Intelligent speed regulation method for mining long-distance two-way conveying belt conveyor

Abstract

An intelligent speed regulation method for a mining long-distance bidirectional conveying belt conveyor comprises the steps that in the running process, monitoring signals are collected in real time, amplification and preliminary denoising processing are conducted through a signal preprocessing module, filtering processing is conducted through a Kalman filtering module, and high-precision monitoring data are output; the loads are divided into three grades, namely a low load, a medium load and a high load; according to the actual situation of field operation, establishing a standard fuzzy logic rule base by using an experience induction method; introducing state credibility, and dividing the state credibility into low, medium and high according to the sizes of diagonal elements of an error covariance matrix so as to assist in judging the reliability of the monitoring data and adjusting the fuzzy control rule to obtain an enhanced fuzzy rule base; and the output of the fuzzy controller is converted into an accurate speed regulation instruction by adopting a gravity center method, and accurate regulation and control on the conveying speed are realized by utilizing the speed regulation instruction. The method is high in control precision and short in response time, and the robustness of a control system can be remarkably improved.

Description

Technical Field

[0001] The invention belongs to the technical field of intelligent speed regulation, and in particular relates to an intelligent speed regulation method for a long-distance bidirectional transport belt conveyor for mining. Background Art

[0002] As a key material transportation equipment, belt conveyors play a pivotal role in the coal industry. In recent years, with the continuous growth of coal demand, the mining volume of underground coal needs to be increased in order to meet the requirements of output and efficiency, and the transportation distance of mining belt conveyors is often up to thousands of meters. When there is insufficient coal or no-load operation during the transportation process, it not only reduces the transportation efficiency, but also causes a lot of waste of electricity. At the same time, long-term operation will accelerate the wear of equipment parts (such as rollers, drums, etc.), thereby shortening the service life of the equipment and increasing the cost of maintenance and replacement.

[0003] At present, long-distance transportation for mining mainly relies on the DSJ100 quick-disassembly and assembly bidirectional belt conveyor. The equipment adopts a double-layer structure design, with the upper layer used to transport coal and the lower layer used to transport production auxiliary materials. However, this type of equipment has significant limitations in the kilometer-level transportation scenario: frequent disassembly and assembly lead to increased downtime and higher maintenance costs; although the double-layer structure improves space utilization, it increases the complexity of the machinery and puts forward higher requirements for real-time speed control.

[0004] In the existing technology, the fuzzy logic control method can dynamically adjust the conveying speed through sensor data, but there are some defects. Due to the influence of environmental noise and signal interference, the data collected by the sensor usually has large errors, especially in long-distance transportation scenarios. The sensor is susceptible to more electromagnetic interference, mechanical vibration and environmental temperature and humidity. The noise amplitude of the collected raw data can reach 20% to 30% of the effective signal, which will lead to a significant increase in the controller's misjudgment rate. At the same time, traditional filtering algorithms rely on fixed windows or thresholds, which are difficult to adapt to dynamic conditions such as sudden load changes and slope transients, and the contradiction between denoising and real-time performance is prominent. For this reason, it is urgent to provide an intelligent speed regulation method suitable for long-distance bidirectional conveying belt conveyors for mining. Summary of the invention

[0005] In view of the problems existing in the above-mentioned prior art, the present invention provides an intelligent speed regulation method for a long-distance bidirectional transport belt conveyor for mining. The method can effectively reduce noise interference, has high control accuracy and short response time, can significantly improve the robustness of the control system, and thus can significantly improve the transportation efficiency of the belt conveyor. The method is suitable for energy-saving and optimized control of long-distance bidirectional transport belt conveyors in complex working conditions underground in coal mines.

[0006] In order to achieve the above object, the present invention provides an intelligent speed regulation method for a long-distance bidirectional transport belt conveyor for mining, comprising the following steps: Step 1: Establish a sensor monitoring network; Install multiple sensors at multiple key monitoring nodes on long-distance bidirectional belt conveyors used in mines, and form a sensor monitoring network through multiple sensors; Step 2: Collect operating data and perform filtering; During the operation of the long-distance bidirectional belt conveyor for mining, the monitoring signal is collected in real time through the sensor monitoring network, and the signal preprocessing module is used to amplify and preliminarily remove noise, and then the multi-modal data is fused and filtered using the distributed Kalman filter architecture to output high-precision monitoring data; Step 3: Fuzzify the key parameters in the high-precision monitoring data; The load is divided into three levels, namely low load, medium load and high load; the slope is divided into three levels, namely downhill, relatively flat and uphill; the conveying speed is divided into three levels, namely deceleration, normal working speed and acceleration; Step 4: Based on the actual situation of on-site operation, use the empirical induction method to establish a standard fuzzy logic rule base; IF LOAD = LOW LOAD AND SLOPE = UPGRADING THEN CONVEYING SPEED = INCREASE 1 SPEED; IF Load = Low Load AND Slope = Flatter THEN Conveying Speed ​​= Increase 2 Speeds; IF LOAD = LOW LOAD AND SLOPE = DOWN SLOPES THEN CONVEYING SPEED = REDUCED BY 1 SPEED; IF LOAD = MEDIUM LOAD AND SLOPE = UPSCALING THEN CONVEYING SPEED = NORMAL WORKING SPEED; IF Load = Medium Load AND Slope = Flatter THEN Conveying Speed ​​= Increase 1 Speed; IF load = medium load AND slope = downhill THEN conveying speed = reduce 1 gear speed; IF LOAD = HIGH LOAD AND SLOPE = UPGRADING THEN CONVEYING SPEED = REDUCED BY 1 SPEED; IF load = high load AND slope = relatively flat THEN conveying speed = normal working speed; IF LOAD = HIGH LOAD AND SLOPE = DOWN SLOPES THEN CONVEYING SPEED = REDUCED 2 SPEED; Step 5: Expand the fuzzy logic control rules; The state credibility is introduced and divided into low, medium and high according to the size of the diagonal elements of the error covariance matrix to assist in judging the reliability of monitoring data and adjusting the fuzzy control rules. Step 6: Output speed control instructions to intelligently control the long-distance bidirectional belt conveyor for mining; The center of gravity method is used to convert the output of the fuzzy controller into an accurate speed control command, and the speed control command is sent to the motor drive control system of the long-distance bidirectional transport belt conveyor for mining, so as to use the speed control command to achieve precise control of the speed of the long-distance bidirectional transport belt conveyor for mining.

