Coal quality detection method, system, equipment and medium

Abstract

The invention relates to a coal quality detection method, system and device and a medium, and the method comprises the steps: obtaining color texture characteristic parameters according to a pre-obtained standard coal image, and obtaining Raman spectrum characteristic parameters according to a pre-obtained standard coal Raman spectrum chart. And on the basis of the Raman spectrum characteristic parameters, by increasing the weights of the color texture characteristic parameters, constructing a prediction model of the ash content in the coal. And on the basis of the color texture characteristic parameters, by increasing the weight of the Raman spectrum characteristic parameters, constructing a prediction model of the water content and the volatile component content in the coal. And coupling the prediction model of the ash content in the coal and the prediction model of the moisture and volatile content in the coal to obtain a target coal quality prediction model. Color texture characteristic parameters and Raman spectrum characteristic parameters of a to-be-detected coal sample are obtained, and the component content of the to-be-detected coal sample is obtained based on the target coal quality prediction model. The problem of low accuracy of coal quality detection in related technologies is solved.

Description

Technical Field

[0001] The present application relates to the technical field of coal quality detection, and in particular to a coal quality detection method, system, equipment and medium. Background Art

[0002] As an important industrial raw material, coal occupies an irreplaceable position in the current fossil energy system. Especially in the field of thermal power, the demand for coal is huge. However, the supply of coal sources is not stable, which means that power plants may use dozens or even hundreds of different types of coal at the same time. At the same time, real-time monitoring of coal quality components (water, volatile matter, fixed carbon and ash, etc.) is conducive to the realization of efficient thermal conversion of coal (combustion, liquefaction, vaporization, etc.). Therefore, rapid detection of coal quality is of great significance.

[0003] Although traditional industrial analysis methods are accurate, their cumbersome processes and long cycles make it difficult to meet the needs of rapid detection. In recent years, coal quality detection methods based on spectral technology have made significant progress, providing new possibilities for rapid analysis. However, such methods mainly focus on the characterization of the specific chemical structure of coal, face bottlenecks in improving detection accuracy, and are easily interfered by on-site environmental factors, affecting the stability and reliability of detection. Therefore, the coal quality detection in related technologies has the problem of low accuracy. Summary of the invention

[0004] The embodiments of the present application provide a coal quality detection method, system, device and medium to at least solve the problem of low accuracy in coal quality detection in the related art.

[0005] In a first aspect, an embodiment of the present application provides a coal quality detection method, the method comprising:

[0006] Acquire color texture characteristic parameters according to the pre-acquired standard coal image, and acquire Raman spectrum characteristic parameters according to the pre-acquired standard coal Raman spectrum image;

[0007] Based on the Raman spectrum characteristic parameters, a prediction model for ash content in coal is constructed by increasing the weight of the color texture characteristic parameters;

[0008] Based on the color texture characteristic parameters, a prediction model for the moisture and volatile content in coal is constructed by increasing the weight of the Raman spectrum characteristic parameters;

[0009] Coupling the prediction model of the ash content in the coal and the prediction model of the moisture and volatile matter content in the coal to obtain a target coal quality prediction model;

[0010] The color texture characteristic parameters and Raman spectrum characteristic parameters of the coal sample to be detected are obtained, and based on the target coal quality prediction model, the component content of the coal sample to be detected is obtained.

[0011] In one embodiment, before acquiring the color texture characteristic parameters according to the pre-acquired standard coal image and acquiring the Raman spectrum characteristic parameters according to the pre-acquired standard coal Raman spectrum image, the method further includes:

[0012] Perform image acquisition and Raman spectrum test on standard coals of various coal quality components to obtain images and Raman spectra of the standard coals; wherein,

[0013] The coal quality components include moisture, volatile matter, fixed carbon and ash. The same reference is used for standard coal samples with different coal quality components, and the reference is an air-dried basis.

[0014] In one embodiment, before performing image acquisition and Raman spectrum testing on standard coals of various coal quality components, the method further includes:

[0015] Preprocessing the Raman spectrum of the standard coal, wherein the preprocessing includes segmenting the Raman spectrum into a first-order Raman spectrum and a second-order Raman spectrum;

[0016] The wavelength range of the first-order Raman spectrum is selected to be 400-2400cm -1 The wavelength range of the second-order Raman spectrum is 2400-3800cm -1 , and perform baseline removal processing on the first-order Raman spectrum and the second-order Raman spectrum.

[0017] In one embodiment, the color texture characteristic parameters include grayscale first-order moment, grayscale second-order moment, grayscale third-order moment, color standard deviation, main color tone, energy, contrast, correlation, entropy and uniformity;

[0018] The characteristic parameters of the Raman spectrum include peak area, total peak area of ​​the first-order Raman spectrum and total peak area of ​​the second-order Raman spectrum, and a combination of the peak area and the total peak area of ​​the first-order Raman spectrum and the total peak area of ​​the second-order Raman spectrum.

[0019] In one embodiment, before constructing the prediction model of ash content in coal based on the Raman spectrum characteristic parameters by increasing the weight of the color texture characteristic parameters, the method further includes:

[0020] The color texture characteristic parameter and the Raman spectrum characteristic parameter are scaled by using Z-score standardization, so that the scales of the color texture characteristic parameter and the Raman spectrum characteristic parameter are within a preset range.

