Network model, dense medium shallow slot sorting machine system and coal sorting method
By combining the dynamic filtering network model of coal signals with the front-end weighing device, the problems of inaccurate coal quantity signals and control lag in the traditional heavy medium shallow trough separator system are solved, efficient and intelligent coal sorting control is achieved, and the equipment operating efficiency and equipment life are improved.
Patent Information
- Application Number
- CN202511090568.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-08-05
- Publication Date
- 2025-09-12
AI Technical Summary
Traditional heavy medium shallow trough separator systems have difficulty providing accurate and reliable real-time data in the coal quantity signal detection and processing links, and there are problems of control lag and low efficiency at the operation control level, which limits the intelligence and efficiency of the sorting process.
A dynamic filtering network model of coal signals is adopted, including the first filtering unit, the second filtering unit and the third filtering unit. By dynamically adjusting the filtering window length, exponential decay weighting and outlier removal, a multi-stage filtering chain is formed to process the coal quantity signal; combined with the front weighing device and the frequency converter, real-time adjustment of the scraper speed is achieved.
It improves the response speed and accuracy of the coal quantity signal, reduces the interference effect, achieves accurate matching of scraper speed and feed load, improves the operating efficiency and equipment life of the separator, and saves energy consumption and maintenance costs.
Smart Images
Figure CN120618680A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of separators, in particular to a network model, a heavy medium shallow trough separator system and a coal separation method. Background Art
[0002] As core equipment for lump coal washing in coal preparation plants, the operating efficiency and control accuracy of a heavy medium shallow trough separator directly impact clean coal recovery and equipment life. Traditional heavy medium shallow trough separator systems suffer from two key technical bottlenecks, hindering the intelligent and efficient separation process.
[0003] On the one hand, existing technologies struggle to provide accurate and reliable real-time coal quantity data when detecting and processing coal quantity signals. In traditional coal preparation systems, coal quantity weighing signals are susceptible to multiple interferences: uneven coal flow and belt vibration during transportation can cause dramatic fluctuations, making it difficult to balance signal real-time performance and stability. Filtering methods using fixed window lengths either suffer from response delays due to excessively large windows, preventing them from capturing sudden changes in coal quantity, or suffer from insufficient signal smoothness due to excessively small windows. Historical and current data are processed with equal weight, making it difficult for the filtering results to accurately track coal quantity trends, especially during periods of continuous increases and decreases in coal quantity. Furthermore, pulse interference signals caused by the impact of large coal lumps and mechanical vibration can be mixed into the weighing data, directly leading to distortion in coal quantity measurement and further impacting subsequent control decisions. These signal processing flaws make it difficult for existing systems to output high-quality signals that can both quickly respond to actual coal quantity changes and effectively suppress interference, creating hidden dangers for precise control.
[0004] On the other hand, at the level of sorting machine operation control, the traditional system has significant control lag and low efficiency problems. The original shallow trough sorting machine motor mostly uses the industrial frequency operation mode with a fixed speed, and cannot dynamically adjust the scraper operation speed according to the amount of coal fed: when the amount of coal increases sharply, the scraper cannot speed up in time, which can easily cause material accumulation and blockage in the trough, exacerbating the friction and wear between the scraper and the chain; when the amount of coal decreases suddenly, the scraper still runs at high speed, resulting in energy waste and equipment idleness. More importantly, the existing coal quantity detection device is mostly installed on the rear-end gangue belt conveyor to detect the weight of the gangue after sorting. This post-detection method has a signal lag of up to several minutes - from the coal entering the sorting machine to the gangue being detected, it needs to go through multiple links such as sorting and transportation, making the control system only able to "remediate after the fact" rather than "control in advance." Even if attempts are made to move the detection device forward to the feed conveyor belt, the front-end signal is more affected by the impact of falling materials and belt vibration. Traditional filtering technology cannot effectively eliminate the noise, resulting in worse signal quality after moving forward, making it difficult to use as a reliable control basis. Summary of the Invention
[0005] Some simplifications or omissions may be made in this section and the abstract and title of the present application to avoid obscuring the purpose of this section, the abstract and the title of the invention, and such simplifications or omissions shall not be used to limit the scope of the invention.
