Monitoring management method and system for coal sample assay process

Abstract

The invention relates to the technical field of coal sample management, and discloses a monitoring management method and system for a coal sample test process, and the method comprises the steps: carrying out the image shooting of a target monitoring management coal sample, obtaining a monitoring management coal sample image, carrying out the gray conversion, and obtaining a monitoring management coal sample gray image; the method comprises the following steps: acquiring a monitored and managed coal sample standard grayscale image, comparing pixel points in the monitored and managed coal sample grayscale image with pixel points in the monitored and managed coal sample standard grayscale image, determining a plurality of pixel point deviation sets, and calculating a crack doubt coefficient; curve fitting is carried out on all pixel points, and the coal sample difference degree is calculated; adjusting the crack doubt coefficient based on the adjustment coefficient to obtain a target crack doubt coefficient; according to the target crack doubt coefficient, whether the target monitoring management coal sample has cracks is judged, comprehensive, accurate and efficient monitoring of the cracks on the surface of the coal sample is achieved, the monitoring efficiency and the monitoring reliability are improved, and powerful technical support is provided for coal quality control and optimization.

Description

Technical Field

[0001] The present invention relates to the technical field of coal sample management, and in particular to a monitoring management method and system for a coal sample testing process. Background Art

[0002] Coal sampling is a key part of coal quality control and assessment, which involves detailed analysis of the chemical composition, physical properties and process performance of coal. These test results are crucial to determine the grade, use, combustion efficiency and environmental impact of coal.

[0003] In the coal industry, the surface cracks of coal samples are one of the important indicators for evaluating coal quality and combustion performance. The presence of surface cracks will affect key parameters such as coal calorific value, combustion efficiency, and environmental emissions. Therefore, accurate monitoring and analysis of the crack characteristics on the surface of coal samples is crucial to ensuring coal quality. However, the traditional method of monitoring surface cracks in coal samples mainly relies on manual visual inspection, which is not only time-consuming and labor-intensive, but also difficult to achieve high-precision and high-repeatability monitoring. With the development of science and technology, an advanced surface crack monitoring and management method is urgently needed to improve the monitoring accuracy and efficiency of surface crack characteristics of coal samples. Summary of the invention

[0004] The embodiment of the present invention provides a monitoring management method and system for a coal sample testing process, which realizes comprehensive, accurate and efficient monitoring of surface cracks of coal samples, and improves monitoring efficiency and monitoring reliability.

[0005] In order to achieve the above object, the present invention provides a monitoring and management method for a coal sample testing process, comprising: Determine a target monitoring and management coal sample, capture an image of the target monitoring and management coal sample to obtain a monitoring and management coal sample image, and perform grayscale conversion on the monitoring and management coal sample image to obtain a monitoring and management coal sample grayscale image; Acquire a monitoring and management coal sample standard grayscale image corresponding to the target monitoring and management coal sample, compare pixel points in the monitoring and management coal sample grayscale image with pixel points in the monitoring and management coal sample standard grayscale image, determine multiple pixel point deviation sets, and calculate the crack suspicion coefficient of the target monitoring and management coal sample based on the pixel point deviation sets; Performing curve fitting on the pixel points in the grayscale image of the monitored coal sample and the pixel points in the standard grayscale image of the monitored coal sample, respectively, and calculating the coal sample difference of the target monitored coal sample based on the curve fitting result; An adjustment coefficient of the target monitoring and management coal sample is set based on the coal sample difference, and the crack suspicion coefficient is adjusted based on the adjustment coefficient to obtain a target crack suspicion coefficient of the target monitoring and management coal sample; According to the relationship between the target crack doubt coefficient and the preset target crack doubt coefficient, it is judged whether the target monitored coal sample has cracks. If so, an alarm is issued.

[0006] Further, when grayscale conversion is performed on the monitoring and management coal sample image to obtain the monitoring and management coal sample grayscale image, it includes: Mapping each color pixel in the monitoring and management coal sample image to a mapping point in a three-dimensional color space, wherein the three-dimensional color space includes a red channel, a green channel, and a blue channel; Applying a weight vector to each mapping point in the three-dimensional color space to generate a corresponding grayscale value; All grayscale values ​​are recombined and transformed to obtain the monitoring and management coal sample grayscale image.

