Method and device for identifying abnormal deformation of coal mine channel

Abstract

The invention discloses a method and a device for identifying abnormal deformation of a coal mine channel. The method comprises the following steps: acquiring current video data acquired by each camera device in a mine channel in a current monitoring period; the view field ranges of the camera devices do not coincide and cover the preset range of the channel; extracting current point cloud data of the channel wall surface in a preset range from the current video data, wherein the point cloud data is a feature point set of the mine channel wall surface; determining a target point cloud at the to-be-monitored position from the current point cloud, and extracting a theoretical sub-point cloud corresponding to the target point cloud from an array according to a pre-constructed mapping relation among the array, the start matrix and the termination matrix based on the space coordinate of the target point cloud; the array is used for storing a theoretical overall point cloud of the channel wall surface in a preset range, and the subscript of each matrix is in one-to-one correspondence with coordinates of points in the point cloud; and determining a channel deformation result of the position of the target point cloud based on the distance between the target point cloud and the theoretical sub-point cloud. The method can quickly determine the deformation of any position in the mine channel.

Description

Technical Field

[0001] The present invention relates to the technical field of mine detection, and particularly to a method and device for identifying abnormal deformation of a coal mine roadway. Background Art

[0002] Coal mines are important raw materials for industrial production and are crucial for industrial development. In recent years, with the increase in coal mining scale, mine safety accidents often occur, posing great potential hazards to mining and personnel safety. In the related art, when monitoring the deformation of a mine roadway, the laser ranging method is usually adopted, but this method is inefficient and cannot monitor the deformation state of the mine roadway in real time.

[0003] Based on this, there is an urgent need for a method and device for identifying abnormal deformation of a coal mine roadway to solve the above problems. Summary of the Invention

[0004] The present invention provides a method and device for identifying abnormal deformation of a coal mine roadway, which can quickly and real-time determine the deformation at any position in the mine roadway. The technical solution is as follows:

[0005] In a first aspect, an embodiment of the present invention provides a method for identifying abnormal deformation of a coal mine roadway, the method comprising:

[0006] Obtaining current video data collected by each camera device in the mine roadway during the current monitoring period; the field of view ranges of the camera devices do not overlap and cover a preset range of the roadway;

[0007] Extracting current point cloud data of the roadway wall surface within the preset range from the current video data, the point cloud data being a set of feature points of the mine roadway wall surface;

[0008] Determining a target point cloud at a position to be monitored from the current point cloud, and based on the spatial coordinates of the target point cloud, extracting a theoretical sub-point cloud corresponding to the target point cloud from an array according to the mapping relationship between a pre-constructed array, a starting matrix, and an ending matrix; the array is used to store the theoretical overall point cloud of the roadway wall surface within the preset range, and the subscripts of each matrix respectively correspond one-to-one with the coordinates of the points in the point cloud;

[0009] Determining a roadway deformation result at the position where the target point cloud is located based on the distance between the target point cloud and the theoretical sub-point cloud.

[0010] In a second aspect, an embodiment of the present invention further provides a device for identifying abnormal deformation of a coal mine roadway, the device comprising:

[0011] An acquisition unit for acquiring current video data collected by each camera device in the mine passage during the current monitoring period; the field of view ranges of the camera devices do not overlap and cover a preset range of the passage;

[0012] A first extraction unit for extracting current point cloud data of the passage wall surface within the preset range from the current video data, where the point cloud data is a set of feature points of the mine passage wall surface;

[0013] A second extraction unit for determining a target point cloud at a position to be monitored from the current point cloud, and based on the spatial coordinates of the target point cloud, extracting a theoretical sub-point cloud corresponding to the target point cloud from an array according to the mapping relationship between a pre-constructed array, a starting matrix, and an ending matrix; the array is used to store the theoretical overall point cloud of the passage wall surface within the preset range, and the subscripts of each matrix correspond one by one to the coordinates of the points in the point cloud;

[0014] A determination unit for determining the passage deformation result at the position where the target point cloud is located based on the distance between the target point cloud and the theoretical sub-point cloud.