[0006] In order to address the deficiencies of the prior art, an object of the present invention is to provide a dynamic filtering network model for coal signals, comprising a first filtering unit, a second filtering unit and a third filtering unit; wherein, the first filtering unit is configured to dynamically adjust the filtering window length for smoothing the coal signal according to the real-time variance of the coal signal; the second filtering unit is configured to perform weighted moving average filtering on historical coal signal data using an exponentially attenuated weighting method; and the third filtering unit is configured to identify and eliminate pulse interference data in the coal signal.
[0007] As a preferred solution of the dynamic filtering network model of coal signals described in the present invention, wherein: the first filtering unit is configured to preset a variance threshold, and the variance threshold is used to judge the severity of the fluctuation of the current coal flow state; if the real-time variance of the coal signal is greater than the variance threshold, it is judged to be a violent fluctuation state, and the first filtering window length is used; if the real-time variance of the coal signal is less than or equal to the variance threshold, it is judged to be a stable state, and the second filtering window length is used; wherein the first filtering window length is less than the second filtering window length.
[0008] As a preferred solution of the coal signal dynamic filtering network model of the present invention, the second filtering unit is configured as follows:
[0009] Using the weighted moving average formula
[0010]
[0011] Filter the original coal signal x[n];
[0012] Among them, the weight w k Using exponential decay function
[0013] w k =e -k / τ
[0014] Calculation is performed, τ is the attenuation coefficient;
[0015] Where y[n] is the output signal after filtering, x[nk] is the sampling value of the historical original coal signal, N is the length of the filter window, and w k is the weight, k represents the time distance of the data point, and τ is a preset attenuation coefficient.
[0016] As a preferred solution of the coal signal dynamic filtering network model of the present invention, wherein: the third filtering unit is configured as follows:
[0017] In a sliding window, calculate the median M and standard deviation σ of all sampled data;
[0018] Determine whether the current sampling value x[n] meets the abnormal condition:
[0019] |x[n]-M|>[citestart]3σ
[0020] If the abnormal condition is met, x[n] is determined to be an abnormal pulse interference value, and the median M of the current window is used to replace the abnormal value before subsequent filtering calculations are performed.
[0021] As a preferred solution of the coal signal dynamic filtering network model of the present invention, the first filtering unit, the second filtering unit and the third filtering unit are connected in series to form a multi-stage filtering chain, and process the coal signal in sequence.
[0022] Another object of the present invention is to provide a heavy medium shallow trough separator system, comprising: a separator having a scraper for conveying and separating materials; a driving member for driving the scraper to operate; a weighing member arranged on a feed conveyor belt for conveying coal to the separator, the weighing member being used to collect coal quantity data on the feed conveyor belt in real time and use it as a feedforward control signal; a control module electrically connected to the driving member, the control module being used to filter the feedforward control signal and adjust the output frequency.
[0023] As a preferred solution of the heavy medium shallow trough separator system described in the present invention, the control module includes a frequency converter and a controller; the signal input end of the controller is connected to the weighing piece, and the signal output end is connected to the frequency converter; the controller has a built-in coal signal dynamic filtering network model, which is used to dynamically filter the received raw coal quantity data, and convert the processed raw coal quantity data into frequency instructions and send them to the frequency converter.
[0024] Another object of the present invention is to provide a coal sorting method, including directly detecting the real-time changes in the amount of coal at the feed end by means of a weighing piece arranged on the feed conveyor belt of the sorting machine; dynamically filtering the collected coal amount signal to eliminate fluctuations and interference during the measurement process; and adjusting the operating speed of the sorting machine scraper in real time according to the filtered coal amount signal, so as to match the scraper speed with the feed load.
[0025] As a preferred solution of the heavy medium shallow trough separator system method described in the present invention, the step of dynamically filtering the collected coal quantity signal is used to adaptively adjust the filter window, weightedly smooth the signal, and eliminate abnormal pulses.
[0026] As a preferred solution of the heavy medium shallow trough separator system method described in the present invention, the weighing piece is installed at a position greater than 5 meters away from the drop point of the feed conveyor belt; and a shock-proof gasket and a belt tensioning device are added to the installation area of the weighing piece.
[0027] The beneficial effects of the present invention are as follows: through the organic combination of one or more of the first filtering unit, the second filtering unit and the third filtering unit, various interferences in the industrial field can be effectively overcome, providing a high-quality signal basis for subsequent intelligent control.