[0007] Furthermore, when comparing the pixel points in the grayscale image of the monitored coal sample with the pixel points in the standard grayscale image of the monitored coal sample, determining a plurality of pixel deviation sets, and calculating the crack suspicion coefficient of the target monitored coal sample based on the pixel deviation sets, it includes: Calculate the pixel difference between each pixel in the monitoring and management coal sample grayscale image and each pixel in the monitoring and management coal sample standard grayscale image; Calculate the pixel mean of all pixel difference values, and generate a first pixel deviation set according to all pixel difference values ​​less than the pixel mean value; Generate a second pixel deviation set according to all pixel difference values ​​equal to the pixel mean; Generate a third pixel deviation set according to all pixel difference values ​​greater than the pixel mean; Calculate the first sub-crack doubt coefficient and the second sub-crack doubt coefficient of the target monitoring and management coal sample according to the first pixel point deviation set, the second pixel point deviation set and the third pixel point deviation set; The pixel point deviation set is calculated based on the first sub-crack doubt coefficient and the second sub-crack doubt coefficient to calculate the crack doubt coefficient of the target monitored and managed coal sample.

[0008] Further, when calculating the first sub-crack suspicion coefficient and the second sub-crack suspicion coefficient of the target monitoring and management coal sample according to the first pixel point deviation set, the second pixel point deviation set and the third pixel point deviation set, it includes: The first sub-crack doubt coefficient of the target monitoring and management coal sample is calculated according to the following formula; ; Among them, a1 is the first sub-crack doubt coefficient, s1 is the first calculation coefficient, f1 is the weight corresponding to the first pixel deviation set, n1 is the number of pixel difference values ​​in the first pixel deviation set, g i is the difference value of the i-th pixel in the first pixel deviation set, g1 i is the weight corresponding to the ith pixel difference value, f2 is the weight corresponding to the second pixel deviation set, n2 is the number of pixel difference values ​​in the second pixel deviation set, and p j is the jth pixel difference value in the second pixel deviation set, p1 j is the weight corresponding to the j-th pixel difference value, f3 is the weight corresponding to the third pixel deviation set, n3 is the number of pixel difference values ​​in the third pixel deviation set, q k The k-th pixel difference value in the third pixel deviation set, q1 k is the weight corresponding to the k-th pixel difference value; The second sub-crack doubt coefficient of the target monitoring and management coal sample is calculated according to the following formula; ; Among them, a2 is the second sub-sub-crack doubt coefficient, and s2 is the second calculation coefficient.

[0009] Further, when calculating the pixel point deviation set based on the first sub-fracture doubt coefficient and the second sub-fracture doubt coefficient to calculate the fracture doubt coefficient of the target monitoring and management coal sample, it includes: The crack suspicion coefficient of the target monitoring and management coal sample is calculated according to the following formula: ; Among them, a is the crack suspicion coefficient of the target monitored coal sample.

[0010] Further, when curve fitting is performed on the pixel points in the grayscale image of the monitored coal sample and the pixel points in the standard grayscale image of the monitored coal sample respectively, and the coal sample difference of the target monitored coal sample is calculated based on the curve fitting result, it includes: Performing curve fitting on pixel points in the grayscale image of the monitored coal sample to obtain a first fitting curve; Performing curve fitting on the pixel points in the standard grayscale image of the monitored coal sample to obtain a second fitting curve; Extracting intersections of the first fitting curve and the second fitting curve, and counting the number of intersections of all intersections; Extract a pixel value from each intersection and calculate the corresponding pixel value and value; Calculate the pixel value mean of the pixel values ​​corresponding to all intersection points, and delete the pixel values ​​that are smaller than the pixel value mean; Count the number of remaining intersections, extract a pixel value from the remaining intersections, and calculate the corresponding remaining pixel value and value; The coal sample difference of the target monitored and managed coal sample is calculated based on the number of intersections, the pixel values ​​and values, the remaining number of intersections and the remaining pixel values ​​and values.

[0011] Further, when calculating the coal sample difference of the target monitoring and management coal sample according to the number of intersections, the pixel value and value, the remaining number of intersections and the remaining pixel value and value, it includes: The coal sample difference of the target monitoring and management coal sample is calculated according to the following formula: ; Among them, r is the coal sample difference of the target monitoring and management coal sample, t1 is the number of intersection points, t2 is the remaining number of intersection points, u1 is the pixel value and value, and u2 is the remaining pixel value and value.

