Self-adaptive variable frequency speed regulation control method for belt conveyor

Abstract

The invention provides a self-adaptive variable-frequency speed regulation control method for a belt conveyor, which comprises the following steps: acquiring current material loading capacity data through a weight sensor, comparing the current material loading capacity data with a preset standard loading capacity to obtain a deviation value of the loading capacity, and controlling the feeding speed of a feeding device according to the deviation value. The loading capacity of the conveyor is maintained within a reasonable range by adjusting the loading speed of the materials; the speed sensor is adopted to obtain current belt speed data, the current belt speed data is compared with a preset standard speed to obtain a speed deviation value, the output frequency of the frequency converter is controlled according to the size and positive and negative of the deviation value, and the belt speed is maintained within a reasonable range by adjusting the rotating speed of the motor. The control strategy can be automatically optimized according to different working conditions, belt tension and speed fluctuation are effectively reduced, stable operation of the conveyor in a complex and changeable environment is ensured, and conveying efficiency and safety are improved.

Description

Technical Field

[0001] The invention belongs to the field of belt conveyor control, and in particular relates to an adaptive frequency conversion speed regulation control method for a belt conveyor. Background Art

[0002] During the operation of the belt conveyor, the belt tension and speed will fluctuate due to changes in material loading and conveying distance. The traditional constant speed control method is difficult to adapt to such dynamic changes, which can easily cause problems such as belt slippage and deviation, affecting the conveying efficiency and equipment life. At the same time, due to the complex and changeable on-site environment, the belt length required for the conveyor also needs to be adjusted in real time according to the working conditions. How to achieve adaptive control of conveyor speed and belt length to ensure smooth operation under different working conditions is a technical problem that needs to be solved urgently. Summary of the invention

[0003] In order to solve the above technical problems, the present invention proposes an adaptive variable frequency speed control method for a belt conveyor to solve the above problems in the prior art.

[0004] To achieve the above object, the present invention provides an adaptive variable frequency speed control method for a belt conveyor, comprising the following steps:

[0005] Acquire belt tension data, transmit the tension data to the control system to determine whether it exceeds a preset tension threshold range, and if it exceeds, the control system adjusts the belt tension through a control signal;

[0006] Acquire current conveying distance data, compare the current conveying distance data with a preset standard conveying distance, obtain a conveying distance deviation value, and adjust the belt length according to the conveying distance deviation value;

[0007] Acquire current material loading data, compare the current material loading data with a preset standard loading, obtain a loading deviation value, and adjust the material loading speed according to the loading deviation value;

[0008] Acquire current belt speed data, compare the current belt speed data with a preset standard speed, obtain a speed deviation value, and adjust the motor speed according to the speed deviation value;

[0009] The running status of the belt is monitored in real time through machine vision technology to determine whether slippage and deviation occur. If slippage or deviation occurs, the output of the inverter and the tension of the tensioning device are adjusted according to the degree of slippage and deviation.

[0010] The control system adopts an adaptive control algorithm to calculate the optimal parameter combination of the PID controller according to the operating conditions of the conveyor, and regulates the belt conveyor according to the optimal parameter combination.

[0011] Preferably, adjusting the belt tension includes: the control system issues an adjustment instruction, and changes the rotation speed and torque of the motor by adjusting the output frequency and voltage amplitude of the inverter, thereby adjusting the running speed and tension of the belt.

[0012] Preferably, adjusting the belt length includes: controlling the telescopic amount and telescopic direction of the telescopic mechanism to adjust the belt length according to the size and positive and negative sign of the conveying distance deviation value, so as to compensate for the belt tension fluctuation caused by the change of the conveying distance.

[0013] Preferably, adjusting the loading speed of the material includes: controlling the feeding speed of the feeding device according to the size of the loading amount deviation value, and controlling the loading amount by adjusting the loading speed of the material.