[0021] In one embodiment, after scaling the color texture characteristic parameter and the Raman spectrum characteristic parameter by using Z-score standardization, the method further includes:

[0022] The number of the color texture characteristic parameters and the number of the Raman spectrum characteristic parameters are selected using neighborhood component analysis, wherein the color texture characteristic parameters select the first several parameters with stronger correlation, and the Raman characteristic parameters select the first several parameters with stronger correlation.

[0023] In one embodiment, the component contents of the coal sample to be tested include fixed carbon content, ash content, moisture content and volatile matter content, wherein the fixed carbon content is determined based on the ash content, the moisture content and the volatile matter content.

[0024] In a second aspect, an embodiment of the present application provides a coal quality detection system, the system comprising a module for acquiring characteristic parameters, a module for constructing a prediction model for ash content in coal, a module for constructing a prediction model for moisture and volatile matter content in coal, a target coal quality prediction model, and a module for obtaining coal quality detection results, wherein:

[0025] The characteristic parameter acquisition module is used to acquire color and texture characteristic parameters according to a pre-acquired standard coal image, and to acquire Raman spectrum characteristic parameters according to a pre-acquired standard coal Raman spectrum image;

[0026] The module for constructing a prediction model for ash content in coal constructs a prediction model for ash content in coal based on the Raman spectrum characteristic parameters by increasing the weight of the color texture characteristic parameters;

[0027] The module for constructing a prediction model for the moisture and volatile content in coal constructs a prediction model for the moisture and volatile content in coal based on the color texture characteristic parameters by increasing the weight of the Raman spectrum characteristic parameters;

[0028] The target coal quality prediction model is used to couple the prediction model of the ash content in the coal and the prediction model of the moisture and volatile matter content in the coal to obtain the target coal quality prediction model;

[0029] The module for obtaining coal quality detection results is used to obtain the color texture characteristic parameters and Raman spectrum characteristic parameters of the coal sample to be detected, and obtain the component content of the coal sample to be detected based on the target coal quality prediction model.

[0030] In a third aspect, an embodiment of the present application provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, a coal quality detection method as described in the first aspect above is implemented.

[0031] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a coal quality detection method as described in the first aspect above.

[0032] The coal quality detection method, system, equipment and medium provided in the embodiments of the present application have at least the following technical effects.

[0033] The color texture characteristic parameters are obtained according to the pre-acquired standard coal image, and the Raman spectrum characteristic parameters are obtained according to the pre-acquired standard coal Raman spectrum. Based on the Raman spectrum characteristic parameters, by increasing the weight of the color texture characteristic parameters, a prediction model for the ash content in the coal is constructed. Based on the color texture characteristic parameters, by increasing the weight of the Raman spectrum characteristic parameters, a prediction model for the moisture and volatile content in the coal is constructed. The prediction model for the ash content in the coal and the prediction model for the moisture and volatile content in the coal are coupled to obtain the target coal quality prediction model. The color texture characteristic parameters and the Raman spectrum characteristic parameters of the coal sample to be tested are obtained, and the component content of the coal sample to be tested is obtained based on the target coal quality prediction model. The present application realizes accurate prediction of moisture, volatile matter, ash and fixed carbon in the coal by coupling image recognition and Raman spectrum detection, and dynamically adjusting the feature weights. It has the characteristics of fast detection speed, high accuracy and good stability, and solves the problem of low accuracy in coal quality detection in related technologies.

[0034] Details of one or more embodiments of the present application are set forth in the following drawings and description to make other features, objects, and advantages of the present application more readily apparent. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0036] Figure 1 is a flow chart of a coal quality detection method according to an embodiment of the present application;

[0037] Figure 2 is a schematic diagram of standard coal according to an exemplary embodiment;

[0038] Figure 3 is a schematic diagram of a Raman spectrum according to an exemplary embodiment;

[0039] Figure 4 is a schematic diagram of baseline correction of a first-order Raman spectrum and a second-order Raman spectrum according to an exemplary embodiment;

[0040] Figure 5 is a block diagram of a system for coal quality detection according to an exemplary embodiment;

[0041] Figure 6 is a block diagram of an electronic device according to an exemplary embodiment. DETAILED DESCRIPTION

[0042] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is described and illustrated below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. Based on the embodiments provided in the present application, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present application.

[0043] Obviously, the drawings described below are only some examples or embodiments of the present application. For ordinary technicians in this field, the present application can also be applied to other similar scenarios based on these drawings without creative work. In addition, it can also be understood that although the efforts made in this development process may be complicated and lengthy, for ordinary technicians in this field related to the content disclosed in this application, some changes in design, manufacturing or production based on the technical content disclosed in this application are just conventional technical means, and should not be understood as insufficient content disclosed in this application.

[0044] Reference to "embodiments" in this application means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those of ordinary skill in the art that the embodiments described in this application may be combined with other embodiments without conflict.

[0045] Unless otherwise defined, the technical terms or scientific terms involved in this application should be understood by people with ordinary skills in the technical field to which this application belongs. The words "one", "a", "a", "the" and the like involved in this application do not indicate a quantitative limitation, and may represent the singular or plural. The terms "include", "comprise", "have" and any of their variations involved in this application are intended to cover non-exclusive inclusions; for example, a process, method, system, product or device that includes a series of steps or modules (units) is not limited to the listed steps or units, but may also include steps or units that are not listed, or may also include other steps or units inherent to these processes, methods, products or devices. The words "connect", "connected", "coupled" and the like involved in this application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The "multiple" involved in this application refers to two or more. "And / or" describes the association relationship of associated objects, indicating that there may be three relationships, for example, "A and / or B" can represent: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the objects before and after are in an "or" relationship. The terms "first", "second", "third", etc. involved in this application are only used to distinguish similar objects and do not represent a specific ordering of the objects.