[0028] Furthermore, the control module adjusts the speed of the separator's scrapers in real time. When the feed coal volume is high, the scraper speed is increased to ensure timely material transport and prevent blockage. When the feed coal volume is low, the scraper speed is reduced to avoid energy waste and equipment wear caused by no-load or light-load operation. This achieves precise dynamic matching of scraper speed and feed load. BRIEF DESCRIPTION OF THE DRAWINGS
[0029] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.
[0030] Figure 1 Schematic diagram of the coal signal dynamic filtering network model.
[0031] Figure 2 This is the logic diagram of the coal signal dynamic filtering network model.
[0032] Figure 3 This is the connection diagram of the heavy medium shallow trough separator system. DETAILED DESCRIPTION
[0033] In order to make the objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.
[0034] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.
[0035] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.
[0036] Example 1
[0037] See Figure 1 This embodiment provides a dynamic coal signal filtering network model designed to address inaccurate weighing signals from belt scales in coal preparation systems caused by factors such as coal flow fluctuations, mechanical vibration, and impact from falling materials. The core concept of this network model is to deeply process the raw coal signal through the coordinated operation of one or more functionally complementary filtering units, producing an accurate signal that is both smooth and responsive to actual coal quantity changes.
[0038] Specifically, the coal signal dynamic filtering network model includes a combination of a first filtering unit 101 , a second filtering unit 102 and a third filtering unit 103 .
[0039] The primary function of the first filter unit 101 is to reduce system response delay. During the coal preparation process, the incoming coal quantity can fluctuate dramatically within a short period of time. The first filter unit 101 intelligently identifies the stable and fluctuating states of the coal flow and automatically adjusts its filtering parameters (such as the length of the filter window) to ensure a rapid system response when the coal quantity fluctuates dramatically, while providing a highly smooth signal when the coal quantity is stable, thus balancing response speed and signal quality.
[0040] The primary function of the second filtering unit 102 is to improve tracking of coal quantity trends. Traditional arithmetic mean filtering treats all historical data equally, making it susceptible to interference from historical data and failing to accurately reflect current trends. The second filtering unit 102 employs a weighted processing approach, giving more weight to recent data and less weight to older data. This allows the filtered signal to more sensitively and accurately track actual coal quantity changes.
[0041] The third filter unit 103's primary function is to reduce the impact of pulse interference on weighing. During conveying, the instantaneous impact of large coal lumps or occasional belt vibrations can generate abnormal pulse signals. These signals represent actual changes in coal quantity and, if left untreated, can lead to misadjustments in the separator speed. The third filter unit 103 is specifically designed to identify and eliminate these glitches, ensuring signal purity and reliability.
[0042] The network model of the present invention can effectively overcome various interferences in industrial sites and provide a high-quality signal foundation for subsequent intelligent control.
[0043] Example 2
[0044] See Figure 1 and Figure 2 This embodiment is the second embodiment of the invention, and this embodiment is based on embodiment 1.
[0045] Regarding the implementation of the first filtering unit 101 , the first filtering unit 101 can achieve a balance between fast response and smooth output by adaptively adjusting the filter window length.
[0046] The specific steps are as follows: First, a variance threshold is preset. According to historical data and process requirements, a variance threshold is preset. This threshold is used to define the violent fluctuation state and stable state of the coal flow.
[0047] Secondly, in real-time judgment, the controller calculates the variance of the coal signal in the current data window in real time.
[0048] Dynamic adjustments are then made. If the real-time variance exceeds a preset threshold, indicating significant coal flow fluctuations, the controller uses a smaller first filter window length (for example, a window containing five sampling points), sacrificing some smoothness in exchange for a faster response. If the real-time variance is less than or equal to the threshold, indicating stable coal flow, a larger second filter window length (for example, a window containing 30 sampling points) is used to obtain a smoother and more stable output signal. The first filter window length is smaller than the second filter window length.
[0049] Regarding the implementation of the second filtering unit 102 , the second filtering unit 102 may adopt an exponentially decayed weighted moving average filter.
[0050] The calculation formula is:
[0051]
[0052] Where: y[n] is the output signal after filtering, x[nk] is the sampling value of the historical original coal signal, and N is the length of the filtering window. k is the weight, which is calculated as follows:
[0053] w k =e -k / τ
[0054] k represents the time distance of the data points, k = 0 represents the latest data, and a larger k indicates older data.
[0055] τ is a preset decay coefficient. The smaller τ is, the faster the weight of historical data decays and the more sensitive the system is to new data.
