Intelligent coal preparation plant platform data processing system

Abstract

The invention relates to the technical field of computers, in particular to an intelligent coal preparation plant platform data processing system, and the system comprises a data collection module which is used for collecting raw coal parameters, quality parameters and environment parameters; the raw coal quality analysis module is used for analyzing the raw coal quality state; the grading module is used for grading the rotating speed of the sorting machine; the feedback monitoring module is used for constructing a density adjustment index and a rotating speed adjustment index; the data processing module is used for managing the medium density of the sorting machine and the rotating speed of the sorting machine in the next monitoring period; the dust analysis module is used for improving the management process of the rotating speed of the sorting machine in the next monitoring period according to the dust concentration collected in the monitoring period; and the noise analysis module is used for improving the analysis process of the dust state of the coal preparation plant according to the noise level collected in the monitoring period. According to the invention, the processing efficiency of the coal preparation plant platform data is effectively improved.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and particularly to an intelligent coal preparation plant platform data processing system. Background Art

[0002] Traditional coal preparation processes usually rely on manual detection and empirical judgment, resulting in low process efficiency, resource waste, and environmental pollution problems. With the progress of information technology and automation technology, the data processing system of intelligent coal preparation plants plays an increasingly important role in the coal separation process. By integrating various sensors, automated equipment, and data analysis technologies, intelligent coal preparation plants can monitor various quality parameters of raw coal in real time, thereby performing efficient quality analysis and dynamically adjusting separation parameters to achieve the goals of improving coal separation efficiency, improving coal quality, and reducing production costs.

[0003] Chinese Patent Publication No. CN114817385A discloses a data management system for a coal preparation plant application platform, including: a data acquisition module configured to collect multi-source heterogeneous data of the coal preparation plant information system according to batch data and stream data; a data governance module configured to govern the data collected by the data acquisition module; and a data quality control module configured to perform data quality control based on a pre-established data quality index system. This embodiment provides a data stream batch fusion processing mechanism, which collects multi-source heterogeneous data of the coal preparation plant information system according to batch data and stream data and performs data governance and quality control, improving the availability of coal preparation plant data; thus, it can be seen that when processing the data of the coal preparation plant, this solution only analyzes the quality of the data through the attributes of the data itself and does not process the data in combination with other influencing factors, resulting in the problem of low data processing efficiency of the coal preparation plant platform. Summary of the Invention

[0004] The purpose of the present invention is to provide an intelligent coal preparation plant platform data processing system to solve at least one of the problems existing in the prior art.

[0005] To achieve the above purpose, the present invention adopts the following technical solutions:

[0006] An intelligent coal preparation plant platform data processing system, including:

[0007] A data acquisition module for collecting raw coal parameters, quality parameters, and environmental parameters;

[0008] A raw coal quality analysis module for analyzing the quality status of raw coal according to the collected ash content, sulfur content, and moisture content of raw coal;

[0009] A grading module for grading the rotational speed of the separator according to the rotational speed of the separator collected within the monitoring period;

[0010] A feedback monitoring module, which is used to analyze the state of clean coal according to the clean coal ash content collected within a monitoring period, construct a clean coal anomaly index based on the state of clean coal, and is also used to analyze the state of tail coal according to the tail coal ash content collected within the monitoring period, construct a tail coal anomaly index based on the state of tail coal, and is further used to construct a density adjustment index and a rotational speed adjustment index according to the construction results of the clean coal anomaly index and the tail coal anomaly index;

[0011] A data processing module, which is used to manage the medium density and rotational speed of the separator in the next monitoring period according to the analysis result of the raw coal quality state, the classification result of the separator rotational speed level, and the construction results of the density adjustment index and the rotational speed adjustment index within the monitoring period.

[0012] Furthermore, the raw coal quality analysis module includes a quality index construction unit, which is used to construct an ash quality index according to the collected raw coal ash content a0 and the preset ash content a1. If a0 ≤ a1, the quality index construction unit sets the ash quality index to HF1. Otherwise, the quality index construction unit sets the ash quality index to HF2;

[0013] The quality index construction unit is also used to construct a sulfur quality index according to the collected raw coal sulfur content s0 and the preset sulfur content s1. If s0 ≤ s1, the quality index construction unit sets the sulfur quality index to SF1. Otherwise, the quality index construction unit sets the sulfur quality index to SF2;

[0014] The quality index construction unit is also used to construct a moisture quality index according to the collected raw coal moisture content h0 and the preset moisture content h1. If h0 ≤ h1, the quality index construction unit sets the moisture quality index to ZF1. Otherwise, the quality index construction unit sets the moisture quality index to ZF2.

[0015] Furthermore, the raw coal quality analysis module also includes a quality analysis unit, which is used to construct a raw coal quality index Y according to the ash quality index, the sulfur quality index, and the moisture quality index, and analyze the raw coal quality state according to the raw coal quality index Y and the preset quality index threshold Y0 to determine whether the raw coal quality state is abnormal.

[0016] Furthermore, the level classification module classifies the separator rotational speed level according to the collected separator rotational speed z0 and the preset rotational speed threshold z1 within the monitoring period, so as to classify the separator rotational speed level into a low rotational speed or a high rotational speed.

[0017] Further, the feedback monitoring module includes a status analysis unit, which is used to analyze the clean coal status based on the clean coal ash content jf0 collected within the monitoring period and the preset clean coal ash content jf1. If jf0 ≤ jf1, the status analysis unit determines that the clean coal status is normal and sets the clean coal anomaly index to JY1; otherwise, the status analysis unit determines that the clean coal status is abnormal and sets the clean coal anomaly index to JY2;

[0018] The status analysis unit also analyzes the tail coal status based on the tail coal ash content wf0 collected within the monitoring period and the preset tail coal ash content wf1. If wf0 ≤ wf1, the status analysis unit determines that the tail coal status is abnormal and sets the tail coal anomaly index to WY1; otherwise, the status analysis unit determines that the tail coal status is normal and sets the tail coal anomaly index to WY2.

[0019] Further, the feedback monitoring module also includes a construction unit, which is used to construct the density adjustment index MT based on the construction results of the clean coal anomaly index and the tail coal anomaly index, and set MT = r1 × clean coal anomaly index - r2 × tail coal anomaly index;

[0020] The construction unit also constructs the rotational speed adjustment index ZT based on the construction results of the clean coal anomaly index and the tail coal anomaly index, and sets ZT = -r3 × clean coal anomaly index + r4 × tail coal anomaly index;

[0021] Wherein, r1 is the first clean coal weight, r2 is the first tail coal weight, r3 is the second clean coal weight, and r4 is the second tail coal weight.

[0022] Further, the data processing module includes a medium density management unit, which is used to manage the medium density of the separator in the next monitoring period based on the analysis result of the raw coal quality status and the construction result of the density adjustment index within the monitoring period, where:

[0023] When the raw coal quality status is normal, the medium density management unit sets the medium density of the separator in the next monitoring period to M1, and sets M1 = m0 × (1 + MT);

[0024] When the raw coal quality status is abnormal, the medium density management unit sets the medium density of the separator in the next monitoring period to M2, and sets M2 = m0 × {1 + ln[5 × (Y - Y0)] / ln6 + MT}, where m0 is the first preset medium density.

[0025] Further, the data processing module also includes a rotational speed management unit, which is used to manage the rotational speed of the separator in the next monitoring period based on the classification result of the separator rotational speed level, the construction result of the rotational speed adjustment index, and the management result of the medium density in the next monitoring period, where:

[0026] When Mx < m2, if the sorting machine speed level is low speed, the speed management unit sets the sorting machine speed in the next monitoring period to V1, and sets V1 = v0×(1 + ZT); if the sorting machine speed level is high speed, the speed management unit sets the sorting machine speed in the next monitoring period to V2, and sets V2 = v0×(1 + η1×ZT), where η1 is the first preset adjustment coefficient;

[0027] When Mx > m2, if the sorting machine speed level is high speed, the speed management unit sets the sorting machine speed in the next monitoring period to V3, and sets V3 = v0×(1 + ZT); if the sorting machine speed level is high speed, the speed management unit sets the sorting machine speed in the next monitoring period to V4, and sets V4 = v0×(1 + η2×ZT), where η2 is the first preset adjustment coefficient;

[0028] Among them, Mx is the management result of the medium density in the next monitoring period, m2 is the second preset density, x = 1, 2, η1 > η2, and v0 is the preset reference speed.

[0029] Further, it also includes a dust analysis module, which is used to analyze the dust state of the coal preparation plant in the monitoring period according to the dust concentration f0 collected in the monitoring period and the preset dust concentration f1, where:

[0030] If f0 ≤ f1, the dust analysis module determines that the dust state of the coal preparation plant in the current monitoring period is normal; otherwise, the dust analysis module determines that the dust state of the coal preparation plant in the current monitoring period is abnormal;

[0031] When the dust state of the coal preparation plant is abnormal, the dust analysis module improves the management process of the sorting machine speed in the next monitoring period, and sets the improved preset reference speed to v0'.

[0032] Further, it also includes a noise analysis module, which is used to analyze the noise state of the coal preparation plant in the monitoring period according to the noise level p0 collected in the monitoring period and the preset noise p1, where:

[0033] If p0 ≤ p1, the noise analysis module determines that the noise state of the coal preparation plant in the current monitoring period is normal; otherwise, the noise analysis module determines that the noise state of the coal preparation plant in the current monitoring period is abnormal;

[0034] When the noise state of the coal preparation plant is abnormal, the noise analysis module improves the analysis process of the dust state of the coal preparation plant, and sets the improved preset dust concentration to f2.

[0035] The beneficial effects of the present invention are as follows: By integrating functional modules such as data acquisition, quality analysis, environmental monitoring, and dynamic regulation, the efficiency and reliability of the coal preparation process are significantly improved. The raw coal quality analysis module combines key indicators such as ash content, sulfur content, and moisture content to construct a multi-dimensional quality index, accurately evaluate the state of raw coal, and provide a scientific basis for optimizing separation parameters. The feedback monitoring module dynamically calculates the abnormal indexes of clean coal and tail coal, and adjusts the medium density and the rotating speed of the separator in real time, effectively improving the separation accuracy and the product qualification rate. At the same time, the system introduces a dust and noise analysis module, and optimizes the equipment operation in combination with environmental parameters, not only reducing energy consumption and pollution, but also greatly improving the safety of the working environment. In addition, the data processing module realizes the dynamic management of parameters through intelligent algorithms, enabling the system to have the ability to adapt to different working conditions and reducing the cost of manual intervention. Overall, the present invention solves the problems of low data processing efficiency and unstable separation quality in the existing technology through full-process intelligent management, and provides reliable technical support for the efficient and environmentally friendly operation of coal preparation plants. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, 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 invention, and those of ordinary skill in the art can also obtain other drawings based on these drawings without creative efforts.

[0037] Figure 1 It is a schematic structural diagram of the data processing system of the intelligent coal preparation plant platform in this embodiment.

[0038] Figure 2 It is a schematic structural diagram of the raw coal quality analysis module in this embodiment.

[0039] Figure 3 It is a schematic structural diagram of the feedback monitoring module in this embodiment.

[0040] Figure 4 It is a schematic structural diagram of the data processing module in this embodiment. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0041] In order to more clearly illustrate the present invention, the present invention will be further described below in conjunction with preferred embodiments and drawings. Similar components in the drawings are denoted by the same reference numerals. Those skilled in the art should understand that the specific content described below is illustrative rather than restrictive, and should not be used to limit the protection scope of the present invention.

[0042] It should be noted that although terms such as first, second, and third may be used in the embodiments of the present application for description, these descriptions should not be limited to these terms. These terms are only used to distinguish the descriptions. For example, without departing from the scope of the embodiments of the present application, the first can also be called the second, and similarly, the second can also be called the first.

[0043] Please refer to Figure 1 as shown, which is a schematic structural diagram of the intelligent coal preparation plant platform data processing system of this embodiment. The system includes

[0044] a data acquisition module for acquiring raw coal parameters, quality parameters, and environmental parameters. The raw coal parameters include raw coal ash content, raw coal sulfur content, and raw coal moisture content. The quality parameters include clean coal ash and tail coal ash. The environmental parameters include dust concentration and noise level. The clean coal ash is the mass percentage of ash in the clean coal product, and the tail coal ash is the mass percentage of ash in the tail coal. In this embodiment, the acquisition methods of raw coal parameters, quality parameters, and environmental parameters are not specifically limited, and those skilled in the art can freely set them as long as the acquisition requirements of raw coal parameters, quality parameters, and environmental parameters are met. Among them, the raw coal ash content can be measured by an X-ray fluorescence spectrometer, the raw coal sulfur content can be determined by the high-temperature combustion infrared absorption method, the raw coal moisture content can be measured by the drying loss method, the clean coal ash and tail coal ash are detected by an ash analyzer, the dust concentration is collected in real time by a laser dust sensor, and the noise level is measured by a decibel meter.

[0045] Please continue to refer to Figure 1 as shown, the system includes:

[0046] a raw coal quality analysis module connected to the data acquisition module, and the raw coal quality analysis module is used to analyze the raw coal quality status according to the collected raw coal ash content, raw coal sulfur content, and raw coal moisture content.

[0047] Please refer to Figure 2 as shown, the raw coal quality analysis module includes:

[0048] a quality index construction unit for constructing an ash quality index according to the collected raw coal ash content a0 and the preset ash content a1. If a0 ≤ a1, the quality index construction unit sets the ash quality index to HF1 and sets HF1 = 0. Otherwise, the quality index construction unit sets the ash quality index to HF2 and sets HF2 = (a0 - a1) / a0;

[0049] The quality index construction unit is also used to construct the sulfur content quality index according to the collected raw coal sulfur content s0 and the preset sulfur content s1. If s0 ≤ s1, the quality index construction unit sets the sulfur content quality index to SF1 and sets SF1 = 0. Otherwise, the quality index construction unit sets the sulfur content quality index to SF2 and sets SF2 = ln[(s0 - s1) / (s0 + s1) + 1];

[0050] The quality index construction unit is also used to construct the moisture content quality index according to the collected raw coal moisture content h0 and the preset moisture content h1. If h0 ≤ h1, the quality index construction unit sets the moisture content quality index to ZF1 and sets ZF1 = 0. Otherwise, the quality index construction unit sets the moisture content quality index to ZF2 and sets:

[0051] ZF2 = lg[(h0 - h1) / h1 + 1] / lg2; Through the construction of ash, sulfur, and moisture content quality indexes, the quality index construction unit can quantify the quality status of raw coal. Through the preset ash, sulfur, and moisture content thresholds, the system can automatically judge whether the raw coal quality meets the standard and generate corresponding quality indexes according to the exceeding standard situation. This dynamic quality assessment mechanism enables the system to adjust the sorting parameters in real time to ensure the accuracy and stability of the sorting process.

[0052] It can be understood that in this embodiment, the settings of the preset ash content, preset sulfur content, and preset moisture content are not specifically limited, and those skilled in the art can freely set them as long as they meet the setting requirements of the preset ash content, preset sulfur content, and preset moisture content. Among them, the best value of a1 is 0.25, the best value of s1 is 0.02, and the best value of h1 is 0.08.

[0053] Please continue to refer to Figure 1 as shown, the system further includes:

[0054] A quality analysis unit, which is connected to the quality index construction unit. The quality analysis unit constructs the raw coal quality index Y according to the ash quality index, sulfur quality index, and moisture quality index, and sets Y = w1 × ash quality index + w2 × sulfur quality index + w3 × moisture quality index;

[0055] The quality analysis unit analyzes the raw coal quality status according to the raw coal quality index Y and the preset quality index threshold Y0. If Y ≤ Y0, the quality analysis unit determines that the raw coal quality status is normal. Otherwise, the quality analysis unit determines that the raw coal quality status is abnormal;

[0056] Among them, w1 is the ash weight, w2 is the sulfur weight, w3 is the moisture weight, and w1 + w2 + w3 = 1; the quality analysis unit generates the raw coal quality index Y through the weighted calculation of the ash, sulfur, and moisture quality indexes, and combines the preset quality index threshold Y0 to quickly determine whether the raw coal quality status is normal. Through this intelligent quality analysis mechanism, the system can automatically adjust the sorting parameters to ensure the stability and efficiency of the sorting process, reduce manual intervention, and improve the automation level of the coal preparation plant.

[0057] It can be understood that in this embodiment, the setting of each weight and the preset quality index threshold is not specifically limited, and those skilled in the art can freely set them as long as the setting requirements of each weight and the preset quality index threshold are met. Among them, the best value of w1 is 0.5, the best value of w2 is 0.2, the best value of w3 is 0.3, and the best value of Y0 is 0.27.

[0058] Please continue to refer to Figure 1 As shown, the system further includes:

[0059] A grade division module, which is connected to the data acquisition module. The grade division module is used to divide the rotational speed grade of the separator according to the rotational speed z0 of the separator collected within the monitoring period and the preset rotational speed threshold z1, where:

[0060] If z0 ≤ z1, the grade division module determines that the rotational speed grade of the separator in the current monitoring period is a low rotational speed;

[0061] If z0 ≥ z1, the grade division module determines that the rotational speed grade of the separator in the current monitoring period is a high rotational speed; the grade division module divides the rotational speed into high and low rotational speed grades by monitoring the rotational speed of the separator and according to the preset rotational speed threshold. This division of rotational speed grades provides a basis for subsequent rotational speed adjustment to ensure that the separator can maintain the best working state under different working conditions of the separator, improving the sorting efficiency and product quality.

[0062] It can be understood that in this embodiment, the setting of the monitoring period is not specifically limited, and those skilled in the art can freely set it as long as the setting requirements of the monitoring period are met. Among them, the monitoring period can be set to 24h, 48h, etc.; in this embodiment, the acquisition method of the rotational speed of the separator is not specifically limited, and those skilled in the art can freely set it as long as the setting requirements of the rotational speed of the separator are met. Among them, the rotational speed of the separator can be collected by a Hall effect sensor.

[0063] It can be understood that in this embodiment, the setting of the preset rotational speed threshold is not specifically limited, and those skilled in the art can freely set it as long as the setting requirements of the preset rotational speed threshold are met. Among them, the best value of z1 is 1200 rpm.

[0064] Please continue to refer to Figure 1 As shown, the system further includes:

[0065] A feedback monitoring module, which is connected to the data acquisition module. The feedback monitoring module is used to analyze the clean coal status according to the clean coal ash collected within the monitoring period, construct a clean coal anomaly index based on the clean coal status, and is also used to analyze the tail coal status according to the tail coal ash collected within the monitoring period, construct a tail coal anomaly index based on the tail coal status, and is also used to construct a density adjustment index and a speed adjustment index according to the construction results of the clean coal anomaly index and the tail coal anomaly index.

[0066] Please refer to Figure 3 As shown, the feedback monitoring module includes:

[0067] A status analysis unit, which is used to analyze the clean coal status according to the clean coal ash jf0 collected within the monitoring period and the preset clean coal ash jf1. If jf0 ≤ jf1, the status analysis unit determines that the clean coal status is normal, sets the clean coal anomaly index to JY1, and sets: JY1 = 0; otherwise, the status analysis unit determines that the clean coal status is abnormal, sets the clean coal anomaly index to JY2, and sets JY2 = (jf0 - jf1) / α1;

[0068] The status analysis unit also analyzes the tail coal status according to the tail coal ash wf0 collected within the monitoring period and the preset tail coal ash wf1. If wf0 ≤ wf1, the status analysis unit determines that the tail coal status is abnormal, sets the tail coal anomaly index to WY1, and sets WY1 = (wf1 - wf0) / α2; otherwise, the status analysis unit determines that the tail coal status is normal, sets the tail coal anomaly index to WY2, and sets WY2 = 0, where α1 is the first preset reference and α2 is the second preset reference.

[0069] It can be understood that in this embodiment, the settings of the preset clean coal ash, the preset tail coal ash, the first preset reference, and the second preset reference are not specifically limited, and those skilled in the art can freely set them as long as they meet the setting requirements of the preset clean coal ash, the preset tail coal ash, the first preset reference, and the second preset reference. Among them, the optimal value of jf1 is 0.09, the optimal value of wf1 is 0.65, the optimal value of α1 is 0.1, and the optimal value of α2 is 0.5.

[0070] Please continue to refer to Figure 3 As shown, the feedback monitoring module further includes:

[0071] A construction unit, which is connected to the status analysis unit. The construction unit is used to construct a density adjustment index MT according to the construction results of the clean coal anomaly index and the tail coal anomaly index, and sets MT = r1 × clean coal anomaly index - r2 × tail coal anomaly index;

[0072] The building unit also constructs a rotational speed adjustment index ZT according to the construction results of the clean coal anomaly index and the tail coal anomaly index, and sets ZT = -r3 × clean coal anomaly index + r4 × tail coal anomaly index;

[0073] Wherein, r1 is the first clean coal weight, r2 is the first tail coal weight, r1 + r2 = 1, r3 is the second clean coal weight, r4 is the second tail coal weight, r3 + r4 = 1; the building unit dynamically adjusts the medium density and rotational speed of the separator through the ash anomaly indexes of clean coal and tail coal. Through the construction of the clean coal anomaly index and the tail coal anomaly index, the system can adjust the separation parameters in real time to ensure the accuracy and stability of the separation process. This feedback mechanism can effectively improve the separation accuracy and product qualification rate, reduce the waste rate, and enhance the economic benefits of the coal preparation plant.

[0074] It can be understood that in this embodiment, no specific limitations are imposed on the setting of each weight. Those skilled in the art can freely set them as long as the setting requirements of each weight are met. Among them, the optimal value of r1 is 0.7, the optimal value of r2 is 0.3, the optimal value of r3 is 0.4, and the optimal value of r4 is 0.6.

[0075] Please continue to refer to Figure 1 As shown, the system further includes:

[0076] A data processing module, which is connected to the raw coal quality analysis module, the grade division module, and the feedback monitoring module. The data processing module is used to manage the medium density and rotational speed of the separator in the next monitoring period according to the analysis results of the raw coal quality status within the monitoring period, the division results of the separator rotational speed grades, and the construction results of the density adjustment index and the rotational speed adjustment index.

[0077] Please refer to Figure 4 As shown, the data processing module includes:

[0078] A medium density management unit, which is used to manage the medium density of the separator in the next monitoring period according to the analysis results of the raw coal quality status within the monitoring period and the construction results of the density adjustment index, wherein:

[0079] When the raw coal quality status is normal, the medium density management unit sets the medium density of the separator in the next monitoring period to M1, and sets M1 = m0 × (1 + MT);

[0080] When the quality state of the raw coal is abnormal, the medium density management unit sets the medium density of the separator in the next monitoring cycle to M2, where M2 = m0 × {1 + ln[5 × (Y - Y0)] / ln6 + MT}, and m0 is the first preset medium density; the medium density management unit dynamically adjusts the medium density of the separator according to the raw coal quality state and the density adjustment index. Through this dynamic adjustment mechanism, the system can ensure that the separation process can maintain the best separation effect under different working conditions, improve the separation accuracy and product qualification rate, and reduce the reject rate.

[0081] It can be understood that in this embodiment, the setting of the first preset medium density is not specifically limited, and those skilled in the art can freely set it as long as the setting requirements of the first preset medium density are met. Among them, the optimal value of m0 is 1.5 g / cm 3 。

[0082] Please continue to refer to Figure 4 as shown, the data processing module further includes:

[0083] A rotational speed management unit, which is connected to the medium density management unit. The rotational speed management unit is used to manage the rotational speed of the separator in the next monitoring cycle according to the division result of the rotational speed level of the separator, the construction result of the rotational speed adjustment index, and the management result of the medium density in the next monitoring cycle, where:

[0084] When Mx < m2, if the rotational speed level of the separator is low rotational speed, the rotational speed management unit sets the rotational speed of the separator in the next monitoring cycle to V1, where V1 = v0 × (1 + ZT); if the rotational speed level of the separator is high rotational speed, the rotational speed management unit sets the rotational speed of the separator in the next monitoring cycle to V2, where V2 = v0 × (1 + η1 × ZT), and η1 is the first preset adjustment coefficient;

[0085] When Mx > m2, if the rotational speed level of the separator is high rotational speed, the rotational speed management unit sets the rotational speed of the separator in the next monitoring cycle to V3, where V3 = v0 × (1 + ZT); if the rotational speed level of the separator is high rotational speed, the rotational speed management unit sets the rotational speed of the separator in the next monitoring cycle to V4, where V4 = v0 × (1 + η2 × ZT), and η2 is the second preset adjustment coefficient;

[0086] Among them, Mx is the management result of the medium density in the next monitoring cycle, m2 is the second preset medium density, x = 1, 2, η1 > η2, and v0 is the preset reference rotational speed; the rotational speed management unit dynamically adjusts the rotational speed of the separator according to the division result of the rotational speed level of the separator, the rotational speed adjustment index, and the management result of the medium density. Through this intelligent rotational speed adjustment mechanism, the system can ensure that the separator can maintain the best working state under different working conditions, improve the separation efficiency and product quality.

[0087] It is understandable that in this embodiment, there is no specific limitation on the setting of the second preset medium density, the first preset adjustment coefficient, the second preset adjustment coefficient, and the preset reference speed. Those skilled in the art can set them freely, as long as the setting requirements of the second preset medium density, the first preset adjustment coefficient, the second preset adjustment coefficient, and the preset reference speed are met. Among them, the best value of m2 is 1.8 g / cm 3 , the best value of v0 is 1000 rpm, the best value of η1 is 0.8, and the best value of η2 is 0.56.

[0088] Please continue to refer to Figure 1 As shown, the system further includes:

[0089] A dust analysis module, which is connected to the data processing module. The dust analysis module is used to analyze the dust state of the coal preparation plant during the monitoring period according to the dust concentration f0 collected during the monitoring period and the preset dust concentration f1, where:

[0090] If f0 ≤ f1, the dust analysis module determines that the dust state of the coal preparation plant in the current monitoring period is normal; otherwise, the dust analysis module determines that the dust state of the coal preparation plant in the current monitoring period is abnormal.

[0091] When the dust state of the coal preparation plant is abnormal, the dust analysis module improves the management process of the separator speed in the next monitoring period, and sets the improved preset reference speed as v0', where v0' = v0 × exp[-(f0 - f1) / (f0 + f1)]; the dust analysis module judges whether the dust state of the coal preparation plant is normal by monitoring the dust concentration. When the dust state is abnormal, the system will automatically adjust the speed of the separator to reduce dust generation and improve the safety of the working environment. Through this environmental monitoring and control mechanism, the system can effectively reduce energy consumption and pollution and improve the environmental protection level of the coal preparation plant.

[0092] It is understandable that in this embodiment, there is no specific limitation on the setting of the preset dust concentration. Those skilled in the art can set it freely, as long as the setting requirements of the preset dust concentration are met. Among them, the best value of f1 is 10 mg / m 3 .

