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CN-122024220-A - Monitoring system and evaluation method for winding state of hoisting steel wire rope for ultra-deep vertical shaft construction

CN122024220ACN 122024220 ACN122024220 ACN 122024220ACN-122024220-A

Abstract

The invention provides a monitoring system and an evaluation method for winding state of a lifting steel wire rope for ultra-deep vertical shaft construction based on photogrammetry and video metering, which comprise a binocular vision acquisition module, an image processing module and an early warning module, and aim to overcome the problems of low efficiency, insufficient precision, difficult installation of monitoring equipment and the like in the existing monitoring technology, and can monitor the abnormal winding state of the lifting steel wire rope in real time and judge and quantitatively evaluate abnormal winding working conditions such as rope biting, rope skipping, rope sinking and the like. The scheme has the characteristics of convenience in installation, simplicity in operation, strong anti-interference capability and nondestructive detection, and simultaneously has high detection precision and detection efficiency. And the grading alarm mechanism is used for detecting suspicious abnormality in a single frame, confirming in 3 continuous frames, triggering a primary alarm if the suspicious abnormality exists continuously, triggering a secondary alarm or stopping the machine to be checked by a worker if the suspicious abnormality exists continuously for a long time, and the alarm information can comprise abnormal winding type, occurrence position, severity and the like so that the worker can quickly judge and take corresponding solving measures.

Inventors

  • FAN WENBO
  • GE SHIRONG
  • WANG DAGANG
  • DENG XIANSONG
  • DENG QIAO
  • SUN YINHE

Assignees

  • 中煤建设集团有限公司
  • 中国矿业大学(北京)
  • 中国矿业大学

Dates

Publication Date
20260512
Application Date
20260214

Claims (16)

