Control method for a scraper conveyor for a fault face dip

Abstract

The present application relates to the technical field of mine conveying control, and particularly relates to a control method for a scraper conveyor used in a fault working face for downward mining, comprising: obtaining the operating parameters of a coal mining machine and the geological parameters of a working face based on a set control adjustment frequency; evaluating the production efficiency of the operating parameters of the coal mining machine and the geological parameters of the working face to obtain the target coal flow density of the scraper conveyor; in response to the collected real-time coal flow density being less than the target coal flow density, collecting real-time monitoring parameters of the scraper conveyor; calculating the coal flow density gap between the real-time coal flow density and the target coal flow density, and combining the same with the real-time monitoring parameters to input into a conveyor control adjustment model to obtain the optimal control parameters of the scraper conveyor under the working condition, and adjusting the scraper conveyor based on the optimal control parameters. The present application can improve the adaptability of the scraper conveyor to complex and changeable working conditions, and improve the efficiency and safety of coal mining.

Description

Technical Field

[0001] The present invention relates to the technical field of mine conveying control, and in particular to a control method for a scraper conveyor used for downward mining on a fault working face. Background Art

[0002] In underground coal mining, the scraper conveyor is the core transportation equipment of the fully mechanized mining face. Its operating stability directly affects the coal production efficiency and safe production. When the working face exposes a large-angle fault and is in a downward mining condition, it faces multiple challenges such as uneven coal rock hardness, complex equipment force, and coal flow downflow impact.

[0003] At present, the control methods for scraper conveyors for fault-face mining usually only operate based on fixed parameters or simple preset modes, which makes it difficult to fully adapt to different geological conditions and the dynamically changing operating conditions of coal mining machines. In particular, in fault areas, there is a tendency for coal flow density to not match equipment load. At the same time, existing control methods mostly focus on monitoring a single parameter of the scraper conveyor, and fail to comprehensively analyze multi-dimensional data such as scraper spacing and key equipment temperature. This makes it difficult to comprehensively evaluate the equipment health status and conveying efficiency, and thus it is impossible to accurately determine the optimal control solution under the current conditions, which can easily lead to problems such as conveyor overload, high energy consumption, or coal flow interruption. Summary of the Invention

[0004] The present invention provides a control method for a scraper conveyor for downward mining on a fault working face. The method can improve the adaptability of the scraper conveyor to complex and changeable working conditions, improve the efficiency and safety of coal mining, and effectively solve the problems in the background technology.

[0005] In order to achieve the above object, the present invention provides a control method for a scraper conveyor for mining on a fault working face, comprising:

[0006] Based on the set control adjustment frequency, the coal mining machine operating parameters and working face geological parameters are obtained;

[0007] Performing a production efficiency evaluation on the operating parameters of the coal mining machine and the geological parameters of the working face to obtain a target coal flow density of the scraper conveyor;

[0008] In response to the collected real-time coal flow density being less than the target coal flow density, collecting real-time monitoring parameters of the scraper conveyor;

[0009] The coal flow density gap between the real-time coal flow density and the target coal flow density is calculated, combined with the real-time monitoring parameters, and input into the conveyor control and adjustment model to obtain the optimal control parameters of the scraper conveyor under this working condition, and the scraper conveyor is adjusted accordingly.

[0010] Furthermore, the coal mining machine operating parameters include at least hauling speed, coal passing height and coal mining speed.

[0011] Furthermore, the geological parameters of the working face include at least coal seam hardness, coal seam thickness and coal seam inclination.

[0012] Furthermore, the real-time monitoring parameters include at least scraper spacing, motor temperature, reducer temperature, chain wear degree and equipment vibration characteristics.

[0013] Furthermore, the method for setting the control adjustment frequency includes:

[0014] Based on the service life of the scraper conveyor, the basic adjustment frequency is mapped in the preset adjustment frequency database;

[0015] In response to at least one characteristic parameter in the preset monitoring characteristic parameter set exceeding its own preset threshold range, the basic adjustment frequency is corrected by comprehensively considering the excess magnitude of each characteristic parameter to obtain a dynamic adjustment frequency, and the dynamic adjustment frequency is used as the control adjustment frequency;

[0016] In response to the fact that no characteristic parameter in the preset monitoring characteristic parameter set exceeds its own preset threshold range, the basic adjustment frequency is used as the control adjustment frequency.

[0017] Furthermore, the preset monitoring characteristic parameter set includes the traction speed, the coal mining speed, the motor temperature, the reducer temperature and the chain wear degree.

[0018] Furthermore, the conveyor control and adjustment model is constructed based on a multi-objective optimization algorithm, which includes at least any one of the NSGA-II algorithm, MOEA / D algorithm and Pareto frontier optimization algorithm, and is used to perform collaborative optimization between the coal flow density gap and the real-time monitoring parameters to determine the optimal control parameters.

[0019] Furthermore, in the production efficiency evaluation, equipment load safety boundary constraints are introduced, and an objective function containing inequality constraints is constructed, so that the target coal flow density output by the production efficiency evaluation meets both the production efficiency requirements and the equipment safety operation threshold.

[0020] Furthermore, the equipment load safety boundary constraint condition adopts at least one of the motor rated power, chain allowable tension and scraper strength limit of the scraper conveyor.

[0021] Furthermore, the coal seam hardness is obtained by reverse calculation of the current fluctuation amplitude of the coal mining machine cutting motor, and the reverse calculation formula is:

[0022] ;

[0023] Indicates the hardness of the coal seam; k 1 represents the working condition correction factor; Indicates the exploration preset hardness; I actual Indicates the real-time current of the cutting motor; I 空载 Indicates the no-load current of the cutting motor; I 额定 Indicates the rated current of the cutting motor.

