Distribution line pole extraction method and system based on laser point cloud

Abstract

The invention discloses a laser point cloud-based distribution line pole extraction method and system, and the method comprises the steps: obtaining laser point cloud data of a linear distribution line, and carrying out the preprocessing of the data, and obtaining the preprocessed distribution line laser point cloud data; performing spatial conversion on the preprocessed distribution line laser point cloud data to obtain distribution line laser point cloud data parallel to an x axis; conducting wire representative coordinates are obtained from distribution line laser point cloud data parallel to the x axis in a partitioned mode, and distribution line electric pole coordinates are obtained in combination with the maximum value and the maximum value constraint. According to the method, the power distribution tower can be effectively extracted from the laser point cloud data.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of line inspection, and relates to a power distribution line pole extraction method and system based on laser point clouds. BACKGROUND

[0002] Line inspection is a key work of power grid operation and maintenance, which needs to invest a large amount of manpower and material resources. The transmission line is high, and the poles can be roughly extracted through the different spatial features of the ground points, wire points and tower points, and then further extracted. The channel of the distribution line is very complex, and the poles are relatively low, so that the pole, wire, tree and ground point cloud are mixed together, unlike the transmission tower which is much larger than the trees and other objects in the channel, so the method for extracting the transmission tower from the laser point cloud data is no longer applicable to extracting the distribution tower. SUMMARY

[0003] To solve the problems in the prior art, the application provides a power distribution line pole extraction method and system based on laser point clouds, which selects representative points to form wire coordinates from the spatial features of the wire and tower of the power distribution line, determines the position of the power distribution line pole by combining the maximum value and maximum value constraints, and finally obtains the three-dimensional coordinates of the power distribution line pole.

[0004] The application adopts the following technical solutions.

[0005] The application provides a power distribution line pole extraction method based on laser point clouds, which comprises the following steps: Step 1: Obtain the laser point cloud data of the straight-line power distribution line and perform preprocessing to obtain the preprocessed power distribution line laser point cloud data; Step 2: Perform spatial conversion on the preprocessed power distribution line laser point cloud data to obtain the power distribution line laser point cloud data parallel to the x-axis; Step 3: Obtain the wire representative coordinates by partitioning the power distribution line laser point cloud data parallel to the x-axis, and obtain the power distribution line pole coordinates by combining the maximum value and maximum value constraints.

[0006] Preferably, the preprocessing in step 1 comprises the following steps: Extract the x, y and z coordinates of the laser point cloud data of the straight-line power distribution line, and the coordinate unit is meter; Round off the extracted x, y and z coordinates to two decimal places, and remove the data with the same x, y and z coordinates; Filter the laser point cloud data with the z coordinates located between the upper and lower limits of the elevation threshold, wherein the upper and lower limits of the elevation threshold are set according to the average ground elevation and the height of the power distribution line pole, so as to filter out the ground point cloud and noise points in the air.

[0007] Preferably, the spatial conversion of the pre-processed power distribution line laser point cloud data in step 2 comprises: projecting the pre-processed power distribution line laser point cloud data to an x-o-y two-dimensional plane and fitting a plane first-order equation of the power distribution line, and obtaining an angle θ between the power distribution line and the x-axis according to the plane first-order equation; rotating the pre-processed power distribution line laser point cloud data by an angle θ clockwise in the plane range, so that the power distribution line trend is parallel to the x-axis, and obtaining laser point cloud data parallel to the x-axis.

