution histogram specifically involves: [The text abruptly ends here, likely due to an incomplete sentence or a formatting error.] Gaussian distribution The total echo distribution is obtained by superimposing the signals: ,in, and These are the weighting coefficients for the scattering noise and the target signal, respectively.

[0024] Solving the total echo distribution yields the expected time of the target reflection peak in the second photon distribution histogram, and then calibrating it. Specifically, this involves differentiating the total echo distribution. =0 and solve the equation to obtain the expected peak time of the scattered echo and the expected peak time of the target reflected echo under the corresponding total echo distribution. Select the expected peak time of the target reflected echo that is close to the interference-free state. The expected time of the target reflection peak under interference conditions ; The expected time based on the target reflection peak in the second photon distribution histogram Using this as a benchmark, it is calibrated, and the time threshold in the second photon distribution histogram is dynamically adjusted, as shown below:

[0025] ;

[0026] in, The expected time of the target reflection peak under interference conditions; This is the dynamically adjusted time offset.

[0027] In one embodiment of the present invention, the dynamically adjusted time offset satisfy:

[0028] When the detected transmission line signal is greater than the preset strength, i.e., the attenuation coefficient of the Gamma distribution. Larger, Gamma distribution scattering number When it is small, then decrease When the strength of the transmission line being tested is less than the preset strength, i.e., the attenuation coefficient of the Gamma distribution... Smaller, Gamma distribution scattering number When it is large, it will increase. .

[0029] In one embodiment of the present invention, the method for calculating the depth distance of each pixel in the calibrated second photon distribution histogram in step S2 includes:

[0030] The peak time of the second photon distribution histogram for each pixel is identified and used as the true time it takes for a photon to be emitted by the radar, strike the surface of the transmission line, and reflect back. The depth distance information of the transmission line under that pixel is calculated based on the speed of light and the peak time.

[0031] ;

[0032] in, For the first The depth distance corresponding to each pixel At the speed of light, For the first The peak time of the second photon distribution histogram corresponding to each pixel.

[0033] In one embodiment of the present invention, the method further includes filtering the calibrated second photon distribution histogram between steps S4 and S5 to reduce noise, the method comprising:

[0034] The calibrated second photon distribution histogram was filtered using a Savitzky-Golay filter. First, a data window with respect to the filter was selected and slid along the second photon distribution histogram, processing each point individually: for each time variable within the data window... The photon count value on the Y-axis of the corresponding second photon distribution histogram is fitted with a low-order polynomial using the least squares method. The value of each point in the data window is replaced with the fitted value of that point on the low-order polynomial, thereby achieving a smoothing effect.

[0035] To solve the above-mentioned technical problems, the present invention provides a transmission line detection imaging system, comprising:

[0036] First acquisition module: used to photograph the power transmission line using single-photon lidar in an interference-free environment, and obtain a first photon distribution histogram of 256*256 pixels.

[0037] First calculation module: used to calculate the peak time of each pixel in the first photon distribution histogram;

[0038] The second acquisition module is used to photograph the power transmission line using a single-photon lidar under interference conditions to obtain a second photon distribution histogram of 256*256 pixels.

[0039] Calibration module: used to calibrate the expected time position of the target reflection peak in the second photon distribution histogram based on the peak time of each pixel in the first photon distribution histogram;

[0040] The second calculation module is used to calculate the depth distance of each pixel in the calibrated second photon distribution histogram.

[0041] Synthesis module: Used to synthesize the depth distance of the second photon distribution histogram of 256*256 pixels under interference conditions, to obtain the depth distance map of the transmission line.

[0042] To solve the above-mentioned technical problems, the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the above-described transmission line detection imaging method.

[0043] To address the aforementioned technical problems, the present invention provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the above-described transmission line detection imaging method.

[0044] Compared with the prior art, the above-described technical solution of the present invention has the following advantages:

[0045] The transmission line detection imaging method described in this invention can effectively detect the target location of transmission lines, improve the detection accuracy of transmission lines in complex environments, reduce the influence of scattering noise, and ultimately achieve high-precision target imaging and high robustness. Attached Figure Description

[0046] To make the content of this invention easier to understand, the invention will be further described in detail below with reference to specific embodiments and accompanying drawings.

[0047] Figure 1 This is a flowchart of the method of the present invention;

[0048] Figure 2 This is the first photon distribution histogram obtained by the single-photon lidar in the interference-free state in this embodiment of the invention;

[0049] Figure 3 This is a true depth map of the transmission line under interference-free conditions in an embodiment of the present invention;

[0050] Figure 4 This is a diagram showing the identification results of power transmission lines in an embodiment of the present invention;

[0051] Figure 5 This is a total echo distribution diagram in an embodiment of the present invention;

[0052] Figure 6 This is a flowchart illustrating the calibration process for the expected time position of the target reflection peak in an embodiment of the present invention.

[0053] Figure 7 This is a gamma distribution map of scattered photons obtained by performing maximum likelihood estimation on the photon distribution histogram under interference conditions in an embodiment of the present invention.

[0054] Figure 8 This is a Guass distribution map of reflected photons obtained by performing maximum likelihood estimation on the photon distribution histogram under interference-free conditions in an embodiment of the present invention.

[0055] Figure 9 This is the total echo distribution map obtained by superimposing the time distributions of the target reflected photons and the noise scattered photons in the embodiment of the present invention;

[0056] Figure 10 This is a schematic diagram of the Savitzky-Golay filtering effect in an embodiment of the present invention;

[0057] Figure 11 This is an estimated depth map of the transmission line under interference conditions in an embodiment of the present invention;

[0058] Figure 12 This is a comparison diagram of the depth difference between the interference-free state and the interference state in an embodiment of the present invention. Detailed Implementation

[0059] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, so that those skilled in the art can better understand and implement the present invention. However, the embodiments described are not intended to limit the present invention.

[0060] Example 1

[0061] Reference Figure 1 As shown, this invention relates to an imaging method for detecting power transmission lines, comprising:

[0062] Step S1: In an interference-free environment, the power transmission line is photographed using a single-photon lidar to obtain a first photon distribution histogram of 256*256 pixels;

[0063] Step S2: Calculate the peak time of each pixel in the first photon distribution histogram;

[0064] Step S3: Under interference conditions, the transmission line is photographed using a single-photon lidar to obtain a second photon distribution histogram of 256*256 pixels;

[0065] Step S4: Based on the peak time of each pixel in the first photon distribution histogram, the expected time position of the target reflection peak in the second photon distribution histogram is determined;

[0066] Step S5: Calculate the depth distance of each pixel in the calibrated second photon distribution histogram;

[0067] Step S6: Synthesize the depth distance of the second photon distribution histogram of 256*256 pixels under interference conditions to obtain the depth distance map of the transmission line.

[0068] The following is a detailed description of this embodiment:

[0069] Please refer to Figure 1 The transmission line detection imaging method of the present invention includes:

[0070] S1: Obtain the first photon distribution histogram of 256*256 pixels captured by the single-photon lidar under interference-free environment;

[0071] Furthermore, a time-correlated single-photon count was performed on the transmission line under interference-free conditions using a single-photon lidar to obtain the photon distribution of 256*256 pixels within one frame. The data from a total of 100 frames were summed to obtain the first photon distribution histogram for each pixel, as shown below. Figure 2 As shown.

[0072] S2: In addition to counting peak time, this embodiment can also identify the depth distance of each pixel in the first photon distribution histogram under each pixel by using the peak method, and obtain the specific location distribution of the transmission line based on the depth distance. The depth distance of each pixel and the location distribution of the transmission line are used as prior knowledge.

[0073] Furthermore, noise is filtered out by smoothing the first photon distribution histogram. The peak time of the first photon distribution histogram at each pixel is identified using the peak method, which is regarded as the real time it takes for a photon to be emitted by the radar, illuminate the surface of the transmission line, and reflect back. The depth distance information of the transmission line at that pixel is deduced based on the speed of light and the peak time, and is expressed as:

[0074] ;

[0075] in, For the first The depth distance corresponding to each pixel At the speed of light, For the first The peak time of the first photon distribution histogram corresponding to each pixel.

[0076] Furthermore, after obtaining the depth and distance information of all pixels, a depth map of the transmission line under interference-free conditions can be synthesized, such as... Figure 3 As shown, after image filtering pre-processing and edge detection (e.g., using the Sobel operator) on the depth map, the specific location distribution of the transmission lines is identified. This serves as prior knowledge for subsequent foreign object (ice) detection on the transmission lines under interference conditions. Figure 4 As shown.

[0077] S3: Obtain the second photon distribution histogram of 256*256 pixels captured by the single-photon lidar under interference conditions;

[0078] Furthermore, a time-correlated single-photon count was performed on the transmission line under interference conditions using a single-photon lidar to obtain the photon distribution of 256*256 pixels within one frame. The data from a total of 100 frames were summed to obtain the second photon distribution histogram for each pixel, as shown below. Figure 4 As shown.

[0079] S4: Based on the peak time of each pixel in the first photon distribution histogram, the expected time position of the target reflection peak in the second photon distribution histogram is determined;

[0080] It should be noted that during transmission line testing, due to the multiple scattering effect in the complex environment, the echo signal will exhibit a certain time shift. At this time, the time of the target reflection peak... Compared to the time of the undisturbed state (i.e., the subsequent time) It will move forward ( Figure 5 middle It's a short period of time (moved forward). ,like Figure 5 As shown. Therefore, simply calibrating the target reflection peak time under undisturbed conditions. And set thresholds before and after it. This can affect the accuracy and robustness of target echo peak detection. Therefore, this embodiment employs the following method to calibrate the expected time of the target reflection peak. .

[0081] Furthermore, calibrating the expected time of the target reflection peak in the second photon distribution histogram (i.e., dynamically adjusting the time threshold) includes: modeling the scattered echo photons in the second photon distribution histogram using maximum likelihood estimation; modeling the reflected echo photons in the second photon distribution histogram using maximum likelihood estimation in conjunction with the peak time of each pixel in the first photon distribution histogram; modeling the total echo photons in the second photon distribution histogram using the scattered echo photons modeled by maximum likelihood estimation and the reflected echo photons modeled by maximum likelihood estimation; obtaining the total echo distribution by modeling the total echo photons, solving for the total echo distribution to obtain the expected time position of the target reflection peak in the second photon distribution histogram, and calibrating it, specifically as follows: Figure 6 As shown.

[0082] Furthermore, the scattered echo photons in the second photon distribution histogram are modeled using maximum likelihood estimation. Specifically, the time distribution of the backscattered photons in the scattering noise follows a Gamma distribution, with the probability density function being: ,in, The attenuation coefficient of the Gamma distribution. Here, is the Gamma function, used to represent the normalization of the Gamma distribution; The number of scattering events is the Gamma distribution. The time variable represents the time it takes for a photon to travel from emission to the reflected echo. The scattered echo model is modeled as follows: Figure 7 As shown.

[0083] Furthermore, by combining the peak time of each pixel in the first photon distribution histogram with the maximum likelihood estimation model of the reflected photons in the second photon distribution histogram, specifically: the time distribution of the target reflected photons follows a Gaussian distribution, and the probability density function is: Its peak time is the actual propagation time of the target: , , The peak time of the first photon distribution histogram for each pixel. It is the first photon in the histogram of the first photon distribution. Peak time of the first photon distribution histogram corresponding to each pixel The set of, where, Let be the population standard deviation of the Guass distribution. Let be the mean of the Guass distribution, representing the peak time of the target reflected echo. The reflected echo model is modeled as follows: Figure 8 As shown.

[0084] Furthermore, the total echo photon superposition in the second photon distribution histogram is modeled as follows: the total echo distribution is a Gamma distribution. Gaussian distribution Superposition: ,in, and These are the weighting coefficients for the scattered noise and the target signal, respectively. The total echo model is modeled as follows: Figure 9 As shown.

[0085] Furthermore, differentiate the total echo distribution and solve the equation:

[0086] =0

[0087] It should be noted that this equation generally has two positive analytical solutions, corresponding to the peak time of the scattered echo under the total echo distribution and the peak time of the target reflected echo, respectively. In this embodiment, we choose the target reflection peak time which is closer to the undisturbed state (because, in principle, the target reflected echo under the total echo distribution only undergoes a slight forward shift due to scattering, and overall it is closer to the target reflection peak time under the undisturbed state). The solution is taken as the time of the target reflection peak under interference conditions. In short, the mean of the Guass distribution This only indicates the peak time of the target's reflected echo under interference-free conditions. The expected peak time of the target reflected echo in the result of the total echo differentiation equation. It will shift forward due to scattering.

[0088] It should be noted that, due to the complexity of analytical solutions, the numerical optimization method Newton's iteration method is usually used to solve the above equations. =0.

[0089] Furthermore, based on the calibration of the expected time and location of the target reflection peak under interference-free conditions, and combined with the real-time measured power line signal strength, dynamic adjustments are made. The expected time of the target reflection peak in the second photon distribution histogram is used as the basis for these adjustments. As a benchmark, it is calibrated, and the time threshold in the second photon distribution histogram is dynamically adjusted, as follows:

[0090]

[0091] in, The expected time of the target's reflection peak under interference conditions; The time offset is dynamically adjusted and optimized in real time based on the strength of the target signal and the interference of background noise.

[0092] It should be noted that, under different detection scenarios, the signal strength of the transmission line changes. It will be adjusted based on real-time feedback, as shown below:

[0093]

[0094] in, This is a dynamically adjusted time offset. The attenuation coefficient of the scattered noise Gamma distribution. The scattering number is the noise Gamma distribution, and the specific formula can be obtained through simulation experiments. Specifically, when the detected transmission line signal is greater than a preset strength, i.e., the attenuation coefficient of the Gamma distribution... The number of scattering events in the Gamma distribution is relatively large (greater than the first comparison threshold). If the value is small (less than the second comparison threshold), then decrease. The time threshold accurately locks onto the target signal; when the detected transmission line strength is less than the preset strength, i.e., the attenuation coefficient of the Gamma distribution... The number of scattering events in the Gamma distribution is relatively small (less than the first comparison threshold). If the value is large (greater than the second comparison threshold), then increase the value. This ensures that the target echo signal can still be effectively extracted even if the target signal is interfered with.

[0095] S5: Filter the calibrated second photon distribution histogram (the second photon distribution histogram corresponding to each pixel) using Savitzky-Golay filtering;

[0096] Furthermore, the residual suppression method filters the calibrated second photon distribution histogram using a Savitzky-Golay filter. A data window is selected and slides along the second photon distribution histogram, processing each point individually. For each time variable within the data window... The photon count values ​​on the Y-axis of the corresponding second photon distribution histogram (i.e., each point) are fitted to a low-order polynomial using the least squares method. The value of each point within the data window is replaced with the fitted value of that point on the polynomial, thus achieving a smoothing effect. Generally, an odd number of points is chosen for the sliding window size, such as 5 or 7 points; a larger data window results in a more pronounced smoothing effect, but may lose more detail. A 3rd or 4th order low-order polynomial is chosen; higher orders result in a finer fit, but are prone to overfitting. Residual suppression techniques can effectively remove noise from multiple scattering, improve signal quality, and further improve the detection accuracy of transmission lines, such as... Figure 10 As shown.

[0097] S6: Extract the peak time of the photon distribution histogram within the target signal window;

[0098] Furthermore, after obtaining the dynamic threshold and filtered photon distribution histogram, the peak time of the second photon distribution histogram is identified and considered as the actual time it takes for a photon to be emitted by the radar, strike the surface of the transmission line, and reflect back. This is then calculated based on the speed of light. and peak time Calculate the depth distance information of the transmission line at this pixel. ( ).

[0099] S7: Confidence judgment of pixels around the power transmission line; Specifically, foreign objects (ice) on the power transmission line need to be detected. If the depth distance of the ice pixel is close to that of the power transmission line pixel and it is around the power transmission line, it is judged to be part of the power transmission line.

[0100] Specifically, after obtaining the depth distance and specific location distribution information of each power line pixel through the first photon distribution histogram, the next step is to determine whether pixels that did not detect the power line in the interference-free environment might have foreign objects (ice) attached, causing the power line volume to increase, in the interference environment. For these pixels that did not detect the power line target in the interference-free environment, based on the similarity between the depth value of these foreign object pixels and the power line pixels, if the depth distance of the foreign object pixel is relatively close to that of the power line pixel (less than a preset threshold) and is close to or in contact with the power line pixel, then the pixel is considered valid, indicating that it may be part of the power line; otherwise, it is treated as a background pixel and does not participate in the identification of the target area.

[0101] S8: Generates a high-quality transmission line depth map;

[0102] Furthermore, after determining which pixels belong to the transmission line target, a 256*256 pixel depth map of the transmission line under interference conditions is synthesized, such as... Figure 11 As shown. The estimated depth map is used to compare with the depth map of the transmission line under undisturbed conditions to detect whether the target transmission line is covered with ice or has foreign objects attached, such as... Figure 12As shown (the black continuous line segment has potential problems with icing or foreign object adhesion).

[0103] Example 2

[0104] This embodiment provides a power transmission line detection imaging system, including:

[0105] First acquisition module: used to photograph the power transmission line using single-photon lidar in an interference-free environment, and obtain a first photon distribution histogram of 256*256 pixels.

[0106] First calculation module: used to calculate the peak time of each pixel in the first photon distribution histogram;

[0107] The second acquisition module is used to photograph the power transmission line using a single-photon lidar under interference conditions to obtain a second photon distribution histogram of 256*256 pixels.

[0108] Calibration module: used to calibrate the expected time position of the target reflection peak in the second photon distribution histogram based on the peak time of each pixel in the first photon distribution histogram;

[0109] The second calculation module is used to calculate the depth distance of each pixel in the calibrated second photon distribution histogram.

[0110] Synthesis module: Used to synthesize the depth distance of the second photon distribution histogram of 256*256 pixels under interference conditions, to obtain the depth distance map of the transmission line.

[0111] Example 3

[0112] This embodiment provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the transmission line detection imaging method described in Embodiment 1.

[0113] Example 4

[0114] This embodiment provides a computer-readable storage medium storing a computer program thereon. When the computer program is executed by a processor, it implements the steps of the transmission line detection imaging method described in Embodiment 1.

[0115] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of this application can be implemented in various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0116] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0117] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0118] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0119] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations here. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. A method for imaging transmission line detection, characterized in that, include: Step S1: In an interference-free environment, the power transmission line is photographed using a single-photon lidar to obtain a first photon distribution histogram of 256*256 pixels; Step S2: Calculate the peak time of each pixel in the first photon distribution histogram; Step S3: Under interference conditions, the transmission line is photographed using a single-photon lidar to obtain a second photon distribution histogram of 256*256 pixels; Step S4: Based on the peak time of each pixel in the first photon distribution histogram, the expected time position of the target reflection peak in the second photon distribution histogram is determined; Step S5: Calculate the depth distance of each pixel in the calibrated second photon distribution histogram; Step S6: Synthesize the depth distance of the second photon distribution histogram of 256*256 pixels under interference conditions to obtain the depth distance map of the transmission line.

2. The transmission line detection imaging method according to claim 1, characterized in that: The methods for obtaining the first photon distribution histogram of 256*256 pixels in step S1 and the second photon distribution histogram of 256*256 pixels in step S3 include: A single-photon lidar is used to perform time-correlated single-photon counting on the power line under both interference-free and interference-affected conditions. The photon distribution of 256*256 pixels within one frame is obtained. A total of 100 frames of data are summed to obtain the first or second photon distribution histogram for each pixel.

3. The transmission line detection imaging method according to claim 1, characterized in that: The method for calibrating the expected time position of the target reflection peak in the second photon distribution histogram based on the peak time of each pixel in the first photon distribution histogram includes: The scattered echo photons in the second photon distribution histogram are modeled using maximum likelihood estimation; The peak time of each pixel in the first photon distribution histogram is used to model the reflected photons in the second photon distribution histogram using maximum likelihood estimation. The total echo photons in the second photon distribution histogram are superimposed and modeled based on the scattered echo photons modeled by maximum likelihood estimation and the reflected echo photons modeled by maximum likelihood estimation. The total distribution is obtained by modeling the superposition of total echo photons. The total distribution is then solved to obtain the expected time position of the target reflection peak in the second photon distribution histogram, and the peak is then calibrated.

4. The transmission line detection imaging method according to claim 3, characterized in that: The scattered echo photons in the second photon distribution histogram are modeled using maximum likelihood estimation. Specifically, the time distribution of the scattered echo photons follows a Gamma distribution, and the probability density function is: ,in, The attenuation coefficient of the Gamma distribution. is the Gamma function, used to represent the normalization factor of the Gamma distribution; The number of scattering events is the Gamma distribution. It is a time variable, representing the time it takes for a photon to travel from emission to reflection. The reflected photons in the second photon distribution histogram are modeled using maximum likelihood estimation, based on the peak time of each pixel in the first photon distribution histogram. Specifically, the time distribution of the target reflected photons follows a Gaussian distribution, and the probability density function is: Its peak time is the actual propagation time of the target: , , The peak time of the first photon distribution histogram for each pixel. It is the first photon in the histogram of the first photon distribution. Peak time of the first photon distribution histogram corresponding to each pixel The set of, where, Let be the population standard deviation of the Guass distribution. The mean of the Guass distribution represents the peak time of the target's reflected echo; Modeling the total echo photon superposition in the second photon distribution histogram specifically involves: [The text abruptly ends here, likely due to an incomplete sentence or a formatting error.] Gaussian distribution The total echo distribution is obtained by superimposing the signals: ,in, and These are the weighting coefficients for the scattering noise and the target signal, respectively. Solving the total echo distribution yields the expected time of the target reflection peak in the second photon distribution histogram, and then calibrating it. Specifically, this involves differentiating the total echo distribution. =0 and solve the equation to obtain the expected peak time of the scattered echo and the expected peak time of the target reflected echo under the corresponding total echo distribution. Select the expected peak time of the target reflected echo that is close to the interference-free state. The expected time of the target reflection peak under interference conditions ; The expected time based on the target reflection peak in the second photon distribution histogram Using this as a benchmark, it is calibrated, and the time threshold in the second photon distribution histogram is dynamically adjusted, as shown below: ； in, The expected time of the target reflection peak under interference conditions; This is the dynamically adjusted time offset.

5. The transmission line detection imaging method according to claim 4, characterized in that: The dynamically adjusted time offset satisfy: When the detected transmission line signal is greater than the preset strength, i.e., the attenuation coefficient of the Gamma distribution. Larger, Gamma distribution scattering number When it is small, then decrease When the strength of the transmission line being tested is less than the preset strength, i.e., the attenuation coefficient of the Gamma distribution... Smaller, Gamma distribution scattering number When it is large, it will increase. .

6. The transmission line detection imaging method according to claim 1, characterized in that: The method for calculating the depth distance of each pixel in the calibrated second photon distribution histogram in step S2 includes: The peak time of the second photon distribution histogram for each pixel is identified and used as the true time it takes for a photon to be emitted by the radar, strike the surface of the transmission line, and reflect back. The depth distance information of the transmission line under that pixel is calculated based on the speed of light and the peak time. ； in, For the first The depth distance corresponding to each pixel At the speed of light, For the first The peak time of the second photon distribution histogram corresponding to each pixel.

7. The transmission line detection imaging method according to claim 1, characterized in that: Between steps S4 and S5, the method further includes filtering the calibrated second photon distribution histogram to reduce noise, including: The calibrated second photon distribution histogram was filtered using a Savitzky-Golay filter. First, a data window with respect to the filter was selected and slid along the second photon distribution histogram, processing each point individually: for each time variable within the data window... The photon count value on the Y-axis of the corresponding second photon distribution histogram is fitted with a low-order polynomial using the least squares method. The value of each point in the data window is replaced with the fitted value of that point on the low-order polynomial, thereby achieving a smoothing effect.

8. A transmission line detection imaging system, characterized in that, include: First acquisition module: used to photograph the power transmission line using single-photon lidar in an interference-free environment, and obtain a first photon distribution histogram of 256*256 pixels. First calculation module: used to calculate the peak time of each pixel in the first photon distribution histogram; The second acquisition module is used to photograph the power transmission line using a single-photon lidar under interference conditions to obtain a second photon distribution histogram of 256*256 pixels. Calibration module: used to calibrate the expected time position of the target reflection peak in the second photon distribution histogram based on the peak time of each pixel in the first photon distribution histogram; The second calculation module is used to calculate the depth distance of each pixel in the calibrated second photon distribution histogram. Synthesis module: Used to synthesize the depth distance of the second photon distribution histogram of 256*256 pixels under interference conditions, to obtain the depth distance map of the transmission line.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, it implements the steps of the transmission line detection imaging method as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the transmission line detection imaging method as described in any one of claims 1 to 7.

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