[0014] Preferably, adjusting the output of the frequency converter and the tensioning force of the tensioning device according to the degree of slippage and deviation comprises:

[0015] Construct a skidding and deviation discrimination model based on support vector machine;

[0016] Acquire the belt surface image, use image preprocessing technology to denoise and enhance the image; extract the belt surface texture features through texture feature extraction algorithm to obtain texture feature vector;

[0017] Inputting the texture feature vector into a slip and deviation discrimination model to obtain a belt state;

[0018] The output of the frequency converter and the tensioning force of the tensioning device are adjusted according to the belt state.

[0019] Preferably, regulating the belt conveyor includes:

[0020] The neural network is trained through historical operation data to establish the mapping relationship between the conveyor operating conditions and the optimal control parameters, and an adaptive control model is obtained;

[0021] Calculate the optimal parameter combination of PID controller through adaptive control model;

[0022] The belt conveyor is regulated according to the optimal parameter combination.

[0023] The present invention also provides an adaptive variable frequency speed control system for a belt conveyor, comprising:

[0024] A tension control module is used to obtain the tension data of the belt, and transmit the tension data to the control system to determine whether it exceeds a preset tension threshold range. If it exceeds, the control system adjusts the belt tension through a control signal;

[0025] A conveying distance compensation module is used to obtain current conveying distance data, compare the current conveying distance data with a preset standard conveying distance, obtain a conveying distance deviation value, and adjust the belt length according to the conveying distance deviation value;

[0026] A loading control module, used to obtain current material loading data, compare the current material loading data with a preset standard loading, obtain a loading deviation value, and adjust the material loading speed according to the loading deviation value;

[0027] The speed control module is used to obtain current belt speed data, compare the current belt speed data with a preset standard speed, obtain a speed deviation value, and adjust the motor speed according to the speed deviation value;

[0028] The running status monitoring module is used to monitor the running status of the belt in real time through machine vision technology to determine whether slippage and deviation occur. If slippage or deviation occurs, the output of the inverter and the tension of the tensioning device are adjusted according to the degree of slippage and deviation.

[0029] The adaptive control module and the control system adopt an adaptive control algorithm to calculate the optimal parameter combination of the PID controller according to the operating conditions of the conveyor, and regulate the belt conveyor according to the optimal parameter combination.

[0030] The present invention also provides a computer device, comprising a memory, a processor and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method.

[0031] The present invention also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the method are implemented.

[0032] The present invention also provides a computer program product, comprising a computer program, which implements the steps of the method when executed by a processor.

[0033] Compared with the prior art, the present invention has the following advantages and technical effects:

[0034] The present invention discloses an adaptive variable frequency speed control method for a belt conveyor. Key parameters such as belt tension, conveying distance, material loading and running speed are monitored in real time by a variety of sensors, and the data is transmitted to a control system for analysis and processing. When it is detected that the parameters deviate from the preset range, the present invention automatically adjusts the inverter output, telescopic mechanism and feeding device, etc. to maintain the stability of the belt tension and speed. At the same time, the present invention uses machine vision technology to monitor the running state of the belt, identify slippage and deviation, and make timely adjustments. Through the adaptive control algorithm, the present invention can automatically optimize the control strategy according to different working conditions, effectively reduce belt tension and speed fluctuations, ensure the stable operation of the conveyor in a complex and changeable environment, and improve the conveying efficiency and safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The drawings constituting a part of the present application are used to provide a further understanding of the present application. The illustrative embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0036] Figure 1 The figure is a flow chart of a method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0037] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0038] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0039] Embodiment 1

[0040] like Figure 1 As shown, in this embodiment, an adaptive variable frequency speed control method for a belt conveyor is provided, which is characterized by comprising the following steps:

[0041] Acquire belt tension data, transmit the tension data to the control system to determine whether it exceeds a preset tension threshold range, and if it exceeds, the control system adjusts the belt tension through a control signal;

[0042] Acquire current conveying distance data, compare the current conveying distance data with a preset standard conveying distance, obtain a conveying distance deviation value, and adjust the belt length according to the conveying distance deviation value;

[0043] Acquire current material loading data, compare the current material loading data with a preset standard loading, obtain a loading deviation value, and adjust the material loading speed according to the loading deviation value;

[0044] Acquire current belt speed data, compare the current belt speed data with a preset standard speed, obtain a speed deviation value, and adjust the motor speed according to the speed deviation value;

[0045] The running status of the belt is monitored in real time through machine vision technology to determine whether slippage and deviation occur. If slippage or deviation occurs, the output of the inverter and the tension of the tensioning device are adjusted according to the degree of slippage and deviation.

