High-steep slope disaster monitoring method and system based on optimized views photogrammetry

Abstract

The present invention relates to the technical field of slope monitoring. Disclosed are a high-steep slope disaster monitoring method and system based on optimized views photogrammetry. The method comprises: acquiring initial data of a high-steep slope by means of a sensor and a GPS device, and constructing an initial three-dimensional model of the high-steep slope; acquiring accurate image data of each point of the high-steep slope by means of an optimized views photogrammetry device; on the basis of the accurate image data combined with an image processing technique, optimizing the initial three-dimensional model, so as to generate an optimized three-dimensional model of the high-steep slope; and establishing a real-time monitoring and early-warning mechanism, collecting the accurate image data of each point of the high-steep slope in real time, performing deformation analysis on the optimized three-dimensional model, so as to promptly find the deformation situation of the slope, and raising an alarm. In the present invention, optimized views photogrammetry is used, such that high-resolution image data can be acquired in a relatively complex area that is difficult to approach, and terrain features of a high-steep slope area can be clearly displayed, thereby providing more comprehensive and detailed information for monitoring and disaster investigation of a high-steep slope.

Description

A method and system for monitoring disasters on steep slopes based on superior view photogrammetry Technical Field

[0001] This invention relates to the field of slope monitoring technology, and more specifically to a method and system for monitoring disasters on steep slopes based on superior-view photogrammetry. Background Technology

[0002] The crests of steep slopes often contain unfavorable geological features such as weathering unloading zones, unstable rock masses, and deformed, fractured rock masses, frequently leading to sudden high-altitude collapses and landslides that pose serious threats to roads and buildings below. Due to the difficulty of navigating steep slopes, investigators rarely reach the top, and traditional surveying and mapping methods struggle to accurately obtain information on the distribution, location, and geometric dimensions of unfavorable geological features above steep slopes, hindering the analysis, evaluation, and prevention of these hazards.

[0003] In recent years, 3D laser scanning technology has been widely used in geological hazard investigations, particularly for steep slopes, enabling rapid slope mapping and measurement of rock mass structural parameters. However, 3D scanning, utilizing ground-based scanners, struggles to scan the top of slopes and concealed areas. Currently, 3D modeling primarily focuses on two levels: terrain and urban 3D. Traditional satellite and airborne remote sensing systems can provide fundamental observational data for large-scale terrain and urban 3D modeling. However, the flight altitude and observation methods of these platforms limit the resolution and completeness of the acquired data, making it difficult to meet the high spatial resolution, high temporal resolution, and multi-view observation data requirements urgently needed for the construction and updating of detailed 3D models of complex scenes.

[0004] Therefore, how to provide a method and system for monitoring disasters on steep slopes based on superior vision photogrammetry, acquire high-resolution image data in complex and inaccessible areas, and clearly show the topographic features of steep slope areas is a problem that urgently needs to be solved by those skilled in the art.

[0005] Summary of the Invention

[0006] In view of this, the present invention provides a method and system for monitoring high and steep slope disasters based on superior-view photogrammetry. Employing superior-view photogrammetry technology, high-resolution image data can be acquired in complex and inaccessible areas, including higher locations and concealed areas, providing more comprehensive and detailed information for monitoring and investigating high and steep slopes. It can clearly display the topographic features of high and steep slope areas, providing an important data foundation and scientific basis for disaster monitoring, risk assessment, and response measures. The present invention improves the accuracy of high and steep slope monitoring, automates monitoring work, and enables non-contact measurement, reducing labor input.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: a method for monitoring disasters on steep slopes based on superior-view photogrammetry, comprising:

[0008] Initial data of steep slopes is acquired using sensors and GPS devices, and an initial 3D model of the slope is constructed. Ground sensors, such as inclinometers, displacement sensors, and strain gauges, monitor the slope's parameters in real time, including tilt angle, displacement changes, and stress conditions. This data provides real-time information on slope changes.

[0009] Precise image data of various points on steep slopes can be obtained using the U-View photogrammetry equipment. The U-View photogrammetry equipment can acquire high-resolution image data, which can cover all parts of the steep slope, including the top and hidden parts.

[0010] Based on the precise image data and combined with image processing technology, the initial three-dimensional model is optimized to generate an optimized three-dimensional model of the steep slope.

[0011] A real-time monitoring and early warning mechanism is established to collect precise image data of various points on steep slopes in real time, perform deformation analysis on the optimized 3D model, promptly detect slope deformation, and issue alarms. Potential geological hazard risks are identified to reduce disaster risks.

