Intelligent control method and system of wind compressor in coal preparation plant based on big data analysis

Abstract

The present invention discloses an intelligent control method and system for a wind compressor in a coal preparation plant based on big data analysis, which belongs to the field of big data. The present invention imports the separation effect, retention effect and particle distribution data of this coal obtained by analysis into a wind compressor power analysis strategy to analyze the required power of the wind compressor, and adjusts the wind compressor power to the required power of the wind compressor obtained by analysis in real time. The power matching degree of the wind compressor is accurately analyzed by comprehensively analyzing the coal particle separation effect and retention effect of historical periods, and then the required power of the wind compressor for this coal is comprehensively analyzed according to the power matching degree of the wind compressor, the particle distribution data of this coal and the particle distribution data of coal in historical periods, thereby improving the accuracy of matching the required power of the wind compressor with the particle distribution data of this coal, and further improving the separation effect of the coal.

Description

Technical Field

[0001] The present invention belongs to the field of big data, and specifically relates to an intelligent control method and system for an air compressor in a coal preparation plant based on big data analysis. Background Art

[0002] A coal preparation plant is a coal processing plant that sorts coal, removes mineral impurities from raw coal, and separates it into products of different specifications. The main products of a coal preparation plant are clean coal and lump coal, in addition to by-products such as medium coal and coal slime. In the production process of a coal preparation plant, the raw coal is first screened and crushed in a screening and crushing workshop, and then sorted by a jig in the main plant to obtain three products: clean coal, medium coal, and gangue. Medium coal and gangue are dehydrated and shipped out as the final product; clean coal is further graded and dehydrated to obtain the final product. The function of the wind compressor in a coal preparation plant is mainly to provide gas power so that the materials can be separated during the coal preparation process. In the physical sorting stage of coal, the wind compressor mainly plays the role of compressing air and spraying it onto the material, and the material is separated by the impact force of the gas. Therefore, how to ensure that the output power of the wind compressor matches the particle distribution data of this coal is a problem that needs to be solved. If the wind is strong, it is easy to cause the coal of the particle size that needs to be retained to be blown away, and if the wind is weak, it is easy to cause the coal of the particle size that needs to be separated to be retained;

[0003] When ensuring that the output power of the wind compressor matches the particle distribution data of the coal this time, the prior art ignores the comprehensive analysis of the coal particle separation effect and retention effect of the historical period on the power matching degree of the wind compressor, resulting in the inability to comprehensively analyze the power required by the wind compressor for this coal through the power matching degree of the wind compressor, the particle distribution data of the coal this time and the particle distribution data of the coal in the historical period, resulting in unsatisfactory coal separation effect. Most of the prior art has the above problems;

[0004] The above patents all have the problems raised by this background technology. In order to solve the problems raised by this background technology, this application designs an intelligent control method and system for wind compressors in coal preparation plants based on big data analysis. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention proposes an intelligent control method and system for air compressors in a coal preparation plant based on big data analysis.

[0006] To achieve the above object, the present invention provides the following technical solution: an intelligent control method for wind compressors in coal preparation plants based on big data analysis, which comprises the following specific steps:

[0007] Obtain coal image data on the conveying mechanism during the operation of the air compressor, and obtain coal particle distribution data;

[0008] Obtain coal image data on the conveying mechanism during the operation of the air compressor, and obtain coal particle distribution data;

[0009] Import the coal particle distribution data of historical coal into the retention effect analysis strategy to analyze the retention effect;

[0010] The separation effect, retention effect and particle distribution data of the coal obtained from the analysis are imported into the wind compressor power analysis strategy to analyze the required power of the wind compressor;

[0011] The wind compressor power is adjusted in real time to the wind compressor required power obtained through analysis.

