Method and device for measuring cloud position and cloud speed based on all-sky cloud picture networking

Abstract

The invention relates to a cloud position and cloud speed measuring method. The method comprises the following steps: acquiring an all-sky image shot by each all-sky imager and a coordinate point relation between pixel coordinates and space coordinates in a three-dimensional space coordinate system; identifying spatial feature matching points of all-sky images shot by the paired all-sky imagers at the same moment; calculating a direction angle of a connecting line of two points in each spatial feature point pair; calculating an elevation angle and an azimuth angle of the cloud feature point; calculating space coordinates of the cloud feature points; calculating the correlation between the pixel coordinate variable quantity and the space coordinate variable quantity of the same all-sky imager at two adjacent moments; and calculating the cloud moving speed. In the technical scheme of the invention, based on foundation cloud picture networking observation and three-dimensional space positioning of the feature points on the cloud cluster in the cloud picture, analysis modeling is carried out on the all-sky cloud picture time sequence, quantitative observation of the movement speed of the cloud is realized, and more accurate data support is provided for movement prediction of the cloud and new energy application.

Description

Technical Field

[0001] The present invention relates to the field of meteorological data processing technology, and in particular to a cloud position and cloud speed measurement method, system, readable storage medium and computer equipment based on all-sky cloud map networking. Background Art

[0002] Cloud occlusion is an important factor affecting the efficiency of photovoltaic power generation. The difficulty and key point of photovoltaic power prediction lies in the prediction of the local cloud movement speed and direction. In the cloud prediction business, the real value or observed value of the current cloud movement speed is often required as input to obtain a more accurate prediction result of the local cloud movement speed. However, due to the limitation of observation methods, the observation data of cloud movement speed is very scarce, which is also one of the important limiting factors affecting the accuracy of cloud movement prediction.

[0003] The means of observing clouds include three methods: satellite remote sensing, ground-based radar remote sensing and ground-based all-sky imagers.

[0004] The most commonly used method for obtaining large-scale, large-scale cloud movement speed and direction observation data is to use satellite remote sensing images to calculate cloud wind guidance. However, the spatial resolution of satellite observation methods is often above 1 km, which is a coarse resolution, and the local observation time is often greater than 1 hour, which cannot meet the requirements of a specific single photovoltaic power station for short-term photovoltaic power forecasting, especially the forecast needs in the near time.

[0005] Ground-based radar remote sensing observations are mainly carried out through millimeter-wave cloud radar or lidar, which scans the sky from bottom to top. This observation method obtains cloud height and layer information by actively emitting millimeter waves or lasers, measuring the signal return time and phase information, and can also estimate cloud amount information based on the movement and occlusion of clouds. However, this type of equipment also has obvious disadvantages. First, the scanning angle of the equipment is often very small, and it is impossible to quickly obtain cloud amount information for the entire sky; second, this type of equipment cannot obtain the horizontal movement speed of clouds, and the movement of clouds is often quasi-horizontal; finally, the purchase and maintenance costs of active remote sensing radar equipment are often high, which limits the methods of this type of equipment.

[0006] Ground-based all-sky imagers generally observe the entire sky through a visible light camera in a fisheye projection mode. By using projection transformation and pixel extraction of cloud coverage areas, the distribution of clouds with a radius of about 2 kilometers can be obtained, and the spatial resolution and temporal resolution are high. Therefore, the movement of clouds can be judged through time series analysis. However, the image of a single ground-based all-sky imager only has azimuth angle information, and it is impossible to observe the height distance of the cloud, so it is impossible to accurately and quantitatively observe the movement speed of the cloud. Through network observation, cloud observation information at different angles can be provided to further calculate the cloud height and spatial coordinate information. For example, Tao Fa et al. (2013) proposed a cloud base height measurement method based on binocular imaging. This method established an observation system with a baseline length of 60 meters and obtained a relatively accurate cloud base height based on two images at the same time. However, the system did not discuss feature matching between different times, and assumed that the two cameras were at the same height, which is difficult to meet in a system of two all-sky imagers at a long distance. Similarly, Li Guosheng (2011) systematically discussed the method and system of binocular imaging cloud base height measurement in his doctoral dissertation, but the system used a cloud camera instead of an all-sky imager. In terms of the method of using dual cameras for cloud movement observation, the patent "A photovoltaic power station cloud tracking prediction method, system and process, application number CN202110850460.3" proposed from the process and concept that two real-time videos in the area to be tracked are obtained by two cameras respectively; the two real-time videos are decomposed into two corresponding sets of frame images respectively, and the sun and multiple cloud areas in the two sets of frame images are identified through the trained target detection segmentation model; the cloud height, cloud speed and cloud range of each cloud area are calculated. However, the patent does not give a specific calculation method and implementation steps, but only gives a general execution process. The patent also has many fuzzy places. For key information such as the camera projection method, installation, cloud feature matching method, and cloud height calculation method, only module information is given, not the calculation method. Therefore, the patent is limited in guidance and specificity. Summary of the invention

[0007] The present invention aims to solve at least one of the technical problems existing in the prior art or related art.

[0008] To this end, an object of the present invention is to provide a method for measuring cloud position and cloud speed based on all-sky cloud map networking, which utilizes ground-based cloud map networking observations, and on the basis of three-dimensional spatial positioning of characteristic points on cloud clusters in the cloud map, analyzes and models the all-sky cloud map time series to achieve quantitative observation of cloud movement speed, thereby providing more accurate data support for cloud movement prediction and new energy applications.

[0009] To achieve the above-mentioned purpose, the technical solution of the first aspect of the present invention provides a cloud position and cloud speed measurement method based on the all-sky cloud map network, comprising the following steps:

[0010] Get the full sky image taken by each full sky imager and the coordinate point relationship between the pixel coordinates of the corresponding full sky image and the spatial coordinates in the three-dimensional spatial coordinate system, specifically:

[0011]

[0012] Wherein, the viewing angle FOV of the all-sky imager is 180°, N is the number of pixels of the all-sky image, and the point at the i-th pixel in the horizontal direction and the j-th pixel in the vertical direction of the all-sky image corresponds to the spatial coordinates (x, y, z) of the point P in the three-dimensional spatial coordinate system;

[0013] Identify the spatial feature matching points of the all-sky images taken by the paired all-sky imagers at the same time, and obtain the spatial feature point pairs;

[0014] Calculate the direction angle of the connecting line between the two points in each spatial feature point pair, specifically:

[0015] Among them, the two full sky images I at time t 1，t and I 2，t The coordinates of P are 1，t，k (i 1，t，k , j 1，t，k ) and P 2，t，k (i 2，t，k , j 2，t，k );

[0016] The spatial feature pairs are quality controlled according to the preset threshold corresponding to the direction angle to obtain the matching feature point set. The expression of the matching feature point set PF is:

[0017] P F =[(P 1，t，k , P 2，t，k )ifθ t，k <median(θ t )-10|k=1,2,...,N];wherein,

[0018] θ t，k Represented as the kth matching spatial feature point pair P at time t 1，t，k (i 1，t，k , j 1，t，k ) and P 2，t，k (i 2，t，k , j 2，t，k )’s connecting line direction angle, median(θ t) is expressed as the average value of the connecting line direction angles of all spatial feature point pairs at time t. The preset threshold is that the difference between the connecting line direction angle of the kth matching spatial feature point pair at time t and the average value of the connecting line direction angles of all spatial feature point pairs at time t is less than 10, that is, θ t，k <median(θ t )-10;

[0019] Calculate the elevation and azimuth of the cloud feature points based on the pixel coordinates of the matching feature point set in the full sky image;

[0020] Calculate the spatial coordinates of the cloud feature points according to the elevation angle and azimuth angle of the cloud feature points and the spatial coordinates of the paired all-sky imagers in the three-dimensional spatial coordinate system;

[0021] According to the spatial coordinates of the cloud feature points, the correlation between the pixel coordinate change and the spatial coordinate change of the same all-sky imager at two adjacent moments is calculated;

[0022] The cloud movement speed is calculated based on the correlation between the pixel coordinate change and the spatial coordinate change.

[0023] In the above technical solution, preferably, calculating the elevation angle and azimuth angle of the cloud feature point according to the pixel coordinates of the matching feature point set in the full sky image comprises the following steps:

[0024] The azimuth angle β and elevation angle α of each all-sky imager are calculated according to the spatial coordinates of the all-sky imager and the coordinate point relationship between the pixel coordinates and the spatial coordinates, specifically:

[0025]

[0026] Substitute the pixel coordinates of the cloud feature points in the matching feature point set into the azimuth and elevation of the all-sky imager to obtain the elevation angle α of the cloud feature point 1，t，k and α 2，t，k and the azimuth angle β 1，t，k and β 2，t，k .

[0027] In the above technical solution, preferably, calculating the spatial coordinates of the cloud feature points according to the elevation angle and azimuth angle of the cloud feature points and the spatial coordinates of the paired all-sky imagers in the three-dimensional spatial coordinate system comprises the following steps:

[0028] According to the definition of trigonometric tangent function, the positional relationship between cloud feature points and paired all-sky imagers in the three-dimensional spatial coordinate system is obtained, and the expression is:

[0029]

[0030] Among them, the spatial coordinates of the paired sky imagers are A1(x1, y1, z1) and A2(x2, y2, z2), and the cloud feature point P c The spatial coordinates (x c ,y c , z c ), the elevation angles α1 and α2 of the paired sky imagers;

[0031] According to the cosine theorem of triangle, the distance relationship between the cloud feature points and the all-sky imager in the three-dimensional space coordinate system is obtained, and the expression is:

[0032] d xy，12 2 =d xy，1c 2 +d xy，2c 2 -2×d xy，1c ×d xy，2c ×cos(β1-β2); where d xy，12 refers to the horizontal distance between two all-sky imagers, d xy，1c and d xy，2c They refer to the horizontal distances between the two all-sky imagers and the cloud feature points respectively;

[0033]

[0034] The spatial coordinates of the cloud feature points in the three-dimensional space coordinate system are calculated based on the elevation angle, azimuth angle, position relationship and distance relationship of the cloud feature points. The expression is:

[0035] in,

[0036]

[0037] Then, according to the coordinate point relationship between pixel coordinates and spatial coordinates, we get:

[0038]

[0039] The elevation angle α 1，t，k and α 2，t，k , and the azimuth angle β 1，t，k and β 2，t，k Substituting the above formula, we can get the spatial coordinates z of the cloud feature point in the three-dimensional space coordinate system: c，t，k , then z c，t，k Substituting the coordinate point relationship between pixel coordinates and spatial coordinates, we can obtain the spatial coordinates (x c，t，k ,y c，t，k ).

[0040] In the above technical solution, preferably, the pixel coordinate change is:

[0041] Among them, the pixel coordinates of the spatial feature points captured by any all-sky imager at two adjacent moments are (i1, j1) and (i2, j2); similarly, the kth matching spatial feature point pair P is obtained. 1，t，k (i 1，t，k , j 1，t，k ) and P 2，t，k (i 2，t，k , j 2，t，k ) pixel coordinate change Δi t，k and Δj t，k ;

[0042] Then, according to the coordinate point relationship between the pixel coordinates and the spatial coordinates, the spatial coordinate change is obtained as:

[0043]

[0044] will i 1，t，k and j 1，t，k、 and Δi t，k and Δj t，k and z c，t，k Substituting into the above formula, we can get the spatial coordinate change Δx c，t，k and Δy c，t，k ;

[0045] The cloud movement speed is calculated based on the correlation between the pixel coordinate change and the space coordinate change, specifically:

[0046] Among them, Δt is the time interval between two adjacent moments, so as to obtain the spatial coordinates of the kth feature point and the cloud moving speed at time t, that is, V c，t，k =(x c，t，k ,y c，t，k , z c，t，k ,u c，t，k , v c，t，k ), all observation data sets at time t are V c，t =(V c，t，1 , V c，t，2 , ..., v c，t，M ), where M is the number of valid cloud feature points.

[0047] The technical solution of the second aspect of the present invention provides a cloud position and cloud speed measurement system based on a full sky cloud map network, comprising:

[0048] The pixel coordinate and space coordinate conversion module is configured to obtain the full sky image taken by each full sky imager and the coordinate point relationship between the pixel coordinates of the corresponding full sky image and the space coordinates in the three-dimensional space coordinate system, specifically:

[0049]

[0050] Wherein, the viewing angle FOV of the all-sky imager is 180°, N is the number of pixels of the all-sky image, and the point at the i-th pixel in the horizontal direction and the j-th pixel in the vertical direction of the all-sky image corresponds to the spatial coordinates (x, y, z) of the point P in the three-dimensional spatial coordinate system;

[0051] A spatial feature recognition module is configured to recognize spatial feature matching points of all-sky images captured by paired all-sky imagers at the same time, and obtain spatial feature point pairs;

[0052] The connection direction angle calculation module is configured to calculate the direction angle of the connection line between two points in each spatial feature point pair, specifically:

[0053] Among them, the two full sky images I at time t 1，t and I 2，t The coordinates of P are 1，t，k (i 1，t，k , j 1，t，k ) and P 2，t，k (i 2，t，k , j 2，t，k );

[0054] The data quality control module is configured to perform quality control on the spatial feature pair according to a preset threshold corresponding to the direction angle, and obtain a matching feature point set, the matching feature point set P F The expression is:

[0055] P F =[(P 1，t，k , P 2，t，k )ifθ t，k <median(θ t )-10|k=1,2,...,N];wherein,

[0056] θ t，k Represented as the kth matching spatial feature point pair P at time t 1，t，k (i 1，t，k , j 1，t，k ) and P 2，t，k (i 2，t，k , j 2，t，k )’s connecting line direction angle, median(θ t) is expressed as the average value of the connecting line direction angles of all spatial feature point pairs at time t. The preset threshold is that the difference between the connecting line direction angle of the kth matching spatial feature point pair at time t and the average value of the connecting line direction angles of all spatial feature point pairs at time t is less than 10, that is, θ t，k <median(θ t )-10;

[0057] A cloud feature angle calculation module is configured to calculate the elevation angle and azimuth angle of a cloud feature point based on the pixel coordinates of the matching feature point set in the full sky image;

[0058] A cloud feature point spatial calculation module is configured to calculate the spatial coordinates of the cloud feature point according to the elevation angle and azimuth angle of the cloud feature point and the spatial coordinates of the paired all-sky imagers in a three-dimensional spatial coordinate system;

[0059] A cloud feature coordinate change conversion module is configured to calculate the correlation between the pixel coordinate change and the spatial coordinate change of the same all-sky imager at two adjacent moments according to the spatial coordinates of the cloud feature points;

[0060] The cloud speed calculation module is configured to calculate the cloud movement speed based on the correlation between the pixel coordinate change amount and the space coordinate change amount.

[0061] In the above technical solution, preferably, the cloud feature angle calculation module includes:

[0062] The imager angle calculation unit is configured to calculate the azimuth angle β and elevation angle α of each all-sky imager according to the spatial coordinates of the all-sky imager and the coordinate point relationship between the pixel coordinates and the spatial coordinates, specifically:

[0063]

[0064] The cloud feature angle calculation unit is configured to substitute the pixel coordinates of the cloud feature point in the matching feature point set into the azimuth and elevation of the all-sky imager to obtain the elevation angle α of the cloud feature point. 1，t，k and α 2，t，k and the azimuth angle β 1，t，k and β 2，t，k .

[0065] In the above technical solution, preferably, the cloud feature point space calculation module includes:

[0066] The position relationship calculation unit is configured to obtain the position relationship between the cloud feature point and the paired all-sky imager in the three-dimensional space coordinate system according to the definition of the trigonometric tangent function, and the expression is:

[0067]

[0068] Among them, the spatial coordinates of the paired sky imagers are A1(x1, y1, z1) and A2(x2, y2, z2), and the cloud feature point P c The spatial coordinates (x c ,y c , z c ), the elevation angles α1 and α2 of the paired sky imagers;

[0069] The distance relationship calculation unit is configured to obtain the distance relationship between the cloud feature points and the paired all-sky imagers in the three-dimensional space coordinate system according to the cosine theorem of the triangle, and the expression is:

[0070] d xy，12 2 =d xy，1c 2 +d xy，2c 2 -2×d xy，1c ×d xy，2c ×cos(|β1-β2|); where d xy，12 refers to the horizontal distance between two all-sky imagers, d xy，1c and d xy，2c They refer to the horizontal distances between the two all-sky imagers and the cloud feature points respectively;

[0071]

[0072]

[0073] The cloud feature point space calculation unit is configured to calculate the spatial coordinates of the cloud feature points in the three-dimensional space coordinate system according to the elevation angle and azimuth angle, position relationship and distance relationship of the cloud feature points. The expression is:

[0074] in,

[0075]

[0076] Then, according to the coordinate point relationship between pixel coordinates and spatial coordinates, we get:

[0077]

[0078] Finally, the elevation angle α 1，t，k and α 2，t，k , and the azimuth angle β 1，t，k and β 2，t，k Substituting the above formula, we can get the spatial coordinates z of the cloud feature point in the three-dimensional space coordinate system: c，t，k , then z c，t，kSubstituting the coordinate point relationship between pixel coordinates and spatial coordinates, we can obtain the spatial coordinates (x c，t，k ,y c，t，k ).

[0079] In the above technical solution, preferably, the pixel coordinate change is:

[0080] Among them, the pixel coordinates of the spatial feature points captured by any all-sky imager at two adjacent moments are (i1, j1) and (i2, j2); similarly, the kth matching spatial feature point pair P is obtained. 1，t，k (i 1，t，k , j 1，t，k ) and P 2，t，k (i 2，t，k , j 2，t，k ) pixel coordinate change Δi t，k and Δj t，k ;

[0081] The change in spatial coordinates is:

[0082]

[0083] i 1，t，k and j 1，t，k、 and Δi t，k and Δj t，k and z c，t，k Substituting into the above formula, we can get the spatial coordinate change Δx c，t，k and Δy c，t，k ;

[0084] The expression of cloud movement speed is:

[0085] Among them, Δt is the time interval between two adjacent moments, so as to obtain the spatial coordinates of the kth feature point and the cloud moving speed at time t, that is, V c，t，k =(x c，t，k ,y c，t，k , z c，t，k ,u c，t，k , v c，t，k ), all observation data sets at time t are V c，t =(V c，t，1 , V c，t，2 , ..., v c，t，M ), where M is the number of valid cloud feature points.

[0086] The technical solution of the third aspect of the present invention provides a readable storage medium on which a computer program is stored. When the program is executed by a processor, the steps of the cloud position and cloud speed measurement method based on the all-sky cloud map network provided by the technical solution of the first aspect are implemented.

[0087] The technical solution of the fourth aspect of the present invention provides a computer device, including a storage medium and a processor; the storage medium is used to store a computer program; the processor is used to execute the computer program to implement the steps of the cloud position and cloud speed measurement method based on the all-sky cloud map network provided by the technical solution of the first aspect above.

[0088] Compared with the prior art, the advantages of the cloud position and cloud speed measurement method and equipment based on the all-sky cloud map networking provided by the present invention are: utilizing ground-based cloud map networking observations, on the basis of the three-dimensional spatial positioning of characteristic points on the cloud clusters in the cloud map, the all-sky cloud map time series is analyzed and modeled, to achieve quantitative observation of the cloud movement speed, and to provide more accurate data support for cloud movement prediction and new energy applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0089] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0090] Figure 1 A schematic diagram of a three-dimensional spatial coordinate system centered on the all-sky imager is shown;

[0091] Figure 2 A schematic diagram of the imaging principle of the all-sky imager is shown;

[0092] Figure 3 A schematic diagram showing the connecting lines of the original and matched Shi-Tomasi feature points;

[0093] Figure 4 A schematic diagram of the bottom-height intelligent observation method is shown;

[0094] Figure 5 A flowchart of a method according to an embodiment of the present invention is shown;

[0095] Figure 6 A flowchart of step S5 according to an embodiment of the present invention is shown;

[0096] Figure 7 A flowchart of step S6 according to an embodiment of the present invention is shown;

[0097] Figure 8 A structural block diagram of a system involved in an embodiment of the present invention is shown;