[0012] Preferably, image data of various parts of the steep slope are acquired using a photogrammetry device, including:

[0013] By adjusting the shooting angle of the U-View photogrammetry equipment, the flight path of the drone is planned, enabling the drone to fly over steep slopes and cover concealed areas, thus determining the optimal flight path.

[0014] Preferably, based on the initial data of the steep slope, an improved Dijkstra algorithm is used to plan the flight path, configure the flight plan, and load the flight plan into the UAV flight control system; the initial data includes various parameter information of the steep slope, specifically including tilt angle, altitude, and GPS position, etc.

[0015] Based on the initial data of the steep slope, a flight plan is formulated and loaded into the UAV flight control system. When the UAV flight system executes the flight plan, it acquires slope images through the UAV photogrammetry equipment. The flight plan includes the selection of flight altitude, flight speed, shooting angle, and shooting frequency values.

[0016] Based on the initial data of the steep slope, flight path planning and flight scheme configuration are carried out, and the slope images are acquired and processed using the U-View photogrammetry equipment. Finally, complete and continuous images of the steep slope are obtained, providing reliable data support for slope monitoring and analysis.

[0017] Preferably, based on the initial data of the steep slope, an improved Dijkstra algorithm is used for flight path planning, a flight plan is configured, and the flight plan is loaded into the UAV flight control system, including:

[0018] The execution area of ​​the flight plan is determined based on the initial data of the steep slope. The initial three-dimensional model and the position coordinates of the GPS device are called. The initial three-dimensional model is simplified by performing a layer-order traversal of the point set starting from the source point of the initial three-dimensional model, generating a direct graph for the Dijkstra algorithm, and obtaining a simplified three-dimensional model of the slope. The source point of the initial three-dimensional model is any point in the model.

[0019] Starting from a source point, the hidden points are sequentially included in the path according to the shortest path principle until the shortest path for all hidden points is generated. The improved Dijkstra algorithm is used for the three-dimensional simplified model of the slope to complete the path calculation and obtain non-repeating flight path points covering all hidden points.

[0020] Export the actual route calculation and path planning waypoint information to configure the flight plan.

[0021] Preferably, the flight plan development process includes:

[0022] The flight altitude is calculated using the formula h = Df / a, where h is the flight altitude, f is the lens focal length, a is the dimensional parameter of the U-Vision photogrammetry equipment, and D is the slope width.

[0023] The flight speed is calculated using the formula v = Thb / f(1-r), where v is the flight speed, T is the shooting interval, r is the image overlap rate, h is the drone's flight altitude, and b is the size parameter of the U-View photogrammetry device.

[0024] By adjusting the shooting angle, we can ensure that we can capture images of the hidden parts of the steep slope in a comprehensive manner.

[0025] The coordinates of the path obtained by calculating the slope range using the Dijkstra algorithm are used to determine the flight plan based on the flight altitude, flight speed, shooting angle, and each coordinate point.

[0026] Preferably, based on the precise image data and combined with image processing technology, the initial 3D model is optimized to generate an optimized 3D model of the steep slope, including:

[0027] The slope images captured by the U-View photogrammetry equipment at different shooting angles are transmitted to the ground computer equipment. After the slope images are processed to remove lens distortion, an image fusion algorithm is used to fuse the images to obtain complete and continuous images of steep slopes.

[0028] Preferably, an image fusion algorithm is used to perform image fusion to obtain a complete and continuous image of the steep slope, including:

[0029] Extract the position of the target feature point pixels in multiple slope images, calculate the relative coordinates of the feature point pixels in space, and obtain the relative coordinates of all feature point pixels.

[0030] An optimized 3D model is obtained by fitting the relative coordinates of all feature point pixels.

[0031] Preferably, the position of the target feature point pixels in the image is extracted from multiple slope images, and the relative coordinates of the feature point pixels in space are calculated to obtain the relative coordinates of all feature point pixels. This includes: placing the image in space according to the shooting angle and shooting distance of each slope image; establishing a specific reference coordinate system after the first image is taken; and constraining each subsequent image by two constraints to ensure the accuracy of coordinate system fusion.

[0032] Calculate the relative coordinates of feature point pixels in space to obtain the relative coordinates of all feature point pixels, and then calculate a weighted average of the relative coordinates of all feature point pixels to obtain a more accurate weighted average.