[0007] As a preference, in step one, the multiple sensors include a load sensor, a speed sensor, a current sensor, a slope sensor, a temperature sensor and a torque sensor.

[0008] Furthermore, in order to effectively remove the interference of noise and effectively ensure the accuracy of monitoring data, in step 2, the process of filtering using the distributed Kalman filter architecture is as follows: S21: During the operation of the long-distance bidirectional belt conveyor for mining, the monitoring signal is collected in real time through the sensor monitoring network, and the signal preprocessing module is used to amplify and perform preliminary noise removal; S22: Define the state vector according to formula (1) , according to formula (2), the state equation based on the motion model of the long-distance bidirectional belt conveyor for mining is established, and according to formula (3), the observation equation based on the motion model of the long-distance bidirectional belt conveyor for mining is established; (1); (2); (3); In the formula, For conveyor belt The speed of time, For conveyor belt The slope of the moment, conveyor The load at the moment, is the transpose operation; is the state transfer matrix based on the motion model of long-distance bidirectional belt conveyor for mining. , is the sampling time interval, is the acceleration due to gravity, For conveyor belt The sine value of the slope at that moment, is the friction coefficient of the conveyor belt, For conveyor belt The load at the moment, for The state of the moment, is the control input matrix, , is the equivalent mass of the conveyor belt, is the motor torque control quantity, is the process noise; for The sensor measurement value at the moment; is the observation matrix, , is the load sensor calibration factor; is the observation noise; for The state of the moment; S23: Continuously correct the state equation through the observation equation and use the Kalman gain K K Recursively update the state estimate and covariance matrix to output high-precision monitoring data.

[0009] Furthermore, in order to more accurately control the long-distance bidirectional belt conveyor for mining, in step three, the specific process of fuzzy processing is as follows: For load :0≤ The load within the range of <150kg / m is regarded as low load, and the load within the range of 150kg / m≤ The load within the range of <300kg / m is regarded as medium load, and 300kg / m≤ Loads within the range of ≤ 600kg / m are considered high loads; For slope :Will The slope within the range of <-5° is regarded as downhill, and -5°≤ The slopes within the range of <5° are considered relatively flat, and the slopes within the range of 5°≤ The slope within the range is taken as uphill; For speed : Keeping the speed constant is the normal working speed, increasing the speed is acceleration, and decreasing the speed is deceleration.

[0010] Furthermore, in order to effectively ensure that the conveyor can still be stably and accurately controlled under dynamic conditions such as load mutation or slope transient, in step 5, the state credibility is calculated according to formula (4): ;when When 0.3≤, an external PID is used for compensation so that the output of the fuzzy controller is multiplied by the attenuation coefficient of 0.5. <0.6, the standard fuzzy rule base is used for control. When , the enhanced fuzzy rule base is enabled to reduce the response speed; (4); In the formula, is the trace of the error covariance matrix, the sum of the diagonal elements, is the error covariance matrix of the Kalman filter, To set the maximum acceptable error matrix.

[0011] Furthermore, in order to realize effective monitoring of data and status at key monitoring nodes, in step one, the load sensor is installed on the roller support in the middle area of ​​the long-distance bidirectional conveyor belt conveyor for mining, so as to collect the actual load signal in real time; the speed sensor is installed at the driving wheel of the long-distance bidirectional conveyor belt conveyor for mining, so as to collect the conveying speed signal in real time; the current sensor is installed on the driving motor of the long-distance bidirectional conveyor belt conveyor for mining, so as to collect the current signal in real time; a plurality of slope sensors are installed on the frame of the long-distance bidirectional conveyor belt conveyor for mining at set intervals in sequence, so as to collect the belt slope signal in the area in real time; the temperature sensor is installed on the driving motor and controller of the long-distance bidirectional conveyor belt conveyor for mining, so as to collect the temperature signal in real time; the torque sensor is installed on the driving motor of the long-distance bidirectional conveyor belt conveyor for mining, so as to collect the torque signal in real time.