[0008] Preferably, the step 3 comprises: Step 3.1, sorting the laser point cloud data parallel to the x-axis according to the x-axis coordinates from small to large, to obtain sorted laser point cloud data; Step 3.2, marking the smallest x-axis coordinate in the laser point cloud data as x_min, and the largest x-axis coordinate as x_max; Step 3.3, setting a variable x_state and initializing it as x_min; Step 3.4, filtering the laser point cloud data with x coordinates between x_state and x_state + step from the sorted laser point cloud data, and marking it as data, where step is the filtering step; Step 3.5, for data, interval division is performed in the x-axis direction and data variance is analyzed to obtain the representative horizontal and vertical coordinates of the conductor corresponding to the interval, and the representative horizontal and vertical coordinates of the conductor are recorded in the lists x_list and z_list; Step 3.6, based on the maximum value and maximum value constraint analysis list z_list, x_list, to obtain the x-axis position of the power distribution line pole; Step 3.7, according to the x-axis position of the power distribution line pole, taking a meters before and after the x-axis position, the laser point cloud data within the range of 2a meters is the three-dimensional coordinates of the power distribution line pole, where a is the x-axis position parameter; Step 3.8, rotating the three-dimensional coordinates of the power distribution line pole counterclockwise by an angle θ to restore the three-dimensional coordinates of the power distribution line pole in the original coordinate system, where θ is the angle between the power distribution line and the x-axis; Step 3.9, updating x_state to x_state + step / 2, and judging whether x_state is greater than x_max, if greater, then terminate the program, otherwise go to step 3.4.

[0009] Preferably, step 3.5 comprises: Step 3.5.1, setting lists x_list and z_list for recording the representative horizontal and vertical coordinates of the conductor respectively, and initializing them as empty lists; Step 3.5.2: For data, divide the interval along the x-axis, starting from x_state, with an interval length of 2a meters and an overlap length of a meter; Step 3.5.3: For each interval, further divide it into b small intervals and proceed to step 3.5.4. If the length of the distribution line in the interval in the x direction is less than 2a meters, do not further divide it into small intervals and skip the interval directly, where b is the number of small intervals. Step 3.5.4: For each interval, obtain the laser point cloud data point with the largest z coordinate value in each small interval, and obtain a total of b laser point cloud data points with the largest z coordinate values. Calculate the z coordinate value variance of these b laser point cloud data points with the largest z coordinate values: If the variance is less than c, the average z coordinate value of the b laser point cloud data points with the largest z coordinate values ​​is calculated and added to z_list as the conductor representative z coordinate value. At the same time, the average x coordinate value of the b laser point cloud data points with the largest z coordinate values ​​is calculated and added to x_list as the conductor representative x coordinate value. If the variance is greater than or equal to c, the interval and its subintervals are not considered, where c is the variance threshold.

[0010] Preferably, the value range of step is 150~250, the value range of a is 0.4~0.6, the value range of b is 8~15, and the value range of c is 0.1~1.

[0011] Preferably, the step 3.6 of analyzing the lists x_list and z_list based on the maximum and maximum value constraints to obtain the x-axis position of the distribution line pole includes: Step 3.6.1: Remove points in z_list that are outside the discrete limit range and the corresponding points in x_list based on the variance of the elements in z_list. Set the list xg_list used to record the x-axis positions of the distribution line poles and initialize it to an empty list. Step 3.6.2, traverse all elements of z_list. For the i-th element point, take the larger value of 0 and id, denoted as left, and take the smaller value of len(z_list) and i+d, denoted as right, where len(z_list) represents the number of elements in z_list and d is the traversal parameter; If the i-th element point satisfies the maximum and maximum constraints, then the i-th element point value x in x_list is i Added to xg_list as the x-axis position of the distribution line poles, with the maximum and maximum constraints as follows: z i > z i-1 And z i > z i+1, z i is the maximum value of z_list in the interval from left to right; wherein z i-1 , z i+1 is the maximum value of z_list in the interval from left to right; i left and right adjacent point values.

[0012] Preferably, d is in the range of 15-30.

[0013] The second aspect of the present application provides a power distribution line pole extraction system based on laser point cloud, comprising: A data acquisition module is configured to acquire laser point cloud data of a straight power distribution line and perform preprocessing to obtain preprocessed power distribution line laser point cloud data. A space conversion module is configured to perform space conversion on the preprocessed power distribution line laser point cloud data to obtain power distribution line laser point cloud data parallel to the x-axis. A coordinate extraction module is configured to acquire wire representative coordinates in intervals from the power distribution line laser point cloud data parallel to the x-axis, and obtain power distribution line pole coordinates in combination with maximum value and maximum value constraints.

