Intelligent dryer monitoring system and method
Through the intelligent dryer monitoring system, the coal drying process is monitored in real time by using historical data, coal quality coefficient, image processing and adjustment modules, and the coal drying process is solved in the existing technology, and more efficient and stable coal drying is achieved.
Patent Information
- Application Number
- CN202411919202.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-24
- Publication Date
- 2025-05-30
AI Technical Summary
Existing coal dryers cannot monitor the drying process in real time, resulting in poor drying effect and low efficiency.
An intelligent dryer monitoring system is designed to obtain historical coal sample drying data through the analysis module, and the calculation module calculates the coal mass coefficient and drying characteristic coefficient of the coal sample to be dried. The image module determines the drying state coefficient through the coal sample image, and adjusts the unit drying amount according to the drying state coefficient through the adjustment module.
It improves the accuracy and stability of the dryer temperature control and improves the drying quality of coal samples.
Smart Images
Figure CN120062972A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the technical field of coal drying, and more specifically, to an intelligent dryer monitoring system and method. Background Art
[0002] A coal dryer is a device specifically used for drying coal materials, mainly applicable to the drying of coal materials with high water content, fine particle size, and high viscosity. Its working principle is mainly to provide heat through a heat source, heat the wet coal materials to a certain temperature, evaporate the water in them and discharge it, so as to obtain dried coal.
[0003] The coal dryers in the prior art can only set the coal to be dried within a fixed time and cannot monitor the drying process in real time, resulting in poor drying effect and low drying efficiency. Summary of the Invention
[0004] The present invention provides an intelligent dryer monitoring system and method to solve the problems of poor drying effect and low drying efficiency of the dryer in the prior art, including: An analysis module, configured to obtain historical coal sample drying data and determine a drying characteristic coefficient of the coal sample to be dried according to the historical coal sample drying data; A calculation module, configured to obtain the coal quality coefficient and the drying characteristic coefficient of the coal sample to be dried, and determine the unit drying amount according to the coal quality coefficient and the drying characteristic coefficient of the coal sample to be dried; An image module, configured to obtain a coal sample image of the coal sample to be dried during the drying process and determine a drying state coefficient according to the coal sample image of the coal sample to be dried during the drying process; An adjustment module, configured to determine a supplementary drying characteristic coefficient according to the drying state coefficient and adjust the unit drying amount according to the supplementary drying characteristic coefficient.
[0005] Further, the determining the drying characteristic coefficient of the coal sample to be dried according to the historical coal sample drying data includes: Determining the change in moisture content of each coal sample type during the drying process according to the historical coal sample drying data, and drawing a moisture content change curve according to the change in moisture content of each coal sample type during the drying process; Calculating a first correlation coefficient of the moisture content change curve, and clustering each coal sample type according to the first correlation coefficient of the moisture content change curve; Determining the clustering center value of each coal sample type according to the clustering result, and determining the clustering center value of the coal sample to be dried according to the coal sample type of the coal sample to be dried, so as to obtain the drying characteristic coefficient of the coal sample to be dried.
[0006] Further, the clustering each coal sample type according to the first correlation coefficient of the moisture content change curve includes: A sample data set is established based on the first correlation coefficient of the moisture content change curves of various coal sample types, and k initial clustering centers of the sample data set are randomly selected; Calculate the Euclidean distance from the first correlation coefficient in the sample data set to the initial clustering centers, and classify the coal sample types corresponding to the first correlation coefficient into the corresponding clustering clusters according to the Euclidean distance from the first correlation coefficient in the sample data set to the initial clustering centers; Calculate the average value of the first correlation coefficient within each clustering cluster, and recalculate the clustering centers according to the average value of the first correlation coefficient within each clustering cluster; Repeat the above steps iteratively until the clustering centers no longer change or the number of iterations reaches the preset maximum number of iterations, and obtain the clustering result of the coal sample types.
[0007] Further, the determination of the unit drying amount according to the coal quality coefficient and the drying characteristic coefficient of the coal sample to be dried includes: Determine the coal quality coefficient, the drying characteristic coefficient and the corresponding unit drying amount of the historical coal samples according to the historical coal sample drying data, and establish a training sample set according to the coal quality coefficient, the drying characteristic coefficient and the corresponding unit drying amount of the historical coal samples; Establish an initial drying amount evaluation model according to the training sample set and train the initial drying amount evaluation model to obtain a trained drying amount evaluation model; Input the coal quality coefficient and the drying characteristic coefficient of the coal sample to be dried into the trained drying amount evaluation model to obtain the unit drying amount of the coal sample to be dried.
[0008] Further, the coal quality coefficient is specifically the initial moisture content and the density of the coal sample.
[0009] Further, the determination of the drying state according to the coal sample image of the coal sample to be dried during the drying process includes: Perform grayscale processing on the coal sample image of the coal sample to be dried during the drying process to obtain a coal sample grayscale image; Segment the coal sample grayscale image to obtain a number of sub-coal sample grayscale images; Calculate the average grayscale value of each sub-coal sample grayscale image, and draw a change curve of the average grayscale value of the coal sample grayscale image within a preset period; Calculate the second correlation coefficient of any two change curves of the average grayscale value, screen out the change curves of the average grayscale value with the second correlation coefficient greater than the first preset threshold, and set the number of the second correlation coefficients of the change curves of the average grayscale value greater than the first preset threshold as the drying fluctuation value; Obtain the change amount of the average grayscale value of the coal sample grayscale image within a preset period, and multiply the change amount of the average grayscale value within the preset period by the drying fluctuation value to obtain a drying state coefficient.
[0010] Further, determining the supplementary drying characteristic coefficient according to the drying state coefficient includes: Determining the supplementary drying characteristic coefficient according to the supplementary drying characteristic coefficient calculation formula, and the specific supplementary drying characteristic coefficient calculation formula is
[0011] Wherein, is the supplementary drying characteristic coefficient, is the average gray value of the current coal sample gray image, is the drying state coefficient, is the preset standard state coefficient, is the preset range coefficient, is the natural exponential function.
[0012] Further, adjusting the unit drying amount according to the supplementary drying characteristic coefficient includes: Obtaining the preset standard supplementary coefficient, and calculating the ratio of the supplementary drying characteristic coefficient to the preset standard supplementary coefficient; Multiplying the ratio of the supplementary drying characteristic coefficient to the preset standard supplementary coefficient by the unit drying amount of the next preset period to obtain the unit drying amount of the next preset period, and completing the adjustment of the unit drying amount.
[0013] Further, it further includes an evaluation module for: Obtaining the coal sample gray image after drying is completed, calculating the difference between the average gray value of the coal sample gray image after drying is completed and the average gray value of the initial coal sample gray image, and obtaining the drying completion degree; Judging whether the drying completion degree is greater than the second preset threshold. If the drying completion degree is greater than the second preset threshold, setting the first level as the completion level of the dryer; If the drying completion degree is less than or equal to the second preset threshold, judging whether the drying completion degree is greater than the third preset threshold; If the drying completion degree is greater than the third preset threshold, setting the second level as the completion level of the dryer; If the drying completion degree is less than or equal to the third preset threshold, setting the third level as the completion level of the dryer.
[0014] To achieve the above object, the present invention also provides an intelligent dryer monitoring method, including: Obtaining historical coal sample drying data, and determining the drying characteristic coefficient of the coal sample to be dried according to the historical coal sample drying data; Obtaining the coal quality coefficient and the drying characteristic coefficient of the coal sample to be dried, and determining the unit drying amount according to the coal quality coefficient and the drying characteristic coefficient of the coal sample to be dried; Obtain the coal sample image of the coal sample to be dried during the drying process, and determine the drying state coefficient according to the coal sample image of the coal sample to be dried during the drying process; Determine the supplementary drying characteristic coefficient according to the drying state coefficient, and adjust the unit drying amount according to the supplementary drying characteristic coefficient.
[0015] The beneficial effects of the present invention are as follows: By applying the above technical solutions, the present invention calculates the unit drying amount through the coal quality coefficient and drying characteristic coefficient of the coal sample to perform the drying operation on the coal sample. At the same time, the coal sample during the drying process is monitored in real time through image processing technology, and the unit drying amount is adjusted in a timely manner through the supplementary drying characteristic coefficient during the monitoring process, increasing the accuracy and stability of the dryer temperature control and improving the drying quality of the coal sample. Description of the Drawings
[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application. For those skilled in the art, without creative efforts, other drawings can also be obtained based on these drawings.
[0017] Figure 1 Shows a schematic structural diagram of an intelligent dryer monitoring system proposed in an embodiment of the present invention; Figure 2 Shows an overall flowchart of an intelligent dryer monitoring method proposed in an embodiment of the present invention. Detailed Embodiments
[0018] The following will clearly and completely describe the technical solutions in the embodiments of the present application in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present application.
[0019] The terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the present application, unless otherwise stated, the meaning of "a plurality" is two or more.
[0020] In the description of the present application, it should be noted that unless otherwise clearly specified and defined, the terms "installation", "connection", and "coupling" 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 an indirect connection through an intermediate medium, and it can be the communication inside two components. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to specific situations.
[0021] An embodiment of the present application provides an intelligent dryer monitoring system, as Figure 1 shown, including: An analysis module, configured to obtain historical coal sample drying data, and determine a drying characteristic coefficient of the coal sample to be dried according to the historical coal sample drying data; a calculation module, configured to obtain a coal quality coefficient and a drying characteristic coefficient of the coal sample to be dried, and determine a unit drying amount according to the coal quality coefficient and the drying characteristic coefficient of the coal sample to be dried; an image module, configured to obtain a coal sample image of the coal sample to be dried during the drying process, and determine a drying state coefficient according to the coal sample image of the coal sample to be dried during the drying process; an adjustment module, configured to determine a supplementary drying characteristic coefficient according to the drying state coefficient, and adjust the unit drying amount according to the supplementary drying characteristic coefficient.
[0022] In this embodiment, the drying characteristic coefficient of the historical coal sample during the drying process is calculated through the historical coal sample drying data, and then the drying characteristic coefficient of the current coal sample to be dried is obtained. The unit drying amount of the coal sample is accurately judged by combining the coal quality coefficient of the current coal sample to be dried. The drying state of the coal sample within a preset period is determined by real-time monitoring of the coal sample image during the drying process, and the unit drying amount of the next preset period is adjusted according to the drying state of the coal sample within the preset period.
[0023] In some embodiments of the present application, the determining the drying characteristic coefficient of the coal sample to be dried according to the historical coal sample drying data includes: determining the moisture content change situation of each coal sample type during the drying process according to the historical coal sample drying data, and drawing a moisture content change curve according to the moisture content change situation of each coal sample type during the drying process; calculating a first correlation coefficient of the moisture content change curve, and clustering each coal sample type according to the first correlation coefficient of the moisture content change curve; determining a clustering center value of each coal sample type according to the clustering result, and determining a clustering center value of the coal sample to be dried according to the coal sample type of the coal sample to be dried, so as to obtain the drying characteristic coefficient of the coal sample to be dried.
[0024] In this embodiment, the first correlation coefficient of each coal sample type is calculated through the moisture content change curve of each coal sample type, and the coal sample types are clustered based on the first correlation coefficient, so as to obtain the drying characteristic coefficient of the coal sample type corresponding to the coal sample to be dried.
[0025] In some embodiments of the present application, clustering the types of coal samples according to the first correlation coefficient of the moisture content change curve includes: establishing a sample data set according to the first correlation coefficients of the moisture content change curves of the types of coal samples, and randomly selecting k initial clustering centers of the sample data set; calculating the Euclidean distance from the first correlation coefficient in the sample data set to the initial clustering centers, and classifying the types of coal samples corresponding to the first correlation coefficient into the corresponding clustering clusters according to the Euclidean distance from the first correlation coefficient in the sample data set to the initial clustering centers; calculating the average value of the first correlation coefficients within each clustering cluster, and recalculating the clustering centers according to the average values of the first correlation coefficients within each clustering cluster; repeating the above steps iteratively until the clustering centers no longer change or the number of iterations reaches the preset maximum number of iterations, to obtain the clustering result of the types of coal samples.
[0026] In this embodiment, the first correlation coefficients of the types of coal samples are clustered based on the k-means clustering algorithm, so as to cluster the types of coal samples according to different moisture content change situations.
[0027] In some embodiments of the present application, determining the unit drying amount according to the coal quality coefficient and the drying characteristic coefficient of the coal sample to be dried includes: determining the coal quality coefficient, the drying characteristic coefficient and the corresponding unit drying amount of the historical coal sample according to the historical coal sample drying data, and establishing a training sample set according to the coal quality coefficient, the drying characteristic coefficient and the corresponding unit drying amount of the historical coal sample; establishing an initial drying amount evaluation model according to the training sample set and training the initial drying amount evaluation model to obtain a trained drying amount evaluation model; inputting the coal quality coefficient and the drying characteristic coefficient of the coal sample to be dried into the trained drying amount evaluation model to obtain the unit drying amount of the coal sample to be dried.
[0028] In this embodiment, an initial drying amount evaluation model is established based on a deep learning neural network model, and the initial drying amount evaluation model is trained by the coal quality coefficient, the drying characteristic coefficient and the corresponding unit drying amount of the historical coal sample, so as to evaluate the unit drying amount of the coal sample to be dried according to the trained drying amount evaluation model. The unit drying amount is the drying amount of the dryer per unit time.
[0029] In some embodiments of the present application, the coal quality coefficient is specifically the initial moisture content and the density of the coal sample.
[0030] In some embodiments of the present application, determining the drying state based on the coal sample image during the drying process of the coal sample to be dried includes: performing grayscale processing on the coal sample image during the drying process of the coal sample to be dried to obtain a coal sample grayscale image; segmenting the coal sample grayscale image to obtain several sub-coal sample grayscale images; calculating the average grayscale value of each sub-coal sample grayscale image, and drawing a curve of the change in the average grayscale value of the coal sample grayscale image within a preset period; calculating the second correlation coefficient of any two curves of the change in the average grayscale value, screening out the curves of the change in the average grayscale value with the second correlation coefficient greater than the first preset threshold, and setting the number of the second correlation coefficients of the curves of the change in the average grayscale value greater than the first preset threshold as the drying fluctuation value; obtaining the change amount of the average grayscale value of the coal sample grayscale image within a preset period, and multiplying the change amount of the average grayscale value within the preset period by the drying fluctuation value to obtain the drying state coefficient.
[0031] In this embodiment, the drying fluctuation value is calculated by collecting the image of the coal sample to be dried during the drying process, and the drying fluctuation value is corrected according to the change amount of the average grayscale value of the coal sample grayscale image within a preset period, so as to calculate the drying state coefficient.
[0032] In some embodiments of the present application, determining the supplementary drying characteristic coefficient according to the drying state coefficient includes: determining the supplementary drying characteristic coefficient according to the supplementary drying characteristic coefficient calculation formula, and the specific supplementary drying characteristic coefficient calculation formula is
[0033] where is the supplementary drying characteristic coefficient, is the average grayscale value of the current coal sample grayscale image, is the drying state coefficient, is the preset standard state coefficient, is the preset range coefficient, is the natural exponential function.
[0034] In this embodiment, the supplementary drying characteristic coefficient is calculated through the drying state coefficient, and the coal sample to be dried is supplemented and dried according to the supplementary drying characteristic coefficient.
[0035] In some embodiments of the present application, adjusting the unit drying amount according to the supplementary drying characteristic coefficient includes: obtaining the preset standard supplementary coefficient, and calculating the ratio of the supplementary drying characteristic coefficient to the preset standard supplementary coefficient; multiplying the ratio of the supplementary drying characteristic coefficient to the preset standard supplementary coefficient by the unit drying amount of the next preset period to obtain the unit drying amount of the next preset period, and completing the adjustment of the unit drying amount.
[0036] In this embodiment, the unit drying amount in the next preset cycle is adjusted according to the ratio of the supplementary drying characteristic coefficient to the preset standard supplementary coefficient, so as to achieve the supplementary drying of the coal sample to be dried.
[0037] In some embodiments of the present application, it further includes an evaluation module, which is used to: obtain the gray-scale image of the coal sample after drying, calculate the difference between the average gray-scale value of the gray-scale image of the coal sample after drying and the average gray-scale value of the initial coal sample gray-scale image to obtain the drying completion degree; judge whether the drying completion degree is greater than the second preset threshold. If the drying completion degree is greater than the second preset threshold, set the first level as the completion level of the dryer; if the drying completion degree is less than or equal to the second preset threshold, judge whether the drying completion degree is greater than the third preset threshold; if the drying completion degree is greater than the third preset threshold, set the second level as the completion level of the dryer; if the drying completion degree is less than or equal to the third preset threshold, set the third level as the completion level of the dryer.
[0038] In this embodiment, the completion level of the dryer is evaluated based on the calculated drying completion degree of the coal sample after drying. The greater the Hong'an completion degree, the higher the corresponding completion level ranking. By evaluating the completion level of the dryer, it is convenient to more accurately adjust the dryer during the next coal sample drying.
[0039] Based on the same technical concept, as Figure 2 shown, the present invention also provides an intelligent dryer monitoring method, including: S101, obtaining historical coal sample drying data, and determining the drying characteristic coefficient of the coal sample to be dried according to the historical coal sample drying data; S102, obtaining the coal quality coefficient and the drying characteristic coefficient of the coal sample to be dried, and determining the unit drying amount according to the coal quality coefficient and the drying characteristic coefficient of the coal sample to be dried; S103, obtaining the coal sample image of the coal sample to be dried during the drying process, and determining the drying state coefficient according to the coal sample image of the coal sample to be dried during the drying process; S104, determining the supplementary drying characteristic coefficient according to the drying state coefficient, and adjusting the unit drying amount according to the supplementary drying characteristic coefficient.
[0040] By applying the above technical solution, the present invention includes an analysis module for obtaining historical coal sample drying data and determining a drying characteristic coefficient of the coal sample to be dried based on the historical coal sample drying data; a calculation module for obtaining the coal quality coefficient and the drying characteristic coefficient of the coal sample to be dried and determining the unit drying amount based on the coal quality coefficient and the drying characteristic coefficient of the coal sample to be dried; an image module for obtaining the coal sample image during the drying process of the coal sample to be dried and determining a drying state coefficient based on the coal sample image during the drying process of the coal sample to be dried; and an adjustment module for determining a supplementary drying characteristic coefficient based on the drying state coefficient and adjusting the unit drying amount according to the supplementary drying characteristic coefficient. By monitoring the coal sample drying process in real time, the present invention improves the accuracy and stability of the dryer temperature control and enhances the drying quality of the coal sample.
[0041] Through the description of the above embodiments, those skilled in the art can clearly understand that the present invention can be implemented by hardware or by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present invention can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, a USB flash drive, a mobile hard disk, etc.) and includes several instructions for causing a computer device (such as a personal computer, a server, or a network device, etc.) to execute the methods described in various implementation scenarios of the present invention.
[0042] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and are not intended to limit them. Although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments or perform equivalent replacements for some of the technical features. However, such modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present application.
Claims
1. An intelligent dryer monitoring system, characterized in that: include: An analysis module is used to obtain historical coal sample drying data and determine the drying characteristic coefficient of the coal sample to be dried based on the historical coal sample drying data; A calculation module, used for obtaining the coal quality coefficient and drying characteristic coefficient of the coal sample to be dried, and determining the unit drying amount according to the coal quality coefficient and drying characteristic coefficient of the coal sample to be dried; An image module is used to obtain a coal sample image of the coal sample to be dried during the drying process, and determine a drying state coefficient according to the coal sample image of the coal sample to be dried during the drying process; The adjustment module is used to determine the supplementary drying characteristic coefficient according to the drying state coefficient, and adjust the unit drying amount according to the supplementary drying characteristic coefficient.
2. The intelligent dryer monitoring system according to claim 1 is characterized in that: The method of determining the drying characteristic coefficient of the coal sample to be dried according to the historical coal sample drying data includes: Determine the moisture content change of each type of coal sample during the drying process based on historical coal sample drying data, and draw a moisture content change curve based on the moisture content change of each type of coal sample during the drying process; Calculating the first correlation coefficient of the moisture content change curve, and clustering the types of coal samples according to the first correlation coefficient of the moisture content change curve; The cluster center value of each coal sample type is determined according to the clustering result, and the cluster center value of the coal sample to be dried is determined according to the coal sample type of the coal sample to be dried, so as to obtain the drying characteristic coefficient of the coal sample to be dried.
3. The intelligent dryer monitoring system according to claim 2 is characterized in that: The clustering of the coal sample types according to the first correlation coefficient of the moisture content variation curve includes: A sample data set is established according to the first correlation coefficient of the moisture content variation curve of each coal sample type, and k initial clustering centers of the sample data set are randomly selected; Calculate the Euclidean distance from the first correlation coefficient in the sample data set to the initial cluster center, and divide the coal sample types corresponding to the first correlation coefficient into corresponding clusters according to the Euclidean distance from the first correlation coefficient in the sample data set to the initial cluster center; Calculate the average value of the first correlation coefficient in each cluster, and recalculate the cluster center according to the average value of the first correlation coefficient in each cluster; The above steps are iterated repeatedly until the cluster center no longer changes or the number of iterations reaches the preset maximum number of iterations, and the clustering results of the coal sample types are obtained.
