Shaft monitoring method and shaft monitoring device

Abstract

The invention relates to the technical field of mine shaft monitoring, and discloses a shaft monitoring method and a shaft monitoring device.The shaft monitoring method comprises the steps that an original model and an original image of a shaft are obtained; the three-dimensional laser scanning device and the image collecting device are controlled to move in the axial direction of the shaft so as to obtain point cloud data and monitoring images of the shaft, and a monitoring model is determined based on the point cloud data; comparing the original model with the monitoring model, comparing the original image with the monitoring image, and determining deformation data of the shaft. All-round deformation of the shaft is modeled and macroscopically analyzed through point cloud data collected by laser, micro strain and crack analysis is conducted on the shaft through a digital image technology, global monitoring of the shaft is achieved, limitation of traditional point type sensor monitoring is broken through, real-time analysis of full-section deformation distribution is achieved, the monitoring coverage range is expanded, and the monitoring precision is improved. The monitoring blind area is reduced, the accuracy of the monitoring result is improved, and an effective basis is provided for shaft instability failure prediction.

Description

Technical Field

[0001] This application relates to the technical field of mine shaft monitoring, and particularly to a shaft monitoring method and a shaft monitoring device. Background Art

[0002] With the increasing annual intensity of mineral resource exploitation, shallow mineral resources have been exhausted, and countries have to increase the mining depth to obtain mineral resources. As a key hub connecting surface ore treatment facilities with underground ore mining and transportation, the shaft plays an important role in the exploitation of deep mineral resources.

[0003] Since the increase in the burial depth of mined mineral resources leads to an increase in the construction depth of the shaft, the stress on the shaft lining becomes complex and variable. Ultra-deep shafts pass through many strata. In addition to the different lithologies of each stratum, under the action of high ground pressure, strong excavation disturbance, and high rock temperature, problems such as dilation and large deformation are likely to occur, resulting in the deformation and damage of the shaft lining, ultimately posing a serious threat to the safe production of the mine and causing serious economic losses.

[0004] Shaft deformation is a slow process, so long-term monitoring of shaft deformation is required to prevent shaft deformation and damage. In related monitoring technologies, strain gauges and displacement gauges buried in the surrounding rock can obtain discrete point data of the shaft, but the accuracy of the detection results is low, the coverage range is small, and the installation is difficult. Summary of the Invention

[0005] This application aims to solve at least one of the technical problems existing in the prior art or related technologies.

[0006] A first aspect of the technical solution of this application provides a shaft monitoring method, which includes: obtaining the original model and original image of the shaft; controlling a three-dimensional laser scanning device and an image acquisition device to move along the axial direction of the shaft to obtain the point cloud data and monitoring images of the shaft, and determining a monitoring model based on the point cloud data; comparing the original model with the monitoring model, and comparing the original image with the monitoring image to determine the deformation data of the shaft.

[0007] In some technical solutions provided by this application, before obtaining the original model and original image of the shaft, it further includes: determining the key monitoring area of the shaft based on the surrounding rock information of the shaft; setting marks in the key monitoring area; wherein the acquisition targets of the original image and the monitoring image include the marks.

[0008] In some technical solutions provided by this application, the three-dimensional laser scanning device and the image acquisition device are arranged on the chamber platform. The steps of controlling the three-dimensional laser scanning device and the image acquisition device to move along the axial direction of the wellbore specifically include: controlling the chamber platform to move downward, and controlling the three-dimensional laser scanning device to continuously acquire point cloud data during the movement; controlling the chamber platform to pause when reaching the height of any key monitoring area, and controlling the image acquisition device to acquire monitoring images; controlling the chamber platform to move downward to the bottom of the wellbore.

[0009] In some technical solutions provided by this application, the steps of determining the key monitoring area of the wellbore specifically include: based on the core logging of the wellbore, determining the fault distribution information of the surrounding rock; determining the fault positions of the surrounding rock; and determining the circumferential area corresponding to the fault positions of the wellbore as the key monitoring area.

[0010] In some technical solutions provided by this application, the steps of determining the key monitoring area of the wellbore specifically include: according to the in-situ stress information of the surrounding rock, determining the direction of the minimum principal stress of the wellbore; dividing the rock mass quality grade of the surrounding rock according to the geological information of the surrounding rock and the rock mass mechanical parameters; determining the positions of the surrounding rock with low quality grades; and determining the positions of the surrounding rock with low quality grades corresponding to the wellbore in the direction of the minimum principal stress as the key monitoring area.

