CN116148800A - Heliostat deviation rectifying method, device, equipment and medium based on radar - Google Patents
Heliostat deviation rectifying method, device, equipment and medium based on radar Download PDFInfo
- Publication number
- CN116148800A CN116148800A CN202310267212.5A CN202310267212A CN116148800A CN 116148800 A CN116148800 A CN 116148800A CN 202310267212 A CN202310267212 A CN 202310267212A CN 116148800 A CN116148800 A CN 116148800A
- Authority
- CN
- China
- Prior art keywords
- radar
- heliostat
- normal
- target
- point
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
- G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D3/00—Control of position or direction
- G05D3/12—Control of position or direction using feedback
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Computer Networks & Wireless Communication (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Automation & Control Theory (AREA)
- Radar Systems Or Details Thereof (AREA)
Abstract
The invention provides a radar-based heliostat correction method, device, equipment and medium, and relates to the field of solar energy application, wherein the method comprises the following steps: determining a target radar in at least one radar according to heliostat information to be calibrated; acquiring first point cloud data of a heliostat to be calibrated through a target radar; setting a point at a preset position in the first point cloud data as a target point, and calculating the normal of the target point to obtain a first normal coordinate under a target radar coordinate system; and converting the first normal coordinate into a coordinate system of the heat absorption tower to obtain a second normal coordinate, and correcting the heliostat to be calibrated according to the second normal coordinate. The invention can simultaneously rectify the deviation of a plurality of heliostats in parallel by utilizing the radar, and has high rectification speed; the radar does not need to irradiate the heat absorption tower, and the heat absorption tower is not damaged; the radar is less affected by weather and other objections, and heliostat information can be timely and accurately obtained.
Description
Technical Field
The invention relates to the field of solar energy application, in particular to a heliostat correction method, device, equipment and medium based on radar.
Background
Currently, the world faces extremely serious energy and environmental problems, and solar thermal power generation is one of the key technologies for solving the problems. Tower-type molten salt photo-thermal power generation is one of the technical routes of solar thermal power generation, and can be generally divided into three systems, including a condensation heat collection system, a heat storage and exchange system and a conventional power generation system. The condensing and heat collecting system consists of a condensing system and a heat collecting system, wherein the cost of a condenser (heliostat) accounts for 45-70% of one-time investment, and the annual average efficiency of a condensing field is generally 58-72%, so that the research of the condensing process has great influence on the system efficiency and the cost. The tracking accuracy of heliostats is affected by various errors in the process of manufacturing, installing and operating heliostats, and thus other correction methods for improving the tracking accuracy are required.
The existing heliostat correction method generally has two modes, namely, an optical target is arranged on a heat absorption tower, a single-tower heliostat reflection light spot is arranged on the optical target, the reflection light spot of the heliostat is shot through an optical camera arranged at a fixed position in a mirror field, the light spot is measured through an image recognition technology, whether the heliostat tracking is accurate or not is judged, and the correction quantity of each heliostat is given; 2. and arranging a plurality of optical cameras on the heat absorption tower, reflecting light spots of heliostats of a single tower onto the optical cameras, shooting the light spots of the heliostats by the optical cameras, judging whether the heliostats are accurately tracked by an image recognition technology, and giving the correction quantity of each heliostat.
The existing two methods can realize heliostat tracking correction, but have own problems. The first method is the most commonly used correction method, but the correction speed is very slow because only the light spot of one heliostat can be shot on one heliostat at a time (one heat absorption tower can only be arranged on four sides of the east, west and south). Because the cameras are arranged on the heat absorption tower, eight cameras are usually arranged, each camera covers a 45-degree sector area of a heliostat field, and 8-72 heliostats can be measured simultaneously in parallel in a mode of shooting pictures by each camera and dividing the pictures into areas, but the image method is complex, the initial algorithm correction is usually carried out by matching the first method, and most dangerously, the method needs that the heliostats directly irradiate the cameras positioned on the heat absorption tower, although the direct irradiation time is short and the direct irradiation areas of the heat absorption tower can be coated with sunlight reflection paint, long-term thermal damage can be caused to the heat absorption tower because the heliostat light spots are continuously irradiated to the heat absorption tower in the correction time.
Disclosure of Invention
The invention provides a heliostat correction method, device, equipment and medium based on radar.