  1. 1. The monitoring system for the winding state of the hoisting steel wire rope for ultra-deep vertical shaft construction is arranged on one side of a hoisting steel wire rope winding drum and is characterized by comprising a binocular vision acquisition module, an image processing module and an early warning module; The binocular vision acquisition module comprises two camera assemblies and a light supplementing system which are symmetrically arranged and is used for synchronously acquiring image data of a winding area of the steel wire rope; the light supplementing system comprises a directional light source and a light source controller, wherein the directional light source is arranged between two camera assemblies and leads light to face to the hoisting wire rope reel; The image processing module comprises a computer component and a dual-gigabit network port component, wherein the computer component is provided with an independent GPU, and the computer component is connected with the binocular vision acquisition module through the dual-gigabit network port component; The early warning module comprises an acousto-optic warning component and an abnormal data display component, and the acousto-optic warning component and the abnormal data display component are connected with the image processing module through cables.
  2. 2. The system for monitoring the winding state of the hoisting steel wire rope for ultra-deep vertical shaft construction according to claim 1, further comprising a power supply bracket, wherein the directional light source is arranged on the power supply bracket, and the power supply bracket comprises supporting legs capable of adjusting the height.
  3. 3. The ultra-deep vertical shaft construction hoisting steel wire rope winding state monitoring system is characterized in that the camera assembly comprises an industrial camera, a three-dimensional tripod head and a tripod head support, the tripod head support comprises supporting legs capable of adjusting the height, the three-dimensional tripod head is mounted on the tripod head support, and the industrial camera is mounted on the three-dimensional tripod head.
  4. 4. The monitoring system for winding state of the hoisting steel wire rope for ultra-deep vertical shaft construction according to claim 1 is characterized in that the sound-light warning component comprises a buzzer and a warning lamp, and the abnormal data display component comprises a display.
  5. 5. The method for evaluating the winding state of the hoisting steel wire rope for ultra-deep vertical shaft construction is characterized by comprising the following steps of: step a, image acquisition and preprocessing, namely acquiring a high-quality binocular image, and laying a foundation for subsequent three-dimensional reconstruction; step b, stereo matching, namely determining the corresponding relation of the same scene point in the two images by comparing pixels in the left image and the right image so as to calculate parallax, and further calculating depth information of the three-dimensional scene by combining a parallax image generated by stereo matching and parameters obtained by camera calibration; C, generating a point cloud, namely converting a depth map into a three-dimensional point cloud by combining an image coordinate system to a world coordinate system through conversion of the image coordinate system and camera internal parameters, and accurately restoring the three-dimensional geometric form of the rope loop by utilizing a PCL library; step d, point cloud segmentation, namely segmenting the point cloud into independent rope loops of each layer; Step e, winding characteristic extraction, namely quantifying key geometric characteristics of a winding state, namely establishing a characteristic distribution baseline of normal winding by collecting normal winding data, and determining an algorithm detection threshold value; step f, identifying abnormal winding conditions such as rope biting, rope skipping, rope sinking and the like by an abnormal detection algorithm; and g, early warning decision, namely comprehensively judging, triggering early warning, simultaneously calling video or pictures in abnormal time for display, continuously confirming 3 frames of suspicious abnormality detected by a single frame, triggering a primary alarm if the suspicious abnormality exists continuously, and triggering a secondary alarm or triggering a shutdown program to be checked by staff if the continuous time exceeds 3 frames.
  6. 6. The method for evaluating the winding state of the hoisting steel wire rope for ultra-deep vertical shaft construction according to claim 5, wherein the step a comprises the following steps: A1, calibrating a camera by adopting a Zhang Zhengyou calibration method, extracting angular point coordinates by shooting calibration board images of different angles for multiple times by utilizing a regular geometric structure of a checkerboard calibration board, calculating internal and external parameters of the camera by the corresponding relation between pixel positions of the angular points in the images and known actual three-dimensional coordinates on the calibration board, and judging a calibration result according to the obtained average re-projection error; Step a2, carrying out polar correction on the collected left and right original images by using internal and external parameters of the two cameras obtained through calibration through a Bouguet algorithm in an OpenCV library, so that the two corrected images are positioned on the same plane and are aligned, and the searching of the same-name points of the subsequent stereo matching algorithm is positioned on a straight line; And a3, performing image preprocessing on the corrected left and right images, firstly graying the color images based on a weighted average method to reduce the complexity of an algorithm, secondly performing filtering processing on the images by adopting median filtering to remove noise, and meanwhile, the method can keep more edge information, and then enhancing the contrast of the images by utilizing histogram equalization, thereby being convenient for further analysis and processing.