[0011] In some technical solutions provided by this application, the steps of determining the direction of the minimum principal stress of the wellbore specifically include: obtaining in-situ stress information according to the hydraulic fracturing method; obtaining formation information according to geological exploration holes, and determining the conditional area where the surrounding rock is intact and composed of brittle rocks; and correcting the in-situ stress information in the conditional area by the stress relief method to determine the direction of the minimum principal stress.

[0012] In some technical solutions provided by this application, the steps of determining the deformation data of the wellbore specifically include: spatially aligning the monitoring model and the monitoring image through the key monitoring area; calculating the monitoring model according to the ICP algorithm to determine the radial displacement of the wellbore; analyzing the monitoring image according to sub-pixel to determine the strain information, displacement information and crack information of the key monitoring area; and using a convolutional neural network to perform pixel-level segmentation on the cracks in the monitoring image and calculate the crack propagation rate.

[0013] In some technical solutions provided by this application, before obtaining the original model and original image of the wellbore, it further includes: controlling the image acquisition device to acquire calibration images of multiple calibration plates; determining the position of the feature points of the calibration plate in any calibration image; and optimizing the internal parameters and external parameters of the image acquisition device.

[0014] In some technical solutions provided by the present application, after the step of determining the deformation data of the shaft, it further includes: determining a threshold based on the monitoring objects of the shaft, where the monitoring objects include: strain, displacement, and cracks, and the number of thresholds corresponding to any category of monitoring objects is at least three; determining the area where the monitoring result of the monitoring object exceeds the threshold as a risk area; determining the coordinates of the risk area through a monitoring model.

[0015] A second aspect of the technical solution of the present application provides a shaft monitoring device, which is used for the shaft monitoring method provided by any one of the above technical solutions. The shaft monitoring device includes: a cabin platform, a three-dimensional laser scanning device, and a plurality of image acquisition devices. The cabin platform is used to move along the axial direction of the shaft. The three-dimensional laser scanning device and the plurality of image acquisition devices are arranged on the cabin platform. The horizontal field of view ranges of the plurality of image acquisition devices are connected in sequence and cover the inner wall of the shaft circumferentially. The heights of the vertical fields of view of the plurality of image acquisition devices are the same.

[0016] Compared with the related art, the present invention at least includes the following beneficial effects: Through the point cloud data collected by laser for macro analysis of the full-circle deformation of the shaft, and through digital image technology for micro strain and crack analysis of the shaft, global monitoring of the shaft is realized, breaking through the limitations of traditional point-type sensor monitoring, achieving real-time analysis of the full-section deformation distribution, expanding the monitoring coverage range, reducing monitoring blind spots, improving the accuracy of monitoring results, and providing an effective basis for predicting the instability and failure of the shaft. Moreover, by constructing a non-contact monitoring system, the monitoring equipment is avoided from working in a high-pressure, high-temperature, and strongly corrosive environment for a long time. The anti-damage performance is significantly improved compared with traditional point-line monitoring, ensuring long-term stable monitoring in extreme environments, avoiding the risks of repeated drilling and equipment replacement caused by zero drift and failure of point-type sensors due to environmental interference, and greatly reducing the maintenance cost. In addition, the impact of the embedding construction on the construction progress of the shaft is avoided, achieving the purpose of shortening the construction period. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] By reading the detailed descriptions of some embodiments below, various other advantages and benefits will become clear to those of ordinary skill in the art. The drawings are only for the purpose of showing some embodiments and are not considered to be a limitation of the present application. Moreover, throughout the drawings, the same reference numerals are used to represent the same components. In the drawings: Figure 1 is a schematic structural diagram of a shaft monitoring device according to an embodiment provided by the present application; Figure 2 is a partial schematic structural diagram of a shaft monitoring device according to an embodiment provided by the present application; Figure 3 is a comparison diagram of an original model and a monitoring model according to an embodiment provided by the present application; Figure 4 Schematic diagram of a key monitoring area provided for this application; Figure 5 Speckle and strain nephogram provided for this application; Figure 6 Schematic diagram of a key monitoring area provided for this application; Figure 7 Among them, (a) is a top view of a key monitoring area provided for this application; Figure 7 Among them, (b) is a front view of a key monitoring area provided for this application; Figure 8 Flow schematic diagram of a shaft shaft monitoring method provided for this application; Figure 9 Risk early warning flow chart of a shaft shaft provided for this application.