[0014] The third aspect of the present application provides a terminal comprising a processor and a storage medium; the storage medium is configured to store instructions; the processor is configured to operate according to the instructions to perform the steps of the method.

[0015] The fourth aspect of the present application provides a computer readable storage medium having a computer program stored thereon, which is executed by a processor to implement the steps of the method.

[0016] Compared with the prior art, the present application has at least the following beneficial effects: The present application takes into account the differences between power distribution lines and transmission lines. The power distribution line may have trees higher than the wire, so the highest point is not necessarily the wire. By calculating and analyzing the local variance, the overall variance, and the maximum value, maximum value, etc. of the coordinate representative value, the false detection of towers caused by noise points such as trees with a sharp tip is excluded. The present application is suitable for positioning the tower position from complex power distribution line point clouds, has a high fault tolerance rate, and solves the problem that the current technology for extracting transmission towers from transmission line point clouds is completely unsuitable for power distribution lines. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 is a flowchart of the method of the present application; Figure 2 is a two-dimensional projection of a power distribution line; Figure 3 is a representative wire and extreme point position; Figure 4 is a three-dimensional view of a power distribution line pole. DETAILED DESCRIPTION

[0018] In order to make the objects, technical solutions and advantages of the present application clearer, the technical solutions will be described clearly and completely below with reference to the accompanying drawings of the embodiments of the present application. The embodiments described in the present application are only a part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by those skilled in the art without creative work on the basis of the spirit of the present application shall fall within the protection scope of the present application.

[0019] Embodiment 1 of the present application provides a power distribution line pole extraction method based on laser point cloud, as shown in Figure 1 , which comprises the following steps: Step 1: Obtain the laser point cloud data of the straight power distribution line and pre-process it to obtain the pre-processed power distribution line laser point cloud data; Further preferably, the unmanned aerial vehicle is used to carry the laser radar to collect the laser point cloud data of the straight section power distribution line, and the data format is las. When a corner tower with a corner greater than a threshold value is encountered, the current data is archived, and a new data collection record is started. In fact, only an approximate straight section is needed. The corner between adjacent towers is definitely a straight power distribution line, and the corner of the corner tower is generally not more than 10°. When a corner tower with a particularly large corner (greater than a set threshold value) is encountered during collection, the current data can be archived, and a new data collection record can be started.

[0020] The obtained power distribution line laser point cloud data is pre-processed to obtain the pre-processed power distribution line laser point cloud data, which comprises the following steps: (1) The x, y, z coordinates of the laser point cloud data are extracted by using the laspy library of the python language, and the coordinate unit is meter; (2) According to the result of (1), the x, y, z coordinates are rounded to two decimal places, i.e. each coordinate is accurate to the centimeter level, and then the data with the same x, y, z coordinates is de-duplicated; (3) According to the result of (2), a height threshold is set, and the laser point cloud data with z coordinates in a reasonable interval is filtered out. Taking A city as an example, the average elevation of A city is 7 meters (the laser point cloud data takes sea level as the starting point of height, and the starting point of the power distribution pole can be considered as the average elevation of A city), and the height of the power distribution pole is generally 8-15 meters. Therefore, the lower limit of the height threshold is set to 10 meters, and the upper limit of the height threshold is set to 30 meters, so that most of the ground point cloud and noise points in the air can be filtered out.

[0021] Step 2: Spatially convert the pre-processed power distribution line laser point cloud data to obtain power distribution line laser point cloud data parallel to the x-axis; Further preferably, performing spatial transformation on the pre-processed distribution line laser point cloud data obtained in step 1 to obtain distribution line laser point cloud data parallel to the x-axis includes: Step 2.1, based on the result of step 1, project the laser point cloud data onto the xoy two-dimensional plane; In step 2.2, the RANSACRegressor function in Python is used to fit the projection result of step 2.1 to obtain the plane linear equation of the distribution line. The angle θ between the distribution line and the x-axis is obtained based on the plane linear equation. The plane linear equation is a linear equation of the point cloud data on the xoy plane, and the angle θ is the counterclockwise angle between the equation and the x-axis.