[0012] Further, when the adjustment coefficient of the target monitoring and management coal sample is set based on the coal sample difference, and the crack suspicion coefficient is adjusted based on the adjustment coefficient to obtain the target crack suspicion coefficient of the target monitoring and management coal sample, it includes: Presetting a first preset coal sample difference degree and a second preset coal sample difference degree; Presetting a first preset adjustment coefficient, a second preset adjustment coefficient, and a third preset adjustment coefficient; When the coal sample difference is less than the first preset coal sample difference, the product value of the first preset adjustment coefficient and the crack suspicion coefficient is calculated and used as the target crack suspicion coefficient of the target monitoring and management coal sample; When the coal sample difference is greater than or equal to the first preset coal sample difference and less than the second preset coal sample difference, the product value of the second preset adjustment coefficient and the crack suspicion coefficient is calculated and used as the target crack suspicion coefficient of the target monitoring and management coal sample; When the coal sample difference is greater than or equal to the second preset coal sample difference, the product value of the third preset adjustment coefficient and the crack suspicion coefficient is calculated and used as the target crack suspicion coefficient of the target monitoring and management coal sample.

[0013] Further, when judging whether the target monitoring and management coal sample has cracks according to the relationship between the target crack suspicion coefficient and the preset target crack suspicion coefficient, it includes: When the target crack doubt coefficient is less than the preset target crack doubt coefficient, it is judged that the target monitoring and management coal sample does not have cracks; When the target crack doubt coefficient is greater than or equal to the preset target crack doubt coefficient, it is determined that cracks exist in the target monitored and managed coal sample.

[0014] In order to achieve the above object, the present invention also provides a monitoring and management system for the coal sample testing process, comprising: The first module is used to determine a target monitoring and management coal sample, capture an image of the target monitoring and management coal sample to obtain a monitoring and management coal sample image, and perform grayscale conversion on the monitoring and management coal sample image to obtain a monitoring and management coal sample grayscale image; The second module is used to obtain a standard grayscale image of a monitoring and management coal sample corresponding to the target monitoring and management coal sample, compare the pixel points in the grayscale image of the monitoring and management coal sample with the pixel points in the standard grayscale image of the monitoring and management coal sample, determine a plurality of pixel point deviation sets, and calculate the crack suspicion coefficient of the target monitoring and management coal sample based on the pixel point deviation sets; The third module is used to perform curve fitting on the pixel points in the grayscale image of the monitored coal sample and the pixel points in the standard grayscale image of the monitored coal sample, and calculate the coal sample difference of the target monitored coal sample based on the curve fitting result; The fourth module is used to set the adjustment coefficient of the target monitoring and management coal sample based on the coal sample difference, and adjust the crack suspicion coefficient based on the adjustment coefficient to obtain the target crack suspicion coefficient of the target monitoring and management coal sample; The fifth module is used to determine whether the target monitored coal sample has cracks based on the relationship between the target crack suspicion coefficient and the preset target crack suspicion coefficient. If so, an alarm is issued.

[0015] Compared with the prior art, the present invention has the following beneficial effects: The present invention discloses a monitoring and management method and system for a coal sample testing process, which comprises the following steps: capturing an image of a target monitoring and management coal sample to obtain an image of the monitoring and management coal sample, performing grayscale conversion to obtain a grayscale image of the monitoring and management coal sample; obtaining a standard grayscale image of the monitoring and management coal sample, comparing pixel points in the grayscale image of the monitoring and management coal sample with pixel points in the standard grayscale image of the monitoring and management coal sample, determining a plurality of pixel point deviation sets, and calculating a crack suspicion coefficient; performing curve fitting on all pixel points to calculate the coal sample difference; adjusting the crack suspicion coefficient based on an adjustment coefficient to obtain a target crack suspicion coefficient; judging whether a target monitoring and management coal sample has cracks according to the target crack suspicion coefficient, and realizing comprehensive, accurate and efficient monitoring of cracks on the surface of the coal sample, improving monitoring efficiency and monitoring reliability, and providing strong technical support for coal quality control and optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings: Figure 1 A schematic flow chart of a monitoring and management method for a coal sample testing process in an embodiment of the present invention is shown; Figure 2 The schematic diagram of the structure of the monitoring and management system for the coal sample testing process in the embodiment of the present invention is shown. DETAILED DESCRIPTION

[0017] The specific implementation of the present invention is further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0018] In the description of the present application, it should be understood that the terms "center", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present application.

[0019] The terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, unless otherwise specified, "plurality" means two or more.

[0020] In the description of this application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0021] The following is a description of preferred embodiments of the present invention with reference to the accompanying drawings.

[0022] like Figure 1 As shown, an embodiment of the present invention discloses a monitoring and management method for a coal sample testing process, comprising: S110: determining a target monitoring and management coal sample, capturing an image of the target monitoring and management coal sample to obtain a monitoring and management coal sample image, and performing grayscale conversion on the monitoring and management coal sample image to obtain a monitoring and management coal sample grayscale image; In some embodiments of the present application, when grayscale conversion is performed on the monitoring and management coal sample image to obtain the monitoring and management coal sample grayscale image, the process includes: Mapping each color pixel in the monitoring and management coal sample image to a mapping point in a three-dimensional color space, wherein the three-dimensional color space includes a red channel, a green channel, and a blue channel; Applying a weight vector to each mapping point in the three-dimensional color space to generate a corresponding grayscale value; All grayscale values ​​are recombined and transformed to obtain the monitoring and management coal sample grayscale image.