[0046] In this document, it should be understood that the terms involved may be technical means for implementing a part of the present invention or other summary technical terms. For example, the terms may include:

[0047] Raman spectroscopy is a scattering spectrum that is used to characterize the structural characteristics of coal and quickly identify the type of coal.

[0048] Z-score normalization normalizes the data by subtracting the mean and dividing by the standard deviation so that each feature has zero mean and unit variance.

[0049] Neighborhood Components Analysis (NCA) is a supervised learning method mainly used for feature selection.

[0050] In a first aspect, an embodiment of the present application provides a coal quality detection method, which is applied to the stator winding end of a steam turbine generator. Figure 1 This is a flow chart of coal quality testing, such as Figure 1 As shown, a coal quality detection method includes:

[0051] Step S101, obtaining color texture characteristic parameters according to a pre-acquired standard coal image, and obtaining Raman spectrum characteristic parameters according to a pre-acquired standard coal Raman spectrum image.

[0052] Step S102: Based on the Raman spectrum characteristic parameters, a prediction model for the ash content in the coal is constructed by increasing the weight of the color and texture characteristic parameters.

[0053] Step S103: Based on the color texture characteristic parameters, a prediction model for the moisture and volatile content in the coal is constructed by increasing the weight of the Raman spectrum characteristic parameters.

[0054] Step S104: coupling the prediction model of the ash content in the coal and the prediction model of the moisture and volatile matter content in the coal to obtain a target coal quality prediction model.

[0055] Step S105: obtaining color texture characteristic parameters and Raman spectrum characteristic parameters of the coal sample to be tested, and obtaining the component content of the coal sample to be tested based on the target coal quality prediction model.

[0056] In summary, the embodiment of the present application provides a method for coal quality detection, by obtaining color texture feature parameters according to a pre-acquired standard coal image, and obtaining Raman spectrum feature parameters according to a pre-acquired standard coal Raman spectrum image. Based on the Raman spectrum feature parameters, by increasing the weight of the color texture feature parameters, a prediction model for the ash content in coal is constructed. Based on the color texture feature parameters, by increasing the weight of the Raman spectrum feature parameters, a prediction model for the moisture and volatile content in coal is constructed. The prediction model for the ash content in coal and the prediction model for the moisture and volatile content in coal are coupled to obtain a target coal quality prediction model. The color texture feature parameters and Raman spectrum feature parameters of the coal sample to be detected are obtained, and the component content of the coal sample to be detected is obtained based on the target coal quality prediction model. The present application achieves accurate prediction of moisture, volatile matter, ash and fixed carbon in coal by coupling image recognition and Raman spectrum detection, and dynamically adjusting the feature weights. It has the characteristics of fast detection speed, high accuracy and good stability, and solves the problem of low accuracy in coal quality detection in related technologies.

[0057] In one embodiment, before step S101, obtaining color texture characteristic parameters according to a pre-acquired standard coal image, and obtaining Raman spectrum characteristic parameters according to a pre-acquired standard coal Raman spectrum image, the method further includes:

[0058] Image acquisition and Raman spectrum testing are performed on standard coals of various coal quality components to obtain images and Raman spectra of standard coals;

[0059] Coal quality components include moisture, volatile matter, fixed carbon and ash. The same benchmark is used for standard coal samples with different coal quality components, which is the air-dried basis.

[0060] Optionally, a plurality of standard coals with different coal quality components are selected, and image acquisition and Raman testing are performed on the powder of the standard coal to obtain images and Raman spectra of the standard coal. The selected standard coals are all raw ore coals, and the coal quality components of the standard coals are obtained by industrial analysis with reference to GB / T212-2008. The coal quality components include moisture, volatile matter, fixed carbon and ash, and the same benchmark is used for different standard coals, and the benchmarks are all air-dried basis.

[0061] Among them, the powder of standard coal is imaged and Raman tested to obtain the image and Raman spectrum of standard coal. It can be: use a high-resolution camera or other imaging equipment to take photos or videos of each standard coal sample to obtain the image of standard coal. When shooting, factors such as light source, background and shooting angle should be considered to ensure image quality. Use a Raman spectrometer to measure each coal sample and obtain the Raman spectrum of standard coal. During the experiment, it is necessary to control the laser power, exposure time and other conditions to obtain high-quality spectral data. An image-Raman database of standard coal can be established, and the image and Raman spectrum of standard coal can be entered into the database for subsequent query and data analysis.

[0062] By acquiring images of standard coal, we can intuitively understand the appearance characteristics of coal, such as color and texture. By acquiring the Raman spectrum of standard coal, we can provide information about the chemical composition and structure of coal. The combination of the two forms a comprehensive data set that covers both physical and chemical properties.

[0063] In one embodiment, before performing image acquisition and Raman spectrum testing on standard coals of various coal quality components, the method further includes:

[0064] Preprocessing the Raman spectrum of standard coal includes segmenting the Raman spectrum into a first-order Raman spectrum and a second-order Raman spectrum;

[0065] The wavelength range of the first-order Raman spectrum is 400-2400cm -1 , the wavelength range of the second-order Raman spectrum is selected to be 2400-3400cm -1 , and the first-order Raman spectra and the second-order Raman spectra were baseline-removed.

