Cloud shadow recognition and local radiation intensity monitoring method for tower-type CSP power station mirror field

Abstract

The present invention discloses a method for identifying cloud shadows and monitoring local radiation intensity in a mirror field of a tower-type solar thermal power station, including: monitoring the cloud shadow condition of the mirror field to obtain a real-time image of the mirror field and a binary image of the heliostat mirror field; setting a cloud shadow area percentage threshold Y, if the shadow area ratio S of the binary image of the heliostat mirror field is greater than or equal to Y, it is judged that cloud shadowing is detected, the binary image of the heliostat mirror field is analyzed to obtain the outline of the cloud shadowing area; the outline of the cloud shadowing area is transmitted to an unmanned aerial vehicle group, and the flight path of the unmanned aerial vehicle group is planned; and the direct normal radiation intensity (DNI) data of the cloud shadowing area is measured according to the detection result of the unmanned aerial vehicle group. The present invention can monitor the DNI under the cloud in real time, while ensuring a high detection accuracy, thereby providing reliable information for the dynamic scheduling of the mirror field to ensure the efficient and stable operation of the solar thermal power station.

Description

Technical Field

[0001] The present invention relates to the technical field of direct normal irradiance (DNI) monitoring of a mirror field of a tower-type solar thermal power station, and in particular to a method for cloud shadow recognition and local radiation intensity monitoring of a mirror field of a tower-type solar thermal power station. Background Art

[0002] Tower-type solar thermal power stations are green, low-carbon, grid-friendly power sources due to their high light-to-heat conversion efficiency, high focusing temperature, a concentrating heat absorption device at the front end, a synchronous generator set at the back end, and a molten salt and other heat storage systems. They can generate electricity continuously and stably, and are one of the current mainstream commercial solar thermal power generation technologies with the largest proportion, accounting for the highest proportion of grid-connected solar thermal power stations in my country.

[0003] Tower-type solar thermal power stations are mainly composed of heliostats, absorption towers, absorbers, high and low temperature molten salt storage tanks, steam generators and steam turbine generator sets; among them, the investment in the heliostat field accounts for a large proportion, accounting for more than 50% of the total investment cost; its basic operating principle leads to the energy flux density on the absorber surface and its safe and stable operation being directly determined by the field.

[0004] Existing cloud-shading and shadow-monitoring methods for tower-type solar thermal power stations mostly use indirect methods such as photovoltaic-related ground-based cloud maps and are not systematically standardized. For scenarios such as cloud-shading conditions that require precise scheduling of the mirror field, there are problems such as insufficient accuracy of the monitoring methods.

[0005] CN109871996A discloses a cloud monitoring system and method for a tower-type solar thermal power station based on photovoltaic panels. A number of photovoltaic panels evenly distributed in the heliostat mirror field are electrically connected to a cloud analysis and prediction unit, and information on whether the location is blocked by clouds and the degree of blocking is collected in real time and transmitted to the cloud analysis and prediction unit; the meteorological station is electrically connected to the cloud analysis and prediction unit, and the real-time measured weather information is transmitted to the cloud analysis and prediction unit; the cloud analysis and prediction unit calculates the area blocked by clouds in the mirror field in real time, and predicts the direction of the area blocked by clouds in the mirror field in the future. The principle of the invention is simple and easy to implement, and can accurately reflect the situation of the mirror field being blocked by clouds, providing a basis for the operation control of the heat absorption system of the tower-type solar thermal power station. However, this method has problems such as high cost, difficulty in data processing and prediction technology, limited coverage, and maintenance and management requirements in practical applications.

[0006] The UAV group is flexible and can be used with portable global horizontal irradiance (GHI) and direct radiation intensity (DNI) meters. As the cost is greatly reduced, it can be widely used in the field of renewable energy. However, there is no patent or related literature on the precise monitoring method of DNI under cloud cover conditions using such equipment. Summary of the invention

[0007] The purpose of the present invention is to provide a method for identifying cloud shadows and monitoring local radiation intensity in a mirror field of a tower-type CSP power station, which can monitor DNI under cloud shadows in real time while ensuring high detection accuracy, thereby providing reliable information for dynamic scheduling of the mirror field to ensure efficient and stable operation of the CSP power station.