[0012] Furthermore, in order to improve the control accuracy, in step 5, the construction process of the enhanced fuzzy rule base is as follows: S51: Enhance the fuzzy processing of key parameters in the high-precision monitoring data; The load is divided into five levels, namely low load, slightly low load, medium load, slightly high load and high load; the slope is divided into five levels, namely steep downhill, downhill, flat, uphill and steep uphill; S52: Based on the actual situation of field operation, an enhanced fuzzy logic rule base is established using the empirical induction method; IF LOAD = LOW LOAD AND SLOPE = STEEP UPGRADED THEN CONVEYING SPEED = INCREASE 2 SPEED; IF LOAD = LOW LOAD AND SLOPE = UPGRADED THEN CONVEYING SPEED = INCREASE 3 SPEED; IF LOAD = LOW LOAD AND SLOPE = FLAT THEN CONVEYING SPEED = INCREASE 4 SPEED; IF LOAD = LOW LOAD AND SLOPE = DOWNSLOPE THEN CONVEYING SPEED = INCREASE 2 SPEED; IF LOAD = LOW LOAD AND SLOPE = STEEP DOWNSCALING THEN CONVEYING SPEED = NORMAL WORKING SPEED; IF LOAD = Slightly underloaded AND SLOPE = Steep uphill THEN CONVEYING SPEED = Normal working speed; IF Load = slightly lower load AND Slope = uphill THEN Conveying speed = increase 1 speed; IF Load = Slightly light load AND Slope = Flat THEN Conveying speed = Increase 2 speeds; IF load = slightly lower load AND slope = downhill THEN conveying speed = increase 1 speed; IF load = slightly lower load AND slope = steep downhill THEN conveying speed = reduce 1 speed; IF LOAD = MEDIUM LOAD AND SLOPE = STEEP UPGRADED THEN CONVEYING SPEED = REDUCED 2 SPEED; IF LOAD = MEDIUM LOAD AND SLOPE = UPSCALING THEN CONVEYING SPEED = NORMAL WORKING SPEED; IF LOAD = MEDIUM LOAD AND SLOPE = FLAT THEN CONVEYING SPEED = NORMAL WORKING SPEED; IF LOAD = MEDIUM LOAD AND SLOPE = DOWNSCALING THEN CONVEYING SPEED = NORMAL WORKING SPEED; IF Load = Medium Load AND Slope = Steep Downhill THEN Conveying Speed ​​= Reduce 1 Speed; IF load = slightly higher load AND slope = steep uphill THEN conveying speed = reduce 3 speeds; IF load = slightly higher load AND slope = uphill THEN conveying speed = reduce 2 gear speed; IF Load = Slightly higher load AND Slope = Flat THEN Conveying speed = Reduce by 1 speed; IF LOAD = Slightly higher load AND SLOPE = Downhill THEN CONVEYING SPEED = Normal working speed; IF load = slightly higher load AND slope = steep downhill THEN conveying speed = reduce 2 gear speed; IF LOAD = HIGH LOAD AND SLOPE = STEEP UPGRADED THEN CONVEYING SPEED = REDUCED 4TH SPEED; IF LOAD = HIGH LOAD AND SLOPE = UPGRADING THEN CONVEYING SPEED = REDUCED 3 SPEED; IF LOAD = HIGH LOAD AND SLOPE = FLAT THEN CONVEYING SPEED = REDUCED 2 SPEED; IF LOAD = HIGH LOAD AND SLOPE = DOWN SLOPE THEN CONVEYING SPEED = REDUCED 3 SPEED; IF LOAD = HIGH LOAD AND SLOPE = STEEP DOWNGRID THEN CONVEYING SPEED = REDUCED 4TH SPEED.

[0013] Furthermore, in order to improve the control accuracy, in S51, the specific process of enhancing the fuzzy processing is as follows: For load :0≤ The load within the range of <150kg / m is regarded as low load, and the load within the range of 150kg / m≤ The load within the range of <225kg / m is regarded as slightly lower load, and 225kg / m≤ The load within the range of 300kg / m is regarded as medium load, and the load within the range of 300kg / m≤ The load within the range of 350kg / m is regarded as slightly higher load, and the load within the range of 350kg / m≤ Loads within the range of ≤ 600kg / m are considered high loads; For slope :Will The slope within the range of <-8° is regarded as a steep downhill slope, and -8°≤ The slope within the range of <-4° is regarded as downhill, and -4°≤ The slopes within the range of <4° are regarded as flat, and the slopes within the range of 4°≤ The slope within the range of <8° is regarded as uphill, and 8°≤ Slopes within the range are considered steep uphill.

[0014] In the present invention, the sensor monitoring network is first used to continuously monitor the running state of the conveyor, and the sensor data is optimized by Kalman filtering, which can remove the noise and other interference data in the monitoring data, and effectively ensure the accuracy of the monitoring data. Then the key parameters in the monitoring data are fuzzified, and fuzzy reasoning is performed according to the preset fuzzy rule base. At the same time, the center of gravity method is used to convert the fuzzy output into an accurate speed adjustment instruction, and then the speed adjustment instruction is fed back to the motor drive control system to achieve closed-loop control of the motor to accelerate, maintain and decelerate. This method can ensure that the optimal control instruction is obtained efficiently and accurately, thereby significantly improving the control accuracy and greatly shortening the control response time. Through the introduction of state credibility, the filtering and fuzzy control parameters can be updated in real time according to the actual operating state during the operation process, effectively ensuring that the conveyor can still maintain stable and accurate speed regulation performance under dynamic conditions such as load mutation or slope transient. The present invention adopts a distributed Kalman filter architecture to optimize sensor data, and combines fuzzy logic control strategy and state credibility evaluation to effectively reduce sensor noise interference and improve the accuracy of state estimation. Compared with traditional speed regulation methods, the present invention is more adaptable to load mutations and environmental changes in long-distance transportation environments, has higher steady-state accuracy and stronger robustness, and significantly improves the transportation efficiency and equipment service life of the conveyor.

