High and steep slope rock mass stability intelligent monitoring system and monitoring method

Abstract

The invention discloses a high and steep slope rock mass stability intelligent monitoring system and monitoring method, and relates to the technical field of intelligent monitoring, and the method comprises the steps: 1, collecting images, vibration and inclinometer data in real time, and carrying out comprehensive analysis to generate a landslide coefficient; 2, if the coefficient exceeds a threshold value, triggering a first-level alarm, otherwise, entering trend analysis; and step 3, judging the trend speed through the ratio of the low frequency to the high frequency of the vibration signal and the time slope, triggering secondary early warning when the trend speed exceeds a threshold value, or else, circularly monitoring. Vector conversion and included angle cosine are matched with a landslide direction threshold value, effective deformation is quantified by combining bending radius and stress analysis, multidirectional bending interference of an inclinometer is eliminated, displacement direction measurement precision is improved, early warning errors are reduced, a trend variable is constructed by utilizing a ratio of low-frequency amplitude to high-frequency amplitude, trend speed is analyzed by combining time sequence slope, and the method has the advantages of being high in accuracy and high in accuracy. The rock body loosening sign is comprehensively pre-judged, early warning is achieved, and the sudden landslide risk is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent monitoring, and in particular to an intelligent monitoring system and method for the stability of rock masses on high-steep slopes. Background Art

[0002] Monitoring the stability of rock masses on high-steep slopes is the core means to prevent geological disasters and ensure the safety of life and property. Due to the steep terrain and complex geological conditions of high-steep slopes, they are prone to disasters such as landslides and collapses induced by factors such as rainfall and earthquakes, which directly threaten the safety of residential areas, transportation arteries, and water conservancy facilities below. By using Beidou satellites to monitor the surface displacement of slopes in real time, and using inclinometers to monitor environmental parameters such as the deep deformation of rock masses in real time, and using these data to identify the signs of the initiation of the sliding zone of rock masses in advance, such as sudden changes in the displacement rate of rock masses and sudden increases in seepage pressure in rock masses, millimeter-level early warnings can be achieved.

[0003] Among them, before the inclinometer is buried, workers need to drill holes in the rock mass first and place an inclinometer tube perpendicular to the rock mass. There are grooves in the inclinometer tube, and the inclinometer slides freely along the grooves. If the rock mass inside undergoes displacement, it will squeeze the inclinometer tube and cause it to bend and deform. The inclinometer calculates the displacement amount of the rock mass by measuring the bending angle of the inclinometer tube and using the bending angle.

[0004] When the rock mass undergoes displacement, the inclinometer tube generates multi-directional bending due to the uneven extrusion of the surrounding rock mass, resulting in the inclinometer being unable to accurately measure the displacement direction of the rock mass, and ultimately increasing the early warning error of the rock mass landslide. Summary of the Invention

[0005] In view of the deficiencies of the prior art, the present invention provides an intelligent monitoring system and method for the stability of rock masses on high-steep slopes, which solves the problem that the multi-directional bending of the inclinometer tube caused by the displacement of the rock mass leads to deviation in the measurement direction and increases the early warning error of the landslide.

[0006] To achieve the above objectives, the present invention is realized through the following technical solutions: an intelligent monitoring system and method for the stability of rock masses on high-steep slopes, including the following specific steps and modules: Step 1: Obtain image data, vibration data, and inclinometer tube state data sets in real time and perform comprehensive analysis to obtain the rock mass landslide coefficient. Among them, by converting the inclinometer tube state data set into a bending vector and using the cosine formula of the included angle to match the preset landslide direction threshold, only the bending vectors consistent with the landslide direction are statistically analyzed, and the multi-directional bending of the inclinometer tube caused by the displacement of the rock mass is solved by combining the bending radius and force analysis; Step 2: Judge whether the rock mass landslides according to the rock mass landslide coefficient. If it is judged that the rock mass landslides, a first early warning is given. If it is judged that the rock mass is stable, Step 3 is executed; Step 3: Judge whether the rock mass has a tendency to landslide. If it is judged that the rock mass has a tendency to slide, a second early warning is given. If it is judged that the rock mass has no tendency to slide, return to Step 1 and continue to repeat the above step process.