[0008] In order to achieve the above technical objectives, the technical solution adopted by the present invention is:

[0009] A method for identifying cloud shadows and monitoring local radiation intensity in a mirror field of a tower-type solar thermal power station, wherein the cloud shadow area identification and DNI monitoring method comprises the following steps:

[0010] Step 1: Monitor the cloud cover condition of the mirror field and obtain the real-time image H(x, y, t) of the mirror field;

[0011] Step 2: Preprocess the real-time image H(x, y, t) of the heliostat field to remove the structures and heliostats in the tower CSP power station, and obtain the cloud shadow binary image B(x, y, t) of the heliostat field;

[0012] Step 3: Set a percentage threshold Y of the cloud-covered area / total mirror field area. If the shadow area ratio S of the heliostat field cloud-covered shadow binary image B(x, y, t) is less than Y, it is determined that no cloud cover is detected, the drone group does not take action, and the process goes to step 1. If S≥Y, it is determined that cloud cover is detected, and the heliostat field binary image B(x, y, t) is analyzed to obtain the cloud-covered area contour v(x, y, t), and the process goes to step 4.

[0013] Step 4: The cloud-covered area outline v(x, y, t) is transmitted to the UAV group. The cloud-covered heliostat information c(x, y, t) is obtained by the intersection of the cloud-covered area outline v(x, y, t) and the heliostat position information m(x, y, t). The flight path f(x, y, t) of the UAV group is calculated based on the cloud-covered heliostat information c(x, y, t), the current sun position information s(α, β, t), the flight altitude h of the UAV group, and the initial position u(x, y, t) of the UAV group.

[0014] Step 5: Start the drone group and detect movement according to the flight path f(x, y, t);

[0015] Step 6: Analyze the detection results of the portable total radiation and direct radiation intensity measuring instruments carried by the drone group, feed the data back to the data center through the wireless communication system, and obtain the real-time DNI data DNI (x, y, t) corresponding to the cloud-covered area v (x, y, t).

[0016] Furthermore, in step 1, a plurality of cameras are arranged on the top of the heat absorption tower of the solar thermal power station, and the plurality of cameras are evenly arranged circumferentially at the bottom of the tower top platform, each camera faces a mirror field sub-area, and the mirror field sub-areas of different cameras overlap, and the sum of the shooting areas of all cameras completely covers the mirror field of the solar thermal power station.

[0017] Furthermore, in step 2, the process of obtaining the heliostat field binary image B(x, y, t) includes the following steps:

[0018] Preprocess the real-time image H(x, y, t) of the mirror field and convert it into a grayscale image g(x, y, t);

[0019] According to the position and size information of the heliostats and other structures in the tower CSP power station, the corresponding area pixels in the grayscale image g(x, y, t) are set to 255 to exclude these areas, and M(x, y, t) is obtained to obtain the processed mirror field real-time image h(x, y, t);

[0020] The Otsu method is used to obtain the segmentation threshold T of the cloud shadow image. All pixels in h(x, y, t) are traversed. If h(x, y, t) is less than T, h(x, y, t) = 0, otherwise h(x, y, t) = 255 to obtain the binary image B(x, y, t) of the heliostat field containing only cloud shadows.

[0021] Furthermore, in step 3, the cloud cover area percentage threshold Y is calculated by the ratio of the shadow area of ​​the mirror field itself at time t to the total area actually occupied by the mirror field image; wherein the shadow area of ​​the mirror field itself at time t is calculated by the angle γ between the heliostat and the horizontal plane at time t, the heliostat area, and the inter-mirror shielding coefficient.

[0022] Furthermore, in step 4, the process of planning the flight path f(x, y, t) for the drone group includes the following steps:

[0023] According to the cloud-covered heliostat information c(x, y, t), the sun position s(α, β, t) and the UAV flight altitude h, the projection c′(x′, y′, t) of the cloud-covered heliostat group at the UAV flight altitude is obtained, and it is used as the UAV flight path point set c′(x′, y′, t), where x′=xh / tanα·cosβ, y′=y+h / tanα·sinβ, α represents the solar altitude angle, and β represents the solar azimuth angle;

[0024] Convert the set of UAV flight path points c'(x', y', t) in the plane rectangular coordinate system into the set of flight path points in the polar coordinate system

[0025] According to the UAV flight path point set c′(r, θ, t) in the polar coordinate system and the initial position information u(x, y, t) of the UAV group, when and When θ is corrected, θ′ is obtained.

