Coal rock identification method and system based on full-vision-field microscopic image

Abstract

The invention provides a coal rock recognition method and system based on a full-vision-field microscopic image, and relates to the technical field of coal rock recognition, and the method comprises the steps: obtaining a coal rock microscopic image; extracting a coincident region of the adjacent coal rock microscopic images after preprocessing, performing feature point matching on the coincident region by adopting a full-vision-field feature point matching image splicing method, and finding out matching point pairs in the coincident region; matching point pairs of different images are transformed and aligned through a geometric change estimation method, image splicing is carried out through a weighted average image fusion method, and a full-vision-field image is generated; inputting the full-vision-field image into a component identification model, extracting multi-scale semantic features of the full-vision-field image, reconstructing spatial information of the multi-scale semantic features, and outputting an identified coal rock maceral category result; and performing quantitative analysis on the identified coal rock macerals, obtaining the proportion of each component, and performing summary statistics. According to the invention, the coal rock type can be intelligently identified on the aspect of multi-scale characteristics.

Description

Technical Field

[0001] The present disclosure relates to the technical field of coal-rock identification, and in particular to a coal-rock identification method and system based on full-view microscopic images. Background Art

[0002] The statements in this section merely provide background information related to the present disclosure and do not necessarily constitute prior art.

[0003] Coal rock type identification is essential for assessing coal quality, predicting its combustion characteristics, and determining its economic value. This technology helps understand the impact of different coal characteristics on coal's calorific value, combustion efficiency, and ash generation, thereby optimizing coal utilization and processing, reducing environmental pollution, and providing a scientific basis for coal mining and resource management. Traditional coal rock type identification relies on microscopic observation and analysis, and manual judgment of coal types such as lignite, bituminous coal, and anthracite. Although this method can provide detailed identification information, the identification process is highly dependent on the operator's experience and judgment and is easily affected by subjective factors. In addition, manual identification is slow and labor-intensive when processing a large number of samples, and there may be inconsistencies in standards and results between different operators.

[0004] With the continuous development of artificial intelligence technology, computer vision has shown broad application prospects in the field of coal and rock microscopic image recognition. It can automatically process and analyze a large number of microscopic images, significantly improve recognition speed and efficiency, and reduce human errors. With the help of advanced algorithms such as deep learning, recognition accuracy and reliability have been greatly improved.

[0005] In recent years, existing technologies have widely applied image segmentation technology in coal and rock microscopic image recognition, and have made significant progress. The semantic segmentation method can accurately distinguish different coal and rock components by classifying each pixel in the microscopic image, thereby realizing the automatic recognition of coal and rock types, greatly improving the accuracy and efficiency of recognition. This technology not only significantly reduces the workload of traditional manual annotation, but also effectively avoids human errors and supports the automatic processing of large-scale samples.

[0006] However, there is still a lack of standardized coal and rock microscopic image datasets, which limits the versatility and reliability of the algorithm. Secondly, a deep learning model capable of quantitative analysis of coal and rock microscopic images has not yet been developed. In addition, the intelligent coal and rock identification system needs to be further improved. Summary of the invention

[0007] In order to solve the above-mentioned problems, the present invention proposes a coal and rock identification method and system based on full-view microscopic images, which uses full-view feature point matching image stitching to match the overlapping areas of adjacent coal and rock microscopic images, and splices them to form a full-view digital image. Based on the image segmentation algorithm, the spliced ​​full-view image is automatically segmented to achieve accurate identification of coal and rock microscopic components, and quantitative analysis of the identified coal and rock microscopic components, thereby achieving automatic identification of coal and rock types and facilitating subsequent data retrieval, management and application.