[0098] Fig. 9The structure block diagram of the cloud feature angle calculation module involved in the embodiment of the present invention is shown;

[0099] Fig.10 The structure block diagram of the cloud feature point space calculation module involved in the embodiment of the present invention is shown. DETAILED DESCRIPTION

[0100] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0101] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0102] like Figures 1 to 7 As shown, according to an embodiment of the present invention, a cloud position and cloud speed measurement method based on a full sky cloud map network includes the following steps:

[0103] S1, obtaining the full sky image taken by each full sky imager and the coordinate point relationship between the pixel coordinates of the corresponding full sky image and the spatial coordinates in the three-dimensional spatial coordinate system, specifically:

[0104] Assume that the center of the image is the origin, and assume that we have a point in the image, which is the i-th pixel in the horizontal direction and the j-th pixel in the vertical direction. At the same time, the actual position of the object corresponding to this pixel in the three-dimensional coordinate system of the space world is P(x, y, z). Then according to the relationship of isometric projection ( Figure 2 ), we get the relationship between the pixel in the image and the image coordinates and the world coordinate system as follows:

[0105]

[0106] Wherein, the viewing angle FOV of the all-sky imager is 180°, N is the number of pixels of the all-sky image, and the point at the i-th pixel in the horizontal direction and the j-th pixel in the vertical direction of the all-sky image corresponds to the spatial coordinates (x, y, z) of the point P in the three-dimensional spatial coordinate system;

[0107] In this step, the present invention uses two or more all-sky imagers for networked observation. Since two all-sky cloud images are matched to calculate the cloud base height and cloud moving speed, if there are more than two all-sky imagers, they can be paired one by one to calculate the cloud height and cloud moving speed. Figure 1As shown, there are two all-sky imagers A1 and A2 in this system, and the installation positions of these two all-sky imagers are A1 (x1, y1, z1) and A1 (x2, y2, z2) respectively. Each all-sky imager is installed in a standardized manner and meets the following two conditions: 1) The imager is installed vertically upward, that is, the center point of the all-sky cloud map is the zenith point position of the imager; 2) The azimuth angle of the imager installation must ensure that the column direction of the captured image corresponds to the north-south direction of the actual environment, and the row direction of the image corresponds to the east-west direction of the actual environment.

[0108] After the installation of the all-sky imager is completed, the all-sky imager is synchronized and calibrated to ensure that the time of the two cameras is consistent. When actually shooting images, the two all-sky imagers shoot at the same time. The all-sky images obtained by A1 and A2 at time t are I and I respectively. 1，t and I 2，t In order to record and analyze the movement of cloud images, two all-sky imagers are controlled to take synchronous and continuous photos of the sky, and the time interval between the two all-sky imagers is Δt.

[0109] In this network observation scheme, the projection method of the image taken by each all-sky imager is equidistant projection, such as Figure 2 Under the condition of equidistant projection, assuming that the diameter of the full sky cloud map is N, and the FOV of the full sky imager is 180°, then a single pixel represents a consistent viewing angle and

[0110] S2, identifying the spatial feature matching points of the all-sky images taken by the paired all-sky imagers at the same time, and obtaining the spatial feature point pairs;

[0111] S3, calculate the direction angle of the connecting line between the two points in each spatial feature point pair, specifically:

[0112] Among them, the two full sky images I at time t 1，t and I 2，t The coordinates of P are 1，t，k (i 1，t，k , j 1，t，k ) and P 2，t，k (i 2，t，k , j 2，t，k );

[0113] In this step, in order to calculate the cloud base height, we need to match the cloud features in the paired all-sky images. In this study, we implemented the Shi-Tomasi-SIFT corner detection and matching algorithm to obtain and match cloud features. The algorithm uses the Shi-Tomasi algorithm to detect candidate good features, and then uses the SIFT algorithm to describe and match these features in the two images of the paired camera. The original image is normalized to 0-255 to minimize the impact of brightness changes. The Shi-Tomasi algorithm is insensitive to viewing angle, brightness, rotation, etc., and is computationally efficient. The SIFT algorithm is very effective in describing the direction and scale of features, and is also very robust to brightness, scale and rotation. The combination of Shi-Tomasi and SIFT algorithms can accurately and quickly detect the features of cloud images. According to this method, assuming that in a set of images I of two stations 1，t and I 2，t After corner point detection and matching, a set of N matching points is obtained. For the kth matching spatial feature point, the two images I at time t 1，t and I 2，t The coordinates in are P 1，t，k (i 1，t，k , j 1，t，k ) and P 2，t，k (i 2，t，k , j 2，t，k ).

[0114] In order to evaluate the direction between feature points, the average direction of the connecting line between the matching feature points is calculated, and the images of the two cameras at the same time are spliced ​​left and right, such as Figure 3 As shown, the left and right figures are I 1，t and I 2，t Considering that the all-sky cameras have the same installation azimuth, after the two pre-processed images are spliced ​​in left-right arrangement, the connecting lines of all feature points should be basically parallel, without crossing at too large an angle, such as Figure 3 For each individual matching point pair, for example, at time t, the image coordinates of the kth matching feature point in the two images are P 1，t，k and P 2，t，k ,according to Figure 3 The arrangement shown describes the direction angle of the connecting line as θ, where the angle θ is 0° in the horizontal right direction and increases in the clockwise direction, and the kth connecting line direction angle θ can be obtained.

[0115] S4, quality control is performed on the spatial feature pair according to the preset threshold corresponding to the direction angle to obtain a matching feature point set, the matching feature point set P F The expression is:

[0116] P F =[(P1，t，k , P 2，t，k )ifθ t，k <median(θ t )-10|k=1,2,...,N];wherein,

[0117] θ t，k Represented as the kth matching spatial feature point pair P at time t 1，t，k (i 1，t，k , j 1，t，k ) and P 2，t，k (i 2， t ， k, j 2， t ， k), median(θ t ) is expressed as the average value of the connecting line direction angles of all spatial feature point pairs at time t. The preset threshold is that the difference between the connecting line direction angle of the kth matching spatial feature point pair at time t and the average value of the connecting line direction angles of all spatial feature point pairs at time t is less than 10, that is, θ t，k <median(θ t )-10; the quality-controlled matching feature point set P F The number is M.

[0118] S5, calculating the elevation angle and azimuth angle of the cloud feature point according to the pixel coordinates of the matching feature point set in the full sky image;

[0119] S6, calculating the spatial coordinates of the cloud feature points according to the elevation angle and azimuth angle of the cloud feature points and the spatial coordinates of the paired all-sky imagers in the three-dimensional spatial coordinate system;

[0120] S7, calculating the correlation between the pixel coordinate change and the spatial coordinate change of the same all-sky imager at two adjacent moments according to the spatial coordinates of the cloud feature points;

[0121] S8, calculating the cloud movement speed according to the correlation between the pixel coordinate change amount and the space coordinate change amount.