In a first aspect, the present invention provides a radar-based heliostat rectification method, including: determining a target radar in at least one radar according to heliostat information to be calibrated; acquiring first point cloud data of a heliostat to be calibrated through a target radar, wherein the coordinates of each point in the first point cloud data are in a target radar coordinate system; setting a point at a preset position in the first point cloud data as a target point, and calculating the normal of the target point to obtain a first normal coordinate under the target radar coordinate system; and converting the first normal coordinate into a coordinate system of the heat absorption tower to obtain a second normal coordinate, and correcting the heliostat to be calibrated according to the second normal coordinate.
Further, the heliostat information to be calibrated includes the position of the heliostat to be calibrated and the coordinates of the original normal line of the heliostat to be calibrated, and the coordinates of the original normal line are in the coordinate system of the heat absorption tower.
Further, the method further comprises: at least one auxiliary radar is determined from heliostat information to be calibrated.
Further, the obtaining, by the target radar, the point cloud data of the heliostat to be calibrated includes: and acquiring at least one group of corresponding point cloud data through the target radar and at least one auxiliary radar, and fusing and registering the at least one group of point cloud data to obtain first point cloud data.
Further, fusing and registering at least one set of point cloud data, comprising: and inputting 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 heliostats to be calibrated, and fusing and registering the at least one group of point cloud data corresponding to the heliostats to be calibrated.
Further, the setting a point at a preset position in the first point cloud data as a target point, calculating a normal line of the target point, and obtaining a first normal line coordinate under the target radar coordinate system includes: and setting a point at a preset position in the first point cloud data as a target point, and calculating the normal line of the target point by a point cloud normal vector estimation method based on PCA to obtain a first normal line coordinate under the target radar coordinate system.
Further, converting the first normal coordinate to a coordinate system of an endothermic tower to obtain a second normal coordinate, correcting the heliostat according to the second normal coordinate, including: converting the first normal coordinate into a coordinate system of the heat absorption tower according to a preset conversion matrix to obtain a second normal coordinate; comparing the second normal coordinate with the original normal coordinate, determining a deviation value and judging whether the deviation value is in a preset range or not; if the deviation value is within the preset range, the heliostat to be calibrated is not corrected; if the deviation value is not within the preset range, the azimuth and/or the pitching angle of the heliostat to be calibrated need to be corrected.
In a second aspect, the present invention further provides a radar-based heliostat correction device, including: the first processing module is used for determining a target radar in at least one radar according to heliostat information to be calibrated; the second processing module is used for acquiring first point cloud data of the heliostat to be calibrated through the target radar, and the coordinates of each point in the first point cloud data are in a target radar coordinate system; the third processing module is used 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 under the target radar coordinate system; and the fourth processing module is used for converting the first normal coordinate into the coordinate system of the heat absorption tower to obtain a second normal coordinate, and correcting the heliostat to be calibrated according to the second normal coordinate.
In a third aspect, the present invention also provides an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of any one of the radar-based heliostat rectification methods described above when the program is executed.
In a fourth aspect, the present invention also provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of a radar-based heliostat rectification method as described in any of the above.
In a fifth aspect, the present invention also provides a computer program product comprising a computer program which, when executed by a processor, implements the steps of a radar-based heliostat rectification method as described in any one of the above.
The heliostat correction method, device, equipment and medium based on the radar provided by the invention have the following technical effects:
1. the radar can be utilized to simultaneously rectify thousands of heliostats/tens of thousands of heliostats in parallel in the whole field, so that the rectification speed is high;
2. the radar does not need to irradiate the heat absorption tower, and the heat absorption tower is not damaged;
3. the radar is less affected by weather and other objections, and heliostat information can be timely and accurately obtained.
Drawings
In order to more clearly illustrate the invention or the technical solutions of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of one application scenario in which heliostats operate;
FIG. 2 is a flow diagram of some embodiments of a radar-based heliostat rectification method provided in accordance with the invention;
FIG. 3 is a schematic diagram of an application scenario of radar and endothermic tower coordinate conversion;
fig. 4 is a schematic diagram of an application scenario of a radar-based heliostat rectification method according to the invention;
fig. 5 is a schematic structural view of some embodiments of a radar-based heliostat correction apparatus provided in accordance with the invention.