  7. 7. The method for evaluating the winding state of the hoisting steel wire rope for ultra-deep vertical shaft construction according to claim 5, wherein the step b comprises the following steps: Step b1, cost calculation, namely, the cost calculation is the first step of the stereo matching process, and the similarity or matching degree between pixel points is estimated by comparing the characteristics of pixels in the left image and the right image; in the step b2, cost aggregation, namely in practical application, the matching cost of a single pixel point is easily influenced by noise and illumination variation, and error can be effectively reduced and matching performance is improved by aggregating information of local neighborhood; Step b3, parallax calculation, namely after cost calculation and cost aggregation are completed, selecting a parallax value corresponding to the minimum aggregation cost from the aggregated cost matrix through a winner general eating (WTA) algorithm to generate a preliminary parallax map, and further refining the parallax value by adopting sub-pixel fitting; And b4, parallax optimization, namely, the initially generated parallax map contains a large number of noise points and mismatching areas under the influence of the environment in an actual scene. Therefore, after parallax extraction, parallax image optimization is needed, parallax errors caused by shielding are eliminated by adopting left-right consistency inspection, noise points are removed through median filtering, and small invalid areas are filled so as to improve quality and accuracy.
  8. 8. The method for evaluating the winding state of the hoisting steel wire rope for ultra-deep vertical shaft construction according to claim 5, wherein the step d comprises the following steps: step d1, point cloud filtering, namely setting X, Y minimum and maximum thresholds on a Z axis by adopting a straight-through filtering algorithm, reserving points in a threshold range, removing all other points, and reserving only the point cloud of a winding drum area; step d2, converting a reel coordinate system, namely fitting a reel cylindrical model by using a random sampling consistency fitting algorithm, converting a point cloud into the reel coordinate system, and establishing a winding analysis reference; step d3, rope loop segmentation, namely converting a three-dimensional point cloud into a two-dimensional annular section through axial slicing, slicing the sheet along the axial direction (Z axis) of the winding drum, adopting an improved DBSCAN algorithm integrating axial and radial distance measurement in each slice (theta, rho) to realize cluster segmentation of adjacent rope loops, and constructing a complete rope loop through cross-slice centroid track association and shortest path optimization; And d4, separating the winding layers, namely determining the boundary positions of the layers based on valley value detection of the radial density histogram, and allocating layer number labels by combining the average radial coordinates of the rope loops.
  9. 9. The method for evaluating the winding state of the hoisting steel wire rope for ultra-deep vertical shaft construction according to claim 5, wherein the step e comprises the following steps: Step e1, extracting and calculating the center line of each rope loop through a point cloud framework; step e2, quantifying the bending degree by analyzing the spatial distribution of the neighborhood of the center line of the rope loop, calculating the local curvature based on the covariance matrix eigenvalue, and effectively identifying the abnormal bending area of the rope biting and sinking of the steel wire rope by setting a threshold value; And e3, extracting the space characteristics of the rope loops, such as the axial space and the radial space between adjacent rope loops, through double-dimensional quantization, and providing a quantitative basis for rope skipping detection through setting a threshold value of the space.
  10. 10. The method for evaluating the winding state of the hoisting steel wire rope for ultra-deep vertical shaft construction according to claim 5, wherein the step f comprises the following steps: step f1, rope biting detection (overlapping winding) that the same layer of rope loops are abnormally overlapped and locally deformed; step f2, rope sinking detection (interlayer embedding) is carried out, namely, sinking and local deformation of the upper layer rope ring; Step f3, rope skipping detection (winding across grooves), wherein the steel wire rope enters adjacent rope grooves after crossing the current winding rope grooves, the spacing between the adjacent rope rings is abnormal, and the axial spacing ratio is calculated; In the formula, Is the identification value of the axial distance between adjacent rope loops, And in order to avoid the occurrence of the axial distance value of normal adjacent rope loops of the rope skipping, triggering the rope skipping alarm when R θ is more than 1.5.
  11. 11. The method for evaluating the winding state of the hoisting steel wire rope for ultra-deep vertical shaft construction according to claim 8, wherein the process of fitting the cylindrical model of the winding drum by the consistency fitting algorithm in the step d2 is specifically as follows: in RANSAC cylinder fitting, points are randomly selected as an initial model, and initial parameters of the cylinder are calculated according to the points, including coordinates (x 0, y0, z 0) of one point on the cylinder axis, cylinder axis vectors (a 0, b0, c 0) and radius r0; Then calculating the distance from other points to the model and comparing the distance with a predefined threshold value; If the distance is smaller than the threshold value, the points are regarded as the inner points of the model, otherwise, the points are regarded as the outer points of the model; The end of each iteration calculates an iteration ending judgment factor according to the expected error rate, the maximum number of the inner points, the total sample number and the current iteration times; after the iteration is finished, the optimal model parameter is the final model parameter estimated value.