[0033] Preferably, an optimized 3D model is obtained by fitting the relative coordinates of all feature point pixels, including:

[0034] The first fitting step: Fit feature point pixels within the first adjacent distance using a straight line fitting method;

[0035] The second fitting sub-step: Fit feature point pixels within the adjacent second distance using a multi-vertex beta curve fitting method;

[0036] The third fitting sub-step: Fit feature point pixels within the adjacent third distance using a multi-vertex polygonal line fitting method;

[0037] The length of the first distance is less than the length of the second distance, and the length of the second distance is less than the length of the third distance.

[0038] Preferably, a high-steep slope disaster monitoring system based on superior view photogrammetry includes:

[0039] The initial model building module is used to acquire initial data of steep slopes through sensors and GPS devices, and to build an initial three-dimensional model of the steep slopes.

[0040] The data acquisition module is used to acquire accurate image data of various points on steep slopes using the U-View photogrammetry equipment;

[0041] The optimized model building module is used to optimize the initial three-dimensional model based on the precise image data and combined with image processing technology to generate an optimized three-dimensional model of the steep slope.

[0042] The early warning module is used to establish a real-time monitoring and early warning mechanism, collect accurate image data of various points on steep slopes in real time, perform deformation analysis on the optimized three-dimensional model, detect slope deformation in a timely manner, and issue an alarm.

[0043] As can be seen from the above technical solution, compared with the prior art, this invention discloses a method and system for monitoring high and steep slope disasters based on ultra-high-definition photogrammetry, including: acquiring initial data of the high and steep slope through sensors and GPS devices, and constructing an initial three-dimensional model of the high and steep slope; monitoring the parameter data of the high and steep slope through ground sensors to provide real-time changes in the slope; acquiring precise image data of each point on the high and steep slope through ultra-high-definition photogrammetry equipment; using ultra-high-definition photogrammetry equipment can acquire high-resolution image data, covering all parts of the entire high and steep slope, including the top and hidden parts. Based on the precise image data, combined with image processing technology, the initial three-dimensional model is optimized to generate an optimized three-dimensional model of the high and steep slope; a real-time monitoring and early warning mechanism is established, real-time acquisition of precise image data of each point on the high and steep slope, deformation analysis of the optimized three-dimensional model, timely detection of slope deformation, and alarm issuance. Potential geological disaster risks are identified to reduce disaster risks. This invention utilizes superior imagery technology to acquire high-resolution image data in complex and inaccessible areas, including higher locations and concealed areas, providing more comprehensive and detailed information for the monitoring and disaster investigation of steep slopes. This invention can clearly demonstrate the topographic features of steep slope areas, providing an important data foundation and scientific basis for disaster monitoring, risk assessment, and response measures. This invention improves the accuracy of steep slope monitoring, automates monitoring work, and enables non-contact measurement, reducing labor input. Attached Figure Description

[0044] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0045] Figure 1 is a schematic diagram of the process of a high and steep slope disaster monitoring method based on superior view photogrammetry provided by the present invention.

[0046] Figure 2 is a schematic diagram of a high and steep slope disaster monitoring system based on superior view photogrammetry provided by the present invention. Detailed Implementation

[0047] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0048] This invention discloses a method and system for monitoring high and steep slope disasters based on superior-view photogrammetry. Utilizing superior-view photogrammetry technology, high-resolution image data can be acquired in complex and inaccessible areas, including higher locations and concealed areas, providing more comprehensive and detailed information for monitoring and disaster investigation of high and steep slopes. This invention can clearly demonstrate the topographic features of high and steep slope areas, providing an important data foundation and scientific basis for disaster monitoring, risk assessment, and response measures. This invention improves the accuracy of high and steep slope monitoring, automates monitoring work, and enables non-contact measurement, reducing labor input.

[0049] This invention discloses a method for monitoring disasters on steep slopes based on superior-view photogrammetry, as shown in Figure 1, including:

[0050] Initial data of steep slopes is acquired using sensors and GPS devices, and an initial 3D model of the slope is constructed. Ground sensors, such as inclinometers, displacement sensors, and strain gauges, monitor the slope's parameters in real time, including tilt angle, displacement changes, and stress conditions. This data provides real-time information on slope changes.

[0051] Accurate image data of various points on steep slopes can be obtained using the U-View photogrammetry equipment. The U-View photogrammetry equipment can obtain high-resolution image data, which can cover all parts of the steep slope, including the top and hidden parts.