[0066] Optionally, the Raman spectrum of standard coal is preprocessed, and the preprocessing includes segmenting the Raman spectrum into a first-order Raman spectrum and a second-order Raman spectrum, and the wavelength range of the selected first-order Raman spectrum is 400-2400cm -1 The wavelength range of the selected second-order Raman spectrum is 2400-3400cm -1 In this example, the wavelength range of the first-order Raman spectrum can be 800-2200cm -1 The wavelength range of the second-order Raman spectrum can be 2200-3200cm-1 , and the first-order Raman spectra and the second-order Raman spectra were baseline-removed.

[0067] By segmenting the Raman spectrum, different types of vibration modes can be better identified. The first-order Raman spectrum mainly contains the fundamental vibration mode of the molecule, while the second-order Raman spectrum may include combined frequency and frequency doubling information. There is usually a fluorescent background or other non-specific signal in the Raman spectrum, and these interfering signals will affect the identification of characteristic peaks. By removing the baseline processing (for example, using polynomial fitting), these background noises can be removed, thereby enhancing the true Raman signal, making the data clearer, and improving the quality of the sample data.

[0068] In step S101, color texture characteristic parameters are obtained according to a pre-acquired standard coal image, and Raman spectrum characteristic parameters are obtained according to a pre-acquired standard coal Raman spectrum image.

[0069] Optionally, the color texture characteristic parameters are obtained according to the standard coal image, which can be obtained by color space conversion (such as RGB to HSV) to obtain grayscale, average color value, color saturation, color standard deviation, etc. The texture information such as energy and contrast can be extracted by using methods such as local binary pattern (LBP). Specifically, the color texture characteristic parameters can be grayscale first-order moment, grayscale second-order moment, grayscale third-order moment, color standard deviation, main color, energy, contrast, correlation, entropy and uniformity. These features can reflect the fine structure and color distribution of the coal surface, which is helpful to identify different types of coal.

[0070] The Raman spectrum characteristic parameters of the standard coal Raman spectrum can be obtained by extracting the peak area, the total peak area of ​​the first-order Raman spectrum and the total peak area of ​​the second-order Raman spectrum from the standard coal Raman spectrum. Specifically, the Raman spectrum characteristic parameters include the peak area I D ,I V ,I G ,I 2D ,I 2G ,I (D+G) ,I 2S , the total peak area of ​​the first-order Raman spectrum and the total peak area of ​​the second-order Raman spectrum S1, S2 and their corresponding combination I D / S1、I V / S1、I G / S1、I 2D / S2、I 2G / S2、I (D+G) / S2、S1 / S2、I V / I 2D ,I 2D / I 2S ,I (D+G) / I 2S ,I (D+G) / I 2S wait.

[0071] Obtaining color and texture characteristic parameters can directly reflect the physical appearance of coal, such as glossiness, particle size, impurity content, etc., which helps to improve detection accuracy. Obtaining Raman spectrum characteristic parameters provides detailed information about the chemical composition of coal, which can help determine the type and structure of organic matter in coal.

[0072] In one embodiment, before step S102, based on the Raman spectrum characteristic parameters, the prediction model of the ash content in the coal is constructed by increasing the weight of the color texture characteristic parameters, the method further includes:

[0073] The color texture characteristic parameters and the Raman spectrum characteristic parameters are scaled by using Z-score standardization so that the scales of the color texture characteristic parameters and the Raman spectrum characteristic parameters are within a preset range.

[0074] Neighborhood component analysis is used to select the number of color texture feature parameters and Raman spectrum feature parameters, wherein the color texture feature parameters select the first several parameters with stronger correlation, and the Raman feature parameters select the first several parameters with stronger correlation.

[0075] Optionally, Z-score standardization is used to scale the color texture feature parameters and Raman spectrum feature parameters to ensure that the scales of all feature parameters are within a preset range. In this example, the preset range can be a range of 0-1. Different features may have different dimensions and scales. Direct use will cause some features to have too much or too little impact on the model. Z-score standardization eliminates this effect, allowing all features to be compared on the same scale. The standardized data is more stable, which helps to improve the robustness and generalization ability of the model. Neighborhood component analysis (NCA) is used for feature selection to reduce the number of features and improve model performance. In this embodiment, the color texture feature parameters can select the first 8 parameters with stronger correlation, and the Raman feature parameters can select the first 12 parameters with stronger correlation.

[0076] Standardizing feature parameters and selecting features can improve the prediction accuracy of the model. Reducing the number of features can significantly reduce the time and computing resource requirements for model training. Standardization ensures data consistency and reduces deviations caused by differences in feature scales.

[0077] In one embodiment, in step S102, based on the Raman spectrum characteristic parameters, a prediction model for the ash content in coal is constructed by increasing the weight of the color texture characteristic parameters. Specifically, the model includes:

[0078] After using neighborhood component analysis (NCA) for feature selection, weight factors are set for color and texture feature parameters, and the weights of color and texture feature parameters are increased by multiplying the weight factors. Support vector machine (SVM) is used as the prediction model. SVM is a powerful classification and regression algorithm suitable for high-dimensional data. A prediction model for ash content in coal is constructed. The input of the prediction model for ash content in coal is color and texture feature parameters and Raman spectrum feature parameters, and the output is ash content in coal.

