A radar-based heliostat correction method, apparatus, device and medium

Abstract

This invention provides a radar-based method, apparatus, device, and medium for correcting the polarization of heliostats, relating to the field of solar energy applications. The method includes: determining a target radar among at least one radars based on information about the heliostat to be calibrated; acquiring first point cloud data of the heliostat to be calibrated through the target radar; setting a point at a preset location in the first point cloud data as a target point, calculating the normal to the target point to obtain first normal coordinates in the target radar coordinate system; transforming the first normal coordinates to the heat absorber tower coordinate system to obtain second normal coordinates; and correcting the heliostat to be calibrated based on the second normal coordinates. This invention utilizes radar to simultaneously and in parallel correct multiple heliostats, achieving rapid correction; the radar does not need to illuminate the heat absorber tower, thus avoiding damage; the radar is less affected by weather and other objective factors, enabling timely and accurate acquisition of heliostat information.

Description

Technical Field

[0001] This invention relates to the field of solar energy applications, and in particular to a radar-based heliostat correction method, apparatus, device, and medium. Background Technology

[0002] The world is currently facing extremely serious energy and environmental problems, and solar thermal power generation is one of the key technologies for solving these problems. Tower molten salt solar thermal power generation is one of the technical routes for solar thermal power generation, which can generally be divided into three systems: a concentrating solar collector system, a heat storage and exchange system, and a conventional power generation system. The concentrating solar collector system consists of a concentrating system and a collecting system. The cost of the concentrator (heliostat) accounts for 45% to 70% of the initial investment, and the average annual efficiency of the concentrating field is generally 58% to 72%. Therefore, research on the concentrating process has a significant impact on system efficiency and cost. The tracking accuracy of the heliostat is affected by various errors in its manufacturing, installation, and operation, thus requiring other methods to improve tracking accuracy and correct deviations.

[0003] Existing methods for heliostat correction typically fall into two categories: 1. Placing an optical target on the absorber tower, with the reflected light spot from a single heliostat landing on the target. An optical camera positioned at a fixed location within the mirror field captures the reflected light spot, and image recognition technology is used to measure the spot, determining whether the heliostat is tracking accurately and providing the required correction amount for each heliostat. 2. Placing multiple optical cameras on the absorber tower, with the reflected light spot from a single heliostat landing on the camera. The camera captures the reflected light spot, and image recognition technology is used to determine whether the heliostat is tracking accurately and provide the required correction amount for each heliostat.

[0004] Both existing methods can achieve heliostat tracking and correction, but each has its own problems. Method 1 is the most commonly used correction method, but because it can only capture the light spot of one heliostat on one target at a time (usually, a heat absorber tower can only set up four targets in the east, west, south, and north directions), its correction speed is very slow. Method 2, because the cameras are set on the heat absorber tower, usually eight cameras are set up, each camera covering a 45-degree fan-shaped area of ​​the heliostat field. It can also divide the image captured by each camera into regions, and can measure 8 to 72 heliostats in parallel and simultaneously. However, its image method is more complex, and it usually needs to be combined with Method 1 for initial algorithm correction. The most dangerous thing is that this method requires the heliostat to directly illuminate the camera located on the heat absorber tower. Although the direct illumination time is very short and the heat absorber tower may be coated with solar reflective paint in the directly illuminated area, the heliostat light spot will continuously illuminate the heat absorber tower during the correction time, which may cause permanent thermal damage to the heat absorber tower. Summary of the Invention

[0005] This invention provides a radar-based heliostat correction method, apparatus, device, and medium.

[0006] In a first aspect, the present invention provides a radar-based heliostat correction method, comprising: determining a target radar among at least one radar based on information of the heliostat to be calibrated; acquiring first point cloud data of the heliostat to be calibrated through the target radar, wherein the coordinates of each point in the first point cloud data are in the coordinate system of the target radar; setting a point at a preset position in the first point cloud data as a target point, calculating the normal of the target point to obtain a first normal coordinate in the coordinate system of the target radar; transforming the first normal coordinate to the coordinate system of the heat absorber to obtain a second normal coordinate, and correcting the heliostat to be calibrated based on the second normal coordinate.