[0015] In a third aspect, an embodiment of the present invention further provides an electronic device, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the method described in any embodiment of this specification is implemented.

[0016] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed on a computer, the computer is made to execute the method described in any embodiment of this specification.

[0017] In a fifth aspect, an embodiment of the present invention further provides a computer program product, including a computer program, and when the computer program is executed by a processor, the steps of the method described above are implemented.

[0018] An embodiment of the present invention provides a method and device for identifying abnormal deformation of a coal mine roadway. By installing multiple camera devices in the roadway, video data in the roadway can be collected in real time through the camera devices, including video data in the normal state and monitoring data at any time. The theoretical overall point cloud is extracted from the video data in the normal state, and this theoretical overall point cloud is used as a standard for determining whether the roadway is deformed. Also, since the theoretical overall point cloud has been pre-stored in an array, and there is a mapping relationship between the array, matrix, and spatial coordinates of points, the local point cloud adjacent to a point can be quickly searched according to the spatial coordinates of the point. Finally, when the position to be monitored is determined, only the distance between the target point cloud and the theoretical sub-point cloud at this position needs to be determined, and then it can be determined whether the roadway at this position is deformed according to the calculation result. The method of this application can obtain the video data in the coal mine roadway in real time, and quickly identify the deformation in the coal mine roadway based on the obtained video data, with high identification efficiency. By quickly identifying the abnormal deformation in the coal mine roadway, risk early warning can be realized, and casualties and property losses can be avoided. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0020] Figure 1 is a flowchart of a method for identifying abnormal deformation of a coal mine roadway provided by an embodiment of the present invention;

[0021] Figure 2 is a structural diagram of a device for identifying abnormal deformation of a coal mine roadway provided by an embodiment of the present invention;

[0022] Figure 3 is a hardware architecture diagram of a computer device provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0023] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0024] The following describes the specific implementation manners of the above concepts.

[0025] Please refer toFigure 1 , a method for identifying abnormal deformation of a coal mine shaft passage provided by an embodiment of the present invention, the method comprising:

[0026] Step 100, obtaining current video data collected by each camera device in the mine passage during the current monitoring period; the field of view ranges of the camera devices do not overlap and cover a preset range of the passage;

[0027] Step 102, extracting current point cloud data of the passage wall surface within the preset range from the current video data, the point cloud data being a set of feature points of the mine passage wall surface;

[0028] Step 104, determining a target point cloud at the position to be monitored from the current point cloud, and based on the spatial coordinates of the target point cloud, extracting a theoretical sub-point cloud corresponding to the target point cloud from an array according to the mapping relationship between a pre-constructed array, a starting matrix, and an ending matrix; the array is used to store the theoretical overall point cloud of the passage wall surface within the preset range, and the subscripts of each matrix correspond one-to-one with the coordinates of the points in the point cloud;

[0029] Step 106, determining the passage deformation result at the position where the target point cloud is located based on the distance between the target point cloud and the theoretical sub-point cloud.

[0030] In this embodiment, by installing multiple camera devices in the mine passage, video data in the mine passage can be collected in real time by the camera devices, including video data in a normal state and monitoring data at any time. The theoretical overall point cloud is extracted from the video data in the normal state, and this theoretical overall point cloud is used as a standard for determining whether the passage is deformed. Also, since the theoretical overall point cloud has been pre-stored in the array, and there is a mapping relationship between the array, the matrix, and the spatial coordinates of the points, therefore, the local point cloud adjacent to a point can be quickly searched according to the spatial coordinates of the point. Finally, when the position to be monitored is determined, only the distance between the target point cloud at this position and the theoretical sub-point cloud needs to be determined, and then it can be determined whether the passage at this position is deformed according to the calculation result. The method of the present application can obtain video data in the mine passage in real time and quickly identify deformations in the mine passage based on the obtained video data, with high identification efficiency.

[0031] In addition, when an abnormal deformation of the mine passage is identified, the risk can be sent to an alarm LED screen for display and an alarm broadcast can be sent to timely remind relevant personnel to handle the risk and avoid casualties and property losses.

[0032] The following describes Figure 1 the execution manners of the respective steps shown.

