A mine surface analysis method based on coal mining

Abstract

The present invention relates to the technical field of coal mining, and in particular to a mine face analysis method based on coal mining, comprising: continuously collecting coal mine layer image data, performing color inversion processing on the continuously collected coal mine layer image data, capturing coal mine layer contour images in the coal mine layer image data, and analyzing coal mine layer uniformity based on the coal mine layer contour images in the coal mine layer image data; selecting coal blocks in the mined coal mine, taking the coal blocks as identification targets, identifying coal block contours, analyzing coal block fracture smoothness based on the coal block contours, and analyzing coal mine layer fracture smoothness based on the coal block fracture smoothness; the present invention analyzes the uniformity of the coal mine layer and the smoothness of the coal mine layer fracture through coal mine layer image data collection and coal mine sample contour identification, thereby evaluating the coal mine layer quality in combination with the coal mine layer uniformity and the coal mine layer fracture smoothness, providing accurate reference data for staff, and assisting staff in making more adaptive management operations on the mined coal mine.

Description

Technical Field

[0001] The invention relates to the technical field of coal mining, and in particular to a mine face analysis method based on coal mining. Background Art

[0002] Coal mining refers to the process of extracting coal from underground. After the coal is mined, people will classify the mined coal according to its quality and apply it to different scenarios.

[0003] At present, coal mine quality assessment is often carried out by operating personnel through on-site observation of coal mines. In this way, the assessment results will be affected by many factors such as the assessor's own experience, and the assessment accuracy is poor. In addition, compared with large-area coal mines, coal mines in different regions are prone to quality differences. Therefore, it is considered that the assessment is obviously not suitable for the quality assessment of coal mines, which in turn has a certain impact on subsequent coal mine excavation, classification, processing and other treatments. Summary of the invention

[0004] In view of the above-mentioned shortcomings of the prior art, the present invention provides a mine surface analysis method based on coal mining, which solves the technical problems raised in the above-mentioned background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0006] A mine face analysis method based on coal mining, comprising:

[0007] Continuously collect coal mine layer image data, perform color inversion processing on the continuously collected coal mine layer image data, capture coal mine layer contour images in the coal mine layer image data, and analyze coal mine layer uniformity based on the coal mine layer contour images in the coal mine layer image data;

[0008] Select coal blocks in the mined coal mine, take the coal blocks as the identification target, identify the coal block contour, analyze the coal block fracture smoothness based on the coal block contour, and analyze the coal mine layer fracture smoothness based on the coal block fracture smoothness;

[0009] Obtain the coal mine layer uniformity analysis results and the coal mine layer fracture smoothness analysis results, combine the two sets of analysis results, evaluate the coal mine quality, set a judgment threshold, compare the judgment threshold with the coal mine quality evaluation results, and when the coal mine quality evaluation results are not within the judgment threshold, perform a re-analysis of the coal mine layer uniformity and coal mine layer fracture smoothness, and end when the coal mine quality evaluation results are within the judgment threshold;

[0010] The coal quality analysis logic is expressed as:

[0011]

[0012] Where: ε is the coal quality judgment value; F is the symmetry of the coal layer; Q is the smoothness of the coal layer fracture; η is the correction factor;

[0013] Among them, the larger the coal mine quality judgment value ε is, the better the coal mine quality is, vice versa, the worse the coal mine quality is. The coal mine quality assessment result is not within the judgment threshold. When performing re-analysis of the coal mine layer uniformity and the coal mine layer fracture smoothness, each group of coal mine layer image data is used as an independent analysis target. The coal mine quality analysis results obtained based on each group of analysis targets represent the coal mine quality analysis results of the areas corresponding to each group of coal mine layer image data in the coal mine layer.