[0007] Furthermore, the information of the heliostat to be calibrated includes the position of the heliostat to be calibrated and the coordinates of the original normal of the heliostat to be calibrated, wherein the coordinates of the original normal are in the coordinate system of the heat-absorbing tower.

[0008] Furthermore, the method also includes: determining at least one auxiliary radar based on the heliostat information to be calibrated.

[0009] Furthermore, the step of acquiring point cloud data of the heliostat to be calibrated through the target radar includes: acquiring at least one set of corresponding point cloud data through the target radar and at least one auxiliary radar, fusing and registering the at least one set of point cloud data to obtain the first point cloud data.

[0010] Furthermore, at least one set of point cloud data is fused and registered, including: inputting the point data in each set of point cloud data into a deep learning network for classification to obtain at least one set of point cloud data corresponding to the heliostat to be calibrated, and fusing and registering the at least one set of point cloud data corresponding to the heliostat to be calibrated.

[0011] Further, the step of setting a point at a preset location in the first point cloud data as a target point, calculating the normal of the target point, and obtaining the first normal coordinates in the target radar coordinate system includes: setting a point at a preset location in the first point cloud data as a target point, calculating the normal of the target point using a point cloud normal vector estimation method based on PCA, and obtaining the first normal coordinates in the target radar coordinate system.

[0012] Further, the first normal coordinates are transformed into the heat-absorbing tower coordinate system to obtain the second normal coordinates. The heliostat is then corrected based on the second normal coordinates, including: transforming the first normal coordinates into the heat-absorbing tower coordinate system according to a preset transformation matrix to obtain the second normal coordinates; comparing the second normal coordinates with the original normal coordinates to determine the deviation value and whether the deviation value is within a preset range; if the deviation value is within the preset range, the heliostat to be calibrated does not need to be corrected; if the deviation value is not within the preset range, the azimuth and / or elevation angle of the heliostat to be calibrated needs to be corrected.

[0013] Secondly, the present invention also provides a radar-based heliostat correction device, comprising: a first processing module for determining a target radar among at least one radar based on the information of the heliostat to be calibrated; a second processing module for acquiring first point cloud data of the heliostat to be calibrated through the target radar, wherein the coordinates of each point in the first point cloud data are in the coordinate system of the target radar; a third processing module for setting a point at a preset position in the first point cloud data as a target point, calculating the normal of the target point, and obtaining a first normal coordinate in the coordinate system of the target radar; and a fourth processing module for converting the first normal coordinate to the coordinate system of the heat absorber tower to obtain a second normal coordinate, and correcting the heliostat to be calibrated based on the second normal coordinate.

[0014] Thirdly, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of any of the radar-based heliostat correction methods described above.

[0015] Fourthly, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the radar-based heliostat correction method as described above.

[0016] Fifthly, the present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of any of the radar-based heliostat correction methods described above.

[0017] The present invention provides a radar-based heliostat correction method, device, equipment, and medium, which have the following technical advantages:

[0018] 1. Radar can be used to simultaneously correct the deviation of thousands or tens of thousands of heliostats across the entire field, with a fast correction speed;

[0019] 2. The radar does not need to illuminate the heat absorption tower, and will not cause damage to the heat absorption tower;

[0020] 3. Radar is less affected by objective factors such as weather, and can obtain heliostat information in a timely and accurate manner. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0022] Figure 1 This is a schematic diagram of an application scenario for heliostat operation;

[0023] Figure 2 This is a flowchart illustrating some embodiments of the radar-based heliostat correction method provided by the present invention;

[0024] Figure 3 This is a schematic diagram of an application scenario for coordinate transformation between radar and heat absorption tower;

[0025] Figure 4 This is a schematic diagram of an application scenario of the radar-based heliostat correction method provided by the present invention;

[0026] Figure 5 This is a schematic diagram of some embodiments of the radar-based heliostat correction device provided by the present invention. Detailed Implementation

[0027] A heliostat, also known as a star-fixing mirror, is an optical device that reflects light from the sun or other celestial bodies in a fixed direction. For example... Figure 1 As shown, multiple heliostats 2 are set up on the site, and a heat-absorbing tower 4 is located at the center of the site. Each heliostat reflects sunlight or other celestial body light 1 onto a heat collector 3 on the heat-absorbing tower. The heat from the collected light drives a generator to rotate, thus performing photoelectric conversion. The heliostats 2 can be connected to a control device 5 to adjust their azimuth and angle.

