Slope deformation monitoring method and device based on binocular vision, electronic equipment and storage medium

Abstract

The application discloses a slope deformation monitoring method and device based on binocular vision, electronic equipment and a storage medium. The method and device are applied to the electronic equipment and are used for monitoring the deformation of a to-be-monitored slope. Specifically, image data collected by two high-definition cameras which are arranged on the opposite side of the slope and keep a preset distance apart is acquired. A plurality of reference points are arranged on the slope. Second three-dimensional coordinates of each reference point measured by a measuring robot arranged on the opposite side of the slope are acquired. The image data is processed based on the internal parameters and external parameters of the high-definition cameras, so that the first three-dimensional coordinates of each reference point are obtained. The first three-dimensional coordinates and the second three-dimensional coordinates are analyzed and verified, so that the deformation trend of the slope is obtained. Through the monitoring of the deformation trend, disposal measures can be taken as soon as potential dangers are found, so that personnel casualties and property losses can be avoided.

Description

Technical Field

[0001] This application relates to the field of mining production technology, and more specifically, to a method, device, electronic device, and storage medium for monitoring slope deformation based on binocular vision. Background Technology

[0002] In open-pit mines, slope stability is a crucial factor in ensuring safe production and construction. Slope deformation monitoring can provide early warnings of potential geological disasters, preventing casualties and property damage. Existing slope monitoring technologies largely rely on traditional surveying methods such as total stations and GPS. While these methods have been well-proven in terms of accuracy and reliability, they have some technical limitations, such as limited measurement points, long data acquisition cycles, and poor real-time performance. Particularly in complex terrain conditions, they cannot effectively conduct large-scale, high-precision real-time monitoring. Summary of the Invention

[0003] In view of this, this application provides a method, device, electronic device and storage medium for monitoring slope deformation based on binocular vision, for monitoring the deformation of slopes in open-pit mines, so that timely measures can be taken when potential hazards are discovered to avoid casualties and property damage.

[0004] To achieve the above objectives, the following solution is proposed:

[0005] A method for monitoring slope deformation based on binocular vision, applied to electronic devices, is used to monitor the deformation of a slope to be monitored. The method includes the following steps:

[0006] The system acquires image data from two high-definition cameras deployed on the opposite side of the slope and maintaining a prediction setup. Multiple reference points are deployed on the slope. Simultaneously, the system acquires the second three-dimensional coordinates of each reference point measured by a measuring robot deployed on the opposite side of the slope.

[0007] The image data is processed based on the intrinsic and extrinsic parameters of the high-definition camera to obtain the first three-dimensional coordinates of each reference point;

[0008] The deformation trend of the slope is obtained by analyzing and verifying the first three-dimensional coordinates and the second three-dimensional coordinates.

[0009] Optionally, the baseline length between the two high-definition cameras is proportional to the length of the slope, and the overlap area of ​​the images captured by the two high-definition cameras exceeds 80% of either image.

[0010] Optionally, the step of analyzing and verifying the first three-dimensional coordinates and the second three-dimensional coordinates to obtain the deformation trend of the slope includes the following steps:

[0011] The first three-dimensional coordinates and the second three-dimensional coordinates are analyzed and verified to obtain the coordinate data of the slope;

[0012] The temporal changes of the coordinate data are compared and analyzed to obtain the trend of change.

[0013] Optional steps may also be included:

[0014] Each of the received high-definition cameras is calibrated using Zhang Zhengyou's calibration method to obtain the intrinsic parameters.

[0015] Optionally, it also includes the following steps:

[0016] Each of the high-definition cameras is calibrated using the PnP calibration method to obtain the extrinsic parameters.

[0017] A slope deformation monitoring device based on binocular vision, applied to electronic devices, is used to monitor the deformation of a slope to be monitored. The slope deformation monitoring device includes:

[0018] The parameter acquisition module is configured to acquire image data collected by two high-definition cameras deployed on the opposite side of the slope and maintaining the prediction setup. Multiple reference points are deployed on the slope. At the same time, it acquires the second three-dimensional coordinates of each of the reference points measured by the measuring robot deployed on the opposite side of the slope.

[0019] The data processing module is configured to process the image data based on the intrinsic and extrinsic parameters of the high-definition camera to obtain the first three-dimensional coordinates of each reference point;

[0020] The analysis and verification module is configured to analyze and verify the first three-dimensional coordinates and the second three-dimensional coordinates to obtain the deformation trend of the slope.