[0056] Regarding the implementation of the third filtering unit 103 , the third filtering unit 103 may adopt an outlier elimination method based on the 3σ criterion.
[0057] The specific steps are as follows:
[0058] In a sliding data window, calculate the median M and standard deviation σ of all sampled data in the window.
[0059] The latest sample value x[n] is evaluated to see if it meets the abnormal condition: |x[n]-M|>citestart]3σ. This condition means that the current sample value deviates from the majority of the data in the window by more than three standard deviations, which is an extremely low probability event and can be determined as abnormal pulse interference.
[0060] If the conditions are met, x[n] is determined to be an outlier and the window median M is used to replace the value of x[n]. If not, the original value is retained. The processed data then enters the subsequent filtering stage.
[0061] Regarding multi-stage serial filtering: In the preferred embodiment, these three units are connected in series, forming a powerful multi-stage filtering chain. The processing flow is as follows: the original signal first passes through the third filter unit to remove pulse spikes, then enters the second filter unit for weighted smoothing to follow the trend. Finally, the logic of the first filter unit dynamically adjusts the response speed of the entire filtering process.
[0062] Assume that the controller collects the following seven original coal quantity signals (unit: tons / hour) within a time window: [50.2, 50.5, 51.0, 85.8, 50.9, 51.3, 51.5].
[0063] The third filter unit 103 is used to remove pulses. The controller calculates the median of this window as 51.0, with a standard deviation σ of approximately 12.9. The current sample value 85.8 satisfies the condition |85.8 - 51.0| > 3σ and is therefore considered an outlier. Therefore, the system replaces 85.8 with the median 51.0. The processed data becomes [50.2, 50.5, 51.0, 51.0, 50.9, 51.3, 51.5].
[0064] Second filtering unit 102 performs weighted smoothing, feeding the cleaned data into a weighted moving average filter. In this embodiment, τ = 3, with the most recent data value 51.5 receiving the highest weight and 50.2 receiving the lowest weight. The calculated result will be very close to recent values such as 51.3 and 51.5, and will be largely unaffected by earlier data.
[0065] The first filter unit 101 is used for dynamic windowing. Since the raw data has a large variance due to the large fluctuations such as 85.8, the first unit will determine that the current state is a large fluctuation and instruct the entire filtering system to use a smaller window, such as N=5, to ensure that it can quickly keep up with the actual changes in the signal.
[0066] Example 3
[0067] Reference Figure 3 This embodiment is the second embodiment of the invention, and is based on embodiment 1. This embodiment provides a heavy medium shallow trough separation system.
[0068] The separator 200 is a heavy medium shallow trough separator body, which is provided with a scraper 201 for conveying and separating materials. Its working principle is to use heavy medium to separate coal and gangue under the action of buoyancy.
[0069] The driving member 300 is usually a motor, which provides power for the operation of the scraper 201 .
[0070] The weighing unit 400 is a key detection component in this system, preferably a high-precision electronic belt scale. Its installation location is crucial: it is placed on the infeed conveyor 202 that feeds the sorting machine 200. This pre-installed position allows it to directly measure the amount of coal entering the sorting machine and use this data as a feedforward control signal, enabling predictive regulation.
[0071] The control module 500 is the system's intelligent control core and is electrically connected to the drive unit 300. The control module 500 preferably consists of a frequency converter 501 and a controller 502 (e.g., a PLC control board). The input of the controller 502 is connected to the weighing unit 400 to receive the raw coal quantity signal. The controller 502 internally implements the coal signal dynamic filtering network model described in Examples 1 and 2. After filtering the received signal, the controller 502 calculates the optimal scraper operating speed based on the processed precise coal quantity and converts this speed into a corresponding frequency command.
[0072] The output terminal of the controller 502 is connected to the frequency converter 501, and sends a frequency instruction to the frequency converter 501. The frequency converter 501 adjusts the current frequency and voltage output to the driving member 300 accordingly, thereby accurately controlling the running speed of the scraper 201.
[0073] Example 4
[0074] This embodiment is the third embodiment of the invention, and is based on Embodiments 1 to 3. This embodiment provides a coal separation method.
[0075] S100: Feedforward detection. Using a weighing device 400 mounted on the infeed conveyor 202 at the front end of the sorting machine 200, changes in the coal level at the infeed end are directly and in real time detected. This is fundamentally different from traditional feedback control that measures the amount of coal discharged at the rear end, enabling predictive control rather than hindsight.