Detailed Description
Heliostats, also known as fixed star mirrors, can reflect rays of the sun or other celestial bodies to fixed-direction optics. As shown in fig. 1, a plurality of heliostats 2 are arranged on a field, a heat absorption tower 4 is arranged in the center of the field, each heliostat in the field reflects the light rays 1 of the sun or other celestial bodies to a heat collector 3 on the heat absorption tower, and the heat of the collected light is utilized to push a generator to rotate so as to realize photoelectric conversion. The heliostat 2 may be connected to a control device 5 for adjusting the orientation and angle of the heliostat.
The shapes of the heliostat are various, and the mirror surface of the heliostat may be a plane (as shown in fig. 1) or a curved surface.
Wherein the plurality of heliostats form a field of heliostats. The orientation of each heliostat in the field of mirrors is different, typically expressed in terms of azimuth-elevation angle.
Point cloud data refers to a set of vectors in a three-dimensional coordinate system. The scan data is recorded in the form of dots, each dot containing three-dimensional coordinates, some possibly containing color information (RGB) or reflected intensity information.
Relationship between point cloud and three-dimensional image: the three-dimensional image is a special information expression form, and is characterized by three-dimensional data in the expression space, wherein the expression form comprises: depth map (expressing object-to-camera distance in grayscale), geometric model (built by CAD software), point cloud model (all reverse engineering devices sample objects as point clouds). Compared with the two-dimensional image, the three-dimensional image can realize the decoupling of the natural object, namely the background by the information of the third dimension. Point cloud data is the most common and fundamental three-dimensional model. The point cloud model is usually obtained directly by measurement, each point corresponds to one measurement point, and other processing means are not adopted, so that the maximum information quantity is contained. The information is hidden in the point cloud and needs to be extracted by other extraction means, and the process of extracting the information in the point cloud is three-dimensional image processing.
Concept of point cloud: the Point Cloud is a massive Point set expressing the target space distribution and the target surface characteristics under the same space reference system, and after the space coordinates of each sampling Point of the object surface are obtained, the Point Cloud is obtained and is called as Point Cloud.
The content of the point cloud: the point cloud obtained according to the laser measurement principle comprises three-dimensional coordinates (XYZ) and laser reflection intensity, wherein the intensity information is related to the surface material, roughness and incident angle direction of a target, and the emission energy and the laser wavelength of the instrument.
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It should be noted that, for convenience of description, only the portions related to the present invention are shown in the drawings. Embodiments of the invention and features of the embodiments may be combined with each other without conflict.
It should be noted that the terms "first," "second," and the like herein are merely used for distinguishing between different devices, modules, or units and not for limiting the order or interdependence of the functions performed by such devices, modules, or units.
It should be noted that references to "one", "a plurality" and "a plurality" in this disclosure are intended to be illustrative rather than limiting, and those skilled in the art will appreciate that "one or more" is intended to be construed as "one or more" unless the context clearly indicates otherwise.
The names of messages or information interacted between the devices in the embodiments of the present invention are for illustrative purposes only and are not intended to limit the scope of such messages or information.
The invention will be described in detail below with reference to the accompanying drawings in combination with an embodiment, with reference to fig. 2:
and step 1, determining a target radar in at least one radar according to heliostat information to be calibrated. Radar finds objects and determines their spatial location by radio. Radar is an electronic device that detects objects with magnetic waves. The radar emits electromagnetic waves to irradiate the target and receives echoes thereof, thereby obtaining information such as the distance from the target to the electromagnetic wave emission point, the distance change rate (radial velocity), the azimuth, the altitude and the like. According to the frequency range of the radar, the radar can be divided into beyond-sight radar, microwave radar, millimeter wave radar, laser radar and the like. The invention does not limit the type of radar, and can select proper radar according to specific requirements. Light rays in the lens field often influence a camera to acquire an image, so that the image acquired by the camera needs to be repaired and processed through algorithms such as illumination compensation and the like, the image is more or less distorted, so that the radar is inaccurately positioned and rectified, the radar irradiates a target by emitting electromagnetic waves and receives echoes of the target, and the electromagnetic waves can be well distinguished from the illumination, so that compared with the camera, radar data are more accurate, and the practicability is higher.