[0007] Further, in Step 1, the image data is subjected to filtering and noise reduction processing, and the image data is processed through an image contour segmentation algorithm and an image contour tracking algorithm to obtain a rock mass image. The rock mass image includes the number of pixel points of the rock mass image and the pixel point coordinates of the rock mass image. The pixel point coordinates of the rock mass image are summed and averaged according to the number of pixel points of the rock mass image to obtain the center point coordinates of the rock mass image, obtaining the initial coordinates, and recalculating the center point coordinates of the rock mass image according to time to obtain the current coordinates. The Euclidean distance formula is used to calculate the current coordinates and the initial coordinates to obtain the displacement coefficient of the rock mass image.

[0008] Further, the specific method for obtaining the displacement coefficient of the rock mass image is as follows: ; where represents the displacement coefficient of the rock mass image, reflecting whether the rock mass is displaced, represents the abscissa of the initial coordinates, represents the abscissa of the current coordinates, represents the ordinate of the initial coordinates, represents the ordinate of the current coordinates.

[0009] Further, in Step 1, the vibration data is subjected to data cleaning processing, and the vibration data is averaged to obtain a vibration influence coefficient.

[0010] Further, the specific steps of converting the inclinometer tube state data set into a bending vector, using the cosine formula of the included angle to match the preset landslide direction threshold, only counting the bending vectors consistent with the landslide direction, and combining the bending radius and force analysis to solve the multi-directional bending of the inclinometer tube caused by rock mass displacement are as follows: performing three-dimensional modeling on the rock mass image to obtain a rock mass model, taking the center point of the model as the origin, establishing a three-dimensional coordinate system, and dividing the inclinometer tube state data set into Copies are obtained to get the inclinometer tube status data of different depth layers. The inclinometer tube status data includes the pipeline bending angle and the pipeline bending radius. The inclinometer tube status data is cleaned, and the pipeline bending angle is vector-transformed to obtain the pipeline bending vector. A bending vector database and a bending radius database are established. The pipeline bending vector data is stored in the bending vector database, and the pipeline bending radius data is stored in the bending radius database. The pipeline bending vectors and the pipeline bending radii correspond one by one. A landslide direction threshold is set, and the included angle cosine formula of vectors is used to calculate the pipeline bending vectors and the vectors within the landslide direction threshold in turn to obtain the landslide direction matching value. The landslide direction matching value is compared with one. If the landslide direction matching value is equal to one, the number of pipeline bending vectors with the landslide direction matching value equal to one is counted to obtain the number of times of pushing the landslide. And according to the force analysis and the friction formula, a pipeline bending direction influence factor is set. The pipeline bending radius and the pipeline bending direction influence factor corresponding to the pipeline bending vector with the landslide direction matching value equal to one are normalized and multiplied in turn to obtain the rock mass anomaly value. The rock mass anomaly values are summed up according to the number of times of pushing the landslide to obtain the rock mass distortion coefficient. If the landslide direction matching value is not equal to one, the inclinometer continues to detect.

[0011] Further, normalization processing and comprehensive analysis are performed according to the rock mass image displacement coefficient, the vibration influence coefficient, and the rock mass distortion coefficient to obtain the rock mass landslide coefficient; ; where represents the rock mass landslide coefficient, reflecting whether the rock mass landslides, represents the rock mass image displacement coefficient, reflecting whether the rock mass image is displaced, represents the vibration influence coefficient, reflecting whether there is vibration inside the rock mass, represents the rock mass distortion coefficient, reflecting the influence on the rock mass sliding, represents the quantity of the inclinometer tube status data of different depth layers.

[0012] Further, in step two, the rock mass landslide threshold is obtained through historical experiments and compared with the rock mass landslide coefficient. If the rock mass landslide coefficient is greater than or equal to the rock mass landslide threshold, it is judged that the rock mass is landsliding. If the rock mass landslide coefficient is less than the rock mass landslide threshold, it is judged that the rock mass is stable.

[0013] Further, in step three, through the rock mass stability prediction algorithm, the rock mass sliding trend coefficient is calculated, and a rock mass sliding trend threshold is set. The rock mass sliding trend threshold is assigned a value of 2M. The rock mass sliding trend threshold is compared with the rock mass sliding trend coefficient. If the rock mass sliding trend coefficient is equal to the rock mass sliding trend threshold, it is judged that the rock mass has a sliding trend. If the rock mass sliding trend coefficient is not equal to the rock mass sliding trend threshold, it is judged that the rock mass has no sliding trend.

