Wind turbine tower inclination determination method and device, electronic equipment, and storage medium

Abstract

The present invention provides a method and device for determining the inclination of a wind turbine tower, an electronic device, and a storage medium. The present invention first obtains point cloud data in a target area including a wind turbine tower, and extracts the point cloud data of the wind turbine tower in the point cloud data; then, the point cloud data of the wind turbine tower is layered according to a preset height, and plane circle fitting is performed on the point cloud data of each layer of the wind turbine tower respectively, and the coordinates of the center points of each plane circle are determined; then, two center points are randomly selected, and a straight line fitting process is performed using the coordinates of the selected center points to obtain multiple initial straight lines, and the center points matching each initial straight line are determined respectively, and the initial straight line with the largest number of matching center points is used as a target straight line model, and the center point matching the target straight line model is used as a target center point; finally, the highest point and the lowest point among the target center points are determined, and the inclination of the wind turbine tower is determined according to the coordinates of the highest point and the coordinates of the lowest point.

Description

Technical Field

[0001] The present disclosure relates to the technical field of wind power generation, and in particular to a method and device for determining the inclination of a wind turbine tower, an electronic device, and a storage medium. Background Art

[0002] At present, my country's wind power technology is developing rapidly, and a large number of wind power devices have begun to be used. The structure of wind power devices is complex, and their wind turbine towers bear multiple loads such as the gravity, bending moment and torque of the generator set. Under the action of long-term complex loads, the wind turbine tower is prone to tilting, which not only affects its normal operation, but may also cause the tower to collapse, causing huge economic losses.

[0003] At present, most of the solutions for detecting the tilt of wind turbine towers require the installation of information collection equipment used for detection on the wind turbine towers, or require accurate data collection of points on the wind turbine towers. This increases the difficulty of detecting the tilt of wind turbine towers and reduces the efficiency and accuracy of tilt detection. Summary of the invention

[0004] The present disclosure at least provides a method and device for determining the inclination of a wind turbine tower, an electronic device, and a storage medium to reduce the difficulty of detecting the inclination of a wind turbine tower and improve the efficiency and accuracy of the inclination detection.

[0005] According to one aspect of the present disclosure, a method for determining an inclination of a wind turbine tower is provided, comprising:

[0006] Acquire point cloud data within a target area including a wind turbine tower, and extract the wind turbine tower point cloud data from the point cloud data; wherein the point cloud data includes point cloud data obtained by scanning the target area in at least one direction;

[0007] The wind turbine tower point cloud data is layered according to a preset height, and plane circle fitting is performed on each layer of wind turbine tower point cloud data to obtain plane circles corresponding to each layer of wind turbine tower point cloud data; and the coordinates of the center points of each plane circle are determined;

[0008] Randomly select two center points, and use the coordinates of the selected center points to perform straight line fitting processing to obtain multiple initial straight lines; and respectively determine the center points that match each initial straight line, and use the initial straight line with the largest number of matching center points as the target straight line model, and use the center point that matches the target straight line model as the target center point;

[0009] The highest point and the lowest point in the target center point are determined, and the inclination of the wind turbine tower is determined according to the coordinates of the highest point and the coordinates of the lowest point.

[0010] In a possible implementation manner, extracting wind turbine tower point cloud data from the point cloud data includes:

[0011] In a case where the point cloud data includes point cloud data obtained by scanning the target area in multiple directions, point cloud stitching is performed on the point cloud data obtained by scanning the target area in each direction to obtain target point cloud data;

[0012] The target point cloud data is classified to obtain the wind turbine tower point cloud data.

[0013] In a possible implementation manner, the height of the plane circle is determined according to the following steps:

[0014] The average height of the wind turbine tower point cloud data corresponding to the plane circle is used as the height of the plane circle.

[0015] In a possible implementation, the randomly selecting two center points and performing straight line fitting processing using the coordinates of the selected center points to obtain multiple initial straight lines include:

[0016] Randomly select two center points, and perform straight line fitting on the coordinates of the two selected center points to obtain an initial straight line;

[0017] Two center points are randomly selected multiple times, and the straight line fitting process is performed to obtain multiple initial straight lines.

[0018] In a possible implementation, respectively determining the center points that match the initial straight lines and taking the initial straight line with the largest number of matching center points as the target straight line model includes:

[0019] Determine the center point that matches each initial straight line respectively;

[0020] Eliminate the initial straight lines whose number of matching center points is less than the preset value;

[0021] Among the remaining initial straight lines, the initial straight line with the largest number of matching center points is used as the target straight line model.