[0021] Optional, also includes:

[0022] The intrinsic parameter calibration module is configured to calibrate each of the received high-definition cameras based on the Zhang Zhengyou calibration method to obtain the intrinsic parameters.

[0023] Optional, also includes:

[0024] The extrinsic parameter calibration module is configured to calibrate each of the high-definition cameras based on the PnP calibration method to obtain the extrinsic parameters.

[0025] An electronic device includes at least one processor and a memory connected to the processor, wherein:

[0026] The memory is used to store computer programs or instructions;

[0027] The processor is used to execute the computer program or instructions to enable the electronic device to implement the slope deformation monitoring method based on binocular vision as described above.

[0028] A computer-readable storage medium is applied to an electronic device, the storage medium carrying one or more computer programs that can be executed by the electronic device, thereby enabling the electronic device to implement the binocular vision-based slope deformation monitoring method as described above.

[0029] As can be seen from the above technical solution, this application discloses a method, device, electronic device, and storage medium for slope deformation monitoring based on binocular vision. The method and device are applied to an electronic device for monitoring the deformation of a slope. Specifically, it acquires image data from two high-definition cameras deployed opposite the slope and maintaining predictive positioning. Multiple reference points are deployed on the slope, and the second three-dimensional coordinates of each reference point are acquired simultaneously by a measuring robot deployed opposite the slope. The image data is processed based on the intrinsic and extrinsic parameters of the high-definition cameras to obtain the first three-dimensional coordinates of each reference point. The first and second three-dimensional coordinates are analyzed and verified to obtain the slope deformation trend. By monitoring the deformation trend, timely measures can be taken when potential hazards are detected, thereby avoiding casualties and property damage. Attached Figure Description

[0030] To more clearly illustrate the technical solutions in the embodiments of this application 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 some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0031] Figure 1 This is a schematic diagram of a reference point on a slope according to an embodiment of this application;

[0032] Figure 2 This is a flowchart illustrating a slope deformation monitoring method based on binocular vision, according to an embodiment of this application.

[0033] Figure 3 This is a schematic diagram of a PnP calibration method;

[0034] Figure 4 This is a block diagram of a slope deformation monitoring device based on binocular vision, according to an embodiment of this application.

[0035] Figure 5 This is a block diagram of another slope deformation monitoring device based on binocular vision, according to an embodiment of this application.

[0036] Figure 6 This is a block diagram of an electronic device according to an embodiment of this application. Detailed Implementation

[0037] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0038] Given the current inability to effectively monitor slopes over large areas with high precision in real time, this application provides a slope deformation monitoring scheme based on binocular vision for monitoring deformation in open-pit mines, roadbeds, and mountains. Specifically, this application describes a physical device for monitoring slopes in open-pit mines, comprising electronic equipment, two high-definition cameras connected to the electronic equipment, and a measuring robot. This device uses the high-definition cameras and the measuring robot to acquire images and position data from multiple reference points on the slope to be monitored, thereby monitoring deformation.

[0039] The two high-definition cameras deployed in this application are located at two stable high points opposite the slope, with the location chosen to capture the entire slope. The distance between the two high-definition cameras, i.e., the baseline length, is designed to cover the entire length of the slope. Furthermore, the overlap of the areas captured by the two high-definition cameras must be greater than 80%. The high-definition cameras used are Canon EOS 90D cameras, which have a 32.5-megapixel APS-C sensor and a maximum image resolution of 6960×4640. After the high-definition cameras acquire images of the target, the first three-dimensional coordinates of the reference point are calculated using the pixel coordinates extracted by the camera and the intrinsic and extrinsic parameters of the high-definition cameras.

[0040] Multiple reference points are set up on the slope to be monitored, such as Figure 1 As shown, stable targets such as triangular pyramids, reflectors, or prisms are selected as reference points to facilitate identification by the measurement robot. The measurement robot automatically searches for and locks onto the target using its telescope and sensor system. During target deployment, steel wire is driven into the reference point and raised by approximately 60 centimeters. The prism is reinforced by embedding three ground anchors to tighten the steel wire. This solves the problems of inaccurate positioning caused by the low position and swaying of the target. Field tests showed that even in winds of force 5-6, the target remained largely stationary and could still be scanned by the measurement robot.