[0018] Among them, Figures 1 to 4 、 Figure 6 and Figure 7 The corresponding relationship between the reference numerals and the component names in is as follows: 10 Shaft monitoring device; 100 Compartment platform; 200 Three-dimensional laser scanning device; 300 Image acquisition device; 310 Series shielded wire; 320 Terminal shielded wire; 400 Steel wire rope; 500 Monitoring compartment; 600 Computer equipment; 20 Shaft. Detailed implementation manners

[0019] In order to better understand the above technical solutions, the technical solutions of the embodiments of this application will be described in detail below through the accompanying drawings and specific embodiments. It should be understood that the specific features in the embodiments of this application and the embodiments are detailed descriptions of the technical solutions of the embodiments of this application, rather than limitations on the technical solutions of this application. Without conflict, the technical features in the embodiments of this application and the embodiments can be combined with each other.

[0020] The first aspect of the embodiments of this application proposes a shaft shaft monitoring method, as Figure 8 shown, the shaft shaft monitoring method includes: Step 101, obtaining the original model and original image of the shaft; Step 102, controlling the three-dimensional laser scanning device and the image acquisition device to move along the axial direction of the shaft to obtain the point cloud data and monitoring images of the shaft, and determining the monitoring model based on the point cloud data; Step 103, comparing the original model with the monitoring model, comparing the original image with the monitoring image, and determining the deformation data of the shaft.

[0021] In this embodiment, the shaft can be an ultra-deep shaft. When monitoring the deformation of the shaft wall, a three-dimensional laser scanning device and an image acquisition device are placed into the shaft. The three-dimensional laser scanning device can be a high-frequency three-dimensional laser scanner (≥ 1 million point clouds per second). The three-dimensional laser scanning device obtains the three-dimensional point cloud data of the entire circumference of the shaft wall by emitting laser beams and receiving the reflected signals, and establishes a monitoring model based on the point cloud data. The image acquisition device can be a high-resolution industrial-grade high-definition camera. Multiple image acquisition devices are arranged according to the perimeter of the cross-section of the shaft to form a shooting array, and a ring-shaped fill light is equipped to collect the monitoring images covering the circumference of the shaft.

[0022] The original model and the original images, as the original data for comparing the deformation of the shaft wall, can be collected by the three-dimensional laser scanning device and the image acquisition device at the initial stage of shaft construction. When the shaft wall deforms, there are deviations between the monitoring model or the monitoring images and the original data. As Figure 3 shown, there are deviations between the monitoring model A1 and the original model A0. By comparing the specific shapes and positions of the deviations, the amount and location of the deformation of the shaft wall can be determined. Based on the three-dimensional laser scanning and DIC (Digital Image Correlation) technology, the global deformation of the shaft and the micro-deformations in key areas such as shear zones and hidden fractures can be accurately captured, and then the deformation of the shaft can be monitored.

[0023] Exemplarily, after the data collection is completed, the point cloud data and the monitoring images are transmitted to the ground data processing platform through a wireless transmission method. The wireless transmission method can use a computer 5G private network to avoid signal attenuation caused by wired transmission.

[0024] By modeling the point cloud data collected by laser to macroscopically analyze the global deformation of the shaft wall, and using digital image technology to microscopically analyze the strain and cracks of the shaft wall, the global monitoring of the shaft is realized, breaking through the limitations of traditional point-type sensor monitoring, achieving real-time analysis of the full-section deformation distribution, expanding the monitoring coverage, reducing monitoring blind spots, improving the accuracy of monitoring results, and providing an effective basis for predicting the instability and failure of the shaft wall. Moreover, by constructing a non-contact monitoring system, the monitoring equipment is avoided from working in an environment of high pressure, high temperature, and strong corrosion for a long time, and its anti-damage ability is significantly improved compared with traditional point-line monitoring. It ensures long-term stable monitoring in extreme environments, avoids the risks of repeated drilling and equipment replacement caused by zero drift and failure of point-type sensors due to environmental interference, and greatly reduces the maintenance cost. In addition, it avoids the impact of the embedding construction on the construction progress of the shaft, achieving the purpose of shortening the construction period.