[0022] In a possible implementation manner, determining the highest point and the lowest point among the target center points includes:

[0023] Using the target center point to perform straight line fitting to obtain a target straight line segment;

[0024] According to the target straight line segment, the highest point and the lowest point among the target center points are determined.

[0025] In a possible implementation, the point cloud data includes three-dimensional laser scanning data.

[0026] According to another aspect of the present disclosure, a device for determining the inclination of a wind turbine tower is provided, comprising:

[0027] A data processing module, used to obtain point cloud data within a target area including a wind turbine tower, and extract the wind turbine tower point cloud data from the point cloud data; wherein the point cloud data includes point cloud data obtained by scanning the target area in at least one direction;

[0028] A circle fitting processing module is used to layer the wind turbine tower point cloud data according to a preset height, and perform plane circle fitting on each layer of wind turbine tower point cloud data to obtain a plane circle corresponding to each layer of wind turbine tower point cloud data; and determine the coordinates of the center point of each plane circle;

[0029] A straight line fitting processing module is used to randomly select two center points and perform straight line fitting processing using the coordinates of the selected center points to obtain multiple initial straight lines; and to respectively determine the center points that match each initial straight line, and use the initial straight line with the largest number of matching center points as the target straight line model, and use the center point that matches the target straight line model as the target center point;

[0030] The inclination determination module is used to determine the highest point and the lowest point in the target center point, and determine the inclination of the wind turbine tower according to the coordinates of the highest point and the coordinates of the lowest point.

[0031] According to another aspect of the present disclosure, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory, wherein the processor implements any of the above methods when executing the computer program.

[0032] According to another aspect of the present disclosure, a computer-readable storage medium is provided, wherein a computer program is stored in the computer-readable storage medium, and when the computer program is executed by a processor, any of the above-mentioned methods is implemented.

[0033] The method for determining the inclination of a wind turbine tower disclosed in the present invention first obtains point cloud data within a target area including a wind turbine tower, and extracts the point cloud data of the wind turbine tower from the point cloud data; then, the point cloud data of the wind turbine tower is layered according to a preset height, and plane circle fitting is performed on the point cloud data of each layer of the wind turbine tower, and the coordinates of the center points of each plane circle are determined; then, a straight line fitting process is performed using the coordinates of the selected center points to obtain a plurality of initial straight lines, and the center points matching each initial straight line are determined respectively, and the initial straight line with the largest number of matching center points is used as a target straight line model, and the center point matching the target straight line model is used as a target center point; finally, the highest point and the lowest point among the target center points are determined, and the inclination of the wind turbine tower is determined according to the coordinates of the highest point and the coordinates of the lowest point. The above-mentioned scheme utilizes the collected laser point cloud data to calculate the inclination of the wind turbine tower in a non-contact manner, avoiding the installation of related collection equipment on the wind turbine tower, and effectively improving the convenience and detection efficiency, and effectively reducing the manpower and device maintenance costs; and the detection results will not be adversely affected by the errors in the collection of specific point data. The data collected by the scheme disclosed in the present invention is of high accuracy and a large number of point clouds. Compared with other schemes, the results are more robust and have higher accuracy.

[0034] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure.

[0036] Figure 1 is a flow chart of a method for determining the inclination of a wind turbine tower according to the present disclosure;

[0037] Figure 2A According to the plane circle schematic diagram of the embodiment of the present disclosure Figure 1 ;

[0038] Figure 2B This is a second schematic diagram of a plane circle according to an embodiment of the present disclosure;

[0039] Figure 2C According to the plane circle schematic diagram of the embodiment of the present disclosure Figure 3 ;

[0040] Figure 3 is a schematic structural diagram of a device for determining the inclination of a wind turbine tower according to the present disclosure;

[0041] Figure 4 is a schematic structural diagram of an electronic device according to the present disclosure. DETAILED DESCRIPTION

[0042] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0043] In view of the technical defects of the current wind turbine tower inclination detection scheme, such as high difficulty, low efficiency and accuracy, the present invention provides a method and device for determining the inclination of a wind turbine tower, an electronic device and a storage medium. The present invention utilizes the collected laser point cloud data to calculate the inclination of the wind turbine tower in a non-contact manner, thus avoiding the installation of related collection equipment on the wind turbine tower and eliminating the need for precise measurement of specific points on the wind turbine tower. The method has high accuracy and high efficiency, and at the same time, improves the convenience of measuring the inclination of the wind turbine tower and reduces the cost of manpower and device maintenance.