[0015] This method can effectively reduce noise interference, has high control accuracy and short response time, can significantly improve the robustness of the control system, and thus significantly improve the transportation efficiency of the belt conveyor. It is suitable for energy-saving and optimized control of long-distance bidirectional belt conveyors in complex working conditions underground in coal mines. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a control principle block diagram of the present invention. DETAILED DESCRIPTION

[0017] The present invention will be further described below in conjunction with the accompanying drawings.

[0018] like Figure 1 As shown, the present invention provides an intelligent speed regulation method for a long-distance bidirectional belt conveyor for mining, comprising the following steps: Step 1: Establish a sensor monitoring network; Install multiple sensors at multiple key monitoring nodes on long-distance bidirectional belt conveyors for mining, and form a sensor monitoring network through multiple sensors to achieve comprehensive monitoring of the conveyor status; Step 2: Collect operating data and perform filtering; During the operation of the long-distance bidirectional belt conveyor for mining, the monitoring signals are collected in real time through the sensor monitoring network, and the signal preprocessing module is used to amplify and preliminarily remove noise, and then the multi-modal data is fused and filtered using the distributed Kalman filter architecture; Step 3: Fuzzify the key parameters in the high-precision monitoring data; The load is divided into three levels, namely low load, medium load and high load; the slope is divided into three levels, namely downhill, relatively flat and uphill; the conveying speed is divided into three levels, namely deceleration, normal working speed and acceleration; Step 4: According to the actual situation of on-site operation, use the empirical induction method to establish a standard fuzzy logic rule base, as shown in Table 1; Table 1: Standard fuzzy logic rule base

[0019] In Table 1, 0 represents the normal working speed, a negative sign represents a deceleration, and the specific value of the negative sign corresponds to the speed gear, and a positive number represents an increase in speed, and the specific value corresponds to the speed gear; The standard fuzzy logic rule base is expressed in the form of "IF THEN" as follows: IF LOAD = LOW LOAD AND SLOPE = UPGRADING THEN CONVEYING SPEED = INCREASE 1 SPEED; IF Load = Low Load AND Slope = Flatter THEN Conveying Speed ​​= Increase 2 Speeds; IF LOAD = LOW LOAD AND SLOPE = DOWN SLOPES THEN CONVEYING SPEED = REDUCED BY 1 SPEED; IF LOAD = MEDIUM LOAD AND SLOPE = UPSCALING THEN CONVEYING SPEED = NORMAL WORKING SPEED; IF Load = Medium Load AND Slope = Flatter THEN Conveying Speed ​​= Increase 1 Speed; IF load = medium load AND slope = downhill THEN conveying speed = reduce 1 gear speed; IF LOAD = HIGH LOAD AND SLOPE = UPGRADING THEN CONVEYING SPEED = REDUCED BY 1 SPEED; IF load = high load AND slope = relatively flat THEN conveying speed = normal working speed; IF LOAD = HIGH LOAD AND SLOPE = DOWN SLOPES THEN CONVEYING SPEED = REDUCED 2 SPEED; Step 5: Expand the fuzzy logic control rules; The state credibility is introduced and divided into low, medium and high according to the size of the diagonal elements of the error covariance matrix to assist in judging the reliability of monitoring data and adjusting the fuzzy control rules. Step 6: Output speed control instructions to intelligently control the long-distance bidirectional belt conveyor for mining; The center of gravity method is used to convert the output of the fuzzy controller into an accurate speed control command, and the speed control command is sent to the motor drive control system of the long-distance bidirectional transport belt conveyor for mining, so as to use the speed control command to achieve precise control of the speed of the long-distance bidirectional transport belt conveyor for mining.

[0020] As a preference, in step one, the multiple sensors include a load sensor, a speed sensor, a current sensor, a slope sensor, a temperature sensor and a torque sensor.

[0021] In order to effectively remove the interference of noise and effectively ensure the accuracy of monitoring data, in step 2, the process of filtering using the distributed Kalman filter architecture is as follows: S21: During the operation of the long-distance bidirectional belt conveyor for mining, the monitoring signal is collected in real time through the sensor monitoring network, and the signal preprocessing module is used to amplify and perform preliminary noise removal; S22: Based on the kinematic characteristics of the long-distance bidirectional belt conveyor for mining and the sensor layout, the state vector is defined according to formula (1): ; According to formula (2), the state equation based on the motion model of the long-distance bidirectional belt conveyor for mining is established. In this way, each node can estimate the current state based on the state at the previous moment; According to formula (3), the observation equation based on the motion model of the long-distance bidirectional belt conveyor for mining is established to fuse the observation data; (1); (2); (3); In the formula, For conveyor belt The speed of time, For conveyor belt The slope of the moment, conveyor The load at the moment, is the transpose operation, for The transpose of is the state transfer matrix based on the motion model of a long-distance bidirectional belt conveyor for mining. When the slope changes slowly (constant in a short time), the load change is linearly related to the transport volume, and the speed change is affected by the motor torque, slope resistance and load, the discretized state equation is The speed change in the first row is affected by the slope resistance and belt friction. The short-term changes of the slope and load in the second and third rows are ignored and assumed to be constant. is the sampling time interval, is the acceleration due to gravity, For conveyor belt The sine value of the slope at that moment, is the friction coefficient of the conveyor belt, For conveyor belt The load at the moment, for The state of the moment, is the control input matrix, , is the equivalent mass of the conveyor belt, is the motor torque control quantity, is the process noise; for The sensor measurement value at the moment; is the observation matrix, the pressure and load are linearly related, , is the load sensor calibration factor; is the observation noise; for The state of the moment; S23: Continuously correct the state equation through the observation equation and use the Kalman gain K K Recursively update the state estimate and covariance matrix to output high-precision monitoring data.