  12. 12. The method for evaluating the winding state of the hoisting wire rope for ultra-deep vertical shaft construction according to claim 8 is characterized in that the step d2 of establishing a winding analysis standard comprises the following steps of firstly, fitting a cylindrical initial axis direction vector (a 0, b0, c 0) through a least square method, calculating the distances from all sample points to an axis, taking a median as an initial radius r0, setting 10% of a rope diameter as a distance threshold value for inner point judgment, adopting adaptive iteration, selecting all inner points corresponding to an optimal model, and obtaining a finally fitted reel center point coordinate (x, y, z), a reel axis vector (a, b, c) and a radius r 0 ; Then, a local coordinate system with the center of the reel as the origin and the axis as the Z axis is established, then a rotational translation matrix is calculated to convert the global point cloud into the coordinate system, and Cartesian coordinates are converted into cylindrical coordinates (ρ, θ, Z), wherein the radial distance ρ is determined by the point-to-axis distance, θ is normalized to the [0,2ρ ] range, and the axial coordinate Z is kept unchanged by transforming the coordinate system for subsequent winding structure analysis.
  13. 13. The method for evaluating the winding state of the hoisting wire rope for ultra-deep vertical shaft construction according to claim 9, wherein the step e1 comprises: step e1-1, initializing seed points as skeleton candidate points; step e1-2, calculating the L1 distance from each point in the point cloud to the seed point, and distributing the attraction relation; step e1-3, updating the seed points to the gravity center positions according to the attracted point set; step e1-4, thinning skeleton points, and removing too dense points; Step e1-5, iterative optimization is carried out until the positions of skeleton points are converged; and e1-6, performing post-treatment to generate a connecting line structure of the skeleton, namely the center line of the rope loop.
  14. 14. The method for evaluating the winding state of the hoisting wire rope for ultra-deep vertical shaft construction according to claim 9, wherein the step e2 comprises, when quantifying the bending degree by analyzing the spatial distribution of the point cloud neighborhood, selecting the neighborhood point set with the target point as the center and calculating the covariance matrix thereof, then performing eigenvalue decomposition on the matrix to obtain three eigenvalues (λ 1 ≥λ 2 ≥λ 3 ), and finally defining the curvature k=λ 3 /(λ 1 +λ 2 +λ 3 by the ratio of the minimum eigenvalue λ 3 to the sum of the eigenvalues, and specifically further comprises the steps of: Step e2-1, selecting a point on the central line of the rope loop as a target point, wherein the neighborhood radius r=αd, d is the diameter of the steel wire rope, and a certain value between 1.5 and 2 is selected to balance noise suppression and detail maintenance, so as to construct a covariance matrix: The diagonal elements are variances in X, Y and Z coordinate directions and represent the point set dispersion degree in each coordinate axis direction, and the off-diagonal elements are covariance items and represent the correlation of the change of different coordinate axis directions, namely the correlation of X-Y coordinates, X-Z coordinates and Y-Z coordinates; Step e2-2, performing eigenvalue solution by using jacobian iterative method, to obtain an eigenvalue λ 1 ≥λ 2 ≥λ 3 , where λ 1 represents the variance of the principal direction (the maximum distribution direction), λ 2 represents the variance of the secondary direction, λ 3 represents the variance of the normal direction (the minimum distribution direction), and the curvature is defined as the ratio of the minimum eigenvalue λ 3 to the sum of eigenvalues, that is, curvature κ=λ 3 /(λ 1 +λ 2 +λ 3 .
  15. 15. The method for evaluating the winding state of the hoisting wire rope for ultra-deep vertical shaft construction according to claim 10, wherein the step f1 comprises: F1-1, positioning an overlapping area, namely calculating the intersection of the axial intervals of the two ropes; step f1-2, quantifying the overlapping degree: Wherein O ij is the standardized overlapping length, d is the diameter of the steel wire rope; when O ij is more than 0.3, the steel wire ropes are abnormally overlapped, and the overlapping length is more than 30% of the rope diameter; step f1-3, deformation characteristic verification: wherein, kappa ratio calculates curvature fluctuation ratio for overlapping area point cloud, sigma κ is curvature standard deviation; when kappa ratio is more than 2.0, the curvature fluctuation of the point cloud of the overlapping area is abnormal, which means that the curvature fluctuation of the overlapping area is doubled; step f1-4, synthesizing a decision function: Wherein, w 1 and w 2 are weight coefficients, and the invention takes w 1 =0.6,w 2 =0.4; and when F bite is more than 1.0, triggering rope biting alarm.
  16. 16. The method for evaluating the winding state of the hoisting wire rope for ultra-deep vertical shaft construction according to claim 10, wherein the step f2 comprises: step f2-1, calculating the embedding depth: wherein ρ max and ρ min are the maximum radial coordinate and the minimum radial coordinate of the centers of adjacent loops when the rope is trapped; when delta is more than 0.4d, the steel wire rope abnormally sinks, which means that embedding exceeds 40% of the rope diameter; f2-2, deformation analysis, namely extracting curvature distribution of the rope sinking part: Wherein, kappa ratio ' is the point cloud of the rope trapping area to calculate the curvature fluctuation ratio, sigma κ is the curvature standard deviation; When kappa ratio ' > 2.0 is abnormal in curvature fluctuation of the point cloud of the rope trapping region, the curvature fluctuation of the rope trapping region is doubled; Step f2-3, synthesizing a decision function: Wherein w 1 ' and w 2 ' are weight coefficients, and w 1 '=0.6,w 2 ' =0.4; when F sink is more than 1.0, the rope trapping alarm is triggered.

