Dynamic evaluation method and system for trafficability of monorail crane in underground coal mine roadway

Abstract

The invention discloses a method and system for dynamically evaluating trafficability of a monorail crane in an underground coal mine roadway, and the method comprises the steps: obtaining roadway video information through video collection equipment, and analyzing and recognizing the position of roadway rock damage or deformation; for the damaged or deformed position, the running speed of the monorail crane is reduced in advance, and the deformation amount is analyzed through the three-dimensional laser point cloud technology; preliminarily judging the trafficability of the monorail crane in the current roadway by referring to the range of the key size of the monorail crane, and if the monorail crane is impassable, performing multipoint cloud offline fitting accurate judgment; and evaluating the trafficability of the monorail crane according to the judgment result, and providing driving suggestions for the monorail crane. According to the method, the trafficability of the monorail crane in the underground coal mine roadway can be dynamically evaluated, and safe operation of the monorail crane is guaranteed.

Description

Technical Field

[0001] The invention belongs to the technical field of auxiliary transportation equipment in coal mines, and in particular relates to a method for dynamically evaluating the trafficability of a monorail crane in an underground tunnel of a coal mine in a deep confined space. Background Art

[0002] As an important transportation equipment in the auxiliary transportation system of coal mines, monorail cranes in tunnels play a vital role in the production and transportation of coal mines. The evaluation of the trafficability of monorail cranes in underground mine tunnels is an important part of coal mine safety management and intelligent mining equipment, and its purpose is to ensure the safe operation of auxiliary transportation equipment in coal mines.

[0003] At present, the evaluation of monorail crane trafficability in underground coal mine tunnels mainly relies on traditional manual inspection and visual inspection methods. These methods have many problems and limitations, including:

[0004] Traditional manual inspections mainly rely on the driver's experience and visual observation. This method has low accuracy and the inspection frequency is also greatly limited. The underground environment of coal mines is complex, and special working conditions such as anchor bolts falling off, roof sinking, two sides moving closer, and surrounding rock deformation often occur. In this case, it is difficult for manual inspections to detect the type of damage in time, which creates a greater safety risk. For example, the falling of anchors may cause the roof to lose support, which in turn causes dangerous conditions such as roof sinking, and manual inspections often fail to detect such problems in time.

[0005] Visual detection methods mainly use cameras and other sensors to obtain tunnel cross-sectional shape information. However, since coal mine tunnels are deep and confined spaces, which are quite different from the ground road environment, there are problems such as uneven lighting and insufficient light, which leads to incomplete and insignificant tunnel cross-sectional shape features obtained by visual images, and it is difficult to accurately provide tunnel risk prediction and assessment, and it is difficult to meet the needs of monorail crane passability assessment in coal mine underground tunnels. In addition, existing methods are mostly based on static measurement and assessment, which makes it difficult to quickly detect the deformation and passability of the entire tunnel line.

[0006] LiDAR sensors can measure the details of the tunnel surface and are not affected by ambient light. Therefore, LiDAR sensors are widely used in environmental perception. For example, a feasible domain extraction method for structured roads based on three-dimensional LiDAR with application publication number CN 201810169285 X takes single-line point cloud as the research object, uses K-means clustering optimization to extract obstacles, and combines DBSCAN clustering algorithm to extract curbs to obtain feasible domains. However, this method is designed for structured roads. The working conditions of underground tunnels are very different from those of ground structured roads. There are a large number of special obstacle types and factors that have unique effects on the passability of monorail cranes underground. These make this method not universal in road conditions such as underground mine tunnels, and it is difficult to effectively evaluate the passability of monorail cranes.

[0007] The application publication number is CN 116929363 A, which is an autonomous navigation method for mining vehicles based on a traversable map. It uses lidar data to conduct a feasibility analysis of mining transport vehicles. However, this method is mostly used to identify ground obstacles in mining areas and cannot identify the traversability of monorail cranes under special working conditions such as the movement of the two walls in the tunnel and the deformation of the roof.