[0022] Step 2.3, rotate the laser point cloud data obtained in step 1 clockwise by an angle θ in the plane range so that the distribution line is parallel to the x-axis, and obtain the laser point cloud data parallel to the x-axis. Along the x-axis direction, its projection on the yoz plane is shown as Figure 2 .

[0023] Step 3: Obtain representative coordinates of the conductors between the laser point cloud data partitions of the distribution line parallel to the x-axis, and obtain the coordinates of the distribution line poles by combining the maximum and maximum constraints.

[0024] Further preferably, according to the laser point cloud data of the distribution line parallel to the x-axis obtained in step 2, the pole coordinates are proposed based on the spatial characteristics of the poles and conductors, including: Step 3.1: Since the laser point cloud data is disordered, sort the laser point cloud data obtained in step 2 from small to large according to the x-axis coordinate; Step 3.2: Use the Python language min function to get the minimum x-axis coordinate, denoted as x_min, and use the Python language max function to get the maximum x-axis coordinate, denoted as x_max; Step 3.3, set the variable x_state with an initial value of x_min; Step 3.4: Based on the result of step 3.1, filter out the laser point cloud data whose x coordinates are between x_state and x_state + step, denoted as data, and step can be 150-250; Step 3.5: Based on the data obtained in step 3.4, obtain the representative coordinate values ​​of the wires in segments along the x-axis direction, and obtain the representative coordinates of the wires of the corresponding segments, including: Step 3.5.1, set the variables x_list and z_list to empty lists initially, which are used to record the coordinates of the wire representatives; Step 3.5.2, according to the data obtained in step 3.4, divide the interval in the x-axis direction, with x_state as the starting point, interval length of 2a meters, and overlap length of a meters, to obtain [x_state, x_state+2a], [x_state+a, x_state+3a], [x_state+2a, x_state+4a]…, the overlap length is set to increase the fault tolerance rate and prevent local high trees and other noise data from affecting the subsequent tower point cloud extraction, a can be 0.4~0.6; Step 3.5.3, according to the interval division result obtained in step 3.5.2, for each interval, continue to divide into b small intervals, each small interval length is a / b meters, for example, for the interval [x_state+i, x_state+i+2a], continue to divide into [x_state+i, x_state+i+a / b], [x_state+i+a / b, x_state+i+2a / b], …, [x_state+i+9a / b, x_state+i+2a] these b small intervals, enter step 3.5.4; if the length of the distribution line in the x direction is less than 2a meters, do not further divide into small intervals, directly skip the interval; where b can be 8~15; Step 3.5.4, according to the b small intervals obtained in step 3.5.3, use the max function to get the z coordinate value of the largest laser point cloud data point in each small interval, if the variance of the z coordinate values of the b laser point cloud data points is less than c, calculate the average value of the b z coordinate values and add it to z_list, at the same time calculate the average value of the x coordinate values of the b laser point cloud data points and add it to x_list, if the variance of the z coordinate values of the b laser point cloud data points is greater than or equal to c, do not consider the b small intervals, because there are noise points such as trees with sharp tips, c can be 0.1~1; In fact, taking step=200 as an example, the length of the straight line segment is not necessarily a multiple of 200, so every time 200 meters are extracted, the last part will not be 200 meters, according to the actual situation, the last part is merged into the previous part or established as a separate part. For example, less than 100 meters is merged into the previous part, and greater than or equal to 100 meters is established as a separate part. The value of step can also be selected according to the length of the straight line segment in advance, so that the straight line segment can be divided into equal segments, and there is no remaining part.