[0052] Based on the precise image data and combined with image processing technology, the initial three-dimensional model is optimized to generate an optimized three-dimensional model of the steep slope.

[0053] A real-time monitoring and early warning mechanism is established to collect precise image data of various points on steep slopes in real time, perform deformation analysis on the optimized 3D model, promptly detect slope deformation, and issue alarms. Potential geological hazard risks are identified to reduce disaster risks.

[0054] U-View photogrammetry is a novel UAV photogrammetric technique. Based on a rapidly generated 3D rough model, it optimizes and selects high-quality observation and shooting angles that better suit the geometry of the scene objects and meet the needs of 3D reconstruction, as well as alternative ground control points, through model observation sampling, visibility and reconstructability analysis, and other techniques. Automated planning processes then generate the UAV flight path and field ground control deployment plan.

[0055] The embodiments of the present invention combine photogrammetry and image processing technologies to monitor the deformation of steep slopes in real time, provide early warning of potential disaster risks, and effectively protect the safety of personnel and facilities.

[0056] Specifically, using the U-View photogrammetry equipment, image data of various parts of the steep slope are acquired, including:

[0057] By adjusting the shooting angle of the U-View photogrammetry equipment and planning the drone's flight path, the drone can fly over steep slopes and cover concealed areas, determining the optimal flight path. Considering the slope terrain and obstacle locations, the best flight path is selected to ensure comprehensive image acquisition of concealed areas. Adjusting the drone's shooting angle and path ensures images of concealed areas on steep slopes are captured. Methods such as tilted flight or fixed-point shooting can be used to cover inaccessible areas. The U-View photogrammetry equipment provides high-resolution image data. Images captured by this equipment clearly show concealed areas on steep slopes, aiding in analysis and assessment. During flight, the drone's operational status is monitored to ensure flight stability and safety. Simultaneously, attention is paid to the image quality of concealed areas to ensure sufficiently clear data for subsequent analysis. After the flight, the acquired impact data is transmitted to the monitoring center for analysis. Combined with other data and information, the stability and potential risks of steep slopes are assessed, and necessary measures are taken for repair and reinforcement.

[0058] Specifically, based on the initial data of the steep slope, an improved Dijkstra algorithm is used to plan the flight path, configure the flight plan, and load the flight plan into the UAV flight control system; the initial data includes various parameter information of the steep slope, specifically including tilt angle, altitude, and GPS position.

[0059] Based on the initial data of the steep slope, a flight plan is formulated and loaded into the UAV flight control system. When the UAV flight system executes the flight plan, it acquires slope images through the UAV photogrammetry equipment. The flight plan includes the selection of flight altitude, flight speed, shooting angle, and shooting frequency values.

[0060] Based on the initial data of the steep slope, flight path planning and flight scheme configuration are carried out, and the slope images are acquired and processed using the U-View photogrammetry equipment. Finally, complete and continuous images of the steep slope are obtained, providing reliable data support for slope monitoring and analysis.

[0061] Specifically, based on the initial data of the steep slope, an improved Dijkstra algorithm is used for flight path planning, a flight plan is configured, and the flight plan is loaded into the UAV flight control system, including:

[0062] The execution area of ​​the flight plan is determined based on the initial data of the steep slope. The initial three-dimensional model and the position coordinates of the GPS device are called. The initial three-dimensional model is simplified by performing a layer-order traversal of the point set starting from the source point of the initial three-dimensional model, generating a direct graph for the Dijkstra algorithm, and obtaining a simplified three-dimensional model of the slope. The source point of the initial three-dimensional model is any point in the model.

[0063] Starting from a source point, the hidden points are sequentially included in the path according to the shortest path principle until the shortest path for all hidden points is generated. The improved Dijkstra algorithm is used for the three-dimensional simplified model of the slope to complete the path calculation and obtain non-repeating flight path points covering all hidden points.

[0064] Export the actual route calculation and path planning waypoint information to configure the flight plan.

[0065] Dijkstra's algorithm essentially starts from a source point, adds hidden points to the path one by one according to the shortest path principle, and uses a table to record the added hidden points until the shortest path for all hidden points has been generated.