[0044] The technical solution of the present disclosure is described below through specific embodiments.

[0045] like Figure 1 As shown, it is a flow chart of the method for determining the inclination of the wind turbine tower of this embodiment. The execution subject of this embodiment is a computing device or component with data processing capability. The specific method of this embodiment may include the following steps:

[0046] S110, acquiring point cloud data within a target area including a wind turbine tower, and extracting the wind turbine tower point cloud data from the point cloud data; wherein the point cloud data includes point cloud data obtained by scanning the target area in at least one direction.

[0047] In some embodiments, a 3D laser scanner is used to collect point cloud data in the target area. The collected point cloud data is point cloud data with the wind turbine tower as the main body, which can be collected by a single station or by multiple stations. The above-mentioned multi-station collection refers to using multiple 3D laser scanners to scan the wind turbine tower from multiple directions to obtain point cloud data; single station refers to using a 3D laser scanner to scan the wind turbine tower from a single direction to obtain point cloud data.

[0048] In some embodiments, the wind turbine tower point cloud data in the point cloud data may be extracted by classification using machine learning.

[0049] S120, stratifying the wind turbine tower point cloud data according to preset heights, and performing plane circle fitting on each layer of wind turbine tower point cloud data to obtain plane circles corresponding to each layer of wind turbine tower point cloud data; and determining the coordinates of the center points of each plane circle.

[0050] In some embodiments, the following steps can be used to perform plane circle fitting:

[0051] The circle optimization formula is established based on the least squares fitting method. After simplification, the formula for solving the center point and radius of the circle can be obtained.

[0052] In some embodiments, the average height of the wind turbine tower point cloud data corresponding to the plane circle may be used as the height of the plane circle.

[0053] Through this step, each layer of wind turbine tower point cloud data corresponds to a plane circle, and the coordinates of the center point of the plane circle can be obtained by fitting.

[0054] If the point cloud data obtained by single-station acquisition is used to perform the above steps S110 and S120, the plane circle obtained by fitting is as follows: Figure 2A As shown; if the point cloud data obtained by multi-station acquisition is used to perform the above steps S110 and S120, the plane circle obtained by fitting is as follows Figure 2B As shown; superimpose the plane circles fitted by the two methods, and get Figure 2C It can be seen that the point cloud data collected by the single-station acquisition method can only fit half of the plane circle after processing, and the point cloud data collected by the multi-station method can only fit the complete plane circle after processing; and through Figure 2C It can be seen that the two fitting results are close when the point cloud density is evenly distributed and there are fewer noise points. The point cloud data collected by the multi-station method scans the wind turbine tower from different angles, and these point cloud data are spliced ​​and processed. The fitted plane circle is more accurate, that is, the coordinates of the center point of the determined plane circle are more accurate, but in fact, the difference in the inclination calculated using the above two fitting results is not much, and is smaller than the inclination danger threshold set during the detection process. Therefore, it can be concluded that although the fitting results of the point cloud data collected by the multi-station method are more accurate, the calculation results of the point cloud data collected by the single-station method and the multi-station method both meet the actual engineering requirements.

[0055] S130, randomly selecting two center points, and performing straight line fitting processing using the coordinates of the selected center points to obtain multiple initial straight lines; and, respectively determining the center points that match each initial straight line, and taking the initial straight line with the largest number of matching center points as the target straight line model, and taking the center point that matches the target straight line model as the target center point.

[0056] This step is to fit a straight line based on all the center points using the random sampling consensus method to obtain the point cloud that best meets the "straight line characteristics", that is, the target center point mentioned above. This can be achieved using the following steps:

[0057] S1301, randomly selecting two center points, and performing straight line fitting processing on the coordinates of the selected center points to obtain an initial straight line. Randomly selecting two center points multiple times, and performing the straight line fitting processing to obtain multiple initial straight lines.

[0058] S1302, respectively determine the center point that matches each initial straight line.

[0059] For each initial straight line, this step is to test other point cloud data based on the initial straight line. If the result is good, for example, the distance between other center points and the initial straight line is less than the preset distance, it is regarded as an inner point, that is, a center point that matches the initial straight line.