[0022] In order to more accurately control the long-distance bidirectional belt conveyor, in step three, the specific process of fuzzy processing is as follows: For load :0≤ The load within the range of <150kg / m is regarded as low load, and the load within the range of 150kg / m≤ The load within the range of <300kg / m is regarded as medium load, and 300kg / m≤ Loads within the range of ≤ 600kg / m are considered high loads; For slope :Will The slope within the range of <-5° is regarded as downhill, and -5°≤ The slopes within the range of <5° are considered relatively flat, and the slopes within the range of 5°≤ The slope within the range is taken as uphill; For speed : Keeping the speed constant is the normal working speed, increasing the speed is acceleration, and decreasing the speed is deceleration.

[0023] In order to effectively ensure that the conveyor can still be stably and accurately controlled under dynamic conditions such as sudden load changes or transient slope changes, in step 5, the state credibility is calculated according to formula (4): ;when When 0.3≤, an external PID is used for compensation so that the output of the fuzzy controller is multiplied by the attenuation coefficient of 0.5. <0.6, the standard fuzzy rule base is used for control. When , the enhanced fuzzy rule base is enabled to reduce the response speed; (4); In the formula, is the trace of the error covariance matrix, the sum of the diagonal elements, is the error covariance matrix of the Kalman filter, To set the maximum acceptable error matrix.

[0024] In order to realize the effective monitoring of the data and status at the key monitoring nodes, in step one, the load sensor is installed on the roller support in the middle section of the long-distance bidirectional conveyor belt conveyor for mining, preferably at the key node of the load-bearing section, for real-time acquisition of the actual load signal; the speed sensor is installed at the driving wheel of the long-distance bidirectional conveyor belt conveyor for mining, for real-time acquisition of the conveying speed signal; the current sensor is installed on the driving motor of the long-distance bidirectional conveyor belt conveyor for mining, for real-time acquisition of the current signal; a plurality of slope sensors are installed on the frame of the long-distance bidirectional conveyor belt conveyor for mining at set intervals in sequence, for real-time acquisition of the belt slope signal in the area; the temperature sensor is installed on the driving motor and controller of the long-distance bidirectional conveyor belt conveyor for mining, for real-time acquisition of the temperature signal, so as to effectively prevent the driving motor and the controller from overheating; the torque sensor is installed on the driving motor of the long-distance bidirectional conveyor belt conveyor for mining, for real-time acquisition of the torque signal.

[0025] In order to improve the control accuracy, in step 5, the construction process of the enhanced fuzzy rule base is as follows: S51: Enhance the fuzzy processing of key parameters in the high-precision monitoring data; The load is divided into five levels, namely low load, slightly low load, medium load, slightly high load and high load; the slope is divided into five levels, namely steep downhill, downhill, flat, uphill and steep uphill; In order to improve the control accuracy, in S51, the specific process of enhancing the fuzzy processing is as follows: For load :0≤ The load within the range of <150kg / m is regarded as low load, and the load within the range of 150kg / m≤ The load within the range of <225kg / m is regarded as slightly lower load, and 225kg / m≤ The load within the range of 300kg / m is regarded as medium load, and the load within the range of 300kg / m≤ The load within the range of <350kg / m is regarded as slightly higher load, and the load within the range of 350kg / m≤ Loads within the range of ≤ 600kg / m are considered high loads; For slope :Will The slope within the range of <-8° is regarded as a steep downhill slope, and -8°≤ The slope within the range of <-4° is regarded as downhill, and -4°≤ The slopes within the range of <4° are regarded as flat, and the slopes within the range of 4°≤ The slope within the range of <8° is regarded as uphill, and 8°≤ Slopes within the range are considered steep uphill.

[0026] S52: Based on the actual situation of field operation, an enhanced fuzzy logic rule base is established using the empirical induction method, as shown in Table 2; Table 2: Enhanced fuzzy logic rule base

[0027] Among them, 0 represents the normal working speed, the negative sign represents deceleration, and the specific value of the negative sign corresponds to the number of gears that reduce the speed. The positive number represents the speed increase, and the specific value corresponds to the number of gears that increase the speed.