[0046] The control system adopts an adaptive control algorithm to calculate the optimal parameter combination of the PID controller according to the operating conditions of the conveyor, and regulates the belt conveyor according to the optimal parameter combination.

[0047] The specific steps include:

[0048] Step S101, obtain the tension data of the belt and transmit the tension data to the control system. If the tension data exceeds the preset tension threshold range, the control system issues an adjustment instruction to adjust the output frequency and voltage amplitude of the inverter, change the speed and torque of the motor, and then adjust the running speed and tension of the belt to maintain the belt tension within a reasonable range.

[0049] Specifically, the belt tension data is obtained and transmitted to the control system. The control system determines whether the tension data exceeds the preset tension threshold range. If it exceeds, the control system issues an adjustment instruction. According to the adjustment instruction, the speed and torque of the motor are changed by adjusting the output frequency and voltage amplitude of the inverter. The change data of the motor speed and torque are obtained, and the adjustment value of the running speed and tension of the belt is determined according to the change data. The adjustment value is transmitted to the control system, and the control system controls the running speed and tension of the belt according to the adjustment value to maintain the belt tension within a reasonable range. A mathematical model between the belt tension and the motor speed and torque is established, and the model is trained using a machine learning algorithm to obtain an optimized adjustment strategy. The optimized adjustment strategy is applied to the control system to realize adaptive adjustment of the belt tension and improve the stability and reliability of the system.

[0050] In this embodiment, the core of the belt tension control system is to establish an accurate mathematical model and optimization strategy. First, historical data needs to be collected, including belt tension, motor speed and torque data. For example, on a certain production line, belt tension data is collected by a tension sensor, ranging from 1,000 to 1,500 Newtons, the motor speed is between 600 and 800 revolutions per minute, and the torque is between 50 and 70 Newtons. These data form the basis for model training. The multivariate linear regression algorithm can analyze the relationship between these three variables. For example, when the motor speed increases by 100 revolutions per minute, the belt tension increases by 100 Newtons; when the torque increases by 10 Newtons, the tension increases by 50 Newtons. A preliminary mathematical model is established through this relationship. The support vector machine algorithm can handle nonlinear relationships and improve model accuracy. In practical applications, when the production line speed suddenly increases, the support vector machine algorithm can more accurately predict the required tension adjustment amount. When converting the optimized adjustment strategy into a control rule, the actual working conditions need to be considered. For example, in a paper production line, the belt tension should be maintained at around 1,200 Newtons during normal operation. When the tension is detected to be more than 1,300 Newtons, the control system automatically reduces the motor speed by 20 revolutions per minute; when the tension is less than 1,100 Newtons, the torque is increased by 5 Newton meters. In the process of real-time data collection and adjustment by the control system, the adjustment effect needs to be recorded. For example, the change trend of tension after each adjustment, the adjustment time, and whether oscillation occurs are recorded. These data are used for subsequent model optimization. In the application of a steel production line, it was found through analysis that when the ambient temperature is high in summer, the effect of the same adjustment parameters will be reduced, and the adjustment strength needs to be appropriately increased. When retraining the model regularly, it is necessary to combine it with production practice. For example, a conveyor belt system verifies the model once a month and finds that as the belt is used for a longer time, the relationship between tension and speed will change, and the model parameters need to be adjusted in time. In this way, it is ensured that the control strategy always maintains the optimal state and adapts to different working conditions. This adaptive adjustment method significantly improves the stability of the system, reduces the number of shutdowns caused by abnormal tension, and improves production efficiency.