[0024] The technical solution of the present invention can achieve the following technical effects:

[0025] The present invention conducts a multi-dimensional collaborative analysis of the operating parameters of the coal mining machine, the geological parameters of the working face, the real-time coal flow density and the real-time monitoring parameters of the scraper conveyor, rather than processing a certain type of parameter in isolation; the target coal flow density is established through production efficiency evaluation, and the real-time coal flow density is compared with it as a benchmark, and then the real-time monitoring parameters of the scraper conveyor are linked, so that the control decision is no longer based on a single or one-sided data, but from the perspective of the whole process of mining and transportation, to achieve a deep coupling analysis of the operating status of the scraper conveyor and the complex working conditions of the working face; by constructing a dynamic adaptive control closed loop, parameters are continuously acquired at a set frequency, the target coal flow density is continuously adjusted according to changes in working conditions, and dynamic adjustments are made based on real-time monitoring feedback; in the face of dynamic changes in coal and rock conditions and the operating status of the coal mining machine during the process of high-angle fault mining, the operating parameters of the scraper conveyor can be adjusted in a timely and accurate manner. Compared with the traditional fixed parameter or simple preset mode control method, the adaptability of the scraper conveyor to complex and changeable working conditions is improved, and the efficiency and safety of coal mining are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a logic flow chart of the scraper conveyor control method used for fault face mining in the present invention. DETAILED DESCRIPTION

[0027] The present application is described below in conjunction with the accompanying drawings.

[0028] like Figure 1 As shown, the control method of the scraper conveyor for fault working face downward mining of the present invention specifically includes the following steps:

[0029] Step S1: obtaining coal mining machine operating parameters and working face geological parameters based on the set control adjustment frequency;

[0030] Step S2: evaluating the production efficiency of the shearer operating parameters and the working face geological parameters to obtain a target coal flow density of the scraper conveyor;

[0031] Step S3: in response to the collected real-time coal flow density being less than the target coal flow density, collecting real-time monitoring parameters of the scraper conveyor;

[0032] Step S4: Calculate the coal flow density gap between the real-time coal flow density and the target coal flow density, combine it with the real-time monitoring parameters, input it into the conveyor control and adjustment model, obtain the optimal control parameters of the scraper conveyor under this working condition, and adjust the scraper conveyor accordingly.

[0033] In this embodiment, the existing control method is difficult to adapt to different geological conditions and the dynamically changing operating status of the coal mining machine. However, this method can perceive in real time the complex conditions of uneven hardness of coal and rock in the working face, changes in inclination, and changes in the operating status of the coal mining machine by obtaining the operating parameters of the coal mining machine and the geological parameters of the working face based on the set frequency. Then, the production efficiency is evaluated based on the above parameters to obtain the target coal flow density that meets the current working conditions, effectively solving the problem of mismatch between coal flow density and equipment load, and ensuring stable and efficient transportation even in the fault area. In view of the defect of the existing control method that focuses on single parameter monitoring, this method collects multi-dimensional real-time monitoring parameters such as scraper spacing, motor temperature, and reducer temperature when the real-time coal flow density is less than the target coal flow density, and combines them with the coal flow density gap to input into the conveyor control adjustment model. It can comprehensively evaluate the health status of the equipment and the transportation efficiency, accurately determine the optimal control parameters, and avoid problems such as conveyor overload, high energy consumption, or coal flow interruption.

[0034] This method conducts a multi-dimensional collaborative analysis of the operating parameters of the coal mining machine, the geological parameters of the working face, the real-time coal flow density, and the real-time monitoring parameters of the scraper conveyor, rather than treating any particular parameter in isolation. A target coal flow density is established through production efficiency evaluation, which is used as a benchmark for comparison with the real-time coal flow density, and then linked to the real-time monitoring parameters of the scraper conveyor. This ensures that control decisions are no longer based on single or one-sided data, but rather on a full-process perspective of mining and transportation, enabling a deep coupling analysis of the operating status of the scraper conveyor and the complex working conditions of the working face. By constructing a dynamic adaptive control closed loop, parameters are continuously acquired at a set frequency, the target coal flow density is continuously adjusted according to changes in working conditions, and dynamic adjustments are made based on real-time monitoring feedback. In the face of dynamic changes in coal and rock conditions and the operating status of the coal mining machine during the process of down-mining on steep-angle faults, the operating parameters of the scraper conveyor can be adjusted in a timely and accurate manner. Compared with traditional control methods with fixed parameters or simple preset modes, this method improves the adaptability of the scraper conveyor to complex and changing working conditions, thereby enhancing the efficiency and safety of coal mining.

[0035] In some embodiments of the present invention, for step S1, multi-sensor collaborative acquisition and dynamic parameter synchronization are used to obtain real-time data on the shearer status and geological conditions that are strongly related to the operation of the scraper conveyor, providing accurate input for subsequent coal flow density evaluation and control parameter optimization; wherein the shearer operating parameters include at least hauling speed, coal passing height, and coal mining speed;

[0036] The traction speed is the speed at which the shearer moves along the working face, directly affecting the amount of coal passed and the continuity of the coal flow. During downward mining, large inclination angles can cause fluctuations in the traction speed. For example, the impact of the coal flow causing a drop in speed requires high-frequency monitoring to adjust the conveyor's matching capacity. The encoder or Hall effect sensor installed on the shearer's traction unit, combined with the gearbox transmission ratio, can monitor and convert the shearer's movement speed along the working face in real time.