[0012] As a preferred technical solution for the intelligent control method of the wind compressor in a coal preparation plant based on big data analysis, the specific contents of obtaining the coal image data on the conveying mechanism during the operation of the wind compressor and obtaining the coal particle distribution data are as follows:

[0013] S11, real-time acquisition of coal image data on the conveying mechanism during the operation of the wind compressor, acquisition of coal particle images on the conveying mechanism before and after the air outlet, importing into image contour processing software, acquisition of the particle size distribution of coal particles on the corresponding conveying mechanism before and after the air outlet, and acquisition of operating power data during the operation of the wind compressor;

[0014] The specific steps of calculating the particle size distribution of coal particles in the statistical image are as follows:

[0015] S111, Image preprocessing:

[0016] Import image: import the collected coal particle image into the image processing software;

[0017] Denoising: Use filters (such as Gaussian filters, median filters, etc.) to remove noise from the image to improve the accuracy of particle recognition;

[0018] Contrast enhancement: Improve the visibility of particles in an image through histogram equalization or other contrast enhancement methods;

[0019] S112, Particle Identification and Segmentation:

[0020] Apply threshold segmentation: set a suitable threshold to binarize the image in order to separate particles from the background;

[0021] Edge detection: Use edge detection algorithms (such as Canny edge detection) to identify the outline of particles;

[0022] Morphological operations: further cleaning and separation of particles through dilation and erosion operations;

[0023] S113, Particle size measurement:

[0024] Contour tracking: For each detected particle, track its contour and determine its boundary;

[0025] Particle size parameter calculation: Calculate the particle size parameters of each particle based on the profile information, such as equivalent diameter (such as area equivalent diameter, perimeter equivalent diameter, etc.), area, etc.;

[0026] S114, Particle size distribution statistics:

[0027] Particle size classification: classify the measured particle size parameters according to a specific range; Frequency statistics: count the number of particles in each particle size range;

[0028] S12, obtaining the particle size distribution of the coal particles on the corresponding conveying mechanism before the air outlet, comparing the obtained particle size of the coal particles with the rated particle size of the coal particles to be removed, if the particle size of the coal particles is less than or equal to the rated particle size of the coal particles to be removed, it is set as removed particles, if the particle size of the coal particles is greater than the rated particle size of the coal particles to be removed, it is set as retained particles, and the coal particles on the corresponding conveying mechanism before the air outlet are classified into removed particles and retained particles;

[0029] S13, obtaining the distribution of removed particles and retained particles of the coal particles on the conveying mechanism at the corresponding position after passing through the air outlet.

[0030] As a preferred technical solution for the intelligent control method of the wind compressor in a coal preparation plant based on big data analysis, the method of importing the historical coal impurity particle distribution data into the separation effect analysis strategy for analyzing the separation effect includes the following specific steps:

[0031] S21, obtaining the distribution of removed particles and the particle size data of each removed particle on the conveying mechanism at the corresponding positions before and after the air outlet within a set period;

[0032] S22, the obtained distribution of removed particles on the conveying mechanism at the corresponding positions before and after the air outlet within the set period and the particle size data of each removed particle are introduced into the separation effect evaluation value calculation formula to calculate the separation effect evaluation value, wherein the separation effect evaluation value is: , where T is the set time duration, mt is the number of removed particles on the conveying mechanism after passing the corresponding position of the air outlet at time t, kt is the number of removed particles on the conveying mechanism before passing the corresponding position of the air outlet at time t, dt is the time integral, sti is the average diameter of the i-th removed particles on the conveying mechanism after passing the corresponding position of the air outlet at time t, hti is the distance from the i-th removed particles on the conveying mechanism after passing the corresponding position of the air outlet at time t to the vertical center line of the air outlet, dt is the time integral, Stj is the average diameter of the j-th removed particles on the conveying mechanism before passing the corresponding position of the air outlet at time t, and Htj is the distance from the j-th removed particles on the conveying mechanism before passing the corresponding position of the air outlet at time t to the vertical center line of the air outlet.

[0033] As a preferred technical solution for the intelligent control method of the wind compressor in a coal preparation plant based on big data analysis, the method of importing the coal particle distribution data of historical coal into the retention effect analysis strategy for analyzing the retention effect includes the following specific steps:

[0034] S31, obtaining the distribution of retained particles on the conveying mechanism at corresponding positions before and after the air outlet within a set period and the particle size data of each retained particle;

[0035] S32, importing the obtained distribution of retained particles on the conveying mechanism at the corresponding positions before and after the air outlet within the set period and the particle size data of each retained particle into the retention effect evaluation value calculation formula to calculate the retention effect evaluation value, wherein the retention effect evaluation value calculation formula is: , where Mt is the number of retained particles on the conveying mechanism after passing the corresponding position of the air outlet at time t, and Kt is the number of retained particles on the conveying mechanism before passing the corresponding position of the air outlet at time t, is the average diameter of the sth retained particles on the conveying mechanism before passing the corresponding position of the air outlet at time t, and ptc is the average diameter of the cth retained particles on the conveying mechanism after passing the corresponding position of the air outlet at time t.

