A method for fitting and displaying slope radar monitoring data and video images

Abstract

The present invention discloses a method for fitting and displaying slope radar monitoring data and video images, which includes arranging a plurality of corner reflectors on the slope surface of the monitoring area; constructing a radar image coordinate system for the slope radar image, determining the position of the corner reflector in the slope radar image, and determining the image coordinate values of the corner reflector through the image coordinate system; constructing a video image coordinate system, and manually marking the image coordinate values of the corner reflector in the slope video monitoring image of this frame; establishing an affine transformation matrix between the slope radar image and the slope video monitoring image, synchronously fitting the slope radar image with the slope video stream at a magnification of 1 based on the affine transformation matrix, and determining the position of the target query point in the slope video monitoring image at different magnifications in the slope video monitoring image at a magnification of 1; the present invention improves the visualization effect of slope radar monitoring data and enhances the real-time performance and accuracy of monitoring.

Description

Technical Field

[0001] The present invention relates to the technical field of slope monitoring, and particularly relates to a method for fitting and displaying slope radar monitoring data and video images. Background Art

[0002] The slope radar realizes real-time imaging of the slope in the monitoring area by emitting microwaves. In order to more intuitively view and display the slope displacement monitoring data in the imaging diagram, it is usually necessary to perform three-dimensional spatial registration on the slope radar imaging diagram and the UAV three-dimensional aerial survey diagram.

[0003] UAV aerial survey, massive data processing and modeling, aerial survey base map and elevation processing, and the development of registration software are the basic work to complete this registration. The entire registration process not only requires high computer computing power but is also very cumbersome.

[0004] The specific implementation method of fitting the radar data in the traditional method to the video surveillance image is as follows: Take a whole image of the entire open-pit mine, use three-dimensional modeling software to establish a three-dimensional real-scene model of the open-pit mine, and use the video surveillance image as the base map. Different computers have different computing powers, and the modeling time for 100 photos usually takes more than 4 hours.

[0005] When performing the fitting operation on the radar surveillance image and the video surveillance image, it is also necessary to construct a radar stereo imaging diagram based on the radar coordinates (which need to be measured in advance with RTK or total station), 84 coordinate point cloud, and UTM coordinate point cloud, and then fit the radar stereo imaging diagram with the video surveillance base map. The entire fitting operation process takes at least 30 minutes. Therefore, the fitting and display of traditional video surveillance images and radar surveillance images take a long time and cannot realize the real-time update of video surveillance data and radar surveillance data. Especially for the slopes of open-pit mines, the surface scenes of the slopes continuously change with the progress of mining activities, resulting in a problem of temporal mismatch between the radar monitoring images and the aerial survey base maps, which seriously affects the radar monitoring effect of the slopes. Summary of the Invention

[0006] The purpose of the present invention is to provide a method for fitting and displaying slope radar monitoring data and video images to solve the technical problem that there is a temporal mismatch between the radar monitoring image and the aerial survey base map in the prior art, which seriously affects the radar monitoring effect of the slope.

[0007] To solve the above technical problem, the present invention specifically provides the following technical solutions:

[0008] A method for fitting and displaying slope radar monitoring data and video images includes the following steps:

[0009] Step 100: Divide the open-pit mine into different monitoring areas, deploy a plurality of corner reflectors on the slope surface of each monitoring area, and install corresponding slope radars and video monitoring devices opposite to each monitoring area;

[0010] Step 200: Construct a radar image coordinate system for the slope radar image, determine the position of the corner reflector in the slope radar image based on the strength of the echo signal in the slope radar image, and determine the image coordinate value of the corner reflector through the image coordinate system. ;

[0011] Step 300: Intercept slope video monitoring images from the slope video stream of the monitoring area, construct a video image coordinate system, and manually mark the image coordinate value of the corner reflector in this frame of slope video monitoring image. ;

[0012] Step 400: Establish an affine transformation matrix between the slope radar image and the slope video monitoring image, so that the image coordinate value of the corner reflector in the image coordinate system and the image coordinate value of the corner reflector in the video image coordinate system correspond one by one;

[0013] Step 500: Synchronously fit the slope radar image and the 1x magnification slope video stream based on the affine transformation matrix, determine the position of the target query point in the 1x magnification slope video monitoring image in the slope video monitoring image at different magnification factors, and obtain the radar monitoring data corresponding to the target query point in the slope radar image of the slope video monitoring image at different magnification factors.