[0041] The measuring robot is positioned at pre-selected control points. These control points are stable and have good visibility to ensure accurate measurement of the positions of various targets on the slope. A sturdy tripod is used during setup, and the robot is leveled to ensure its horizontality and minimize measurement errors. During measurement, the robot transmits signals to the targets and calculates the distance to the targets by measuring the round-trip time or phase difference of the signals, while simultaneously measuring the horizontal and vertical angles. Based on these measurements, along with the instrument's own coordinates and attitude information, the three-dimensional coordinates (X, Y, Z) of each target center are calculated using triangulation principles and related mathematical models. Multiple measurements are performed on multiple targets to improve the accuracy and reliability of the data. The average value is then taken as the final measurement result, which is described in this application as the second three-dimensional coordinates of a reference point.

[0042] Based on the above, this application proposes the following scheme to achieve slope deformation monitoring.

[0043] Figure 2 This is a flowchart illustrating a slope deformation monitoring method based on binocular vision, as described in an embodiment of this application.

[0044] like Figure 2 As shown, the slope deformation monitoring method provided in this embodiment is applied to an electronic device, which can be understood as a computer, server, or cloud platform with data computing and information processing capabilities. The slope deformation monitoring method specifically includes the following steps:

[0045] S1. Acquire image data and second / third-dimensional coordinates from two high-definition cameras.

[0046] The two high-definition cameras periodically or irregularly capture images of the slope, obtaining image data including the slope itself and multiple reference points deployed on the slope, which serves as the basis for further processing. The second three-dimensional coordinate refers to the coordinate data obtained by the measuring robot monitoring the reference points.

[0047] S2. Process the image data based on the intrinsic and extrinsic parameters of the high-definition camera.

[0048] The intrinsic and extrinsic parameters are obtained by calibrating the high-definition camera in advance. Based on the obtained intrinsic and extrinsic parameters, the image data is processed to obtain the first three-dimensional coordinates based on binocular vision.

[0049] S3. By comparing and analyzing the first and second three-dimensional coordinates, the changing trend of the slope can be obtained.

[0050] In the specific analysis, the first three-dimensional coordinates and the second three-dimensional coordinates are analyzed and verified to obtain the coordinate data of the slope; then the temporal changes of the coordinate data are compared and analyzed to obtain the trend of slope change.

[0051] The analysis and verification of the first and second three-dimensional coordinates are performed through the following steps.

[0052] First, data preprocessing is performed, including removing business trips and standardizing data formats.

[0053] Gross error removal: A preliminary check is performed on the target's 3D coordinate data measured by the measuring robot and calculated by the binocular vision system to remove data points that deviate significantly from the normal range. These data points may be caused by random errors during the measurement process, external interference, or other factors, and are called gross errors. For example, if the coordinate value of a target differs too much from the coordinate values ​​of other adjacent targets, it may be a gross error and needs to be removed.

[0054] Data format consistency: Ensure that the coordinate data measured by the robot and the coordinate data calculated by the binocular vision system have the same coordinate system and data format for subsequent comparative analysis. If the two coordinate systems are inconsistent, coordinate transformation is required to unify them into the same coordinate system.

[0055] Then, geometric relationship verification is performed, including checking relative positional relationships and spatial distribution consistency.

[0056] Relative positional relationship check: Check whether the relative positional relationships between targets measured by the robot and between targets calculated by the binocular vision system are consistent. For example, the geometric parameters such as distance and angle between adjacent targets should be similar under both measurement methods. If there is a large difference between the two, it indicates that there may be measurement error or systematic error.

[0057] Spatial distribution consistency: Analyze the overall spatial distribution of the target. By drawing a 3D distribution map of the target, visually compare whether the spatial distribution of the target presented by the measurement robot and the binocular vision system matches. If the distribution differs significantly, the measurement process and calculation methods need further examination.

[0058] Error analysis was then performed, with Boakye calculating error statistics and analyzing the sources of error.