[0036] As a preferred technical solution for the intelligent control method of the wind compressor in the coal preparation plant based on big data analysis, the separation effect, retention effect and particle distribution data of the coal obtained by analysis are imported into the wind compressor power analysis strategy to analyze the required power of the wind compressor, including the following specific contents:

[0037] S41, obtaining the particle size distribution of the coal particles to be separated this time and the particle size distribution of the coal particles in the previous cycle, and importing them into the particle size distribution comparison value calculation formula to calculate the particle size distribution comparison value, wherein the particle size distribution comparison value calculation formula is: ,in, To retain the particle fraction coefficient, is the particle removal ratio, The average particle size of the retained particles in the coal particles that need to be separated this time, is the average particle size of the retained particles in the coal particles separated in the previous cycle, is the average particle size of the particles to be removed from the coal particles to be separated this time, is the average particle size of the particles removed from the coal particles separated in the previous cycle;

[0038] S42, obtaining the calculated particle size distribution comparison value, the separation effect evaluation value of the previous cycle, the retention effect evaluation value of the previous cycle and the wind compressor output power of the previous cycle, and importing them into the wind compressor required power calculation formula to calculate the wind compressor required power, wherein the wind compressor required power calculation formula is: , Pm is the wind compressor output power of the previous cycle, ln () is the logarithm with the natural constant e as the base, is the separation effect ratio.

[0039] As a preferred technical solution for the intelligent control method of the wind compressor in a coal preparation plant based on big data analysis, the real-time adjustment of the wind compressor power to the wind compressor required power obtained by analysis includes the following specific contents:

[0040] The required power of the wind compressor obtained by analysis is obtained, and the power of the wind compressor is adjusted in real time to the required power of the wind compressor obtained by analysis.

[0041] An intelligent control system for a wind compressor in a coal preparation plant based on big data analysis, which is implemented based on the above-mentioned intelligent control method for a wind compressor in a coal preparation plant based on big data analysis, and specifically includes a data acquisition module, a separation effect analysis module, a retention effect analysis module, a required power analysis module, a power adjustment module and a control module;

[0042] The data acquisition module is used to acquire the coal image data on the conveying mechanism during the operation of the wind press and acquire the coal particle distribution data;

[0043] The separation effect analysis module is used to import the historical coal impurity particle distribution data into the separation effect analysis strategy to analyze the separation effect;

[0044] The retention effect analysis module is used to import the coal particle distribution data of historical coal into the retention effect analysis strategy to analyze the retention effect;

[0045] The required power analysis module is used to import the separation effect, retention effect and particle distribution data of the coal obtained by analysis into the wind compressor power analysis strategy to analyze the required power of the wind compressor;

[0046] The power adjustment module is used to adjust the power of the wind compressor to the power required by the wind compressor obtained by analysis;

[0047] The control module is used for controlling the operation of the data acquisition module, the separation effect analysis module, the retention effect analysis module, the required power analysis module and the power regulation module.

[0048] An electronic device comprises: a processor and a memory, wherein the memory stores a computer program that can be called by the processor;

[0049] The processor executes the above-mentioned intelligent control method of the wind compressor in the coal preparation plant based on big data analysis by calling the computer program stored in the memory.

[0050] A computer-readable storage medium stores instructions, which, when executed on a computer, enable the computer to execute the above-mentioned intelligent control method for an air compressor in a coal preparation plant based on big data analysis.