[0014] As a preferred solution of the present invention, in the step 100, all corner reflectors are evenly dispersed on the slope surface, the number of corner reflectors deployed on the slope surface is at least 3, and each reflecting surface of each corner reflector is unobstructed.

[0015] As a preferred solution of the present invention, in the step 200, the radar image coordinate system is a coordinate system for expressing the position of each pixel point in the slope radar image, and the origin of the radar image coordinate system is set at the upper left corner of the slope radar image;

[0016] The echo signal of the corner reflector in the slope radar image is stronger than the echo signal of the slope surface in the slope radar image, so that the pixel value of the corner reflector in the slope radar image is greater than the pixel value corresponding to the slope rock surface in the slope radar image.

[0017] As a preferred embodiment of the present invention, the image resolution of the slope radar imaging map is m×n pixels. Wave intensity filtering is performed on the slope radar imaging map, and the pixel values corresponding to each pixel point in the slope radar imaging map after wave intensity filtering are collected. Pixel points are selected based on the pixel values, and the image coordinates of the selected pixel points in the radar image coordinate system of the slope radar imaging map are determined. 。

[0018] As a preferred embodiment of the present invention, in the step 300, the image resolution of the slope video surveillance image is o×p pixels.

[0019] As a preferred embodiment of the present invention, in the step 400, the method for implementing the affine transformation matrix between the slope radar imaging map and the slope video surveillance image is as follows:

[0020] Taking the slope video surveillance image as the base map, the slope radar imaging map is mapped into the slope video surveillance image through the affine transformation matrix, and the pixel point coordinates of the slope radar imaging map after affine transformation by the affine transformation matrix are in one-to-one correspondence and the same as the pixel point coordinates on the slope video surveillance image;

[0021] Among them, the expression of the affine transformation matrix is: ;

[0022] is the coordinate of the slope radar imaging map after affine transformation by the affine transformation matrix;

[0023] is the coordinate of the slope radar imaging map before affine transformation;

[0024] is the affine transformation matrix, where 、 、 、 、 and are affine transformation parameters.

[0025] As a preferred embodiment of the present invention, the method for mapping the slope radar imaging map into the slope video surveillance image through the affine transformation matrix is as follows:

[0026] Input the coordinate values corresponding to each of the corner reflectors in the slope radar imaging map and the slope video surveillance image respectively to obtain the 、 、 、 、 and ;

[0027] Input the slope radar imaging map, the affine transformation matrix, and the size of the output image into Python software. After the affine transformation matrix, the pixel coordinates of the slope radar imaging map are the same as those of the same pixel on the slope video surveillance image.

[0028] As a preferred embodiment of the present invention, traverse all the pixel points on the slope radar imaging map one by one, and obtain the coordinate values corresponding to each pixel point based on the image coordinate system;

[0029] After the coordinate values of each pixel point on the slope radar imaging map are transformed by the affine transformation matrix, they are the same as the coordinate values of the same pixel on the slope video surveillance image, so that the slope radar imaging map is transformed into a slope radar monitoring cloud map that matches the pixel points of the slope video surveillance image one by one.

[0030] As a preferred embodiment of the present invention, the implementation method for obtaining the radar monitoring data corresponding to the target query point of the slope video surveillance image at different magnification ratios in the slope radar imaging map is:

[0031] First, determine the position of the target query point in the slope video surveillance image at different magnification ratios in the slope video surveillance image at 1 magnification ratio;

[0032] Based on the position of the target query point in the slope video surveillance image at 1 magnification ratio, determine the corresponding position of the target query point in the slope radar imaging map after affine transformation, so as to obtain the radar monitoring data corresponding to the target query point in the slope radar imaging map.

[0033] As a preferred embodiment of the present invention, the implementation method for determining the position of the target query point in the slope video surveillance image at different magnification ratios in the slope video surveillance image at 1 magnification ratio is:

[0034] ;

[0035] Among them, is the image plane coordinate of the target query point in the slope video surveillance image at different magnification ratios in the 1 magnification ratio image;

[0036] is the image plane coordinate of the optical axis center of the slope video surveillance image at different magnification ratios in the 1 magnification ratio image;

[0037] are the image plane coordinates of the target query point in the slope video surveillance image at different magnification ratios respectively;

[0038] M is the magnification ratio of the video image.