[0014] Furthermore, when the coal mine layer image data is collected, the coal mine layer is used as the image center to ensure that the coal mine layer runs through the coal mine layer image data horizontally, and the center line of the coal mine layer image data is in the coal mine layer image area in the coal mine layer image data, and after the color inversion processing is performed on the continuously collected coal mine layer image data, the image pixel is used as the minimum identification unit, and the image center pixel is used as the search target to search for the same pixel in the coal mine layer image data, and the area in the coal mine layer image data where all the found same pixels are located, that is, the coal mine layer image in the coal mine layer image data, and each group of boundary pixels in the coal mine layer image is connected to form a coal mine layer contour image;

[0015] Wherein, when the coal mine layer image data is collected, the coal mine layers in any two groups of continuously collected coal mine layer image data are adjacent to each other, and after the coal mine layer contour images of each group of coal mine layer image data are captured, the coal mine layer contour images of each group are connected to each other based on the collection order of their source coal mine layer image data to form a complete coal mine layer total contour image;

[0016] After color inversion, the same pixel search logic of the central pixel in the coal mine layer image data is expressed as:

[0017]

[0018] Where: s(q,b) is the similarity between pixels q and b; q(i,j) is the grayscale value of pixel q at position (i,j); b(i+1,j+1) is the grayscale value of pixel b at position (i+1,j+1) adjacent to pixel q; γ is a constant;

[0019] When the constant γ≥0 and s(q,b)≥99.99%, the pixels q and b are determined to be the same pixel.

[0020] Furthermore, the coal seam uniformity analysis logic is expressed as:

[0021]

[0022] Where: F is the symmetry of the coal mine layer; n is the set of contour vertices on the coal mine layer contour image; h α is the height of the αth group of vertices; h min is the lowest point in the upper contour vertex set; d α→α+1 is the distance from the αth group of vertices to the α+1th group of vertices; m is the set of contour vertices under the coal mine layer contour image; h β is the height of the βth group of vertices; h max is the highest point in the vertex set of the lower contour; d β→β+1 is the distance from the βth group vertex to the β+1th group vertex; ω 1 ,ω 2 is the weight;

[0023] Among them, the closer the value of coal mine layer uniformity F is to 1, the better the coal mine layer uniformity is. 1 +ω 2 =1,ω 1 Obey, the larger the coal mine layer span, ω 1 The larger the value, the greater the setting logic, ω 2 =1-ω 1 .

[0024] Furthermore, when selecting coal blocks as identification targets in the mined coal mine, at least one group of selected coal blocks is set every ten meters, and each group of coal blocks is in a range of length from 20 to 30 centimeters, a range of width from 20 to 30 centimeters, and a range of height from 20 to 30 centimeters.

[0025] Furthermore, when the outline of the coal block is identified, the coal block is placed on a plane, and a plurality of groups of distance measuring modules are arranged equidistantly in a straight line above the plane, and the distances between the plurality of groups of distance measuring modules and the plane where the coal block is placed are all equal. From a bird's-eye view, the distance measuring modules arranged equidistantly in a straight line cover the surface of the coal block, and each group of distance measuring modules operates synchronously to measure the distance between itself and the surface of the coal block or the surface where the coal block is placed. Based on the distances of each group, line segments representing the distances of each group are drawn on the same plane, and the fixed points of each group of line segments are adjacent to each other and connected to form the outline of the coal block;

[0026] The operation of obtaining the coal block contour is no less than three times, and the more the coal block surface faces, the more operations are performed to obtain the coal block contour, and the total length of the coal block contour is no less than the maximum cross-sectional circumference of the coal block.

[0027] Furthermore, the coal block fracture smoothness analysis logic is expressed as:

[0028]

[0029] Where: Q s is the coal block fracture smoothness performance value based on coal block contour analysis; u is the set of distance measurement points in the coal block contour; ctv is the curvature of the vth group of distance measurement points at the corresponding position on the coal block contour; ct v+1 is the curvature of the corresponding position of the v+1th group of distance measuring points on the coal block contour; n u is the total number of ranging points in set u; The distance between the maximum curvature measuring point in the coal block contour and the coal block placement surface; The distance between the minimum curvature measuring point in the coal block contour and the coal block placement surface; is the performance value of coal block fracture smoothness; Qsx is the performance value of coal block fracture smoothness based on the x-th group of coal block contour analysis;

[0030] Based on the coal block fracture smoothness analysis logic, the fracture smoothness performance value of each group of coal blocks is obtained Further, the fracture smoothness performance value of each group of coal blocks Sum and then average, and the average result is recorded as the smoothness of the coal mine seam fracture.