[0008] On the other hand, some current dynamic measurement methods mainly rely on point cloud data, but the amount of point cloud data is often very large, which places extremely high demands on computing power. In the actual underground coal mine environment, excessive data volume and computing power requirements will seriously affect the timeliness and effectiveness of the evaluation. Summary of the invention

[0009] In order to overcome the deficiencies of the above-mentioned prior art, the present invention provides a method and system for dynamically evaluating the passability of a monorail crane in an underground tunnel of a coal mine, which can realize dynamic evaluation of the passability of a monorail crane in an underground tunnel of a coal mine and assist in the safe driving of a monorail crane.

[0010] To achieve the above object, the present invention adopts the following technical solution:

[0011] A method for dynamically evaluating the trafficability of a monorail crane in an underground coal mine tunnel comprises the following steps:

[0012] S1: Obtain visual information of the tunnel rock surface through the video acquisition equipment installed on the monorail crane to identify the damaged or deformed areas of the tunnel rock;

[0013] S2. For places where rock damage or deformation is identified, the monorail crane's travel speed is reduced to 30%-50% of the original speed in advance; at the same time, the point cloud data of the tunnel is obtained through the point cloud data acquisition equipment installed on the monorail crane, and the deformation of the tunnel rock is analyzed using the three-dimensional laser point cloud analysis method;

[0014] S3: Preliminary evaluation of the passability of the monorail crane in the current lane, by referring to the range of the height and width of the monorail crane. If it is preliminarily judged to be passable, the monorail crane maintains normal speed; if it is preliminarily judged to be impassable, proceed to step S4;

[0015] S4. Accurately evaluate the trafficability of the monorail crane in the current tunnel, perform multi-point cloud offline fitting through the acquired point cloud data of the tunnel, obtain the tunnel section profile data, and calculate the accurate deformation of the tunnel rock;

[0016] S5: Provide driving suggestions for the monorail crane based on the precise deformation of the tunnel rock calculated in step S4, the size of the vehicle and the provisions of the Coal Mine Safety Regulations.

[0017] In step S2, for the case where there is no surface damage in the tunnel, the three-dimensional laser point cloud is sparsely processed to determine the deformation of the tunnel.

[0018] In step S1, a target detection algorithm is used to identify where the tunnel rock is damaged or deformed, tunnel images containing normal and damaged deformed rocks are collected as a training set, the rock areas and states are annotated, and a target detection model is trained.

[0019] In step S2, the three-dimensional laser point cloud analysis method adopts a point cloud registration method or a point cloud comparison method.

[0020] In step S3, the preliminarily judged passability of the monorail in the current lane is that if the following two formulas are satisfied at the same time, it is preliminarily judged to be passable, otherwise it is preliminarily judged to be impassable;

[0021] 0<Δh≤0.1h

[0022] 0<Δw≤0.05w

[0023] Wherein, h is the height of the monorail crane body; w is the width of the monorail crane body.

[0024] In step S4, the method for offline fitting of multiple point clouds specifically includes the following steps:

[0025] S41, extracting the tunnel centerline data based on the ground and tunnel side projection, including the following sub-steps:

[0026] S411, taking the sensor of the point cloud acquisition device that acquires the tunnel point cloud data as the coordinate origin O, establishing an XYZ three-axis coordinate system, wherein the X axis is the axial direction of the tunnel, the XOY plane is the ground, and the XOZ plane is the side of the tunnel;

[0027] S412, projecting the lane point cloud data onto the XOY plane and the XOZ plane in the three-axis coordinate system respectively, and extracting a two-dimensional boundary point set of the lane projection;

[0028] S413, set a certain step length Δx along the axial direction of the lane in the XOY plane and the XOZ plane, perform equal-interval sampling on the boundary point set, and extract x i The maximum value y of the boundary point set in the y-axis direction corresponding to (i=1,2,3,...,n) i(max) With the minimum value y i(min) , the maximum value z of the corresponding boundary point set in the z-axis direction i(max) With the minimum value z i(min) ;