[0023] In this embodiment, a weight vector is applied to each mapping point in the three-dimensional color space (i.e., the color information of each pixel). This weight vector is not fixed, but is dynamically adjusted according to the color information of the pixel. For example, for the dark areas commonly seen in coal sample images, the weight of the blue channel may be increased because coal is usually dark gray or black, and the blue channel dominates these colors. In this way, the actual color and texture of the coal sample can be more accurately reflected, and the quality of the grayscale image can be improved.

[0024] In this embodiment, all generated grayscale values ​​are recombined to form a final monitoring and management coal sample grayscale image.

[0025] The beneficial effect of the above technical solution is that the present invention improves the quality and accuracy of monitoring and managing coal sample grayscale images. By dynamically adjusting the weight vector and optimizing using artificial intelligence, a more efficient and accurate grayscale conversion process can be achieved.

[0026] S120: obtaining a standard grayscale image of a monitoring and management coal sample corresponding to the target monitoring and management coal sample, comparing pixel points in the grayscale image of the monitoring and management coal sample with pixel points in the standard grayscale image of the monitoring and management coal sample, determining a plurality of pixel point deviation sets, and calculating a crack suspicion coefficient of the target monitoring and management coal sample based on the pixel point deviation sets; In some embodiments of the present application, when comparing the pixel points in the grayscale image of the monitored coal sample with the pixel points in the standard grayscale image of the monitored coal sample, determining a plurality of pixel deviation sets, and calculating the crack suspicion coefficient of the target monitored coal sample based on the pixel deviation sets, the method includes: Calculate the pixel difference between each pixel in the monitoring and management coal sample grayscale image and each pixel in the monitoring and management coal sample standard grayscale image; Calculate the pixel mean of all pixel difference values, and generate a first pixel deviation set according to all pixel difference values ​​less than the pixel mean value; Generate a second pixel deviation set according to all pixel difference values ​​equal to the pixel mean; Generate a third pixel deviation set according to all pixel difference values ​​greater than the pixel mean; Calculate the first sub-crack doubt coefficient and the second sub-crack doubt coefficient of the target monitoring and management coal sample according to the first pixel point deviation set, the second pixel point deviation set and the third pixel point deviation set; The pixel point deviation set is calculated based on the first sub-crack doubt coefficient and the second sub-crack doubt coefficient to calculate the crack doubt coefficient of the target monitored and managed coal sample.

[0027] In this embodiment, the pixel point difference value is calculated based on the one-to-one correspondence between the pixel points in the monitoring and management coal sample grayscale image and the monitoring and management coal sample standard grayscale image.

[0028] The beneficial effect of the above technical scheme is: the present invention calculates the first sub-crack doubt coefficient and the second sub-crack doubt coefficient of the target monitored and managed coal sample according to the first pixel point deviation set, the second pixel point deviation set and the third pixel point deviation set, thereby ensuring the calculation accuracy of the first sub-crack doubt coefficient and the second sub-crack doubt coefficient, and further laying the foundation for the calculation of the crack doubt coefficient.

[0029] In some embodiments of the present application, when calculating the first sub-crack suspicion coefficient and the second sub-crack suspicion coefficient of the target monitoring and management coal sample according to the first pixel deviation set, the second pixel deviation set and the third pixel deviation set, it includes: The first sub-crack doubt coefficient of the target monitoring and management coal sample is calculated according to the following formula; ; Among them, a1 is the first sub-crack doubt coefficient, s1 is the first calculation coefficient, f1 is the weight corresponding to the first pixel deviation set, n1 is the number of pixel difference values ​​in the first pixel deviation set, g i is the difference value of the i-th pixel in the first pixel deviation set, g1 i is the weight corresponding to the ith pixel difference value, f2 is the weight corresponding to the second pixel deviation set, n2 is the number of pixel difference values ​​in the second pixel deviation set, and p j is the jth pixel difference value in the second pixel deviation set, p1 j is the weight corresponding to the j-th pixel difference value, f3 is the weight corresponding to the third pixel deviation set, n3 is the number of pixel difference values ​​in the third pixel deviation set, q kThe k-th pixel difference value in the third pixel deviation set, q1 k is the weight corresponding to the k-th pixel difference value; The second sub-crack doubt coefficient of the target monitoring and management coal sample is calculated according to the following formula; ; Among them, a2 is the second sub-sub-crack doubt coefficient, and s2 is the second calculation coefficient.