[0031] Furthermore, the correction factor η is obtained by the following formula:

[0032]

[0033] Where: q is the total amount of selected coal blocks in the coal block selection stage; is the fracture smoothness performance value of the p-th group of coal blocks; λp is the surface reflectivity of the p-th group of coal blocks.

[0034] Furthermore, the coal quality assessment result is not in the determination threshold state, indicating that the coal quality of the area corresponding to each group of coal layer image data in the coal layer is obtained, and the determination threshold is further applied to compare the analysis results of each group, and feedback is given to the user end;

[0035] Among them, the coal mine quality analysis result within the judgment threshold is derived from the coal mine in the coal mine layer area corresponding to the coal mine layer image data, that is, the area with better coal mine quality compared with the coal mine as a whole.

[0036] Compared with the known public technology, the technical solution provided by the present invention has the following beneficial effects:

[0037] The present invention provides a mine face analysis method based on coal mining. During the execution of the method, the uniformity of the coal mine layer and the smoothness of the coal mine layer fracture are analyzed through coal mine layer image data collection and coal mine sample contour recognition, so as to evaluate the coal mine quality in combination with the uniformity of the coal mine layer and the smoothness of the coal mine layer fracture, and provide accurate reference data for coal mine mining staff, assist coal mine mining staff, and make more adaptive management operations for the mined coal mine;

[0038] At the same time, when this method is executed, the cost of manual analysis is low and it is less affected by the size of the coal mine layer. It can be easily applied to coal mine mining operations of different sizes, making the coal mining process more intelligent and efficient. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0040] Figure 1 It is a schematic diagram of a process of a mine surface analysis method based on coal mining;

[0041] Figure 2 It is a logical schematic diagram of coal mine layer image data collection in the present invention;

[0042] Figure 3 It is a schematic diagram of the logic of coal block contour acquisition in the present invention;

[0043] Figure 4 It is a schematic diagram of the outline of the coal block in the present invention;

[0044] The numbers in the figure represent: 1. coal block; 2. ranging module. DETAILED DESCRIPTION

[0045] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0046] The present invention will be further described below in conjunction with the embodiments.

[0047] Embodiment 1:

[0048] A mine surface analysis method based on coal mining in this embodiment, such as Figure 1 As shown, including:

[0049] Continuously collect coal mine layer image data, perform color inversion processing on the continuously collected coal mine layer image data, capture coal mine layer contour images in the coal mine layer image data, and analyze coal mine layer uniformity based on the coal mine layer contour images in the coal mine layer image data;

[0050] Select coal blocks in the mined coal mine, take the coal blocks as the identification target, identify the coal block contour, analyze the coal block fracture smoothness based on the coal block contour, and analyze the coal mine layer fracture smoothness based on the coal block fracture smoothness;

[0051] Obtain the coal mine layer uniformity analysis results and the coal mine layer fracture smoothness analysis results, combine the two sets of analysis results, evaluate the coal mine quality, set a judgment threshold, compare the judgment threshold with the coal mine quality evaluation results, and when the coal mine quality evaluation results are not within the judgment threshold, perform a re-analysis of the coal mine layer uniformity and coal mine layer fracture smoothness, and end when the coal mine quality evaluation results are within the judgment threshold;

[0052] The logic of coal mine quality analysis is expressed as:

[0053]

[0054] Where: ε is the coal quality judgment value; F is the coal layer symmetry; Q is the coal layer fracture smoothness; η is the correction factor;

[0055] Among them, the larger the coal mine quality judgment value ε is, the better the coal mine quality is, and vice versa, the worse the coal mine quality is. The coal mine quality assessment result is not within the judgment threshold. When performing the re-analysis of the coal mine layer uniformity and the coal mine layer fracture smoothness, each group of coal mine layer image data is used as an independent analysis target. The coal mine quality analysis results obtained based on each group of analysis targets represent the coal mine quality analysis results of the corresponding areas of each group of coal mine layer image data in the coal mine layer;