[0037] The coal-passing height is the relative height between the shearer's cutting drum and the coal seam roof, reflecting the matching of the cutting depth and coal seam thickness. If the coal-passing height is too low, the equipment may be overloaded due to cutting hard rock, while if it is too high, coal may be missed, affecting the coal flow density. By using a laser rangefinder or ultrasonic sensor on the shearer's rocker arm to measure the vertical distance between the shearer drum and the middle trough of the scraper conveyor, the cutting height of the coal-passing channel can be indirectly reflected.

[0038] The coal mining speed is the actual amount of coal mined per unit time, which directly determines the rate at which the coal flow is fed into the conveyor. The current sensor or speed encoder installed on the shearer's cutting motor can be used to obtain the real-time power or speed of the drum cutting the coal and rock. The coal mining speed is calculated based on the drum diameter and cutting depth. The specific calculation formula is:

[0039] ;

[0040] in, v c Indicates coal mining speed; k 2 represents the coal mining efficiency coefficient; Indicates coal density; P d Indicates the real-time power of the shearer cutting motor; H It indicates the vertical thickness of the coal seam, that is, the actual thickness of the coal seam in the vertical direction at the current mining position of the coal mining machine.

[0041] Geological parameters are key factors affecting coal flow density and equipment load, and need to be dynamically updated through the integration of in-situ sensing and geological exploration data. The geological parameters of the working face include at least coal seam hardness, coal seam thickness, and coal seam inclination, among which:

[0042] Coal seam hardness affects the shearer's cutting power consumption and efficiency. Hard coal seams require a lower traction speed to reduce equipment wear, while soft coal seams can increase the speed to improve efficiency. Coal seam hardness can be measured directly or indirectly. Direct measurement involves installing a stress sensor on the shearer's cutting section to monitor the cutting resistance in real time, and inferring the coal seam hardness based on the density of the drum pick arrangement. Indirect measurement involves inferring the current fluctuation amplitude of the shearer's cutting motor, establishing a mapping model between the effective current value, cutting resistance, and hardness coefficient. The calculation formula is:

[0043] ;

[0044] Indicates the hardness of the coal seam; k 1 represents the working condition correction factor; Indicates the exploration preset hardness; I actual Indicates the real-time current of the cutting motor; I 空载 Indicates the no-load current of the cutting motor; I 额定 Indicates the rated current of the cutting motor;

[0045] Coal seam thickness affects the reasonable range of coal cutting height. During downward mining, large inclination angles may lead to uneven coal seam thickness, such as roof collapse or floor uplift, requiring real-time monitoring to optimize cutting strategies. By installing a geological radar or ultrasonic probe on the top of the coal mining machine, it transmits signals to the coal seam ahead and calculates the coal seam thickness based on the echo time difference.

[0046] The greater the inclination of the coal seam, the stronger the impact of the coal flow, and the higher the requirements for the conveyor chain tension and scraper spacing; the control parameters need to be dynamically adjusted to balance stability and efficiency; by deploying high-precision inclination sensors at the head and tail of the scraper conveyor and the body of the coal mining machine, the inclination changes of the working face direction and tendency can be monitored in real time; or inclination sensors can be installed on the support columns to collect the coal seam inclination of each support column.

[0047] As a preferred embodiment of the above, under the fault working face downward mining condition, the coal mining environment is complex and changeable, and the coal mining machine operating parameters interact with the working face geological parameters, jointly affecting the coal mining efficiency and the operating status of the scraper conveyor; if the scraper conveyor operating indicators are set only based on experience or fixed parameters, it will not be able to adapt to the real-time changing working conditions, which will easily lead to a mismatch between the coal flow density and the equipment load, causing equipment overload, coal flow interruption and other problems; therefore, it is necessary to integrate and analyze the coal mining machine operating parameters and the working face geological parameters to obtain the scraper conveyor target coal flow density that meets the current actual working conditions.

[0048] Specifically, the traction speed, coal passing height, and coal mining speed in the coal mining machine operating parameters, and the coal seam hardness, coal seam thickness, and coal seam inclination in the working face geological parameters are used as input data and imported into a pre-built production efficiency evaluation model that has been trained and optimized with a large amount of actual working condition data; the production efficiency evaluation model is based on machine learning algorithms or mathematical modeling methods to deeply explore the intrinsic relationship between various parameters and production efficiency; for example, by analyzing a large amount of historical data, the model can learn the relationship between coal mining efficiency and coal flow density corresponding to different combinations of coal mining machine traction speeds, coal passing heights, and coal mining speeds under specific coal seam hardness, thickness, and inclination conditions; after inputting the current actual working condition parameters, the model outputs an accurate scraper conveyor target coal flow density value through complex calculations and reasoning. The target coal flow density value represents the ideal coal flow density state in which the scraper conveyor can achieve efficient and stable operation under the current working conditions.

[0049] In order to verify the output accuracy of the production efficiency evaluation model, the following tests were performed:

[0050] When the shearer cuts coal seams of different hardness (f=3, 4, 5) at different traction speeds (8m / min, 10m / min, 12m / min), the laser coal flow density sensor is used to collect coal flow cross-sectional data in real time and input it into the production efficiency evaluation model to calculate the target coal flow density. The actual coal flow rate transported by the scraper conveyor (obtained by weighing on an electronic scale at the transfer point) is also recorded simultaneously, and the error rate between the model output value and the actual value is calculated.

[0051] Data Record Table 1:

[0052] Experimental group Pulling speed (m / min) Coal seam hardness (f) Target coal flow density (t / h) Actual coal flow density (t / h) Error rate (%) 1 10 4 480 475 1.04 2 12 5 520 512 1.54 3 8 3 450 448 0.44

[0053] Among them, the target coal flow density is calculated by the production efficiency evaluation model; the actual coal flow density is obtained by weighing on an electronic scale at the transfer point, recorded every 5 minutes, and the average value is taken; the data shows that the output value of the production efficiency evaluation model is highly consistent with the actual value (error ≤1.54%), verifying the adaptability of the production efficiency evaluation model to complex working conditions (hardness changes, inclination changes).