[0066] 1. If l(v0,v1) is the edge with the smallest weight, then the shortest path must contain this edge;

[0067] 2. The second shortest path satisfies either of the following two conditions:

[0068] (1) The source point reaches v2 through an edge l(v0,v2);

[0069] (2) If the source point reaches v2 through two edges, then the two edges must be l(v0,v1) and l(v1,v2), and pass through v1;

[0070] 3. The third shortest path satisfies any one of the following four conditions:

[0071] (1) The source point reaches v3 through an edge l(v0,v3);

[0072] (2) The source point reaches v3 through two edges, so the two edges are l(v0,v1) and l(v1,v3), and pass through v1;

[0073] (3) The source point reaches v3 through two edges, so the two edges are l(v0,v2) and l(v2,v3), and pass through v2;

[0074] (4) The source point reaches v3 through three edges, then the three edges are l(v0,v1), l(v1,v2) and l(v2,v3);

[0075] By recursively applying this strategy, the shortest paths from the source point to all other points can be obtained.

[0076] Based on the initial data of the steep slope, a grid method is typically used to plan and spatially model the slope. The grid divides the steep slope into several small segments, each segment corresponding to two hidden points and one edge in the graph. The point set of the initial 3D model is traversed beforehand to generate a simplified graph, which is the direct graph used in Dijkstra's algorithm. A layer-order traversal is performed on the point set starting from the source point, checking if a certain point v... i arc i ={ <v i ,v j >, <v i ,v k >,…, <v m ,v i >} and temporarily store in table arc[v i In the next layer of hidden points, the entire set of edges Arc = {arc} is then checked. j ,arc k ,…,arc m The elements in these sub-edge sets are those that can potentially connect to arc[v]. i The edges in the grid are merged. Check the coordinates of these hidden edge points in the grid. If the three points have a rotation angle greater than 103°, merge the corresponding edges into a shortcut edge, and use the parent endpoint as the starting point of the shortcut edge.

[0077] Next, additional constraints are added to adjust the final route calculation result. Navigation strategy calculations are performed, and the weight distribution of the generated simplified map is adjusted according to the strategy. Then, the starting and ending points of the path planning are input into the route calculation method (the starting and ending points can be determined according to the actual situation) to obtain the final route planning result. This step ultimately calculates the coordinates of the waypoints. In the successful route calculation callback, all waypoints are exported to a linear list, and the entire task of the algorithm is completed.

[0078] Finally, the navigation points need to be output to a digital map. By marking these points on the map, the operator can clearly see the path planning results. If there are errors in the planning process, they can choose to replan or output debugging information for the developers to modify the program. After determining the waypoints, the flight plan is configured and loaded into the UAV flight control system via wireless communication.

[0079] Specifically, the flight plan development process includes:

[0080] The flight altitude is calculated using the formula h = Df / a, where h is the flight altitude, f is the lens focal length, a is the dimensional parameter of the U-Vision photogrammetry equipment, and D is the slope width.

[0081] The flight speed is calculated using the formula v = Thb / f(1-r), where v is the flight speed, T is the shooting interval, r is the image overlap rate, h is the drone's flight altitude, and b is the size parameter of the U-View photogrammetry device.

[0082] By adjusting the shooting angle, we can ensure that we can capture images of the hidden parts of the steep slope in a comprehensive manner.

[0083] The coordinates of the path obtained by calculating the slope range using the Dijkstra algorithm are used to determine the flight plan based on the flight altitude, flight speed, shooting angle, and each coordinate point.

[0084] Specifically, based on the precise image data and combined with image processing technology, the initial 3D model is optimized to generate an optimized 3D model of the steep slope, including:

[0085] The slope images captured by the U-View photogrammetry equipment at different shooting angles are transmitted to the ground computer equipment. After the slope images are processed to remove lens distortion, an image fusion algorithm is used to fuse the images to obtain complete and continuous images of steep slopes.

[0086] Specifically, an image fusion algorithm is used to fuse images to obtain complete and continuous images of steep slopes, including:

[0087] Extract the position of the target feature point pixels in multiple slope images, calculate the relative coordinates of the feature point pixels in space, and obtain the relative coordinates of all feature point pixels.

[0088] An optimized 3D model is obtained by fitting the relative coordinates of all feature point pixels.

[0089] Specifically, the positions of target feature point pixels in multiple slope images are extracted, and the relative coordinates of the feature point pixels in space are calculated to obtain the relative coordinates of all feature point pixels. This includes: placing the image in space according to the shooting angle and shooting distance of each slope image; establishing a specific reference coordinate system after the first image is taken; and constraining each subsequent image by two constraints to ensure the accuracy of coordinate system fusion.

