A slope protection deformation monitoring system and method

Abstract

The present invention relates to the field of slope protection health monitoring technology, and specifically to a slope protection deformation monitoring system and method, the system comprising: a speckle spraying drone, which sprays random speckle patterns on the slope protection surface; a binocular camera trolley, which is used to collect stereo images of the slope protection surface; a remote monitoring room, including high-performance computing equipment and a monitoring station, which is used to receive image data transmitted by the binocular camera trolley in real time and display the health status of the slope protection; a cloud data storage module, which is used to store monitoring data persistently, support trend analysis of long-term slope protection deformation, historical data backtracking and structural health assessment; an early warning module, which automatically triggers an early warning mechanism and records alarm information when it is detected that the slope protection deformation exceeds a preset value. Through the present invention, the problems of insufficient real-time performance, low precision, limited local monitoring and strong manual dependence of traditional slope protection monitoring are effectively solved, and efficient, real-time and comprehensive monitoring of slope protection deformation is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of slope protection health monitoring, and in particular to a slope protection deformation monitoring system and method. Background Art

[0002] Slopes are slopes formed by natural or human factors in mountainous areas, river banks, roadbeds, dams and other terrains. Slope protection is an engineering measure taken to prevent slope erosion, collapse and sliding to ensure its stability. The stability of slope protection is crucial. However, due to natural and human factors, slope protection may deform or collapse, causing safety accidents and economic losses. Therefore, real-time monitoring and timely maintenance of slope protection are the key to disaster prevention and mitigation.

[0003] Traditional monitoring methods, such as manual inspections, levels, total stations, settlement monitoring, etc., have problems such as requiring manual work, equipment increasing the burden on the slope, local monitoring and limited resolution. These methods are difficult to adapt to complex terrain and severe weather, and cannot provide real-time monitoring. Summary of the invention

[0004] The present invention provides a slope protection deformation monitoring system and method, thereby effectively solving the problems pointed out in the background technology.

[0005] In order to achieve the above object, the technical solution adopted by the present invention is: A slope protection deformation monitoring system, comprising: Speckle spraying drone, spraying random speckle patterns on the slope protection surface for image processing and deformation analysis in subsequent DIC monitoring; Binocular camera trolley, used to collect stereo images of the slope protection surface, and obtain the three-dimensional displacement and deformation data of the slope protection through the binocular camera; Remote monitoring room, including high-performance computing equipment and monitoring station, used to receive image data transmitted by the binocular camera trolley in real time, run the DIC algorithm to generate slope deformation and displacement analysis results, and display the slope health status; Furthermore, the system further comprises: Cloud data storage module, used for persistent storage of monitoring data, supports long-term slope deformation trend analysis, historical data backtracking and structural health assessment; The early warning module sets a safety threshold based on the DIC analysis results. When the slope deformation exceeds the preset value, the early warning mechanism is automatically triggered and the alarm information is recorded.

[0006] Furthermore, the binocular camera car includes: left and right cameras, left and right camera protection covers, a first connecting component, a second connecting component, a body, a bumper, wheels and a wireless data antenna.

[0007] Furthermore, the left and right cameras of the binocular camera car are connected to the vehicle body through the first connecting component and the second connecting component, the first connecting component is used to control the angle adjustment of the camera in the horizontal direction, and the second connecting component is used to control the angle adjustment of the camera in the vertical direction.

[0008] Furthermore, the remote monitoring room includes a computing server, a storage server and a monitoring station. The computing server is configured with a multi-core processor and a high-performance graphics processing unit for efficiently processing and analyzing image data transmitted from the binocular camera cart and supporting real-time operation of the DIC algorithm.

[0009] Furthermore, the cloud data storage module monitors data through a secure data encryption transmission mechanism and adopts a distributed storage architecture for redundant data backup.