[0051] Compared with the prior art, the beneficial effects of this application are:

[0052] The present application obtains coal image data on the conveying mechanism during the operation of the wind compressor, obtains coal particle distribution data, imports historical coal impurity particle distribution data into the separation effect analysis strategy to analyze the separation effect, imports historical coal coal particle distribution data into the retention effect analysis strategy to analyze the retention effect, imports the separation effect, retention effect and the particle distribution data of this coal obtained by analysis into the wind compressor power analysis strategy to analyze the required power of the wind compressor, adjusts the wind compressor power to the required power of the wind compressor obtained by analysis in real time, accurately analyzes the power matching degree of the wind compressor by comprehensively analyzing the coal particle separation effect and retention effect of the historical period, and then comprehensively analyzes the required power of the wind compressor for this coal according to the power matching degree of the wind compressor, the particle distribution data of this coal and the particle distribution data of the coal in the historical period, thereby improving the accuracy of matching the required power of the wind compressor with the particle distribution data of this coal, and further improving the separation effect of the coal. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 It is a schematic diagram of the overall process of the intelligent control method of the wind compressor in a coal preparation plant based on big data analysis of the present invention;

[0054] Figure 2 This is a schematic diagram of step S1 of the intelligent control method for wind compressors in coal preparation plants based on big data analysis of the present invention;

[0055] Figure 3It is a schematic diagram of the particle size statistics step of the intelligent control method of the wind compressor in the coal preparation plant based on big data analysis of the present invention;

[0056] Figure 4 It is a schematic diagram of step S4 of the intelligent control method of the wind compressor in a coal preparation plant based on big data analysis of the present invention;

[0057] Figure 5 The figure is a schematic diagram of the composition of the intelligent control system of the wind compressor in the coal preparation plant based on big data analysis of the present invention. DETAILED DESCRIPTION

[0058] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. The following description of at least one exemplary embodiment is actually only illustrative and is by no means intended to limit the present application and its application or use.

[0059] Example 1

[0060] In order to solve the technical problems raised in the background technology, the present invention provides a preferred embodiment: Figure 1-Figure 2 As shown, the intelligent control method of the wind compressor in a coal preparation plant based on big data analysis includes the following specific steps:

[0061] S1. Obtaining coal image data on the conveying mechanism during the operation of the air compressor, and obtaining coal particle distribution data;

[0062] In one specific embodiment, the specific contents of obtaining the coal image data on the conveying mechanism during the operation of the wind press and obtaining the coal particle distribution data are as follows:

[0063] S11, real-time acquisition of coal image data on the conveying mechanism during the operation of the wind compressor, acquisition of coal particle images on the conveying mechanism before and after the air outlet, importing into image contour processing software, acquisition of the particle size distribution of coal particles on the corresponding conveying mechanism before and after the air outlet, and acquisition of operating power data during the operation of the wind compressor;

[0064] Among them, Figure 3 As shown in the figure, the specific steps of statistically analyzing the particle size distribution of coal particles in the image are as follows:

[0065] S111, Image preprocessing:

[0066] Import image: import the collected coal particle image into the image processing software;

[0067] Denoising: Use filters (such as Gaussian filters, median filters, etc.) to remove noise from the image to improve the accuracy of particle recognition;

[0068] Contrast enhancement: Improve the visibility of particles in an image through histogram equalization or other contrast enhancement methods;

[0069] S112, Particle Identification and Segmentation:

[0070] Apply threshold segmentation: set a suitable threshold to binarize the image in order to separate particles from the background;

[0071] Edge detection: Use edge detection algorithms (such as Canny edge detection) to identify the outline of particles;

[0072] Morphological operations: further cleaning and separation of particles through dilation and erosion operations;

[0073] S113, Particle size measurement:

[0074] Contour tracking: For each detected particle, track its contour and determine its boundary;

[0075] Particle size parameter calculation: Calculate the particle size parameters of each particle based on the profile information, such as equivalent diameter (such as area equivalent diameter, perimeter equivalent diameter, etc.), area, etc.;

[0076] S114, Particle size distribution statistics:

[0077] Particle size classification: classify the measured particle size parameters according to a specific range; Frequency statistics: count the number of particles in each particle size range;

[0078] S12, obtaining the particle size distribution of the coal particles on the corresponding conveying mechanism before the air outlet, comparing the obtained particle size of the coal particles with the rated particle size of the coal particles to be removed, if the particle size of the coal particles is less than or equal to the rated particle size of the coal particles to be removed, it is set as removed particles, if the particle size of the coal particles is greater than the rated particle size of the coal particles to be removed, it is set as retained particles, and the coal particles on the corresponding conveying mechanism before the air outlet are classified into removed particles and retained particles;

[0079] S13, obtaining the distribution of removed particles and retained particles of coal particles on the conveying mechanism at the corresponding position after the air outlet;