[0025] In some embodiments provided by the present application, as Figure 4 and Figure 5As shown, before obtaining the original model and original image of the wellbore, it further includes: determining the key monitoring area of the wellbore based on the surrounding rock information of the wellbore; setting marks in the key monitoring area; wherein, the acquisition targets of the original image and the monitoring image include the marks.

[0026] In this embodiment, the key monitoring area of the wellbore is determined according to the quality of the surrounding rock. The key monitoring area is the area with poor surrounding rock quality, which is the area where the wellbore is prone to deformation and cracks. Taking the key monitoring area as the acquisition area B of the image acquisition device realizes the collaborative diagnosis of the whole and the local, and improves the monitoring efficiency of the DIC technology. Moreover, laser scanning and image acquisition are carried out simultaneously in the key monitoring area, which improves the accuracy of the monitoring results and makes the judgment result of the wellbore deformation more reliable.

[0027] First, pretreatment work is carried out in the key monitoring area. The surface of the wellbore wall is cleaned, and the floating dust on the wellbore wall is removed by a high-pressure air gun, and the wall surface in the key monitoring area is polished flat. Then, marks are set in the key monitoring area B to provide traceable feature points on the inner wall of the wellbore, which is convenient for calculating the displacement field through the DIC algorithm. Exemplarily, the marks can be speckles. First, a layer of white matte paint is sprayed on the wellbore wall surface, and after drying, a professional speckle production tool is used to print black circular patterns on the wellbore wall to form marks. The close-packing rate of the speckles should be 45% to 55%, and they are randomly distributed so that the corresponding speckles can be accurately matched before and after deformation. Exemplarily, the marks can be Poisson disk distributions (minimum spacing ≥ 0.7d, d is the disk diameter) generated by MATLAB.

[0028] In some embodiments provided by the present application, as Figure 1 shown, the steps of controlling the three-dimensional laser scanning device and the image acquisition device to move along the axial direction of the wellbore, which are arranged on the cabin platform, specifically include: controlling the cabin platform to move downward, and controlling the three-dimensional laser scanning device to continuously acquire point cloud data during the movement; controlling the cabin platform to pause when it reaches the height of any key monitoring area, and controlling the image acquisition device to acquire the monitoring image; controlling the cabin platform to move downward to the bottom of the wellbore.

[0029] In this embodiment, the cabin platform is a liftable platform. The cabin platform moves downward at the working speed, and the three-dimensional laser scanning device continuously acquires point cloud data during the movement. The cabin platform stops when it passes through the designated key monitoring area, and the image acquisition device takes pictures to acquire the monitoring image. After the shooting is completed, the cabin platform continues to move downward and acquire point cloud data. The cabin platform stops again and takes pictures when it reaches the next key monitoring area, and so on until the cabin platform reaches the bottom of the well, so that the image acquisition device obtains the monitoring images of all key monitoring areas, and the three-dimensional laser scanning device acquires the complete point cloud data of the wellbore, completing the acquisition work of this monitoring data.

[0030] In some embodiments provided by the present application, the steps of determining the key monitoring area of the wellbore specifically include: determining the fault distribution information of the surrounding rock based on the core logging of the wellbore; determining the fault positions of the surrounding rock; and determining the circumferential area corresponding to the fault positions of the wellbore as the key monitoring area.

[0031] In this embodiment, a fault is a structure in which the crust is stressed and fractured, and significant relative displacement occurs between rock blocks on both sides of the fracture surface. Due to the fault, the rock strength is weak, resulting in the wellbore being more prone to damage. By conducting core logging on the ultra-deep vertical shaft geological exploration boreholes during well construction, the fault distribution at different depths of the vertical shaft is obtained, as Figure 6 shown. Based on the fault distribution, the fault positions are determined. Based on the height of the fault positions, the axial position of the key monitoring area on the wellbore is determined, and the circumferential annular area where the axial position is located is determined as the key monitoring area B. In this way, the key monitoring area is delimited based on the fault information of the surrounding rock, making the key monitoring range more reasonable and accurate.