[0014] Furthermore, the specific method for obtaining the rock mass sliding trend coefficient is as follows: Filter the vibration data through a software filtering algorithm to obtain the low-frequency amplitude and the high-frequency amplitude. Divide the low-frequency amplitude by the high-frequency amplitude to obtain the sliding trend variable. When the sliding trend variable increases, assign the value M to the sliding trend variable. When the sliding trend variable decreases, assign the value N to the sliding trend variable. Set the initial variable coordinates according to the sliding trend variable and time. And the current variable coordinates , represents the initial time, represents the current time, represents the initial sliding trend variable, represents the current sliding trend variable. Calculate the initial variable coordinates and the current variable coordinates through the slope formula to obtain the sliding trend speed. When the sliding trend speed increases, assign the value M to the sliding trend speed. When the sliding trend speed decreases, assign the value N to the sliding trend speed. Set the rock mass sliding trend coefficient. When both the sliding trend variable and the sliding trend speed increase, assign the value 2M to the rock mass sliding trend coefficient. When the sliding trend variable and the sliding trend speed do not both increase, assign the value M + N or 2N to the rock mass sliding trend coefficient.

[0015] Furthermore, there are a rock mass sliding monitoring module, a rock mass sliding analysis module, and a rock mass sliding trend prediction module; the rock mass sliding monitoring module is used to obtain image data, vibration data, and the inclinometer tube status data set in real time and perform comprehensive analysis to obtain the rock mass landslide coefficient; the rock mass sliding analysis module is used to judge whether the rock mass landslides according to the rock mass landslide coefficient. If it is judged that the rock mass landslides, a first warning is given. If it is judged that the rock mass is stable, the judgment result of the rock mass being stable is sent to the rock mass sliding trend prediction module; the rock mass sliding trend prediction module is used to receive the judgment result of the rock mass being stable and judge whether the rock mass has a landslide trend. If it is judged that the rock mass has a sliding trend, a second warning is given. If it is judged that the rock mass has no sliding trend, it returns to the rock mass sliding monitoring module and continues to repeat the execution of the rock mass sliding monitoring module, the rock mass sliding analysis module, and the rock mass sliding trend prediction module.

[0016] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects:

[0017] 1. By converting the deflection angle of the inclinometer tube into a vector and using the cosine formula of the included angle to match the preset landslide direction threshold, only the bending vectors consistent with the landslide direction are counted. Combining the bending radius and force analysis, the action of forces in different directions is quantified, avoiding multi-directional bending of the inclinometer tube caused by uneven extrusion of the surrounding rock mass, which may lead to the inability of the inclinometer to accurately measure the displacement direction of the rock mass, and improving the accuracy of rock mass landslide warning.

[0018] 2. By calculating the ratio of the low-frequency amplitude to the high-frequency amplitude as the sliding trend variable, and combining with the slope analysis of the time series, the sliding trend velocity is obtained. The rock mass sliding is predicted by comprehensively considering the sliding trend variable and the sliding trend velocity, reducing the risk of rock mass sliding.

[0019] Of course, it is not necessary for any product implementing the present invention to achieve all the above-mentioned advantages simultaneously. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a flowchart of an intelligent monitoring method for the stability of rock mass on a high-steep slope according to the present invention.

[0021] Figure 2 It is a structural diagram of an intelligent monitoring system for the stability of rock mass on a high-steep slope according to the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0022] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0023] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variation thereof is intended to cover a non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or elements inherent to such process, method, article or device.