[0030] In some embodiments of the present application, when calculating the pixel point deviation set based on the first sub-fracture suspicion coefficient and the second sub-fracture suspicion coefficient to calculate the fracture suspicion coefficient of the target monitoring and management coal sample, it includes: The crack suspicion coefficient of the target monitoring and management coal sample is calculated according to the following formula: ; Among them, a is the crack suspicion coefficient of the target monitored coal sample.

[0031] The beneficial effect of the above technical scheme is: the present invention calculates the crack doubt coefficient of the target monitored coal sample based on the first sub-crack doubt coefficient and the second sub-crack doubt coefficient by calculating the pixel point deviation set, thereby realizing high-precision monitoring of surface cracks of the coal sample, reducing the time and cost of manual operation and improving monitoring efficiency.

[0032] S130: performing curve fitting on the pixel points in the grayscale image of the monitored coal sample and the pixel points in the standard grayscale image of the monitored coal sample, respectively, and calculating the coal sample difference of the target monitored coal sample based on the curve fitting result; In some embodiments of the present application, when performing curve fitting on the pixel points in the grayscale image of the monitoring and management coal sample and the pixel points in the standard grayscale image of the monitoring and management coal sample respectively, and calculating the coal sample difference of the target monitoring and management coal sample based on the curve fitting result, it includes: Performing curve fitting on pixel points in the grayscale image of the monitored coal sample to obtain a first fitting curve; Performing curve fitting on the pixel points in the standard grayscale image of the monitored coal sample to obtain a second fitting curve; Extracting intersections of the first fitting curve and the second fitting curve, and counting the number of intersections of all intersections; Extract a pixel value from each intersection and calculate the corresponding pixel value and value; Calculate the pixel value mean of the pixel values ​​corresponding to all intersection points, and delete the pixel values ​​that are smaller than the pixel value mean; Count the number of remaining intersections, extract a pixel value from the remaining intersections, and calculate the corresponding remaining pixel value and value; The coal sample difference of the target monitored and managed coal sample is calculated based on the number of intersections, the pixel values ​​and values, the remaining number of intersections and the remaining pixel values ​​and values.

[0033] In this embodiment, each intersection point corresponds to the same pixel value.

[0034] In this embodiment, the intersection points corresponding to the pixel values ​​that are smaller than the average pixel value are deleted.

[0035] The beneficial effect of the above technical solution is: the present invention calculates the coal sample difference of the target monitored and managed coal sample according to the number of intersections, pixel values ​​and values, the number of remaining intersections and the remaining pixel values ​​and values, thereby ensuring the calculation accuracy of the coal sample difference and providing a reliable basis for the dynamic adjustment of the fracture doubt coefficient.

[0036] In some embodiments of the present application, when calculating the coal sample difference of the target monitoring and management coal sample according to the number of intersections, the pixel value and value, the remaining number of intersections and the remaining pixel value and value, it includes: The coal sample difference of the target monitoring and management coal sample is calculated according to the following formula: ; Among them, r is the coal sample difference of the target monitoring and management coal sample, t1 is the number of intersection points, t2 is the remaining number of intersection points, u1 is the pixel value and value, and u2 is the remaining pixel value and value.

[0037] S140: setting an adjustment coefficient of the target monitoring and management coal sample based on the coal sample difference, and adjusting the fracture suspicion coefficient based on the adjustment coefficient to obtain a target fracture suspicion coefficient of the target monitoring and management coal sample; In some embodiments of the present application, when the adjustment coefficient of the target monitoring and management coal sample is set based on the coal sample difference, and the crack suspicion coefficient is adjusted based on the adjustment coefficient to obtain the target crack suspicion coefficient of the target monitoring and management coal sample, it includes: Presetting a first preset coal sample difference degree and a second preset coal sample difference degree; Presetting a first preset adjustment coefficient, a second preset adjustment coefficient, and a third preset adjustment coefficient; When the coal sample difference is less than the first preset coal sample difference, the product value of the first preset adjustment coefficient and the crack suspicion coefficient is calculated and used as the target crack suspicion coefficient of the target monitoring and management coal sample; When the coal sample difference is greater than or equal to the first preset coal sample difference and less than the second preset coal sample difference, the product value of the second preset adjustment coefficient and the crack suspicion coefficient is calculated and used as the target crack suspicion coefficient of the target monitoring and management coal sample; When the coal sample difference is greater than or equal to the second preset coal sample difference, the product value of the third preset adjustment coefficient and the crack suspicion coefficient is calculated and used as the target crack suspicion coefficient of the target monitoring and management coal sample.

