Classified extraction method for power transmission line tower and wire parameters based on cloud data

Abstract

The invention relates to the technical field of laser point cloud data acquisition, and particularly discloses a cloud data-based power transmission line tower and lead parameter classification extraction method, which comprises the following steps of: acquiring operation state data of a laser radar in an operation process and environment data in a laser radar detection area in real time through a data acquisition unit; by combining the aging analysis result of the laser diode of the laser radar and the atmospheric attenuation degree analysis result of the effective detection distance of the laser radar, the transmitting power of the laser radar is adjusted in real time, so that the dynamic adjustment of the transmitting power of the laser radar can be realized, thereby ensuring the detection distance of the laser radar and improving the detection accuracy of the laser radar. Therefore, the situation that the intensity of the received reflection signal is weakened due to the reduction of the detection distance of the laser radar, the generated point cloud data becomes sparse, and the quality of the point cloud data is influenced is avoided, and the reliability of the parameter extraction result of the transmission line tower and the lead is improved.

Description

Technical Field

[0001] This invention relates to the field of laser point cloud data acquisition technology, specifically a method for classifying and extracting parameters of transmission line towers and conductors based on cloud data. Background Technology

[0002] The classification and extraction of parameters of transmission line towers and conductors can obtain a variety of key parameter data of towers and conductors in transmission lines. These data are of great significance for the design, operation and maintenance, and safety assessment of transmission lines.

[0003] In existing technologies, common methods for classifying and extracting parameters of transmission line towers and conductors generally involve first turning on the lidar at a preset operating power to scan the transmission line and obtain laser point cloud data of targets such as towers and conductors. Then, through data preprocessing, coarse extraction, fine extraction, parameter extraction and modeling, the parameters of transmission line towers and conductors can be extracted.

[0004] In existing technologies, when extracting parameters of transmission line towers and conductors, it is necessary to use lidar to scan the transmission line in real time to obtain laser point cloud data of targets such as towers and conductors. However, during use, lidar is affected by external factors and its own operating status, which reduces its effective detection range. Under these circumstances, the collected laser point cloud data will be sparse, resulting in a decrease in the quality of the laser point cloud data. This will further affect the reliability of the subsequent extraction results of transmission line tower and conductor parameters. Summary of the Invention

[0005] The purpose of this invention is to provide a method for classifying and extracting parameters of transmission line towers and conductors based on cloud data, thereby solving the following technical problems: How to improve the reliability of the extracted parameters of transmission line towers and conductors.

[0006] The objective of this invention can be achieved through the following technical solutions: A method for classifying and extracting parameters of transmission line towers and conductors based on cloud data, the method comprising: S1: The data acquisition unit collects real-time data on the operating status of the lidar and environmental data within the lidar's detection area. S2: By combining the real-time collected operating status data of the lidar during operation with the data analysis unit, the aging status of the lidar's laser diode is analyzed. S3: By combining the environmental data collected in real time within the lidar detection area with the environmental monitoring unit, the atmospheric attenuation of the effective detection range of the lidar is analyzed; S4: By combining the aging analysis results of the lidar's lidar diodes with the atmospheric attenuation analysis results of the lidar's effective detection range, the lidar's transmission power is adjusted in real time. S5: The three-dimensional point cloud data of towers and conductors in the transmission line are obtained by combining the laser radar with the transmission power adjusted, and ground point filtering and noise removal are performed. S6: By combining the processed 3D point cloud data, the parameter data of transmission line towers and conductors are extracted and classified.

[0007] Furthermore, the data collected in S1 includes: Operational status data of the lidar during operation and environmental data within the lidar detection area; The operating status data of the lidar during operation includes: the lidar's wavelength offset, operating current, beam divergence angle, and the minimum current required for the laser diode to start laser emission. The environmental data within the lidar detection area includes: ambient humidity, rainfall, airborne particulate matter content, and visibility within the lidar detection area.

[0008] Furthermore, the analysis process in S3 includes: Through formula Calculate the wavelength offset of the lidar after correction during the i-th data acquisition. ; Where i represents a data collection at fixed time intervals. Let be the wavelength offset of the lidar acquired during the i-th data acquisition. Let i be the total number of data collections during the i-th data collection. Let be the operating current of the lidar during the i-th data acquisition. For all The average value, The preset operating current, for The standard value, To define a function, if Then let Otherwise, let .