[0080] Through this step, the removed particles and the retained particles are classified to obtain the distribution of the removed particles and the retained particles of the coal particles on the conveying mechanism at the corresponding position;

[0081] S2. Obtaining coal image data on the conveying mechanism during the operation of the air compressor, and obtaining coal particle distribution data;

[0082] In one specific embodiment, the process of importing historical coal impurity particle distribution data into the separation effect analysis strategy to analyze the separation effect includes the following specific steps:

[0083] S21, obtaining the distribution of removed particles and the particle size data of each removed particle on the conveying mechanism at the corresponding positions before and after the air outlet within a set period; it should be noted that the set period here is set manually by experience, and the removal time close to the time of this analysis is selected, preferably the removal time of the last batch of removed particles;

[0084] S22, the obtained distribution of removed particles on the conveying mechanism at the corresponding positions before and after the air outlet within the set period and the particle size data of each removed particle are introduced into the separation effect evaluation value calculation formula to calculate the separation effect evaluation value, wherein the separation effect evaluation value is: , where T is the set time duration, mt is the number of removed particles on the conveying mechanism after passing the corresponding position of the air outlet at time t, kt is the number of removed particles on the conveying mechanism before passing the corresponding position of the air outlet at time t, dt is the time integral, sti is the average diameter of the i-th removed particles on the conveying mechanism after passing the corresponding position of the air outlet at time t, hti is the distance from the i-th removed particles on the conveying mechanism after passing the corresponding position of the air outlet at time t to the vertical center line of the air outlet, dt is the time integral, Stj is the average diameter of the j-th removed particles on the conveying mechanism before passing the corresponding position of the air outlet at time t, and Htj is the distance from the j-th removed particles on the conveying mechanism before passing the corresponding position of the air outlet at time t to the vertical center line of the air outlet.

[0085] This step is used to analyze the removal effect of the particles to be removed, and in this step, the distance from the removed particles to the vertical center line of the air outlet and the average diameter of the removed particles (i.e., particle size) are combined to analyze the difficulty of removing the particles, i.e. , that is, the closer the distance to the vertical center line of the air outlet is, the easier it is to remove the particles with smaller particle sizes. Then, the removal effect is weighted according to the difficulty of removal. That is, the smaller the removal difficulty, the greater the removal effect. That is, if the particles with smaller removal difficulty are not removed, the removal effect is worse.

[0086] S3, importing the coal particle distribution data of historical coal into the retention effect analysis strategy to analyze the retention effect;

[0087] In one specific embodiment, importing the coal particle distribution data of historical coal into the retention effect analysis strategy to analyze the retention effect includes the following specific steps:

[0088] S31, obtaining the distribution of retained particles on the conveying mechanism at corresponding positions before and after the air outlet within a set period and the particle size data of each retained particle;

[0089] S32, importing the obtained distribution of retained particles on the conveying mechanism at the corresponding positions before and after the air outlet within the set period and the particle size data of each retained particle into the retention effect evaluation value calculation formula to calculate the retention effect evaluation value, wherein the retention effect evaluation value calculation formula is: , where Mt is the number of retained particles on the conveying mechanism after passing the corresponding position of the air outlet at time t, and Kt is the number of retained particles on the conveying mechanism before passing the corresponding position of the air outlet at time t, is the average diameter of the sth retained particles on the conveying mechanism before passing the corresponding position of the air outlet at time t, and ptc is the average diameter of the cth retained particles on the conveying mechanism after passing the corresponding position of the air outlet at time t;

[0090] S4, importing the separation effect, retention effect and particle distribution data of the coal obtained from the analysis into the wind compressor power analysis strategy to analyze the required power of the wind compressor;

[0091] In one specific embodiment, the separation effect, retention effect and particle distribution data of the coal obtained by analysis are introduced into the wind compressor power analysis strategy to analyze the required power of the wind compressor, including the following specific contents:

[0092] S41, obtaining the particle size distribution of the coal particles to be separated this time and the particle size distribution of the coal particles in the previous cycle, and importing them into the particle size distribution comparison value calculation formula to calculate the particle size distribution comparison value, wherein the particle size distribution comparison value calculation formula is: ,in, To retain the particle fraction coefficient, is the particle removal ratio, The average particle size of the retained particles in the coal particles that need to be separated this time, is the average particle size of the retained particles in the coal particles separated in the previous cycle, is the average particle size of the particles to be removed from the coal particles to be separated this time, is the average particle size of the particles removed from the coal particles separated in the previous cycle;