[0033] First, for step 100, obtain the current video data collected by each camera device in the mine passage during the current monitoring period.

[0034] In this step, the camera devices are arranged at intervals and respectively installed at key points in the channel. The field of view ranges of the camera devices do not overlap and cover a preset range of the channel. Each camera device uses a camera with no less than 4 million pixels, and each camera is respectively used to collect video signals in the mine tunnel.

[0035] For step 102, extract the current point cloud data of the channel wall surface within the preset range from the current video data.

[0036] In this step, extracting point cloud data from video is a common method in the field of feature extraction, which will not be elaborated here. The point cloud data is a set of feature points on the mine tunnel wall surface, and the feature points are points with gray values or texture characteristics higher than the preset value. The preset value is determined according to user requirements and is not specifically limited in this application.

[0037] For step 104, determine the target point cloud at the position to be monitored from the current point cloud, and based on the spatial coordinates of the target point cloud, extract the corresponding theoretical sub-point cloud from the array according to the mapping relationship between the pre-constructed array, start matrix, and end matrix.

[0038] In this step, the theoretical overall point cloud is obtained by using camera devices to collect video data within the preset range of the coal mine tunnel under normal conditions and extracting feature points from the video data. The theoretical overall point cloud is used to represent the normal state of the mine tunnel wall surface. In this normal state, there are no risks such as collapse in the mine tunnel, and safe production can be carried out.

[0039] In some embodiments, the dimensions of the start matrix and the end matrix are both determined based on the boundaries of the overall point cloud of the channel wall surface within the preset range and the size of the preset grid. The calculation formula is as follows:

[0040]

[0041] In the formula, A x , A y and A z are the dimensions of the matrix in the x, y, and z directions respectively; x max , x min , y max , y min , z max and z min are the maximum and minimum boundary coordinates of the overall point cloud in the x, y, and z directions respectively; w x , w x and w x are the sizes of the grid in the x, y, and z directions respectively; represents rounding up.

[0042] In some embodiments, the mapping relationship between the array and each matrix is determined in the following manner:

[0043] S1. Calculate the position coordinates of the grid corresponding to each point in the theoretical overall point cloud in the entire spatial grid, and calculate the position encoding of the grid based on the position coordinates of the grid.

[0044] In this step, the position encoding of the grid is calculated by the following formula:

[0045] p = x'+A x (y'+z'A y )

[0046] In the formula, p represents the position encoding of the grid; (x, y, z) represents the spatial coordinates of the point in the point cloud, and (x', y', z') are the position coordinates of the grid corresponding to the point (x, y, z), where, represents rounding down.

[0047] S2. Take the encoding of each point in the theoretical overall point cloud and the position encoding of its corresponding grid as a data pair, and store each data pair in the array in descending order of the position encoding. Each data pair corresponds to an array subscript.

[0048] In this step, the overall point cloud includes many points, and each point has an encoding, which is used to represent which point it is in the point cloud. When the point cloud is determined, the encoding of each point in the point cloud and its spatial coordinates are also determined accordingly. Suppose the encoding of a certain point is d, then the data pair can be expressed as (d, p). Store them in the array in order of the p value. For example, suppose the encoding of a point is 231 and the position encoding of its corresponding grid is 12, then the data pair is expressed as (231, 12). Let (231, 12) be stored in the position where the 10th element of the array is located, which means that the data pair (231, 12) corresponds to the array subscript 10, that is, the 10th element in the array is (231, 12). By analogy, the corresponding relationship between each data pair and the array subscript can be obtained.

[0049] S3. Traverse the array and calculate the starting matrix subscript and the ending matrix subscript corresponding to each position encoding in each data pair.

[0050] In this step, after storing each data pair in the array, the corresponding relationship between each data pair and the array subscript can be determined, that is, the corresponding relationship between the grid position encoding in the data pair and the array subscript.

[0051] S4. When the number of the same position encodings is multiple, record the starting subscript and the ending subscript of the position encoding in the array; otherwise, use the array subscript corresponding to the position encoding as both the starting subscript and the ending subscript. Let the element position corresponding to the starting matrix subscript store the starting subscript of the position encoding in the array, and let the element position corresponding to the ending matrix subscript store the ending subscript of the position encoding in the array.