[0024] As Figure 1 shown, the embodiments of the present invention provide an intelligent monitoring method for the stability of rock mass on a high-steep slope, including the following specific steps:

[0025] Step 1: Real-time obtain image data through a camera. The image data includes the number of pixel points, pixel point coordinates, and pixel point brightness values. Filter and denoise the image data, which helps improve the quality of the image data. Process the image data using an image contour segmentation algorithm and an image contour tracking algorithm. For the image contour segmentation algorithm, such as the sobel algorithm, first convert the image data into a grayscale image to simplify the calculation. Then, use 3×3 convolution kernels in the horizontal and vertical directions (the horizontal convolution kernel focuses on detecting vertical edges, and the vertical convolution kernel focuses on detecting horizontal edges) to perform convolution operations on the grayscale image, obtaining a horizontal gradient matrix and a vertical gradient matrix. Next, perform a square root calculation of the sum of squares of the horizontal gradient matrix and the vertical gradient matrix to obtain the gradient magnitude. The gradient magnitude is used to determine the edges, and the arctangent function is used to calculate the gradient direction. The gradient direction is used to determine the direction in which the edges extend. Finally, perform binarization processing on the gradient magnitude by setting a threshold, and mark the pixel point coordinates with a gradient magnitude higher than the threshold as the contour, obtaining a rock mass image, which is used to reflect the external condition of the rock mass. For the image contour tracking algorithm, such as the Lucas - Kanade optical flow method, first make an assumption of constant pixel point brightness value, obtaining an assumption formula, that is, the pixel point brightness values of the same object in two adjacent frames of images are unchanged. Then, perform a first-order Taylor expansion on the assumption formula to obtain an optical flow constraint equation. Next, use the sobel algorithm and the difference between adjacent frames to calculate the spatial gradient and the temporal gradient, which are used to reflect the influence of the changes in the spatial gradient and the temporal gradient on the pixel point brightness value in the optical flow constraint equation. Since there are two unknowns in the optical flow constraint equation, assume that the pixel points in the neighborhood move the same. Therefore, combine the optical flow constraint equations of the pixel points in the neighborhood into an overdetermined system of equations, and then use the least squares method to solve this system of equations to avoid errors, obtaining an estimated value of the optical flow velocity. Finally, in practical applications, select feature points (such as corner points), perform optical flow calculation and tracking on them, and then update the positions of the feature points in each frame to achieve target tracking. If there is a displacement of the rock mass, the accuracy of analyzing whether the rock mass is displaced can be improved by tracking the rock mass image;

[0026] The rock mass image includes the number of pixel points of the rock mass image and the pixel point coordinates of the rock mass image. Sum up the pixel point coordinates of the rock mass image according to the number of pixel points of the rock mass image and then take the average to obtain the center point coordinates of the rock mass image, obtaining the initial coordinates. As time goes by, recalculate the center point coordinates of the rock mass image according to the time to obtain the current coordinates. Calculate the current coordinates and the initial coordinates using the Euclidean distance formula to obtain the displacement coefficient of the rock mass image. , where represents the displacement coefficient of the rock mass image, reflecting whether the rock mass is displaced. represents the abscissa of the initial coordinates. represents the abscissa of the current coordinates. represents the ordinate of the initial coordinates. Represents the ordinate of the current coordinate.

[0027] Vibration data is obtained in real time through a vibration sensor to reflect the internal condition of the rock mass. The vibration data is processed by data cleaning to remove redundant values, which helps to improve the quality of the data. The average value of the vibration data is calculated to obtain the vibration influence coefficient.

[0028] Three-dimensional modeling is performed on the rock mass image to obtain a rock mass model. Taking the center point of this model as the origin, a three-dimensional coordinate system is established. The inclinometer is used to obtain the inclinometer tube state data set in real time. The inclinometer tube state data set is discrete data. Since the inclinometer slides in the inclinometer tube and stops each time it slides to a limited distance and then measures at this position, the inclinometer needs to measure times from the top to the bottom of the inclinometer tube. The inclinometer tube state data set is evenly divided into parts to obtain the inclinometer tube state data of different depth layers. The inclinometer tube state data includes the pipe bending angle and the pipe bending radius. Since the pipe bending angle and the pipe bending radius are caused by the internal forces in the rock mass, the internal force condition of the rock mass can be analyzed by analyzing the pipe bending angle and the pipe bending radius. Data cleaning is performed on the inclinometer tube state data to remove redundant values, which helps to improve the quality of the data. Vector transformation is performed on the pipe bending angle to obtain the pipe bending vector. A bending vector database and a bending radius database are established. The pipe bending vector data is stored in the bending vector database, and the pipe bending radius data is stored in the bending radius database, and each pipe bending vector is associated with the corresponding pipe bending radius;