[0028] The enhanced fuzzy logic rule base is expressed in the form of "IF THEN" as follows: IF LOAD = LOW LOAD AND SLOPE = STEEP UPGRADED THEN CONVEYING SPEED = INCREASE 2 SPEED; IF LOAD = LOW LOAD AND SLOPE = UPGRADED THEN CONVEYING SPEED = INCREASE 3 SPEED; IF LOAD = LOW LOAD AND SLOPE = FLAT THEN CONVEYING SPEED = INCREASE 4 SPEED; IF LOAD = LOW LOAD AND SLOPE = DOWNSLOPE THEN CONVEYING SPEED = INCREASE 2 SPEED; IF LOAD = LOW LOAD AND SLOPE = STEEP DOWNSCALING THEN CONVEYING SPEED = NORMAL WORKING SPEED; IF LOAD = Slightly underloaded AND SLOPE = Steep uphill THEN CONVEYING SPEED = Normal working speed; IF Load = slightly lower load AND Slope = uphill THEN Conveying speed = increase 1 speed; IF Load = Slightly light load AND Slope = Flat THEN Conveying speed = Increase 2 speeds; IF load = slightly lower load AND slope = downhill THEN conveying speed = increase 1 speed; IF load = slightly lower load AND slope = steep downhill THEN conveying speed = reduce 1 speed; IF LOAD = MEDIUM LOAD AND SLOPE = STEEP UPGRADED THEN CONVEYING SPEED = REDUCED 2 SPEED; IF LOAD = MEDIUM LOAD AND SLOPE = UPSCALING THEN CONVEYING SPEED = NORMAL WORKING SPEED; IF LOAD = MEDIUM LOAD AND SLOPE = FLAT THEN CONVEYING SPEED = NORMAL WORKING SPEED; IF LOAD = MEDIUM LOAD AND SLOPE = DOWNSCALING THEN CONVEYING SPEED = NORMAL WORKING SPEED; IF Load = Medium Load AND Slope = Steep Downhill THEN Conveying Speed ​​= Reduce 1 Speed; IF load = slightly higher load AND slope = steep uphill THEN conveying speed = reduce 3 speeds; IF load = slightly higher load AND slope = uphill THEN conveying speed = reduce 2 gear speed; IF Load = Slightly higher load AND Slope = Flat THEN Conveying speed = Reduce by 1 speed; IF LOAD = Slightly higher load AND SLOPE = Downhill THEN CONVEYING SPEED = Normal working speed; IF load = slightly higher load AND slope = steep downhill THEN conveying speed = reduce 2 gear speed; IF LOAD = HIGH LOAD AND SLOPE = STEEP UPGRADED THEN CONVEYING SPEED = REDUCED 4TH SPEED; IF LOAD = HIGH LOAD AND SLOPE = UPGRADING THEN CONVEYING SPEED = REDUCED 3 SPEED; IF LOAD = HIGH LOAD AND SLOPE = FLAT THEN CONVEYING SPEED = REDUCED 2 SPEED; IF LOAD = HIGH LOAD AND SLOPE = DOWN SLOPE THEN CONVEYING SPEED = REDUCED 3 SPEED; IF LOAD = HIGH LOAD AND SLOPE = STEEP DOWNGRID THEN CONVEYING SPEED = REDUCED 4TH SPEED.

[0029] In the present invention, the sensor monitoring network is first used to continuously monitor the running state of the conveyor, and the sensor data is optimized by Kalman filtering, which can remove the noise and other interference data in the monitoring data, and effectively ensure the accuracy of the monitoring data. Then the key parameters in the monitoring data are fuzzified, and fuzzy reasoning is performed according to the preset fuzzy rule base. At the same time, the center of gravity method is used to convert the fuzzy output into an accurate speed adjustment instruction, and then the speed adjustment instruction is fed back to the motor drive control system to achieve closed-loop control of the motor to accelerate, maintain and decelerate. This method can ensure that the optimal control instruction is obtained efficiently and accurately, thereby significantly improving the control accuracy and greatly shortening the control response time. Through the introduction of state credibility, the filtering and fuzzy control parameters can be updated in real time according to the actual operating state during the operation process, effectively ensuring that the conveyor can still maintain stable and accurate speed regulation performance under dynamic conditions such as load mutation or slope transient. The present invention adopts a distributed Kalman filter architecture to optimize sensor data, and combines fuzzy logic control strategy and state credibility evaluation to effectively reduce sensor noise interference and improve the accuracy of state estimation. Compared with traditional speed regulation methods, the present invention is more adaptable to load mutations and environmental changes in long-distance transportation environments, has higher steady-state accuracy and stronger robustness, and significantly improves the transportation efficiency and equipment service life of the conveyor.

[0030] This method can effectively reduce noise interference, has high control accuracy and short response time, can significantly improve the robustness of the control system, and thus significantly improve the transportation efficiency of the belt conveyor. It is suitable for energy-saving and optimized control of long-distance bidirectional belt conveyors in complex working conditions underground in coal mines.