[0093] S42, obtaining the calculated particle size distribution comparison value, the separation effect evaluation value of the previous cycle, the retention effect evaluation value of the previous cycle and the wind compressor output power of the previous cycle, and importing them into the wind compressor required power calculation formula to calculate the wind compressor required power, wherein the wind compressor required power calculation formula is: , Pm is the wind compressor output power of the previous cycle, ln () is the logarithm with the natural constant e as the base, is the separation effect ratio;

[0094] S5. Adjust the wind compressor power in real time to the wind compressor required power obtained by analysis;

[0095] In one specific embodiment, adjusting the wind compressor power to the wind compressor required power obtained by analysis in real time includes the following specific contents:

[0096] The required power of the wind compressor obtained by analysis is obtained, and the power of the wind compressor is adjusted in real time to the required power of the wind compressor obtained by analysis.

[0097] It should be noted here that the method for determining the values ​​of the retained particle ratio coefficient, the removed particle ratio coefficient and the separation effect ratio is preferably: obtaining 500 sets of coal image data and output power data on the conveying mechanism during the operation of the wind compressor, substituting them into the wind compressor required power calculation formula to calculate the wind compressor required power, obtaining the coal screening conditions at each power level, hiring experts to score the screening conditions of these coals, and then arranging the scores in descending order, importing the highest scoring power value and the calculated wind compressor required power into the fitting software, and outputting the values ​​of the retained particle ratio coefficient, the removed particle ratio coefficient and the separation effect ratio that meet the highest scoring power value judgment accuracy.

[0098] The advantages of this embodiment over the prior art are as follows: obtaining coal image data on the conveying mechanism during the operation of the wind compressor, obtaining coal particle distribution data, importing historical coal impurity particle distribution data into the separation effect analysis strategy to analyze the separation effect, importing historical coal coal particle distribution data into the retention effect analysis strategy to analyze the retention effect, importing the separation effect, retention effect and the particle distribution data of this coal obtained by analysis into the wind compressor power analysis strategy to analyze the required power of the wind compressor, adjusting the wind compressor power to the required power of the wind compressor obtained by analysis in real time, accurately analyzing the matching degree of wind compressor power by comprehensively analyzing the coal particle separation effect and retention effect of historical periods, and then comprehensively analyzing the required power of the wind compressor for this coal according to the matching degree of wind compressor power, the particle distribution data of this coal and the particle distribution data of coal in historical periods, thereby improving the accuracy of matching the required power of the wind compressor with the particle distribution data of this coal, and further improving the separation effect of coal.

[0099] Example 2

[0100] like Figure 5As shown, the intelligent control system of the wind compressor in the coal preparation plant based on big data analysis is implemented based on the intelligent control method of the wind compressor in the coal preparation plant based on big data analysis in Example 1, which specifically includes a data acquisition module, a separation effect analysis module, a retention effect analysis module, a required power analysis module, a power adjustment module and a control module;

[0101] The data acquisition module is used to acquire the coal image data on the conveying mechanism during the operation of the wind compressor and acquire the coal particle distribution data;

[0102] The separation effect analysis module is used to import the historical coal impurity particle distribution data into the separation effect analysis strategy to analyze the separation effect;

[0103] A retention effect analysis module is used to import the coal particle distribution data of historical coal into the retention effect analysis strategy to analyze the retention effect;

[0104] A power analysis module is required to import the separation effect, retention effect and particle distribution data of the coal obtained from the analysis into the wind compressor power analysis strategy to analyze the power required by the wind compressor;

[0105] A power regulating module, used for regulating the power of the wind compressor to the power required by the wind compressor obtained through analysis;

[0106] The control module is used for controlling and operating the data acquisition module, the separation effect analysis module, the retention effect analysis module, the power required analysis module and the power regulation module.

[0107] Example 3

[0108] This embodiment provides an electronic device, including: a processor and a memory, wherein the memory stores a computer program that can be called by the processor;

[0109] The processor executes the intelligent control method of the wind compressor in the coal preparation plant based on big data analysis in Example 1 by calling the computer program stored in the memory.