[0025] Step 3.6, based on the maximum value and maximum value constraint analysis list x_list, z_list, to obtain the x-axis position of the distribution line pole; Further preferably, according to the conductor representative coordinates obtained in step 3.5, calculate the maximum value position (z i > zi-1 and z i > z i+1 ), the x-axis position of the distribution pole is determined (the maximum value position is not necessarily the tower position, and further determination is needed through z i is the maximum value of z_list in the interval from left to right), including: Step 3.6.1, calculate the variance of the elements in z_list, remove the points in z_list that are particularly discrete and the points in x_list at the corresponding positions to avoid false detection, and set the variable xg_list, the initial value is an empty list, to record the tower position coordinates; Step 3.6.2, according to step 3.5, the wire representative coordinates are obtained, including x_list and z_list, all elements of z_list are traversed, for the i-th point, take the maximum value of 0 and i-d, denoted as left, and take the minimum value of len(z_list) and i+d, denoted as right, where len(z_list) represents the number of elements of z_list, and d can be taken as 15-30; If the i-th point satisfies: z i > z i-1 and z i > z i+1 , z i is the maximum value of z_list in the interval from left to right, then x i is added to xg_list, because the wire will produce sag, the tower position must be the maximum value point of the z coordinate value curve, and since there may be noise points in the local position, such as trees, the second condition must be met, that is, the maximum value point is the highest point within d / 2 meters in front and back in the x direction, where z i-1 , z i+1 are the left and right adjacent point values of z_list in the interval from left to right with the maximum value z i ; the final tower position is shown in Figure 3 .

[0026] Step 3.7, according to the x-axis position of the distribution pole obtained in step 3.6, take a meters in front and back of the x-axis position, and the laser point cloud data in the range of 2a meters is the three-dimensional coordinates of the distribution pole; the three-dimensional coordinates of a single tower are shown in Figure 4 .

[0027] Step 3.8, according to the three-dimensional coordinates of the distribution pole obtained in step 3.7, rotate counterclockwise by an angle θ to restore the three-dimensional coordinates of the distribution pole in the original coordinate system; Step 3.9, update x_state as x_state+ step / 2, judge whether x_state is greater than x_max, if greater, terminate the program, otherwise, go to step 3.4.

[0028] Embodiment 2 of the present application provides a power distribution line pole extraction system based on laser point cloud, comprising: The data acquisition module is configured to acquire laser point cloud data of the linear power distribution line and perform preprocessing to obtain preprocessed power distribution line laser point cloud data. The spatial conversion module is configured to perform spatial conversion on the preprocessed power distribution line laser point cloud data to obtain power distribution line laser point cloud data parallel to the x-axis. The coordinate extraction module is configured to acquire conductor representative coordinates by partitioning the power distribution line laser point cloud data parallel to the x-axis, and obtain power distribution line pole coordinates by combining maximum value and maximum value constraints.

[0029] Embodiment 3 of the present application provides a terminal comprising a processor and a storage medium; the storage medium is configured to store instructions; the processor is configured to operate according to the instructions to perform the steps of the method.

[0030] Embodiment 4 of the present application provides a computer readable storage medium having a computer program stored thereon, the program being executed by a processor to implement the steps of the method.

[0031] Compared with the prior art, the present application has at least the following beneficial effects: The present application takes into account the differences between power distribution lines and transmission lines. The power distribution line may have trees higher than the conductor, so the highest point is not necessarily the conductor. By calculating the coordinate representative value, analyzing the local variance, overall variance, and maximum value, maximum value, etc., the false detection of towers caused by noise points such as trees with a sharp tip is excluded. The channel trees are the biggest obstacle to extracting towers, and are suitable for positioning the tower position from complex power distribution line point clouds, with high fault tolerance, solving the problem that the current technology for extracting transmission towers from transmission line point clouds is completely unsuitable for power distribution lines.

[0032] The present disclosure can be a system, a method, and / or a computer program product. The computer program product can include a computer readable storage medium having computer readable program instructions loaded thereon for causing a processor to implement various aspects of the present disclosure.