[0038] In this embodiment, the first preset coal sample difference is smaller than the second preset coal sample difference. The first preset coal sample difference is preferably 4, and the second preset coal sample difference is preferably 8.

[0039] In this embodiment, the first preset adjustment coefficient is smaller than the second preset adjustment coefficient and smaller than the third preset adjustment coefficient. The first preset adjustment coefficient is preferably 0.85, the second preset adjustment coefficient is preferably 1.15, and the third preset adjustment coefficient is preferably 1.25.

[0040] The beneficial effect of the above technical solution is: the present invention selects the corresponding adjustment coefficient according to the relationship between the coal sample difference, the first preset coal sample difference and the second preset coal sample difference, adjusts the crack doubt coefficient, further ensures the accuracy of crack monitoring and avoids errors.

[0041] S150: judging whether the target monitored coal sample has cracks according to the relationship between the target crack doubt coefficient and the preset target crack doubt coefficient, and if so, issuing an alarm.

[0042] In some embodiments of the present application, when judging whether the target monitoring and management coal sample has cracks according to the relationship between the target crack suspicion coefficient and the preset target crack suspicion coefficient, it includes: When the target crack doubt coefficient is less than the preset target crack doubt coefficient, it is judged that the target monitoring and management coal sample does not have cracks; When the target crack doubt coefficient is greater than or equal to the preset target crack doubt coefficient, it is determined that cracks exist in the target monitored and managed coal sample.

[0043] In this embodiment, the preset target crack doubt coefficient is preferably 12.

[0044] The beneficial effects of the above technical solution are: the present invention realizes comprehensive, accurate and efficient monitoring of surface cracks of coal samples, improves monitoring efficiency and monitoring reliability, and provides strong technical support for coal quality control and optimization.

[0045] In order to further explain the technical idea of ​​the present invention, the technical solution of the present invention is now described in combination with specific application scenarios.

[0046] Correspondingly, such as Figure 2 As shown, the present application also provides a monitoring and management system for the coal sample testing process, including: The first module is used to determine a target monitoring and management coal sample, capture an image of the target monitoring and management coal sample to obtain a monitoring and management coal sample image, and perform grayscale conversion on the monitoring and management coal sample image to obtain a monitoring and management coal sample grayscale image; The second module is used to obtain a standard grayscale image of a monitoring and management coal sample corresponding to the target monitoring and management coal sample, compare the pixel points in the grayscale image of the monitoring and management coal sample with the pixel points in the standard grayscale image of the monitoring and management coal sample, determine a plurality of pixel point deviation sets, and calculate the crack suspicion coefficient of the target monitoring and management coal sample based on the pixel point deviation sets; The third module is used to perform curve fitting on the pixel points in the grayscale image of the monitored coal sample and the pixel points in the standard grayscale image of the monitored coal sample, and calculate the coal sample difference of the target monitored coal sample based on the curve fitting result; The fourth module is used to set the adjustment coefficient of the target monitoring and management coal sample based on the coal sample difference, and adjust the crack suspicion coefficient based on the adjustment coefficient to obtain the target crack suspicion coefficient of the target monitoring and management coal sample; The fifth module is used to determine whether the target monitored coal sample has cracks based on the relationship between the target crack suspicion coefficient and the preset target crack suspicion coefficient. If so, an alarm is issued.

[0047] In the description of the above embodiments, specific features, structures, materials or characteristics may be combined in a suitable manner in any one or more embodiments or examples.

[0048] Although the present invention has been described above with reference to the embodiments, various modifications may be made thereto and parts thereof may be replaced with equivalents without departing from the scope of the present invention. In particular, as long as there is no structural conflict, the various features in the embodiments disclosed by the present invention may be used in combination with each other in any manner, and the fact that these combinations are not fully described in this specification is only for the sake of omitting space and saving resources.