[0026] According to the monitoring requirements and the flight time of the drone, the following two drone paths are planned:

[0027] The first flight path: According to the corrected UAV flight path point set c″(r, θ′, t) in the polar coordinate system, the maximum and minimum values ​​of r and θ′ are taken respectively: max(r), min(r), max(θ′), min(θ′). When there are multiple maximum values, the corresponding r and θ′ are averaged to obtain the four path points of the UAV flight. The flight path is set to fly in a straight line from the initial position to (min(r), θ′), according to the trajectory of (min(r), θ′), (max(r), θ′), (r, max(θ′)), (r, min(θ′)), or (min(r), θ′), (max(r), θ′), (r, min(θ′)), (r, max(θ′)), and finally return to the initial position;

[0028] The second flight path: According to the corrected UAV flight path point set c″(r, θ′, t) in the polar coordinate system, the maximum value of θ′ in the point set composed of flight path points with equal r is taken, max(θ′) r 、Minimum value min(θ′) r , and obtain several waypoints of the UAV flight. Among the above waypoints, take the minimum value min(θ′) according to θ′ r When r increases from small to large, θ′ takes the maximum value max(θ′) r When r is sorted from large to small, we get two sets of waypoints for the UAV to fly. and The flight path is set from the initial position to the point (min(r), min(θ′)m in(r) ) arrive The trajectory flies in a straight line and finally returns to the initial position.

[0029] Furthermore, if there are multiple cloud shadows in the field of view, the drone groups are set to different flight altitudes h to avoid possible collisions between drone groups. Each drone corresponds to one cloud shadow, and the path planning is performed for each drone separately.

[0030] Compared with the prior art, the present invention has the following beneficial effects:

[0031] First, the method for identifying cloud cover and shadows in a mirror field of a tower-type solar thermal power station and monitoring local radiation intensity of the tower-type solar thermal power station of the present invention obtains high-definition cloud shadow images through a monitoring system, and uses image recognition technology to extract real-time cloud shadow information to monitor the cloud cover status of the mirror field, which can achieve high temporal and spatial resolution.

[0032] Second, the method for identifying cloud cover and shadows in the mirror field of a tower-type solar thermal power station and monitoring local radiation intensity of the present invention pre-processes the graphics through the location and size information of the structures existing in the tower-type solar thermal power station, which can avoid the interference of related structures on image recognition and does not affect the cloud cover recognition result, thereby ensuring the dynamic scheduling effect of the mirror field.

[0033] Third, the cloud shadow recognition and local radiation intensity monitoring method of the tower-type solar thermal power station mirror field of the present invention effectively avoids the deviation of the final measurement result caused by the oblique sunlight when there is cloud cover by correcting the flight area of ​​the drone group before path planning; the original rectangular coordinate system is converted into a polar coordinate system, and the layout characteristics of the tower-type solar thermal power station mirror field are utilized to facilitate subsequent path planning.

[0034] Fourth, the method for identifying cloud shadows and monitoring local radiation intensity in the mirror field of a tower-type solar thermal power station of the present invention provides accurate and reliable DNI information of cloud-shaded areas for dynamic scheduling of the mirror field through an unmanned aerial vehicle group equipped with portable full-radiation and direct-radiation intensity measuring instruments, thereby realizing optimized scheduling of the heliostats in the entire field based on the uniformity of energy flux density and achieving safe and stable operation of the absorber. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 The figure is a schematic diagram of the cloud shadow camera system and the drone group structure; the accompanying drawings are marked as follows: ① cloud shadow camera system, ② drone group, ③ data center;

[0036] Figure 2 This is a flow chart of the method for identifying cloud shadows and monitoring local radiation intensity in a mirror field of a tower-type solar thermal power station of the present invention;

[0037] Figure 3 This is a schematic diagram of binarization of the cloud-covered area detection area;

[0038] Figure 4 This is a schematic diagram of the first flight path of the drone group;

[0039] Figure 5 Schematic diagram of the second flight path of the drone group. DETAILED DESCRIPTION

[0040] The embodiments of the present invention are further described in detail below in conjunction with the accompanying drawings.