[0059] Error statistics are calculated: This involves determining the error statistics between the coordinate data measured by the robot and the coordinate data calculated by the binocular vision system, such as root mean square error (RMSE) and mean absolute error (MAE). The formula for calculating the root mean square error is:

[0060] RMSE=n1∑i=1n(xim−xiv)2+(yim−yiv)2+(zim−ziv)2,

[0061] Where (xim, yim, zim) are the 3D coordinates of the i-th target measured by the robot, (xiv, yiv, ziv) are the 3D coordinates of the i-th target calculated by the binocular vision system, and n is the number of targets. These error statistics are used to quantitatively evaluate the magnitude of the error between the two measurement methods.

[0062] Error Source Analysis: Analyze the possible causes of errors, such as the accuracy limitations of the measurement robot, algorithm errors in the binocular vision system, and the influence of external environmental factors (such as lighting, wind, etc.) on the measurement. Based on the error sources, take corresponding measures to reduce errors, such as calibrating the measurement robot, optimizing the algorithm of the binocular vision system, and selecting a suitable measurement environment.

[0063] Through the above processing, the coordinate data of multiple reference points on the slope are obtained.

[0064] When comparing and analyzing the temporal changes of coordinate data, the following steps are performed.

[0065] First, time-series data is processed, including data grouping and data correlation.

[0066] Data grouping: The coordinate data measured by the robot and the coordinate data calculated by the binocular vision system are grouped in chronological order to form coordinate datasets at different time points. For example, the target is measured at regular intervals (such as once a day or once a week), and the data from each measurement is used as a dataset.

[0067] Data correlation: Ensure that the coordinate data of the measured robot and the coordinate data of the binocular vision system at each time point correspond one-to-one, so as to make accurate comparison and analysis.

[0068] Then, the changes are calculated, including the coordinate changes and the change index.

[0069] Coordinate change calculation: For each target, calculate the change between the coordinates measured by the robot and the coordinates calculated by the binocular vision system at different time points, i.e., ΔX=Xt+1−Xt, ΔY=Yt+1−Yt, ΔZ=Zt+1−Zt, where (Xt,Yt,Zt) and (Xt+1,Yt+1,Zt+1) are the three-dimensional coordinates at time t and time (t+1), respectively. Similarly, the coordinate change calculated by the binocular vision system can also be calculated.

[0070] Calculation of comprehensive change indicators: In addition to the change in a single coordinate, some comprehensive change indicators can also be calculated, such as the magnitude of the target's position change and the deformation rate. The magnitude of the position change can be obtained by calculating the vector magnitude of the coordinate change, i.e., S = ΔX² + ΔY² + ΔZ²; the deformation rate can be calculated by dividing the magnitude of the position change by the time interval.

[0071] Finally, trend analysis is conducted, including graphical representation, correlation analysis, and early warning analysis.

[0072] Graphical Display: A curve showing the change in target coordinates over time visually illustrates the slope's deformation trend. By observing the shape and pattern of the curve, one can determine whether the slope is in a stable, slowly deforming, or rapidly deforming state. For example, if the curve shows a gradually rising trend, it indicates that the slope may be undergoing continuous deformation.

[0073] Correlation analysis: This involves analyzing the correlation between the coordinate changes measured by the robot and the coordinate changes calculated by the binocular vision system. A high correlation indicates that the two measurement methods are consistent in reflecting slope deformation and can mutually verify and complement each other. A low correlation requires further analysis, which may indicate a significant error in one measurement method or a complex slope deformation.

[0074] Early warning analysis: Based on the deformation trend and rate of change of the slope, combined with relevant engineering experience and safety standards, the stability of the slope is assessed and an early warning is issued. When the deformation rate of the slope exceeds a certain threshold or the magnitude of the positional change reaches a certain level, an early warning signal is issued in a timely manner to remind relevant personnel to take corresponding measures, such as strengthening monitoring and carrying out reinforcement treatment.

[0075] As can be seen from the above technical solution, this embodiment provides a slope deformation monitoring method based on binocular vision. This method is applied to an electronic device to monitor the deformation of a slope. Specifically, it acquires image data from two high-definition cameras deployed opposite the slope and maintaining predictive positioning. Multiple reference points are set up on the slope. Simultaneously, the second three-dimensional coordinates of each reference point are acquired by a measuring robot deployed opposite the slope. The image data is processed based on the intrinsic and extrinsic parameters of the high-definition cameras to obtain the first three-dimensional coordinates of each reference point. The first and second three-dimensional coordinates are analyzed and verified to obtain the slope deformation trend. By monitoring the deformation trend, timely measures can be taken when potential hazards are detected, thereby avoiding casualties and property damage.