[0056] The correction factor η is obtained by the following formula:

[0057]

[0058] Where: q is the total amount of selected coal blocks in the coal block selection stage; is the fracture smoothness performance value of the p-th group of coal blocks; p is the surface reflectivity of the p-th group of coal blocks;

[0059] When collecting coal mine layer image data, the coal mine layer is used as the image center to ensure that the coal mine layer runs through the coal mine layer image data horizontally, and the center line of the coal mine layer image data is in the coal mine layer image area in the coal mine layer image data, and after color inversion processing is performed on the continuously collected coal mine layer image data, the image pixel is used as the minimum identification unit, and the image center pixel is used as the search target to search for the same pixel in the coal mine layer image data, and the area in the coal mine layer image data where all the found same pixels are located, that is, the coal mine layer image in the coal mine layer image data, and each group of boundary pixels in the coal mine layer image is connected to form a coal mine layer contour image;

[0060] Wherein, when the coal mine layer image data is collected, the coal mine layers in any two groups of continuously collected coal mine layer image data are adjacent to each other, and after the coal mine layer contour images of each group of coal mine layer image data are captured, the coal mine layer contour images of each group are connected to each other based on the collection order of their source coal mine layer image data to form a complete coal mine layer total contour image;

[0061] When identifying the outline of a coal block, the coal block is placed on a plane, and several groups of distance measuring modules are deployed equidistantly in a straight line above it, and the distances between the several groups of distance measuring modules and the plane where the coal block is placed are equal. From a bird's-eye view, the distance measuring modules deployed equidistantly in a straight line cover the surface of the coal block. Each group of distance measuring modules runs synchronously to measure the distance between itself and the surface of the coal block or the surface where the coal block is placed. Based on the distance of each group, line segments representing the distance of each group are drawn on the same plane, and the fixed points of each group of line segments are adjacent and connected to each other to form the outline of the coal block;

[0062] The operation of obtaining the coal block contour is no less than three times, and the more the coal block surface faces, the more operations are performed to obtain the coal block contour, and the total length of the coal block contour is no less than the maximum cross-sectional circumference of the coal block.

[0063] In this embodiment, through the analysis of coal mine layer uniformity and coal mine layer fracture smoothness, a global and high-precision quality assessment of the coal mine layer surface is provided, thereby assisting coal mining personnel to make more adaptive management of the coal mining process based on the data obtained by executing the method in this embodiment;

[0064] At the same time, the present embodiment provides a specified coal mine quality analysis logic, further limits the coal mine quality analysis method, and gradually converts the coal mine layer image data into digital data for easy reading by the staff, so that the method in the embodiment can be used more quickly by the staff in the application stage;

[0065] See also Figure 2 As shown, the figure gives an example of a group of coal mine layer image data, and the dotted line on the coal mine layer image data in the figure is the center line of the image data, which is located on the coal mine layer image in the coal mine layer image data;

[0066] See also Figure 3 As shown in the figure, the collection logic of the coal block contour is further demonstrated. Several distance measuring modules are arranged as shown in the figure, equidistant and vertically parallel above the coal block. When the distance measuring modules are operated synchronously, each group of distance measuring modules obtains a group of distances. The distances measured by each group of distance measuring modules are Figure 4 In the figure, the coal block profile required for the method in Example 1 is formed (i.e. Figure 4 The curves in the window defined by the two groups of dotted lines are shown in the figure);

[0067] It should be noted that based on Figure 3Under the recorded positions and postures of the ranging modules and the coal blocks, the denser the arrangement of the ranging modules and the better the ranging accuracy, the more accurate the constructed coal block contour will be, and the higher the accuracy of the coal seam fracture smoothness analysis results will be.

[0068] Embodiment 2:

[0069] In terms of specific implementation, based on Example 1, this example refers to Figure 1 A mine surface analysis method based on coal mining in Example 1 is further specifically described:

[0070] After color inversion, the same pixel search logic of the central pixel in the coal mine layer image data is expressed as:

[0071]

[0072] Where: s(q,b) is the similarity between pixels q and b; q(i,j) is the grayscale value of pixel q at position (i,j); b(i+1,j+1) is the grayscale value of pixel b at position (i+1,j+1) adjacent to pixel q; γ is a constant;

[0073] When the constant γ≥0 and s(q,b)≥99.99%, the pixels q and b are determined to be the same pixel.