[0029] Set a landslide direction threshold. The rock mass model includes a part located underground and a part exposed to the air. Calculate the vectors from any point on the surface of the rock mass model exposed to the air to the origin. The set of obtained vectors is the range of the landslide direction threshold. Calculate the landslide direction matching value by successively calculating the cosine of the angle between the pipeline bending vector and the vectors within the landslide direction threshold using the cosine formula for the angle between vectors. Compare the landslide direction matching value with one. If the landslide direction matching value is equal to one, it indicates that the force corresponding to this pipeline bending vector can push the rock mass to cause a landslide. Count the number of pipeline bending vectors with a landslide direction matching value equal to one to obtain the number of times of pushing the landslide. Set the pipeline bending direction influence factor according to the force analysis and the friction formula. Taking the horizontal plane of the center point of the rock mass model as the reference, divide the pipeline bending vectors into three categories, namely, one category where the arrow of the pipeline bending vector points below the horizontal plane, denoted as A, and the corresponding force is a; one category where the arrow of the pipeline bending vector coincides with the horizontal plane, denoted as B, and the corresponding force is b; one category where the arrow of the pipeline bending vector points above the horizontal plane, denoted as C, and the corresponding force is c. Decompose the force a to obtain a force tending to B and a force tending to A. Decompose the force b to obtain a force tending to B as a whole. Decompose the force c to obtain a force tending to B and a force tending to C. Additionally, according to the friction formula, that is, the friction force is equal to the product of the friction coefficient and the pressure, a has a force tending to A, so the friction force for pushing the rock mass to slide is the largest. c has a force tending to C, so the friction force for pushing the rock mass to slide is the smallest. b is medium. Therefore, the difficulty of a, b, and c in pushing the rock mass to slide from large to small is a, b, and c respectively. Subsequently, the category where the arrow of the pipeline bending vector points above the horizontal plane has the greatest influence on the rock mass sliding, and the category where the arrow of the pipeline bending vector points below the horizontal plane has the smallest influence on the rock mass sliding. The value range of the pipeline bending direction influence factor is , in degrees. That is, a two-dimensional coordinate system is established based on the origin and a unit vector k passing through the origin. Combining with the physical reality, k is rotated around the origin from an angle infinitely close to and greater than zero degrees to an angle infinitely close to and less than 180 degrees to obtain the value range of the pipeline bending direction influence factor. If the included angle between the arrow of the pipeline bending vector corresponding to the pipeline bending radius and the upper side of the horizontal plane is larger, the pipeline bending direction influence factor is larger. If the included angle between the arrow of the pipeline bending vector corresponding to the pipeline bending radius and the lower side of the horizontal plane is larger, the pipeline bending direction influence factor is smaller. The pipeline bending radius and the pipeline bending direction influence factor corresponding to the pipeline bending vector with a landslide direction matching value equal to one are normalized and multiplied in sequence to obtain the rock mass anomaly value. The rock mass anomaly values are summed according to the number of times of pushing the landslide to obtain the rock mass distortion coefficient. Combining the bending radius and force analysis, the action of forces in different directions is quantified to avoid multi-directional bending of the inclinometer tube due to uneven extrusion of the surrounding rock mass, resulting in the inability of the inclinometer to accurately measure the displacement direction of the rock mass and improving the accuracy of rock mass landslide warning. If the landslide direction matching value is not equal to one, the inclinometer continues to detect.

[0030] The specific method for obtaining the landslide direction matching value is as follows:

[0031] ;

[0032] Among them, represents the landslide direction matching value, with a range of -1 to 1. represents the pipeline bending vector. represents the landslide direction threshold. represents the modulus of the pipeline bending vector. represents the modulus of the landslide direction threshold.

[0033] According to the rock mass image displacement coefficient, vibration influence coefficient, and rock mass distortion coefficient, normalization processing and comprehensive analysis are carried out to obtain the rock mass landslide coefficient. , where represents the rock mass landslide coefficient, reflecting whether the rock mass landslides. represents the rock mass image displacement coefficient, reflecting whether the rock mass image is displaced. represents the vibration influence coefficient, reflecting whether there is vibration inside the rock mass. represents the rock mass distortion coefficient, reflecting the influence on the rock mass sliding. represents the number of inclinometer tube state data at different depth layers.