[0122] like Figure 6 As shown, in the above embodiment, preferably, S5, calculating the elevation angle and azimuth angle of the cloud feature point according to the pixel coordinates of the matching feature point set in the full sky image, includes the following steps:

[0123] S51, calculating the azimuth angle β and elevation angle α of each all-sky imager according to the spatial coordinates of the all-sky imager and the coordinate point relationship between the pixel coordinates and the spatial coordinates, specifically:

[0124]

[0125] In this step, the feature set P obtained after quality controlF It will be used to estimate the cloud moving speed. After finding the cloud feature points in the two images, the cloud height can be calculated based on the azimuth and elevation of the feature points in the two cloud images. The calculation process is shown in the figure below. Figure 4 As shown. In the network observation, the spatial coordinates of the two all-sky imagers A1(x1, y1, z1) and A1(x2, y2, z2) are known. At the same time, according to the imaging relationship of the all-sky imager Figure 2 , the azimuth angle β and elevation angle α can be calculated.

[0126] S52, substituting the pixel coordinates of the cloud feature point in the matching feature point set into the azimuth and elevation of the all-sky imager to obtain the elevation angle α of the cloud feature point 1，t，k and α 2，t，k and the azimuth angle β 1，t，k and β 2，t，k .

[0127] like Figure 7 As shown, in the above embodiment, preferably, S6, calculating the spatial coordinates of the cloud feature points according to the elevation angle and azimuth angle of the cloud feature points and the spatial coordinates of the paired all-sky imagers in the three-dimensional spatial coordinate system, comprises the following steps:

[0128] S61, according to the definition of trigonometric tangent function, the positional relationship between the cloud feature points and the paired all-sky imagers in the three-dimensional spatial coordinate system is obtained, and the expression is:

[0129]

[0130] Among them, the spatial coordinates of the paired sky imagers are A1(x1, y1, z1) and A2(x2, y2, z2), and the cloud feature point P c The spatial coordinates (x c ,y c , z c ), the elevation angles α1 and α2 of the paired sky imagers;

[0131] S62, according to the cosine theorem of triangle, the distance relationship between the cloud feature points and the all-sky imager in the three-dimensional space coordinate system is obtained, and the expression is:

[0132] d xy，12 2 =d xy，1c 2 +d xy，2c 2 -2×d xy，1c ×d xy，2c ×cos(|β1-β2|); where d xy，12 refers to the horizontal distance between two all-sky imagers, d xy，1c and d xy，2cThey refer to the horizontal distances between the two all-sky imagers and the cloud feature points respectively;

[0133]

[0134] S63, calculating the spatial coordinates of the cloud feature points in the three-dimensional spatial coordinate system according to the elevation angle and azimuth angle, position relationship and distance relationship of the cloud feature points, and the expression is:

[0135] in,

[0136]

[0137] Then, according to the coordinate point relationship between pixel coordinates and spatial coordinates, we get:

[0138]

[0139] The elevation angle α 1，t，k and α 2，t，k , and the azimuth angle β 1，t，k and β 2，t，k Substituting the above formula, we can get the spatial coordinates z of the cloud feature point in the three-dimensional space coordinate system: c，t，k , then z c，t，k Substituting the coordinate point relationship between pixel coordinates and spatial coordinates, we can obtain the spatial coordinates (x c，t，k ,y c，t，k ).

[0140] In the above embodiment, preferably, the pixel coordinate change is:

[0141] Among them, the pixel coordinates of the spatial feature points captured by any all-sky imager at two adjacent moments are (i1, j1) and (i2, j2); similarly, the kth matching spatial feature point pair P is obtained. 1，t，k (i 1，t，k , j 1，t，k ) and P 2，t，k (i 2，t，k , j 2，t，k ) pixel coordinate change Δi t，k and Δj t，k ;

[0142] In order to calculate the coordinate changes of the spatial points corresponding to the changes of the coordinate points in the cloud map, considering that the horizontal speed of the cloud is much greater than the vertical speed in most cases, it can be assumed that the height of the cloud remains basically unchanged and the cloud only moves in the horizontal direction.

[0143] Then, according to the coordinate point relationship between the pixel coordinates and the spatial coordinates, the spatial coordinate change is obtained as:

[0144]

[0145] will i 1，t，k and j 1，t，k、 and Δi t，k and Δj t，k and z c，t，k Substituting into the above formula, we can get the spatial coordinate change Δx c，t，k and Δy c，t，k ;

[0146] The cloud movement speed is calculated based on the correlation between the pixel coordinate change and the space coordinate change, specifically:

[0147] Among them, Δt is the time interval between two adjacent moments, so as to obtain the spatial coordinates of the kth feature point and the cloud moving speed at time t, that is, V c，t，k =(x c，t，k ,y c，t，k , z c，t，k ,u c，t，k , v c，t，k ), all observation data sets at time t are V c，t =(V c，t，1 , V c，t，2 , ..., v c，t，M ), where M is the number of valid cloud feature points.

[0148] like Figures 1 to 4 , Figures 8 to 10 As shown, according to another embodiment of the present invention, a cloud position and cloud velocity measurement system 100 based on all-sky cloud image networking includes:

[0149] The pixel coordinate and space coordinate conversion module 1 is configured to obtain the full sky image captured by each full sky imager and the coordinate point relationship between the pixel coordinates of the corresponding full sky image and the space coordinates in the three-dimensional space coordinate system, specifically:

[0150]

[0151] Wherein, the viewing angle FOV of the all-sky imager is 180°, N is the number of pixels of the all-sky image, and the point at the i-th pixel in the horizontal direction and the j-th pixel in the vertical direction of the all-sky image corresponds to the spatial coordinates (x, y, z) of the point P in the three-dimensional spatial coordinate system;

[0152] A spatial feature recognition module 2 is configured to recognize spatial feature matching points of all-sky images captured by paired all-sky imagers at the same time, and obtain spatial feature point pairs;

[0153] The connection direction angle calculation module 3 is configured to calculate the direction angle of the connection line between two points in each spatial feature point pair, specifically:

[0154] Among them, the two full sky images I at time t 1，t and I 2，t The coordinates of P are 1，t，k (i 1，t，k , j 1，t，k ) and P 2，t，k (i 2，t，k , j 2，t，k )

[0155] The data quality control module 4 is configured to perform quality control on the spatial feature pair according to a preset threshold value corresponding to the direction angle, and obtain a matching feature point set, the matching feature point set P F The expression is:

[0156] P F =[(P 1，t，k , P 2，t，k )ifθ t，k <median(θ t )-10|k=1,2,...,N];wherein,

[0157] θ t，k Represented as the kth matching spatial feature point pair P at time t 1，t，k (i 1，t，k , j 1，t，k ) and P 2，t，k (i2, t，k , j 2，t，k )’s connecting line direction angle, median(θ t ) is expressed as the average value of the connecting line direction angles of all spatial feature point pairs at time t. The preset threshold is that the difference between the connecting line direction angle of the kth matching spatial feature point pair at time t and the average value of the connecting line direction angles of all spatial feature point pairs at time t is less than 10, that is, θ t，k <median(θ t )-10;