[0033] Computer readable storage media can be tangible storage media which can retain and store instructions for use by an instruction execution device. Computer readable storage media can be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of computer readable storage media include the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire.

[0034] Computer readable program instructions described herein can be downloaded to respective computing / processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and / or a wireless network. The network can comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and / or edge servers. A network adapter card or network interface in each computing / processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing / processing device.

[0035] Computer readable program instructions for carrying out operations of the present disclosure can be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++ or the like, and conventional procedural programming languages such as the "C" programming language or similar programming languages. The computer readable program instructions can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate array (FPGA), or programmable logic array (PLA) can execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present disclosure.

[0036] Finally, it should be noted that the above-mentioned embodiments are merely used to illustrate the technical solutions of the present application, but not to limit it. Although the present application has been described in detail with reference to the above-mentioned embodiments, those skilled in the art should understand that the specific embodiments of the present application can be modified or replaced, and any modification or replacement without departing from the spirit and scope of the present application should be covered in the protection scope of the claims of the present application.

Claims

1. A method for extracting distribution line poles based on laser point cloud, characterized in that: include: Step 1: Acquire laser point cloud data of a linear distribution line and perform preprocessing to obtain preprocessed laser point cloud data of the distribution line; Step 2: spatially transform the pre-processed distribution line laser point cloud data to obtain distribution line laser point cloud data parallel to the x-axis; Step 3: Obtain representative coordinates of the conductors between the laser point cloud data partitions of the distribution line parallel to the x-axis, and obtain the coordinates of the distribution line poles by combining the maximum and maximum constraints.

2. The method for extracting distribution line poles based on laser point cloud according to claim 1, characterized in that: The pretreatment in step 1 includes: Extract the x, y, and z coordinates of the laser point cloud data of the straight-line distribution line. The coordinate unit is meters. The extracted x, y, and z coordinates are rounded to two decimal places, and the data with the same x, y, and z coordinates are removed; The laser point cloud data with z coordinates between the upper and lower limits of the elevation threshold are filtered out. The upper and lower limits of the elevation threshold are set according to the average altitude of the ground and the height of the distribution line poles to filter out the noise points on the ground point cloud and in the air.

3. The method for extracting distribution line poles based on laser point cloud according to claim 1, characterized in that: The spatial conversion of the pre-processed distribution line laser point cloud data in step 2 includes: The pre-processed distribution line laser point cloud data is projected onto the xoy two-dimensional plane and fitted to obtain the plane linear equation of the distribution line. The angle θ between the distribution line and the x-axis is obtained according to the plane linear equation. The pre-processed distribution line laser point cloud data is rotated clockwise by an angle θ in the plane range so that the distribution line direction is parallel to the x-axis, and the laser point cloud data parallel to the x-axis is obtained.

4. The method for extracting distribution line poles based on laser point cloud according to claim 1, characterized in that: The step 3 comprises: Step 3.1, sorting the laser point cloud data of the distribution lines parallel to the x-axis from small to large according to the x-axis coordinates to obtain sorted laser point cloud data; Step 3.2: Label the minimum x-axis coordinate in the laser point cloud data as x_min and the maximum x-axis coordinate as x_max; Step 3.3, set the variable x_state and initialize it to x_min; Step 3.4: Filter out the laser point cloud data whose x coordinates are between x_state and x_state + step from the sorted laser point cloud data, denoted as data, where step is the filtering step length; Step 3.5: For data, divide the intervals along the x-axis and analyze the data variance to obtain the horizontal and vertical coordinates of the corresponding intervals. Record the horizontal and vertical coordinate values ​​of the wires in the lists x_list and z_list; Step 3.6, based on the maximum and maximum constraint analysis lists z_list and x_list, obtain the x-axis position of the distribution line pole; Step 3.7: Based on the x-axis position of the distribution line pole, take a meter before and after the x-axis position. The corresponding laser point cloud data within a range of 2a meters is the 3D coordinate of the distribution line pole, where a is the x-axis position parameter. Step 3.8: Rotate the three-dimensional coordinates of the distribution line pole counterclockwise by an angle θ to restore the three-dimensional coordinates of the distribution line pole in the original coordinate system, where θ is the angle between the distribution line and the x-axis. Step 3.9: Update x_state to x_state + step / 2 and check whether x_state is greater than x_max. If so, terminate the program; otherwise, go to step 3.