4. The intelligent dryer monitoring system according to claim 1 is characterized in that: The method of determining the unit drying amount according to the coal quality coefficient and the drying characteristic coefficient of the coal sample to be dried includes: Determine the coal quality coefficient and drying characteristic coefficient of the historical coal sample and the corresponding unit drying amount according to the historical coal sample drying data, and establish a training sample set according to the coal quality coefficient and drying characteristic coefficient of the historical coal sample and the corresponding unit drying amount; An initial drying amount evaluation model is established according to the training sample set, and the initial drying amount evaluation model is trained to obtain a trained drying amount evaluation model; The coal quality coefficient and drying characteristic coefficient of the coal sample to be dried are input into the trained drying quantity evaluation model to obtain the unit drying quantity of the coal sample to be dried.
5. The intelligent dryer monitoring system according to claim 4 is characterized in that: The coal quality coefficient specifically refers to the initial moisture content and density of the coal sample.
6. The intelligent dryer monitoring system according to claim 1 is characterized in that: The step of determining the drying state according to the coal sample image of the coal sample to be dried during the drying process includes: Grayscale processing is performed on the coal sample image of the dried coal sample in the drying process to obtain a coal sample grayscale image; Segment the coal sample grayscale image to obtain a number of sub-coal sample grayscale images; Calculate the average grayscale value of the grayscale image of each sub-coal sample, and draw a curve of the average grayscale value change of the coal sample grayscale image within a preset period; Calculate the second correlation coefficient of any two average gray value change curves, screen out the average gray value change curves whose second correlation coefficients are greater than the first preset threshold, and set the number of the second correlation coefficients of the average gray value change curves greater than the first preset threshold as the drying fluctuation value; The variation of the average grayscale value of the coal sample grayscale image within a preset period is obtained, and the variation of the average grayscale value within the preset period is multiplied by the drying fluctuation value to obtain the drying state coefficient.
7. The intelligent dryer monitoring system according to claim 1 is characterized in that: The method of determining the supplementary drying characteristic coefficient according to the drying state coefficient comprises: The supplementary drying characteristic coefficient is determined according to the supplementary drying characteristic coefficient calculation formula, wherein the supplementary drying characteristic coefficient calculation formula is specifically: ， in, To supplement the drying characteristic coefficient, is the average grayscale value of the current coal sample grayscale image, is the drying state coefficient, is the preset standard state coefficient, is the preset range coefficient, is a natural exponential function.
8. The intelligent dryer monitoring system according to claim 7 is characterized in that: The step of adjusting the unit drying amount according to the supplementary drying characteristic coefficient includes: Obtaining a preset standard supplementary coefficient, and calculating a ratio of the supplementary drying characteristic coefficient to the preset standard supplementary coefficient; The unit drying amount of the next preset cycle is obtained by multiplying the ratio of the supplementary drying characteristic coefficient to the preset standard supplementary coefficient to complete the adjustment of the unit drying amount.
9. The intelligent dryer monitoring system according to claim 1, characterized in that: Also included are evaluation modules for: Obtaining a grayscale image of the coal sample after drying, calculating the difference between the average grayscale value of the grayscale image of the coal sample after drying and the average grayscale value of the initial coal sample grayscale image, and obtaining the degree of drying completion; determining whether the drying completion degree is greater than a second preset threshold, and if the drying completion degree is greater than the second preset threshold, setting the first level as the completion level of the dryer; If the drying completion degree is less than or equal to the second preset threshold, determining whether the drying completion degree is greater than a third preset threshold; If the drying completion degree is greater than the third preset threshold, setting the second level as the completion level of the dryer; If the drying completion degree is less than or equal to the third preset threshold, the third level is set as the completion level of the dryer.
10. An intelligent dryer monitoring method, characterized in that: include: Obtain historical coal sample drying data, and determine the drying characteristic coefficient of the coal sample to be dried based on the historical coal sample drying data; Obtaining the coal quality coefficient and drying characteristic coefficient of the coal sample to be dried, and determining the unit drying amount according to the coal quality coefficient and drying characteristic coefficient of the coal sample to be dried; Acquire a coal sample image of the coal sample to be dried during the drying process, and determine a drying state coefficient according to the coal sample image of the coal sample to be dried during the drying process; The supplementary drying characteristic coefficient is determined according to the drying state coefficient, and the unit drying amount is adjusted according to the supplementary drying characteristic coefficient.