[0039] The present invention has the following beneficial effects compared with the prior art:

[0040] By performing radar monitoring and video monitoring on the slope, the present invention fits and displays the slope displacement monitoring data contained in the slope radar imaging map in the slope video surveillance image, realizes real-time update of the slope video surveillance base map, significantly improves the visualization effect of the slope radar monitoring data, enhances the real-time and accuracy of monitoring, and provides more intuitive and effective technical support for slope monitoring and early warning. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only exemplary, and for those of ordinary skill in the art, other implementation drawings can be obtained by extension based on the provided drawings without creative efforts.

[0042] Figure 1 It is a flowchart of the method for fitting and displaying monitoring data and video images according to an embodiment of the present invention;

[0043] Figure 2 It is a schematic diagram of the coordinate position of the corner reflector in the radar image coordinate system according to an embodiment of the present invention;

[0044] Figure 3 It is a schematic diagram of the coordinate position of the corner reflector in the video image coordinate system according to an embodiment of the present invention;

[0045] Figure 4 It is the slope radar imaging map before affine transformation according to an embodiment of the present invention;

[0046] Figure 5 It is the slope radar imaging map after affine transformation according to an embodiment of the present invention;

[0047] Figure 6 It is a fitting diagram of the slope radar imaging map after affine transformation and the slope video surveillance image according to an embodiment of the present invention;

[0048] Figure 7 It is an effect diagram of the fitting display of the slope video surveillance base map and the radar monitoring cloud map according to an embodiment of the present invention;

[0049] Figure 8 It is a schematic diagram of the principle of pixel positioning of images with different magnifications according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0050] Next, in combination with the accompanying drawings in the embodiments of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0051] As Figure 1 shown, the present invention provides a method for fitting and displaying slope radar monitoring data and video images, including the following steps:

[0052] Step 100: Divide the open-pit mine into different monitoring areas, deploy a plurality of corner reflectors on the slope surface of each monitoring area, and install corresponding slope radars and video monitoring devices opposite to each monitoring area.

[0053] All corner reflectors are evenly dispersed on the slope surface. The number of corner reflectors deployed on the slope surface is at least 3, and each reflecting surface of each corner reflector has no occlusion.

[0054] Perform zonal monitoring on the video and radar monitoring operations of the open-pit mine. Divide the open-pit mine into multiple planar blocks, and install slope radars and video monitoring devices in the opposite directions of each planar block, so as to realize radar monitoring and video monitoring of each planar block. Since the video stream images are transmitted and updated in real time, without considering the time consumption, and the radar monitoring data and video monitoring data are two-dimensionally fitted, there is no three-dimensional modeling problem, and there is no requirement for the computer performance, without considering the time consumption.

[0055] Step 200: Construct a radar image coordinate system for the slope radar image of the monitoring area, determine the position of the corner reflector in the slope radar image based on the strength of the echo signal in the slope radar image, and determine the image coordinate value of the corner reflector through the image coordinate system , as Figure 2 shown.

[0056] Step 300: Intercept the slope video monitoring image from the slope video stream of the monitoring area, construct a video image coordinate system, and manually mark the image coordinate value of the corner reflector in this frame of slope video monitoring image , as Figure 3 shown.

[0057] It should be noted that in this embodiment, a slope radar and a video monitoring device are specifically arranged opposite to the monitoring area. The slope radar and the video monitoring device can respectively collect radar data and video data of the same monitoring area. Both the radar data and the video data are two-dimensional data. When fitting the radar data and the video data, a frame of image is intercepted from the video stream, and the monitoring time of this image is determined. Then, a radar image of the same monitoring time is obtained. The affine transformation matrix of the video monitoring image and the radar image can be solved using the opencv software library of python. Subsequently, the imaging maps generated by the radar later (here the imaging map is another format of the radar monitoring data) can be transformed through this affine transformation matrix.

[0058] In step 200, the radar image coordinate system is used to represent the coordinate system of each pixel point in the slope radar image, and the origin of the radar image coordinate system is set at the upper left corner of the slope radar image. The abscissa x increases from left to right along the horizontal direction, and the ordinate y increases from top to bottom along the vertical direction.

[0059] The echo signal of the corner reflector in the slope radar image is stronger than the echo signal of the slope surface in the slope radar image, so that the pixel value of the corner reflector in the slope radar image is greater than the pixel value corresponding to the slope rock surface in the slope radar image. Therefore, in the slope radar image, a very bright point will appear at the position where the corner reflector is located.