[0090] Calculate the relative coordinates of feature point pixels in space to obtain the relative coordinates of all feature point pixels, and then calculate a weighted average of the relative coordinates of all feature point pixels to obtain a more accurate weighted average.

[0091] The two constraints are:

[0092] (1) Calculate the rotation angle and distance based on the gyroscope and gravity sensor of the U-Vision photogrammetry equipment itself;

[0093] (2) Based on the flight plan, combined with the initial reference coordinate system and reference map, determine whether the current shooting target part and angle meet the shooting space requirements so that the image can fill the entire space.

[0094] Specifically, the optimized 3D model is obtained by fitting the relative coordinates of all feature point pixels, including:

[0095] The first fitting step: Fit feature point pixels within the first adjacent distance using a straight line fitting method;

[0096] The second fitting sub-step involves fitting feature point pixels within the adjacent second distance using a multi-vertex beta curve fitting method. The beta curve follows the beta distribution function. "The beta distribution is a density function of the conjugate prior distribution of the Bernoulli distribution and the binomial distribution." The continuous multi-segment beta curve is composed of multiple single beta curves with multiple similar starting points. From a continuous form, it resembles a whole beta curve with multiple vertices. Finally, the entire fitting method is used to fit feature point pixels within the adjacent second distance.

[0097] The third fitting sub-step: Fit feature point pixels within the adjacent third distance using a multi-vertex polygonal line fitting method;

[0098] The length of the first distance is less than the length of the second distance, and the length of the second distance is less than the length of the third distance.

[0099] This invention also discloses a high-slope disaster monitoring system based on superior view photogrammetry, as shown in Figure 2, comprising:

[0100] The initial model building module is used to acquire initial data of steep slopes through sensors and GPS devices, and to build an initial three-dimensional model of the steep slopes.

[0101] The data acquisition module is used to acquire accurate image data of various points on steep slopes using the U-View photogrammetry equipment;

[0102] The optimized model building module is used to optimize the initial three-dimensional model based on the precise image data and combined with image processing technology to generate an optimized three-dimensional model of the steep slope.

[0103] The early warning module is used to establish a real-time monitoring and early warning mechanism, collect accurate image data of various points on steep slopes in real time, perform deformation analysis on the optimized three-dimensional model, detect slope deformation in a timely manner, and issue an alarm.

[0104] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0105] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for monitoring disasters on steep slopes based on superior-view photogrammetry, characterized in that, include: Initial data of steep slopes are acquired using sensors and GPS devices, and an initial three-dimensional model of the steep slopes is constructed. Accurate image data of various points on steep slopes are obtained using the U-Vision photogrammetry equipment. Based on the precise image data and combined with image processing technology, the initial three-dimensional model is optimized to generate an optimized three-dimensional model of the steep slope. Establish a real-time monitoring and early warning mechanism to collect accurate image data of various points on steep slopes in real time, perform deformation analysis on the optimized three-dimensional model, detect slope deformation in a timely manner, and issue an alarm.

2. The method for monitoring disasters on steep slopes based on superior-view photogrammetry according to claim 1, characterized in that, Using the U-View photogrammetry equipment, image data of various parts of the steep slope were acquired, including: By adjusting the shooting angle of the U-View photogrammetry equipment, the flight path of the drone is planned, enabling the drone to fly over steep slopes and cover concealed areas, thus determining the optimal flight path.

3. The method for monitoring disasters on steep slopes based on superior-view photogrammetry according to claim 2, characterized in that, Based on the initial data of the steep slope, the improved Dijkstra algorithm is used to plan the flight path, configure the flight plan, and load the flight plan into the UAV flight control system. Based on the initial data of the steep slope, a flight plan is formulated and loaded into the UAV flight control system. When the UAV flight system executes the flight plan, it acquires slope images through the UAV photogrammetry equipment. The flight plan includes the selection of flight altitude, flight speed, shooting angle, and shooting frequency values.