[0074] In this embodiment, by setting the same pixel search logic of the central pixel in the above-mentioned coal mine layer image data, the similarity between each pixel in the coal mine layer image data is calculated, thereby providing necessary data for further acquisition of the coal mine layer image and the coal mine layer contour image in the coal mine layer image data, ensuring the smooth extraction of the coal mine layer image and the coal mine layer contour image, and providing necessary data support for the further execution of the method in Example 1.

[0075] like Figure 1 As shown in the figure, the coal mine layer uniformity analysis logic is expressed as:

[0076]

[0077] Where: F is the symmetry of the coal mine layer; n is the set of contour vertices on the coal mine layer contour image; h α is the height of the αth group of vertices; h min is the lowest point in the upper contour vertex set; d α→α+1 is the distance from the αth group of vertices to the α+1th group of vertices; m is the set of contour vertices under the coal mine layer contour image; h β is the height of the βth group of vertices; h max is the highest point in the vertex set of the lower contour; d β→β+1 is the distance from the βth group vertex to the β+1th group vertex; ω 1 ,ω2 is the weight;

[0078] Among them, the closer the value of coal mine layer uniformity F is to 1, the better the coal mine layer uniformity is. 1 +ω 2 =1,ω 1 Obey, the larger the coal mine layer span, ω 1 The larger the value, the greater the setting logic, ω 2 =1-ω 1 .

[0079] By calculating the above-mentioned coal mine layer uniformity analysis logic formula, the coal mine layer uniformity analysis results are represented in digital form, which is convenient for users to read and provides necessary logic operation data support for further execution of the coal mine quality analysis logic in the method in Example 1.

[0080] like Figure 1 As shown, when selecting coal blocks as identification targets in a mined coal mine, at least one group of selected coal blocks is set every ten meters, and each group of coal blocks is in a range of length from 20 to 30 cm, a range of width from 20 to 30 cm, and a range of height from 20 to 30 cm.

[0081] Through the above settings, the specifications of the coal block from which the coal block contour is extracted are further limited, so that the extracted coal block contour is more convenient for executing the method in Example 1;

[0082] It should be noted that the limitation on the selection range of coal block specifications in the above settings can be adaptively changed and adjusted according to the actual application scenario of the method in Example 1.

[0083] Embodiment 3:

[0084] In terms of specific implementation, based on Example 1, this example refers to Figure 1 A mine surface analysis method based on coal mining in Example 1 is further described in detail:

[0085] The logical expression of coal block fracture smoothness analysis is:

[0086]

[0087] Where: Q s is the coal block fracture smoothness performance value based on coal block contour analysis; u is the set of distance measurement points in the coal block contour; ct v is the curvature of the vth group of distance measurement points at the corresponding position on the coal block contour; ct v+1 is the curvature of the corresponding position of the v+1th group of distance measuring points on the coal block contour; n u is the total number of ranging points in set u; The distance between the maximum curvature measuring point in the coal block contour and the coal block placement surface; The distance between the minimum curvature measuring point in the coal block contour and the coal block placement surface; Q is the fracture smoothness performance value of coal block; sx is the coal block fracture smoothness performance value based on the x-th group of coal block contour analysis;

[0088] Based on the coal block fracture smoothness analysis logic, the fracture smoothness performance value of each group of coal blocks is obtained Further, the fracture smoothness performance value of each group of coal blocks was Sum and then average, and the average result is recorded as the smoothness of the coal mine seam fracture.

[0089] In this embodiment, the coal block port smoothness analysis logic is further limited by the above-mentioned coal block fracture smoothness analysis logic, which provides necessary data support for the execution of the coal mine quality analysis logic in Example 1, and outputs the coal block fracture smoothness in such a digital form, which facilitates user reading.