[0110] The electronic device may have relatively large differences due to different configurations or performances, and may include one or more processors and one or more memories, wherein the memory stores at least one computer program, and the computer program is loaded and executed by the processor to implement the intelligent control method of wind compressors in coal preparation plants based on big data analysis provided by the above method embodiment. The electronic device may also include other components for realizing the functions of the device, for example, the electronic device may also have components such as wired or wireless network interfaces and input and output interfaces for data input and output. This embodiment will not be described in detail here.

[0111] Example 4

[0112] This embodiment provides a computer-readable storage medium having a rewritable computer program stored thereon;

[0113] When the computer program runs on a computer device, the computer device executes the intelligent control method for wind compressors in a coal preparation plant based on big data analysis in Example 1.

[0114] For example, the computer readable storage medium can be a read-only memory, a random access memory, a read-only CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.

[0115] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When a computer instruction or computer program is loaded or executed on a computer, a process or function according to an embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network or other programmable device. Computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center through a wired network or / and a wireless network. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

Claims

1. An intelligent control method for wind compressors in coal preparation plants based on big data analysis, characterized in that: It includes the following specific steps: Obtain coal image data on the conveying mechanism during the operation of the air compressor, and obtain coal particle distribution data; Import the historical coal impurity particle distribution data into the separation effect analysis strategy to analyze the separation effect; Import the coal particle distribution data of historical coal into the retention effect analysis strategy to analyze the retention effect; The separation effect, retention effect and particle distribution data of the coal obtained from the analysis are imported into the wind compressor power analysis strategy to analyze the required power of the wind compressor; Adjust the wind compressor power in real time to the wind compressor required power obtained through analysis; The method of importing the historical coal impurity particle distribution data into the separation effect analysis strategy to analyze the separation effect includes the following specific steps: Obtain the distribution of removed particles and the particle size data of each removed particle on the conveying mechanism at the corresponding positions before and after the air outlet within a set period; The obtained particle removal distribution on the conveying mechanism at the corresponding position before and after the air outlet within the set period and the particle size data of each particle removal are introduced into the separation effect evaluation value calculation formula to calculate the separation effect evaluation value, where the separation effect evaluation value is: , where T is the duration of the set time, mt is the number of particles removed from the conveying mechanism after passing the corresponding position of the air outlet at time t, kt is the number of particles removed from the conveying mechanism before passing the corresponding position of the air outlet at time t, dt is the time integral, sti is the average diameter of the i-th particle removed from the conveying mechanism after passing the corresponding position of the air outlet at time t, hti is the distance from the i-th particle removed from the conveying mechanism after passing the corresponding position of the air outlet at time t to the vertical center line of the air outlet, dt is the time integral, Stj is the average diameter of the j-th particle removed from the conveying mechanism before passing the corresponding position of the air outlet at time t, and Htj is the distance from the j-th particle removed from the conveying mechanism before passing the corresponding position of the air outlet at time t to the vertical center line of the air outlet; The method of importing the coal particle distribution data of historical coal into the retention effect analysis strategy to analyze the retention effect includes the following specific steps: Obtain the distribution of retained particles on the conveying mechanism at corresponding positions before and after the air outlet within a set period and the particle size data of each retained particle; The obtained retained particle distribution on the conveying mechanism at the corresponding position before and after the air outlet within the set period and the particle size data of each retained particle are imported into the retention effect evaluation value calculation formula to calculate the retention effect evaluation value, wherein the retention effect evaluation value calculation formula is: , where Mt is the number of retained particles on the conveying mechanism after passing the corresponding position of the air outlet at time t, and Kt is the number of retained particles on the conveying mechanism before passing the corresponding position of the air outlet at time t, is the average diameter of the sth retained particles on the conveying mechanism before passing the corresponding position of the air outlet at time t, and ptc is the average diameter of the cth retained particles on the conveying mechanism after passing the corresponding position of the air outlet at time t.