[0158] A cloud feature angle calculation module 5 is configured to calculate the elevation angle and azimuth angle of the cloud feature point according to the pixel coordinates of the matching feature point set in the full sky image;

[0159] A cloud feature point spatial calculation module 6 is configured to calculate the spatial coordinates of the cloud feature point according to the elevation angle and azimuth angle of the cloud feature point and the spatial coordinates of the paired all-sky imagers in a three-dimensional spatial coordinate system;

[0160] The cloud feature coordinate change conversion module 7 is configured to calculate the correlation between the pixel coordinate change amount and the spatial coordinate change amount of the same all-sky imager at two adjacent moments according to the spatial coordinates of the cloud feature points;

[0161] The cloud speed calculation module 8 is configured to calculate the cloud movement speed based on the correlation between the pixel coordinate change amount and the space coordinate change amount.

[0162] like Fig. 9 As shown, in the above embodiment, preferably, the cloud feature angle calculation module 5 includes:

[0163] The imager angle calculation unit 51 is configured to calculate the azimuth angle β and elevation angle α of each all-sky imager according to the spatial coordinates of the all-sky imager and the coordinate point relationship between the pixel coordinates and the spatial coordinates, specifically:

[0164]

[0165]

[0166] The cloud feature angle calculation unit 52 is configured to substitute the pixel coordinates of the cloud feature point in the matching feature point set into the azimuth and elevation angle of the all-sky imager to obtain the elevation angle α of the cloud feature point. 1，t，k and α 2，t，k and the azimuth angle β 1，t，k and β 2，t，k .

[0167] like Fig.10 As shown, in the above embodiment, preferably, the cloud feature point space calculation module 6 includes:

[0168] The position relationship calculation unit 61 is configured to obtain the position relationship between the cloud feature point and the paired all-sky imager in the three-dimensional space coordinate system according to the definition of the trigonometric tangent function, and the expression is:

[0169]

[0170] Among them, the spatial coordinates of the paired sky imagers are A1(x1, y1, z1) and A2(x2, y2, z2), and the cloud feature point P c The spatial coordinates (x c ,y c , z c ), the elevation angles α1 and α2 of the paired sky imagers;

[0171] The distance relationship calculation unit 62 is configured to obtain the distance relationship between the cloud feature points and the all-sky imagers in the three-dimensional space coordinate system according to the cosine theorem of the triangle, and the expression is:

[0172] dxy，12 2 =d xy，1c 2 +d xy，2c 2 -2×d xy，1c ×d xy，2c ×cos(β1-β2); where d xy，12 refers to the horizontal distance between two all-sky imagers, d xy，1c and d xy，2c They refer to the horizontal distances between the two all-sky imagers and the cloud feature points respectively;

[0173]

[0174] The cloud feature point space calculation unit 63 is configured to calculate the spatial coordinates of the cloud feature points in the three-dimensional space coordinate system according to the elevation angle and azimuth angle, position relationship and distance relationship of the cloud feature points. The expression is:

[0175] in,

[0176]

[0177] Then, according to the coordinate point relationship between pixel coordinates and spatial coordinates, we get:

[0178]

[0179] Finally, the elevation angle α 1，t，k and α 2，t，k , and the azimuth angle β 1，t，k and β 2，t，k Substituting the above formula, we can get the spatial coordinates z of the cloud feature point in the three-dimensional space coordinate system: c，t，k , then z c，t，k Substituting the coordinate point relationship between pixel coordinates and spatial coordinates, we can obtain the spatial coordinates (x c，t，k ,y c，t，k ).

[0180] In the above embodiment, preferably, the pixel coordinate change is:

[0181] Among them, the pixel coordinates of the spatial feature points captured by any all-sky imager at two adjacent moments are (i1, j1) and (i2, j2); similarly, the kth matching spatial feature point pair P is obtained. 1，t，k (i 1，t，k , j 1，t，k ) and P 2，t，k (i 2，t，k , j 2，t，k) pixel coordinate change Δi t，k and Δj t，k ;

[0182] The change in spatial coordinates is:

[0183]

[0184] i 1，t，k and j 1，t，k、 and Δi t，k and Δj t，k and z c，t，k Substituting into the above formula, we can get the spatial coordinate change Δx c，t，k and Δy c，t，k ;

[0185] The expression of cloud movement speed is:

[0186] Among them, Δt is the time interval between two adjacent moments, so as to obtain the spatial coordinates of the kth feature point and the cloud moving speed at time t, that is, V c，t，k =(x c，t，k ,y c，t，k , z c，t，k ,u c，t，k , v c，t，k ), all observation data sets at time t are V c，t =(V c，t，1 , V c，t，2 , ..., v c，t，M ), where M is the number of valid cloud feature points.

[0187] Based on the above Figure 5 and Figure 7 The method shown, accordingly, an embodiment of the present application also provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the steps of the cloud position and cloud speed measurement method based on the all-sky cloud map network of any of the above embodiments are implemented.

[0188] Based on this understanding, the technical solution of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, USB flash drive, mobile hard disk, etc.), including a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods of various implementation scenarios of the present application.

[0189] Based on the above Figures 5 to 7 The method shown, and Figures 8 to 10The virtual device embodiment shown, in order to achieve the above-mentioned purpose, the embodiment of the present application also provides a computer device, including a storage medium and a processor; the storage medium is used to store computer programs; the processor is used to execute the computer program to implement the steps of the cloud position and cloud speed measurement method based on the all-sky cloud map network of any of the above-mentioned embodiments.

[0190] Optionally, the computer device may further include a user interface, a network interface, a camera, a radio frequency (RF) circuit, a sensor, an audio circuit, a WI-FI module, etc. The user interface may include a display, an input unit such as a keyboard, etc., and the optional user interface may also include a USB interface, a card reader interface, etc. The network interface may optionally include a standard wired interface, a wireless interface (such as a Bluetooth interface, a WI-FI interface), etc.

[0191] Those skilled in the art will appreciate that the computer device structure provided in this embodiment does not limit the computer device, and may include more or fewer components, or a combination of certain components, or different component arrangements.

[0192] The storage medium may also include an operating system and a network communication module. The operating system is a program that manages and saves the hardware and software resources of the computer device, and supports the operation of information processing programs and other software and / or programs. The network communication module is used to realize communication between the components inside the storage medium, and communication with other hardware and software in the physical device.