[0052] In this step, there may be multiple p's. Since each p corresponds to an array subscript, the subscript of the first storage of p is used as its starting subscript, and the last storage of p is used as its ending subscript. When there is only one p, its corresponding array subscript is used as both the starting subscript and the ending subscript. Then, the mapping relationship between the array and each matrix is represented by the following formula:

[0053] S(m,n) = sz s ,E(m,n) = sz e

[0054] In the formula, S(m,n) represents the starting matrix subscript, corresponding to the m-th row and the n-th column of the matrix; E(m,n) represents the ending matrix subscript, corresponding to the m-th row and the n-th column of the matrix; sz s represents the starting subscript of the array; sz e represents the ending subscript of the array.

[0055] And so on, the mapping relationship between the array and each matrix is obtained.

[0056] In some embodiments, step 104 is implemented in the following manner:

[0057] Determine the core points in the target point cloud. For each core point, perform the following operations:

[0058] Calculate the position coordinates of the core grid where the core point is located based on the coordinates of the core point; calculate the starting matrix subscript and the ending matrix subscript corresponding to the core grid based on the position coordinates of the core grid; determine the starting subscript and the ending subscript of the corresponding array based on the starting matrix subscript and the ending matrix subscript, and take the points in the array between the starting subscript and the ending subscript as the neighboring point cloud of the core point;

[0059] Take the set of each neighboring point cloud as the theoretical sub-point cloud corresponding to the target point cloud.

[0060] In this step, the number of core points is at least one. The position where the core point is located can be determined according to the geometric position or according to the customer requirements, and the present application does not make specific limitations.

[0061] Calculate the corresponding starting matrix subscript and ending matrix subscript (m, n) according to the position coordinates of the core grid through the following formula, where: m = x', n = y' + z'·Ay.

[0062] After determining (m, n), determine the corresponding starting subscript sz of the array according to the mapping relationship s and ending subscript sz e , and take the data pairs stored between sz s ~sz e as the neighboring point cloud of this core point.

[0063] Finally, for step 106, based on the distance between the target point cloud and the theoretical sub-point cloud, determine the channel deformation result at the location of the target point cloud, including:

[0064] For each detection point in the target point cloud, perform the following: find the theoretical point closest to this detection point from the theoretical sub-point cloud, and calculate the distance between this detection point and this theoretical point;

[0065] For each distance, determine whether this distance exceeds the distance threshold. If it exceeds, mark the corresponding detection point as an abnormal point;

[0066] Count the total number of all abnormal points. If the total number exceeds the set number, it is determined that the channel at the location of the target point cloud has abnormal deformation; otherwise, it is determined that the deformation of the channel at the location of the target point cloud is within the allowable range.

[0067] In this step, the theoretical point represents the normal state of the mine tunnel. Therefore, the smaller the distance between the detection point and its corresponding theoretical point, the smaller the deformation of the tunnel. When all distances are less than the distance threshold, it is the best state, indicating that the tunnel is very safe. When most distances are less than the distance threshold, it means that although there is deformation, it does not affect the safety of personnel and production safety. Otherwise, it indicates the existence of risks such as collapse.

[0068] In some embodiments, after determining the channel deformation result at the location of the target point cloud, it further includes:

[0069] Send this deformation result to a pre-set warning LED screen to display the recognition result using the LED screen. When the recognition result is that there is a risk in the mine tunnel, an alarm broadcast can also be sent to remind relevant personnel to quickly handle the risk and avoid casualties and property losses.

[0070] As Figure 2 , Figure 3 shown, the embodiments of the present invention provide a device for identifying abnormal deformation of a coal mine tunnel. The device embodiments can be implemented by software, or by hardware or a combination of software and hardware. From the hardware level, as Figure 2As shown in the figure, it is a hardware architecture diagram of a computing device where an identification device for abnormal deformation of a coal mine shaft passage provided by an embodiment of the present invention is located. In addition to Figure 2 the shown processor, memory, network interface, and non-volatile memory, the computing device where the device is located in the embodiment usually may also include other hardware, such as a forwarding chip responsible for processing packets, etc. Taking software implementation as an example, as Figure 3 shown, as a logically meaningful device, it is formed by the CPU of its computing device reading the corresponding computer program in the non-volatile memory into the memory for operation.