[0028] Heliostats come in various shapes, and their mirrors can be flat (e.g., ...). Figure 1 (As shown) can also be a curved surface.

[0029] Multiple heliostats form a field of light. Each heliostat in the field has a different orientation, which is generally expressed by azimuth-elevation angle.

[0030] Point cloud data refers to a set of vectors in a three-dimensional coordinate system. Scanned data is recorded in the form of points, each containing three-dimensional coordinates, and some may contain color information (RGB) or reflectance information.

[0031] The Relationship Between Point Clouds and 3D Images: 3D images are a special form of information representation, characterized by expressing three dimensions of space. These dimensions include: depth maps (using grayscale to represent the distance between an object and the camera), geometric models (created by CAD software), and point cloud models (all reverse engineering equipment samples objects into point clouds). Compared to 2D images, 3D images, by utilizing this third dimension, can achieve a natural decoupling of the object and background. Point cloud data is the most common and fundamental 3D model. Point cloud models are often obtained directly from measurements, with each point corresponding to a measurement point, without any other processing, thus containing the largest amount of information. This information, hidden within the point cloud, needs to be extracted using other methods; the process of extracting information from the point cloud is 3D image processing.

[0032] The concept of point cloud: A point cloud is a massive collection of points that express the spatial distribution and surface characteristics of a target under the same spatial reference frame. After obtaining the spatial coordinates of each sampling point on the surface of an object, what is obtained is a collection of points, called a "point cloud".

[0033] The content of the point cloud: The point cloud obtained according to the laser measurement principle includes three-dimensional coordinates (XYZ) and laser reflection intensity. The intensity information is related to the surface material, roughness, incident angle direction of the target, as well as the emission energy of the instrument and the laser wavelength.

[0034] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0035] It should also be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings. Unless otherwise specified, the embodiments and features described herein can be combined with each other.

[0036] It should be noted that the concepts of "first" and "second" mentioned in this invention are only used to distinguish different devices, modules or units, and are not used to limit the order of functions performed by these devices, modules or units or their interdependencies.

[0037] It should be noted that the terms "a" and "a plurality of" used in this invention are illustrative rather than restrictive. Those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0038] The names of the messages or information exchanged between the multiple devices in the embodiments of the present invention are for illustrative purposes only and are not intended to limit the scope of these messages or information.

[0039] The present invention will now be described in detail with reference to the accompanying drawings and embodiments. Figure 2 :

[0040] Step 1: Determine the target radar among at least one radar based on the information from the heliostat to be calibrated. Radar uses radio waves to detect targets and determine their spatial positions. Radar is an electronic device that uses electromagnetic waves to detect targets. Radar emits electromagnetic waves to illuminate the target and receives its echo, thereby obtaining information such as the distance from the target to the electromagnetic wave emission point, the rate of change of distance (radial velocity), azimuth, and altitude. According to radar frequency bands, they can be divided into over-the-horizon radar, microwave radar, millimeter-wave radar, and lidar, etc. This invention does not limit the type of radar; a suitable radar can be selected according to specific needs. Light in the mirror field often affects the image acquired by the camera. Therefore, images acquired by the camera need to be repaired and processed using algorithms such as illumination compensation, which leads to some degree of image distortion, resulting in inaccurate radar positioning and correction. Radar, on the other hand, illuminates the target by emitting electromagnetic waves and receives its echo. Electromagnetic waves can be easily distinguished from light, therefore, radar data is more accurate and practical than that of a camera.

[0041] In some embodiments, the heliostat information to be calibrated includes the position of the heliostat and the coordinates of its original normal, wherein the coordinates of the original normal are in the receiver tower coordinate system. For example, after receiving the heliostat information, a suitable radar is selected as the target radar based on the position (Xt, Yt, Zt) of the heliostat. As an example, a radar-heliostat coordinate lookup table can be prepared in advance, which records the radar coordinates and the corresponding heliostat coordinates to ensure that the target radar can scan the heliostat relatively completely. The heliostat information to be calibrated can be entered by the operator or selected by the operator according to the options displayed on the page, or it can be automatically generated by the program.