[0076] In addition, one specific embodiment of this application also includes a calibration scheme for internal and external parameters.

[0077] The intrinsic parameter calibration of a high-definition camera is based on Zhang Zhengyou's calibration method to determine the distortion coefficients, focal length f, and principal point coordinates of the two camera lenses; the basic calibration formula is as follows:

[0078] 1. Perspective projection model:

[0079] Three-dimensional points ( , , The basic formula for projecting a two-dimensional point (x, y) onto the image plane is:

[0080] Where: s is the scaling factor, ( , , (x,y) is a 3D point in the world coordinate system; (x,y) is the pixel coordinate in the image coordinate system; K is the camera's intrinsic parameter matrix, which includes parameters such as focal length and optical center; R is the camera's rotation matrix; represents the camera's attitude in the world coordinate system; t is the camera's translation vector, representing the camera's position in the world coordinate system.

[0081] 2. Intrinsic parameter matrix 𝐾:

[0082] The camera intrinsic parameter matrix describes the camera's internal geometry and optical properties, and is typically represented as:

[0083] in: and It is the focal length of the camera in the x and y directions; and These are the pixel coordinates of the optical center; distortion parameters (radial and tangential distortion) are also estimated during calibration. By repeatedly photographing known patterns (such as a checkerboard pattern) and extracting points from these images, a correspondence between two-dimensional image points and three-dimensional world coordinates is established. This information is then used to solve for and optimize the camera intrinsic parameter matrix.

[0084] 3. Reprojection error:

[0085] To optimize camera parameters and minimize reprojection error, the formula is:

[0086] in: These are the actual extracted image coordinates. The projection point is calculated based on camera parameters and 3D coordinates. By minimizing this error, the camera's intrinsic parameters are optimized.

[0087] The calibration of the external parameters of high-definition cameras is based on the PnP calibration method, such as... Figure 3As shown, the known positions in three-dimensional space are obtained in the pre-process, and the projected coordinates of the corresponding three-dimensional points are extracted from the image. In the calibration stage, a matrix describing the internal characteristics of the camera is obtained. The camera's exterior orientation elements can be deduced from the three-dimensional coordinates of the calibration pattern, thereby recovering the camera's extrinsic parameters.

[0088] 1. Establish mapping relationships

[0089] In this step, it is necessary to pair the world coordinates of a 3D point with its corresponding 2D image coordinates. Assume there are... Three-dimensional points and corresponding two-dimensional image points After establishing the mapping relationship, a set of point pairs is formed:

[0090]

[0091] 2. Using the PnP algorithm

[0092] Using the above-mentioned known three-dimensional points and corresponding two-dimensional points The PnP algorithm is applied to solve for the camera's rotation matrix. and displacement vector The core of this process is to obtain the optimal solution by minimizing the reprojection error. The reprojection error is defined as:

[0093]

[0094] in It is the camera's intrinsic parameter matrix. It is the representation of a 3D point in the camera coordinate system. It is the depth value of that point.

[0095] 3. Converting a rotation vector to a rotation matrix

[0096] During the solution process, the rotation matrix It may be given in the form of a rotation vector. In this case, the rotation vector rvec can be converted into a rotation matrix using the Rodrigues formula. The Rodrigues formula is expressed as follows:

[0097]

[0098] in: It is the magnitude (rotation angle) of the rotation vector. It is the antisymmetric matrix form of the rotation vector rvec, calculated as follows:

[0099]

[0100] in, It is a rotation vector.

[0101] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0102] Although the operations are described in a specific order, this should not be construed as requiring these operations to be performed in the specific order shown or in a sequential order. In certain environments, multitasking and parallel processing may be advantageous.

[0103] It should be understood that the steps described in the method embodiments of this disclosure may be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of this disclosure is not limited in this respect.

[0104] Computer program code for performing the operations of this disclosure can be written in one or more programming languages ​​or a combination thereof, including but not limited to object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer.