[0076] S200: Dynamic signal filtering: Dynamic filtering is performed on the collected raw coal quantity signal to eliminate fluctuations and interference during the measurement process. This step specifically utilizes the techniques described in Example 2, such as adaptive filter window adjustment, signal weighted smoothing, and abnormal pulse rejection, to ensure the accuracy of the control signal.
[0077] S300: Real-time speed adjustment. Based on the precise coal quantity signal obtained after filtering, the control module 500 adjusts the operating speed of the scraper 201 of the separator 200 in real time. When the coal feed is large, the scraper speed is increased to ensure timely material transport and prevent blockage. When the coal feed is small, the scraper speed is reduced to avoid energy waste and equipment wear caused by no-load or light-load operation. This achieves precise dynamic matching of scraper speed and feed load.
[0078] Furthermore, to ensure accuracy during the first step of testing, this method preferably includes optimizing the installation location. Specifically, the weighing unit 400 is installed at least 5 meters from the drop point of the infeed conveyor 202 to effectively avoid the impact zone of falling materials. Furthermore, anti-vibration pads and a belt tensioning device are installed in the installation area of the weighing unit 400 to minimize the impact of belt vibration on weighing accuracy.
[0079] Comparative Example 1
[0080] In this comparative example, a weighing piece is installed on the rear end of the gangue belt conveyor to detect the weight of the gangue sorted by the sorting machine 200 and calculate the current signal to the frequency converter 501.
[0081] In this solution, the weighing element 400 is used to detect the weight of the gangue sorted by the separator 200, and the weight signal is integrated and transmitted to the frequency converter 501 for adjusting the speed of the scraper 201 of the separator 200.
[0082] Compared with the solution of the present invention, this solution has the following inherent defects:
[0083] The signal lag is significant. From the moment the coal enters the separator 200, to the moment it is sorted, to the moment the gangue is conveyed by the scraper 201 to the discharge head, and finally to the moment it lands on the gangue conveyor and is detected by the weighing unit, there is a delay of several minutes. This lag prevents the control system from responding promptly to sudden changes in the feed rate. By the time a surge in gangue is detected, the separator 200 is likely already experiencing severe material compression or blockage. Control adjustments are too late, and are essentially a post-event remedy.
[0084] The signal is indirect and inaccurate. The amount of waste rock at the rear end depends not only on the feed rate at the front end but also on a variety of factors, including separation density, medium stability, and the waste rock content of the raw coal. For example, when a decrease in waste rock is detected, the controller cannot determine whether this is due to a decrease in feed rate (in which case the speed should be reduced), a shift in separation medium density causing clean coal to escape (in which case the medium should be adjusted), or a broken scraper (in which case the machine should be shut down for maintenance). This ambiguity in the signal significantly reduces the accuracy of control decisions.
[0085] The beneficial effects of our invention are as follows:
[0086] In contrast, the solution of the present invention places the weighing piece 400 in front, realizing feedforward control, which has significant advantages.
[0087] It responds quickly and directly detects the feed load with a response time of seconds. It can complete speed adjustment before the coal flow fluctuation reaches the sorting machine to achieve preventive control.
[0088] The control is precise. The feed amount is the most direct load indicator of the sorting machine 200. The control target is clear and is not affected by the variables of the sorting process. The control accuracy is high.
[0089] It saves energy and reduces consumption, protects equipment, and avoids unnecessary energy consumption and mechanical wear by precisely matching load and speed. According to actual application calculations, it can save 20% of energy consumption and double the service life of wearing parts such as scrapers and chains, with significant economic benefits.
[0090] In addition, in terms of energy consumption, after the inverter 501 was put into use, the equipment operation basically remained at around 40Hz, saving 20% of energy consumption compared with the industrial frequency operation state. The motor power of the equipment is 30KW, and it is calculated based on 18 hours of operation per day: 30*20%*18*350*0.57=21,546 yuan.
[0091] In terms of materials, the service life of the shallow groove scraper and chain was 3 months before the transformation, and the service life can be extended to 6 months after the transformation. The price of a set of chains and scrapers is about 160,000 yuan, which can save about 300,000 yuan a year.