[0090] like Figure 1 As shown, the coal mine quality assessment result is not in the judgment threshold state, which means that after the coal mine quality of the corresponding area of ​​each group of coal mine layer image data in the coal mine layer is obtained, the judgment threshold is further applied to compare the analysis results of each group, and feedback is given to the user end;

[0091] Among them, the coal mine quality analysis result within the judgment threshold is derived from the coal mine in the coal mine layer area corresponding to the coal mine layer image data, that is, the area with better coal mine quality compared with the coal mine as a whole.

[0092] Through the above settings, further logic is provided so that each coal mine area in the coal mine layer can be further distributedly analyzed, thereby realizing the coal quality analysis of each area in the coal mine layer by the method in Example 1, and based on this, providing more data references for coal mining workers, and then making adaptive plans for subsequent coal mining work.

[0093] In summary, during the execution of the method in the above embodiment, the symmetry of the coal mine layer and the smoothness of the coal mine layer fracture are analyzed through coal mine layer image data collection and coal mine sample contour recognition, so as to evaluate the coal mine quality of the coal mine layer in combination with the symmetry of the coal mine layer and the smoothness of the coal mine layer fracture, and provide accurate reference data for coal mine mining workers, assist coal mine mining workers to make more adaptive management operations for the mined coal mines. At the same time, when the method is executed, the cost of manual analysis is low, and it is less affected by the size of the coal mine layer. It can be conveniently applied to coal mine layer mining operations of different scales, making the coal mining process more intelligent and efficient.

[0094] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A mine surface analysis method based on coal mining, characterized in that: include: Continuously collect coal mine layer image data, perform color inversion processing on the continuously collected coal mine layer image data, capture coal mine layer contour images in the coal mine layer image data, and analyze coal mine layer uniformity based on the coal mine layer contour images in the coal mine layer image data; Select coal blocks in the mined coal mine, take the coal blocks as the identification target, identify the coal block contour, analyze the coal block fracture smoothness based on the coal block contour, and analyze the coal mine layer fracture smoothness based on the coal block fracture smoothness; Obtain the coal mine layer uniformity analysis results and the coal mine layer fracture smoothness analysis results, combine the two sets of analysis results, evaluate the coal mine quality, set a judgment threshold, compare the judgment threshold with the coal mine quality evaluation results, and when the coal mine quality evaluation results are not within the judgment threshold, perform a re-analysis of the coal mine layer uniformity and coal mine layer fracture smoothness, and end when the coal mine quality evaluation results are within the judgment threshold; The coal quality analysis logic is expressed as: ； Where: is the coal quality determination value; Symmetry of coal seams; It is the smoothness of the fracture of the coal mine layer; Correction factor; Among them, the coal mine quality judgment value The larger the value is, the better the coal quality is. On the contrary, the worse the coal quality is. The coal quality assessment result is not within the judgment threshold. When performing the re-analysis of the coal layer uniformity and the coal layer fracture smoothness, each group of coal layer image data is used as an independent analysis target. The coal quality analysis results obtained based on each group of analysis targets represent the coal quality analysis results of the corresponding areas of each group of coal layer image data in the coal layer. The coal mine layer uniformity analysis logic is expressed as: ； Where: Symmetry of coal seams; is the set of contour vertices on the coal mine layer contour image; For the The height of the group's vertices; It is the lowest point in the vertex set of the upper contour; For the Group vertices to The distance between group vertices; is the set of contour vertices under the coal mine layer contour image; For the The height of the group's vertices; It is the highest point in the vertex set of the lower contour; For the Group vertices to The distance between group vertices; , is the weight; Among them, coal seam uniformity The closer the value is to 1, the better the symmetry of the coal mine layer is. , Obey, the larger the coal mine layer span, The larger the value, the higher the setting logic. ; The coal block fracture smoothness analysis logic is expressed as: ； Where: is the coal block fracture smoothness performance value based on coal block contour analysis; is the set of distance measurement points in the coal block contour; is the curvature of the corresponding position of the vth group of distance measuring points on the coal block contour; is the curvature of the corresponding position of the v+1th group of distance measuring points on the coal block contour; For collection The total amount of mid-range measuring points; The distance between the maximum curvature measuring point in the coal block contour and the coal block placement surface; The distance between the minimum curvature measuring point in the coal block contour and the coal block placement surface; It is the performance value of coal block fracture smoothness; is the coal block fracture smoothness performance value based on the x-th group of coal block contour analysis; Based on the coal block fracture smoothness analysis logic, the fracture smoothness performance value of each group of coal blocks is obtained , and further the fracture smoothness performance value of each group of coal blocks Sum and average, and the average result is recorded as the smoothness of the coal mine layer fracture; The correction factor The formula is as follows: ； Where: In the coal selection stage, the total amount of coal is selected; is the fracture smoothness performance value of the p-th group of coal blocks; is the surface reflectivity of the pth group of coal blocks.