[0032] In some embodiments provided by the present application, the steps of determining the key monitoring area of the wellbore specifically include: determining the direction of the minimum principal stress of the wellbore according to the in-situ stress information of the surrounding rock; dividing the rock mass quality grade of the surrounding rock according to the geological information and rock mass mechanical parameters of the surrounding rock; determining the positions of the surrounding rock with low quality grades; and determining the positions of the surrounding rock with low quality grades corresponding to the wellbore in the direction of the minimum principal stress as the key monitoring area.

[0033] In this embodiment, since there are many joints and fissures distributed inside the rock, the strength of the intact rock is relatively large, and it is not easily damaged compared to the rock containing joints. Therefore, the joints weaken the rock strength, making the wellbore more prone to damage. By core logging, the formation lithology corresponding to different depths of the vertical shaft is obtained. By conducting geological surveys on the surrounding rock of the vertical shaft during well construction, information such as the joints, structural attitudes, density, and joint water content of the surrounding rock is obtained. Rock specimens are processed by on-site sampling, and rock mechanical tests are performed on the rock specimens to obtain the rock mechanical parameters of the surrounding rock, and the rock mass mechanical parameters of the vertical shaft surrounding rock are calculated through calculation. Based on the joint information and rock mass mechanical parameters, the surrounding rock grades of the vertical shaft are divided. Exemplarily, the rock mass quality grade of the surrounding rock can be divided by RMR (Rockmass rating, the index value of rock mass geomechanics classification), and the areas with poor and very poor grading results are determined as the positions of the surrounding rock with low quality grades.

[0034] It should be noted that even if the quality of the rock mass is poor, that is, the rock mass quality grade is low, but not in the direction of the minimum principal stress, the wellbore is usually not easily damaged. Therefore, as Figure 7As shown in (a), when dividing the key monitoring area, based on the minimum principal stress direction, the radial direction R on the shaft is determined through the minimum principal stress, and then its radial position B1 is determined. For example, within a preset width range on both the left and right sides in the radial direction is its radial position B1. As Figure 7 As shown in (b), the axial position B2 on the shaft is determined through the position of the low-quality surrounding rock, and the overlapping range of the radial position and the axial position is determined as the key monitoring area B. In this way, based on the joint information, rock mechanics parameters, and in-situ stress distribution, the key monitoring area is delimited, making the key monitoring range more reasonable and accurate.

[0035] In some embodiments provided by the present application, the steps of determining the minimum principal stress direction of the shaft specifically include: obtaining in-situ stress information according to the hydraulic fracturing method; obtaining formation information according to geological exploration holes, and determining a conditional area where the surrounding rock is intact and composed of brittle rocks; using the stress relief method to correct the in-situ stress information within the conditional area to determine the minimum principal stress direction.

[0036] In this embodiment, during the pre-construction period, the in-situ stress distribution of the shaft passing through the formation is initially understood through the hydraulic fracturing method. The hydraulic fracturing method can be used for large-scale preliminary exploration, quickly obtain in-situ stress information, and has low detection cost and high efficiency. The in-situ stress information includes the magnitudes and acting orientations of the maximum principal stress and the minimum principal stress of the shaft. During the construction period, based on the formation information obtained from the geological exploration holes, a conditional area where the surrounding rock is intact and composed of brittle rocks is found, and the accurate in-situ stress situation of this conditional area is obtained using the stress relief method. The stress relief method has high detection accuracy, efficiency, and accuracy. By calibrating at the key position (conditional area) using the stress relief method, the local accuracy can be ensured. By combining the two methods, the minimum principal stress direction is made more accurate.

[0037] In some embodiments provided by the present application, the steps of determining the deformation data of the shaft specifically include: spatially aligning the monitoring model and the monitoring image through the key monitoring area; calculating the monitoring model according to the ICP algorithm to determine the radial displacement of the shaft; analyzing the monitoring image according to sub-pixel to determine the strain information, displacement information, and crack information of the key monitoring area; using a convolutional neural network to perform pixel-level segmentation on the cracks in the monitoring image and calculate the crack propagation rate.