[0010] Furthermore, the early warning module performs DIC analysis through an automated algorithm, including: Based on the image data transmitted by the binocular camera trolley received in real time, the displacement and deformation of the slope protection surface at different time points are calculated through digital image correlation (DIC) analysis; The automated algorithm automatically identifies key areas of displacement and deformation in the image and evaluates the degree of deformation at the monitoring points; When the deformation degree exceeds a set threshold, the automation algorithm automatically triggers the early warning mechanism and generates an alarm.

[0011] Furthermore, the automation algorithm performs self-learning and optimization according to the historical monitoring data in the cloud data storage module, and adjusts the warning threshold and monitoring parameters.

[0012] Furthermore, the speckle spraying UAV adopts program navigation technology to spray the speckle pattern according to a set path.

[0013] Furthermore, the speckle spraying UAV and the binocular camera trolley perform periodic alternating operations according to a predetermined operation plan. After each spraying operation, the speckle spraying UAV will pause and wait for the binocular camera trolley to collect images, and the images are matched one by one with the speckle patterns.

[0014] A slope protection deformation monitoring method, the method comprising: The speckle spraying drone is used to spray random speckle patterns on the surface of the slope protection, and a uniformly distributed speckle pattern is formed on the surface of the slope protection; Use a binocular camera trolley to collect high-definition stereo images on the slope protection surface and transmit them to the remote monitoring system in real time for processing; Based on the collected image data, DIC is used for image processing to calculate the displacement and deformation of the slope protection surface, generate the displacement field and deformation map, and analyze the deformation of the slope protection; According to the DIC analysis results, the monitored deformation data is judged by threshold value. When the deformation exceeds the set threshold, the early warning mechanism is triggered; All monitoring data are uploaded to the cloud for long-term data storage and trend analysis to obtain the long-term change trend of slope deformation, health status assessment and potential risk prediction.

[0015] The technical solution of the present invention can achieve the following technical effects: It effectively solves the problems of traditional slope protection monitoring, such as lack of real-time performance, low accuracy, limitation to local monitoring and strong dependence on manual work, and realizes efficient, real-time and comprehensive monitoring of slope protection deformation. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 is a schematic diagram of a slope deformation monitoring system; Figure 2 This is a schematic diagram of the binocular camera car; Figure 3 Schematic diagram of the path for spraying speckle on a drone; Figure 4 The figure is a flow chart of a slope deformation monitoring method. DETAILED DESCRIPTION

[0018] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items.

[0020] Embodiment 1 like Figure 1 As shown, the present invention provides a slope deformation monitoring system, the system comprising: Speckle spraying drone, spraying random speckle patterns on the slope protection surface for image processing and deformation analysis in subsequent DIC monitoring; Binocular camera trolley, used to collect stereo images of the slope protection surface, and obtain the three-dimensional displacement and deformation data of the slope protection through the binocular camera; Remote monitoring room, including high-performance computing equipment and monitoring station, used to receive image data transmitted by the binocular camera trolley in real time, run the DIC algorithm to generate slope deformation and displacement analysis results, and display the slope health status; Specifically, firstly, the speckle spraying drone uses high-precision spraying technology to evenly spray random speckle patterns on the surface of the slope protection. As an important marker for digital image correlation (DIC) analysis, these speckle patterns use waterproof, dustproof and durable coatings, which can remain clear and stable even under complex climatic conditions. The drone spraying path adopts a line-by-line sweeping return mode to optimize the spraying efficiency and pattern consistency. After the spraying is completed, the drone returns to the starting point and waits for the next stage of operation. The binocular camera car collects high-definition stereo images of the slope protection surface. The car has good adaptability in complex terrain and is equipped with four-wheel drive and shock absorption systems to ensure the safety and stability of the equipment. When collecting images, the car will pause to ensure the clarity and accuracy of the data. The collected image data is transmitted to the remote monitoring room in real time via a wireless antenna. The remote monitoring room is the data processing and analysis center of the system, equipped with high-performance computing servers and monitoring stations. The computing servers perform real-time analysis of the transmitted image data by running the digital image correlation (DIC) algorithm. The DIC algorithm first extracts the feature points of the left and right cameras through stereo matching technology, and uses the triangulation principle to generate three-dimensional point cloud data of the slope protection surface; then, the system calculates the displacement differences at different time points and generates a three-dimensional deformation cloud map of the slope protection; these analysis results are displayed in real time through the interactive interface of the monitoring station, allowing monitoring personnel to view the health status and deformation area of ​​the slope protection.