Claims

1. An intelligent speed regulation method for a long-distance bidirectional belt conveyor for mining, characterized in that: The following steps are involved: Step 1: Establish a sensor monitoring network; Install multiple sensors at multiple key monitoring nodes on long-distance bidirectional belt conveyors used in mines, and form a sensor monitoring network through multiple sensors; Step 2: Collect operating data and perform filtering; During the operation of the long-distance bidirectional belt conveyor for mining, the monitoring signal is collected in real time through the sensor monitoring network, and the signal preprocessing module is used to amplify and preliminarily remove noise, and then the multi-modal data is fused and filtered using the distributed Kalman filter architecture to output high-precision monitoring data; Step 3: Fuzzify the key parameters in the high-precision monitoring data; The load is divided into three levels, namely low load, medium load and high load; the slope is divided into three levels, namely downhill, relatively flat and uphill; the conveying speed is divided into three levels, namely deceleration, normal working speed and acceleration; Step 4: Based on the actual situation of on-site operation, use the empirical induction method to establish a standard fuzzy logic rule base; IF LOAD = LOW LOAD AND SLOPE = UPGRADING THEN CONVEYING SPEED = INCREASE 1 SPEED; IF Load = Low Load AND Slope = Flatter THEN Conveying Speed ​​= Increase 2 Speeds; IF LOAD = LOW LOAD AND SLOPE = DOWN SLOPES THEN CONVEYING SPEED = REDUCED BY 1 SPEED; IF LOAD = MEDIUM LOAD AND SLOPE = UPSCALING THEN CONVEYING SPEED = NORMAL WORKING SPEED; IF Load = Medium Load AND Slope = Flatter THEN Conveying Speed ​​= Increase 1 Speed; IF load = medium load AND slope = downhill THEN conveying speed = reduce 1 gear speed; IF LOAD = HIGH LOAD AND SLOPE = UPGRADING THEN CONVEYING SPEED = REDUCED BY 1 SPEED; IF load = high load AND slope = relatively flat THEN conveying speed = normal working speed; IF LOAD = HIGH LOAD AND SLOPE = DOWN SLOPES THEN CONVEYING SPEED = REDUCED 2 SPEED; Step 5: Expand the fuzzy logic control rules; The state credibility is introduced and divided into low, medium and high according to the size of the diagonal elements of the error covariance matrix to assist in judging the reliability of monitoring data and adjusting the fuzzy control rules. Step 6: Output speed control instructions to intelligently control the long-distance bidirectional belt conveyor for mining; The center of gravity method is used to convert the output of the fuzzy controller into an accurate speed control command, and the speed control command is sent to the motor drive control system of the long-distance bidirectional transport belt conveyor for mining, so as to use the speed control command to achieve precise control of the speed of the long-distance bidirectional transport belt conveyor for mining.

2. The intelligent speed regulation method for a long-distance bidirectional belt conveyor for mining according to claim 1 is characterized in that: In step one, the various sensors include a load sensor, a speed sensor, a current sensor, a slope sensor, a temperature sensor, and a torque sensor.

3. The intelligent speed regulation method for a long-distance bidirectional belt conveyor for mining according to claim 1 is characterized in that: In step 2, the process of filtering using the distributed Kalman filter architecture is as follows: S21: During the operation of the long-distance bidirectional belt conveyor for mining, the monitoring signal is collected in real time through the sensor monitoring network, and the signal preprocessing module is used to amplify and perform preliminary noise removal; S22: Define the state vector according to formula (1) , according to formula (2), the state equation based on the motion model of the long-distance bidirectional belt conveyor for mining is established, and according to formula (3), the observation equation based on the motion model of the long-distance bidirectional belt conveyor for mining is established; （1）； （2）； （3）； In the formula, For conveyor belt The speed of time, For conveyor belt The slope of the moment, conveyor The load at the moment, is the transpose operation; is the state transfer matrix based on the motion model of long-distance bidirectional belt conveyor for mining. , is the sampling time interval, is the acceleration due to gravity, For conveyor belt The sine value of the slope at that moment, is the friction coefficient of the conveyor belt, For conveyor belt The load at the moment, for The state of the moment, is the control input matrix, , is the equivalent mass of the conveyor belt, is the motor torque control quantity, is the process noise; for The sensor measurement value at the moment; is the observation matrix, , is the load sensor calibration factor; is the observation noise; for The state of the moment; S23: Continuously correct the state equation through the observation equation and use the Kalman gain K K Recursively update the state estimate and covariance matrix to output high-precision monitoring data.

4. The intelligent speed regulation method for a long-distance bidirectional belt conveyor for mining according to claim 1 is characterized in that: In step three, the specific process of fuzzification is as follows: For load :0≤ The load within the range of <150kg / m is regarded as low load, and the load within the range of 150kg / m≤ The load within the range of 300kg / m is considered as medium load, and the load within the range of 300kg / m≤ Loads within the range of ≤ 600kg / m are considered high loads; For slope :Will The slope within the range of <-5° is regarded as downhill, and -5°≤ The slopes within the range of <5° are considered relatively flat, and the slopes within the range of 5°≤ The slope within the range is taken as uphill; For speed : Keeping the speed constant is the normal working speed, increasing the speed is acceleration, and decreasing the speed is deceleration.

5. The intelligent speed regulation method for a long-distance bidirectional belt conveyor for mining according to claim 1 is characterized in that: In step 5, the state credibility is calculated according to formula (4): ;when When 0.3≤, an external PID is used for compensation so that the output of the fuzzy controller is multiplied by the attenuation coefficient of 0.

5. <0.6, the standard fuzzy rule base is used for control. When , the enhanced fuzzy rule base is enabled to reduce the response speed; （4）； In the formula, is the trace of the error covariance matrix, the sum of the diagonal elements, is the error covariance matrix of the Kalman filter, To set the maximum acceptable error matrix.