In some embodiments, the heliostat information to be calibrated includes the position of the heliostat to be calibrated and the coordinates of the original normal to the heliostat to be calibrated, the coordinates of the original normal being in the endothermic tower coordinate system. For example, after receiving heliostat information to be calibrated, an appropriate radar is selected as a target radar according to the positions (Xt, yt, zt) of heliostats to be calibrated. As an example, a radar-heliostat coordinate comparison table may be prepared in advance, in which radar coordinates and corresponding heliostat coordinates are recorded, to ensure that the target radar can scan the heliostat relatively completely. The heliostat information to be calibrated can be input by a worker or selected by the worker according to options displayed on a page, and can also be automatically generated by a program.
In some embodiments, at least one radar may be disposed at the outer edge of the field of view, or may be disposed within the field of view, depending on the detection range of the radar and the density of the radar. For example, the lidar is fixedly arranged on the ground or on a short upright post on the ground, one or a group of lidars can correspond to one surface or a group of heliostats, and the radar can scan the angle of each surface heliostat at a preset frequency so as to ensure that more accurate and complete point cloud data are obtained.
And 2, acquiring first point cloud data of the heliostat to be calibrated through a target radar, wherein the coordinates of each point in the first point cloud data are in a target radar coordinate system.
As an example, the target radar is a laser radar, after the target radar sends out a plurality of laser points to the heliostat to be calibrated, the reflected laser points are first point cloud data, and each point in the first point cloud data contains three-dimensional coordinate (XYZ) information under a target radar coordinate system.
In consideration of that the point cloud data acquired only by the target radar may be insufficient in information due to factors such as a shielding object, in some embodiments, at least one auxiliary radar may be further determined according to heliostat information to be calibrated, at least one set of point cloud data corresponding to the target radar and the at least one auxiliary radar may be acquired, and the at least one set of point cloud data may be fused and registered to obtain the first point cloud data.
Because the acquired at least one set of point cloud data may carry information of other heliostats, land, or obstruction, fusing and registering the at least one set of point cloud data includes: and inputting 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 heliostats to be calibrated, and fusing and registering the at least one group of point cloud data corresponding to the heliostats to be calibrated. In this way, the information of other heliostats, lands or shields in each set of point cloud data is removed, only the point cloud data of the heliostats to be calibrated are obtained, the fusion and registration are facilitated to be quickened, and the fusion and registration results are more accurate.
As an example, each heliostat coordinate in the radar-heliostat coordinate comparison table may correspond to multiple radar coordinates, where the radar coordinates include target radar coordinates and auxiliary radar coordinates, that is, each heliostat may be scanned by a target radar and multiple auxiliary radars to obtain multiple sets of point cloud data, and the multiple sets of point cloud data scanned by the multiple auxiliary radars supplement and perfect the point cloud data scanned by the target radar from different angles. The point cloud data of the heliostat to be detected, which is obtained through the auxiliary radar, also need to be subjected to preprocessing operations such as point cloud classification and the like, so as to obtain the point cloud data of the heliostat to be detected. The process of fusing and registering at least one group of point cloud data needs to be fused and registered based on target radar coordinates and/or point cloud data acquired by the target radar.
As an example, the step of fusing and registering point cloud data of the heliostat to be detected obtained by the target radar and a plurality of sets of point cloud data of the heliostat to be detected obtained by the at least one auxiliary radar includes:
and 2.1, registering at least one group of point cloud data by using a Super4pcs algorithm to obtain registered point cloud data of heliostats to be detected, wherein the registered point cloud data is in a target radar coordinate system.
And 2.2, judging an overlapping area through the Euclidean distance, and then carrying out weighted fusion on the overlapping area and the complete point cloud map, wherein the weight value which can be selected is 0.5 respectively. And for the non-overlapping area, the required point cloud data is screened out by clustering, and then the point cloud data is added into the final complete point cloud data of the heliostat to be calibrated to obtain the first point cloud data.
And 3, setting a point at a preset position in the first point cloud data as a target point, and calculating the normal of the target point to obtain a first normal coordinate under the target radar coordinate system.
If the mirror surface of the heliostat is planar, any point of the mirror surface may be the target point, and if the mirror surface of the heliostat is curved, the center point of the mirror surface is the target point.