[0021] The present invention effectively solves the problems of insufficient real-time performance, low precision, limited local monitoring and strong manual dependence of traditional slope protection monitoring, and realizes efficient, real-time and comprehensive monitoring of slope protection deformation.

[0022] As a preferred embodiment of the above, the system further includes: Cloud data storage module, used for persistent storage of monitoring data, supports long-term slope deformation trend analysis, historical data backtracking and structural health assessment; The early warning module sets a safety threshold based on the DIC analysis results. When the slope deformation exceeds the preset value, the early warning mechanism is automatically triggered and the alarm information is recorded.

[0023] The cloud data storage module of the system is used to store monitoring data for a long time, ensure data security through encrypted transmission, support remote access and dynamic backtracking analysis of historical data. Based on long-term stored data, the system can use trend analysis and machine learning algorithms to evaluate the health status of the slope protection and predict future deformation risks, thereby providing a scientific basis for slope protection management. When the deformation of the slope protection surface exceeds the set threshold, the early warning module will automatically trigger the alarm mechanism. The alarm information includes the deformation area, deformation amplitude and risk level, and is sent to relevant monitoring personnel via SMS or email. The early warning module also supports multi-level response measures, such as slope protection site blockade, professional on-site inspection and repair, to ensure that slope protection safety hazards can be dealt with in a timely manner.

[0024] As a preferred embodiment of the above, Figure 2 As shown, the binocular camera car includes: left and right cameras, left and right camera protection covers, a first connecting component, a second connecting component, a body, a bumper, wheels and a wireless data antenna.

[0025] Specifically, the left and right cameras are the core monitoring components of the vehicle, which can synchronously collect high-resolution stereo images of the slope protection surface. The cameras are equipped with durable protective covers to prevent damage in complex terrain or severe weather conditions. The connecting parts are made of high-strength materials, which are stable and durable. They can firmly fix the cameras after adjustment to ensure the clarity and accuracy of the collected images. The body is the main frame structure of the vehicle, carrying all equipment components. It is designed to be light but sturdy and can adapt to different monitoring environments. The front end of the body is equipped with a bumper to protect the vehicle from collision and damage during travel, especially in complex terrain. The wheels are designed with all-terrain tires with good grip and shock absorption performance, which supports the vehicle to travel stably on uneven terrain such as mud and stones. The wireless data antenna equipped with the vehicle is responsible for establishing an efficient data transmission connection with the remote monitoring room. Through the wireless data antenna, the collected images can be transmitted to the remote monitoring room in real time with high bandwidth and low latency, ensuring the immediacy and reliability of the data, providing a basis for subsequent DIC analysis.

[0026] As a preferred embodiment of the above embodiment, the left and right cameras of the binocular camera car are connected to the vehicle body through a first connecting component and a second connecting component. The first connecting component is used to control the angle adjustment of the camera in the horizontal direction, and the second connecting component is used to control the angle adjustment of the camera in the vertical direction.