[0042] In some embodiments, at least one radar can be located at the outer edge of the mirror field, or it can be located within the mirror field depending on the radar's detection range and density. For example, the lidar can be fixedly arranged on the ground or on a short pillar on the ground. One or a group of lidars can correspond to one or a group of heliostats. The radar can scan the angle of each heliostat at a preset frequency to ensure that more accurate and complete point cloud data is obtained.

[0043] Step 2: Acquire the first point cloud data of the heliostat to be calibrated using the target radar. The coordinates of each point in the first point cloud data are in the target radar coordinate system.

[0044] As an example, the target radar is a lidar. After the target radar emits multiple laser points to the heliostat to be calibrated, the reflected laser points are the first point cloud data. Each point in the first point cloud data contains three-dimensional coordinate (XYZ) information in the target radar coordinate system.

[0045] Considering that the point cloud data obtained by the target radar alone may be incomplete due to factors such as obstructions, in some embodiments, at least one auxiliary radar can be determined based on the information of the heliostat to be calibrated, and at least one set of corresponding point cloud data can be obtained through the target radar and at least one auxiliary radar. The at least one set of point cloud data is then fused and registered to obtain the first point cloud data.

[0046] Since the acquired point cloud data may carry information about other heliostats, land, or obstructions, fusing and registering at least one set of point cloud data includes: inputting the point data from each set of point cloud data into a deep learning network for classification to obtain at least one set of point cloud data corresponding to the heliostat to be calibrated; and fusing and registering the at least one set of point cloud data corresponding to the heliostat to be calibrated. This removes information about other heliostats, land, or obstructions from each set of point cloud data, leaving only the point cloud data of the heliostat to be calibrated, which helps to speed up the fusion and registration process and makes the results more accurate.

[0047] As an example, each heliostat coordinate in the aforementioned radar-heliostat coordinate lookup table can correspond to multiple radar coordinates. These radar coordinates include target radar coordinates and auxiliary radar coordinates. That is, each heliostat is scanned by the target radar and multiple auxiliary radars, resulting in multiple sets of point cloud data. The multiple sets of point cloud data from the auxiliary radars supplement and improve the point cloud data scanned by the target radar from different angles. The point cloud data of the heliostat to be tested obtained through the auxiliary radars also needs to undergo preprocessing operations such as point cloud classification to obtain the actual point cloud data of the heliostat to be tested. The process of fusing and registering at least one set of point cloud data requires fusing and registering based on the target radar coordinates and / or the point cloud data obtained through the target radar.

[0048] As an example, the steps for fusing and registering the point cloud data of the heliostat to be tested obtained by the target radar and multiple sets of point cloud data of the heliostat to be tested obtained by at least one auxiliary radar include:

[0049] Step 2.1: Use the Super4pcs algorithm to register at least one set of point cloud data to obtain the registered point cloud data of the heliostat to be tested. The registered point cloud data is in the target radar coordinate system.

[0050] Step 2.2: Identify overlapping areas using Euclidean distance, then perform weighted fusion of the overlapping areas and the complete point cloud map. A weight of 0.5 can be selected for each area. For non-overlapping areas, segmentation is performed using clustering to filter out the required point cloud data. This data is then added to the final complete point cloud data of the heliostat to be calibrated, resulting in the first point cloud data.

[0051] Step 3: Set the point at the preset position in the first point cloud data as the target point, calculate the normal of the target point, and obtain the first normal coordinates in the target radar coordinate system.

[0052] If the mirror of a heliostat is flat, then any point on the mirror can be the target point; if the mirror of a heliostat is curved, then the center point of the mirror is the target point.

[0053] The heliostat's mirror surface can be curved or flat, and the preset position can be the center of the mirror surface. This means the target point can be the center of the heliostat's mirror surface. Because the center point of the heliostat's mirror surface is relatively fixed relative to the radar's position, the coordinates of the center point of each heliostat's mirror surface in each radar coordinate system can be recorded in the database. The heliostat information to be calibrated can include the coordinates of the center point of the heliostat's mirror surface corresponding to the radar.

[0054] If the mirror of a heliostat is flat, a deep learning network can be used to classify the point cloud data of the mirror, and then any point in the point cloud data can be selected as the target point.

[0055] In some embodiments, the mirror surface of a heliostat can be flat or curved. Therefore, in order to determine the mirror normal more quickly and accurately, different normal determination methods can be selected for different heliostats.