[0193] In the present invention, the terms "first", "second", and "third" are used for descriptive purposes only and should not be understood as indicating or implying relative importance; the term "plurality" refers to two or more, unless otherwise clearly defined. The terms "installed", "connected", "connected", "fixed", etc. should be understood in a broad sense. For example, "connected" can be a fixed connection, a detachable connection, or an integral connection; "connected" can be a direct connection or an indirect connection through an intermediate medium. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0194] In the description of the present invention, it is necessary to understand that the directions or positional relationships indicated by terms such as “upper”, “lower”, “left”, “right”, “front” and “back” are based on the directions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or unit referred to must have a specific direction, be constructed and operated in a specific orientation, and therefore, cannot be understood as a limitation on the present invention.

[0195] In the description of this specification, the description of the terms "one embodiment", "some embodiments", "specific embodiments", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0196] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A cloud position and cloud velocity measurement method based on all-sky cloud image networking, characterized in that: The following steps are involved: The full sky image captured by each full sky imager and the coordinate point relationship between the pixel coordinates of the full sky image and the spatial coordinates in the three-dimensional spatial coordinate system are obtained, specifically: Wherein, the viewing angle FOV of the all-sky imager is 180°, N is the number of pixels of the all-sky image, and the point of the i-th pixel in the horizontal direction and the j-th pixel in the vertical direction of the all-sky image corresponds to the spatial coordinates (x, y, z) of the point P in the three-dimensional spatial coordinate system; Identify spatial feature matching points of the all-sky image taken by the pairs of all-sky imagers at the same time to obtain spatial feature point pairs; Calculate the direction angle of the connecting line between the two points in each pair of spatial feature points, specifically: Among them, the two full sky images I at time t 1，t and I 2，t The coordinates of P are 1，t，k (i 1，t，k , j 1，t，k ) and P 2，t，k (i 2，t，k , j 2，t，k ); The spatial feature pair is quality-controlled according to a preset threshold value corresponding to the direction angle to obtain a matching feature point set, the matching feature point set P F The expression is: P F =[(P 1，t，k , P 2，t，k )ifθ t，k <median(θ t )-10|k=1,2,...,N];where, θ t，k Represented as the kth matching spatial feature point pair P at time t 1，t，k (i 1，t，k , j 1，t，k ) and P 2，t，k (i 2，t，k , j 2，t，k )’s connecting line direction angle, median(θ t ) is expressed as the average value of the connecting line direction angles of all spatial feature point pairs at time t. The preset threshold is that the difference between the connecting line direction angle of the kth matching spatial feature point pair at time t and the average value of the connecting line direction angles of all spatial feature point pairs at time t is less than 10, that is, θ t，k <median(θ t )-10; Calculate the elevation angle and azimuth angle of the cloud feature point according to the pixel coordinates of the matching feature point set in the full sky image; Calculate the spatial coordinates of the cloud feature point according to the elevation angle and the azimuth angle of the cloud feature point and the spatial coordinates of the paired all-sky imager in the three-dimensional spatial coordinate system; Calculate the correlation between the pixel coordinate change and the spatial coordinate change of the same all-sky imager at two adjacent moments according to the spatial coordinates of the cloud feature points; The cloud movement speed is calculated based on the correlation between the pixel coordinate change amount and the space coordinate change amount.

2. The cloud position and cloud velocity measurement method according to claim 1, characterized in that: Calculating the elevation angle and azimuth angle of the cloud feature point according to the pixel coordinates of the matching feature point set in the full sky image comprises the following steps: The azimuth angle β and elevation angle α of each of the all-sky imagers are calculated according to the spatial coordinates of the all-sky imager and the coordinate point relationship between the pixel coordinates and the spatial coordinates, specifically: Substitute the pixel coordinates of the cloud feature point in the matching feature point set into the azimuth and elevation of the all-sky imager to obtain the elevation angle α of the cloud feature point 1，t，k and α 2，t，k and the azimuth angle β 1，t，k and β 2，t，k .

3. The cloud position and cloud velocity measurement method according to claim 2, characterized in that: Calculating the spatial coordinates of the cloud feature points according to the elevation angle and the azimuth angle of the cloud feature points and the spatial coordinates of the paired all-sky imagers in the three-dimensional spatial coordinate system comprises the following steps: According to the definition of trigonometric tangent function, the positional relationship between the cloud feature points and the paired all-sky imagers in the three-dimensional spatial coordinate system is obtained, and the expression is: Among them, the spatial coordinates of the paired sky imagers are A1(x1, y1, z1) and A2(x2, y2, z2), and the cloud feature point P c The spatial coordinates (x c ,y c , z c ), the elevation angles α1 and α2 of the paired sky imagers; According to the cosine theorem of triangle, the distance relationship between the cloud feature points and the all-sky imager in the three-dimensional space coordinate system is obtained, and the expression is: d xy，12 2 =d xy，1c 2 +d xy，2c 2 -2×d xy，1c ×d xy，2c ×cos(β1-β2); where d xy，12 refers to the horizontal distance between two all-sky imagers, d xy，1c and d xy，2c They refer to the horizontal distances between the two all-sky imagers and the cloud feature points; The spatial coordinates of the cloud feature points in the three-dimensional spatial coordinate system are calculated according to the elevation angle and azimuth angle of the cloud feature points, the position relationship and the distance relationship. The expression is: in, Then, according to the coordinate point relationship between pixel coordinates and spatial coordinates, we get: The elevation angle α 1，t，k and α 2，t，k , and the azimuth angle β 1，t，k and β 2，t，k Substituting the above formula, we can get the spatial coordinate z of the cloud feature point in the three-dimensional space coordinate system: c，t，k , then z c，t，k Substituting the pixel coordinates and the spatial coordinates into the coordinate point relationship, the spatial coordinates (x c，t，k ,y c，t，k ).

4. The cloud position and cloud velocity measurement method according to claim 3, characterized in that: The pixel coordinate change is: Among them, the pixel coordinates of the spatial feature points captured by any all-sky imager at two adjacent moments are (i1, j1) and (i2, j2); similarly, the kth matching spatial feature point pair P is obtained. 1，t，k (i 1，t，k , j 1，t，k ) and P 2，t，k (i 2，t，k , j 2，t，k ) pixel coordinate change Δi t，k and Δj t，k ; Then, according to the coordinate point relationship between the pixel coordinates and the spatial coordinates, the spatial coordinate change is obtained as: will i 1，t，k and j 1，t，k、 and Δi t，k and Δj t，k and z c，t，k Substituting into the above formula, we can get the spatial coordinate change Δx c，t，k and Δy c，t，k ; The cloud movement speed is calculated according to the correlation between the pixel coordinate change amount and the space coordinate change amount, specifically: Among them, Δt is the time interval between two adjacent moments, so as to obtain the spatial coordinates of the kth feature point and the cloud moving speed at time t, that is, V c，t，k =(x c，t，k ,y c，t，k , z c，t，k ,u c，t，k , v c，t，k ), all observation data sets at time t are V c，t =(V c，t，1 , V c，t，2 , ..., v c，t，M ), where M is the number of valid cloud feature points.