[0027] Specifically, the first connecting component is used to control the angle adjustment of the camera in the horizontal direction. Its design adopts a rotating axis structure, allowing the left and right cameras to be accurately adjusted within the horizontal angle range of ±45° to cover a wider monitoring area. The connecting component is also equipped with a locking mechanism, which can fix the camera angle after adjustment to ensure stability and accuracy during shooting. The second connecting component is used to control the angle adjustment of the camera in the vertical direction. Its structure adopts a sliding guide rail or a rotating joint, allowing the left and right cameras to be flexibly adjusted within the vertical angle range of ±30° to meet the monitoring needs of different slope heights or slope slopes. The vertical angle adjustment is also equipped with precise positioning and locking functions to prevent the camera from shaking or offsetting during shooting. Through the collaborative adjustment of the first and second connecting components, the binocular camera can achieve multi-angle monitoring, significantly improve the flexibility and applicability of image acquisition, and provide comprehensive and accurate data support for the three-dimensional displacement and deformation analysis of different areas on the slope surface.

[0028] As a preferred embodiment of the above embodiment, the remote monitoring room includes a computing server, a storage server and a monitoring station. The computing server is configured with a multi-core processor and a high-performance graphics processing unit for efficiently processing and analyzing image data transmitted from the binocular camera cart and supporting real-time operation of the DIC algorithm.

[0029] Specifically, the computing server is the core component of the remote monitoring room, responsible for efficiently processing and analyzing the image data transmitted from the binocular camera cart. The computing server is equipped with a multi-core processor (such as a 32-core Intel Xeon series or AMD EPYC processor) and a high-performance graphics processing unit (GPU, such as NVIDIAA100 or RTX 3090) to ensure the real-time and efficient operation of image processing and DIC algorithms. The core tasks of the DIC algorithm include stereo matching, 3D reconstruction, and displacement field calculation. These tasks require large-scale parallel computing. The powerful computing power of the GPU can significantly improve the data processing speed. The computing server is also equipped with a large-capacity memory (such as 128GB DDR4 or higher) to support efficient loading and caching of large amounts of image data and avoid performance bottlenecks in the data processing process; the storage server is responsible for reliable storage of monitoring data to ensure data security and integrity. The storage server also supports remote access, allowing users to retrieve historical monitoring data for analysis or backtracking through authorized interfaces; the monitoring console provides an interactive interface for displaying monitoring data and analysis results. Through the monitoring console, monitoring personnel can view the three-dimensional displacement cloud map and deformation distribution of slope protection deformation in real time. The interface design is intuitive, providing accurate values ​​and trend charts of the deformation area, which facilitates monitoring personnel to quickly identify potential hazards. In addition, the monitoring console supports custom settings. Users can adjust monitoring parameters, safety thresholds or data display modes according to actual needs to meet slope protection monitoring needs in different scenarios.

[0030] As a preferred embodiment of the above embodiment, the cloud data storage module monitors data through a secure data encryption transmission mechanism and adopts a distributed storage architecture for redundant data backup.

[0031] Specifically, the cloud data storage module uses the Advanced Encryption Standard (AES) algorithm and the Transport Layer Security Protocol (TLS) during the data transmission process to encrypt the data transmitted from the remote monitoring room or the binocular camera car. This mechanism can effectively prevent the interception, tampering or leakage of data during transmission, ensuring the security and integrity of the monitoring data. At the same time, the cloud storage module supports lossless data compression technology to reduce transmission delays and improve network transmission efficiency while ensuring data quality. In terms of data storage, the cloud data storage module adopts a distributed storage architecture and combines data redundancy technology (such as RAID6 or RAID 10) to achieve high-reliability storage. Distributed storage stores monitoring data on multiple nodes to meet the storage needs of large-scale data. At the same time, a redundant backup mechanism is used to ensure that in the event of hardware failure or other unexpected situations, data can be quickly restored to avoid data loss. In addition, the storage module has dynamic expansion capabilities and can flexibly increase storage capacity according to the needs of monitoring tasks to meet the needs of long-term slope protection monitoring projects.

[0032] As a preferred embodiment of the above, the early warning module performs DIC analysis through an automated algorithm, including: Based on the image data transmitted by the binocular camera trolley in real time, the displacement and deformation of the slope protection surface at different time points are calculated through DIC analysis; The automated algorithm automatically identifies key areas of displacement and deformation in the image and assesses the degree of deformation at the monitoring points; When the degree of deformation exceeds the set threshold, the automated algorithm automatically triggers the early warning mechanism and generates an alarm.