[0105] Figure 4 This is a block diagram of a slope deformation monitoring device based on binocular vision, according to an embodiment of this application. Figure 4As shown, the slope deformation monitoring device provided in this embodiment is applied to an electronic device, which can be understood as a computer, server, or cloud platform with data computing and information processing capabilities. Specifically, the slope deformation monitoring device includes a parameter acquisition module 10, a data processing module 20, and an analysis and verification module 30.

[0106] The parameter acquisition module is used to acquire image data and second / third-dimensional coordinates from two high-definition cameras.

[0107] The two high-definition cameras periodically or irregularly capture images of the slope, obtaining image data including the slope itself and multiple reference points deployed on the slope, which serves as the basis for further processing. The second three-dimensional coordinate refers to the coordinate data obtained by the measuring robot monitoring the reference points.

[0108] The data acquisition module is used to process image data based on the intrinsic and extrinsic parameters of the high-definition camera.

[0109] The intrinsic and extrinsic parameters are obtained by calibrating the high-definition camera in advance. Based on the obtained intrinsic and extrinsic parameters, the image data is processed to obtain the first three-dimensional coordinates based on binocular vision.

[0110] The analysis and verification module is used to compare and analyze the first and second three-dimensional coordinates to obtain the trend of slope change.

[0111] In the specific analysis, the first and second three-dimensional coordinates are analyzed and verified to obtain the coordinate data of the slope; then, the temporal changes of the coordinate data are compared and analyzed to obtain the trend of slope change. The specific analysis process has been described in detail above and will not be repeated here.

[0112] As can be seen from the above technical solution, this embodiment provides a slope deformation monitoring device based on binocular vision. This device is applied to electronic equipment to monitor the deformation of the slope to be monitored. Specifically, it acquires image data collected by two high-definition cameras deployed on the opposite side of the slope and maintaining a predictive setup. Multiple reference points are deployed on the slope, and the second three-dimensional coordinates of each reference point are acquired by a measuring robot deployed on the opposite side of the slope. The image data is processed based on the intrinsic and extrinsic parameters of the high-definition cameras to obtain the first three-dimensional coordinates of each reference point. The first and second three-dimensional coordinates are analyzed and verified to obtain the deformation trend of the slope. By monitoring the deformation trend, timely measures can be taken when potential hazards are discovered, thereby avoiding casualties and property damage.

[0113] In addition, one specific embodiment of this application further includes an internal parameter calibration module 40 and an external parameter calibration module 50, such as... Figure 5As shown, these are used to calibrate the internal and external parameters of a high-definition camera, respectively. The specific calibration process has been described in detail above and will not be repeated here.

[0114] The units described in the embodiments of this disclosure can be implemented in software or in hardware. The name of a unit does not necessarily limit the unit itself; for example, the first acquisition unit can also be described as "a unit that acquires at least two Internet Protocol addresses".

[0115] The functions described above in this document can be performed at least in part by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), system-on-a-chip (SoCs), complex programmable logic devices (CPLDs), and so on.

[0116] Figure 5 This is a block diagram of an electronic device according to an embodiment of this application.

[0117] The following is for reference. Figure 5 This document illustrates a structural diagram suitable for implementing the electronic device in the embodiments of this disclosure. The terminal device in the embodiments of this disclosure may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. This electronic device is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this disclosure.

[0118] The electronic device may include a processing unit (e.g., a central processing unit, a graphics processing unit, etc.) 601, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 602 or a program loaded from an input device 506 into a random access memory (RAM) 603. The RAM also stores various programs and data required for the operation of the electronic device. The processing unit, ROM, and RAM are interconnected via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0119] Typically, the following devices can be connected to the I / O interface: input devices including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 607 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 608 including, for example, magnetic tapes, hard disks, etc.; and communication devices 609. Communication device 609 allows the electronic device to communicate wirelessly or wiredly with other devices to exchange data. Although electronic devices with various devices are shown in the figures, it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed alternatively.

[0120] This application also provides an embodiment of a computer-readable storage medium.

[0121] The aforementioned computer-readable storage medium is used in an electronic device and carries one or more computer programs. When these programs are executed by the electronic device, the device acquires image data from two high-definition cameras positioned opposite the slope and maintaining a predictive setup. Multiple reference points are positioned on the slope. Simultaneously, the device acquires the second three-dimensional coordinates of each reference point measured by a surveying robot positioned opposite the slope. Based on the intrinsic and extrinsic parameters of the high-definition cameras, the image data is processed to obtain the first three-dimensional coordinates of each reference point. The first and second three-dimensional coordinates are analyzed and verified to determine the deformation trend of the slope. By monitoring the deformation trend, timely intervention measures can be taken when potential hazards are detected, thereby preventing casualties and property damage.