[0009] Furthermore, the analysis process in S3 also includes: Through formula Calculate the aging index of the lidar laser diode during the i-th data acquisition. ; in, Let be the beam divergence angle of the lidar during the i-th data acquisition. The preset beam divergence angle, for The standard value, For the bounding function, if ,make Otherwise, let , For all The average value, Let be the minimum current required for the laser diode in the lidar to begin laser emission during the i-th data acquisition. This is the minimum current threshold at which the laser diode in a lidar begins to emit laser light.

[0010] Furthermore, the analysis process in S3 also includes: By measuring the aging index of the lidar laser diode during the i-th data acquisition Compared with the preset laser diode aging index threshold Perform a comparison; like It is determined that the laser diode in the lidar is severely aged during the i-th data acquisition, which will lead to a weakening of the long-distance reflection signal, i.e. a reduction in the effective detection range, and will also cause the point cloud to be sparse and increase noise, affecting the accuracy of point cloud data extraction. like It is determined that the aging of the laser diode in the lidar is not severe during the i-th data acquisition, which will not lead to a weakening of the long-distance reflection signal, i.e., a reduction in the effective detection distance, and has little impact on the accuracy of point cloud data extraction.

[0011] Furthermore, the adjustment process in S4 includes: Through formula Calculate the environmental impact coefficient within the lidar detection area during the i-th data acquisition. ; in, Let be the ambient humidity within the lidar detection area during the i-th data acquisition. The preset ambient humidity, for The standard value, Let be the rainfall within the lidar detection area during the i-th data acquisition. The preset rainfall amount, for The standard value, Let be the concentration of airborne particulate matter in the lidar detection area during the i-th data acquisition. The preset concentration of suspended particles in the air, Let be the visibility within the lidar detection area during the i-th data acquisition. This is the preset visibility level.

[0012] Furthermore, the adjustment process in S4 also includes: By using the environmental influence coefficient within the lidar detection area during the i-th data acquisition... Compared with the preset environmental impact coefficient threshold Perform a comparison; like It is determined that during the i-th data acquisition, the pulsed laser emitted by the lidar experiences severe atmospheric attenuation within the detection area, which leads to a reduction in detection range. like It is determined that during the i-th data acquisition, the pulsed laser emitted by the lidar has a low degree of atmospheric attenuation in the detection area, and thus has a low impact on the detection distance.

[0013] Furthermore, the adjustment process in S4 also includes: Through formula Calculate the corrected operating power of the lidar at the i-th data acquisition. ; in, The preset operating power for the lidar and The weighting coefficients are set based on empirical fitting. To adjust the coefficient lookup table function, based on empirical data... The impact of the numerical range on the operating power of the lidar was obtained through testing.

[0014] Furthermore, the correction process in S5 includes: Based on the original LiDAR point cloud data, ground points are filtered out to generate a digital elevation model to extract all non-ground points. Noise points are removed by calculating the area threshold of the point cloud data or comparing the density of a point with its neighboring points.

[0015] Furthermore, the data extracted in S6 includes: Geometric position parameters, structural characteristic parameters, functional parameters, and environmental parameters of transmission line towers; The data extracted in S6 also includes: geometric position parameters, physical characteristic parameters, structural parameters, electrical parameters, mechanical parameters and safety distance parameters of the transmission line conductors.

[0016] The beneficial effects of this invention are: (1) By combining the laser diode aging analysis results of the laser radar with the atmospheric attenuation analysis results of the effective detection range of the laser radar, the present invention can adjust the transmission power of the laser radar in real time, thereby realizing the dynamic adjustment of the transmission power of the laser radar, thus ensuring the detection range of the laser radar, and avoiding the situation where the intensity of the received reflected signal weakens due to the reduction of the laser radar detection range, which in turn leads to the sparseness of the generated point cloud data and affects the quality of the point cloud data, thereby improving the reliability of the extraction results of transmission line tower and conductor parameters.

[0017] (2) The present invention measures the aging index of the laser diode of the lidar during the i-th data acquisition. Compared with the preset laser diode aging index threshold By comparing the data, we can accurately analyze the aging status of the laser diode in the lidar during the i-th data acquisition. Since the aging of the laser diode will lead to a weakening of the long-distance reflection signal, based on the analysis results, we can analyze whether the effective detection range of the lidar has decreased, thus providing reliable data support for subsequent adjustment of the lidar power.