[0029] S414, using the midpoint formula and Calculate the boundary point concentration values ​​in the y-axis and z-axis directions, and obtain the point set M in the XOY plane = {m1 ,m 2 ,m 3 ,...,m n}, the set of points in the XOZ plane N = {n 1 ,n 2 ,n 3 ,...,n n}, and based on the midpoint set of the XOY plane and the midpoint set of the XOZ plane, the central axis of the lane is fitted;

[0030] S42, using a tunnel cross-section profile separation model based on the RANSAC algorithm to obtain tunnel cross-section profile data and calculate deformation, including the following sub-steps:

[0031] S421, calculating the angle α between the central axis of the lane extracted in step S414 and the XOY plane, and the angle β between the central axis of the lane and the XOZ plane;

[0032] S422, based on the angle α and the angle β, the coordinates of the roadway point cloud data are converted so that the central axis of the converted roadway point cloud data is parallel to the X axis. The calculation formulas before and after the conversion are:

[0033]

[0034] Where [XYZ] T is the original coordinate of the roadway point cloud, [X'Y'Z'] T is the converted roadway point cloud coordinate;

[0035] S423, slice the adjusted tunnel point cloud data along the X-axis at a certain interval δ and thickness d according to the point cloud processing speed, perform projection fitting on the sliced ​​point cloud data based on the RANSAC algorithm, obtain the tunnel section contour data, and calculate the precise deformation amount.

[0036] In step S423, the adjusted lane point cloud data is sliced ​​along the X-axis at a certain interval δ and thickness d according to the point cloud processing speed. To achieve continuous processing of the slices, the vehicle is still located before the processed slice after the processing of the slice. The time required to process the point cloud of one slice is recorded as T 计 , the speed of the monorail crane is V, the braking time is t, and the distance between the monorail crane and the processing slice section is δ 1 , then δ 1 The determination satisfies the formula: The distance between the two slice sections is δ 2 , in order to enable the monorail crane to brake in time when identifying the impassable area, δ 2 The determination satisfies the formula at the same time: and

[0037]

[0038] Step S5 is specifically: the monorail crane needs to take into account the shaking that occurs during the operation of the equipment, and its maximum swing amplitude must be considered to be 10°. In addition to the maximum swing amplitude, a safety distance of at least 200mm is set between the monorail crane and the tunnel wall, and a safety distance of 500mm is set between the monorail crane and the top of the tunnel, and a safety distance of 400mm is set between the bottom of the monorail crane and the bottom of the tunnel. Design a safe and passable area model for transport vehicles based on this regulation.

[0039] The specific driving suggestions provided for the monorail crane are: the monorail crane brakes in time or changes the driving route.

[0040] The present invention further discloses a dynamic evaluation system for monorail crane trafficability in underground coal mine tunnels, comprising:

[0041] Video acquisition module, used to obtain video information of the lane;

[0042] Video analysis module, used to perform frame-by-frame analysis on video information to identify locations where the tunnel rock is damaged or deformed;

[0043] Point cloud acquisition module, used to obtain roadway point cloud data;

[0044] Point cloud processing module, used to process 3D laser point cloud and analyze the precise deformation of tunnel rock;

[0045] The trafficability assessment module is used to accurately assess the trafficability of the monorail crane based on the precise deformation of the roadway rock;

[0046] The decision-making recommendation module is used to provide driving recommendations for the monorail crane based on the evaluation results.

[0047] The beneficial effects of the present invention are:

[0048] First, the present invention provides a method for dynamically evaluating the passability of a monorail crane in an underground coal mine tunnel. Compared with the existing traditional static measurement and evaluation, the present invention utilizes vehicle-mounted sensors to achieve rapid detection of mobile deformation, and can realize dynamic evaluation of deformation and passability of the entire tunnel line.