[0034] Step 2: Determine whether the rock mass is landsliding based on the rock mass landslide coefficient. Obtain the rock mass landslide threshold through historical experiments and compare it with the rock mass landslide coefficient. If the rock mass landslide coefficient is greater than or equal to the rock mass landslide threshold, it is determined that the rock mass is landsliding. If it is determined that the rock mass is landsliding, a first warning is given. If the rock mass landslide coefficient is less than the rock mass landslide threshold, it is determined that the rock mass is stable. If it is determined that the rock mass is stable, then proceed to Step 3.

[0035] Step 3: Determine whether the rock mass has a tendency to landslide. Calculate through the rock mass stability prediction algorithm to obtain the rock mass sliding tendency coefficient, and set the rock mass sliding tendency threshold. Assign a value of 2M to the rock mass sliding tendency threshold. Compare the rock mass sliding tendency threshold with the rock mass sliding tendency coefficient. If the rock mass sliding tendency coefficient is equal to the rock mass sliding tendency threshold, that is, the rock mass sliding tendency coefficient is equal to 2M, it is determined that the rock mass has a sliding tendency. If it is determined that the rock mass has a sliding tendency, a second warning is given. If the rock mass sliding tendency coefficient is not equal to the rock mass sliding tendency threshold, that is, the rock mass sliding tendency coefficient is not equal to 2M, it is determined that the rock mass has no sliding tendency. If it is determined that the rock mass has no sliding tendency, return to Step 1 and continue to repeat the above step process.

[0036] The specific method for obtaining the rock mass sliding tendency coefficient is as follows:

[0037] Filter the vibration data through the software filtering algorithm to obtain the low-frequency amplitude and the high-frequency amplitude. Divide the low-frequency amplitude by the high-frequency amplitude to obtain the sliding tendency variable. Since the interior of the rock mass gradually loosens before landsliding, resulting in more and larger voids, the low-frequency amplitude can continue to pass through the voids without energy loss, while the high-frequency amplitude is scattered due to the voids, resulting in a weakening of the high-frequency amplitude. Therefore, when the voids inside the rock mass become more and larger, the sliding tendency variable increases monotonically. The larger the sliding tendency variable, the more obvious the sliding tendency. Assign a value of M to the sliding tendency variable, and the smaller the sliding tendency variable, the less obvious the sliding tendency. Assign a value of N to the sliding tendency variable. As time goes by, set the initial variable coordinates according to the sliding tendency variable and time and the current variable coordinates , represents the initial time, represents the current time, represents the initial sliding tendency variable, represents the current sliding tendency variable. Through the slope formula for the initial variable coordinates and the current variable coordinates Perform calculations to obtain the sliding trend velocity. When the sliding trend velocity is greater, i.e., the slope is greater, the sliding trend is more obvious. Assign the value M to the sliding trend velocity. When the sliding trend velocity is slower, i.e., the slope is smaller, the sliding trend is less obvious. Assign the value N to the sliding trend velocity. Set the rock mass sliding trend coefficient. Since it is impossible to accurately determine whether the rock mass has a sliding trend only by the increase of the sliding trend variable or the increase of the sliding trend velocity, when both the sliding trend variable and the sliding trend velocity increase simultaneously, assign the value 2M to the rock mass sliding trend coefficient. When the sliding trend variable and the sliding trend velocity do not increase simultaneously, assign the value M + N or 2N to the rock mass sliding trend coefficient, where M is not equal to N.

[0038] An embodiment of the present invention provides an intelligent monitoring system for the stability of a high-steep slope rock mass, including the following specific modules: a rock mass sliding monitoring module, a rock mass sliding analysis module, and a rock mass sliding trend prediction module.

[0039] The rock mass sliding monitoring module is used to obtain image data, vibration data, and the inclinometer tube state data set in real time and perform comprehensive analysis to obtain the rock mass landslide coefficient.

[0040] The rock mass sliding analysis module is used to judge whether the rock mass landslides according to the rock mass landslide coefficient. If it is judged that the rock mass landslides, a first warning is given. If it is judged that the rock mass is stable, the judgment result of the rock mass being stable is sent to the rock mass sliding trend prediction module.