The mirror surface of the heliostat is a curved surface or a plane, the preset positions can be the positions of the mirror surface centers, namely the target points can be the points of the mirror surface centers of the heliostat, and because the center points of the mirror surfaces of the heliostat and the positions of the radars are relatively fixed, the coordinates of the center points of the mirror surfaces of the heliostat corresponding to each radar coordinate system can be recorded in a database. The heliostat information to be calibrated may include coordinates of a heliostat mirror center point corresponding to the radar.
If the mirror surface of the heliostat is a plane, the point cloud data of the mirror surface can be classified by using the deep learning network, and then one point is selected from the point cloud data of the mirror surface to serve as a target point.
In some embodiments, the mirror surface of the heliostat may be flat or curved, so that different normal determination methods may be selected for different heliostats in order to enable faster and accurate determination of the mirror surface normal.
In some embodiments, a point at a preset position in the first point cloud data may be set as a target point, and a normal line of the target point is calculated by a point cloud normal vector estimation method based on PCA, to obtain a first normal line coordinate under the target radar coordinate system.
PCA-based point cloud normal vector estimation is derived from the least squares method. Assuming that the normal vector of a certain point (target point) is to be estimated, it is necessary to estimate a plane by using the neighboring points of the point and then calculate the normal vector of the point, that is, by minimizing an objective function (the required parameter is the normal vector) such that the vector formed by the point and each neighboring point thereof is multiplied by 0 (i.e., perpendicular) to the point of the normal vector. As an example, the normal coordinates (i.e., the first normal coordinates) in the target radar coordinate system obtained can be expressed as: (x) 1l ，y 1l ，z 1l )-(x 2l ，y 2l ，z 2l ) Wherein (x) 1l ，y 1l ，z 1l ) And (x) 2l ，y 2l ，z 2l ) Representing two coordinates of the normal under the target radar coordinate system.
And 4, converting the first normal coordinate into a coordinate system of the heat absorption tower to obtain a second normal coordinate, and correcting the heliostat according to the second normal coordinate.
In some embodiments, step 4 may be implemented by:
and 4.1, converting the first normal coordinate into a coordinate system of the heat absorption tower according to a preset conversion matrix to obtain a second normal coordinate.
Because the relative positions of the target radar, the heliostat to be calibrated and the heat absorption tower are unchanged, xyz axes of the three coordinate systems are parallel to each other, and the conversion relation of the target radar coordinate system and the heat absorption tower coordinate system is as follows:
(x 0t ，y 0 t ，z 0 t )+(x 1l ，y 1l ，z 1l )＝(x 1t ，y 1t ，z 1t )
(x 0t ，y 0 t ，z 0 t )+(x 2l ，y 2l ，z 2l )＝(x 2t ，y 2t ，z 2t )
wherein, (x) 0t ，y 0 t ，z 0 t ) Representing coordinates of the target radar in the endothermic tower coordinate system (coordinates of each radar in the endothermic tower coordinate system, which are known, may be stored in a database) (x 1l ，y 1l ，z 1l ) And ((x) 2l ，y 2l ，z 2l ) Two coordinates (obtained in step 3) representing the normal line in the target radar coordinate system, (x) 1t ，y 1t ，z 1t ) And (x) 2t ，y 2t ，z 2t ) Two coordinates of the point cloud data in the coordinate system of the heat absorption tower are represented.
And respectively converting two coordinates of the normal under the target radar coordinate system into the coordinate system of the heat absorption tower according to the formula to obtain a second normal coordinate.
As shown in fig. 3, the point a is a radar, a radar coordinate system is established with the point a as an origin, the coordinate axis of the radar coordinate system is parallel to the coordinate axis of the heat absorption tower coordinate system, the coordinate of the radar in the heat absorption tower coordinate system is (5,0,0), the coordinate of the point cloud data B in the radar coordinate system is (1,2,0), and the coordinate of the point cloud data B in the heat absorption tower coordinate system is (5,0,0) + (1,2,0) = (6,2,0). In other point cloud data (x l ，y l ，z l ) In the coordinate system of the heat absorption tower, the coordinates are (5+x l ，y l ，z l )。
And 4.2, comparing the second normal line coordinate with the original normal line coordinate, determining an offset value and judging whether the offset value is in a preset range or not.
Step 4.3, if the deviation value is within the preset range, the heliostat to be calibrated is not corrected; if the deviation value is not within the preset range, the azimuth and/or the pitching angle of the heliostat to be calibrated need to be corrected.