[0033] Specifically, first, the module calculates the three-dimensional displacement and deformation of the slope protection surface at different time points through DIC analysis, and generates the displacement field and deformation distribution map of the slope protection surface by comparing the image data at different times, providing data support for the subsequent key area identification; then, the automated algorithm deeply analyzes the DIC analysis results, automatically identifies the areas with significant deformation and their key feature points, and the algorithm can accurately locate the areas with large deformation amplitudes and evaluate their deformation degrees, including displacement values, change rates, and deformation types (such as tension, compression, or shear). Based on the evaluation results, the module determines the severity of slope deformation according to the preset standards, quantifies the deformation degree, and provides a scientific basis for the triggering of the early warning; when the identified deformation degree exceeds the preset safety threshold, the early warning module will immediately trigger the automated early warning mechanism, generate alarm information, and notify relevant personnel in real time. The alarm information includes the location of the deformation area, deformation amplitude, specific parameters exceeding the threshold, and warning level (such as slight deformation, significant deformation, or major hidden dangers). The notification method includes SMS, email, or pop-up window on the monitoring station to ensure that the monitoring personnel can quickly understand the status of the slope protection and respond. At the same time, the module will record and archive the detailed data and analysis results of each early warning to provide support for subsequent data verification and trend analysis. The early warning module supports a multi-level early warning mechanism, which is divided into three levels according to the degree of deformation and the severity of potential risks: slight deformation, significant deformation and major hidden dangers. For slight deformation, the system recommends strengthening monitoring and continuously observing regional changes; for significant deformation, it indicates that there may be structural risks, and recommends on-site inspection or reinforcement measures; for major hidden dangers, the system will notify the relevant management department to take emergency measures immediately, such as blocking the slope protection area or organizing repair operations.

[0034] As a preferred embodiment of the above, the automated algorithm performs self-learning and optimization based on the historical monitoring data in the cloud data storage module to adjust the warning thresholds and monitoring parameters.

[0035] Specifically, the cloud data storage module stores a large amount of historical data accumulated in the long-term monitoring of slope protection, including the displacement field distribution, deformation characteristics, triggered warning records and their corresponding actual safety status of each monitoring. The automated algorithm identifies the deformation trend of the slope protection, the deformation mode at different stages and the key features related to the warning results by analyzing these historical data. For example, the algorithm can summarize the changing law of the deformation characteristics of the slope protection and its impact on the overall stability under certain specific geological conditions (such as terrain changes in rainy seasons or after earthquakes); based on the results of historical data analysis, the automated algorithm optimizes monitoring parameters and warning thresholds through machine learning techniques (such as supervised learning or unsupervised learning models). For example, for long-term stable slope protection, the algorithm can automatically relax the warning thresholds of certain monitoring points to reduce the false alarm rate; and for areas with large deformation amplitudes or frequent warning triggers in history, the algorithm will tighten the thresholds to strengthen monitoring and warning responses to high-risk areas. In addition, the algorithm can also dynamically adjust parameters in DIC analysis, such as feature point extraction density or image matching window size, to improve the accuracy of displacement field calculation. Through self-learning capabilities, the automated algorithm can also be optimized based on feedback data from actual warning effects. For example, for cases where the warning is triggered but no slope damage occurs, the algorithm will adjust relevant parameters to reduce false alarms in similar scenarios. For situations where the warning is not triggered in time and hidden dangers occur, the algorithm will increase its sensitivity to relevant features to enhance the system's early identification capabilities. This closed-loop learning mechanism can effectively improve the robustness and intelligence of the system.

[0036] As a preferred embodiment of the above, the speckle spraying UAV adopts program navigation technology to spray the speckle pattern according to a set path.