5. A cloud position and cloud velocity measurement system based on a full sky cloud map network, characterized in that: include: The pixel coordinate and space coordinate conversion module is configured to obtain the full sky image taken by each full sky imager and the coordinate point relationship between the pixel coordinates of the full sky image and the space coordinates in the three-dimensional space coordinate system, specifically: Wherein, the viewing angle FOV of the all-sky imager is 180°, N is the number of pixels of the all-sky image, and the point of the i-th pixel in the horizontal direction and the j-th pixel in the vertical direction of the all-sky image corresponds to the spatial coordinates (x, y, z) of the point P in the three-dimensional spatial coordinate system; A spatial feature recognition module is configured to recognize spatial feature matching points of the all-sky image captured by the paired all-sky imagers at the same time, and obtain spatial feature point pairs; The connection direction angle calculation module is configured to calculate the direction angle of the connection line between two points in each pair of spatial feature points, specifically: Among them, the two full sky images I at time t 1，t and I 2，t The coordinates of P are 1，t，k (i 1，t，k , j 1，t，k ) and P 2，t，k (i 2，t，k , j 2，t，k ); The data quality control module is configured to perform quality control on the spatial feature pair according to a preset threshold value corresponding to the direction angle to obtain a matching feature point set, the matching feature point set P F The expression is: P F =[(P 1，t，k , P 2，t，k )ifθ t，k <median(θ t )-10|k=1,2,...,N];wherein, θ t，k Represented as the kth matching spatial feature point pair P at time t 1，t，k (i 1，t，k , j 1，t，k ) and P 2，t，k (i 2，t，k , j 2，t，k )’s connecting line direction angle, median(θ t ) is expressed as the average value of the connecting line direction angles of all spatial feature point pairs at time t. The preset threshold is that the difference between the connecting line direction angle of the kth matching spatial feature point pair at time t and the average value of the connecting line direction angles of all spatial feature point pairs at time t is less than 10, that is, θ t，k <median(θ t )-10; a cloud feature angle calculation module, configured to calculate the elevation angle and azimuth angle of the cloud feature point according to the pixel coordinates of the matching feature point set in the full sky image; a cloud feature point space calculation module, configured to calculate the space coordinates of the cloud feature point according to the elevation angle and azimuth angle of the cloud feature point and the space coordinates of the paired all-sky imager in the three-dimensional space coordinate system; A cloud feature coordinate change conversion module is configured to calculate the correlation between the pixel coordinate change amount and the spatial coordinate change amount of the same all-sky imager at two adjacent moments according to the spatial coordinates of the cloud feature points; The cloud speed calculation module is configured to calculate the cloud movement speed based on the correlation between the pixel coordinate change amount and the space coordinate change amount.

6. The cloud position and cloud velocity measurement system according to claim 5, characterized in that: The cloud feature angle calculation module includes: The imager angle calculation unit is configured to calculate the azimuth angle β and elevation angle α of each of the all-sky imagers according to the spatial coordinates of the all-sky imager and the coordinate point relationship between the pixel coordinates and the spatial coordinates, specifically: A cloud feature angle calculation unit is configured to substitute the pixel coordinates of the cloud feature point in the matching feature point set into the azimuth and elevation angle of the all-sky imager to obtain the elevation angle α of the cloud feature point 1，t，k and α 2，t，k and the azimuth angle β 1，t，k and β 2，t，k .

7. The cloud position and cloud velocity measurement system according to claim 6, characterized in that: The cloud feature point space calculation module includes: The position relationship calculation unit is configured to obtain the position relationship between the cloud feature point and the paired all-sky imager in the three-dimensional space coordinate system according to the definition of the trigonometric tangent function, and the expression is: Among them, the spatial coordinates of the paired sky imagers are A1(x1, y1, z1) and A2(x2, y2, z2), and the cloud feature point P c The spatial coordinates (x c ,y c , z c ), the elevation angles α1 and α2 of the paired sky imagers; The distance relationship calculation unit is configured to obtain the distance relationship between the cloud feature points and the all-sky imager in the three-dimensional space coordinate system according to the cosine theorem of the triangle, and the expression is: d xy，12 2 =d xy，1c 2 +d xy，2c 2 -2×d xy，1c ×d xy，2c ×cos(β1-β2); where d xy，12 refers to the horizontal distance between two all-sky imagers, d xy，1c and d xy，2c They refer to the horizontal distances between the two all-sky imagers and the cloud feature points; The cloud feature point space calculation unit is configured to calculate the spatial coordinates of the cloud feature point in the three-dimensional space coordinate system according to the elevation angle and azimuth angle of the cloud feature point, the position relationship and the distance relationship, and the expression is: in, Then, according to the coordinate point relationship between pixel coordinates and spatial coordinates, we get: Finally, the elevation angle α 1，t，k and α 2，t，k , and the azimuth angle β 1，t，k and β 2，t，k Substituting the above formula, we can get the spatial coordinate z of the cloud feature point in the three-dimensional space coordinate system: c，t，k , then z c，t，k Substituting the pixel coordinates and the spatial coordinates into the coordinate point relationship, the spatial coordinates (x c，t，k ,y c，t，k ).

8. The cloud position and cloud velocity measurement system according to claim 7, characterized in that: The pixel coordinate change is: Among them, the pixel coordinates of the spatial feature points captured by any all-sky imager at two adjacent moments are (i1, j1) and (i2, j2); similarly, the kth matching spatial feature point pair P is obtained. 1，t，k (i 1，t，k , j 1，t，k ) and P 2，t，k (i 2，t，k , j 2，t，k ) pixel coordinate change Δi t，k and Δj t，k ; The spatial coordinate variation is: i 1，t，k and j 1，t，k、 and Δi t，k and Δj t，k and z c，t，k Substituting into the above formula, we can get the spatial coordinate change Δx c，t，k and Δy c，t，k ; The expression of cloud movement speed is: Among them, Δt is the time interval between two adjacent moments, so as to obtain the spatial coordinates of the kth feature point and the cloud moving speed at time t, that is, V c，t，k =(x c，t，k ,y c，t，k , z c，t，k ,u c，t，k , v c，t，k ), all observation data sets at time t are V c，t =(V c，t，1 , V c，t，2 , ..., v c，t，M ), where M is the number of valid cloud feature points.

9. A readable storage medium, characterized in that: A computer program is stored thereon, and when the program is executed by a processor, the steps of the cloud position and cloud speed measurement method based on the all-sky cloud map networking as described in any one of claims 1 to 4 are implemented.

10. A computer device, characterized in that: It includes a storage medium and a processor; the storage medium is used to store a computer program; the processor is used to execute the computer program to implement the steps of the cloud position and cloud speed measurement method based on the all-sky cloud map networking as described in any one of claims 1 to 4.

Images