[0041] The rock mass sliding trend prediction module is used to receive the judgment result of the rock mass being stable and judge whether the rock mass has a sliding trend. If it is judged that the rock mass has a sliding trend, a second warning is given. If it is judged that the rock mass has no sliding trend, it returns to the rock mass sliding monitoring module and continues to repeat the execution of the rock mass sliding monitoring module, the rock mass sliding analysis module, and the rock mass sliding trend prediction module.

[0042] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific embodiments described. Obviously, many modifications and changes can be made according to the content of this specification. These embodiments are selected and specifically described in this specification to better explain the principle and practical application of the present invention, so that those skilled in the relevant technical field can understand and utilize the present invention well. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. An intelligent monitoring method for the stability of rock masses on high and steep slopes, characterized in that: It includes the following specific steps: Step 1: Obtain the image data, vibration data, and inclinometer tube status data set in real time and conduct comprehensive analysis to obtain the rock mass landslide coefficient. Among them, by converting the inclinometer tube status data set into a bending vector and using the cosine formula of the included angle to match the preset landslide direction threshold, only the bending vectors consistent with the landslide direction are counted, and the multi-directional bending of the inclinometer tube caused by the rock mass displacement is solved by combining the bending radius and force analysis; Step 2: Judge whether the rock mass landslides according to the rock mass landslide coefficient. If it is judged that the rock mass landslides, a first warning is given. If it is judged that the rock mass is stable, step 3 is executed; Step 3: Judge whether the rock mass has a tendency to landslide. If it is judged that the rock mass has a sliding tendency, a second warning is given. If it is judged that the rock mass has no sliding tendency, return to step 1 and continue to repeat the above step process.

2. The intelligent monitoring method for the stability of high-steep slope rock mass according to claim 1, characterized in that: In step 1, the image data is filtered and denoised, and the image data is processed by the image contour segmentation algorithm and the image contour tracking algorithm to obtain the rock mass image. The rock mass image includes the number of pixel points of the rock mass image and the pixel point coordinates of the rock mass image. The pixel point coordinates of the rock mass image are summed and averaged according to the number of pixel points of the rock mass image to obtain the center point coordinates of the rock mass image, obtain the initial coordinates, and recalculate the center point coordinates of the rock mass image according to time to obtain the current coordinates. The Euclidean distance formula is used to calculate the current coordinates and the initial coordinates to obtain the rock mass image displacement coefficient.

3. The intelligent monitoring method for the stability of rock mass on high-steep slopes according to claim 2, characterized in that: The specific method for obtaining the rock mass image displacement coefficient is: ； Among them, represents the displacement coefficient of the rock mass image, reflecting whether the rock mass is displaced, represents the abscissa of the initial coordinate, represents the abscissa of the current coordinate, represents the ordinate of the initial coordinate, represents the ordinate of the current coordinate.

4. The intelligent monitoring method for the stability of high-steep slope rock mass according to claim 3, characterized in that: In step 1, the vibration data is cleaned and averaged to obtain the vibration influence coefficient.

5. An intelligent monitoring method for the stability of rock masses on high-steep slopes according to claim 4, characterized in that: The specific step 1 is: Perform 3D modeling on the rock mass image to obtain a rock mass model. Taking the center point of this model as the origin, establish a 3D coordinate system, and evenly divide the inclinometer tube status data set into parts to obtain the inclinometer tube status data of different depth layers. The inclinometer tube status data includes the pipe bending angle and the pipe bending radius. Clean the inclinometer tube status data and perform vector transformation on the pipe bending angle to obtain the pipe bending vector; Set the landslide direction threshold, and calculate the pipeline bending vector and the vectors within the landslide direction threshold in turn through the cosine formula of the included angle of the vector to obtain the landslide direction matching value. Compare the landslide direction matching value with one. If the landslide direction matching value is equal to one, count the number of pipeline bending vectors with the landslide direction matching value equal to one to obtain the number of times of promoting the landslide, and set the pipeline bending direction influence factor according to the force analysis and the friction formula. The pipeline bending radius and the pipeline bending direction influence factor corresponding to the pipeline bending vector with the landslide direction matching value equal to one are normalized and multiplied in turn to obtain the rock mass abnormal value. The rock mass abnormal values are summed according to the number of times of promoting the landslide to obtain the rock mass distortion coefficient.