[0060] S1303, eliminating the initial straight lines whose number of matched center points is less than a preset value;

[0061] S1304. Among the remaining initial straight lines, the initial straight line with the largest number of matched center points is used as a target straight line model, and the center point matched with the target straight line model is used as a target center point.

[0062] It should be noted that the random sampling consensus method is a method to obtain the most reasonable result under a certain probability. The probability can be increased by increasing the number of iterations. This method can effectively remove the influence of noise.

[0063] In some embodiments, the specific steps of the straight line fitting process are as follows:

[0064] The straight line is determined by a direction vector and a geometric center point. The geometric center point is the mean point of all points, and the direction vector can be solved using the PCA principal component analysis method. The steps of the PCA method are as follows: first calculate the mean point of all points, then calculate its covariance matrix, and finally perform eigendecomposition on the covariance matrix. The vector corresponding to its maximum eigenvalue is the direction vector of the straight line.

[0065] S140, determining the highest point and the lowest point in the target center point, and determining the inclination of the wind turbine tower according to the coordinates of the highest point and the coordinates of the lowest point.

[0066] In some embodiments, the target center point may be used to perform straight line fitting to obtain a target straight line segment; then, based on the target straight line segment, the highest point and the lowest point in the target center point may be determined.

[0067] In some embodiments, the formula for calculating the inclination is as follows: ; Among them, Inclination is the inclination, P h is the coordinate of the highest point, P l is the coordinate of the lowest point, x is the coordinate value on the x-axis, y is the coordinate value on the y-axis, and z is the coordinate value on the z-axis.

[0068] The present disclosure proposes a method for calculating the inclination of a wind turbine tower based on laser point cloud data. First, a three-dimensional laser scanner is used to scan the target area where the wind turbine tower is located at a single station or multiple stations, and then the multi-station data is merged to obtain its final point cloud data, that is, the target point cloud data in the above embodiment; then the target point cloud data is manually or automatically classified and processed. Then, the classified wind turbine tower point cloud data is layered according to the height, and each layer of data is fitted with a plane circle to obtain the radius of the circle and the coordinates of the center point; then a random sampling consistency method is used to perform linear fitting on the coordinates of multiple center points, and noise points are filtered out to obtain multiple center points that best meet the "straight line characteristics". Finally, the coordinates of the highest point and the lowest point are obtained by linear fitting these center points, and then the inclination is calculated according to the coordinates of the highest point and the lowest point. The embodiment of the present disclosure is based on laser point cloud data, and the inclination of the wind turbine tower can be non-contactly calculated, and the result is high in precision and fast in processing efficiency; a single-station three-dimensional laser scanner or a multi-station three-dimensional laser scanner can be used to measure point cloud data, reducing manual scanning and point cloud splicing work, greatly reducing workload, and saving labor costs.

[0069] In some embodiments, if the point cloud data obtained by collecting the target area includes point cloud data obtained by scanning the target area in multiple directions, then the above-mentioned extraction of the wind turbine tower point cloud data in the point cloud data can be specifically implemented by the following steps:

[0070] Point cloud data obtained by scanning the target area in various directions are spliced ​​to obtain target point cloud data; and the target point cloud data are classified to obtain the wind turbine tower point cloud data.

[0071] If the point cloud data collected from the target area only includes point cloud data obtained by scanning the target area in one direction, there is no need to perform the above-mentioned point cloud stitching step, and the point cloud data can be directly classified to obtain the above-mentioned wind turbine tower point cloud data.

[0072] The above point cloud stitching is to select the same feature point in the common scanning area, calculate the rotation and translation matrix based on multiple feature points, and finally transform the point cloud into the same coordinate system according to the matrix.

[0073] The above classification of point cloud data can be performed using a trained deep learning model or manually, and the present disclosure does not limit this.

[0074] In the above classification process, it is necessary to remove noise points and fan blade point clouds, and finally extract the wind tower point cloud data.

[0075] Based on the same inventive concept, the present disclosure provides a device for determining the inclination of a wind turbine tower, and the steps performed by the components of the device are the same or similar to those of the above method, so similar parts are not repeated. Figure 3 As shown, the wind turbine tower inclination determination device of this embodiment includes:

[0076] The data processing module 310 is used to obtain point cloud data within a target area including a wind turbine tower, and extract the wind turbine tower point cloud data from the point cloud data; wherein the point cloud data includes point cloud data obtained by scanning the target area in at least one direction.