[0071] Please refer to Figure 3 , an embodiment of the present invention provides an identification device for abnormal deformation of a coal mine shaft passage, and the device includes:

[0072] An acquisition unit 300, configured to acquire current video data collected by each camera device in the mine shaft passage during the current monitoring period; the field of view ranges of each camera device do not overlap and cover a preset range of the passage;

[0073] A first extraction unit 302, configured to extract current point cloud data of the passage wall surface within a preset range from the current video data, and the point cloud data is a set of feature points of the mine shaft passage wall surface;

[0074] A second extraction unit 304, configured to determine a target point cloud at a position to be monitored from the current point cloud, and based on the spatial coordinates of the target point cloud, according to the mapping relationship between a pre-constructed array, a starting matrix, and an ending matrix, extract a theoretical sub-point cloud corresponding to the target point cloud from the array; the array is used to store the theoretical overall point cloud of the passage wall surface within a preset range, and the subscripts of each matrix respectively correspond one by one to the coordinates of the points in the point cloud;

[0075] A determination unit 306, configured to determine the passage deformation result at the position where the target point cloud is located based on the distance between the target point cloud and the theoretical sub-point cloud.

[0076] In some embodiments, each camera device uses a camera with a pixel count of not less than 4 million pixels, and each camera is respectively used to collect video signals inside the mine shaft passage.

[0077] In some embodiments, the theoretical overall point cloud is obtained by using a camera device to collect video data within a preset range of the coal mine shaft passage under normal conditions and performing feature point extraction on the video data;

[0078] The theoretical overall point cloud is used to represent the normal state of the mine shaft passage wall surface.

[0079] In some embodiments, the dimension of each matrix is determined based on the boundary of the overall point cloud of the passage wall surface within a preset range and the size of a preset grid, and the calculation formula is as follows:

[0080]

[0081] Wherein, A x 、A y and A z are the dimensions of the matrix in the x, y, and z directions respectively; x max 、x min 、y max 、y min 、z max and z min are the maximum boundary coordinates and minimum boundary coordinates of the overall point cloud in the x, y, and z directions respectively; w x 、w y and w z are the sizes of the grid in the x, y, and z directions respectively; represents rounding up.

[0082] In some embodiments, the mapping relationship between the array and each matrix is determined in the following manner:

[0083] Calculate the position coordinates of the grid corresponding to each point in the theoretical overall point cloud in the entire spatial grid, and calculate the position encoding of the grid based on the position coordinates of the grid;

[0084] Take the encoding of each point in the theoretical overall point cloud and the position encoding of its corresponding grid as a data pair, and store each data pair in the array in descending order of the position encoding. Each data pair corresponds to an array subscript;

[0085] Traverse the array and calculate the starting matrix subscript and ending matrix subscript corresponding to each position encoding in each data pair;

[0086] When the number of the same position encodings is multiple, record the starting subscript and ending subscript of this position encoding in the array; otherwise, take the array subscript corresponding to this position encoding as both the starting subscript and the ending subscript; Let the element position corresponding to the starting matrix subscript store the starting subscript of this position encoding in the array, and let the element position corresponding to the ending matrix subscript store the ending subscript of this position encoding in the array;

[0087] And so on, to obtain the mapping relationship between the array and each matrix.

[0088] In some embodiments, the second extraction unit 304 is used to perform the following operations:

[0089] Determine the core points in the target point cloud, and for each core point, perform:

[0090] Calculate the position coordinates of the core grid where the core point is located based on the coordinates of the core point; calculate the starting matrix subscript and the ending matrix subscript corresponding to the core grid based on the position coordinates of the core grid; determine the starting subscript and the ending subscript of the corresponding array based on the starting matrix subscript and the ending matrix subscript, and use the points in the array between the starting subscript and the ending subscript as the neighboring point cloud of the core point;

[0091] Take the set of each neighboring point cloud as the theoretical sub-point cloud corresponding to the target point cloud.