6. The intelligent monitoring method for the stability of high-steep slope rock mass according to claim 5, characterized in that: Normalize and comprehensively analyze the rock mass image displacement coefficient, the vibration influence coefficient, and the rock mass distortion coefficient to obtain the rock mass landslide coefficient; ； Among them, represents the rock mass landslide coefficient, reflecting whether the rock mass landslides, represents the rock mass image displacement coefficient, reflecting whether the rock mass image is displaced, represents the vibration influence coefficient, reflecting whether there is vibration inside the rock mass, represents the rock mass distortion coefficient, reflecting the influence on the rock mass sliding, represents the quantity of inclinometer tube state data at different depth layers.

7. The intelligent monitoring method for the stability of high-steep slope rock mass according to claim 1, characterized in that: In step two, obtain the rock mass landslide threshold through historical experiments and compare it with the rock mass landslide coefficient. If the rock mass landslide coefficient is greater than or equal to the rock mass landslide threshold, it is determined that the rock mass is landsliding. If the rock mass landslide coefficient is less than the rock mass landslide threshold, it is determined that the rock mass is stable.

8. A method for intelligent monitoring of the stability of a high-steep slope rock mass according to claim 1, characterized in that: In step three, calculate through the rock mass stability prediction algorithm to obtain the rock mass sliding trend coefficient, set the rock mass sliding trend threshold, assign the value of 2M to the rock mass sliding trend threshold, and compare the rock mass sliding trend threshold with the rock mass sliding trend coefficient. If the rock mass sliding trend coefficient is equal to the rock mass sliding trend threshold, it is determined that the rock mass has a sliding trend. If the rock mass sliding trend coefficient is not equal to the rock mass sliding trend threshold, it is determined that the rock mass has no sliding trend.

9. The intelligent monitoring method for the stability of high-steep slope rock mass according to claim 8, characterized in that: The specific method for obtaining the rock mass sliding trend coefficient is as follows: Filter the vibration data through a software filtering algorithm to obtain the low-frequency amplitude and the high-frequency amplitude. Divide the low-frequency amplitude by the high-frequency amplitude to obtain the sliding trend variable. When the sliding trend variable increases, assign the value M to the sliding trend variable. When the sliding trend variable decreases, assign the value N to the sliding trend variable. Set the initial variable coordinates according to the sliding trend variable and time. and the current variable coordinates , represents the initial time, represents the current time, represents the initial sliding trend variable, represents the current sliding trend variable. Calculate the initial variable coordinates and the current variable coordinates using the slope formula to obtain the sliding trend velocity. When the sliding trend velocity increases, assign the value M to the sliding trend velocity. When the sliding trend velocity decreases, assign the value N to the sliding trend velocity. Set the rock mass sliding trend coefficient. When both the sliding trend variable and the sliding trend velocity increase, assign the value 2M to the rock mass sliding trend coefficient. When the sliding trend variable and the sliding trend velocity do not both increase, assign the value M + N or 2N to the rock mass sliding trend coefficient.

10. An intelligent monitoring system for the stability of high-steep slope rock masses, which is used to implement the intelligent monitoring method for the stability of high-steep slope rock masses described in any one of claims 1-9, characterized in that, The system includes: a rock mass sliding monitoring module, a rock mass sliding analysis module, and a rock mass sliding trend prediction module; The rock mass sliding monitoring module is used to obtain image data, vibration data, and inclinometer tube status data sets in real time and perform comprehensive analysis to obtain the rock mass landslide coefficient; The rock mass sliding analysis module is used to judge whether the rock mass is landsliding according to the rock mass landslide coefficient. If it is judged that the rock mass is landsliding, a first warning is issued. If it is judged that the rock mass is stable, the judgment result of the stable rock mass is sent to the rock mass sliding trend prediction module; The rock mass sliding trend prediction module is used to receive the judgment result of the stable rock mass and judge whether the rock mass has a landsliding trend. If it is judged that the rock mass has a landsliding trend, a second warning is issued. If it is judged that the rock mass has no landsliding trend, it returns to the rock mass sliding monitoring module and continues to repeat the execution of the rock mass sliding monitoring module, the rock mass sliding analysis module, and the rock mass sliding trend prediction module.

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