[0054] In this embodiment, by inputting the real-time collected coal mining machine operating parameters and working face geological parameters into the production efficiency evaluation model, the target coal flow density that meets the current complex working conditions can be accurately generated; compared with the traditional fixed parameters or experience setting method, this method can dynamically adjust the target coal flow density according to the actual conditions such as uneven hardness of coal rock in the fault area and changes in inclination, so that the scraper conveyor operating parameters are highly adapted to the actual working conditions, and effectively avoid the problem of mismatch between coal flow density and equipment load; based on the precise target coal flow density, the scraper conveyor can operate in the optimal state and give full play to its transportation capacity; it will not cause equipment overload and blockage due to excessive coal flow density, affecting production continuity; nor will it cause equipment idling and waste of resources due to too low coal flow density, thereby significantly improving the overall efficiency of coal mining and transportation and increasing coal production per unit time; a reasonable target coal flow density can keep the load of the scraper conveyor within a safe and stable range, reduce equipment wear and failure risks caused by overload, impact, etc., extend equipment service life, and reduce equipment maintenance costs.

[0055] As a preferred embodiment of the above, the target coal flow density is an ideal transport indicator set based on coal mining machine parameters and geological conditions. However, the actual coal flow density may deviate from the target value due to factors such as equipment operating status and coal flow transmission losses. When the real-time coal flow density is less than the target value, it indicates that the current scraper conveyor's transport capacity is not fully utilized or there are potential anomalies, such as coal flow accumulation, equipment jamming, and component wear leading to reduced transmission efficiency. In this case, the root cause of the problem cannot be identified solely based on mining-end parameters. Further real-time monitoring parameters of the scraper conveyor are required to comprehensively analyze the causes of insufficient coal flow.

[0056] Specifically, the coal flow density is monitored in real time by coal flow sensors installed at key positions of the scraper conveyor. The coal flow sensors use microwave radar, laser scanning devices, etc. When the monitored value is continuously lower than the target coal flow density output in step S2, step S3 is automatically triggered to obtain key status data of the scraper conveyor in real time through the distributed sensor network, specifically including:

[0057] Scraper spacing: Laser distance sensors or encoders installed on both sides of the scraper chain monitor the spacing changes between adjacent scrapers in real time. During high-angle mining, abnormal scraper spacing may be caused by chain slack, scraper deformation, or coal flow impact, directly affecting the continuity of coal flow.

[0058] Motor and reducer temperature: Deploy temperature sensors, such as thermocouples and infrared temperature probes, at key locations on the motor housing and reducer to collect real-time data on device temperature rises. Abnormally high temperatures often indicate excessive equipment load, poor lubrication, or mechanical failures, such as bearing wear.

[0059] Chain wear: The chain tensioner's displacement sensor or visual inspection camera monitors chain elongation, chain link wear thickness, and other parameters. Under downward mining conditions, the chain is subjected to long-term impact from coal flow and tilting forces. Increased wear can lead to chain breakage.

[0060] Equipment vibration characteristics: Install vibration acceleration sensors at the head, tail and middle trough of the scraper conveyor to collect data such as vibration frequency and amplitude. Abnormal vibration can reflect problems such as scraper jamming, bearing failure or body instability, such as sudden frequency changes and amplitude exceeding the limit.

[0061] In the edge controller inside the scraper conveyor control box, the raw data is subjected to noise reduction processing. For example, the motor temperature data is filtered using a sliding average, and the vibration signal is Fourier transformed to extract characteristic parameters such as the effective value, peak value, and root mean square value, thereby reducing the amount of data uploaded to the main controller.

[0062] As a preferred embodiment of the above, relying solely on real-time coal flow density or a single equipment monitoring parameter cannot comprehensively and accurately regulate the scraper conveyor; the gap between the real-time coal flow density and the target coal flow density reflects the difference between the current coal flow transportation efficiency and the ideal state, while the real-time monitoring parameters of the scraper conveyor reflect the operating status and health status of the equipment itself; if the coupling relationship between the two is not considered at the same time, blind adjustments are likely to occur. For example, simply increasing the conveying speed based on the coal flow density gap while ignoring existing equipment wear, overload and other problems, leading to equipment failure; or reducing the operating load based on a single parameter such as equipment temperature, which cannot meet the actual coal flow transportation needs; therefore, the coal flow density gap is combined with the real-time monitoring parameters and input into the control and adjustment model, and the production needs and equipment status are comprehensively considered to obtain the optimal control parameters that are truly suitable for the current working conditions; the specific implementation is as follows:

[0063] Step S41: Compare the value collected by the real-time coal flow density sensor with the target coal flow density output by the production efficiency evaluation model in step S2, and calculate the difference between the two, i.e., the coal flow density gap. For example, if the target coal flow density is 500 tons per hour and the real-time coal flow density is 400 tons per hour, the coal flow density gap is 100 tons per hour.