4.

5. The method for extracting distribution line poles based on laser point cloud according to claim 4, characterized in that: Step 3.5 includes: Step 3.5.1, set up lists x_list and z_list for recording the horizontal and vertical coordinate values ​​of the wires, respectively, and initialize them to empty lists; Step 3.5.2: For data, divide the interval along the x-axis, starting from x_state, with an interval length of 2a meters and an overlap length of a meter; Step 3.5.3: For each interval, further divide it into b small intervals and proceed to step 3.5.

4. If the length of the distribution line in the interval in the x direction is less than 2a meters, do not further divide it into small intervals and skip the interval directly, where b is the number of small intervals. Step 3.5.4: For each interval, obtain the laser point cloud data point with the largest z coordinate value in each small interval, and obtain a total of b laser point cloud data points with the largest z coordinate values. Calculate the z coordinate value variance of these b laser point cloud data points with the largest z coordinate values: If the variance is less than c, the average z coordinate value of the b laser point cloud data points with the largest z coordinate values ​​is calculated and added to z_list as the conductor representative z coordinate value. At the same time, the average x coordinate value of the b laser point cloud data points with the largest z coordinate values ​​is calculated and added to x_list as the conductor representative x coordinate value. If the variance is greater than or equal to c, the interval and its subintervals are not considered, where c is the variance threshold.

6. The method for extracting distribution line poles based on laser point cloud according to claim 5, characterized in that: The value range of step is 150~250, the value range of a is 0.4~0.6, the value range of b is 8~15, and the value range of c is 0.1~1.

7. The method for extracting distribution line poles based on laser point cloud according to claim 4, characterized in that: The x-axis position of the distribution line pole is obtained based on the maximum and maximum value constraint analysis lists x_list and z_list described in step 3.6, including: Step 3.6.1: Remove points in z_list that are outside the discrete limit range and the corresponding points in x_list based on the variance of the elements in z_list. Set the list xg_list used to record the x-axis positions of the distribution line poles and initialize it to an empty list. Step 3.6.2, traverse all elements of z_list. For the i-th element point, take the larger value of 0 and id, denoted as left, and take the smaller value of len(z_list) and i+d, denoted as right, where len(z_list) represents the number of elements in z_list and d is the traversal parameter; If the i-th element point satisfies the maximum and maximum constraints, then the i-th element point value x in x_list is i Added to xg_list as the x-axis position of the distribution line poles, with the maximum and maximum constraints as follows: z i > z i-1 And z i > z i+1 , z i It is the maximum value of z_list from left to right; Where z i-1 、z i+1 The maximum value z in the range from left to right of z_list i The point values ​​adjacent to the left and right.

8. The method for extracting distribution line poles based on laser point cloud according to claim 7, characterized in that: The value range of d is 15~30.

9. A distribution line pole extraction system based on laser point cloud, running the method according to any one of claims 1 to 8, characterized in that: The system comprises: A data acquisition module is used to acquire laser point cloud data of a linear distribution line and perform preprocessing to obtain preprocessed laser point cloud data of the distribution line; A spatial conversion module is used to perform spatial conversion on the pre-processed distribution line laser point cloud data to obtain distribution line laser point cloud data parallel to the x-axis; The coordinate extraction module is used to obtain the representative coordinates of the conductors in the partitioned area of ​​the laser point cloud data of the distribution line parallel to the x-axis, and obtain the coordinates of the distribution line poles by combining the maximum value and maximum value constraints.

10. A terminal comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to execute the steps of the method according to any one of claims 1 to 8.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

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