[0038] In this embodiment, a data processing platform based on point cloud data and monitoring images is built. The laser point cloud data and the monitoring images, which are DIC images, are spatially aligned through matching in the key monitoring area. An overall monitoring model of the shaft is extracted based on the point cloud data. By comparing with the original model, the radial displacement of the shaft is calculated through the ICP (Iterative Closest Point) algorithm. Sub-pixel analysis is performed on the DIC images to extract the maximum principal strain, shear strain distribution, and displacement changes, and the local strain, displacement field, and cracks of the DIC are obtained to determine the strain information, displacement information, and crack information in the key monitoring area. The crack information includes the position and size of the cracks. The convolutional neural network (CNN) is used to perform pixel-level segmentation on the cracks in the DIC images to achieve intelligent identification of the cracks and calculate the crack propagation rate. And by comparing and analyzing the gradually increasing situation of the cracks after multiple measurements, the development of the cracks is determined.

[0039] Through the collaboration of multi-modal data fusion and intelligent algorithms, a multi-parameter fusion early warning model based on displacement-strain-crack in the monitoring of shaft surrounding rock improves the accuracy of the shaft deformation analysis results.

[0040] In some embodiments provided by the present application, before obtaining the original model and original images of the shaft, it further includes: controlling the image acquisition device to obtain calibration images of multiple calibration plates; determining the position of the feature points of the calibration plate in any calibration image; optimizing the internal parameters and external parameters of the image acquisition device.

[0041] In this embodiment, the calibration plate can be a high-precision dot array plane, such as a checkerboard or a dot array. The calibration plate is placed within the field of view of the image acquisition device, and at least 50 sets of calibration images with different poses are taken from different angles and positions to improve the calibration accuracy. In any calibration image, the position of the feature points on the calibration plate is detected and extracted. An optimization algorithm is used to calculate the internal parameters and external parameters of the camera. The optimization algorithm can be the least squares method. The internal parameters include: focal length, principal point coordinates, and distortion coefficients. The external parameters include: rotation matrix and translation vector. The image acquisition device is optimized using the calibration images to improve the acquisition performance of the image acquisition device, enabling the image acquisition device to establish an accurate imaging model, eliminate optical distortion, improve pixel-level accuracy, unify the coordinate system, and achieve dynamic pose tracking and large-scale scene measurement, laying a foundation for the accuracy of subsequent acquisition of monitoring images.

[0042] In some embodiments provided by the present application, after the step of determining the deformation data of the shaft, it further includes: determining thresholds based on the monitoring objects of the shaft. The monitoring objects include: strain, displacement, and cracks. The number of thresholds corresponding to any category of monitoring objects is at least three; the area where the monitoring results of the monitoring objects exceed the thresholds is determined as the risk area; the coordinates of the risk area are determined through the monitoring model.

[0043] In this embodiment, by obtaining the radial deformation data of the wellbore, the strain change contour map of the key monitoring area, and the fracture images, and combining visual recognition means, the early warning function of the wellbore deformation is realized. Thresholds for the strain, displacement, and fracture propagation of different vertical wellbores are defined according to long-term monitoring data and actual conditions to ensure the effectiveness and reliability of the early warning system. Three-level early warning thresholds can be set, corresponding to mild, moderate, and structural failure respectively, and the coordinates of the risk area are located through a three-dimensional laser scanning model.

[0044] Exemplarily, as Figure 9 shown, the point cloud data, monitoring images, geological information, concrete parameters, and rock mass parameters are learned through a concrete deformation and cracking database to obtain a wellbore failure prediction model and the vertical shaft risk prediction result.

[0045] In a second aspect of the embodiments of the present application, a wellbore monitoring device 10 is proposed. As Figure 1 and Figure 2 shown, the wellbore monitoring device 10 is used for the vertical wellbore monitoring method provided in any one of the above embodiments. The wellbore monitoring device 10 includes: a cabin platform 100, a three-dimensional laser scanning device 200, and a plurality of image acquisition devices 300. The cabin platform 100 is used to move along the axial direction of the wellbore 20. The three-dimensional laser scanning device 200 and the plurality of image acquisition devices 300 are arranged on the cabin platform 100. The horizontal field of view ranges of the plurality of image acquisition devices 300 are sequentially connected and circumferentially cover the inner wall of the wellbore 20. The heights of the vertical fields of view of the plurality of image acquisition devices 300 are the same.