Wherein, step 4.2 further comprises:
and 4.2.1, determining the current azimuth and the current pitching angle of the heliostat to be calibrated according to the second normal coordinate.
And 4.2.2, determining the original azimuth and the original pitching angle of the original heliostat to be calibrated according to the coordinates of the original normal line.
And 4.2.3, determining an azimuth deviation value according to the current azimuth and the original azimuth.
And 4.2.4, determining a pitch angle deviation value according to the current pitch angle and the original pitch angle.
And 4.2.5, judging whether the azimuth deviation value and the pitch angle deviation value are in a preset range or not respectively.
As shown in fig. 4, a schematic flow diagram of one embodiment of radar-based heliostat rectification is provided. The laser radars are fixedly arranged on the ground or on short stand columns positioned on the ground, one or a group of laser radars can scan one surface or a group of heliostats at high frequency, the angle of each surface heliostat is fed back to a given heliostat control system, the angle is compared with an ideal angle calculated by each surface heliostat, and if the angle is within an error range, the heliostat tracking is accurate; if the tracking error exceeds the error range, the heliostat is required to correct the tracking error. The angle given by the laser radar point cloud mode and the ideal heliostat angle calculated by the heliostat control system are calculated, so that the initial compensation quantity required by heliostat correction can be given, and the correction effect is achieved. The specific deviation rectifying step is as follows:
(1) Fixedly arranging a laser radar on the ground or a short stand column positioned on the ground;
(2) Scanning a heliostat surface by using a laser radar;
(3) The 3D structure of the scanned heliostat is restored through laser radar scanning point cloud data, and the azimuth/pitching angle of the scanned heliostat is calculated;
(4) Comparing the azimuth/pitch angle of the heliostat calculated in the step (3) with an ideal angle of the heliostat corresponding to the heliostat calculated by the heliostat control system at the same time to obtain an angle difference value;
(5) Comparing the angle difference value obtained in the step (4) with an allowable error value of heliostat tracking, and if the angle difference value is within an error range, indicating that the heliostat tracking is accurate; if the tracking error exceeds the error range, indicating that the heliostat is in error in tracking, and correcting the deviation is needed;
(6) If the conclusion obtained in the step (5) is that correction is needed, feeding back the angle difference value obtained in the step (5) to a heliostat control system, obtaining the initial compensation quantity needed by correction of the heliostat, and entering the next correction process; if the conclusion obtained in the step (5) is that correction is not needed, the correction process of the round is finished, and the next round of correction is started.
Referring to fig. 5, fig. 5 is a schematic structural diagram of some embodiments of a radar-based heliostat correction apparatus according to the present invention, and as an implementation of the method shown in the foregoing drawings, the present invention further provides some embodiments of a radar-based heliostat correction apparatus, where the embodiments of the apparatus correspond to the embodiments of the methods shown in fig. 1, and the apparatus may be applied to various electronic devices.
As shown in fig. 5, a radar-based heliostat rectification apparatus 500 of some embodiments includes a first processing module 501, a second processing module 502, a third processing module 503, a fourth processing module 504: the first processing module is used for determining a target radar in at least one radar according to heliostat information to be calibrated; the second processing module is used for acquiring first point cloud data of the heliostat to be calibrated through the target radar, and the coordinates of each point in the first point cloud data are in a target radar coordinate system; the third processing module is used 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 under the target radar coordinate system; and the fourth processing module is used for converting the first normal coordinate into the coordinate system of the heat absorption tower to obtain a second normal coordinate, and correcting the heliostat to be calibrated according to the second normal coordinate.
Further, the heliostat information to be calibrated includes the position of the heliostat to be calibrated and the coordinates of the original normal line of the heliostat to be calibrated, the coordinates of the original normal line are in the coordinate system of the heat absorption tower, and the plurality of radars corresponding to the heliostat to be calibrated include target radars and/or auxiliary radars.
Further, the apparatus further comprises a fifth processing module for: at least one auxiliary radar is determined from heliostat information to be calibrated.
Further, the second processing module is further configured to: and acquiring at least one group of corresponding point cloud data through the target radar and at least one auxiliary radar, and fusing and registering the at least one group of point cloud data to obtain first point cloud data.
Further, the second processing module is further configured to: and inputting 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 heliostats to be calibrated, and fusing and registering the at least one group of point cloud data corresponding to the heliostats to be calibrated.