[0049] Second, unlike the existing dynamic measurement point cloud method, the current dynamic measurement point cloud data volume is large and requires high computing power. The present invention combines vision and point cloud, and uses visual information to assist point cloud data processing, which effectively reduces the amount of data calculation and improves the data processing speed. It is more conducive to timely and accurate evaluation of the trafficability of monorail cranes in the complex environment of coal mines.

[0050] Third, the present invention is mainly implemented based on point cloud sensors. For underground vehicles with existing laser radar sensors, the software can be directly upgraded, and for underground vehicles without laser radar sensors, the hardware can be upgraded by simply adding laser radar sensors. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 It is a flowchart of a method for dynamically evaluating the trafficability of a monorail crane in an underground coal mine tunnel of the present invention;

[0052] Figure 2 This is a module diagram of a dynamic evaluation system for monorail crane trafficability in underground coal mine tunnels of the present invention;

[0053] Figure 3 It is a structural schematic diagram of the present invention;

[0054] Figure 4 A schematic diagram of the cross-sectional spacing selection rule of the present invention;

[0055] Figure 5 This is a schematic diagram of the safe and passable area of ​​the present invention. DETAILED DESCRIPTION

[0056] The present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0057] Please refer to Figure 1 The present invention discloses a method for dynamically evaluating the trafficability of a monorail crane in an underground coal mine tunnel, comprising the following steps:

[0058] S1: Obtain visual information of the tunnel rock surface through the video acquisition equipment installed on the monorail crane to identify the damaged or deformed areas of the tunnel rock;

[0059] S2. For places where rock damage or deformation is identified, the monorail crane's travel speed is reduced to 30%-50% of the original speed in advance; at the same time, the point cloud data of the tunnel is obtained through the point cloud data acquisition equipment installed on the monorail crane, and the deformation of the tunnel rock is analyzed using the three-dimensional laser point cloud analysis method;

[0060] S3: Preliminary evaluation of the passability of the monorail crane in the current lane, by referring to the range of the height and width of the monorail crane. If it is preliminarily judged to be passable, the monorail crane maintains normal speed; if it is preliminarily judged to be impassable, proceed to step S4;

[0061] S4. Accurately evaluate the trafficability of the monorail crane in the current tunnel, perform multi-point cloud offline fitting through the acquired point cloud data of the tunnel, obtain the tunnel section profile data, and calculate the accurate deformation of the tunnel rock;

[0062] S5: Provide driving suggestions for the monorail crane based on the precise deformation of the tunnel rock calculated in step S4, the size of the vehicle and the provisions of the Coal Mine Safety Regulations.

[0063] The video acquisition equipment in step S1 includes but is not limited to cameras, video cameras, etc., and the point cloud acquisition equipment includes but is not limited to laser radar, binocular cameras, etc.

[0064] The identification of tunnel rock damage or deformation may use a target detection algorithm, such as Faster R-CNN and YOLO series algorithms, to collect a large number of tunnel images containing normal and damaged deformed rocks as training sets, annotate rock areas and states, and train a target detection model.

[0065] The method of analyzing deformation using the three-dimensional laser point cloud technology in step S2 includes but is not limited to point cloud matching, point cloud comparison, etc.

[0066] In step S3, the passability is preliminarily judged by referring to the range of key dimensions of the monorail crane. Considering the height h of the monorail crane body, when the spatial change Δh caused by the deformation in the direction of the top of the tunnel satisfies 0<Δh≤0.1h, and the width w of the monorail crane body, when the deformation Δw in the direction of both sides of the tunnel satisfies 0<Δw≤0.05w, it can be preliminarily judged as passable, otherwise it is preliminarily judged as unpassable.