[0092] In some embodiments, the determination unit 306 is configured to perform the following operations:

[0093] For each detection point in the target point cloud, perform: find the theoretical point closest to the detection point from the theoretical sub-point cloud, and calculate the distance between the detection point and the theoretical point;

[0094] For each distance, determine whether the distance exceeds the distance threshold. If it exceeds, mark the corresponding detection point as an abnormal point;

[0095] Count the total number of all abnormal points. If the total number exceeds the set number, determine that there is abnormal deformation in the channel where the target point cloud is located; otherwise, determine that the deformation of the channel where the target point cloud is located is within the allowable range.

[0096] It should be noted that: the identification device for abnormal deformation of the coal mine shaft passage provided in the above embodiments is only illustrated by the division of the above functional modules. In practical applications, the above functions can be allocated to different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the identification device for abnormal deformation of the coal mine shaft passage provided in the above embodiments and the embodiments of the identification method for abnormal deformation of the coal mine shaft passage belong to the same concept. The specific implementation process is detailed in the method embodiments and will not be repeated here.

[0097] The embodiments of the present application also provide a computer device. Please refer to Figure 3 . The computer device includes a processor and a memory. At least one instruction, at least one program, a code set or an instruction set is stored in the memory. The at least one instruction, at least one program, the code set or the instruction set is loaded and executed by the processor to implement the identification method for abnormal deformation of the coal mine shaft passage provided in each of the above method embodiments.

[0098] An embodiment of the present application further provides a computer-readable storage medium, on which at least one instruction, at least one program, a code set or an instruction set is stored. The at least one instruction, at least one program, the code set or the instruction set is loaded and executed by a processor to implement the method for identifying abnormal deformation of a coal mine roadway provided in each of the above method embodiments.

[0099] An embodiment of the present application further provides a computer program product, which includes a computer program. The processor of the computer device reads the computer program from the computer-readable storage medium, and the processor executes the computer program, so that the computer device executes the method for identifying abnormal deformation of a coal mine roadway described in any one of the above embodiments.

[0100] For convenience of description, when describing the above system or device, it is divided into various modules or units according to functions for description. Of course, when implementing the present application, the functions of each unit can be implemented in one or more software and / or hardware.

[0101] From the description of the above embodiments, those skilled in the art can clearly understand that the present application can be implemented by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disc, etc., and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments of the present application.

[0102] Finally, it should be noted that in this article, relational terms such as first, second, third, and fourth are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover a non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the phrase "including a..." does not exclude the existence of additional identical elements in the process, method, article or device including the element.

[0103] The above are only the preferred embodiments of the present application. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present application, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present application.

Claims

1. A method for identifying abnormal deformation of a coal mine passage, characterized in that: The method comprises: Acquire current video data collected by each camera device in the mine channel during the current monitoring period; the field of view of each camera device does not overlap and covers a preset range of the channel; Extracting current point cloud data of the channel wall within the preset range from the current video data, wherein the point cloud data is a set of feature points of the mine channel wall; Determine the target point cloud at the position to be monitored from the current point cloud, and based on the spatial coordinates of the target point cloud, extract the theoretical sub-point cloud corresponding to the target point cloud from the array according to the mapping relationship between the pre-constructed array, the starting matrix and the ending matrix; the array is used to store the theoretical overall point cloud of the channel wall within the preset range, and the subscripts of each matrix correspond to the coordinates of the midpoint of the point cloud one by one; Based on the distance between the target point cloud and the theoretical sub-point cloud, a channel deformation result at the location of the target point cloud is determined.

2. The method according to claim 1, characterized in that Each of the camera devices uses a camera with no less than 400W pixels, and each of the cameras is used to collect video signals in the mine passage.

3. The method according to claim 1, characterized in that The theoretical overall point cloud is obtained by collecting video data within a preset range of the coal mine passage using the camera device and extracting feature points from the video data when the coal mine passage is normal; The theoretical overall point cloud is used to characterize the normal state of the mine passage wall.