[0056] In some embodiments, a point at a preset location in the first point cloud data can be set as a target point, and the normal of the target point can be calculated using a point cloud normal vector estimation method based on PCA to obtain the first normal coordinates in the target radar coordinate system.

[0057] PCA-based point cloud normal vector estimation is actually derived from the least squares method. Suppose we want to estimate the normal vector of a point (target point), we need to estimate a plane using the point's nearest neighbors, and then calculate the normal vector of that point. That is, we minimize an objective function (with the normal vector as the parameter) such that the dot product of the vector formed by the point and each of its nearest neighbors and the normal vector is 0 (i.e., perpendicular). As an example, the obtained normal coordinates (i.e., the first normal coordinates) in the target radar coordinate system can be expressed as: (x... 1l y 1l , z 1l )-(x 2l y 2l , z2l ), where (x 1l y 1l , z 1l ) and (x 2l y 2l , z 2l ) represents the two coordinates of the normal in the target radar coordinate system.

[0058] Step 4: Transform the first normal coordinates to the heat-absorbing tower coordinate system to obtain the second normal coordinates, and correct the heliostat according to the second normal coordinates.

[0059] In some embodiments, step 4 can be implemented through the following steps:

[0060] Step 4.1: Transform the first normal coordinates to the heat absorption tower coordinate system according to the preset transformation matrix to obtain the second normal coordinates.

[0061] Since the relative positions of the target radar, the heliostat to be calibrated, and the heat absorber are constant, and the x, y, and z axes of their coordinate systems are parallel, the transformation relationship between the target radar coordinate system and the heat absorber coordinate system is as follows:

[0062] (x 0t y0 t z0 t )+(x 1l y 1l , z 1l )=(x 1t y 1t , z 1t )

[0063] (x 0t y0 t z0 t )+(x 2l y 2l , z 2l )=(x 2t y 2t , z 2t )

[0064] Among them, (x 0t y0 t z0 t This represents the target radar's coordinates in the heat absorber tower coordinate system (the coordinates of each radar in the heat absorber tower coordinate system can be stored in the database; this is known). 1l y 1l , z 1l ) and ((x 2l y 2l , z 2l (x) represents the two coordinates of the normal in the target radar coordinate system (obtained from step 3), (x)1t y 1t , z 1t ) and (x 2t y 2t , z 2t ) represents two coordinates of the point cloud data in the coordinate system of the heat absorption tower.

[0065] Based on the above formula, the two coordinates of the normal in the target radar coordinate system are transformed to the heat absorption tower coordinate system to obtain the coordinates of the second normal.

[0066] like Figure 3 As shown, point A is the radar. A radar coordinate system is established with point A as the origin. The coordinate axes of the radar coordinate system are parallel to the coordinate axes of the heat absorber tower coordinate system. The coordinates of the radar in the heat absorber tower coordinate system are (5, 0, 0), and the coordinates of point cloud data B in the radar coordinate system are (1, 2, 0). Therefore, the coordinates of point cloud data B in the heat absorber tower coordinate system are (5, 0, 0) + (1, 2, 0) = (6, 2, 0). Other point cloud data in the radar coordinate system (x... l y l , z l The coordinates of the heat exchanger in the heat exchanger tower coordinate system are (5+x) l y l , z l ).

[0067] Step 4.2: Compare the coordinates of the second normal with the coordinates of the original normal, determine the deviation value, and judge whether the deviation value is within the preset range.

[0068] Step 4.3: If the deviation value is within the preset range, the heliostat to be calibrated does not need to be corrected; if the deviation value is not within the preset range, the azimuth and / or elevation angle of the heliostat to be calibrated needs to be corrected.

[0069] Step 4.2 further includes:

[0070] Step 4.2.1: Determine the current azimuth and current elevation angle of the heliostat to be calibrated based on the second normal coordinates.

[0071] Step 4.2.2: Determine the original azimuth and original elevation angle of the heliostat to be calibrated based on the coordinates of the original normal.

[0072] Step 4.2.3: Determine the azimuth deviation value based on the current azimuth and the original azimuth.

[0073] Step 4.2.4: Determine the pitch angle deviation value based on the current pitch angle and the original pitch angle.