Further, the third processing module is further configured to: and setting a point at a preset position in the first point cloud data as a target point, and calculating the normal line of the target point by a point cloud normal vector estimation method based on PCA to obtain a first normal line coordinate under the target radar coordinate system.
Further, the fourth processing module is further configured to: converting the first normal coordinate into a coordinate system of the heat absorption tower according to a preset conversion matrix to obtain a second normal coordinate; comparing the second normal coordinate with the original normal coordinate, determining a deviation value and judging whether the deviation value is in a preset range or not; if the deviation value is within the preset range, the heliostat to be calibrated is not corrected; if the deviation value is not within the preset range, the azimuth and/or the pitching angle of the heliostat to be calibrated need to be corrected.
It will be appreciated that the modules described in the apparatus 400 correspond to the steps in the method described with reference to fig. 1. Thus, the operations, features and advantages described above for the method are equally applicable to the apparatus 400 and the modules and units contained therein, and are not described here again.
The present invention also provides a computer program product comprising a computer program stored on a non-transitory computer readable storage medium, the computer program comprising program instructions that, when executed by a computer, enable the computer to perform the radar-based heliostat rectification method provided by the methods described above.
In yet another aspect, the present invention also provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, is implemented to perform the radar-based heliostat rectification methods provided above.
The apparatus embodiments described above are merely illustrative, wherein the elements described above as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
From the above description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or of course may be implemented by means of hardware. Based on this understanding, the foregoing technical solution may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a computer readable storage medium, such as ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the respective embodiments or some parts of the methods described above for the embodiments.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. A radar-based heliostat rectification method, comprising:
determining a target radar in at least one radar according to heliostat information to be calibrated;
acquiring first point cloud data of a heliostat to be calibrated through a target radar, wherein the coordinates of each point in the first point cloud data are in a target radar coordinate system;
setting a point at a preset position in the first point cloud data as a target point, and calculating the normal of the target point to obtain a first normal coordinate under the target radar coordinate system;
and converting the first normal coordinate into a coordinate system of the heat absorption tower to obtain a second normal coordinate, and correcting the heliostat to be calibrated according to the second normal coordinate.
2. The radar-based heliostat correction method of claim 1, wherein the heliostat information to be calibrated comprises a position of the heliostat to be calibrated and coordinates of a primary normal to the heliostat to be calibrated, the coordinates of the primary normal being in an endothermic tower coordinate system.
3. The radar-based heliostat correction method of claim 2, further comprising:
at least one auxiliary radar is determined from heliostat information to be calibrated.
4. The radar-based heliostat correction method of claim 3, wherein the obtaining, by the target radar, first point cloud data for the heliostat to be calibrated comprises:
and acquiring at least one group of corresponding point cloud data through the target radar and at least one auxiliary radar, and fusing and registering the at least one group of point cloud data to obtain first point cloud data.
5. The radar-based heliostat rectification method of claim 4, wherein said fusing and registering at least one set of point cloud data comprises:
and inputting 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 heliostats to be calibrated, and fusing and registering the at least one group of point cloud data corresponding to the heliostats to be calibrated.
6. The radar-based heliostat correction method according to claim 2, wherein the setting a point at a preset position in the first point cloud data as a target point, calculating a normal line of the target point, and obtaining a first normal line coordinate in the target radar coordinate system, comprises:
and setting a point at a preset position in the first point cloud data as a target point, and calculating the normal line of the target point by a point cloud normal vector estimation method based on PCA to obtain a first normal line coordinate under the target radar coordinate system.
7. The radar-based heliostat correction method of claim 2, wherein converting the first normal coordinate into an endothermic tower coordinate system to obtain a second normal coordinate, correcting the heliostat according to the second normal coordinate, comprising:
converting the first normal coordinate into a coordinate system of the heat absorption tower according to a preset conversion matrix to obtain a second normal coordinate;
comparing the second normal coordinate with the original normal coordinate, determining a deviation value and judging whether the deviation value is in a preset range or not;
if the deviation value is within the preset range, the heliostat to be calibrated is not corrected;
if the deviation value is not within the preset range, the azimuth and/or the pitching angle of the heliostat to be calibrated need to be corrected.