[0079] By increasing the weight of color and texture feature parameters, image information can be better utilized, thereby improving the prediction accuracy of the model.

[0080] In one embodiment, step S103, based on the color texture characteristic parameters, by increasing the weight of the Raman spectrum characteristic parameters, a prediction model for the moisture and volatile content in the coal is constructed. Specifically, it includes:

[0081] After using neighborhood component analysis (NCA) for feature selection, a weight factor is set for the Raman spectrum characteristic parameter, and the weight of the Raman spectrum characteristic parameter is increased by multiplying the weight factor, which is suitable for high-dimensional data. A prediction model for the moisture and volatile content in coal is constructed. The prediction model for the moisture and volatile content in coal inputs the Raman spectrum characteristic parameter parameters and the color texture characteristic parameters, and the output is the moisture content and volatile content.

[0082] By increasing the weight of the Raman spectral characteristic parameters, the chemical composition information can be better utilized, thereby improving the prediction accuracy of the model.

[0083] It is worth noting that in step S102 and step S103, different weights are used for the prediction model of ash content in coal and the prediction model of moisture and volatile matter content in coal, respectively. This is because there are differences in the strength of the correlation between parameters with different weights and the prediction object. For example, in the prediction model of ash content in coal, the direct correlation between color texture parameters and ash is strong (linear correlation), and Raman parameters are relatively weak, but an accurate prediction model cannot be directly established by color texture parameters alone. That is, ash is associated with both color texture parameters and Raman parameters. When constructing a prediction model, color texture parameters are mainly used, and Raman parameters are supplementary. Therefore, the color texture parameters are weighted. For the prediction model of moisture and volatile matter content in coal, the Raman parameters are weighted. In one embodiment, step S104 couples the prediction model of ash content in coal and the prediction model of moisture and volatile matter content in coal to obtain a target coal quality prediction model. Specifically including:

[0084] The prediction model of ash content in coal is combined with the prediction model of moisture and volatile content in coal to obtain the target coal quality prediction model. By coupling the prediction model of ash content in coal with the prediction model of moisture and volatile content in coal, a comprehensive target coal quality prediction model is constructed to achieve rapid detection of coal quality and improve overall prediction accuracy.

[0085] In one embodiment, step S105, obtaining color texture characteristic parameters and Raman spectrum characteristic parameters of the coal sample to be tested, and obtaining the component content of the coal sample to be tested based on the target coal quality prediction model. Specifically includes:

[0086] The coal sample to be tested is subjected to image acquisition and Raman testing in the same manner as step S101, and the corresponding color texture and Raman spectrum characteristic parameters are extracted. The color texture and Raman spectrum characteristic parameters are then processed using Z-score standardization and neighborhood component analysis to obtain the color texture characteristic parameters and Raman spectrum characteristic parameters of the target coal sample to be tested. At this time, the color texture characteristic parameters and Raman spectrum characteristic parameters of the target coal sample to be tested correspond one-to-one to the parameters used in the target coal quality prediction model, and there is no need to use the NCA algorithm for additional parameter screening. The color texture characteristic parameters and Raman spectrum characteristic parameters of the target coal sample to be tested are input into the target coal quality prediction model to obtain the component content of the coal sample to be tested. The component content of the coal sample to be tested includes fixed carbon content, ash content, moisture content and volatile matter content, wherein the fixed carbon content is determined based on the ash content, moisture content and volatile matter content. Specifically, the fixed carbon content of the coal sample to be tested is calculated as follows: FC ad =100–A ad -M ad –V ad , FC ad represents fixed carbon, A ad Indicates ash content, M ad Indicates moisture, V ad Indicates volatile matter.

[0087] Step S105 is based on image recognition and Raman spectroscopy, does not require sample preparation, has wide applicability, is non-destructive, non-toxic, and non-contact with the sample itself, and can be applied to complex production environments. The color texture feature parameters based on the image and the Raman spectrum feature parameters are coupled to make up for the shortcomings of the two in the separate coal quality component detection, and improve the detection accuracy and detection range. In addition, the detection process is simple and the detection speed is fast, which can realize real-time online detection of coal quality and meet the needs of industrial production for rapid acquisition of coal quality information.

[0088] The following is an application example based on the first aspect.

[0089] First, 26 kinds of standard coal with different coal ranks and ash contents were ground to 74-105 μm and numbered 1-30 for industrial analysis. The results were based on air drying. The composition parameters of 6 typical standard coals are listed here, as shown in Table 1.

[0090] Table 1

[0091]

[0092] Then, the 30 types of standard coal were imaged and tested by Raman, and the standard coal images and Raman spectra were obtained. Figure 2 is a schematic diagram of standard coal according to an exemplary embodiment. Figure 3 is a schematic diagram of a Raman spectrum according to an exemplary embodiment, such as Figure 2 and Figure 3 As shown, it can be found that there is baseline drift in the Raman spectrum. In order to accurately obtain the characteristic parameters of the Raman spectrum of the coal sample, the first-order Raman peak and the second-order Raman peak should be baseline corrected respectively. The baseline correction method is the two-point method. Figure 4 is a schematic diagram of baseline correction of a first-order Raman spectrum and a second-order Raman spectrum according to an exemplary embodiment, as shown in Figure 4 As shown, on the left is a schematic diagram of the first-order Raman spectrum baseline correction, wherein the upper left curve graph is the original data and the baseline, and the lower left curve graph is the corrected data; on the right is a schematic diagram of the second-order Raman spectrum baseline correction, wherein the upper right curve graph is the original data and the baseline, and the lower right curve graph is the corrected data.