[0041] See also Figure 2The present invention discloses a method for identifying cloud shadows and local radiation intensity in a mirror field of a tower-type solar thermal power station. The cloud shadow area identification and DNI monitoring method comprises the following steps:

[0042] Step 1: Monitor the cloud cover condition of the mirror field and obtain the real-time image H(x, y, t) of the mirror field.

[0043] like Figure 1 As shown, multiple cameras are arranged on the top of the solar thermal power station's heat absorption tower. The multiple cameras are evenly arranged circumferentially at the bottom of the tower top platform. Each camera faces a mirror field sub-area. The mirror field sub-areas of different cameras overlap, and the sum of the shooting areas of all cameras completely covers the solar thermal power station's mirror field.

[0044] Step 2: Preprocess the real-time image H(x, y, t) of the heliostat field, remove the structures in the tower CSP power station, and obtain the binary image B(x, y, t) of the heliostat field.

[0045] Specifically, the process of obtaining the binary image B(x, y, t) of the heliostat field includes the following steps:

[0046] Preprocess the real-time image H(x, y, t) of the mirror field and convert it into a grayscale image g(x, y, t);

[0047] According to the position and size information of the heliostats and other structures in the tower CSP power station, the corresponding area pixels in the grayscale image g(x, y, t) are set to 255 to exclude these areas, and M(x, y, t) is obtained to obtain the processed mirror field real-time image h(x, y, t);

[0048] The image segmentation threshold T is obtained by using the Otsu method. All pixels in h(x, y, t) are traversed. If h(x, y, t) is less than T, h(x, y, t) = 0, otherwise h(x, y, t) = 255 to obtain the binary image B(x, y, t) of the heliostat field.

[0049] Step 3: Set a percentage threshold Y of the cloud-covered area / total mirror field area. If the shadow area ratio S of the heliostat field binary image B(x, y, t) is less than Y, it is determined that no cloud cover is detected, the drone group does not take any action, and the process goes to step 1. If S≥Y, it is determined that cloud cover is detected, and the heliostat field binary image B(x, y, t) is analyzed to obtain the cloud-covered area contour v(x, y, t), and the process goes to step 4.

[0050] Since the structure shadow in the tower CSP power station will have a certain impact on the cloud cover judgment, based on the above image recognition result B(x, y, t), the cloud cover area percentage threshold Y is obtained and set by the ratio of the shadow area of ​​the mirror field itself at time t to the total area actually occupied by the mirror field image, where the shadow area of ​​the mirror field itself at time t is obtained by the angle γ between the heliostat and the horizontal plane at time t, the heliostat area and the shielding coefficient between the mirrors.

[0051] Step 4: The cloud-covered area outline v(x, y, t) is transmitted to the UAV group. The cloud-covered heliostat information c(x, y, t) is obtained from the intersection of the cloud-covered area outline v(x, y, t) and the heliostat position information m(x, y, t). The flight path f(x, y, t) of the UAV group is planned according to the cloud-covered heliostat information c(x, y, t), the current sun position information s(α, β, t), the flight altitude h of the UAV group, and the initial position u(x, y, t) of the UAV group.

[0052] When the cloud is blocked, there is often a certain angle between the cloud and the sun, that is, oblique radiation. Therefore, if the flight path of the drone group is planned directly along the cloud-blocked area of ​​the heliostat field, that is, the position of the cloud-blocked heliostat is measured, it will cause a certain deviation in the final measurement result. Therefore, we propose the following technical solution. According to the cloud-blocked heliostat c(x, y, t), the sun position s(α, β, t) and the flight altitude h of the drone, where α represents the solar altitude angle and β represents the solar azimuth (0° in the east direction and positive in the counterclockwise direction), the drone flight area c′(x′, y′, t) is obtained, that is, the projection of the cloud-blocked heliostat group on the drone flight plane. From the geometric relationship, x′=xh / tanα·cosβ, y′=y+h / tanα·sinβ. Because the drone flight area is corrected, this problem is effectively solved. Next, the UAV flight area c′(x′, y′, t) is converted into a polar coordinate system c′(r, θ, t), where r represents the distance between the cloud-covered heliostat and the central tower projected on the UAV flight plane. According to the UAV flight area c′(r, θ, t) in the polar coordinate system and the initial position information u(x, y, t) of the UAV group, when and When θ is corrected, θ′ is obtained.