8. A radar-based heliostat correction device, comprising:
the first processing module is used for determining a target radar in at least one radar according to heliostat information to be calibrated;
the second processing module is used for acquiring first point cloud data of the heliostat to be calibrated through the target radar, and the coordinates of each point in the first point cloud data are in a target radar coordinate system;
the third processing module is used 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 under the target radar coordinate system;
and the fourth processing module is used for converting the first normal coordinate into the coordinate system of the heat absorption tower to obtain a second normal coordinate, and correcting the heliostat to be calibrated according to the second normal coordinate.
9. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor, when executing the program, performs the steps of the radar-based heliostat deskewing method of any of claims 1-8.
10. A non-transitory computer readable storage medium having stored thereon a computer program, wherein the computer program when executed by a processor implements the steps of the radar-based heliostat rectification method of any of claims 1 to 8.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310267212.5A CN116148800A (en) | 2023-03-15 | 2023-03-15 | Heliostat deviation rectifying method, device, equipment and medium based on radar |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310267212.5A CN116148800A (en) | 2023-03-15 | 2023-03-15 | Heliostat deviation rectifying method, device, equipment and medium based on radar |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116148800A true CN116148800A (en) | 2023-05-23 |
Family
ID=86352589
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310267212.5A Pending CN116148800A (en) | 2023-03-15 | 2023-03-15 | Heliostat deviation rectifying method, device, equipment and medium based on radar |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116148800A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116659388A (en) * | 2023-08-02 | 2023-08-29 | 沈阳仪表科学研究院有限公司 | System and method for detecting installation position of each plane mirror in heliostat |
-
2023
- 2023-03-15 CN CN202310267212.5A patent/CN116148800A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116659388A (en) * | 2023-08-02 | 2023-08-29 | 沈阳仪表科学研究院有限公司 | System and method for detecting installation position of each plane mirror in heliostat |
| CN116659388B (en) * | 2023-08-02 | 2023-10-20 | 沈阳仪表科学研究院有限公司 | System and method for detecting installation position of each plane mirror in heliostat |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106197312B (en) | A kind of settled date mirror surface-shaped rapid detection system and its method | |
| US8192027B2 (en) | Calibration and tracking control of heliostats in a central tower receiver solar power plant | |
| US11250587B2 (en) | Heliostat error detection | |
| CN109458951B (en) | Heliostat surface shape field detection system and method | |
| CN109508043B (en) | Image-based heliostat secondary reflection pointing correction field system and method | |
| CN109828612B (en) | System and method for rapidly correcting heliostat at night by using unmanned aerial vehicle | |
| CN108413865B (en) | secondary reflection mirror surface type detection method based on three-dimensional measurement and coordinate system conversion | |
| CN116148800A (en) | Heliostat deviation rectifying method, device, equipment and medium based on radar | |
| CN106441149B (en) | It is a kind of based on more range estimations away from tower secondary reflection mirror surface type detection system and method | |
| El Jaouhari et al. | Dual-axis solar tracker design based on a digital hemispherical imager | |
| Ulmer et al. | Slope measurements of parabolic dish concentrators using color-coded targets | |
| CN113205563B (en) | Automatic driving sensor combined calibration target and calibration method | |
| Yellowhair et al. | Development of an aerial imaging system for heliostat canting assessments | |
| CN106569206A (en) | Microwave optical compose-based target detection method | |
| CN109508044B (en) | Heliostat secondary reflection pointing correction system and method | |
| CN110030741B (en) | Method for correcting secondary reflector in tower type solar secondary reflection system | |
| CN117173601B (en) | Photovoltaic power station array hot spot identification method and system | |
| CN110209205A (en) | A kind of heliostat bearing calibration based on mirror surface label | |
| CN107101595B (en) | A kind of settled date mirror mirror attitude detection system and method | |
| WO2024037123A1 (en) | Full-field refined dni prediction method | |
| CN116907535A (en) | Method for checking heliostat by adopting artificial light source and camera | |
| Sproul et al. | The development of the heliostat focusing and canting enhancement technique: an optical heliostat alignment tool for the national solar thermal test facility | |
| Zhou et al. | Three dimensional fully autonomous inspection method for wind power employing unmanned aerial vehicle based on 5G wireless communication and artificial intelligence | |
| CN105758024A (en) | Disc-type tracking system focusing method | |
| Shortis et al. | Photogrammetric monitoring of the construction of a solar energy dish concentrator |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |