Crane boom deflection detection method and system based on laser radar

Abstract

The present application relates to the field of boom deflection detection technology, and specifically to a crane boom deflection detection method and system based on laser radar, the method comprising: collecting the amplitude plane point cloud data, the rotation plane point cloud data, the axial force and the critical force of the crane boom at each collection time; using a clustering algorithm to obtain each cluster of the amplitude plane point cloud data; obtaining the curvature factor of each cluster based on the amplitude plane point cloud data; obtaining the arm head discrimination weight of each cluster based on the distance between the amplitude plane point cloud data, the curvature factor and the distribution of the amplitude plane point cloud data; obtaining the arm head cluster of the amplitude plane point cloud data and the rotation plane point cloud data based on the arm head discrimination weight; obtaining the arm head deflection of the crane boom at each collection time based on the axial force, the critical force of the crane boom and the displacement of the arm head cluster. The present application improves the accuracy of the deflection detection of the crane boom.

Description

Technical Field

[0001] The present application relates to the technical field of boom deflection detection, and in particular to a crane boom deflection detection method and system based on laser radar. Background Art

[0002] The deflection of a crane boom refers to the degree of bending deformation of the boom when it is under load. As crane booms become larger and heavier, the deflection of the boom is a problem that is difficult to overcome during crane hoisting operations. Excessive deflection may cause serious accidents such as crane instability and loss of control of the hoisted objects, posing a serious threat to personnel and equipment.

[0003] Through deflection detection, the deformation of the boom can be discovered in time to avoid safety accidents caused by excessive deflection. At present, common deflection detection methods include visual detection, strain gauge measurement, total station measurement and laser scanning. Visual measurement is suitable for small-scale or slight deformation detection, with low cost and limited accuracy; strain gauge measurement installs strain gauges on the surface of the structure to monitor the strain change of the structure in real time, thereby indirectly calculating the deflection, but it is sensitive to environmental conditions; total station measurement measures the key point positions of the structure multiple times by the total station, and then calculates the deflection of the structure, with high accuracy, but complex operation; laser scanning uses a laser scanner to scan the boom in all directions to generate three-dimensional point cloud data. Through data analysis, the deflection of the structure can be calculated in real time, but this method is easily interfered by other factors in the environment during data collection, so that the collected three-dimensional point cloud data contains a lot of noise, resulting in low accuracy of deflection detection results. Summary of the invention

[0004] In order to solve the above technical problems, the purpose of this application is to provide a crane boom deflection detection method and system based on laser radar. The technical solutions adopted are as follows:

[0005] In a first aspect, an embodiment of the present application provides a method for detecting the deflection of a crane boom based on a laser radar, the method comprising the following steps:

[0006] Collect the luffing plane point cloud data, rotation plane point cloud data, axial force and critical force of the crane boom at each collection time;

[0007] Using clustering algorithm to obtain clusters of variable amplitude plane point cloud data;

[0008] Based on the discrete degree and average situation of the curvature on the fitting surface of the variable amplitude plane point cloud data, the curvature factor of each cluster is obtained;

[0009] The arm head discrimination weight of each cluster is obtained based on the distance between the variable amplitude plane point cloud data, the curvature factor and the distribution of the variable amplitude plane point cloud data;

[0010] Based on the arm head discrimination weight, the arm head clustering clusters of the variable amplitude plane point cloud data and the rotation plane point cloud data are obtained;

[0011] The deflection of the crane boom at each acquisition time is obtained based on the axial force, the critical force of the crane boom and the displacement of the boom head cluster.

[0012] Furthermore, the curvature factor is obtained by:

[0013] Obtain the maximum curvature and minimum curvature of each amplitude plane point cloud data;

[0014] The calculation formula of the curvature factor is: ; In the formula, represents the curvature factor of the i-th cluster; Represents the variance of the maximum curvature of all variable amplitude plane point cloud data in the i-th cluster; Represents the variance of the minimum curvature of all variable amplitude plane point cloud data in the i-th cluster; Represents the average maximum curvature of all amplitude plane point cloud data in the i-th cluster.

[0015] Furthermore, the method for obtaining the maximum curvature and the minimum curvature is:

[0016] The variable amplitude plane point cloud data is used as the input of the surface fitting algorithm, and the fitting surface of the variable amplitude plane point cloud data and the principal curvature of each variable amplitude plane point cloud data in the fitting surface are output; wherein the principal curvature includes the maximum curvature and the minimum curvature.

[0017] Furthermore, the method for obtaining the arm head discrimination weight is:

[0018] Obtain the density of each amplitude plane point cloud data in each cluster;

[0019] Divide each voxel of the variable amplitude plane point cloud data;

[0020] The spatial shape coefficient of each cluster is obtained based on the distance between the variable amplitude plane point cloud data;

[0021] The calculation formula of the arm head discrimination weight is: ; In the formula, is the arm-head discriminant weight of the i-th cluster; is the spatial shape coefficient of the i-th cluster; is the total number of voxels occupied by the i-th cluster; represents the curvature factor of the i-th cluster; is the mean density of all variable amplitude plane point cloud data in the i-th cluster.

[0022] Furthermore, the density of each variable amplitude plane point cloud data is the number of points within the neighborhood radius of each variable amplitude plane point cloud data in each cluster.

[0023] Furthermore, the step of dividing each voxel of the variable amplitude plane point cloud data includes:

[0024] The smallest cube that can include all the variable amplitude plane point cloud data is used as the variable amplitude plane point cloud space, and the variable amplitude plane point cloud space is divided into voxels, and the side length of each voxel is , where a is the minimum side length of the variable amplitude plane point cloud space, z is a preset constant, The rounding function.

[0025] Further, the method for obtaining the spatial shape coefficient is:

[0026] For each cluster, the Euclidean distance between any two variable amplitude plane point cloud data in the cluster is calculated, and the maximum value of the Euclidean distance between all any two variable amplitude plane point cloud data in the cluster is taken as the spatial shape coefficient of each cluster.

[0027] Further, the method for obtaining the arm head cluster is:

[0028] Calculate the arm-head discrimination weight of each cluster, use all arm-head discrimination weights as the input of the Otsu threshold segmentation algorithm, obtain the segmentation threshold, and take the cluster whose arm-head discrimination weight is greater than the segmentation threshold as the arm-head cluster;

[0029] The arm head clustering of the rotation plane point cloud data is obtained by using the same method as the variable amplitude plane point cloud data.

[0030] Further, the obtaining of the boom deflection of the crane boom at each collection time includes:

[0031] For each arm head clustering cluster of the amplitude plane point cloud data and the rotation plane point cloud data, the point where the coordinate mean of all the point cloud data in the arm head clustering cluster is located is taken as the center point of each arm head clustering cluster; for each arm head clustering cluster at each acquisition time, the Euclidean distance between the center point of the arm head clustering cluster and the arm head clustering cluster at the previous adjacent acquisition time is calculated as the relative displacement of each arm head clustering cluster at each acquisition time, and the average of the relative displacements of all arm head clustering clusters at each acquisition time is calculated as the relative displacement at each acquisition time;

[0032] The calculation formula of the boom deflection is: ; In the formula, is the boom deflection of the crane boom in the luffing plane at each sampling moment; is the relative displacement at each acquisition moment; N is the axial force on the crane boom at each acquisition moment; is the critical force of the boom.

[0033] In the second aspect, an embodiment of the present application also provides a laser radar-based crane boom deflection detection system, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of any one of the above-mentioned methods when executing the computer program.

[0034] This application has at least the following beneficial effects:

[0035] This application aims to solve the problem that the crane boom point cloud data scanned by laser radar contains many environmental factors, which makes the boom point cloud data extraction accuracy low, and then leads to inaccurate boom deflection detection. A crane boom deflection detection method and system based on laser radar is proposed. According to the differences in curvature characteristics, shape differences and density differences between the boom and the wires and trees in the point cloud data, the boom weight coefficient is constructed, the interference of environmental factors is eliminated, the arm head clustering clusters in the point cloud data are accurately extracted, and the accuracy of crane boom deflection detection is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0037] Figure 1 A flowchart of a method for detecting crane boom deflection based on laser radar according to an embodiment of the present application;