[0067] The method for offline fitting of multiple point clouds in step S4 comprises the following steps:

[0068] S41, extract the tunnel centerline data based on the ground and tunnel side projection,

[0069] S411, taking the sensor of the point cloud acquisition device that acquires the tunnel point cloud data as the coordinate origin O, establishing an XYZ three-axis coordinate system, wherein the X axis is the axial direction of the tunnel, the XOY plane is the ground, and the XOZ plane is the side of the tunnel;

[0070] S412, projecting the lane point cloud data onto the XOY plane and the XOZ plane in the three-axis coordinate system respectively, and extracting a two-dimensional boundary point set of the lane projection;

[0071] S413, set a certain step length Δx along the axial direction of the lane in the XOY and XOZ planes, perform equal-interval sampling on the boundary point set, and extract x i The maximum value y of the boundary point set in the y-axis direction corresponding to (i=1,2,3,...,n) i(max) With the minimum value y i(min) , the maximum value z of the corresponding boundary point set in the z-axis direction i(max) With the minimum value z i(min) ;

[0072] S314, using the midpoint formula to calculate the boundary point concentration values ​​in the y-axis and z-axis directions, obtain the XOY plane midpoint set M = {m 1 ,m 2 ,m 3 ,...,m n}, the set of points in the XOZ plane N = {n 1 ,n 2 ,n 3 ,...,n n}, and based on the midpoint set of the XOY plane and the midpoint set of the XOZ plane, the central axis of the lane is fitted;

[0073] S42, using the tunnel section profile separation model based on the RANSAC algorithm, obtain the tunnel section profile data and calculate the deformation,

[0074] S421, calculating the angles between the central axis of the tunnel extracted in S1 and the ground (XOY plane) and the side of the tunnel (XOZ plane), which are denoted as α and β respectively;

[0075] S422, based on the angle between the central axis of the lane and the ground and the side of the lane, the coordinates of the lane point cloud data are converted so that the central axis of the lane point cloud data after conversion is parallel to the X axis. The calculation formula before and after the conversion is:

[0076]

[0077] Where [XYZ] T is the original coordinate of the roadway point cloud, [X'Y'Z'] T is the converted roadway point cloud coordinate;

[0078] S423, slice the adjusted tunnel point cloud data along the X-axis at a certain interval δ and thickness d, perform projection fitting on the sliced ​​point cloud data based on the RANSAC algorithm, obtain the tunnel cross-section contour data, and calculate the deformation.

[0079] Please refer to Figure 4 In S423, the adjusted lane point cloud data is sliced ​​along the X axis at a certain interval δ and thickness d according to the point cloud processing speed. In order to realize continuous processing of the slices, the vehicle is still located before the processed slice after the processing of the slice. The time required for the industrial computer to process the point cloud of a slice is recorded as T 计 The speed of the monorail crane is V, the braking time is t, and the distance between the monorail crane and the processing section is δ 1 , then δ 1 The determination needs to satisfy the formula: The distance between the two sections is δ 2 , in order to enable the monorail crane to brake in time when identifying the impassable area, δ 2 The determination of needs to satisfy the formula at the same time: and

[0080]

[0081] Please refer to Figure 5 The basis for accurately evaluating the passability of the monorail crane in S4 includes but is not limited to the deformation of the tunnel, the size and performance of the vehicle, and safety regulations. The "Safety Regulations for Coal Mines" stipulate that the monorail crane must take into account the shaking and other conditions that occur during the operation of the equipment, and its maximum swing amplitude of ±10° must be considered. In addition to the maximum swing amplitude, a safety distance of at least 200mm must be set, and a safety distance of 500mm must be considered at the top and a safety distance of 400mm must be considered at the bottom. The safe and passable area model of the monorail crane is designed based on this regulation.

[0082] The implementation process of providing driving suggestions for the monorail crane in step S5 is as follows: when the evaluation result is passable, the monorail crane maintains normal speed and travels on the original route; when the preliminary evaluation result is impassable, the monorail crane reduces speed and waits for the industrial computer to output accurate evaluation results; when the accurate evaluation is impassable, the monorail crane needs to brake in time, or change the driving route.