[0093] In order to extract as many color and texture characteristic parameters of the image as possible and accurately as possible, the color and texture characteristic parameters extracted here include grayscale first-order moment, grayscale second-order moment, grayscale third-order moment, color standard deviation, energy, contrast, correlation, entropy, and uniformity; Raman spectrum characteristic parameters include peak area I D ,I V ,I G ,I 2D ,I 2G ,I (D+G) ,I 2S , the total peak area of ​​the first-order Raman spectrum and the total peak area of ​​the second-order Raman spectrum S1, S2 and their corresponding combination I D / S1、I V / S1、I G / S1、I 2D / S2、I 2G / S2、I (D+G) / S2、S1 / S2、I V / I 2D ,I 2D / I 2S ,I (D+G) / I 2S ,I (D+G) / I 2S Etc., Raman peak area parameters are obtained by peak fitting. Here, the characteristic parameters of one type of standard coal are given as an example. Table 2 is the color and texture characteristic parameters of standard coal, and Table 3 is the color and texture characteristic parameters of standard coal. As shown in Tables 2 and 3, an image-Raman database is constructed.

[0094] Table 2

[0095]

[0096] Table 3

[0097]

[0098] According to the Z-score standardization and neighborhood component analysis, the characteristic parameters obtained are further processed. Taking the prediction of ash content in coal as an example, after all the characteristic parameters are normalized, the weight factor is set, which can be 0.5-3. 2 can be used in the example. The weight of the color texture parameter is increased by multiplying the weight factor, and the unweighted Raman spectrum characteristic parameters are coupled. Then the NCA algorithm is used to screen the parameters with stronger correlation with ash content in the color texture and Raman spectrum. Among them, the color texture characteristic parameters select the first 8 parameters with stronger correlation, and the Raman characteristic parameters select the first 12 parameters with stronger correlation. The processed and screened characteristic parameters and the corresponding ash content are input into the SVM model to construct a prediction model for coal ash content.

[0099] The same steps are used to construct the prediction model of moisture and volatile matter in coal. Since the study found that Raman parameters are strongly correlated with moisture and volatile matter, the weight of the Raman spectrum characteristic parameters is increased for modeling, and finally all the prediction models are combined.

[0100] The coal sample to be tested is subjected to image acquisition and Raman testing in the same manner as step S101, and the corresponding color texture and Raman spectrum characteristic parameters are extracted. The color texture and Raman spectrum characteristic parameters are then processed using Z-score standardization and neighborhood component analysis to obtain the color texture characteristic parameters and Raman spectrum characteristic parameters of the target coal sample to be tested.

[0101] The color texture characteristic parameters and Raman spectrum characteristic parameters of the processed target coal sample to be tested are input into the prediction model to obtain the contents of ash, moisture and volatile matter in the coal. The calculation method of the fixed carbon content of the coal sample to be tested is: FC ad =100–A ad -M ad –V ad , FC ad represents fixed carbon, A ad Indicates ash content, Mad Indicates moisture, V ad Indicates volatile matter, thereby determining coal quality.

[0102] In summary, the embodiment of the present application provides a method for coal quality detection, by obtaining color texture feature parameters according to a pre-acquired standard coal image, and obtaining Raman spectrum feature parameters according to a pre-acquired standard coal Raman spectrum image. Based on the Raman spectrum feature parameters, by increasing the weight of the color texture feature parameters, a prediction model for the ash content in coal is constructed. Based on the color texture feature parameters, by increasing the weight of the Raman spectrum feature parameters, a prediction model for the moisture and volatile content in coal is constructed. The prediction model for the ash content in coal and the prediction model for the moisture and volatile content in coal are coupled to obtain a target coal quality prediction model. The color texture feature parameters and Raman spectrum feature parameters of the coal sample to be detected are obtained, and the component content of the coal sample to be detected is obtained based on the target coal quality prediction model. The present application achieves accurate prediction of moisture, volatile matter, ash and fixed carbon in coal by coupling image recognition and Raman spectrum detection, and dynamically adjusting the feature weights. It has the characteristics of fast detection speed, high accuracy and good stability, and solves the problem of low accuracy in coal quality detection in related technologies.

[0103] In a second aspect, an embodiment of the present application provides a coal quality detection system. Figure 5 FIG. 1 is a block diagram of a system for grading defect text of a relay protection device according to an exemplary embodiment. Figure 5 As shown, the system includes a module 510 for acquiring characteristic parameters, a module 520 for constructing a prediction model for ash content in coal, a module 530 for constructing a prediction model for moisture and volatile matter content in coal, a target coal quality prediction model 540, and a module 550 for obtaining coal quality detection results, wherein:

[0104] The characteristic parameter acquisition module 510 is used to acquire color texture characteristic parameters according to the pre-acquired standard coal image, and acquire Raman spectrum characteristic parameters according to the pre-acquired standard coal Raman spectrum image;

[0105] A prediction model module 520 for building the ash content in coal is used to build the prediction model for the ash content in coal based on the Raman spectrum characteristic parameters by increasing the weight of the color texture characteristic parameters;

[0106] A prediction model module 530 for the moisture and volatile content in coal is constructed, based on the color texture characteristic parameters, by increasing the weight of the Raman spectrum characteristic parameters to construct a prediction model for the moisture and volatile content in coal;

[0107] The target coal quality prediction model 540 is used to couple the prediction model of the ash content in the coal and the prediction model of the moisture and volatile content in the coal to obtain the target coal quality prediction model;

[0108] The module 550 for obtaining coal quality detection results is used to obtain color texture characteristic parameters and Raman spectrum characteristic parameters of the coal sample to be detected, and to obtain the component content of the coal sample to be detected based on the target coal quality prediction model.