[0018] (3) The present invention uses the environmental influence coefficient of the lidar detection area during the i-th data acquisition. Compared with the preset environmental impact coefficient threshold By comparing the data, we can accurately analyze the atmospheric attenuation of the pulsed laser emitted by the lidar in the detection area during the i-th data acquisition. The analysis results can reflect the attenuation of the lidar detection range in the lidar detection area, thus providing additional data support for subsequent adjustment of the lidar's emission power and further improving the accuracy of the lidar emission power adjustment results.

[0019] (4) This invention combines the environmental influence coefficient of the lidar detection area during the i-th data acquisition. Compared with the aging index of the lidar laser diode at the i-th data acquisition Two sets of data can eliminate the influence of the lidar's operating status and interference from the external environment, ensuring that the laser emitted by the adjusted lidar operating power has sufficient detection range. This avoids the generated point cloud data becoming sparse and affecting the quality of the point cloud data, thereby improving the reliability of the parameter extraction results for transmission line towers and conductors. Attached Figure Description

[0020] The invention will now be further described with reference to the accompanying drawings.

[0021] Figure 1This is a flowchart of the method for classifying and extracting parameters of transmission line towers and conductors based on cloud data in this invention. Detailed Implementation

[0022] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] Please see Figure 1 As shown, in one embodiment, this application provides a method for classifying and extracting parameters of transmission line towers and conductors based on cloud data, the method comprising: S1: The data acquisition unit collects real-time data on the operating status of the lidar and environmental data within the lidar's detection area. S2: By combining the real-time collected operating status data of the lidar during operation with the data analysis unit, the aging status of the lidar's laser diode is analyzed. S3: By combining the environmental data collected in real time within the lidar detection area with the environmental monitoring unit, the atmospheric attenuation of the effective detection range of the lidar is analyzed; S4: By combining the aging analysis results of the lidar's lidar diodes with the atmospheric attenuation analysis results of the lidar's effective detection range, the lidar's transmission power is adjusted in real time. S5: The three-dimensional point cloud data of towers and conductors in the transmission line are obtained by combining the laser radar with the transmission power adjusted, and ground point filtering and noise removal are performed. S6: By combining the processed 3D point cloud data, the parameter data of transmission line towers and conductors are extracted and classified; Through the above technical solution, this example provides a method for classifying and extracting parameters of transmission line towers and conductors based on cloud data. First, the data acquisition unit collects real-time operating status data of the lidar during operation and environmental data within the lidar detection area. Then, the data analysis unit analyzes the aging of the lidar's laser diodes by combining the real-time operating status data. Next, the environmental monitoring unit analyzes the atmospheric attenuation of the lidar's effective detection range by combining the real-time environmental data within the lidar detection area. Based on the lidar's laser diode aging analysis results and atmospheric attenuation analysis results, the lidar's transmission power is adjusted in real time. Then, the three-dimensional point cloud data of the transmission line towers and conductors acquired by the lidar with the adjusted transmission power is combined with ground point filtering and noise removal processing. Finally, the parameter data of the transmission line towers and conductors are extracted and classified by combining the processed three-dimensional point cloud data. By combining the aging analysis results of the lidar's laser diode with the atmospheric attenuation analysis results of the lidar's effective detection range, the lidar's transmission power can be adjusted in real time. This allows for dynamic adjustment of the lidar's transmission power, ensuring the lidar's detection range and preventing a decrease in the intensity of the received reflected signal due to a reduced detection range. This, in turn, would lead to sparse point cloud data, affecting the quality of the point cloud data and thus improving the reliability of the extracted parameters of transmission line towers and conductors.

[0024] The data collected in S1 includes: Operational status data of the lidar during operation and environmental data within the lidar detection area; The operating status data of the lidar during operation includes: the lidar's wavelength offset, operating current, beam divergence angle, and the minimum current required for the laser diode to start laser emission. The environmental data within the lidar detection area includes: ambient humidity, rainfall, airborne particulate matter content, and visibility within the lidar detection area. Through the above technical solution, this example provides operational status data of the lidar during operation and environmental data within the lidar detection area. The operational status data of the lidar during operation includes: the lidar's wavelength offset, operating current, beam divergence angle, and the minimum current required for the laser diode to start laser emission. By combining this data, the aging state of the laser diode in the lidar can be analyzed. Based on this, it is possible to further analyze whether there will be a loss in the lidar's detection range, thereby providing reliable data support for subsequent adjustments to the lidar's power. Furthermore, this example also provides environmental data within the lidar detection area, including ambient humidity, rainfall, airborne particulate matter content, and visibility. This data directly reflects the degree of atmospheric attenuation of the lidar detection range within the detection area. Based on this data, additional data support can be provided for subsequent adjustments to the lidar power. Adjusting the lidar power based on diversified data can improve the rationality of the adjustment results, thereby avoiding the situation where the intensity of the received reflected signal weakens due to the reduction of the lidar detection range, resulting in sparse point cloud data and affecting the quality of the point cloud data.

[0025] The analysis process in S3 includes: Through formula Calculate the wavelength offset of the lidar after correction during the i-th data acquisition. ; Where i represents a data collection at fixed time intervals. Let be the wavelength offset of the lidar acquired during the i-th data acquisition. Let i be the total number of data collections during the i-th data collection. Let be the operating current of the lidar during the i-th data acquisition. For all The average value, The preset operating current, for The standard value mentioned above can be selected and set based on the allowable error in empirical data. To define a function, if Then let Otherwise, let ; Using the above technical solution, this example provides the corrected wavelength offset of the lidar during the i-th data acquisition. It can be done through the formula Calculations show that, obviously, the formula... The current fluctuation value from the moment the lidar is turned on until the i-th data acquisition can be calculated, and combined with the defined function. Definition analysis, when The larger the value, the greater the wavelength offset after correction by the lidar during the i-th data acquisition. The larger it is, the greater it is; conversely, when... If the value is less than 2, then the wavelength offset corrected by the lidar during the i-th data acquisition is... wavelength offset of the lidar acquired during the i-th data acquisition same; Specifically, since current fluctuations directly cause instantaneous changes in wavelength, leading to a decrease in receiver filtering efficiency, the wavelength offset of the lidar will be affected by the current fluctuations of the lidar, resulting in a falsely low value. Therefore, by combining the current fluctuation values ​​from when the lidar is turned on to the i-th data acquisition process, the wavelength offset of the lidar can be corrected, thereby ensuring the accuracy of the lidar wavelength offset data.

[0026] The analysis process in S3 also includes: Through formula Calculate the aging index of the lidar laser diode during the i-th data acquisition. ; in, Let be the beam divergence angle of the lidar during the i-th data acquisition. The preset beam divergence angle, for The standard value mentioned above can be selected and set based on the allowable error in empirical data. For the bounding function, if ,make Otherwise, let , For all The average value, Let be the minimum current required for the laser diode in the lidar to begin laser emission during the i-th data acquisition. This is the minimum current threshold at which the laser diode in a lidar begins to emit laser light; Through the above technical solution, this embodiment provides the aging index of the lidar laser diode during the i-th data acquisition. It can be done through the formula The calculation yields the result, where the formula is... The fluctuation value of the corrected wavelength offset during the period from when the lidar is turned on to the i-th data acquisition can be calculated. Clearly, when the lidar beam divergence angle is greater than the preset beam divergence angle during the i-th data acquisition, and the fluctuation value of the corrected wavelength offset during the period from when the lidar is turned on to the i-th data acquisition is higher than the minimum current required for the lidar's laser diode to begin laser emission during the i-th data acquisition, then the aging index of the lidar's laser diode during the i-th data acquisition will be higher. The larger the value, the more it indicates that the detection range of the lidar has significantly decreased at that data collection point. Specifically, when the beam divergence angle of the lidar is greater than the preset beam divergence angle during the i-th data acquisition, it indicates that the internal material of the laser diode is damaged or aged. The higher the fluctuation value of the corrected wavelength offset after the lidar is turned on until the i-th data acquisition, the more it indicates that the internal material of the laser diode is aging, causing wavelength drift. Finally, when the minimum current required for the laser diode in the lidar to start laser emission is higher during the i-th data acquisition, it also indicates that the laser diode is aging. Therefore, the parameters used in the above formula can reflect whether the laser diode is aging and the degree of aging. When the laser diode is aging, it will lead to a weakening of the long-distance reflection signal. On this basis, it is necessary to increase the operating power of the lidar to enhance the laser beam energy, thereby extending the effective detection distance and increasing the point cloud density. This calculation method provides effective data support for subsequent adjustments to the operating power of the lidar, thereby eliminating the impact of reduced effective detection range caused by laser diode aging and ensuring sufficient detection range.