[0008] According to some embodiments, the present disclosure adopts the following technical solutions: The coal and rock identification method based on full-view microscopic images includes: Acquire coal and rock microscopic images, and pre-process the coal and rock microscopic images; Extract the overlapping areas of adjacent coal and rock microscopic images after preprocessing, use the full-view feature point matching image stitching method to match the feature points of the overlapping areas, and find the matching point pairs in the overlapping areas; The matching point pairs of different images are transformed and aligned using the geometric change estimation method, and the image fusion method of weighted average is used to stitch the images to generate a full-view image. The full-view image is input into the component recognition model, the multi-scale semantic features of the full-view image are extracted, and the spatial information of the multi-scale semantic features is reconstructed to output the classification results of the recognized coal and rock microscopic components; Conduct quantitative analysis on the identified coal and rock microscopic components, obtain the proportion of each component and summarize the statistics; The type of coal rock can be determined based on the distribution and proportion of microscopic components.

[0009] According to some embodiments, the present disclosure adopts the following technical solutions: The coal and rock identification system based on full-view microscopic images includes: An image preprocessing module is used to obtain and preprocess the coal and rock microscopic images; The image stitching module is used to extract the overlapping areas of adjacent coal and rock microscopic images after preprocessing, and use the full-view feature point matching image stitching method to match the feature points of the overlapping areas, and find the matching point pairs in the overlapping areas; the matching point pairs of different images are transformed and aligned using the geometric change estimation method, and the image fusion method of weighted average is used to stitch the images to generate a full-view image; The microscopic component recognition module is used to input the full-view image into the component recognition model, extract the multi-scale semantic features of the full-view image, reconstruct the spatial information of the multi-scale semantic features, and output the recognized coal and rock microscopic component category results; The quantitative analysis module is used to quantitatively analyze the identified coal and rock microscopic components, obtain the proportion of each component and summarize the statistics; The coal-rock intelligent identification module is used to determine the type of coal-rock based on the distribution and proportion of each component.

[0010] According to some embodiments, the present disclosure adopts the following technical solutions: A non-transitory computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by a processor, the coal-rock identification method based on full-view microscopic images is implemented.

[0011] According to some embodiments, the present disclosure adopts the following technical solutions: An electronic device comprises: a processor, a memory and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory so that the electronic device executes the coal and rock identification method based on full-view microscopic images.

[0012] Compared with the prior art, the present invention has the following beneficial effects: The disclosed coal-rock identification method based on full-view microscopic images can quickly remove noise, enhance contrast, and other processes through an automated image preprocessing method, thereby significantly improving image quality and reducing the time for manual intervention. In addition, a large number of images can be processed quickly and accurately, greatly improving identification efficiency and work speed.

[0013] The disclosed coal-rock identification method based on full-view microscopic images uses a full-view feature point matching image stitching method to match feature points in overlapping areas, find matching point pairs in overlapping areas, transform and align matching point pairs of different images using a geometric change estimation method, and use a weighted average image fusion method to stitch images, integrating microscopic images under multiple fields of view into a large image, thereby achieving a wider range of analysis. Through a deep learning model, microscopic components can be extracted and analyzed on multi-scale features to identify complex coal-rock structures.

[0014] The coal and rock identification method based on full-view microscopic images disclosed in the present invention can accurately calculate the proportion of each type of component through an automated quantitative analysis method, realize quantitative analysis, and thus realize automated identification of coal and rock types, reduce errors in manual calculations, and reduce labor intensity.

[0015] The disclosed coal-rock identification method based on full-view microscopic images integrates data storage and management methods, which can effectively manage and preserve analysis data for a long time, facilitating subsequent query and research. In addition, by updating the model or adding training data, the system has good scalability and can adapt to diverse coal-rock intelligent identification needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The accompanying drawings constituting a part of the present disclosure are used to provide a further understanding of the present disclosure. The illustrative embodiments of the present disclosure and their descriptions are used to explain the present disclosure and do not constitute an improper limitation on the present disclosure.

[0017] Figure 1 This is a flow chart of a method for identifying coal and rock based on full-view microscopic images according to an embodiment of the present disclosure; Figure 2 This is a diagram of a coal and rock identification system based on full-viewing-field microscopic images according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0018] The present disclosure is further described below in conjunction with the accompanying drawings and embodiments.

[0019] It should be noted that the following detailed descriptions are all illustrative and are intended to provide further explanation of the present disclosure. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present disclosure belongs.