4. The method for monitoring disasters on steep slopes based on superior-view photogrammetry according to claim 3, characterized in that, Based on the initial data of the steep slope, an improved Dijkstra algorithm is used for flight path planning, a flight plan is configured, and the flight plan is loaded into the UAV flight control system, including: Based on the initial data of the steep slope, the execution area of ​​the flight plan is determined, the initial three-dimensional model and the position coordinates of the GPS device are called, and the point set is traversed in a layer-order manner from the source point of the initial three-dimensional model to simplify the initial three-dimensional model, generate the direct graph of Dijkstra's algorithm, and obtain the simplified three-dimensional model of the slope. Starting from a source point, the hidden points are sequentially included in the path according to the shortest path principle until the shortest path for all hidden points is generated. The improved Dijkstra algorithm is used for the three-dimensional simplified model of the slope to complete the path calculation and obtain non-repeating flight path points covering all hidden points. Export the actual route calculation and path planning waypoint information to configure the flight plan.

5. A method for monitoring disasters on steep slopes based on superior-view photogrammetry according to claim 3, characterized in that, The process of developing a flight plan includes: The flight altitude is calculated using the formula h = Df / a, where h is the flight altitude, f is the lens focal length, a is the dimensional parameter of the U-Vision photogrammetry equipment, and D is the slope width. The flight speed is calculated using the formula v = Thb / f(1-r), where v is the flight speed, T is the shooting interval, r is the image overlap rate, h is the drone's flight altitude, and b is the size parameter of the U-View photogrammetry device. By adjusting the shooting angle, we can ensure that we can capture images of the hidden parts of the steep slope in a comprehensive manner. The coordinates of the path obtained by calculating the slope range using the Dijkstra algorithm are used to determine the flight plan based on the flight altitude, flight speed, shooting angle, and each coordinate point.

6. The method for monitoring disasters on steep slopes based on superior-view photogrammetry according to claim 3, characterized in that, Based on the precise image data and combined with image processing technology, the initial 3D model is optimized to generate an optimized 3D model of the steep slope, including: The slope images captured by the U-View photogrammetry equipment at different shooting angles are transmitted to the ground computer equipment. After the slope images are processed to remove lens distortion, an image fusion algorithm is used to fuse the images to obtain complete and continuous images of steep slopes.

7. A method for monitoring disasters on steep slopes based on superior-view photogrammetry according to claim 6, characterized in that, Image fusion algorithms are used to fuse images and obtain complete and continuous images of steep slopes, including: Extract the position of the target feature point pixels in multiple slope images, calculate the relative coordinates of the feature point pixels in space, and obtain the relative coordinates of all feature point pixels. An optimized 3D model is obtained by fitting the relative coordinates of all feature point pixels.

8. A method for monitoring disasters on steep slopes based on superior-view photogrammetry according to claim 7, characterized in that, Extract the position of target feature point pixels in multiple slope images, calculate the relative coordinates of feature point pixels in space, and obtain the relative coordinates of all feature point pixels. This includes: placing the image in space according to the shooting angle and shooting distance of each slope image; establishing a specific reference coordinate system after the first image is taken; and constraining each subsequent image by two constraints to ensure the accuracy of coordinate system fusion. Calculate the relative coordinates of feature point pixels in space to obtain the relative coordinates of all feature point pixels, and then calculate a weighted average of the relative coordinates of all feature point pixels to obtain a more accurate weighted average.

9. A method for monitoring disasters on steep slopes based on superior-view photogrammetry according to claim 1, characterized in that, An optimized 3D model is obtained by fitting the relative coordinates of all feature point pixels, including: The first fitting step: Fit feature point pixels within the first adjacent distance using a straight line fitting method; The second fitting sub-step: Fit feature point pixels within the adjacent second distance using a multi-vertex beta curve fitting method; The third fitting sub-step: Fit feature point pixels within the adjacent third distance using a multi-vertex polygonal line fitting method; The length of the first distance is less than the length of the second distance, and the length of the second distance is less than the length of the third distance.

10. A high-steep slope disaster monitoring system based on best-view photogrammetry, employing the high-steep slope disaster monitoring method based on best-view photogrammetry as described in any one of claims 1-9, characterized in that, include: The initial model building module is used to acquire initial data of steep slopes through sensors and GPS devices, and to build an initial three-dimensional model of the steep slopes. The data acquisition module is used to acquire accurate image data of various points on steep slopes using the U-View photogrammetry equipment; The optimized model building module is used to optimize the initial three-dimensional model based on the precise image data and combined with image processing technology to generate an optimized three-dimensional model of the steep slope. The early warning module is used to establish a real-time monitoring and early warning mechanism, collect accurate image data of various points on steep slopes in real time, perform deformation analysis on the optimized three-dimensional model, detect slope deformation in a timely manner, and issue an alarm.

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