[0074] Step 4.2.5: Determine whether the azimuth deviation value and the pitch angle deviation value are within the preset range.

[0075] like Figure 4 The diagram illustrates a flowchart of an embodiment of radar-based heliostat correction. A lidar unit is fixed to the ground or mounted on a short pillar. One or a group of lidar units can scan the angle of each heliostat at a high frequency, feeding this information back to the heliostat control system. This angle is compared with the ideal angle calculated for each heliostat. If the angle is within the error range, the heliostat is tracking accurately; if it exceeds the error range, the heliostat is tracking incorrectly and correction is needed. By calculating the angle given by the lidar point cloud and the ideal heliostat angle calculated by the control system, the initial compensation amount required for heliostat correction can be determined to achieve the correction effect. The specific correction steps are as follows:

[0076] (1) Fix the lidar on the ground or on a short column on the ground;

[0077] (2) Use lidar to scan one heliostat;

[0078] (3) By scanning point cloud data with lidar, the 3D structure of the scanned heliostat is restored, and the azimuth / elevation angle of the scanned heliostat is calculated.

[0079] (4) Compare the heliostat azimuth / elevation angle calculated in (3) with the ideal angle of the corresponding heliostat at the same time calculated by the heliostat control system to obtain the angle difference.

[0080] (5) Compare the angle difference obtained in (4) with the allowable error value of the heliostat tracking. If it is within the error range, it means that the heliostat tracking is accurate; if it exceeds the error range, it means that the heliostat tracking has an error and needs to be corrected.

[0081] (6) If the conclusion obtained in (5) is that correction is needed, then the angle difference obtained in step 5 is fed back to the given heliostat control system to obtain the initial compensation amount required for the correction of the heliostat, and then the next round of correction process is entered; if the conclusion obtained in (5) is that correction is not needed, then the current round of correction process ends and the next round of correction is entered.

[0082] Please see Figure 5 , Figure 5 These are schematic diagrams illustrating some embodiments of a radar-based heliostat correction device provided by the present invention. As an implementation of the methods shown in the above figures, the present invention also provides some embodiments of a radar-based heliostat correction device. These device embodiments are similar to... Figure 1 The embodiments of some of the methods shown correspond to this, and the device can be applied to a variety of electronic devices.

[0083] like Figure 5As shown, some embodiments of the radar-based heliostat correction device 500 include a first processing module 501, a second processing module 502, a third processing module 503, and a fourth processing module 504: the first processing module is used to determine a target radar among at least one radar based on the information of the heliostat to be calibrated; the second processing module is used to acquire first point cloud data of the heliostat to be calibrated through the target radar, wherein the coordinates of each point in the first point cloud data are in the coordinate system of the target radar; the third processing module is used to set a point at a preset position in the first point cloud data as a target point, calculate the normal of the target point, and obtain a first normal coordinate in the coordinate system of the target radar; the fourth processing module is used to transform the first normal coordinate to the coordinate system of the heat absorber tower to obtain a second normal coordinate, and correct the heliostat to be calibrated based on the second normal coordinate.

[0084] Furthermore, the information of the heliostat to be calibrated includes the position of the heliostat to be calibrated and the coordinates of the original normal of the heliostat to be calibrated, wherein the coordinates of the original normal are in the coordinate system of the heat-absorbing tower, and the multiple radars corresponding to the heliostat to be calibrated include target radar and / or auxiliary radar.

[0085] Furthermore, the device also includes a fifth processing module for: determining at least one auxiliary radar based on the heliostat information to be calibrated.

[0086] Furthermore, the second processing module is also used to: acquire at least one set of corresponding point cloud data through the target radar and at least one auxiliary radar, and fuse and register the at least one set of point cloud data to obtain the first point cloud data.

[0087] Furthermore, the second processing module is also used to: input the point data in each group of point cloud data into a deep learning network for classification to obtain at least one group of point cloud data corresponding to the heliostat to be calibrated, and fuse and register the at least one group of point cloud data corresponding to the heliostat to be calibrated.

[0088] Furthermore, the third processing module is also used to: set a point at a preset location in the first point cloud data as a target point, calculate the normal of the target point based on the PCA-based point cloud normal vector estimation method, and obtain the first normal coordinates in the target radar coordinate system.