[0020] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present disclosure. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0021] Example 1 In one embodiment of the present disclosure, a method for identifying coal and rock based on full-view microscopic images is provided, comprising: step 1: obtaining a coal and rock microscopic image, and preprocessing the coal and rock microscopic image; Step 2: extract the overlapping areas of adjacent coal and rock microscopic images after preprocessing, use the full-view feature point matching image stitching method to match the feature points of the overlapping areas, and find the matching point pairs in the overlapping areas; Step 3: Use the geometric change estimation method to transform and align the matching point pairs of different images, use the weighted average image fusion method to stitch the images, and generate a full-view image; Step 4: Input the full-view image into the component recognition model, extract the multi-scale semantic features of the full-view image, reconstruct the spatial information of the multi-scale semantic features, and output the recognized coal and rock microscopic component classification results; Step 5: Quantitatively analyze the identified coal rock microscopic components, obtain the proportion of each component and summarize the statistics.

[0022] Step 6: Determine the type of coal rock based on the distribution and proportion of microscopic components.

[0023] Among them, lignite has a low vitrinite content and contains mostly inertinite and minerals; bituminous coal has a high vitrinite content and a compact structure; anthracite has the highest vitrinite content, a high reflectivity, and very little inertinite.

[0024] If the vitrinite content is >80%, the reflectivity is high and the inertinite is very small, it can be identified as anthracite; if the vitrinite content is 40-80% and the inertinite is moderate, it is bituminous coal; if the vitrinite content is <40%, and there are more minerals and inertinite, it is lignite.

[0025] As an embodiment, the specific implementation process of a coal-rock identification method based on full-view microscopic images disclosed in the present invention is as follows: Step 1: Obtaining a coal-rock microscopic image and preprocessing the coal-rock microscopic image; Specifically, a coal-rock microscopic image is acquired, and the coal-rock microscopic image is preprocessed, including image denoising and contrast enhancement processing. Step 2: extract the overlapping areas of adjacent coal and rock microscopic images after preprocessing, use the full-view feature point matching image stitching method to match the feature points of the overlapping areas, and find the matching point pairs in the overlapping areas; Specifically, it includes: 1) Feature point detection: The SIFT feature point detection algorithm is used to find unique and repeatable feature points in each image. This method can extract feature points and descriptors in the image under scale and rotation invariance for subsequent matching processes:

[0026] By adjusting the standard deviation To change the degree of blur, through the multiple relationship k To generate images of different scales, usually k is set to ,in s is the number of image layers in each scale group, is the Gaussian difference function at different scales, is the Gaussian blur function, is the input image.

[0027] 2) Feature point matching: Use Euclidean distance to calculate the distance between the descriptors of each feature point in adjacent images, and select the feature point with the smallest distance as the matching pair. This method can pair the feature points in adjacent images and find the matching point pairs in the overlapping area:

[0028] in, is the descriptor of two feature points.

[0029] Step 3: Use the geometric change estimation method to transform and align the matching point pairs of different images, use the weighted average image fusion method to stitch the images, and generate a full-view image; Specifically, 1) Geometric transformation estimation: Use homography transformation to determine the geometric transformation relationship between adjacent coal and rock microscopic images, and obtain the transformation matrix so that the feature points in one coal and rock microscopic image are aligned with the feature points in another coal and rock microscopic image through transformation:

[0030] in, and are the point coordinates in the original image and the transformed image respectively, H for The homography matrix of .

[0031] 2) Image stitching: Apply the estimated transformation matrix to transform and align the images, and then use the weighted average image fusion method to reduce stitching gaps and color differences:

[0032] in, is the fused image, , For the images involved in stitching, , is the weighting coefficient.

[0033] Repeat steps 2 and 3 for all adjacent coal and rock microscopic images, gradually stitching the images together to generate a full-view image.