[0053] According to the monitoring requirements and the flight time of the drone, the following two drone paths are planned:

[0054] The first flight path: Figure 4According to the corrected UAV flight path point set c″(r, θ′, t) in the polar coordinate system, the maximum and minimum values ​​of r and θ′ are taken respectively: max(r), min(r), max(θ′), min(θ′). When there are multiple maximum values, the corresponding r and θ′ are averaged to obtain the four path points of the UAV flight. The flight path is set to start from the initial position to (min(r), θ′), and fly in a straight line according to the trajectory of (min(r), θ′), (max(r), θ′), (r, max(θ′)), (r, min(θ′)), or (min(r), θ′), (max(r), θ′), (r, min(θ′)), (r, max(θ′)), and finally return to the initial position ( Figure 4 The red dots in the middle are some of the waypoints, and the red and yellow lines show the first flight path);

[0055] The second flight path: Figure 5 According to the corrected UAV flight path point set c″(r, θ′, t) in the polar coordinate system, the maximum value of θ′ in the point set composed of flight path points with equal r is taken, max(θ′) r 、Minimum value min(θ′) r , and obtain several waypoints of the UAV flight. Among the above waypoints, take the minimum value min(θ′) according to θ′ r When r increases from small to large, θ′ takes the maximum value max(θ′) r When r is sorted from large to small, we get two sets of waypoints for the UAV to fly. and The flight path is set from the initial position to the point (min(r), min(θ′) min(r) ) arrive The trajectory flies straight and finally returns to the initial position ( Figure 5 The red dots in the middle are some of the waypoints, and the red and yellow lines show the second flight path).

[0056] As one embodiment, if there are multiple cloud shadows in the field of view, the drone groups are set to different flight altitudes h to avoid possible collisions between drone groups. Each drone corresponds to one cloud shadow, and path planning is performed for each drone separately.

[0057] Step 5: Start the drone group and detect movement based on the flight path f(x, y, t).

[0058] Step 6: Analyze the detection results of the portable total radiation and direct radiation intensity measuring instruments carried by the drone group, feed the data back to the data center through the wireless communication system, and obtain the real-time DNI data DNI (x, y, t) corresponding to the cloud-covered area v (x, y, t).

[0059] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code. The scheme in the embodiments of the present application can be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc.

[0060] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0061] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0062] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0063] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0064] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A method for identifying cloud shadows and monitoring local radiation intensity in a tower-type solar thermal power station mirror field, characterized in that: The method comprises the following steps: Step 1: Monitor the cloud cover condition of the mirror field and obtain the real-time image H(x, y, t) of the mirror field; Step 2: Preprocess the real-time image H(x, y, t) of the heliostat field to remove the structures in the tower CSP power station and obtain the binary image B(x, y, t) of the heliostat field. Step 3: Set a percentage threshold Y of the cloud-covered area / total mirror field area. If the shadow area ratio S of the heliostat field binary image B(x, y, t) is less than Y, it is determined that no cloud cover is detected, the drone group does not take action, and the process goes to step 1. If S≥Y, it is determined that cloud cover is detected, and the heliostat field binary image B(x, y, t) is analyzed to obtain the cloud-covered area contour v(x, y, t), and the process goes to step 4. Step 4: The cloud-covered area outline v(x, y, t) is transmitted to the UAV group. The cloud-covered heliostat information c(x, y, t) is obtained from the intersection of the cloud-covered area outline v(x, y, t) and the heliostat position information m(x, y, t). The flight path f(x, y, t) of the UAV group is planned according to the cloud-covered heliostat information c(x, Y, t), the current sun position information s(α, β, t), the flight altitude h of the UAV group, and the initial position u(x, y, t) of the UAV group. Step 5: Start the drone group and detect movement based on f(x,y,t); Step 6: Analyze the detection results of the portable total radiation and direct radiation intensity measuring instruments carried by the drone group, feed the data back to the data center through the wireless communication system, and obtain the real-time DNI data DNI(x,y,t) corresponding to the cloud-covered area v(x,y,t).

2. The method for identifying cloud shadows and monitoring local radiation intensity in a tower-type solar thermal power station mirror field according to claim 1 is characterized in that: In step 1, multiple cameras are arranged on the top of the solar thermal power station's heat absorption tower. The multiple cameras are evenly arranged circumferentially at the bottom of the tower top platform. Each camera faces a sub-area of ​​the mirror field. The sub-areas of the mirror field of different cameras overlap. The sum of the shooting areas of all cameras completely covers the mirror field of the solar thermal power station.