[0038] Figure 2 A flow chart for obtaining boom deflection is provided for one embodiment of the present application. DETAILED DESCRIPTION

[0039] In order to further explain the technical means and effects adopted by the present application to achieve the predetermined invention purpose, the following, in combination with the accompanying drawings and preferred embodiments, describes in detail the specific implementation method, structure, features and effects of the crane boom deflection detection method and system based on laser radar proposed in the present application. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0040] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0041] The specific scheme of the crane boom deflection detection method and system based on laser radar provided in the present application is described in detail below with reference to the accompanying drawings.

[0042] See also Figure 1 , which shows a flowchart of a crane boom deflection detection method based on laser radar provided by an embodiment of the present application, the method comprising the following steps:

[0043] Step S1, collecting the luffing plane point cloud data, the rotation plane point cloud data, the axial force and the critical force of the crane boom at each collection time.

[0044] During the power construction process, it is often necessary to move various large-scale power equipment, such as generators, transformers, cables, etc. The crane boom can easily complete the transportation of these equipment with its powerful lifting capacity and flexibility, greatly improving the construction efficiency.

[0045] Two laser radars are fixed to the outer side and top surface of the crane boom respectively to collect point cloud data of the crane boom in real time during the power construction process. The laser radar on the outer side collects the point cloud data of the amplitude plane of the crane boom at each collection time, and the laser radar on the top surface collects the point cloud data of the rotation plane of the crane boom at each collection time.

[0046] The obtained three-dimensional point cloud data contains the three-dimensional coordinates of each variable-length plane point cloud data, and obtains the critical force of the crane boom, wherein the method for obtaining the critical force is a well-known technology and is not described in detail in this embodiment. The pressure sensor is installed at the fixed pulley block of the boom to measure the axial force of the boom at each acquisition time in real time.

[0047] The point cloud data is used as the input of the statistical filtering algorithm to denoise the point cloud data. The statistical filtering algorithm is a well-known technology, and the specific process will not be described in detail.

[0048] Step S2, using a clustering algorithm to obtain each cluster of the variable amplitude plane point cloud data; based on the discrete degree and average of the curvature on the fitting surface of the variable amplitude plane point cloud data, obtain the curvature factor of each cluster; based on the distance between the variable amplitude plane point cloud data, the curvature factor and the distribution of the variable amplitude plane point cloud data, obtain the arm head discrimination weight of each cluster; based on the arm head discrimination weight, obtain the arm head cluster of the variable amplitude plane point cloud data and the rotation plane point cloud data.

[0049] Since the laser is divergent, the collected point cloud data contains not only the point cloud data of the crane arm, but also the point cloud data of the objects around the crane arm. Therefore, it is necessary to identify the crane arm in the point cloud data.

[0050] The existing method of extracting boom point cloud data is to judge the maximum and minimum values ​​of the boom in the three directions of the three-dimensional coordinate system based on manual experience, and then cut out the boom point cloud data from the point cloud data, and then use the traditional filtering algorithm to denoise the boom point cloud data to obtain the boom point cloud data. However, when there are objects in the environment that are close to the boom, the above method cannot cut out the environmental objects that are close, so that the obtained boom point cloud data is interfered by the environmental objects, resulting in inaccurate deflection detection results. Therefore, a more accurate crane boom deflection detection method is needed.

[0051] After the boom is extended, the laser radar can be used to obtain the point cloud data of each boom head section and the hook fixed pulley group. The mechanical structure of each boom head section at different positions is relatively similar, so the curvature values ​​of points at different positions in each boom section will be relatively similar. The branches or leaves of trees usually have various shapes, so the curvature values ​​of points belonging to trees will be quite different. The shape of the wire is usually slender, and the surface of the wire is cylindrical. The thinner the wire, the greater the curvature of the wire surface. Since the surface of the wire is more curved than the surface of the boom, the maximum curvature of the point belonging to the wire will be greater than the maximum curvature of the point belonging to the boom.