[0027] The analysis process in S3 also includes: By measuring the aging index of the lidar laser diode during the i-th data acquisition Compared with the preset laser diode aging index threshold Perform a comparison; like It is determined that the laser diode in the lidar is severely aged during the i-th data acquisition, which will lead to a weakening of the long-distance reflection signal, i.e. a reduction in the effective detection range, and will also cause the point cloud to be sparse and increase noise, affecting the accuracy of point cloud data extraction. like It is determined that the aging of the laser diode in the lidar is not severe during the i-th data acquisition, which will not lead to a weakening of the long-distance reflection signal, i.e., a reduction in the effective detection distance, and has little impact on the accuracy of point cloud data extraction; Using the above technical solution, this example demonstrates how to determine the aging index of the lidar laser diode during the i-th data acquisition. Compared with the preset laser diode aging index threshold By comparing the data, we can accurately analyze the aging status of the laser diode in the lidar during the i-th data acquisition. Since the aging of the laser diode will lead to a weakening of the long-distance reflection signal, based on the analysis results, we can analyze whether the effective detection range of the lidar has decreased, thus providing reliable data support for subsequent adjustment of the lidar power.

[0028] The adjustment process in S4 includes: Through formula Calculate the environmental impact coefficient within the lidar detection area during the i-th data acquisition. ; in, Let be the ambient humidity within the lidar detection area during the i-th data acquisition. The preset ambient humidity, for The standard value mentioned above can be selected and set based on the allowable error in empirical data. Let be the rainfall within the lidar detection area during the i-th data acquisition. The preset rainfall amount, for The standard value mentioned above can be selected and set based on the allowable error in empirical data. Let be the concentration of airborne particulate matter in the lidar detection area during the i-th data acquisition. The preset concentration of suspended particles in the air, Let be the visibility within the lidar detection area during the i-th data acquisition. The preset visibility; Through the above technical solution, this embodiment provides the environmental impact coefficient within the lidar detection area during the i-th data acquisition. It can be done through the formula Calculations show that, obviously, when the ambient humidity and rainfall in the lidar detection area are greater than the preset ambient humidity and rainfall amounts during the i-th data acquisition, and the higher the concentration of suspended particulate matter in the air and the lower the visibility during the i-th data acquisition, then the environmental impact coefficient in the lidar detection area during the i-th data acquisition is... The higher the value, the more severe the atmospheric attenuation of the lidar detection range within the lidar detection area at the i-th data acquisition time point; Specifically, when the ambient humidity within the lidar detection area is greater than the preset ambient humidity during the i-th data acquisition, it indicates a higher concentration of water vapor molecules. These water vapor molecules attenuate and scatter the laser beam, and also interact with the laser, converting some light energy into heat energy, resulting in laser energy loss. Conversely, if the rainfall within the lidar detection area is greater than the preset rainfall during the i-th data acquisition, raindrops will scatter and absorb the laser pulse, further reducing laser energy loss and decreasing the lidar detection range. Furthermore, a higher concentration of suspended particles and lower visibility within the lidar detection area during the i-th data acquisition indicate an increase in airborne aerosol particles. This causes strong scattering of the laser beam during propagation, reducing the laser energy reaching the target and lowering the intensity of reflected light, thus shortening the effective detection range. By combining these settings with environmental data within the lidar detection area, the atmospheric attenuation of the lidar detection range can be analyzed, providing additional data support for subsequent adjustments to the lidar's transmission power.

[0029] The adjustment process in S4 also includes: By using the environmental influence coefficient within the lidar detection area during the i-th data acquisition... Compared with the preset environmental impact coefficient threshold Perform a comparison; like It is determined that during the i-th data acquisition, the pulsed laser emitted by the lidar experiences severe atmospheric attenuation within the detection area, which leads to a reduction in detection range. like It is determined that during the i-th data acquisition, the pulsed laser emitted by the lidar has a low degree of atmospheric attenuation in the detection area, and thus has a low impact on the detection distance. Through the above technical solution, this example uses the environmental influence coefficient within the lidar detection area during the i-th data acquisition. Compared with the preset environmental impact coefficient threshold By comparing the data, we can accurately analyze the atmospheric attenuation of the pulsed laser emitted by the lidar in the detection area during the i-th data acquisition. The analysis results can reflect the attenuation of the lidar detection range in the lidar detection area, thus providing additional data support for subsequent adjustment of the lidar's emission power and further improving the accuracy of the lidar emission power adjustment results.