3. The method for identifying cloud shadows and monitoring local radiation intensity in a tower-type solar thermal power station mirror field according to claim 1 is characterized in that: In step 2, the process of obtaining the binary image B(x, y, t) of the heliostat field includes the following steps: Preprocess the real-time image H(x,y,t) of the mirror field and convert it into a grayscale image g(x,y,t); According to the location and size information M(x, Y, T) of the metal structures and reflectors in the tower-type CSP power station, the pixel values ​​of the areas corresponding to the metal structures and reflectors in the grayscale image g(x, y, t) are set to 255 to exclude these areas, and the processed real-time image h(x, y, t) of the mirror field is obtained; The image segmentation threshold T is obtained by using the Otsu method. All pixels in h(x, y, t) are traversed. If h(x, y, t) is less than T, h(x, y, t) = 0, otherwise h(x, y, t) = 255 to obtain the binary image B(x, y, t) of the heliostat field.

4. The method for identifying cloud shadows and monitoring local radiation intensity in a tower-type solar thermal power station mirror field according to claim 1 is characterized in that: In step 3, the cloud cover area percentage threshold Y is calculated by the ratio of the shadow area of ​​the mirror field itself at time t to the total area actually occupied by the mirror field image; wherein the shadow area of ​​the mirror field itself at time t is calculated by the angle γ between the heliostat and the horizontal plane at time t, the heliostat area, and the inter-mirror shielding coefficient.

5. The method for identifying cloud shadows and monitoring local radiation intensity in a tower-type solar thermal power station mirror field according to claim 1 is characterized in that: In step 4, the process of planning the flight path f(x, y, t) for the drone group includes the following steps: According to the cloud-covered heliostat information c(x, y, t), the sun position s(α, β, t) and the UAV flight altitude h, the projection of the cloud-covered heliostat group on the UAV flight plane is obtained and used as the UAV flight path point set c′(x′, y′, t), x′=xh / tanα·cosβ, y′=y+h / tanα·sinβ, α represents the solar altitude angle, and β represents the solar azimuth angle; Convert the UAV flight path point set c′(x′,y′,t) in the plane rectangular coordinate system into the flight path point set c′(r,θ,t) in the polar coordinate system. According to the UAV flight path point set c′(r,θ,t) in the polar coordinate system and the initial position information u(x,y,t) of the UAV group, when and When θ is corrected, θ′ is obtained. According to the monitoring requirements and the flight time of the drone, the following two drone paths are planned: The first flight path: According to the corrected UAV flight path point set c″(r,θ′,t) in the polar coordinate system, the maximum and minimum values ​​of r and θ′ are taken respectively: max(r), min(r), max(θ′), min(θ′). When there are multiple maximum values, the corresponding r and θ′ are averaged to obtain the four path points of the UAV flight. The flight path is set to start from the initial position to (min(r),θ′), and fly in a straight line according to the trajectory of (min(r),θ′), (max(r),θ′), (r,max(θ′)), (r,min(θ′)), or (min(r),θ′), (max(r),θ′), (r,min(θ′)), (r,max(θ′)), and finally return to the initial position; The second flight path: According to the corrected UAV flight path point set c″(r,θ′,t) in the polar coordinate system, the maximum value of θ′ in the point set composed of flight path points with equal r is taken, max(θ′) r 、Minimum value min(θ′) r , and obtain several waypoints of the UAV flight. Among the above waypoints, take the minimum value min(θ′) according to θ′ r When r increases from small to large, θ′ takes the maximum value max(θ′) r When r is sorted from large to small, we get two sets of waypoints for the UAV to fly. and The flight path is set from the initial position to the point (min(r), min(θ′) min(r) ) arrive The trajectory flies in a straight line and finally returns to the initial position.

6. The method for identifying cloud shadows and monitoring local radiation intensity in a tower-type solar thermal power station mirror field according to claim 5 is characterized in that: If there are multiple cloud shadows in the field of view, the drone groups are set to different flight altitudes h to avoid possible collisions between drone groups. Each drone corresponds to one cloud shadow, and the path planning is performed for each drone separately.

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