[0046] In this embodiment, the three-dimensional laser scanning device 200 is installed on the cabin platform 100. The cabin platform 100 is arranged in the monitoring cabin 500. The cabin platform 100 is fixed on the steel wire rope 400 that controls the operation of the monitoring cabin 500. A plurality of balance pan-tilt heads are connected to the plurality of image acquisition devices 300 one by one. The plurality of balance pan-tilt heads are installed on the cabin platform 100, so that the horizontal field of view ranges of the plurality of image acquisition devices 300 are sequentially connected and circumferentially cover the inner wall of the wellbore 20, and the acquisition range covers the circumference of the wellbore 20. The plurality of image acquisition devices 300 are connected through a series shielded wire 310. The heights of the vertical fields of view of the plurality of image acquisition devices 300 are the same to ensure that the acquisition fields of view of the plurality of image acquisition devices 300 are at the same horizontal height and prevent misalignment when splicing the taken photos. The balance pan-tilt head is used to determine the spatial relationship between the vertical shaft wall and the three-dimensional laser scanning device 200 and the image acquisition device 300, compensate for the vibration error of the lifting platform, and determine the position relationship of the point cloud coordinates and the monitoring image shooting in the global coordinate system.

[0047] The shaft monitoring device 10 further includes: an inertial navigation system and a computer device 600. The image acquisition device 300 is connected to the computer device 600 through a terminal shielded wire 320. The computer device 600 is used to control the inertial navigation device, the three-dimensional laser scanning device 200, and the image acquisition device 300, and is responsible for controlling the operating attitude of the shaft monitoring device 10, collecting, storing, and uploading point cloud data and monitoring images.

[0048] The macro analysis of the circumferential deformation of the shaft 20 is modeled by the point cloud data collected by laser, and the micro strain and crack analysis of the shaft 20 is carried out by digital image technology, realizing the global monitoring of the shaft 20, breaking through the limitations of traditional point monitoring, realizing the real-time analysis of the full-section deformation distribution, expanding the monitoring coverage, reducing the monitoring blind area, improving the accuracy of the monitoring results, and providing an effective basis for predicting the instability and failure of the shaft 20. Moreover, by constructing a non-contact monitoring system, the monitoring equipment is avoided from working in a high-pressure, high-temperature, and strongly corrosive environment for a long time, and the anti-damage performance is significantly improved compared with traditional point-line monitoring, ensuring long-term stable monitoring in extreme environments, avoiding the risk of repeated drilling and equipment replacement caused by zero drift and failure of point sensors due to environmental interference, and greatly reducing the maintenance cost. In addition, the influence of the buried construction on the construction progress of the vertical shaft is avoided, achieving the purpose of shortening the construction period.

[0049] In a specific embodiment, a non-contact ultra-deep vertical shaft monitoring method is provided, including: Step 1: Based on geological information and rock mass mechanical parameters, demarcate the key monitoring areas of the vertical shaft.

[0050] Step 2: Construct and arrange a non-contact monitoring system including a three-dimensional laser scanning device and an image acquisition device.

[0051] Step 3: Based on the division results of the key areas of the vertical shaft, carry out pretreatment work such as speckle spraying.

[0052] Step 4: Collect and transmit the data of point cloud data and speckle images.

[0053] Step 5: Build a data processing platform based on point cloud data and speckle images.

[0054] Step 6: Risk warning for the ultra-deep vertical shaft.

[0055] The present invention gets rid of the traditional vertical shaft point sensor wiring monitoring mode, uses three-dimensional laser scanning to model and macroscopically analyze the circumferential deformation of the shaft, uses digital image correlation technology to microscopically analyze the strain and cracks of the entire circumference of the vertical shaft, constructs a non-contact monitoring system, optimizes its layout method, and builds a data processing platform for point cloud data and speckle images on the ground for risk warning of ultra-deep vertical shafts.

[0056] In the present invention, the terms "first", "second", "third" are only used for descriptive purposes and should not be construed as indicating or implying relative importance; the term "plural" means two or more, unless otherwise clearly defined. Terms such as "installed", "connected", "joined", "fixed" should be understood in a broad sense. For example, "connected" can be a fixed connection, a detachable connection, or an integral connection; "joined" can be a direct connection or an indirect connection through an intermediate medium. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0057] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by terms such as "upper", "lower", "left", "right", "front", "rear", etc. is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or unit referred to must have a specific direction, be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation to the present invention.