[0030] The adjustment process in S4 also includes: Through formula Calculate the corrected operating power of the lidar at the i-th data acquisition. ; in, The preset operating power for the lidar and The weighting coefficients are set based on empirical fitting. To adjust the coefficient lookup table function, based on empirical data... The impact of the numerical range on the operating power of the lidar was obtained through testing; Using the above technical solution, this example provides the corrected operating power of the lidar during the i-th data acquisition. It can be done through the formula This calculation method, used in this example, combines the environmental impact coefficient within the lidar detection area during the i-th data acquisition. Compared with the aging index of the lidar laser diode at the i-th data acquisition Two sets of data can eliminate the influence of the lidar's operating status and interference from the external environment, ensuring that the laser emitted by the adjusted lidar operating power has sufficient detection range. This avoids the generated point cloud data becoming sparse and affecting the quality of the point cloud data, thereby improving the reliability of the parameter extraction results for transmission line towers and conductors.

[0031] The correction process in S5 includes: Based on the original LiDAR point cloud data, ground points are filtered out to generate a digital elevation model to extract all non-ground points. Noise points are removed by calculating the area threshold of the point cloud data or comparing the density of a point with its neighboring points. Through the above technical solution, this example provides the correction process in S5. Specifically, based on the original LiDAR point cloud data, ground points are filtered out to generate a digital elevation model to extract all non-ground points. Noise points are removed by calculating the area threshold of the point cloud data or comparing the density of a point with its neighboring points. With this setting, point cloud data preprocessing can be achieved, thereby providing accurate data for subsequent point cloud data extraction to ensure the reliability of the extraction results of transmission line tower and conductor parameters.

[0032] The data extracted in S6 includes: Geometric position parameters, structural characteristic parameters, functional parameters, and environmental parameters of transmission line towers; The data extracted in S6 also includes: geometric position parameters, physical characteristic parameters, structural parameters, electrical parameters, mechanical parameters and safety distance parameters of the transmission line conductors; Through the above technical solution, this example provides the data extracted in S6. By combining this data, the extracted tower and conductor parameters can be used to perform three-dimensional modeling of the transmission line, providing intuitive visualization support for the operation and maintenance management of the transmission line.

[0033] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the claims of this invention should still fall within the patent coverage of this invention.

Claims

1. A method for classifying and extracting parameters of transmission line towers and conductors based on cloud data, characterized in that, The method includes: S1: The data acquisition unit collects real-time data on the operating status of the lidar and environmental data within the lidar's detection area. S2: By combining the real-time collected operating status data of the lidar during operation with the data analysis unit, the aging status of the lidar's laser diode is analyzed. S3: By combining the environmental data collected in real time within the lidar detection area with the environmental monitoring unit, the atmospheric attenuation of the effective detection range of the lidar is analyzed; S4: By combining the aging analysis results of the lidar's lidar diodes with the atmospheric attenuation analysis results of the lidar's effective detection range, the lidar's transmission power is adjusted in real time. S5: The three-dimensional point cloud data of towers and conductors in the transmission line are obtained by combining the laser radar with the transmission power adjusted, and ground point filtering and noise removal are performed. S6: By combining the processed 3D point cloud data, the parameter data of transmission line towers and conductors are extracted and classified.

2. The method for classifying and extracting transmission line tower and conductor parameters based on cloud data according to claim 1, characterized in that, The data collected in S1 includes: Operational status data of the lidar during operation and environmental data within the lidar detection area; The operating status data of the lidar during operation includes: the lidar's wavelength offset, operating current, beam divergence angle, and the minimum current required for the laser diode to start laser emission. The environmental data within the lidar detection area includes: ambient humidity, rainfall, airborne particulate matter content, and visibility within the lidar detection area.