[0060] Perform wave intensity filtering on the slope radar image, collect the pixel values corresponding to each pixel point in the slope radar image after wave intensity filtering, screen out pixel points based on the pixel values, and determine the image coordinates of the screened pixel points in the radar image coordinate system of the slope radar image 。

[0061] In order to present the slope field of view in the video image to the greatest extent, a slope video monitoring device is set beside the slope radar to record and observe the real-time change state of the slope, and the video monitoring is set to a magnification of 1. A frame of image is intercepted from the video stream, and the image coordinates of the corner reflector in this frame of image are manually marked 。

[0062] Since the image resolution of the slope radar image is m×n pixels and the image resolution of the slope video monitoring image is o×p pixels, and since it is impossible for the radar radio frequency transmitting front end and the optical axis of the video image center to coincide, if there is a point in the radar image ,then its coordinates in the video image must not be ,that is, there is a certain deviation in the pixel point distribution of the imaging of the same target slope by the slope radar and the video monitoring.

[0063] This embodiment uses an affine transformation matrix to correct the coordinates of the slope radar image, and accurately fits the imaging pixels of the slope radar image after correction with the slope video surveillance image.

[0064] Among them, this embodiment specifically uses the coordinates of the corner reflector in the slope radar image and the slope video surveillance image respectively and to determine the affine transformation matrix, which establishes a one-to-one correspondence between the slope radar image and the slope video surveillance image.

[0065] Step 400: Establish an affine transformation matrix between the slope radar image and the slope video surveillance image, so that the image coordinate values of the corner reflector in the image coordinate system and the image coordinate values of the corner reflector in the video image coordinate system correspond one-to-one.

[0066] In step 400, the implementation method of establishing the affine transformation matrix between the slope radar image and the slope video surveillance image is as follows:

[0067] Taking the slope video surveillance image as the base map, map the slope radar image into the slope video surveillance image through the affine transformation matrix, and the pixel point coordinates of the slope radar image after affine transformation by the affine transformation matrix match the pixel point coordinates on the slope video surveillance image one by one;

[0068] Among them, the expression of the affine transformation matrix is: ;

[0069] is the coordinate of the slope radar image after affine transformation by the affine transformation matrix;

[0070] is the coordinate of the slope radar image before affine transformation;

[0071] is the affine transformation matrix, where 、 、 、 、 and are affine transformation parameters.

[0072] The implementation method of mapping the slope radar image into the slope video surveillance image through the affine transformation matrix is:

[0073] Input the corresponding coordinate values of each corner reflector in the slope radar image and the slope video surveillance image respectively to obtain the 、 、 , , and .

[0074] Input the slope radar imaging map, the affine transformation matrix, and the size of the output image into Python software. After the affine transformation matrix, the pixel coordinates of the slope radar imaging map are the same as those of the same pixel points on the slope video surveillance image.

[0075] Specifically, the cv2.estimateAffine2D() function in the OpenCV library of Python can be used to calculate the affine transformation matrix. The specific implementation method is as follows: input the coordinate values of multiple corner reflectors in the slope radar imaging map and the slope video surveillance image respectively, and output , , , , and to form the affine transformation matrix .

[0076] Then use the cv2.warpAffine() function to implement the affine transformation of the image. Specifically, input the slope radar imaging map, the affine transformation matrix, and the size of the output image, and output the slope radar imaging map after the affine transformation. As shown in Figure 4 and Figure 5 , where Figure 4 is the slope radar imaging map before the affine transformation, and Figure 5 is the slope radar imaging map after the affine transformation.

[0077] Step 500: Synchronously fit the slope radar imaging map with the 1x magnification slope video stream based on the affine transformation matrix, determine the positions of the target query points in the slope video surveillance image at different magnification factors in the 1x magnification slope video surveillance image, and obtain the radar monitoring data corresponding to the target query points in the slope video surveillance image at different magnification factors in the slope radar imaging map.

[0078] The implementation method of synchronously fitting the slope radar imaging map with the 1x magnification slope video stream based on the affine transformation matrix is as follows: traverse all pixel points on the slope radar imaging map one by one, and obtain the coordinate values corresponding to each pixel point based on the image coordinate system.

[0079] After the coordinate values of each pixel point on the slope radar imaging map are transformed by the affine transformation matrix, they are the same as the coordinate values of the same pixel points on the slope video surveillance image, so that the slope radar imaging map is transformed into a slope radar monitoring cloud map that matches the pixel points of the slope video surveillance image one by one. After synchronously fitting the slope radar imaging map with the 1x magnification slope video stream, as shown in Figure 6As shown, specifically using the slope video monitoring image as the base map and the slope radar monitoring image as the cloud map, the fitting display effect diagram is as Figure 7 shown.