[0109] In summary, a coal quality detection system provided in an embodiment of the present application solves the problem of low accuracy of coal quality detection in the related art by acquiring a characteristic parameter module 510, constructing a prediction model module 520 for ash content in coal, constructing a prediction model module 530 for moisture and volatile content in coal, a target coal quality prediction model and obtaining a coal quality detection result module 540. Specifically, the color and texture characteristic parameters are obtained according to a pre-acquired standard coal image, and the Raman spectrum characteristic parameters are obtained according to a pre-acquired standard coal Raman spectrum image. Based on the Raman spectrum characteristic parameters, a prediction model for ash content in coal is constructed by increasing the weight of the color and texture characteristic parameters. Based on the color and texture characteristic parameters, a prediction model for moisture and volatile content in coal is constructed by increasing the weight of the Raman spectrum characteristic parameters. The prediction model for ash content in coal and the prediction model for moisture and volatile content in coal are coupled to obtain a target coal quality prediction model. The color and texture characteristic parameters and the Raman spectrum characteristic parameters of the coal sample to be detected are obtained, and the component content of the coal sample to be detected is obtained based on the target coal quality prediction model. This application achieves accurate prediction of moisture, volatile matter, ash and fixed carbon in coal by coupling image recognition and Raman spectroscopy detection and dynamically adjusting feature weights. It has the characteristics of fast detection speed, high accuracy and good stability, and solves the problem of low accuracy in coal quality detection in related technologies.

[0110] It should be noted that the coal quality detection system provided in this embodiment is used to implement the above-mentioned implementation mode, and the descriptions that have been made will not be repeated. As used above, the terms "module", "unit", "subunit", etc. can implement a combination of software and / or hardware for a predetermined function. Although the device described in the above embodiment is preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0111] In a third aspect, an embodiment of the present application provides an electronic device, Figure 6 FIG. 1 is a block diagram of an electronic device according to an exemplary embodiment. Figure 6 As shown, the electronic device may include a processor 61 and a memory 62 storing computer program instructions.

[0112] Specifically, the processor 61 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0113] Among them, the memory 62 may include a large capacity memory for data or instructions. By way of example and not limitation, the memory 62 may include a hard disk drive (HDD), a floppy disk drive, a solid state drive (SSD), a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive, or a combination of two or more of these. Where appropriate, the memory 62 may include a removable or non-removable (or fixed) medium. Where appropriate, the memory 62 may be inside or outside the data processing device. In a specific embodiment, the memory 62 is a non-volatile memory. In a specific embodiment, the memory 62 includes a read-only memory (ROM) and a random access memory (RAM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable ROM (Programmable Read-Only Memory, PROM for short), an erasable PROM (Erasable ProgrammableRead-Only Memory, EPROM for short), an electrically erasable PROM (Electrically Erasable ProgrammableRead-Only Memory, EEPROM for short), an electrically alterable ROM (Electrically Alterable Read-Only Memory, EAROM for short) or a flash memory (FLASH) or a combination of two or more of these. Under appropriate circumstances, the RAM can be a static random access memory (SRAM) or a dynamic random access memory (DRAM), wherein the DRAM can be a fast page mode dynamic random access memory (FPMDRAM), an extended data output dynamic random access memory (EDODRAM), a synchronous dynamic random access memory (SDRAM), etc.

[0114] The memory 62 may be used to store or cache various data files required for processing and / or communication, as well as possible computer program instructions executed by the processor 61 .

[0115] The processor 61 implements any one of the coal quality detection methods in the above embodiments by reading and executing computer program instructions stored in the memory 62 .

[0116] In one embodiment, a coal quality detection device may further include a communication interface 63 and a bus 60. Figure 6 As shown, the processor 61, the memory 62, and the communication interface 63 are connected via a bus 60 and communicate with each other.

[0117] The communication interface 63 is used to implement communication between the modules, devices, units and / or equipment in the embodiment of the present application. The communication port 63 can also implement data communication with other components such as: external devices, image / data acquisition equipment, databases, external storage, and image / data processing workstations.

[0118] The bus 60 includes hardware, software or both, and couples the components of a coal quality detection device to each other. The bus 60 includes but is not limited to at least one of the following: a data bus, an address bus, a control bus, an expansion bus, and a local bus. By way of example and not limitation, bus 60 may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an InfiniBand interconnect, a Low Pin Count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local Bus (VLB) bus, or other suitable buses or a combination of two or more of the above. Where appropriate, bus 60 may include one or more buses. Although embodiments of the present application describe and illustrate a particular bus, the present application contemplates any suitable bus or interconnect.

[0119] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium having a program stored thereon, and when the program is executed by a processor, a coal quality detection method provided in the first aspect is implemented.

[0120] The readable storage medium may include but is not limited to: a portable disk, a hard disk, a random access memory, a read-only memory, an erasable programmable read-only memory, an optical storage device, a magnetic storage device or any suitable combination of the above.