[0052] For each acquisition moment, the variable amplitude plane point cloud data is used as the input of the DBSCAN algorithm. In this embodiment, the neighborhood radius is 0.8, and the implementer can select any real number in [0.3, 2] according to the actual situation. In this embodiment, the minimum number of points Minpoints is 6, and the implementer can select other integers in [4, 10] according to the actual situation, and output each clustering cluster; the DBSCAN algorithm is a well-known technology, and the specific process will not be repeated. For each variable amplitude plane point cloud data in each clustering cluster, the number of points within the neighborhood radius of each variable amplitude plane point cloud data is used as the density of each variable amplitude plane point cloud data.

[0053] The variable amplitude plane point cloud data is used as the input of the least square method, and a quadratic surface is fitted to obtain the fitting surface of the variable amplitude plane point cloud data. The least square method is a well-known technology, and the specific process is not repeated here. According to the properties of the fitting surface, the principal curvature of each variable amplitude plane point cloud data in the variable amplitude plane point cloud data can be obtained, wherein the principal curvature includes the maximum curvature and the minimum curvature. The method for obtaining the principal curvature is a well-known technology, and the specific process is not repeated here.

[0054] According to the above analysis, in order to reflect the similarity between the curvature characteristics of the cluster and the curvature characteristics of the arm head cluster, the curvature factor of each cluster is obtained based on the discrete degree of the maximum curvature and the minimum curvature and the average value of the maximum curvature. The calculation formula is: ; In the formula, represents the curvature factor of the i-th cluster; Represents the variance of the maximum curvature of all variable amplitude plane point cloud data in the i-th cluster; Represents the variance of the minimum curvature of all variable amplitude plane point cloud data in the i-th cluster; Represents the average maximum curvature of all amplitude plane point cloud data in the i-th cluster.

[0055] It should be noted that when and When the value of is small, it means that the curvatures of the points in the ith cluster are more similar, which further indicates that the curvature characteristics of the ith cluster are more similar to the curvature characteristics of the arm-head cluster; when The smaller the value of , the smaller the average maximum curvature of the points of the ith cluster is, which further indicates that the curvature characteristics of the ith cluster are more similar to the curvature characteristics of the arm-head cluster.

[0056] Furthermore, since the wires are relatively slender and the branches of trees are usually relatively slender, the point cloud belonging to the wires or trees will occupy a smaller and longer space; the length of the arm head of each boom section is shorter than that of the wires and trees, so the point cloud of each arm head will occupy a larger and shorter space. The surface of the arm head is metal, and the metal has a higher reflectivity to the laser, so the points belonging to the arm head will be denser; the surface of the tree is relatively rough, with a lower reflectivity to the laser, and it will absorb the laser, so the points belonging to the tree in the point cloud data will be sparser; the surface of the wire is an insulating layer, which has a lower reflectivity to the laser than metal, so the points belonging to the wire in the point cloud data will also be sparse.

[0057] According to the above analysis, for each acquisition moment, the smallest cube that can include all the variable amplitude plane point cloud data is used as the variable amplitude plane point cloud space, and the variable amplitude plane point cloud space is divided into voxels, and the side length of each voxel is , a is the minimum side length of the variable amplitude plane point cloud space, z is a preset constant, and the value of z in this embodiment is 200. The implementer can select other values ​​according to the actual situation. The rounding function.

[0058] Furthermore, for each cluster, the Euclidean distance between any two variable amplitude plane point cloud data in the cluster is calculated, and the maximum value of the Euclidean distance between all any two variable amplitude plane point cloud data in the cluster is taken as the spatial shape coefficient of each cluster.

[0059] Furthermore, in order to reflect the possibility that each cluster belongs to the arm-head cluster, the arm-head discrimination weight of each cluster is obtained based on the spatial shape coefficient and curvature factor. The calculation formula is: ; In the formula, is the arm-head discriminant weight of the i-th cluster; is the spatial shape coefficient of the i-th cluster; is the total number of voxels occupied by the i-th cluster; represents the curvature factor of the i-th cluster; is the mean density of all variable amplitude plane point cloud data in the i-th cluster.

[0060] It should be noted that The larger the value of is, the longer the spatial shape of the i-th cluster is. When the value of is large, it means that the spatial shape of the ith cluster is shorter and larger, which means that the ith cluster is more likely to be the arm-head cluster. When the value of is large, it means that the curvature characteristics of the ith cluster are similar to those of the arm-head cluster, which means that the ith cluster is more likely to be the arm-head cluster. When the value of is large, it means that the points of the i-th cluster are denser, which means that the i-th cluster is more likely to belong to the arm-head cluster.

[0061] Furthermore, the arm head discrimination weight of each cluster is calculated, all arm head discrimination weights are used as input of the Otsu threshold segmentation algorithm, the segmentation threshold is obtained, and the clusters whose arm head discrimination weights are greater than the segmentation threshold are used as the arm head clusters. The Otsu threshold segmentation algorithm is a well-known technology, and the specific process will not be repeated.

[0062] At this point, all arm head clustering clusters of the amplitude-variable plane point cloud data at each acquisition moment are obtained.

[0063] Similarly, according to the same method, the arm head clustering clusters of the rotation plane point cloud data at each acquisition time are obtained.

[0064] Step S3, obtaining the boom deflection of the crane boom at each acquisition time based on the axial force, the critical force of the crane boom and the displacement of the boom head cluster.

[0065] For each arm head clustering cluster of the amplitude plane point cloud data and the rotation plane point cloud data, the point where the coordinate mean of all the point cloud data in the arm head clustering cluster is located is taken as the center point of each arm head clustering cluster. For each arm head clustering cluster at each acquisition time, the Euclidean distance between the center point of the arm head clustering cluster and the arm head clustering cluster at the previous adjacent acquisition time is calculated as the relative displacement of each arm head clustering cluster at each acquisition time, and the mean of the relative displacement of all arm head clustering clusters at each acquisition time is calculated as the relative displacement at each acquisition time.

[0066] According to the point cloud data of the crane boom before and after loading, the relative displacement of the center point of each boom head cluster is obtained. The acquisition method is a well-known technology and will not be described in detail in this embodiment.

[0067] According to the above analysis, the boom deflection of the boom of the crane at each acquisition time is calculated using the following formula: ; In the formula, is the boom deflection of the crane boom in the luffing plane at each sampling moment; is the relative displacement at each acquisition moment; N is the axial force on the crane boom at each acquisition moment; is the critical force of the boom. The flow chart of boom deflection acquisition is as follows: Figure 2 shown.

[0068] At this point, the boom deflection of the boom luffing plane of the crane at each acquisition time is obtained.

[0069] Based on the same inventive concept as the above-mentioned method, an embodiment of the present application also provides a laser radar-based crane boom deflection detection system, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein when the processor executes the computer program, the steps of any one of the above-mentioned laser radar-based crane boom deflection detection methods are implemented.

[0070] It should be noted that the above sequence of the embodiments of the present application is for description only and does not represent the advantages and disadvantages of the embodiments. The above is a description of a specific embodiment of this specification. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0071] The various embodiments in the present application are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0072] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present application should be included in the protection scope of the present application.

Claims

1. A crane boom deflection detection method based on laser radar, characterized in that: The method comprises the following steps: Collect the luffing plane point cloud data, rotation plane point cloud data, axial force and critical force of the crane boom at each collection time; Using clustering algorithm to obtain clusters of variable amplitude plane point cloud data; Based on the discrete degree and average situation of the curvature on the fitting surface of the variable amplitude plane point cloud data, the curvature factor of each cluster is obtained; The arm head discrimination weight of each cluster is obtained based on the distance between the variable amplitude plane point cloud data, the curvature factor and the distribution of the variable amplitude plane point cloud data; Based on the arm head discrimination weight, the arm head clustering clusters of the variable amplitude plane point cloud data and the rotation plane point cloud data are obtained; The deflection of the crane boom at each acquisition time is obtained based on the axial force, the critical force of the crane boom and the displacement of the boom head cluster; The obtaining of the boom deflection of the crane boom at each acquisition time comprises: For each arm head clustering cluster of the amplitude plane point cloud data and the rotation plane point cloud data, the point where the coordinate mean of all the point cloud data in the arm head clustering cluster is located is taken as the center point of each arm head clustering cluster; for each arm head clustering cluster at each acquisition time, the Euclidean distance between the center point of the arm head clustering cluster and the arm head clustering cluster at the previous adjacent acquisition time is calculated as the relative displacement of each arm head clustering cluster at each acquisition time, and the average of the relative displacements of all arm head clustering clusters at each acquisition time is calculated as the relative displacement at each acquisition time; The calculation formula of the boom deflection is: ; In the formula, is the boom deflection of the crane boom in the luffing plane at each sampling moment; is the relative displacement at each acquisition moment; N is the axial force on the crane boom at each acquisition moment; is the critical force of the boom.

2. The crane boom deflection detection method based on laser radar as claimed in claim 1, characterized in that: The method for obtaining the curvature factor is: Obtain the maximum curvature and minimum curvature of each amplitude plane point cloud data; The calculation formula of the curvature factor is: ; In the formula, represents the curvature factor of the i-th cluster; Represents the variance of the maximum curvature of all variable amplitude plane point cloud data in the i-th cluster; Represents the variance of the minimum curvature of all variable amplitude plane point cloud data in the i-th cluster; Represents the average maximum curvature of all amplitude plane point cloud data in the i-th cluster.

3. The crane boom deflection detection method based on laser radar as claimed in claim 2, characterized in that: The method for obtaining the maximum curvature and the minimum curvature is: The variable amplitude plane point cloud data is used as the input of the surface fitting algorithm, and the fitting surface of the variable amplitude plane point cloud data and the principal curvature of each variable amplitude plane point cloud data in the fitting surface are output; wherein the principal curvature includes the maximum curvature and the minimum curvature.

4. The crane boom deflection detection method based on laser radar as claimed in claim 1, characterized in that: The method for obtaining the arm head discrimination weight is: Obtain the density of each amplitude plane point cloud data in each cluster; Divide each voxel of the variable amplitude plane point cloud data; The spatial shape coefficient of each cluster is obtained based on the distance between the variable amplitude plane point cloud data; The calculation formula of the arm head discrimination weight is: ; In the formula, is the arm-head discriminant weight of the i-th cluster; is the spatial shape coefficient of the i-th cluster; is the total number of voxels occupied by the i-th cluster; represents the curvature factor of the i-th cluster; is the mean density of all variable amplitude plane point cloud data in the i-th cluster.

5. The crane boom deflection detection method based on laser radar as claimed in claim 4 is characterized in that: The density of each variable amplitude plane point cloud data is the number of points within the neighborhood radius of each variable amplitude plane point cloud data in each cluster.

6. The crane boom deflection detection method based on laser radar as claimed in claim 4, characterized in that: The voxels of the said divided amplitude plane point cloud data include: The smallest cube that can include all the variable amplitude plane point cloud data is used as the variable amplitude plane point cloud space, and the variable amplitude plane point cloud space is divided into voxels, and the side length of each voxel is , where a is the minimum side length of the variable amplitude plane point cloud space, z is a preset constant, The rounding function.

7. The crane boom deflection detection method based on laser radar as claimed in claim 4, characterized in that: The method for obtaining the spatial shape coefficient is: For each cluster, the Euclidean distance between any two variable amplitude plane point cloud data in the cluster is calculated, and the maximum value of the Euclidean distance between all any two variable amplitude plane point cloud data in the cluster is taken as the spatial shape coefficient of each cluster.

8. The crane boom deflection detection method based on laser radar as claimed in claim 1, characterized in that: The method for obtaining the arm head cluster is: Calculate the arm-head discrimination weight of each cluster, use all arm-head discrimination weights as the input of the Otsu threshold segmentation algorithm, obtain the segmentation threshold, and take the cluster whose arm-head discrimination weight is greater than the segmentation threshold as the arm-head cluster; The arm head clustering of the rotation plane point cloud data is obtained by using the same method as the variable amplitude plane point cloud data.

9. A crane boom deflection detection system based on laser radar, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the crane boom deflection detection method based on laser radar as described in any one of claims 1-8 are implemented.

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