[0034] Step 4: Input the full-view image into the component recognition model, extract the multi-scale semantic features of the full-view image, reconstruct the spatial information of the multi-scale semantic features, and output the recognized coal and rock microscopic component category results; Specifically, the component recognition model adopts the DeepLabV3+ network based on the encoder-decoder architecture, and uses the component recognition model to automatically segment the spliced ​​full-view image to achieve accurate recognition of the microscopic components of coal and rock.

[0035] First, the multi-scale semantic features of the image are extracted in the encoder, and then the spatial information of these features is reconstructed in the decoder, so that the predicted image output by the network is consistent with the original input image in spatial resolution.

[0036] The input data of the component recognition model is the full-view image of the microscopic component image of coal and rock. In the encoder, the backbone network is responsible for preliminary feature extraction. The backbone network selects the first five convolution modules of ResNet-101, of which the first module is a 7x7 convolution layer, and the remaining four modules are stacked by different numbers of residual bottleneck structures. Residual learning can effectively prevent the gradient vanishing and gradient exploding phenomena as the number of network layers increases, thereby helping the network to more comprehensively extract local detail features in the image.

[0037] However, local detail features alone are not sufficient to fully identify microscopic components. To this end, an atrous spatial pyramid pooling (ASPP) module is introduced after the backbone network to extract more statistically significant deep semantic information.

[0038] ASPP replaces traditional convolution with dilated convolution, which can significantly expand the receptive field without increasing model parameters and complexity, thereby reducing the loss of spatial resolution. This means that dilated convolution can efficiently extract dense features on images of any resolution. On this basis, ASPP uses four parallel dilated convolution layers, including a 1x1 convolution layer and three 3x3 convolution layers, with expansion rates set to 6, 12, and 18 respectively, and combines global average pooling and convolution fusion to obtain large-scale, multi-scale semantic features. This multi-scale feature contains global and local information of objects in the image, which can help the model simultaneously capture the information brought by the size differences of different microscopic components, helping the model to better understand the content of the image.

[0039] However, the feature information extracted by ASPP is relatively abstract, and the number of channels is large, lacking local detail information including image edge features. Therefore, before these features are input into the decoder, they first need to be reduced through a 1x1 convolution layer to reduce the number of channels, and then fused with the low-level image features extracted by the backbone network to supplement the missing local detail information. For the fused feature map, a 3x3 convolution is used to further reduce the dimension of the feature channel, and then the feature map is restored to the same size as the original input image through bilinear interpolation upsampling to generate the segmentation result of the microscopic component. The segmentation result is actually a set of multi-channel microscopic component category attribution probability maps, each channel corresponds to a recognizable microscopic component category, and each pixel value on the channel represents the probability that the pixel belongs to a certain category.

[0040] Furthermore, the microscopic components in the coal rock are identified to include vitrinite, inertinite, exinite, background resin, etc.

[0041] Step 5: Quantitatively analyze the identified coal and rock microscopic components, obtain the proportion of each component and summarize the statistics.

[0042] Specifically, the identified coal rock microscopic components are quantitatively analyzed to provide the proportion of each component and other relevant data to support further research and analysis. The segmented images are statistically classified and the total number of pixels in each category is calculated. N categories, for each category i , respectively count the number of pixels belonging to this category in the segmented image .

[0043] Calculate the total number of all pixels in the entire image ; The proportion of each component Calculated by the following formula:

[0044] in, Indicates i The proportion of class components, represents the number of pixels of the i-th component, Indicates the total number of pixels in the image.

[0045] The proportion of each type of microscopic component is summarized as the final statistical result, and the type of coal is inferred based on the distribution and proportion of each component: if the vitrinite content is >80%, the reflectivity is high and the inertinite is very small, it can be identified as anthracite; if the vitrinite content is 40-80% and the inertinite is moderate, it is bituminous coal; if the vitrinite content is <40% and there are more minerals and inertinite, it is lignite.

[0046] Finally, the recognition results are automatically saved in the coal and rock microscopic image database to facilitate subsequent data retrieval, management and application.