[0037] Specifically, the speckle spraying drone uses program navigation technology to spray random speckle patterns on the slope protection surface according to a preset path. The drone automatically performs the spraying task according to the set flight route through the positioning system and program control to ensure uniform speckle coverage without missing any monitoring area. During the operation, the drone adjusts the flight altitude and speed according to the shape and size of the slope protection surface, and sprays waterproof, dustproof and durable coatings at the same time to generate high-contrast and clear speckle patterns. The spraying path usually adopts a row-by-row coverage mode to ensure efficient operation. Figure 3 As shown, the speckle spraying drone uses a program to navigate to the predetermined coordinates of each speckle on the slope protection, and uses the paint spraying module to accurately spray the speckle. After completion, the drone automatically moves to the next adjacent speckle coordinate and continues the spraying operation.

[0038] As a preferred embodiment of the above embodiment, the speckle spraying UAV and the binocular camera trolley perform periodic alternating operations according to a predetermined operation plan. After each spraying operation, the speckle spraying UAV will pause and wait for the binocular camera trolley to collect images, and the images are matched one by one with the speckle patterns.

[0039] Specifically, the speckle spraying drone first performs speckle spraying on the slope protection surface according to the set path, ensuring that the random speckle pattern is evenly distributed in the monitoring area, providing marks for subsequent image processing and deformation analysis; after completing the speckle spraying operation, the drone will automatically pause and return to the designated standby position, and then the binocular camera car will enter the operation area according to the predetermined route, move along the slope protection surface and collect images at the designated monitoring points. The high-definition stereo image acquisition of the binocular camera car is matched one by one with the speckle pattern sprayed by the drone, ensuring that the image data collected each time can accurately reflect the displacement and deformation information of the spraying area. This alternating operation mode ensures the efficient coordination of spraying operations and image acquisition: the drone avoids interfering with the car's collection work after completing the spraying, and the car also leaves enough space for the next round of spraying operations after completing the image collection. Through reasonable operation planning and precise time control, the system realizes the seamless connection between spraying and collection tasks, which not only ensures the accurate matching of speckle patterns and images, but also greatly improves the efficiency and quality of overall monitoring.

[0040] Embodiment 2 Based on the same inventive concept as the slope deformation monitoring system in the aforementioned embodiment, the present invention also provides a slope deformation monitoring method, such as Figure 4 As shown, the method includes: S1: Use a speckle spraying drone to spray a random speckle pattern on the slope protection surface, and the slope protection surface forms a uniformly distributed speckle pattern; S2: Use a binocular camera trolley to collect high-definition stereo images on the slope protection surface and transmit them to the remote monitoring system in real time for processing; S3: Based on the collected image data, DIC is used for image processing to calculate the displacement and deformation of the slope protection surface, generate the displacement field and deformation map, and analyze the deformation of the slope protection; S4: Based on the DIC analysis results, threshold judgment is performed on the monitored deformation data. When the deformation exceeds the set threshold, the early warning mechanism is triggered; S5: Upload all monitoring data to the cloud for long-term data storage and trend analysis to obtain the long-term change trend of slope deformation, health status assessment and potential risk prediction.

[0041] The above monitoring method in the present invention can effectively realize the slope deformation monitoring system, and the technical effects that can be achieved are as described in the above embodiments, which will not be repeated here.

[0042] Although the present application has been described in conjunction with specific features and embodiments thereof, it is apparent that various modifications and combinations may be made thereto without departing from the spirit and scope of the present application. Accordingly, this specification and the accompanying drawings are merely exemplary illustrations of the present application as defined herein, and are deemed to have covered any and all modifications, variations, combinations or equivalents within the scope of the present application.

[0043] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalent technologies, the present application is intended to include these modifications and variations.