[0064] Step S42: Integrate the calculated coal flow density gap with the real-time monitoring parameters of the scraper conveyor collected in step S3 to form a comprehensive data set including production demand and equipment status;

[0065] Step S43: The integrated comprehensive data set is input into a pre-trained conveyor control and adjustment model; the conveyor control and adjustment model is constructed based on a large amount of actual working condition data and historical control experience using a machine learning algorithm or a multi-objective optimization algorithm, and the conveyor control and adjustment model adopts a fuzzy control algorithm or a multi-objective optimization algorithm, and the multi-objective optimization algorithm includes at least any one of the NSGA-II algorithm, the MOEA / D algorithm, or the Pareto frontier optimization algorithm; the model takes the coal flow density gap and the real-time monitoring parameters as input, and after complex calculations and reasoning, outputs the optimal control parameters of the scraper conveyor under this working condition, including the chain plate moving speed, chain tension, etc.; for example, if the model determines that the coal flow density gap is large and the chain wear is in a critical state, it will output control parameters that appropriately reduce the chain plate moving speed and increase the chain tension, so as to meet the coal flow transportation needs while ensuring equipment safety;

[0066] Step S44: The optimal control parameters output by the model are transmitted to the control system of the scraper conveyor, and the control system drives the relevant actuators to adjust the scraper conveyor. The actuators include motor inverters, chain tensioning devices, etc., so that the equipment operating parameters adapt to the current working conditions.

[0067] In order to verify the compensation effect of the coal flow density gap, the following tests were conducted:

[0068] A coal flow gap is set in the 2m section in the middle of the scraper conveyor, and the coal flow is blocked by a temporary baffle to simulate the uneven density caused by coal and rock inclusions in the fault zone. The sensor detects the coal flow density in the gap area in real time, with the target value being 500t / h and the actual value being 420t / h, triggering the gap compensation algorithm of the conveyor control adjustment model. The adjustment parameters output by the conveyor control adjustment model are recorded, including but not limited to the chain speed increment, motor torque increment, etc., as well as the time it takes for the coal flow to recover to within ±5% of the target value.

[0069] Data Record Table 2:

[0070] Experimental group Real-time coal flow density (t / h) Coal flow density gap (t / h) Control parameter adjustment (chain speed / m / s) Motor torque adjustment (%) Coal flow recovery time (s) 1 420 80 +0.3 +15 45 2 400 100 +0.5 +20 60

[0071] The coal flow density gap = target value - actual value. For example, in Experimental Group 1, 500 - 420 = 80 t / h. The data showed that after gap compensation, the coal flow density recovered rapidly, with recovery times of 45 seconds for Experimental Group 1 and 60 seconds for Experimental Group 2, both ≤ 90 seconds. The adjustment parameters were linearly positively correlated with the gap value, verifying the accuracy of coal flow density gap calculation and control.

[0072] In this embodiment, by combining the coal flow density gap with the real-time status parameters of the equipment, the complex and changeable production needs and equipment conditions during fault working face mining can be fully considered; whether it is the fluctuation of coal flow density caused by changes in coal rock hardness or the operation abnormality caused by equipment wear, a targeted control scheme can be obtained through model analysis, so that the operating parameters of the scraper conveyor are highly matched with the actual working conditions, effectively solving the problem of mismatch between coal flow density and equipment load; comprehensive consideration of the real-time monitoring parameters of the equipment is used for control to avoid problems such as equipment overload and increased wear caused by blind adjustment; for example, when the model detects that the chain is severely worn, it will give priority to reducing the operating speed or adjusting the tension to reduce the chain force, thereby reducing the risk of equipment failure, extending the equipment service life, and reducing maintenance costs and downtime; precise optimal control parameters can ensure that the scraper conveyor operates stably while meeting production needs; it will not cause coal flow interruption, equipment damage, etc. that affect production efficiency due to excessive coal flow density or excessive equipment load, nor will it reduce coal transportation efficiency due to overly conservative control strategies; at the same time, reasonable operating parameter settings reduce safety hazards caused by equipment failure.

[0073] In some embodiments of the present invention, the method for controlling and adjusting the frequency includes:

[0074] Step T1: Based on the service life of the scraper conveyor, a basic adjustment frequency is mapped in a preset adjustment frequency database;

[0075] Step T2: In response to at least one characteristic parameter in a preset monitoring characteristic parameter set exceeding a preset threshold range, comprehensively considering the extent of the excess of each characteristic parameter, modifying the basic adjustment frequency to obtain a dynamic adjustment frequency, and using the dynamic adjustment frequency as the control adjustment frequency; the preset monitoring characteristic parameter set includes the traction speed, the coal mining speed, the motor temperature, the reducer temperature, and the chain wear degree;

[0076] Step T3: In response to the fact that no characteristic parameter in the preset monitoring characteristic parameter set exceeds its own preset threshold range, the basic adjustment frequency is used as the control adjustment frequency.

[0077] Furthermore, in step T1, the scraper conveyor is affected by multiple factors such as uneven coal rock hardness, complex equipment force, and impact of coal flow during operation. Its equipment status and operating conditions will change with the service life; scraper conveyors in different service stages have different requirements for monitoring and control frequency of operating parameters; new equipment has stable mechanical properties and small fluctuations in operating status, so the control and adjustment frequency can be appropriately reduced to reduce unnecessary data processing and equipment adjustment; while equipment with a longer service time has increased component wear and the probability of failure increases, and the control and adjustment frequency needs to be increased to timely discover and deal with potential problems; therefore, setting the basic adjustment frequency based on the service life of the scraper conveyor can preliminarily match the control needs of the equipment at different use stages, balance the relationship between data acquisition and processing costs and stable equipment operation, and provide a reasonable initial benchmark for subsequent dynamic adjustment.