3. The method for classifying and extracting transmission line tower and conductor parameters based on cloud data according to claim 1, characterized in that, The analysis process in S3 includes: Through formula Calculate the wavelength offset of the lidar after correction during the i-th data acquisition. ; Where i represents a data collection at fixed time intervals. Let be the wavelength offset of the lidar acquired during the i-th data acquisition. Let i be the total number of data collections during the i-th data collection. Let be the operating current of the lidar during the i-th data acquisition. For all The average value, The preset operating current, for The standard value, To define a function, if Then let Otherwise, let .

4. The method for classifying and extracting transmission line tower and conductor parameters based on cloud data according to claim 3, characterized in that, The analysis process in S3 also includes: Through formula Calculate the aging index of the lidar laser diode during the i-th data acquisition. ; in, Let be the beam divergence angle of the lidar during the i-th data acquisition. The preset beam divergence angle, for The standard value, For the bounding function, if ,make Otherwise, let , For all The average value, Let be the minimum current required for the laser diode in the lidar to begin laser emission during the i-th data acquisition. This is the minimum current threshold at which the laser diode in a lidar begins to emit laser light.

5. The method for classifying and extracting transmission line tower and conductor parameters based on cloud data according to claim 4, characterized in that, The analysis process in S3 also includes: By measuring the aging index of the lidar laser diode during the i-th data acquisition Compared with the preset laser diode aging index threshold Perform a comparison; like It is determined that the laser diode in the lidar is severely aged during the i-th data acquisition, which will lead to a weakening of the long-distance reflection signal, i.e. a reduction in the effective detection range, and will also cause the point cloud to be sparse and increase noise, affecting the accuracy of point cloud data extraction. like It is determined that the aging of the laser diode in the lidar is not severe during the i-th data acquisition, which will not lead to a weakening of the long-distance reflection signal, i.e., a reduction in the effective detection distance, and has little impact on the accuracy of point cloud data extraction.

6. The method for classifying and extracting transmission line tower and conductor parameters based on cloud data according to claim 5, characterized in that, The adjustment process in S4 includes: Through formula Calculate the environmental impact coefficient within the lidar detection area during the i-th data acquisition. ; in, Let be the ambient humidity within the lidar detection area during the i-th data acquisition. The preset ambient humidity, for The standard value, Let be the rainfall within the lidar detection area during the i-th data acquisition. The preset rainfall amount, for The standard value, Let be the concentration of airborne particulate matter in the lidar detection area during the i-th data acquisition. The preset concentration of suspended particles in the air, Let be the visibility within the lidar detection area during the i-th data acquisition. This is the preset visibility level.

7. The method for classifying and extracting transmission line tower and conductor parameters based on cloud data according to claim 6, characterized in that, The adjustment process in S4 also includes: By using the environmental influence coefficient within the lidar detection area during the i-th data acquisition... Compared with the preset environmental impact coefficient threshold Perform a comparison; like It is determined that during the i-th data acquisition, the pulsed laser emitted by the lidar experiences severe atmospheric attenuation within the detection area, which leads to a reduction in detection range. like It is determined that during the i-th data acquisition, the pulsed laser emitted by the lidar has a low degree of atmospheric attenuation in the detection area, and thus has a low impact on the detection distance.

8. The method for classifying and extracting transmission line tower and conductor parameters based on cloud data according to claim 7, characterized in that, The adjustment process in S4 also includes: Through formula Calculate the corrected operating power of the lidar at the i-th data acquisition. ; in, The preset operating power for the lidar and The weighting coefficients are set based on empirical fitting. To adjust the coefficient lookup table function, based on empirical data... The impact of the numerical range on the operating power of the lidar was obtained through testing.

9. The method for classifying and extracting transmission line tower and conductor parameters based on cloud data according to claim 1, characterized in that, The correction process in S5 includes: Based on the original LiDAR point cloud data, ground points are filtered out to generate a digital elevation model to extract all non-ground points. Noise points are removed by calculating the area threshold of the point cloud data or comparing the density of a point with its neighboring points.

10. The method for classifying and extracting transmission line tower and conductor parameters based on cloud data according to claim 1, characterized in that, The data extracted in S6 includes: Geometric position parameters, structural characteristic parameters, functional parameters, and environmental parameters of transmission line towers; The data extracted in S6 also includes: geometric position parameters, physical characteristic parameters, structural parameters, electrical parameters, mechanical parameters and safety distance parameters of the transmission line conductors.

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