[0058] In the description of this specification, the descriptions of terms such as "one embodiment", "some embodiments", "specific embodiments", etc. mean that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or instance. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0059] The above are only some embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A shaft monitoring method, characterized in that, Including: Based on the surrounding rock information of the shaft, determine the key monitoring area of the shaft; Set marks in the key monitoring area; Obtain the original model and original image of the shaft; Control the three-dimensional laser scanning device and the image acquisition device to move along the axial direction of the shaft to obtain the point cloud data and monitoring images of the shaft, and determine the monitoring model based on the point cloud data; Compare the original model with the monitoring model, and compare the original image with the monitoring image to determine the deformation data of the shaft; Wherein, the acquisition targets of the original image and the monitoring image include the marks.

2. The shaft monitoring method according to claim 1, characterized in that, The three-dimensional laser scanning device and the image acquisition device are arranged on the cabin platform. The step of controlling the three-dimensional laser scanning device and the image acquisition device to move along the axial direction of the shaft specifically includes: Control the cabin platform to move downward, and control the three-dimensional laser scanning device to continuously obtain the point cloud data during the movement; Control the cabin platform to pause when reaching the height of any key monitoring area, and control the image acquisition device to obtain the monitoring image; Control the cabin platform to move downward to the bottom of the shaft.

3. The shaft monitoring method according to claim 1, characterized in that The step of determining the key monitoring area of the shaft specifically includes: Based on the core logging of the shaft, determine the fault distribution information of the surrounding rock; Determine the fault positions of the surrounding rock; Determine the circumferential area of the shaft corresponding to the fault positions as the key monitoring area.

4. The shaft monitoring method according to claim 1, characterized in that The step of determining the key monitoring area of the shaft specifically includes: According to the in-situ stress information of the surrounding rock, determine the direction of the minimum principal stress of the shaft; Divide the rock mass quality grade of the surrounding rock according to the geological information and rock mass mechanical parameters of the surrounding rock; Determine the positions of the surrounding rock with low quality grades; Determine the positions of the surrounding rock with low quality grades corresponding to the shaft in the direction of the minimum principal stress as the key monitoring area.

5. The shaft monitoring method according to claim 4, characterized in that The step of determining the direction of the minimum principal stress of the shaft specifically includes: Obtain the in-situ stress information according to the hydraulic fracturing method; Obtain the formation information according to the geological exploration holes, and determine the conditional area where the surrounding rock is complete and composed of brittle rocks; Use the stress relief method to correct the in-situ stress information in the conditional area to determine the direction of the minimum principal stress.

6. The shaft monitoring method according to claim 1, characterized in that, The step of determining the deformation data of the shaft specifically includes: Spatially align the monitoring model and the monitoring image through the key monitoring area; Calculate the monitoring model according to the ICP algorithm to determine the radial displacement of the shaft; Analyze the monitoring image according to sub-pixel to determine the strain information, displacement information and crack information of the key monitoring area; Use a convolutional neural network to perform pixel-level segmentation on the cracks in the monitoring image and calculate the crack propagation rate.

7. The shaft monitoring method according to any one of claims 1 to 6, characterized in that, Before obtaining the original model and original image of the shaft, it further includes: Control the image acquisition device to obtain calibration images of multiple calibration plates; Determine the position of the feature points of the calibration plate in any calibration image; Optimize the internal parameters and external parameters of the image acquisition device.

8. The shaft monitoring method according to any one of claims 1 to 6, characterized in that, After the step of determining the deformation data of the shaft, it further includes: Determine a threshold based on the monitoring objects of the wellbore, where the monitoring objects include: strain, displacement, and cracks, and the number of thresholds corresponding to any category of the monitoring objects is at least three; Determine the area where the monitoring result of the monitoring object exceeds the threshold as the risk area; Determine the coordinates of the risk area through the monitoring model.

9. A wellbore monitoring device, characterized in that, For the shaft wellbore monitoring method according to any one of claims 1 to 8, the wellbore monitoring device includes: A cabin platform for moving along the axial direction of the wellbore; A three-dimensional laser scanning device and a plurality of image acquisition devices are arranged on the cabin platform. The horizontal field of view ranges of the plurality of image acquisition devices are connected in sequence and cover the inner wall of the wellbore circumferentially. The heights of the vertical fields of view of the plurality of image acquisition devices are the same, and the acquisition area of the image acquisition device is the key monitoring area of the wellbore.

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