[0083] The present invention also discloses a dynamic assessment system for the passability of a monorail crane in an underground coal mine tunnel, comprising: a video acquisition module, a video analysis module, a point cloud acquisition module, a point cloud processing module, a passability assessment module and a decision suggestion module; the video acquisition module comprises a video acquisition device installed in the monorail crane or the tunnel, for acquiring video information of the tunnel; a video analysis module, for performing frame analysis on the video information, and identifying places where the tunnel rock is damaged or deformed; a point cloud acquisition module, comprising a point cloud acquisition device installed in the monorail crane or the tunnel, for acquiring point cloud data of the tunnel; a point cloud processing module, for processing three-dimensional laser point clouds, analyzing deformation and judging passability; a passability assessment module, for evaluating the passability of the monorail crane according to the deformation and passability judgment results; and a decision suggestion module, for providing driving suggestions for the monorail crane according to the assessment results.

Claims

1. A method for dynamically evaluating the trafficability of a monorail crane in an underground coal mine tunnel, characterized in that: The following steps are involved: S1: Obtain visual information of the tunnel rock surface through the video acquisition equipment installed on the monorail crane to identify the damaged or deformed areas of the tunnel rock; S2. For places where rock damage or deformation is identified, the monorail crane's travel speed is reduced to 30%-50% of the original speed in advance; at the same time, the point cloud data of the tunnel is obtained through the point cloud data acquisition equipment installed on the monorail crane, and the deformation of the tunnel rock is analyzed using the three-dimensional laser point cloud analysis method; S3: Preliminary evaluation of the passability of the monorail crane in the current lane, by referring to the range of the height and width of the monorail crane. If it is preliminarily judged to be passable, the monorail crane maintains normal speed; if it is preliminarily judged to be impassable, proceed to step S4; S4. Accurately evaluate the trafficability of the monorail crane in the current tunnel, perform multi-point cloud offline fitting through the acquired point cloud data of the tunnel, obtain the tunnel section profile data, and calculate the accurate deformation of the tunnel rock; S5: Provide driving suggestions for the monorail crane based on the precise deformation of the tunnel rock calculated in step S4, the size of the vehicle and the provisions of the Coal Mine Safety Regulations.

2. The method for dynamic evaluation of monorail crane trafficability in underground coal mine tunnels according to claim 1 is characterized in that: In step S2, for the case where there is no surface damage in the tunnel, the three-dimensional laser point cloud is sparsely processed to determine the deformation of the tunnel.

3. The method for dynamic evaluation of monorail crane trafficability in underground coal mine tunnels according to claim 1 is characterized in that: In step S1, a target detection algorithm is used to identify where the tunnel rock is damaged or deformed, tunnel images containing normal and damaged deformed rocks are collected as a training set, the rock areas and states are annotated, and a target detection model is trained.

4. The method for dynamic evaluation of monorail crane trafficability in underground coal mine tunnels according to claim 1 is characterized in that: In step S2, the three-dimensional laser point cloud analysis method adopts a point cloud registration method or a point cloud comparison method.

5. The method for dynamic evaluation of monorail crane trafficability in underground coal mine tunnels according to claim 1 is characterized in that: In step S3, the preliminarily judged passability of the monorail in the current lane is that if the following two formulas are satisfied at the same time, it is preliminarily judged to be passable, otherwise it is preliminarily judged to be impassable; 0<Δh≤0.1h 0<Δw≤0.05w Wherein, h is the height of the monorail crane body; w is the width of the monorail crane body.