[0078] Specifically, by analyzing and summarizing the operating data, fault records and maintenance experience of a large number of scraper conveyors at different service times, a preset adjustment frequency database is constructed; the database clearly records different service time intervals and the corresponding basic adjustment frequencies. For example, the service time is divided into multiple intervals such as 0-1 years, 1-3 years, and 3-5 years. The basic adjustment frequency corresponding to 0-1 years is set to parameter acquisition and control evaluation every 30 minutes, 1-3 years corresponds to once every 20 minutes, 3-5 years corresponds to once every 10 minutes, and so on; the setting of this database needs to comprehensively consider factors such as the design life of the equipment, common failure cycles, and coal mine production operation intensity;

[0079] Through the equipment file management system or the operating time recording device carried by the scraper conveyor itself, such as the cumulative operating time counter, the total time of the current scraper conveyor since it was put into use can be accurately obtained; the determined service time of the scraper conveyor is compared with the service time interval in the preset adjustment frequency database, and after finding the corresponding interval, the basic adjustment frequency set for the interval is obtained; for example, if the service time of a scraper conveyor is 2.5 years, which is in the range of 1-3 years, the basic adjustment frequency of every 20 minutes corresponding to the interval is obtained from the database, and this is used as the initial frequency for subsequent parameter acquisition and control adjustment evaluation of the scraper conveyor.

[0080] In order to verify the adaptability of dynamic frequency adjustment, the following experiments are conducted:

[0081] Simulating equipment aging scenarios, the system manually replaces chains with varying amounts of wear, increasing chain tension fluctuations. The system then records the base adjustment frequency and dynamically corrected frequency at different service times (0-1 year, 2-3 years, and 4-5 years). The system also simultaneously monitors the ratio of abnormal parameter detections to the total number of monitoring times. Abnormal parameters include chain vibrations exceeding 5m / s² and motor temperatures exceeding 80°C.

[0082] Data Record Table 3:

[0083] Service length (years) Basic adjustment frequency (times / hour) Frequency after dynamic correction (times / hour) Abnormal parameter detection rate (%) 0-1 30 30 98.2 2-3 20 25 99.5 4-5 10 30 100

[0084] The basic adjustment frequency is the default value when dynamic compensation is disabled, with an initial value of 30 times / hour. After dynamic correction, the frequency is adaptively adjusted based on the equipment's status. For example, after 2-3 years of service, if chain wear leads to increased vibration, the adjustment frequency will automatically increase to 25 times / hour. The abnormal parameter detection rate = number of abnormal events / total number of monitoring times × 100%. Data shows that the dynamic frequency adjustment mechanism improves monitoring sensitivity as equipment ages. After 4-5 years of service, the correction frequency increases from 10 times / hour to 30 times / hour, and the abnormal parameter detection rate is ≥98.2%, meeting the design requirement for the dynamic frequency adjustment mechanism to adapt to equipment aging.

[0085] In this embodiment, by setting the basic adjustment frequency based on the service life, the performance changes and potential risks of the equipment in different use stages can be fully considered; new equipment adopts a lower adjustment frequency during the performance stabilization period, which can reduce system resource usage and additional losses caused by frequent equipment adjustment; old equipment increases the adjustment frequency, which can timely capture operating parameter abnormalities caused by wear, aging and other problems, so that the control adjustment is more in line with the actual operating conditions of the equipment, effectively improving the stability and reliability of the equipment operation; avoiding resource waste or insufficient monitoring caused by a unified fixed adjustment frequency; for new equipment, there is no need for high-frequency monitoring like old equipment, reducing energy consumption and computing resource usage during data collection, transmission and processing; for old equipment, increasing the frequency ensures timely detection of potential faults, avoids equipment failures and production interruptions due to untimely monitoring, and optimizes the allocation of human, material and computing resources in the coal mining process as a whole.

[0086] Furthermore, the operating status of the scraper conveyor will change dynamically due to various factors such as changes in coal rock hardness, coal flow impact, equipment wear, etc.; only the basic adjustment frequency determined based on the service time in step T1 cannot respond in time to abnormal conditions occurring during equipment operation; for example, when the coal mining speed suddenly accelerates, the coal flow density may increase instantly, and the load of the scraper conveyor may surge; or the motor temperature may rise rapidly, indicating that the equipment may be overloaded or have hidden dangers of failure; at this time, if the parameters are still obtained and adjusted according to the basic adjustment frequency, the problem may not be discovered and handled quickly due to untimely monitoring, which may cause equipment failure or affect coal production efficiency and safety; therefore, it is also necessary to dynamically correct the basic adjustment frequency according to the key characteristic parameters of the real-time operation of the equipment to ensure that the control adjustment frequency can closely fit the actual operation needs of the equipment and timely discover and solve potential problems.

[0087] Specifically, various sensors installed on the scraper conveyor and related equipment continuously collect parameters in a preset monitoring feature parameter set in real time, including traction speed, coal mining speed, motor temperature, reducer temperature, and chain wear. For example, speed sensors are used to monitor traction speed and coal mining speed, temperature sensors are used to obtain motor temperature and reducer temperature, and displacement sensors or visual inspection cameras on the chain tensioning device are used to monitor chain wear.

[0088] The various characteristic parameters collected in real time are compared with pre-set thresholds; these thresholds are determined based on the scraper conveyor's design performance, safe operation standards, and a large amount of actual operating data and experience. For example, if the motor temperature exceeds 80°C, the motor temperature parameter is considered to be outside its preset threshold range; if the traction speed changes by more than 20% of the normal range within a short period of time, the traction speed parameter is considered abnormal.

[0089] When at least one characteristic parameter exceeds its own preset threshold range, the system comprehensively considers the excess range of various parameters exceeding the threshold; for example, if the motor temperature exceeds the threshold by 10°C and the coal mining speed exceeds the threshold by 30%, the comprehensive influence coefficient is calculated based on the pre-set weights and algorithms; the basic adjustment frequency is corrected according to this coefficient. For example, if the basic adjustment frequency is once every 20 minutes, if the comprehensive influence coefficient reaches a certain level, the adjustment frequency can be shortened to once every 10 minutes, thereby obtaining a dynamic adjustment frequency, and using it as the current control adjustment frequency; during the correction process, the best adjustment frequency cases under similar parameter excess ranges in historical data will also be referred to to ensure that the corrected frequency is reasonable and effective.

[0090] Through the above embodiments, abnormal changes in the operation of the scraper conveyor can be quickly captured. Once the key characteristic parameters exceed the threshold, the control adjustment frequency is immediately corrected to increase the frequency of parameter acquisition and regulation; the equipment operation status can be monitored more intensively, and potential fault hazards or sudden changes in working conditions can be discovered in time. For example, before the chain wear intensifies but has not yet caused a chain break, maintenance measures can be taken in advance through high-frequency monitoring to avoid equipment failure and ensure production continuity; the adjustment frequency is corrected by considering multiple key characteristic parameters and their excess amplitudes, taking into account the impact of complex and changeable working conditions on equipment operation during fault working face mining; whether it is load fluctuations caused by changes in coal flow or performance changes caused by wear of the equipment itself, the control of the scraper conveyor can be made more precise by dynamically adjusting the adjustment frequency; for example, when the hardness of the coal seam suddenly increases, the coal mining speed decreases, and the motor load increases, timely increasing the adjustment frequency will help the system quickly adjust the operating parameters of the scraper conveyor to adapt it to the new working conditions and maintain efficient and stable operation;

[0091] By dynamically correcting and controlling the adjustment frequency, the operating status of the equipment can be monitored and regulated in a refined manner; equipment anomalies can be discovered and handled in advance, reducing downtime and maintenance costs caused by failures; for example, burnout failures caused by long-term high-temperature operation of the motor can be avoided, the equipment service life can be extended, the reliability and safety of the equipment can be improved, while also reducing the risk of safety accidents caused by equipment failures and protecting the lives of underground workers; ensuring that the scraper conveyor can operate at an appropriate adjustment frequency under various working conditions, and that equipment problems will not be discovered in time due to too low a frequency, affecting production efficiency; nor will unnecessary waste of resources be caused due to too high a frequency; a reasonable adjustment frequency helps maintain a stable rhythm of coal production and transportation, improve production efficiency, while reducing equipment maintenance and repair costs, and maximizing the economic benefits of coal mining.

[0092] On the other hand, when all parameters in the preset monitoring characteristic parameter set are within the normal range, it means that the current operating status of the equipment is stable, and there is no abnormal fluctuation or potential failure risk; at this time, if unnecessary adjustments are still made to the control adjustment frequency frequently, it will not only increase the burden of data collection, transmission and processing, consume additional system resources, but may also cause unnecessary wear to the equipment due to frequent adjustments, affecting the service life of the equipment; based on this, when the equipment is in good operating condition, keeping the basic adjustment frequency unchanged can ensure effective monitoring of the equipment operating status while achieving rational utilization of resources and stable operation of the equipment, and maintaining a smooth rhythm of production operations.

[0093] Specifically, if all parameters do not exceed their respective preset threshold ranges, for example, the motor temperature is always maintained below 80°C, the traction speed variation is within 20% of the normal range, etc.; once it is determined that no characteristic parameter in the preset monitoring characteristic parameter set exceeds its own preset threshold range, the system will directly use the basic adjustment frequency obtained based on the scraper conveyor service time mapping in step T1 as the current control adjustment frequency; in the subsequent operation process, continue to obtain parameters and control evaluation according to the basic adjustment frequency until a parameter abnormality occurs and dynamic adjustment is required.

[0094] Through the above embodiments, unnecessary adjustments to the control frequency are avoided when the equipment is in a stable operating state, the frequency of data collection, transmission and processing is greatly reduced, the consumption of computing resources, network bandwidth, electricity and other resources is reduced, the overall operating efficiency of the system is improved, and the operating costs during coal mining are reduced; the additional wear of the equipment due to frequent adjustments is reduced; for example, the motor does not need to be frequently accelerated or decelerated, and the chain will not suffer increased wear due to frequent tension adjustments, thereby effectively extending the service life of the equipment and reducing the probability of equipment failure; keeping the basic adjustment frequency unchanged when the equipment is in good operating condition helps to maintain a smooth rhythm of production operations; operators can monitor and manage the equipment according to established frequencies and processes, reducing operational troubles and uncertainties caused by frequent changes in the adjustment frequency; at the same time, a stable control rhythm is also conducive to maintaining a good collaborative working relationship with other equipment such as coal mining machines, ensuring the stable operation of the entire coal mining system.

[0095] In some embodiments of the present invention, in order to prevent the scraper conveyor load from exceeding the safety limit and causing safety accidents or equipment failures, it is necessary to ensure that the equipment operates within the safety threshold while pursuing production efficiency, and avoid sacrificing equipment safety due to blindly increasing the coal flow density; by introducing equipment load safety boundary constraints, a dynamic balance between production efficiency and equipment safety is achieved to ensure stable system operation.

[0096] Specifically, the equipment load safety boundary constraint condition adopts one or more of the scraper conveyor's motor rated power, chain allowable tension, scraper strength limit, motor temperature, and scraper vibration. The parameters used in the equipment load safety boundary constraint condition are the safe operating upper limits determined during equipment design and manufacturing, and can effectively reflect the load capacity of the equipment.