Claims

1. A slope deformation monitoring system, characterized in that: include: Speckle spraying drone, spraying random speckle patterns on the slope protection surface for image processing and deformation analysis in subsequent DIC monitoring; Binocular camera trolley, used to collect stereo images of the slope protection surface, and obtain the three-dimensional displacement and deformation data of the slope protection through the binocular camera; The remote monitoring room, including high-performance computing equipment and a monitoring station, is used to receive image data transmitted by the binocular camera trolley in real time, run the DIC algorithm to generate slope deformation and displacement analysis results, and display the slope health status.

2. The slope deformation monitoring system according to claim 1 is characterized in that: The system further comprises: Cloud data storage module, used for persistent storage of monitoring data, supports long-term slope deformation trend analysis, historical data backtracking and structural health assessment; The early warning module sets a safety threshold based on the DIC analysis results. When the slope deformation exceeds the preset value, the early warning mechanism is automatically triggered and the alarm information is recorded.

3. The slope deformation monitoring system according to claim 1 is characterized in that: The binocular camera car includes: left and right cameras, left and right camera protection covers, a first connecting component, a second connecting component, a body, a bumper, wheels and a wireless data antenna.

4. The slope deformation monitoring system according to claim 3 is characterized in that: The left and right cameras of the binocular camera car are connected to the vehicle body through the first connecting component and the second connecting component. The first connecting component is used to control the angle adjustment of the camera in the horizontal direction, and the second connecting component is used to control the angle adjustment of the camera in the vertical direction.

5. The slope deformation monitoring system according to claim 1 is characterized in that: The remote monitoring room includes a computing server, a storage server and a monitoring station. The computing server is configured with a multi-core processor and a high-performance graphics processing unit for efficiently processing and analyzing image data transmitted from the binocular camera cart and supporting real-time operation of the DIC algorithm.

6. The slope deformation monitoring system according to claim 1, characterized in that: The cloud data storage module monitors data through a secure data encryption transmission mechanism and uses a distributed storage architecture to perform redundant data backup.

7. The slope deformation monitoring system according to claim 1, characterized in that: The early warning module performs DIC analysis through an automated algorithm, including: Based on the image data transmitted by the binocular camera trolley received in real time, the displacement and deformation of the slope protection surface at different time points are calculated through digital image correlation (DIC) analysis; The automated algorithm automatically identifies key areas of displacement and deformation in the image and evaluates the degree of deformation at the monitoring points; When the deformation degree exceeds a set threshold, the automation algorithm automatically triggers the early warning mechanism and generates an alarm.

8. The slope deformation monitoring system according to claim 1, characterized in that: The automation algorithm performs self-learning and optimization based on the historical monitoring data in the cloud data storage module to adjust the warning threshold and monitoring parameters.

9. The slope deformation monitoring system according to claim 1, characterized in that: The speckle spraying UAV adopts program navigation technology to spray the speckle pattern according to a set path.

10. The slope deformation monitoring system according to claim 1, characterized in that: The speckle spraying UAV and the binocular camera trolley perform periodic alternating operations according to a predetermined operation plan. After each spraying operation, the speckle spraying UAV will pause and wait for the binocular camera trolley to collect images, and the images are matched one by one with the speckle patterns.

11. A slope deformation monitoring method, characterized in that: The method comprises: The speckle spraying drone is used to spray random speckle patterns on the surface of the slope protection, and a uniformly distributed speckle pattern is formed on the surface of the slope protection; Use a binocular camera trolley to collect high-definition stereo images on the slope protection surface and transmit them to the remote monitoring system in real time for processing; Based on the collected image data, DIC is used for image processing to calculate the displacement and deformation of the slope protection surface, generate the displacement field and deformation map, and analyze the deformation of the slope protection; According to the DIC analysis results, the monitored deformation data is judged by threshold value. When the deformation exceeds the set threshold, the early warning mechanism is triggered; All monitoring data are uploaded to the cloud for long-term data storage and trend analysis to obtain the long-term change trend of slope deformation, health status assessment and potential risk prediction.

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