[0049] Those skilled in the art can understand that the above are only preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions recorded in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A monitoring and management method for coal sample testing process, characterized in that: include: Determine a target monitoring and management coal sample, capture an image of the target monitoring and management coal sample to obtain a monitoring and management coal sample image, and perform grayscale conversion on the monitoring and management coal sample image to obtain a monitoring and management coal sample grayscale image; Acquire a monitoring and management coal sample standard grayscale image corresponding to the target monitoring and management coal sample, compare pixel points in the monitoring and management coal sample grayscale image with pixel points in the monitoring and management coal sample standard grayscale image, determine multiple pixel point deviation sets, and calculate the crack suspicion coefficient of the target monitoring and management coal sample based on the pixel point deviation sets; Performing curve fitting on the pixel points in the grayscale image of the monitored coal sample and the pixel points in the standard grayscale image of the monitored coal sample, respectively, and calculating the coal sample difference of the target monitored coal sample based on the curve fitting result; An adjustment coefficient of the target monitoring and management coal sample is set based on the coal sample difference, and the crack suspicion coefficient is adjusted based on the adjustment coefficient to obtain a target crack suspicion coefficient of the target monitoring and management coal sample; According to the relationship between the target crack doubt coefficient and the preset target crack doubt coefficient, it is judged whether the target monitored coal sample has cracks. If so, an alarm is issued.

2. The monitoring and management method for coal sample testing process according to claim 1, characterized in that: When grayscale conversion is performed on the monitoring and management coal sample image to obtain the monitoring and management coal sample grayscale image, the method includes: Mapping each color pixel in the monitoring and management coal sample image to a mapping point in a three-dimensional color space, wherein the three-dimensional color space includes a red channel, a green channel, and a blue channel; Applying a weight vector to each mapping point in the three-dimensional color space to generate a corresponding grayscale value; All grayscale values ​​are recombined and transformed to obtain the monitoring and management coal sample grayscale image.

3. The monitoring and management method for coal sample testing process according to claim 1, characterized in that: When comparing the pixel points in the grayscale image of the monitored coal sample with the pixel points in the standard grayscale image of the monitored coal sample, determining a plurality of pixel deviation sets, and calculating the crack suspicion coefficient of the target monitored coal sample based on the pixel deviation sets, the method includes: Calculate the pixel difference between each pixel in the monitoring and management coal sample grayscale image and each pixel in the monitoring and management coal sample standard grayscale image; Calculate the pixel mean of all pixel difference values, and generate a first pixel deviation set according to all pixel difference values ​​less than the pixel mean value; Generate a second pixel deviation set according to all pixel difference values ​​equal to the pixel mean; Generate a third pixel deviation set according to all pixel difference values ​​greater than the pixel mean; Calculate the first sub-crack doubt coefficient and the second sub-crack doubt coefficient of the target monitoring and management coal sample according to the first pixel point deviation set, the second pixel point deviation set and the third pixel point deviation set; The pixel point deviation set is calculated based on the first sub-crack doubt coefficient and the second sub-crack doubt coefficient to calculate the crack doubt coefficient of the target monitored and managed coal sample.

4. The monitoring and management method for coal sample testing process according to claim 3 is characterized in that: When calculating the first sub-crack suspicion coefficient and the second sub-crack suspicion coefficient of the target monitoring and management coal sample according to the first pixel point deviation set, the second pixel point deviation set and the third pixel point deviation set, it includes: The first sub-crack doubt coefficient of the target monitoring and management coal sample is calculated according to the following formula; ； Among them, a1 is the first sub-crack doubt coefficient, s1 is the first calculation coefficient, f1 is the weight corresponding to the first pixel deviation set, n1 is the number of pixel difference values ​​in the first pixel deviation set, g i is the difference value of the i-th pixel in the first pixel deviation set, g1 i is the weight corresponding to the ith pixel difference value, f2 is the weight corresponding to the second pixel deviation set, n2 is the number of pixel difference values ​​in the second pixel deviation set, and p j is the jth pixel difference value in the second pixel deviation set, p1 j is the weight corresponding to the j-th pixel difference value, f3 is the weight corresponding to the third pixel deviation set, n3 is the number of pixel difference values ​​in the third pixel deviation set, q k The k-th pixel difference value in the third pixel deviation set, q1 k is the weight corresponding to the k-th pixel difference value; The second sub-crack doubt coefficient of the target monitoring and management coal sample is calculated according to the following formula; ； Among them, a2 is the second sub-sub-crack doubt coefficient, and s2 is the second calculation coefficient.

5. The monitoring and management method for coal sample testing process according to claim 4 is characterized in that: When calculating the pixel point deviation set based on the first sub-fracture doubt coefficient and the second sub-fracture doubt coefficient to calculate the fracture doubt coefficient of the target monitoring and management coal sample, it includes: The crack suspicion coefficient of the target monitoring and management coal sample is calculated according to the following formula: ； Among them, a is the crack suspicion coefficient of the target monitored coal sample.