Description

Monitoring system and evaluation method for winding state of hoisting steel wire rope for ultra-deep vertical shaft construction Technical Field The invention belongs to the technical field of photogrammetry and video metering, and particularly relates to a monitoring system and an evaluation method for realizing winding state of an ultra-deep vertical shaft construction hoisting steel wire rope based on an image measurement analysis technology. Background With the continuous growth of global energy demands and the gradual exhaustion of shallow mineral resources, deep mineral resource exploitation is an important development trend of mining industry. The resource quantity of the maximum monomer gold deposit which is ascertained at home at present is mainly distributed between-1000 meters and-2500 meters, and the depth of the first deep well, the three-mountain island gold deposit and the auxiliary well of the established Asia reaches 2005m. The single-rope winding type lifting system is key equipment for stable operation of transportation operations of personnel, materials, equipment and the like in the ultra-deep vertical shaft construction period, comprises core components such as a winding drum and a lifting steel wire rope, wherein the lifting steel wire rope is subjected to interlayer transition and inter-circle transition stages when being wound on the winding drum in multiple layers, vibration excitation generated by interlayer and inter-circle transition can cause impact on the lifting steel wire rope in the operation process, abnormal winding working conditions such as rope biting and rope skipping of the steel wire rope are easy to cause, abrasion and degradation of the steel wire rope are aggravated, and service life of the steel wire rope is shortened. Therefore, the monitoring system and the evaluation method for the winding state of the lifting steel wire rope for ultra-deep vertical shaft construction can dynamically monitor the abnormal winding phenomenon of the lifting steel wire rope and quantitatively evaluate the abnormal winding state of the steel wire rope, effectively improve the service safety and reliability of the steel wire rope, and have important significance for guaranteeing the safe and efficient construction of the ultra-deep vertical shaft. In recent years, with the development of machine vision, a vision method is gradually applied to various detection of a wire rope. The patent No. CN202410713150.0 discloses a method and a system for detecting damage of a steel wire rope of a mining hoist based on machine vision, which detect broken wires of the steel wire rope in real time by using a deep learning mode, but are complex in calculation and high in implementation and maintenance cost, the patent No. CN202510048892.0 discloses a method and related equipment for identifying defects of the steel wire rope of the mining hoist based on computer vision, the image quality is improved by a Retinex algorithm low-illumination image self-adaptive enhancement network model, but real-time detection of the defects of the steel wire rope cannot be realized, the patent No. CN202411344362.2 discloses a system and a method for detecting winding anomalies of the steel wire rope of the crane based on vision, and the winding anomalies of the steel wire rope are detected by using a neural network model, but a monocular camera adopted by the scheme mainly relies on algorithm to infer depth information, so that three-dimensional perception of winding of the steel wire rope is weak, and misjudgment of winding states of the steel wire rope is very easy to be caused. The monitoring of abnormal winding states (rope biting, rope skipping, rope sinking and the like) of the hoisting steel wire rope for ultra-deep vertical shaft construction is currently studied in China. Disclosure of Invention The invention aims to solve the problems of low efficiency, insufficient precision, difficult installation of monitoring equipment and the like in the prior monitoring technology, provides a monitoring system and an evaluation method for the winding state of an ultra-deep vertical shaft construction hoisting steel wire rope based on photogrammetry and video metering, and can monitor the abnormal winding state of the hoisting steel wire rope in real time and judge and quantitatively evaluate abnormal winding working conditions such as rope biting, rope skipping, rope sinking and the like. The scheme has the characteristics of convenience in installation, simplicity in operation, strong anti-interference capability and nondestructive detection, and simultaneously has high detection precision and detection efficiency. The technical scheme is that the monitoring system for the winding state of the hoisting steel wire rope for ultra-deep vertical shaft construction is arranged on one side of a hoisting steel wire rope winding drum and comprises a binocular vision acquisition module, an image processing module and an ear