[0047] Example 2 In one embodiment of the present disclosure, a coal-rock identification system based on full-view microscopic images is provided, comprising: An image preprocessing module is used to obtain and preprocess the coal and rock microscopic images; The image stitching module is used to extract the overlapping areas of adjacent coal and rock microscopic images after preprocessing, and use the full-view feature point matching image stitching method to match the feature points of the overlapping areas, and find the matching point pairs in the overlapping areas; the matching point pairs of different images are transformed and aligned using the geometric change estimation method, and the image fusion method of weighted average is used to stitch the images to generate a full-view image; The microscopic component recognition module is used to input the full-view image into the component recognition model, extract the multi-scale semantic features of the full-view image, reconstruct the spatial information of the multi-scale semantic features, and output the recognized coal and rock microscopic component category results; The quantitative analysis module is used to quantitatively analyze the identified coal and rock microscopic components, obtain the proportion of each component and summarize the statistics.

[0048] The coal rock intelligent identification module is used to infer the type of coal based on the distribution and proportion of each component: if the vitrinite content is >80%, the reflectivity is high and the inertinite is very small, it can be identified as anthracite; if the vitrinite content is 40-80% and the inertinite is moderate, it is bituminous coal; if the vitrinite content is <40%, and there are more minerals and inertinite, it is lignite.

[0049] Furthermore, it also includes a data storage and management module for automatically saving the identification and analysis results into the coal rock microscopic component image database to facilitate subsequent data retrieval, management and application.

[0050] As an embodiment, a method for using a coal-rock identification system based on a full-view microscopic image includes: A. Obtain microscopic images of coal rocks.

[0051] B. Use the image preprocessing module to preprocess the coal rock microscopic images, including denoising and contrast enhancement processing.

[0052] C. Use the image stitching module to stitch the pre-processed coal and rock microscopic images to generate a full-view image.

[0053] D. Use the micro-component identification module and the deep learning model to identify the micro-components in coal rocks, such as vitrinite, inertinite, exinite, background resin, etc.

[0054] E. Use the quantitative analysis module to conduct quantitative analysis on the identified coal rock microscopic components and provide the proportion of each component and other relevant data. F. Determine the type of coal rock based on the distribution and proportion of microscopic components.

[0055] G. Automatically save the identification results to the coal and rock microscopic image data set to facilitate subsequent data retrieval, management and application.

[0056] H. Repeat steps AG until all coal rock microcomponents are identified.

[0057] Example 3 In one embodiment of the present disclosure, a non-transitory computer-readable storage medium is provided, wherein the non-transitory computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by a processor, the coal and rock identification method based on full-view microscopic images is implemented.

[0058] Example 4 In one embodiment of the present disclosure, an electronic device is provided, including: a processor, a memory, and a computer program; wherein the processor is connected to the memory, and the computer program is stored in the memory. When the electronic device is running, the processor executes the computer program stored in the memory, so that the electronic device executes the coal and rock identification method based on full-field microscopic images.

[0059] The present disclosure is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present disclosure. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0060] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0061] Although the above describes the specific implementation methods of the present disclosure in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present disclosure. Technical personnel in the relevant field should understand that on the basis of the technical solution of the present disclosure, various modifications or variations that can be made by those skilled in the art without creative work are still within the scope of protection of the present disclosure.

Claims

1. A coal-rock identification method based on full-view microscopic images, characterized in that: include: Acquire coal and rock microscopic images, and pre-process the coal and rock microscopic images; Extract the overlapping areas of adjacent coal and rock microscopic images after preprocessing, use the full-view feature point matching image stitching method to match the feature points of the overlapping areas, and find the matching point pairs in the overlapping areas; The matching point pairs of different images are transformed and aligned using the geometric change estimation method, and the image fusion method of weighted average is used to stitch the images to generate a full-view image. The full-view image is input into the component recognition model, the multi-scale semantic features of the full-view image are extracted, and the spatial information of the multi-scale semantic features is reconstructed to output the classification results of the recognized coal and rock microscopic components; Conduct quantitative analysis on the identified coal and rock microscopic components, obtain the proportion of each component and summarize the statistics; The type of coal rock can be determined based on the distribution and proportion of microscopic components.