6. The method for dynamic evaluation of monorail crane trafficability in underground coal mine tunnels according to claim 1, characterized in that: In step S4, the method for offline fitting of multiple point clouds specifically includes the following steps: S41, extracting the tunnel centerline data based on the ground and tunnel side projection, including the following sub-steps: S411, taking the sensor of the point cloud acquisition device that acquires the tunnel point cloud data as the coordinate origin O, establishing an XYZ three-axis coordinate system, wherein the X axis is the axial direction of the tunnel, the XOY plane is the ground, and the XOZ plane is the side of the tunnel; S412, projecting the lane point cloud data onto the XOY plane and the XOZ plane in the three-axis coordinate system respectively, and extracting a two-dimensional boundary point set of the lane projection; S413, set a certain step length Δx along the axial direction of the lane in the XOY plane and the XOZ plane, perform equal-interval sampling on the boundary point set, and extract x i The maximum value y of the boundary point set in the y-axis direction corresponding to (i=1,2,3,...,n) i(max) With the minimum value y i(min) , the maximum value z of the corresponding boundary point set in the z-axis direction i(max) With the minimum value z i(min) ; S414, using the midpoint formula and Calculate the boundary point concentration values ​​in the y-axis and z-axis directions, and obtain the point set M in the XOY plane = {m1,m2,m3,...,m n }, the set of points in the XOZ plane N = {n1,n2,n3,...,n n }, and based on the midpoint set of the XOY plane and the midpoint set of the XOZ plane, the central axis of the lane is fitted; S42, using a tunnel cross-section profile separation model based on the RANSAC algorithm to obtain tunnel cross-section profile data and calculate deformation, including the following sub-steps: S421, calculating the angle α between the central axis of the lane extracted in step S414 and the XOY plane, and the angle β between the central axis of the lane and the XOZ plane; S422, based on the angle α and the angle β, the coordinates of the roadway point cloud data are converted so that the central axis of the converted roadway point cloud data is parallel to the X axis. The calculation formulas before and after the conversion are: Where [XYZ] T is the original coordinate of the roadway point cloud, [X'Y'Z'] T is the converted roadway point cloud coordinate; S423, slice the adjusted tunnel point cloud data along the X-axis at a certain interval δ and thickness d according to the point cloud processing speed, perform projection fitting on the sliced ​​point cloud data based on the RANSAC algorithm, obtain the tunnel section contour data, and calculate the precise deformation amount.

7. The method for dynamically evaluating the trafficability of a monorail crane in an underground coal mine tunnel according to claim 4, characterized in that: In step S423, the adjusted lane point cloud data is sliced ​​along the X-axis at a certain interval δ and thickness d according to the point cloud processing speed. To achieve continuous processing of the slices, the vehicle is still located before the processed slice after the processing of the slice. The time required to process the point cloud of one slice is recorded as T 计 , the speed of the monorail crane is V, the braking time is t, and the distance between the monorail crane and the processing slice section is δ1. Then the determination of δ1 satisfies the formula: The interval between the two slice sections is δ2. In order to ensure that the monorail crane can brake in time when the impassable area is identified, the determination of δ2 also satisfies the formula: and 8. The method for dynamic evaluation of monorail crane trafficability in underground coal mine tunnels according to claim 1, characterized in that: Step S5 is specifically: the monorail crane needs to take into account the shaking that occurs during the operation of the equipment, and its maximum swing amplitude must be considered to be 10°. In addition to the maximum swing amplitude, a safety distance of at least 200mm is set between the monorail crane and the tunnel wall, and a safety distance of 500mm is set between the monorail crane and the top of the tunnel, and a safety distance of 400mm is set between the bottom of the monorail crane and the bottom of the tunnel. Design a safe and passable area model for transport vehicles based on this regulation.

9. The method for dynamically evaluating the trafficability of a monorail crane in an underground coal mine tunnel according to claim 1, characterized in that: In step S5, providing driving advice to the monorail crane specifically includes: the monorail crane brakes in time or changes the driving route.

10. A dynamic evaluation system for monorail crane trafficability in underground coal mine tunnels, characterized in that: include: Video acquisition module, used to obtain video information of the lane; Video analysis module, used to perform frame-by-frame analysis on video information to identify locations where the tunnel rock is damaged or deformed; Point cloud acquisition module, used to obtain roadway point cloud data; Point cloud processing module, used to process 3D laser point cloud and analyze the precise deformation of tunnel rock; The trafficability assessment module is used to accurately assess the trafficability of the monorail crane based on the precise deformation of the tunnel rock; The decision-making recommendation module is used to provide driving recommendations for the monorail crane based on the evaluation results.

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