[0092] In terms of labor, each replacement of the chain and scraper 201 requires 8 workers for 2 shifts. The labor cost for one replacement is about 8*2*300=4800 yuan, which can save 9600 yuan in labor costs per year.
[0093] Other benefits include controlling the equipment speed during equipment inspections based on actual conditions, allowing for a more comprehensive and thorough inspection of equipment conditions.
[0094] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.
Claims
1. A coal signal dynamic filtering network model, characterized by: include, A first filter unit (101), a second filter unit (102), and a third filter unit (103); wherein the first filtering unit (101) is configured to dynamically adjust the filter window length for smoothing the coal signal according to the real-time variance of the coal signal; The second filtering unit (102) is configured to perform weighted moving average filtering on the historical coal signal data using an exponential decay weighting method; The third filtering unit (103) is configured to identify and remove pulse interference data in the coal signal.
2. The coal signal dynamic filtering network model according to claim 1, characterized in that: The first filtering unit (101) is configured as follows: A preset variance threshold is used to judge the severity of the fluctuation of the current coal flow state; If the real-time variance of the coal signal is greater than the variance threshold, it is judged to be in a violent fluctuation state, and the first filter window length is used; If the real-time variance of the coal signal is less than or equal to the variance threshold, it is determined to be in a stable state, and the second filter window length is used; The first filtering window length is smaller than the second filtering window length.
3. The coal signal dynamic filtering network model according to claim 1, characterized in that: The second filtering unit (102) is configured as follows: Using the weighted moving average formula Filter the original coal signal x[n]; Among them, the weight w k Using exponential decay function w k =e -k / τ Calculation is performed, τ is the attenuation coefficient; Where y[n] is the output signal after filtering, x[nk] is the sampling value of the historical original coal signal, N is the length of the filter window, and w k is the weight, k represents the time distance of the data point, and τ is a preset attenuation coefficient.
4. The coal signal dynamic filtering network model according to claim 1, characterized in that: The third filter unit (103) is configured as follows: In a sliding window, calculate the median M and standard deviation σ of all sampled data; Determine whether the current sampling value x[n] meets the abnormal condition: |x[n]-M|>[citestart]3σ If the abnormal condition is met, x[n] is determined to be an abnormal pulse interference value, and the median M of the current window is used to replace the abnormal value before subsequent filtering calculations are performed.
5. The coal signal dynamic filtering network model according to any one of claims 1 to 4, characterized in that: The first filter unit (101), the second filter unit (102) and the third filter unit (103) are connected in series to form a multi-stage filtering chain, and process the coal signal in sequence.
6. A heavy medium shallow trough separator system, characterized by: include, A separator (200) having a scraper (201) for conveying and separating materials; A driving member (300) for driving the scraper (201) to operate; a weighing element (400) provided on a feed conveyor belt (202) for conveying coal to the separator (200), the weighing element (400) being used to collect data on the amount of coal on the feed conveyor belt (202) in real time and use the data as a feedforward control signal; A control module (500) is electrically connected to the driving element (300), and the control module (500) is used to filter the feedforward control signal and adjust the output frequency.
7. The heavy medium shallow trough separator system according to claim 6, characterized in that: The control module (500) includes a frequency converter (501) and a controller (502); The signal input end of the controller (502) is connected to the weighing element (400), and the signal output end is connected to the frequency converter (501); The controller (502) has a built-in coal signal dynamic filter network model, which is used to dynamically filter the received raw coal quantity data and convert the processed raw coal quantity data into a frequency instruction and send it to the frequency converter (501).
8. A coal sorting method, characterized in that: include, By means of a weighing piece (400) provided on the feeding conveyor belt (202) of the sorting machine (200), real-time changes in the amount of coal at the feeding end are directly detected; Perform dynamic filtering on the collected coal quantity signal to eliminate fluctuations and interference during the measurement process; According to the filtered coal quantity signal, the running speed of the scraper (201) of the separator (200) is adjusted in real time, and the scraper speed is matched with the feed load.
9. The coal separation method according to claim 8, wherein: The step of dynamically filtering the collected coal quantity signal is used to adaptively adjust the filter window, weight and smooth the signal, and eliminate abnormal pulses.
10. The coal separation method according to claim 9, wherein: The weighing element (400) is installed at a position greater than 5 meters away from the drop point of the feeding conveyor belt (202); A shock-proof gasket and a belt tensioning device are installed in the installation area of the weighing member (400).