6. The monitoring and management method for coal sample testing process according to claim 1, characterized in that: When pixel points in the monitoring and management coal sample grayscale image and pixel points in the monitoring and management coal sample standard grayscale image are respectively subjected to curve fitting, and the coal sample difference of the target monitoring and management coal sample is calculated based on the curve fitting result, the method includes: Performing curve fitting on pixel points in the grayscale image of the monitored coal sample to obtain a first fitting curve; Performing curve fitting on the pixel points in the standard grayscale image of the monitored coal sample to obtain a second fitting curve; Extracting intersections of the first fitting curve and the second fitting curve, and counting the number of intersections of all intersections; Extract a pixel value from each intersection and calculate the corresponding pixel value and value; Calculate the pixel value mean of the pixel values ​​corresponding to all intersection points, and delete the pixel values ​​that are smaller than the pixel value mean; Count the number of remaining intersections, extract a pixel value from the remaining intersections, and calculate the corresponding remaining pixel value and value; The coal sample difference of the target monitored and managed coal sample is calculated based on the number of intersections, the pixel values ​​and values, the remaining number of intersections and the remaining pixel values ​​and values.

7. The monitoring and management method for coal sample testing process according to claim 6, characterized in that: When calculating the coal sample difference of the target monitoring and management coal sample according to the number of intersections, the pixel value and value, the remaining number of intersections and the remaining pixel value and value, it includes: The coal sample difference of the target monitoring and management coal sample is calculated according to the following formula: ； Among them, r is the coal sample difference of the target monitoring and management coal sample, t1 is the number of intersection points, t2 is the remaining number of intersection points, u1 is the pixel value and value, and u2 is the remaining pixel value and value.

8. The monitoring and management method for coal sample testing process according to claim 1, characterized in that: When the adjustment coefficient of the target monitoring and management coal sample is set based on the coal sample difference, and the crack suspicion coefficient is adjusted based on the adjustment coefficient to obtain the target crack suspicion coefficient of the target monitoring and management coal sample, the method includes: Presetting a first preset coal sample difference degree and a second preset coal sample difference degree; Presetting a first preset adjustment coefficient, a second preset adjustment coefficient, and a third preset adjustment coefficient; When the coal sample difference is less than the first preset coal sample difference, the product value of the first preset adjustment coefficient and the crack suspicion coefficient is calculated and used as the target crack suspicion coefficient of the target monitoring and management coal sample; When the coal sample difference is greater than or equal to the first preset coal sample difference and less than the second preset coal sample difference, the product value of the second preset adjustment coefficient and the crack suspicion coefficient is calculated and used as the target crack suspicion coefficient of the target monitoring and management coal sample; When the coal sample difference is greater than or equal to the second preset coal sample difference, the product value of the third preset adjustment coefficient and the crack suspicion coefficient is calculated and used as the target crack suspicion coefficient of the target monitoring and management coal sample.

9. The monitoring and management method for coal sample testing process according to claim 1, characterized in that: When judging whether the target monitoring and management coal sample has cracks according to the relationship between the target crack suspicion coefficient and the preset target crack suspicion coefficient, it includes: When the target crack doubt coefficient is less than the preset target crack doubt coefficient, it is judged that the target monitoring and management coal sample does not have cracks; When the target crack doubt coefficient is greater than or equal to the preset target crack doubt coefficient, it is determined that cracks exist in the target monitored and managed coal sample.

10. A monitoring and management system for coal sample testing process, characterized in that: include: The first module is used to determine a target monitoring and management coal sample, capture an image of the target monitoring and management coal sample to obtain a monitoring and management coal sample image, and perform grayscale conversion on the monitoring and management coal sample image to obtain a monitoring and management coal sample grayscale image; The second module is used to obtain a standard grayscale image of a monitoring and management coal sample corresponding to the target monitoring and management coal sample, compare the pixel points in the grayscale image of the monitoring and management coal sample with the pixel points in the standard grayscale image of the monitoring and management coal sample, determine a plurality of pixel point deviation sets, and calculate the crack suspicion coefficient of the target monitoring and management coal sample based on the pixel point deviation sets; The third module is used to perform curve fitting on the pixel points in the grayscale image of the monitored coal sample and the pixel points in the standard grayscale image of the monitored coal sample, and calculate the coal sample difference of the target monitored coal sample based on the curve fitting result; The fourth module is used to set the adjustment coefficient of the target monitoring and management coal sample based on the coal sample difference, and adjust the crack suspicion coefficient based on the adjustment coefficient to obtain the target crack suspicion coefficient of the target monitoring and management coal sample; The fifth module is used to determine whether the target monitored coal sample has cracks based on the relationship between the target crack suspicion coefficient and the preset target crack suspicion coefficient. If so, an alarm is issued.

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