[0121] In a possible implementation manner, the present invention can also be implemented in the form of a program product, which includes program code. When the program product is run on a terminal device, the program code is used to enable the terminal device to execute the steps of a coal quality detection method provided in the first aspect.

[0122] Among them, the program code for executing the present invention can be written in any combination of one or more programming languages, and the program code can be executed completely on the user device, partially on the user device, as an independent software package, partially on the user device and partially on a remote device, or completely on the remote device.

[0123] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0124] The above embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A coal quality detection method, characterized in that: The method comprises: Acquire color texture characteristic parameters according to the pre-acquired standard coal image, and acquire Raman spectrum characteristic parameters according to the pre-acquired standard coal Raman spectrum image; Based on the Raman spectrum characteristic parameters, a prediction model for ash content in coal is constructed by increasing the weight of the color texture characteristic parameters; Based on the color texture characteristic parameters, a prediction model for the moisture and volatile content in coal is constructed by increasing the weight of the Raman spectrum characteristic parameters; Coupling the prediction model of the ash content in the coal and the prediction model of the moisture and volatile matter content in the coal to obtain a target coal quality prediction model; The color texture characteristic parameters and Raman spectrum characteristic parameters of the coal sample to be detected are obtained, and based on the target coal quality prediction model, the component content of the coal sample to be detected is obtained.

2. The method according to claim 1, characterized in that: Before acquiring the color texture characteristic parameters according to the pre-acquired standard coal image and acquiring the Raman spectrum characteristic parameters according to the pre-acquired standard coal Raman spectrum, the method further includes: Perform image acquisition and Raman spectrum test on standard coals of various coal quality components to obtain images and Raman spectra of the standard coals; wherein, The coal quality components include moisture, volatile matter, fixed carbon and ash. The same reference is used for standard coal samples with different coal quality components, and the reference is an air-dried basis.

3. The method according to claim 2, characterized in that Before performing image acquisition and Raman spectrum testing on standard coals with different coal quality components, the method further includes: Preprocessing the Raman spectrum of the standard coal, wherein the preprocessing includes segmenting the Raman spectrum into a first-order Raman spectrum and a second-order Raman spectrum; The wavelength range of the first-order Raman spectrum is selected to be 400-2400cm -1 The wavelength range of the second-order Raman spectrum is 2400-3800cm -1 , and perform baseline removal processing on the first-order Raman spectrum and the second-order Raman spectrum.

4. The method according to claim 1, characterized in that: The color texture characteristic parameters include grayscale first-order moment, grayscale second-order moment, grayscale third-order moment, color standard deviation, main color tone, energy, contrast, correlation, entropy and uniformity; The characteristic parameters of the Raman spectrum include peak area, total peak area of ​​the first-order Raman spectrum and total peak area of ​​the second-order Raman spectrum, and a combination of the peak area and the total peak area of ​​the first-order Raman spectrum and the total peak area of ​​the second-order Raman spectrum.

5. The method according to claim 1, characterized in that: Before constructing the prediction model of ash content in coal based on the Raman spectrum characteristic parameters by increasing the weight of the color texture characteristic parameters, the method further includes: The color texture characteristic parameter and the Raman spectrum characteristic parameter are scaled by using Z-score standardization, so that the scales of the color texture characteristic parameter and the Raman spectrum characteristic parameter are within a preset range.

6. The method according to claim 5, characterized in that After scaling the color texture characteristic parameters and the Raman spectrum characteristic parameters by using Z-score standardization, the method further includes: The number of the color texture characteristic parameters and the number of the Raman spectrum characteristic parameters are selected using neighborhood component analysis, wherein the color texture characteristic parameters select the first several parameters with stronger correlation, and the Raman characteristic parameters select the first several parameters with stronger correlation.

7. The method according to claim 1, characterized in that The component contents of the coal sample to be tested include fixed carbon content, ash content, moisture content and volatile matter content, wherein the fixed carbon content is determined based on the ash content, the moisture content and the volatile matter content.

8. A coal quality detection system, characterized in that: The system includes a module for acquiring characteristic parameters, a module for constructing a prediction model for ash content in coal, a module for constructing a prediction model for moisture and volatile matter content in coal, a target coal quality prediction model, and a module for obtaining coal quality detection results, wherein: The characteristic parameter acquisition module is used to acquire color and texture characteristic parameters according to a pre-acquired standard coal image, and to acquire Raman spectrum characteristic parameters according to a pre-acquired standard coal Raman spectrum image; The module for constructing a prediction model for ash content in coal constructs a prediction model for ash content in coal based on the Raman spectrum characteristic parameters by increasing the weight of the color texture characteristic parameters; The module for constructing a prediction model for the moisture and volatile content in coal constructs a prediction model for the moisture and volatile content in coal based on the color texture characteristic parameters by increasing the weight of the Raman spectrum characteristic parameters; The target coal quality prediction model is used to couple the prediction model of the ash content in the coal and the prediction model of the moisture and volatile matter content in the coal to obtain the target coal quality prediction model; The module for obtaining coal quality detection results is used to obtain the color texture characteristic parameters and Raman spectrum characteristic parameters of the coal sample to be detected, and obtain the component content of the coal sample to be detected based on the target coal quality prediction model.

9. An electronic device, characterized in that: The invention comprises a memory and a processor, a computer program stored in the memory and executable on the processor, and the processor implements a coal quality detection method as claimed in any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, a coal quality detection method as described in any one of claims 1 to 7 is implemented.

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