2. The method for identifying coal and rock based on full-view microscopic images according to claim 1, characterized in that: After extracting the overlapping areas of adjacent coal and rock microscopic images after preprocessing, the SIFT feature point detection algorithm is first used to find unique and repeatable feature points in each image, and then the feature points and descriptors in the coal and rock microscopic component images are extracted under scale and rotation invariance: in, is the Gaussian difference function at different scales, is the Gaussian blur function, is the input image.

3. The method for identifying coal and rock based on full-view microscopic images according to claim 1, characterized in that: The full-view feature point matching image stitching method is used to match the feature points in the overlapping area, including: using Euclidean distance to calculate the distance between the descriptors of each feature point in adjacent coal and rock microscopic images, selecting the feature point with the smallest distance as the matching pair, thereby pairing the feature points in adjacent coal and rock microscopic images to find the matching point pairs in the overlapping area.

4. The method for identifying coal and rock based on full-view microscopic images according to claim 1, characterized in that: The matching point pairs of different images are transformed and aligned using a geometric change estimation method, including: using homography transformation to determine the geometric transformation relationship between adjacent coal and rock microscopic images, and obtaining a transformation matrix so that the feature points in one coal and rock microscopic image are aligned with the feature points in another coal and rock microscopic image through transformation.

5. The method for identifying coal and rock based on full-view microscopic images according to claim 1, characterized in that: Use the weighted average image fusion method to stitch images and generate a full-view image, including: transforming and aligning the images using the estimated transformation matrix, and using the weighted average image fusion method to reduce stitching gaps and color differences: in, is the fused image, , For the images involved in stitching, , is the weighting coefficient.

6. The method for identifying coal and rock based on full-view microscopic images according to claim 1, characterized in that: The component recognition model adopts the DeepLabV3+ network based on the encoder-decoder architecture, and uses the component recognition model to automatically segment the spliced ​​full-view image. First, preliminary feature extraction is performed in the encoder's backbone network. After the backbone network, a dilated spatial pyramid pooling module is introduced to extract the deep semantic features of the image. ASPP adopts four parallel dilated convolutional layers and combines global average pooling and convolutional fusion to obtain multi-scale semantic features. Then, the spatial information of these features is reconstructed in the decoder, and finally the microscopic component category attribution probability map output by the network is consistent with the original input image in spatial resolution.

7. The method for identifying coal and rock based on full-view microscopic images according to claim 1, characterized in that: The identified coal rock microscopic components are quantitatively analyzed, including: pixel classification statistics of the output microscopic component category attribution probability map, calculating the total number of pixels in each category, summarizing the proportion of each type of microscopic component as the final statistical result, and judging the type of coal rock based on the distribution and proportion of the microscopic components.

8. The coal-rock identification system based on full-view microscopic images is characterized by: include: An image preprocessing module is used to obtain and preprocess the coal and rock microscopic images; The image stitching module is used to extract the overlapping areas of adjacent coal and rock microscopic images after preprocessing, and use the full-view feature point matching image stitching method to match the feature points of the overlapping areas, and find the matching point pairs in the overlapping areas; the matching point pairs of different images are transformed and aligned using the geometric change estimation method, and the image fusion method of weighted average is used to stitch the images to generate a full-view image; The microscopic component recognition module is used to input the full-view image into the component recognition model, extract the multi-scale semantic features of the full-view image, reconstruct the spatial information of the multi-scale semantic features, and output the recognized coal and rock microscopic component category results; The quantitative analysis module is used to quantitatively analyze the identified coal and rock microscopic components, obtain the proportion of each component and summarize the statistics. The coal-rock intelligent identification module is used to infer the type of coal based on the distribution and proportion of each component.

9. A non-transitory computer-readable storage medium, characterized in that: The non-transitory computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by the processor, the coal and rock identification method based on full-view microscopic images as described in any one of claims 1 to 7 is implemented.

10. An electronic device, characterized in that: include: A processor, a memory and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory so that the electronic device executes the coal rock identification method based on full-view microscopic images as described in any one of claims 1-7.

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