[0097] In the production efficiency evaluation model, an objective function containing inequality constraints is constructed. The objective function takes the coal flow density that can characterize the maximum production efficiency as the main target, and introduces the equipment load safety boundary constraint condition as the inequality constraint. For example, the objective function can be expressed as:

[0098] ;

[0099] Among them, P 电机 is the actual power of the motor, P 额定 is the rated power of the motor; T 链条 is the actual chain tension, T 许用 is the allowable tension of the chain; S 刮板 is the actual strength of the scraper, S 极限 is the scraper strength limit;

[0100] The equipment load safety boundary constraints are integrated into the production efficiency evaluation model; the production efficiency evaluation model calculates the maximum possible coal flow density, i.e., the target coal flow density, while satisfying the equipment load safety boundary constraints through a comprehensive analysis of the shearer operating parameters and the working face geological parameters; an optimization algorithm is used to solve the objective function to obtain the optimal target coal flow density while satisfying the equipment's safe operating conditions; during the solution process, the algorithm automatically adjusts the parameters to ensure that the constraints are met.

[0101] In order to verify whether the conveyor control method in the above embodiment can meet the equipment safety boundary, the following experiments are conducted:

[0102] During the full-operating condition experiment, key parameters such as motor temperature, chain tension, and scraper vibration are continuously monitored, and their maximum values ​​are recorded and compared with the safety threshold.

[0103] Data Record Table 4:

[0104] Monitoring parameters Safety threshold Experimental maximum value Overrun times Motor temperature (°C) ≤80 78 0 Chain tension (kN) ≤120 115 0 Scraper vibration (m / s²) ≤5 4.2 0

[0105] Among them, the maximum motor temperature was 78°C (<80°C), and there was no overheating due to dynamic adjustment of the chain speed; the maximum chain tension was 115kN (<120kN), and there was no risk of chain breakage; the maximum scraper vibration was 4.2m / s² (<5m / s²), and no abnormal wear was caused. The data shows that the control method effectively avoids equipment overload by dynamically adjusting parameters such as chain speed and torque, meeting the design requirement that key equipment parameters are always within the safety threshold.

[0106] In this embodiment, by introducing equipment load safety boundary constraints, the scraper conveyor is ensured to operate within a safe load range, effectively avoiding equipment damage and production accidents due to overload, and improving the reliability and service life of the equipment; while pursuing high production efficiency, the safe operation of the equipment is taken into account, avoiding production interruptions due to equipment overload, and ensuring the continuity and stability of production; the load capacity of the equipment is reasonably utilized to avoid the equipment from operating under conditions exceeding the design limit, reducing energy waste and equipment loss, and improving resource utilization efficiency and economic benefits.

[0107] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the foregoing embodiments. The foregoing embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for controlling a scraper conveyor for mining on a fault working face, characterized in that: include: Based on the set control adjustment frequency, the coal mining machine operating parameters and working face geological parameters are obtained; Performing a production efficiency evaluation on the operating parameters of the coal mining machine and the geological parameters of the working face to obtain a target coal flow density of the scraper conveyor; In response to the collected real-time coal flow density being less than the target coal flow density, collecting real-time monitoring parameters of the scraper conveyor; Calculating the coal flow density gap between the real-time coal flow density and the target coal flow density, combining the gap with the real-time monitoring parameters, and inputting the gap into the conveyor control and adjustment model to obtain the optimal control parameters of the scraper conveyor under this working condition, and adjusting the scraper conveyor accordingly; The method for setting the control adjustment frequency includes: Based on the service life of the scraper conveyor, the basic adjustment frequency is mapped in the preset adjustment frequency database; In response to at least one characteristic parameter in the preset monitoring characteristic parameter set exceeding its own preset threshold range, the basic adjustment frequency is corrected by comprehensively considering the excess magnitude of each characteristic parameter to obtain a dynamic adjustment frequency, and the dynamic adjustment frequency is used as the control adjustment frequency; In response to the fact that no characteristic parameter in the preset monitoring characteristic parameter set exceeds its own preset threshold range, the basic adjustment frequency is used as the control adjustment frequency; The conveyor control and adjustment model is constructed based on a multi-objective optimization algorithm, which includes at least any one of the NSGA-II algorithm, the MOEA / D algorithm and the Pareto frontier optimization algorithm, and is used to collaboratively optimize the coal flow density gap and the real-time monitoring parameters to determine the optimal control parameters; in the production efficiency evaluation, the equipment load safety boundary constraint condition is introduced, and an objective function containing inequality constraints is constructed, so that the target coal flow density output by the production efficiency evaluation meets both the production efficiency requirements and the equipment safety operation threshold.

2. The method for controlling a scraper conveyor for mining a fault face according to claim 1, characterized in that: The coal mining machine operating parameters include at least traction speed, coal passing height and coal mining speed.

3. The method for controlling a scraper conveyor for mining a fault face according to claim 2, characterized in that: The geological parameters of the working face include at least coal seam hardness, coal seam thickness and coal seam inclination.

4. The control method for a scraper conveyor for mining a fault face according to claim 3, characterized in that: The real-time monitoring parameters include at least scraper spacing, motor temperature, reducer temperature, chain wear degree and equipment vibration characteristics.

5. The method for controlling a scraper conveyor for mining a fault face according to claim 4, characterized in that: The preset monitoring characteristic parameter set includes the traction speed, the coal mining speed, the motor temperature, the reducer temperature and the chain wear degree.

6. The method for controlling a scraper conveyor for mining a fault face according to claim 1, characterized in that: The equipment load safety boundary constraint condition adopts at least one of the motor rated power, chain allowable tension and scraper strength limit of the scraper conveyor.

7. The method for controlling a scraper conveyor for mining a fault face according to claim 3, characterized in that: The coal seam hardness is obtained by reverse calculation of the current fluctuation amplitude of the coal mining machine cutting motor. The reverse calculation formula is: ； Indicates the hardness of the coal seam; Indicates the working condition correction factor; Indicates the exploration preset hardness; Indicates the real-time current of the cutting motor; Indicates the no-load current of the cutting motor; Indicates the rated current of the cutting motor.

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