[0122] It should be noted that the computer-readable medium described above in this disclosure can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof.

[0123] In this disclosure, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in connection with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0124] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0125] Although preferred embodiments of the present invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of the present invention.

[0126] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes said element.

[0127] The technical solution provided by the present invention has been described in detail above. Specific examples have been used to illustrate the principle and implementation of the present invention. The description of the above embodiments is only for the purpose of helping to understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation and application scope based on the idea of ​​the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for monitoring slope deformation based on binocular vision, applied to electronic equipment, for monitoring the deformation of a slope to be monitored, characterized in that, The slope deformation monitoring method includes the following steps: The system acquires image data from two high-definition cameras deployed on the opposite side of the slope and maintaining a prediction setup. Multiple reference points are deployed on the slope. Simultaneously, the system acquires the second three-dimensional coordinates of each reference point measured by a measuring robot deployed on the opposite side of the slope. The image data is processed based on the intrinsic and extrinsic parameters of the high-definition camera to obtain the first three-dimensional coordinates of each reference point; The deformation trend of the slope is obtained by analyzing and verifying the first three-dimensional coordinates and the second three-dimensional coordinates.

2. The slope deformation monitoring method as described in claim 1, characterized in that, The baseline length between the two high-definition cameras is proportional to the length of the slope, and the overlap area of ​​the images captured by the two high-definition cameras exceeds 80% of either image.

3. The slope deformation monitoring method as described in claim 1, characterized in that, The step of analyzing and verifying the first three-dimensional coordinates and the second three-dimensional coordinates to obtain the deformation trend of the slope includes the following steps: The first three-dimensional coordinates and the second three-dimensional coordinates are analyzed and verified to obtain the coordinate data of the slope; The temporal changes of the coordinate data are compared and analyzed to obtain the trend of change.

4. The slope deformation monitoring method according to any one of claims 1 to 3, characterized in that, It also includes the following steps: Each of the received high-definition cameras is calibrated using Zhang Zhengyou's calibration method to obtain the intrinsic parameters.

5. The slope deformation monitoring method according to any one of claims 1 to 3, characterized in that, It also includes the following steps: Each of the high-definition cameras is calibrated using the PnP calibration method to obtain the extrinsic parameters.

6. A slope deformation monitoring device based on binocular vision, applied to electronic equipment, for monitoring the deformation of a slope to be monitored, characterized in that, The slope deformation monitoring device includes: The parameter acquisition module is configured to acquire image data collected by two high-definition cameras deployed on the opposite side of the slope and maintaining the prediction setup. Multiple reference points are deployed on the slope. At the same time, it acquires the second three-dimensional coordinates of each of the reference points measured by the measuring robot deployed on the opposite side of the slope. The data processing module is configured to process the image data based on the intrinsic and extrinsic parameters of the high-definition camera to obtain the first three-dimensional coordinates of each reference point; The analysis and verification module is configured to analyze and verify the first three-dimensional coordinates and the second three-dimensional coordinates to obtain the deformation trend of the slope.

7. The slope deformation monitoring device as described in claim 6, characterized in that, Also includes: The intrinsic parameter calibration module is configured to calibrate each of the received high-definition cameras based on the Zhang Zhengyou calibration method to obtain the intrinsic parameters.

8. The slope deformation monitoring device as described in claim 6, characterized in that, Also includes: The extrinsic parameter calibration module is configured to calibrate each of the high-definition cameras based on the PnP calibration method to obtain the extrinsic parameters.

9. An electronic device, characterized in that, The electronic device includes at least one processor and a memory connected to the processor, wherein: The memory is used to store computer programs or instructions; The processor is used to execute the computer program or instructions to enable the electronic device to implement the slope deformation monitoring method based on binocular vision as described in any one of claims 1 to 5.

10. A computer-readable storage medium for use in electronic devices, characterized in that, The storage medium carries one or more computer programs that can be executed by the electronic device, thereby enabling the electronic device to implement the slope deformation monitoring method based on binocular vision as described in any one of claims 1 to 5.

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