[0077] The circle fitting processing module 320 is used to layer the wind turbine tower point cloud data according to preset heights, and perform plane circle fitting on each layer of wind turbine tower point cloud data to obtain plane circles corresponding to each layer of wind turbine tower point cloud data; and determine the coordinates of the center points of each plane circle.

[0078] The straight line fitting processing module 330 is used to randomly select two center points and use the coordinates of the selected center points to perform straight line fitting processing to obtain multiple initial straight lines; and to determine the center points that match each initial straight line respectively, and use the initial straight line with the largest number of matching center points as the target straight line model, and use the center point that matches the target straight line model as the target center point.

[0079] The inclination determination module 340 is used to determine the highest point and the lowest point in the target center point, and determine the inclination of the wind turbine tower according to the coordinates of the highest point and the coordinates of the lowest point.

[0080] In some embodiments, when extracting the wind turbine tower point cloud data from the point cloud data, the data processing module 310 is specifically used to:

[0081] In a case where the point cloud data includes point cloud data obtained by scanning the target area in multiple directions, point cloud stitching is performed on the point cloud data obtained by scanning the target area in each direction to obtain target point cloud data;

[0082] The target point cloud data is classified to obtain the wind turbine tower point cloud data.

[0083] In some embodiments, the circle fitting processing module 320 determines the height of the planar circle using the following steps:

[0084] The average height of the wind turbine tower point cloud data corresponding to the plane circle is used as the height of the plane circle.

[0085] In some embodiments, when the straight line fitting processing module 330 randomly selects two center points and uses the coordinates of the selected center points to perform straight line fitting processing to obtain multiple initial straight lines, it is used to:

[0086] Randomly select two center points, and perform straight line fitting on the coordinates of the two selected center points to obtain an initial straight line;

[0087] Two center points are randomly selected multiple times, and the straight line fitting process is performed to obtain multiple initial straight lines.

[0088] In some embodiments, the straight line fitting processing module 330 is used to determine the center points that match each initial straight line respectively and use the initial straight line with the largest number of matching center points as the target straight line model:

[0089] Determine the center point that matches each initial straight line respectively;

[0090] Eliminate the initial straight lines whose number of matching center points is less than the preset value;

[0091] Among the remaining initial straight lines, the initial straight line with the largest number of matching center points is used as the target straight line model.

[0092] In some embodiments, when determining the highest point and the lowest point in the target center point, the inclination determination module 340 is used to:

[0093] Using the target center point to perform straight line fitting to obtain a target straight line segment;

[0094] According to the target straight line segment, the highest point and the lowest point among the target center points are determined.

[0095] In some embodiments, the point cloud data includes three-dimensional laser scanning data.

[0096] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device and a computer-readable storage medium.

[0097] Figure 4 A schematic block diagram of an example electronic device 400 that can be used to implement an embodiment of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0098] like Figure 4 As shown, the electronic device 400 includes a computing unit 410, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 420 or a computer program loaded from a storage unit 480 to a random access memory (RAM) 430. In the RAM 430, various programs and data required for the operation of the electronic device 400 can also be stored. The computing unit 410, the ROM 420, and the RAM 430 are connected to each other via a bus 440. An input / output (I / O) interface 450 is also connected to the bus 440.

[0099] A number of components in the electronic device 400 are connected to the I / O interface 450, including: an input unit 460, such as a keyboard, a mouse, etc.; an output unit 470, such as various types of displays, speakers, etc.; a storage unit 480, such as a disk, an optical disk, etc.; and a communication unit 490, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 490 allows the electronic device 400 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0100] The computing unit 410 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 410 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 410 performs the various methods and processes described above. For example, in some embodiments, any of the above methods may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 480. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 400 via the ROM 420 and / or the communication unit 490. When the computer program is loaded into the RAM 430 and executed by the computing unit 410, one or more steps of any of the methods described above may be performed. Alternatively, in other embodiments, the computing unit 410 may be configured to perform any of the methods described above in any other appropriate manner (e.g., by means of firmware).

[0101] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0102] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0103] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0104] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0105] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0106] A computer system may include clients and servers. Clients and servers are generally remote from each other and typically interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship to each other. The server may be a cloud server or a server of a distributed system.