2. A mine surface analysis method based on coal mining according to claim 1, characterized in that: When the coal mine layer image data is collected, the coal mine layer is taken as the image center to ensure that the coal mine layer runs through the coal mine layer image data horizontally, and the center line of the coal mine layer image data is in the coal mine layer image area in the coal mine layer image data, and after the color inversion processing is performed on the continuously collected coal mine layer image data, the image pixel is taken as the minimum identification unit, and the image center pixel is used as the search target to search for the same pixel in the coal mine layer image data, and the area in the coal mine layer image data where all the found same pixels are located, that is, the coal mine layer image in the coal mine layer image data, and each group of boundary pixels in the coal mine layer image is connected to form a coal mine layer contour image; Among them, when the coal mine layer image data is collected, the coal mine layers in any two groups of continuously collected coal mine layer image data are adjacent to each other. After the coal mine layer contour images of each group of coal mine layer image data are captured, each group of coal mine layer contour images are connected to each other based on the collection order of their source coal mine layer image data to form a complete coal mine layer overall contour image.

3. A mine surface analysis method based on coal mining according to claim 2, characterized in that: After color inversion, the same pixel search logic of the central pixel in the coal mine layer image data is expressed as: ； Where: is the similarity between pixels q and b; is the gray value of pixel q at position (i, j); is the gray value of pixel b at position (i+1, j+1) adjacent to pixel q; is a constant; Among them, the constant ≥0, When ≥99.99%, pixels q and b are determined to be the same pixel.

4. A mine surface analysis method based on coal mining according to claim 1, characterized in that: When selecting coal blocks as identification targets in a mined coal mine, set up at least one group of selected coal blocks every ten meters, and each group of coal blocks is in a range of length from 20 to 30 centimeters, a range of width from 20 to 30 centimeters, and a range of height from 20 to 30 centimeters.

5. A mine surface analysis method based on coal mining according to claim 1, characterized in that: When identifying the outline of the coal block, the coal block is placed on a plane, and several groups of distance measuring modules are arranged equidistantly in a straight line above it, and the distances between the several groups of distance measuring modules and the plane where the coal block is placed are all equal. From a bird's-eye view, the distance measuring modules arranged equidistantly in a straight line cover the surface of the coal block. Each group of distance measuring modules runs synchronously to measure the distance between itself and the surface of the coal block or the surface where the coal block is placed. Based on the distance of each group, line segments representing the distance of each group are drawn on the same plane, and the fixed points of each group of line segments are adjacent to each other and connected to form the outline of the coal block; The operation of obtaining the coal block contour is no less than three times, and the more the coal block surface faces, the more operations are performed to obtain the coal block contour, and the total length of the coal block contour is no less than the maximum cross-sectional circumference of the coal block.

6. A mine surface analysis method based on coal mining according to claim 1, characterized in that: The coal quality assessment result is not in the judgment threshold state, which means that after the coal quality of the corresponding area of ​​each group of coal layer image data in the coal layer is obtained, the judgment threshold is further applied to compare the analysis results of each group, and feedback is given to the user end; Among them, the coal mine quality analysis result within the judgment threshold is derived from the coal mine in the coal mine layer area corresponding to the coal mine layer image data, that is, the area with better coal mine quality compared with the coal mine as a whole.

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