6. The intelligent speed regulation method for a long-distance bidirectional belt conveyor for mining according to claim 2 is characterized in that: In step one, the load sensor is installed on the roller support in the middle area of ​​the long-distance bidirectional conveyor belt conveyor for mining, and is used to collect the actual load signal in real time; the speed sensor is installed at the driving wheel of the long-distance bidirectional conveyor belt conveyor for mining, and is used to collect the conveying speed signal in real time; the current sensor is installed on the driving motor of the long-distance bidirectional conveyor belt conveyor for mining, and is used to collect the current signal in real time; multiple slope sensors are installed on the frame of the long-distance bidirectional conveyor belt conveyor for mining at set intervals in sequence, and are used to collect the belt slope signal of the area in real time; the temperature sensor is installed on the driving motor and controller of the long-distance bidirectional conveyor belt conveyor for mining, and is used to collect the temperature signal in real time; the torque sensor is installed on the driving motor of the long-distance bidirectional conveyor belt conveyor for mining, and is used to collect the torque signal in real time.

7. The intelligent speed regulation method for a long-distance bidirectional belt conveyor for mining according to claim 5 is characterized in that: In step 5, the construction process of the enhanced fuzzy rule base is as follows: S51: Enhance the fuzzy processing of key parameters in the high-precision monitoring data; The load is divided into five levels, namely low load, slightly low load, medium load, slightly high load and high load; the slope is divided into five levels, namely steep downhill, downhill, flat, uphill and steep uphill; S52: Based on the actual situation of field operation, an enhanced fuzzy logic rule base is established using the empirical induction method; IF LOAD = LOW LOAD AND SLOPE = STEEP UPGRADED THEN CONVEYING SPEED = INCREASE 2 SPEED; IF LOAD = LOW LOAD AND SLOPE = UPGRADED THEN CONVEYING SPEED = INCREASE 3 SPEED; IF LOAD = LOW LOAD AND SLOPE = FLAT THEN CONVEYING SPEED = INCREASE 4 SPEED; IF LOAD = LOW LOAD AND SLOPE = DOWNSLOPE THEN CONVEYING SPEED = INCREASE 2 SPEED; IF LOAD = LOW LOAD AND SLOPE = STEEP DOWNSCALING THEN CONVEYING SPEED = NORMAL WORKING SPEED; IF LOAD = Slightly underloaded AND SLOPE = Steep uphill THEN CONVEYING SPEED = Normal working speed; IF Load = slightly lower load AND Slope = uphill THEN Conveying speed = increase 1 speed; IF Load = Slightly light load AND Slope = Flat THEN Conveying speed = Increase 2 speeds; IF load = slightly lower load AND slope = downhill THEN conveying speed = increase 1 speed; IF load = slightly lower load AND slope = steep downhill THEN conveying speed = reduce 1 speed; IF LOAD = MEDIUM LOAD AND SLOPE = STEEP UPGRADED THEN CONVEYING SPEED = REDUCED 2 SPEED; IF LOAD = MEDIUM LOAD AND SLOPE = UPSCALING THEN CONVEYING SPEED = NORMAL WORKING SPEED; IF LOAD = MEDIUM LOAD AND SLOPE = FLAT THEN CONVEYING SPEED = NORMAL WORKING SPEED; IF LOAD = MEDIUM LOAD AND SLOPE = DOWNSCALING THEN CONVEYING SPEED = NORMAL WORKING SPEED; IF Load = Medium Load AND Slope = Steep Downhill THEN Conveying Speed ​​= Reduce 1 Speed; IF load = slightly higher load AND slope = steep uphill THEN conveying speed = reduce 3 speeds; IF load = slightly higher load AND slope = uphill THEN conveying speed = reduce 2 gear speed; IF Load = Slightly higher load AND Slope = Flat THEN Conveying speed = Reduce by 1 speed; IF LOAD = Slightly higher load AND SLOPE = Downhill THEN CONVEYING SPEED = Normal working speed; IF load = slightly higher load AND slope = steep downhill THEN conveying speed = reduce 2 gear speed; IF LOAD = HIGH LOAD AND SLOPE = STEEP UPGRADED THEN CONVEYING SPEED = REDUCED 4TH SPEED; IF LOAD = HIGH LOAD AND SLOPE = UPGRADING THEN CONVEYING SPEED = REDUCED 3 SPEED; IF LOAD = HIGH LOAD AND SLOPE = FLAT THEN CONVEYING SPEED = REDUCED 2 SPEED; IF LOAD = HIGH LOAD AND SLOPE = DOWN SLOPE THEN CONVEYING SPEED = REDUCED 3 SPEED; IF LOAD = HIGH LOAD AND SLOPE = STEEP DOWNGRID THEN CONVEYING SPEED = REDUCED 4TH SPEED.

8. The intelligent speed regulation method for a long-distance bidirectional belt conveyor for mining according to claim 7 is characterized in that: In S51, the specific process of enhanced fuzzy processing is as follows: For load :0≤ The load within the range of <150kg / m is regarded as low load, and the load within the range of 150kg / m≤ The loads within the range of 225kg / m are regarded as slightly lower loads, and the loads within the range of 225kg / m≤ The load within the range of 300kg / m is regarded as medium load, and the load within the range of 300kg / m≤ The load within the range of 350kg / m is regarded as slightly higher load, and the load within the range of 350kg / m≤ Loads within the range of ≤ 600kg / m are considered high loads; For slope :Will The slope within the range of <-8° is regarded as a steep downhill slope, and -8°≤ The slope within the range of <-4° is regarded as downhill, and -4°≤ The slopes within the range of <4° are regarded as flat, and the slopes within the range of 4°≤ The slope within the range of <8° is regarded as uphill, and 8°≤ Slopes within the range are considered steep uphill.

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