[0107] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0108] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A method for determining the inclination of a wind turbine tower, characterized in that: include: Acquire point cloud data within a target area including a wind turbine tower, and extract the wind turbine tower point cloud data from the point cloud data; wherein the point cloud data includes point cloud data obtained by scanning the target area in at least one direction; The wind turbine tower point cloud data is layered according to a preset height, and plane circle fitting is performed on each layer of wind turbine tower point cloud data to obtain plane circles corresponding to each layer of wind turbine tower point cloud data; and the coordinates of the center points of each plane circle are determined; Randomly select two center points, and use the coordinates of the selected center points to perform straight line fitting processing to obtain multiple initial straight lines; and respectively determine the center points that match each initial straight line, and use the initial straight line with the largest number of matching center points as the target straight line model, and use the center point that matches the target straight line model as the target center point; Determine the highest point and the lowest point of the target center point, and determine the inclination of the wind turbine tower according to the coordinates of the highest point and the coordinates of the lowest point; The method randomly selects two center points and uses the coordinates of the selected center points to perform straight line fitting processing to obtain multiple initial straight lines, including: Randomly select two center points, and perform straight line fitting on the coordinates of the two selected center points to obtain an initial straight line; Randomly selecting two center points multiple times and performing the straight line fitting process to obtain multiple initial straight lines; Wherein, extracting the wind turbine tower point cloud data from the point cloud data includes: In a case where the point cloud data includes point cloud data obtained by scanning the target area in multiple directions, point cloud stitching is performed on the point cloud data obtained by scanning the target area in each direction to obtain target point cloud data; The target point cloud data is classified to obtain the wind turbine tower point cloud data.

2. The method according to claim 1, characterized in that The height of the plane circle is determined according to the following steps: The average height of the wind turbine tower point cloud data corresponding to the plane circle is used as the height of the plane circle.

3. The method according to claim 1, characterized in that The method of respectively determining the center points that match the initial straight lines and taking the initial straight line with the largest number of matching center points as the target straight line model includes: Determine the center point that matches each initial straight line respectively; Eliminate the initial straight lines whose number of matching center points is less than the preset value; Among the remaining initial straight lines, the initial straight line with the largest number of matching center points is used as the target straight line model.

4. The method according to claim 1, characterized in that: Determining the highest point and the lowest point among the target center points includes: Using the target center point to perform straight line fitting to obtain a target straight line segment; According to the target straight line segment, the highest point and the lowest point among the target center points are determined.

5. The method according to claim 1, characterized in that The point cloud data includes three-dimensional laser scanning data.

6. A device for determining the inclination of a wind turbine tower, characterized in that: include: A data processing module, used to obtain point cloud data within a target area including a wind turbine tower, and extract the wind turbine tower point cloud data from the point cloud data; wherein the point cloud data includes point cloud data obtained by scanning the target area in at least one direction; A circle fitting processing module is used to layer the wind turbine tower point cloud data according to a preset height, and perform plane circle fitting on each layer of wind turbine tower point cloud data to obtain a plane circle corresponding to each layer of wind turbine tower point cloud data; and determine the coordinates of the center point of each plane circle; A straight line fitting processing module is used to randomly select two center points and perform straight line fitting processing using the coordinates of the selected center points to obtain multiple initial straight lines; and to respectively determine the center points that match each initial straight line, and use the initial straight line with the largest number of matching center points as the target straight line model, and use the center point that matches the target straight line model as the target center point; An inclination determination module, used to determine the highest point and the lowest point in the target center point, and determine the inclination of the wind turbine tower according to the coordinates of the highest point and the coordinates of the lowest point; The straight line fitting processing module randomly selects two center points and uses the coordinates of the selected center points to perform straight line fitting processing to obtain multiple initial straight lines, which is used to: Randomly select two center points, and perform straight line fitting on the coordinates of the two selected center points to obtain an initial straight line; Randomly selecting two center points multiple times and performing the straight line fitting process to obtain multiple initial straight lines; Wherein, when extracting the wind turbine tower point cloud data from the point cloud data, the data processing module is used to: In a case where the point cloud data includes point cloud data obtained by scanning the target area in multiple directions, point cloud stitching is performed on the point cloud data obtained by scanning the target area in each direction to obtain target point cloud data; The target point cloud data is classified to obtain the wind turbine tower point cloud data.

7. An electronic device comprising a memory, a processor and a computer program stored in the memory, wherein the processor implements the method according to any one of claims 1 to 5 when executing the computer program.

8. A computer-readable storage medium, wherein a computer program is stored in the computer-readable storage medium, and when the computer program is executed by a processor, the method according to any one of claims 1 to 5 is implemented.

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