[0080] In this embodiment, first, the corner reflectors on the slope radar imaging map are used as multiple control points to construct an affine transformation matrix. Then, other pixel points on the slope radar imaging map are sequentially transformed based on the affine transformation matrix established by the "multiple control points", and finally, the slope radar imaging map after affine transformation is formed. The coordinate values of the same pixel points on the transformed slope radar imaging map and the slope video monitoring image are the same.

[0081] Since in the actual application process, the slope video monitoring image cannot always maintain a magnification of 1, in order to more clearly observe the slope rock surface, it is necessary to adjust the camera pan-tilt (the orientation of the optical axis center) and the video magnification M. The implementation method for obtaining the radar monitoring data corresponding to the target query point of the slope video monitoring image at different magnification ratios in the slope radar imaging map is as follows:

[0082] First, determine the position of the target query point in the slope video monitoring image at different magnification ratios in the slope video monitoring image at a magnification of 1. The schematic diagram of pixel positioning for images at different magnification ratios is as Figure 8 shown.

[0083] Based on the position of the target query point in the slope video monitoring image at a magnification of 1, determine the corresponding position of the target query point in the slope radar imaging map after affine transformation, so as to obtain the radar monitoring data corresponding to the target query point in the slope radar imaging map.

[0084] The implementation method for determining the position of the target query point in the slope video monitoring image at different magnification ratios in the slope video monitoring image at a magnification of 1 is as follows:

[0085] ;

[0086] Among them, is the image plane coordinate of the target query point in the slope video monitoring image at different magnification ratios in the image at a magnification of 1;

[0087] is the image plane coordinate of the optical axis center of the slope video monitoring image at different magnification ratios in the image at a magnification of 1;

[0088] are the image plane coordinates of the target query point in the slope video monitoring image at different magnification ratios respectively;

[0089] M is the video image magnification.

[0090] This embodiment conducts zonal monitoring on the video and radar monitoring operations of open-pit mines. The open-pit mine is divided into multiple planar blocks, and slope radars and video monitoring devices are installed in opposite directions of each planar block, so as to achieve radar monitoring and video monitoring of each planar block. Since the video stream images are transmitted and updated in real time, without considering the time consumption, and the radar monitoring data and video monitoring data are two-dimensionally fitted, there is no three-dimensional modeling problem, and there is no requirement for computer performance, without considering the time consumption.

[0091] When specifically fitting the video monitoring images and radar monitoring data of each planar block, intercept a frame of image from the video stream, then obtain a radar imaging map, and use the opencv software library of python to solve the affine transformation matrix of the two images. The subsequent radar-generated imaging maps can be transformed through this affine transformation matrix, and the total time consumption does not exceed 5 minutes. Thus, it can greatly reduce the time consumption of fitting the video monitoring images and radar monitoring data of the mine, and achieve real-time update of the video monitoring base map.

[0092] This embodiment realizes real-time update of the slope video monitoring base map by conducting radar monitoring and video monitoring on the slope, and fitting and displaying the slope displacement monitoring data contained in the slope radar imaging map in the slope video monitoring image, significantly improving the visualization effect of the slope radar monitoring data, enhancing the real-time and accuracy of monitoring, and providing more intuitive and effective technical support for slope monitoring and early warning.

[0093] The above embodiments are only exemplary embodiments of the present application and are not used to limit the present application. The protection scope of the present application is defined by the claims. Those skilled in the art can make various modifications or equivalent replacements within the essence and protection scope of the present application, and such modifications or equivalent replacements should also be regarded as falling within the protection scope of the present application.

Claims

1. A method for displaying slope radar monitoring data and video images, characterized in that: The following steps are involved: Step 100: Divide the open-pit mine into different monitoring areas, arrange multiple corner reflectors on the slope surface of each monitoring area, and install corresponding slope radar and video monitoring equipment opposite to each monitoring area; Step 200: construct a radar image coordinate system of the slope radar image of the monitoring area, determine the position of the corner reflector in the slope radar image based on the strength of the echo signal in the slope radar image, and determine the image coordinate value of the corner reflector through the image coordinate system ; Step 300: intercept the slope video monitoring image from the slope video stream in the monitoring area, construct the video image coordinate system, and manually mark the image coordinate value of the corner reflector in the slope video monitoring image ; Step 400: Establish an affine transformation matrix between the slope radar imaging image and the slope video monitoring image so that the image coordinate value of the corner reflector in the image coordinate system is The image coordinate value of the corner reflector in the video image coordinate system There is a one-to-one correspondence between them; Step 500: Synchronously fit the slope radar imaging image with the slope video stream at 1 magnification based on the affine transformation matrix, and determine the position of the target query point in the slope video surveillance image at different magnifications in the slope video surveillance image at 1 magnification; based on the position of the target query point in the slope video surveillance image at 1 magnification, determine the position corresponding to the target query point in the slope radar imaging image after affine transformation, thereby obtaining the radar monitoring data corresponding to the target query point in the slope radar imaging image, so as to perform thumbnail fit of the slope video surveillance images at different magnifications with the slope radar imaging image.