4. The method according to claim 2, characterized in that: The dimension of each matrix is ​​determined based on the boundary of the overall point cloud of the channel wall within the preset range and the size of the preset grid, and the calculation formula is as follows: In the formula, A x , A y and A z are the dimensions of the matrix in the x, y, and z directions respectively; max 、x min ,y max ,y min 、z max and z min are the maximum boundary coordinates and minimum boundary coordinates of the entire point cloud in the x, y, and z directions respectively; w x 、w y and w z are the sizes of the grid in the x, y, and z directions respectively; Indicates rounding up.

5. The method according to claim 4, characterized in that The mapping relationship between the array and each of the matrices is determined in the following manner: Calculating the position coordinates of the grid corresponding to each point in the theoretical overall point cloud in the entire spatial grid, and calculating the position code of the grid based on the position coordinates of the grid; The code of each point in the theoretical overall point cloud and the position code of the corresponding grid are taken as a data pair, and each data pair is stored in the array in descending order of the position code, and each data pair corresponds to an array subscript; Traversing the array, calculating the starting matrix subscript and the ending matrix subscript corresponding to each position code in each data pair; When there are multiple identical position codes, record the starting subscript and ending subscript of the position code in the array; otherwise, use the array subscript corresponding to the position code as both the starting subscript and the ending subscript; Let the element position corresponding to the starting matrix subscript store the starting subscript of the position code in the array, and let the element position corresponding to the ending matrix subscript store the ending subscript of the position code in the array; By analogy, the mapping relationship between the array and each of the matrices is obtained.

6. The method according to claim 5, characterized in that The method of extracting a theoretical sub-point cloud corresponding to the target point cloud from the array based on the spatial coordinates of the target point cloud and according to a mapping relationship between a pre-constructed array, a starting matrix and a terminal matrix comprises: Determine the core points in the target point cloud, and for each core point, perform: Based on the coordinates of the core point, the position coordinates of the core grid where the core point is located are calculated; based on the position coordinates of the core grid, the starting matrix subscript and the ending matrix subscript corresponding to the core grid are calculated; based on the starting matrix subscript and the ending matrix subscript, the starting subscript and the ending subscript of the corresponding array are determined, and the points of the array between the starting subscript and the ending subscript are used as the neighboring point cloud of the core point; Each set of neighboring point clouds is used as a theoretical sub-point cloud corresponding to the target point cloud.

7. The method according to claim 1, characterized in that The determining, based on the distance between the target point cloud and the theoretical sub-point cloud, the channel deformation result at the location of the target point cloud comprises: For each detection point in the target point cloud, the following steps are performed: searching the theoretical point closest to the detection point from the theoretical sub-point cloud, and calculating the distance between the detection point and the theoretical point; For each distance, it is determined whether the distance exceeds the distance threshold. If it exceeds, the corresponding detection point is marked as an abnormal point; The total number of all abnormal points is counted. If the total number exceeds the set number, it is determined that the channel at the location of the target point cloud has abnormal deformation; otherwise, it is determined that the deformation of the channel at the location of the target point cloud is within the allowable range.

8. A device for identifying abnormal deformation of a coal mine passage, characterized in that: The device comprises: An acquisition unit, used to acquire current video data collected by each camera device in the mine channel during the current monitoring period; the field of view of each camera device does not overlap and covers a preset range of the channel; A first extraction unit, configured to extract current point cloud data of the channel wall within the preset range from the current video data, wherein the point cloud data is a set of feature points of the mine channel wall; A second extraction unit is used to determine a target point cloud at a position to be monitored from the current point cloud, and based on the spatial coordinates of the target point cloud, extract a theoretical sub-point cloud corresponding to the target point cloud from the array according to a mapping relationship between a pre-constructed array, a starting matrix and an ending matrix; the array is used to store a theoretical overall point cloud of the channel wall within the preset range, and the subscripts of each matrix correspond one-to-one to the coordinates of a point in the point cloud; A determination unit is used to determine the channel deformation result at the location of the target point cloud based on the distance between the target point cloud and the theoretical sub-point cloud.

9. A computing device, comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, which, when executed in a computer, causes the computer to execute the method according to any one of claims 1 to 7.

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