[0089] Furthermore, the fourth processing module is also used to: transform the first normal coordinates to the heat-absorbing tower coordinate system according to a preset transformation matrix to obtain the second normal coordinates; compare the second normal coordinates with the original normal coordinates to determine the deviation value and determine whether the deviation value is within a preset range; if the deviation value is within the preset range, the heliostat to be calibrated does not need to be corrected; if the deviation value is not within the preset range, the azimuth and / or elevation angle of the heliostat to be calibrated needs to be corrected.

[0090] It is understandable that the modules described in the device 400 are similar to those in the reference. Figure 1 The steps in the described method correspond to each other. Therefore, the operations, features, and beneficial effects described above for the method also apply to the device 400 and the modules and units contained therein, and will not be repeated here.

[0091] The present invention also provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions, and when the program instructions are executed by a computer, the computer is able to execute the radar-based heliostat correction method provided by the above methods.

[0092] In another aspect, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to perform the radar-based heliostat correction methods provided above.

[0093] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0094] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0095] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A radar-based heliostat correction method, characterized in that, include: The target radar is determined in at least one radar based on the information of the heliostat to be calibrated; the information of the heliostat to be calibrated includes the position of the heliostat to be calibrated and the coordinates of the original normal of the heliostat to be calibrated, the coordinates of the original normal are in the coordinate system of the heat absorber tower. The first point cloud data of the heliostat to be calibrated is acquired by the target radar, and the coordinates of each point in the first point cloud data are in the target radar coordinate system. The points at preset locations in the first point cloud data are set as target points, and the normals of the target points are calculated using the point cloud normal vector estimation method based on PCA, so as to obtain the first normal coordinates in the target radar coordinate system. The first normal coordinates are transformed into the heat tower coordinate system according to the preset transformation matrix to obtain the second normal coordinates; the second normal coordinates are compared with the original normal coordinates to determine the deviation value and determine whether the deviation value is within the preset range; if the deviation value is within the preset range, the heliostat to be calibrated does not need to be corrected; if the deviation value is not within the preset range, the azimuth and / or elevation angle of the heliostat to be calibrated needs to be corrected.

2. The radar-based heliostat correction method according to claim 1, characterized in that, The method further includes: At least one auxiliary radar is determined based on the information from the heliostat to be calibrated.

3. The radar-based heliostat correction method according to claim 2, characterized in that, The acquisition of the first point cloud data of the heliostat to be calibrated via the target radar includes: At least one set of point cloud data is acquired by the target radar and at least one auxiliary radar. The at least one set of point cloud data is then fused and registered to obtain the first point cloud data.

4. The radar-based heliostat correction method according to claim 3, characterized in that, The process of fusing and registering at least one set of point cloud data includes: The point data in each set of point cloud data is input into a deep learning network for classification to obtain at least one set of point cloud data corresponding to the heliostat to be calibrated. The at least one set of point cloud data corresponding to the heliostat to be calibrated is then fused and registered.

5. A radar-based heliostat correction device, characterized in that, include: The first processing module is used to determine the target radar in at least one radar based on the information of the heliostat to be calibrated. The information of the heliostat to be calibrated includes the position of the heliostat to be calibrated and the coordinates of the original normal of the heliostat to be calibrated. The coordinates of the original normal are in the coordinate system of the heat-absorbing tower. The second processing module is used to acquire the first point cloud data of the heliostat to be calibrated through the target radar, wherein the coordinates of each point in the first point cloud data are in the target radar coordinate system. The third processing module is used to set the points at preset positions in the first point cloud data as target points, calculate the normal of the target points based on the PCA point cloud normal vector estimation method, and obtain the first normal coordinates in the target radar coordinate system. The fourth processing module is used to transform the first normal coordinates to the heat tower coordinate system according to the preset transformation matrix to obtain the second normal coordinates, compare the second normal coordinates with the original normal coordinates, determine the deviation value, and determine whether the deviation value is within the preset range. If the deviation value is within the preset range, the heliostat to be calibrated does not need to be corrected. If the deviation value is not within the preset range, the azimuth and / or elevation angle of the heliostat to be calibrated needs to be corrected.

6. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the radar-based heliostat correction method as described in any one of claims 1 to 4.

7. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the radar-based heliostat correction method as described in any one of claims 1 to 4.

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