[0093] Please continue to refer to Figure 1 As shown, the system further includes:

[0094] A noise analysis module, which is connected to the dust analysis module. The noise analysis module is used to analyze the noise state of the coal preparation plant during the monitoring period according to the noise level p0 collected during the monitoring period and the preset noise p1, where:

[0095] If p0 ≤ p1, the noise analysis module determines that the noise state of the coal preparation plant in the current monitoring period is normal; otherwise, the noise analysis module determines that the noise state of the coal preparation plant in the current monitoring period is abnormal.

[0096] When the noise state of the coal preparation plant is abnormal, the noise analysis module improves the analysis process of the dust state of the coal preparation plant, and sets the improved preset dust concentration as f2, where f2 = f1 × {1 - exp[3 × (p0 - p1) / (p0 + p1) - 3]}; the noise analysis module determines whether the noise state of the coal preparation plant is normal by monitoring the noise level. When the noise state is abnormal, the system will automatically adjust the preset value of the dust concentration, further optimize the equipment operation, and reduce the noise pollution. Through this environmental monitoring and control mechanism, the system can effectively improve the safety of the working environment and enhance the environmental protection level of the coal preparation plant.

[0097] Specifically, in this embodiment, the dust concentration and noise level collected during the monitoring period are the average values of the dust concentration and noise level data collected during the monitoring period.

[0098] It can be understood that in this embodiment, the setting of the preset noise is not specifically limited, and those skilled in the art can freely set it as long as the setting requirements of the preset noise are met. Among them, the best value of p1 is 85 dB.

[0099] Specifically, the system in this embodiment is applied to the processing of the data of the steam coal separation process in a large coal preparation plant.

[0100] Obviously, the above embodiments of the present invention are only examples for clearly explaining the present invention, and are not limitations on the implementation manners of the present invention. For those of ordinary skill in the art, other different forms of changes or modifications can be made on the basis of the above description. It is impossible to list all the implementation manners here. All obvious changes or modifications derived from the technical solutions of the present invention are still within the protection scope of the present invention.

Claims

1. An intelligent coal preparation plant platform data processing system, characterized in that, Including: A data acquisition module for acquiring raw coal parameters, quality parameters, and environmental parameters; A raw coal quality analysis module for analyzing the quality status of raw coal based on the collected ash content, sulfur content, and moisture content of raw coal; A grading module for grading the rotational speed level of the separator according to the rotational speed of the separator collected within the monitoring period; A feedback monitoring module for analyzing the clean coal status based on the clean coal ash collected within the monitoring period, constructing a clean coal anomaly index according to the clean coal status, analyzing the tail coal status based on the tail coal ash collected within the monitoring period, constructing a tail coal anomaly index according to the tail coal status, and constructing a density adjustment index and a rotational speed adjustment index according to the construction results of the clean coal anomaly index and the tail coal anomaly index; A data processing module for managing the medium density and rotational speed of the separator in the next monitoring period according to the analysis results of the raw coal quality status, the grading results of the rotational speed level of the separator, and the construction results of the density adjustment index and the rotational speed adjustment index within the monitoring period.

2. The intelligent coal preparation plant platform data processing system according to claim 1, wherein The raw coal quality analysis module includes a quality index construction unit for constructing an ash quality index according to the collected ash content a0 of raw coal and a preset ash content a1. If a0 ≤ a1, the quality index construction unit sets the ash quality index to HF1; otherwise, the quality index construction unit sets the ash quality index to HF2; The quality index construction unit is also used to construct a sulfur quality index according to the collected sulfur content s0 of raw coal and a preset sulfur content s1. If s0 ≤ s1, the quality index construction unit sets the sulfur quality index to SF1; otherwise, the quality index construction unit sets the sulfur quality index to SF2; The quality index construction unit is also used to construct a moisture quality index according to the collected moisture content h0 of raw coal and a preset moisture content h1. If h0 ≤ h1, the quality index construction unit sets the moisture quality index to ZF1; otherwise, the quality index construction unit sets the moisture quality index to ZF2.

3. The intelligent coal preparation plant platform data processing system according to claim 2, wherein The raw coal quality analysis module also includes a quality analysis unit for constructing a raw coal quality index Y according to the ash quality index, sulfur quality index, and moisture quality index, and analyzing the raw coal quality status according to the raw coal quality index Y and a preset quality index threshold Y0 to determine whether the raw coal quality status is abnormal.

4. The intelligent coal preparation plant platform data processing system according to claim 3, characterized in that The grading module grades the rotational speed level of the separator according to the rotational speed z0 of the separator collected within the monitoring period and a preset rotational speed threshold z1 to divide the rotational speed level of the separator into low rotational speed or high rotational speed.