2. The intelligent control method for wind compressor in coal preparation plant based on big data analysis according to claim 1, characterized in that: The specific contents of obtaining the coal image data on the conveying mechanism during the operation of the wind press and obtaining the coal particle distribution data are as follows: Real-time acquisition of coal image data on the conveying mechanism during the operation of the wind compressor, acquisition of coal particle images on the conveying mechanism before and after the air outlet, import into the image contour processing software, acquisition of the particle size distribution of coal particles on the corresponding conveying mechanism before and after the air outlet, and acquisition of operating power data during the operation of the wind compressor; Obtain the particle size distribution of coal particles on the corresponding conveying mechanism before passing through the air outlet, compare the obtained particle size of coal particles with the rated removal particle size of coal particles, if the particle size of coal particles is less than or equal to the rated removal particle size of coal particles, set it as removed particles, if the particle size of coal particles is greater than the rated removal particle size of coal particles, set it as retained particles, and classify the removed particles and retained particles of the coal particles on the corresponding conveying mechanism before passing through the air outlet; The distribution of removed particles and retained particles of the coal particles on the conveying mechanism at the corresponding position after passing through the air outlet is obtained.

3. The intelligent control method for wind compressor in coal preparation plant based on big data analysis according to claim 1 is characterized in that: The analysis of the separation effect, retention effect and the particle distribution data of the coal obtained by the analysis into the wind compressor power analysis strategy to analyze the required power of the wind compressor includes the following specific contents: Obtain the particle size distribution of the coal particles to be separated this time and the particle size distribution of the coal particles in the previous cycle, and import them into the particle size distribution comparison value calculation formula to calculate the particle size distribution comparison value, where the particle size distribution comparison value calculation formula is: ,in, To retain the particle fraction coefficient, is the particle removal ratio, The average particle size of the retained particles in the coal particles that need to be separated this time, is the average particle size of the retained particles in the coal particles separated in the previous cycle, is the average particle size of the particles to be removed from the coal particles to be separated this time, is the average particle size of the particles removed from the coal particles separated in the previous cycle; The calculated particle size distribution comparison value, the separation effect evaluation value of the previous cycle, the retention effect evaluation value of the previous cycle and the wind compressor output power of the previous cycle are obtained and introduced into the wind compressor required power calculation formula to calculate the wind compressor required power, wherein the wind compressor required power calculation formula is: , Pm is the wind compressor output power of the previous cycle, ln () is the logarithm with the natural constant e as the base, is the separation effect ratio.

4. The intelligent control method for wind compressor in coal preparation plant based on big data analysis according to claim 3 is characterized in that: The real-time adjustment of the wind compressor power to the wind compressor required power obtained by analysis includes the following specific contents: The required power of the wind compressor obtained by analysis is obtained, and the power of the wind compressor is adjusted in real time to the required power of the wind compressor obtained by analysis.

5. An intelligent control system for a wind compressor in a coal preparation plant based on big data analysis, which is implemented based on the intelligent control method for a wind compressor in a coal preparation plant based on big data analysis as claimed in any one of claims 1 to 4, characterized in that: It specifically includes a data acquisition module, a separation effect analysis module, a retention effect analysis module, a required power analysis module, a power adjustment module and a control module; The data acquisition module is used to acquire the coal image data on the conveying mechanism during the operation of the wind press and acquire the coal particle distribution data; The separation effect analysis module is used to import the historical coal impurity particle distribution data into the separation effect analysis strategy to analyze the separation effect; The retention effect analysis module is used to import the coal particle distribution data of historical coal into the retention effect analysis strategy to analyze the retention effect; The required power analysis module is used to import the separation effect, retention effect and particle distribution data of the coal obtained by analysis into the wind compressor power analysis strategy to analyze the required power of the wind compressor; The power adjustment module is used to adjust the power of the wind compressor to the power required by the wind compressor obtained by analysis; The control module is used for controlling the operation of the data acquisition module, the separation effect analysis module, the retention effect analysis module, the required power analysis module and the power regulation module.

6. An electronic device comprising: A processor and a memory, wherein the memory stores a computer program that can be called by the processor; It is characterized in that the processor executes the intelligent control method for wind compressor in a coal preparation plant based on big data analysis as described in any one of claims 1 to 4 by calling the computer program stored in the memory.

7. A computer-readable storage medium, characterized in that: Instructions are stored, and when the instructions are executed on a computer, the computer executes the intelligent control method for wind compressors in a coal preparation plant based on big data analysis as described in any one of claims 1 to 4.

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