2. The method for displaying slope radar monitoring data and video images according to claim 1, characterized in that: In step 100, all corner reflectors are evenly dispersed on the slope surface, the number of corner reflectors arranged on the slope surface is at least 3, and each reflective surface of each corner reflector is unobstructed.

3. The method for displaying slope radar monitoring data and video images according to claim 1, characterized in that: In step 200, the radar image coordinate system is used to describe the coordinate system of each pixel position in the slope radar imaging image, and the origin of the radar image coordinate system is set at the upper left corner of the slope radar imaging image; The echo signal of the corner reflector in the slope radar imaging image is stronger than the echo signal of the slope surface in the slope radar imaging image, so that the pixel value of the corner reflector in the slope radar imaging image is greater than the pixel value corresponding to the slope rock surface in the slope radar imaging image.

4. The method for displaying slope radar monitoring data and video images according to claim 3, characterized in that: The image resolution of the slope radar imaging image is m×n pixels, the slope radar imaging image is subjected to wave intensity filtering, the pixel value corresponding to each pixel point in the slope radar imaging image after wave intensity filtering is collected, the pixel points are screened out based on the pixel values, and the image coordinates of the screened pixel points in the radar image coordinate system of the slope radar imaging image are determined .

5. The method for displaying slope radar monitoring data and video images according to claim 4, characterized in that: In step 300, the image resolution of the slope video monitoring image is o×p pixels.

6. The method for displaying slope radar monitoring data and video images according to claim 5, characterized in that: In step 400, the method for establishing the affine transformation matrix between the slope radar imaging image and the slope video monitoring image is: Using the slope video surveillance image as a base map, the slope radar imaging image is mapped to the slope video surveillance image through an affine transformation matrix, and the pixel coordinates of the slope radar imaging image after affine transformation by the affine transformation matrix are matched one by one with the pixel coordinates on the slope video surveillance image; Among them, the expression of the affine transformation matrix is: ; The coordinates of the slope radar image after affine transformation by the affine transformation matrix; is the coordinate of the slope radar image before affine transformation; is the affine transformation matrix, where and are the affine transformation parameters.

7. The method for displaying slope radar monitoring data and video images according to claim 6, characterized in that: The implementation method of mapping the slope radar imaging image to the slope video monitoring image through an affine transformation matrix is: Input the coordinate values ​​of each corner reflector in the slope radar imaging image and the slope video monitoring image to obtain the affine transformation matrix and ; The slope radar imaging image, affine transformation matrix and the size of the output image are input into the Python software. The pixel coordinates of the slope radar imaging image after the affine transformation matrix are identical to the coordinates of the same pixel points on the slope video surveillance image.

8. The method for displaying slope radar monitoring data and video images according to claim 7, characterized in that: Traversing all pixel points on the slope radar imaging image one by one, and obtaining the coordinate value corresponding to each pixel point based on the image coordinate system; After the coordinate value of each pixel point on the slope radar imaging image is converted through the affine transformation matrix, it is the same as the coordinate value of the same pixel point on the slope video surveillance image, so that the slope radar imaging image is transformed into a slope radar monitoring cloud map that matches the pixel points of the slope video surveillance image one by one.

9. The method for displaying slope radar monitoring data and video images according to claim 1, characterized in that: The method for determining the position of the target query point in the slope video surveillance image with different magnifications in the slope video surveillance image with 1 magnification is as follows: ; in, is the image plane coordinates of the target query point in the slope video monitoring image with different magnifications in the 1-magnification image; are the image plane coordinates of the optical axis center of the slope video monitoring image with different magnifications in the 1-magnification image; are respectively the image plane coordinates of the target query point in the slope video monitoring image at different magnifications; M is the video image magnification.

Images