5. The intelligent coal preparation plant platform data processing system according to claim 4, wherein The feedback monitoring module includes a status analysis unit for analyzing the clean coal status according to the clean coal ash jf0 collected within the monitoring period and a preset clean coal ash jf1. If jf0 ≤ jf1, the status analysis unit determines that the clean coal status is normal and sets the clean coal anomaly index to JY1; otherwise, the status analysis unit determines that the clean coal status is abnormal and sets the clean coal anomaly index to JY2; The state analysis unit also analyzes the tail coal state based on the tail coal ash content wf0 collected within the monitoring period and the preset tail coal ash content wf1. If wf0 ≤ wf1, the state analysis unit determines that the tail coal state is abnormal and sets the tail coal abnormality index to WY1. Otherwise, the state analysis unit determines that the tail coal state is normal and sets the tail coal abnormality index to WY2.

6. The intelligent coal preparation plant platform data processing system according to claim 5, characterized in that, The feedback monitoring module further includes a construction unit, which is used to construct the density adjustment index MT based on the construction results of the clean coal abnormality index and the tail coal abnormality index, and sets MT = r1 × clean coal abnormality index - r2 × tail coal abnormality index; The construction unit also constructs the rotational speed adjustment index ZT based on the construction results of the clean coal abnormality index and the tail coal abnormality index, and sets ZT = -r3 × clean coal abnormality index + r4 × tail coal abnormality index; Wherein, r1 is the first clean coal weight, r2 is the first tail coal weight, r3 is the second clean coal weight, and r4 is the second tail coal weight.

7. The intelligent coal preparation plant platform data processing system according to claim 6, wherein The data processing module includes a medium density management unit, which is used to manage the medium density of the separator in the next monitoring period according to the analysis result of the raw coal quality state within the monitoring period and the construction result of the density adjustment index, where: When the raw coal quality state is normal, the medium density management unit sets the medium density of the separator in the next monitoring period to M1, and sets M1 = m0 × (1 + MT); When the raw coal quality state is abnormal, the medium density management unit sets the medium density of the separator in the next monitoring period to M2, and sets M2 = m0 × {1 + ln[5 × (Y - Y0)] / ln6 + MT}, where m0 is the first preset medium density.

8. The intelligent coal preparation plant platform data processing system according to claim 7, wherein The data processing module also includes a rotational speed management unit, which is used to manage the rotational speed of the separator in the next monitoring period according to the classification result of the separator rotational speed level, the construction result of the rotational speed adjustment index, and the management result of the medium density in the next monitoring period, where: When Mx < m2, if the separator rotational speed level is low rotational speed, the rotational speed management unit sets the rotational speed of the separator in the next monitoring period to V1, and sets V1 = v0 × (1 + ZT); if the separator rotational speed level is high rotational speed, the rotational speed management unit sets the rotational speed of the separator in the next monitoring period to V2, and sets V2 = v0 × (1 + η1 × ZT), where η1 is the first preset adjustment coefficient; When Mx > m2, if the separator rotational speed level is high rotational speed, the rotational speed management unit sets the rotational speed of the separator in the next monitoring period to V3, and sets V3 = v0 × (1 + ZT); if the separator rotational speed level is high rotational speed, the rotational speed management unit sets the rotational speed of the separator in the next monitoring period to V4, and sets V4 = v0 × (1 + η2 × ZT), where η2 is the first preset adjustment coefficient; Wherein, Mx is the management result of the medium density in the next monitoring period, m2 is the second preset density, x = 1, 2, η1 > η2, and v0 is the preset reference rotational speed.

9. The intelligent coal preparation plant platform data processing system according to claim 8, characterized in that It further includes a dust analysis module, which is used to analyze the dust state of the coal preparation plant within the monitoring period based on the dust concentration f0 collected within the monitoring period and the preset dust concentration f1, where: If f0 ≤ f1, the dust analysis module determines that the dust status of the coal preparation plant in the current monitoring period is normal; otherwise, the dust analysis module determines that the dust status of the coal preparation plant in the current monitoring period is abnormal. When the dust status of the coal preparation plant is abnormal, the dust analysis module improves the management process of the separator speed in the next monitoring period and sets the improved preset reference speed to v0'.

10. The intelligent coal preparation plant platform data processing system according to claim 9, characterized in that, It also includes a noise analysis module, which is used to analyze the noise status of the coal preparation plant in the monitoring period according to the noise level p0 collected in the monitoring period and the preset noise p1, where: If p0 ≤ p1, the noise analysis module determines that the noise status of the coal preparation plant in the current monitoring period is normal; otherwise, the noise analysis module determines that the noise status of the coal preparation plant in the current monitoring period is abnormal. When the noise status of the coal preparation plant is abnormal, the noise analysis module improves the analysis process of the dust status of the coal preparation plant and sets the improved preset dust concentration to f2.

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