[0051] Step S102, using a laser ranging sensor to obtain current conveying distance data, comparing the current conveying distance data with a preset standard conveying distance to obtain a conveying distance deviation value, and controlling the telescopic amount and telescopic direction of the telescopic mechanism according to the size and positive and negative sign of the deviation value, and compensating for belt tension fluctuations caused by changes in conveying distance by adjusting the length of the belt.

[0052] Specifically, the current conveying distance data collected by the laser ranging sensor is obtained; the current conveying distance data is compared with the preset standard conveying distance to obtain a distance deviation value; the positive or negative value of the distance deviation value is determined, if it is positive, it indicates that the current conveying distance is greater than the standard distance, and if it is negative, it indicates that the current conveying distance is less than the standard distance; according to the size of the distance deviation value, the telescopic amount of the telescopic mechanism is determined, and the larger the deviation value, the larger the telescopic amount; according to the positive or negative value of the distance deviation value, the telescopic direction of the telescopic mechanism is determined, if the deviation is positive, the telescopic direction is the direction of shortening the belt, and if the deviation is negative, the telescopic direction is the direction of extending the belt; the telescopic mechanism is controlled to perform telescopic movement according to the determined telescopic amount and telescopic direction, and the belt tension fluctuation caused by the change in the conveying distance is compensated by adjusting the length of the belt; according to the adjusted belt length, the current conveying distance data is re-acquired, the execution is cyclic, and the belt tension fluctuation is dynamically compensated in real time until the distance deviation value is less than the preset deviation threshold.

[0053] In this embodiment, the laser ranging sensor measures the actual conveying distance of the belt conveyor by emitting a laser beam and receiving a reflected signal, and its measurement accuracy can reach the millimeter level. Taking a belt conveyor for conveying ore as an example, the preset standard conveying distance is 100 meters, and the current conveying distance collected by the laser ranging sensor is 102 meters. At this time, the distance deviation value is positive 2 meters, indicating that the actual conveying distance of the belt exceeds the standard distance. The positive and negative values ​​of the distance deviation value directly determine the movement direction of the telescopic mechanism. When the deviation is positive, the belt length needs to be shortened and the telescopic mechanism shrinks inward; when the deviation is negative, the belt length needs to be extended and the telescopic mechanism stretches outward. Taking the positive deviation of 2 meters in the above example as an example, the telescopic mechanism needs to shrink inward to shorten the belt length. The determination of the telescopic amount is positively correlated with the size of the deviation value. In practical applications, a proportional coefficient can be used for conversion, such as setting the telescopic amount to 0.8 times the deviation value. For a deviation value of 2 meters, the calculated telescopic amount is 1.6 meters. This proportional relationship can be adjusted according to the actual working conditions to ensure the compensation effect. The telescopic mechanism is implemented by an electric push rod or a hydraulic cylinder, and its movement accuracy directly affects the compensation effect. Taking the electric push rod as an example, when it receives the instruction to retract 1.6 meters inward, the internal displacement sensor will feedback the position information in real time to ensure the precise movement. By adjusting the belt length, the tension fluctuation caused by the change of conveying distance can be effectively compensated. The compensation process adopts a closed-loop control method. After completing one adjustment, the system will re-collect the conveying distance data and calculate the new deviation value. Assume that the adjusted distance is 100.3 meters and the new deviation value is positive 0.3 meters. If the preset deviation threshold is 0.5 meters, the current deviation is within the allowable range and the compensation process ends. If it still exceeds the threshold, the compensation adjustment will continue. This dynamic compensation method can respond to the change of conveying distance in real time and maintain the stability of belt tension by accurately controlling the movement of the telescopic mechanism. In actual application, the system can automatically adjust the compensation parameters according to different working conditions. For example, when conveying heavy loads, the telescopic amount can be appropriately increased to ensure the compensation effect. Through continuous monitoring and adjustment, the problem of belt looseness or over-tightness is effectively avoided, and the service life of the equipment is extended.

[0054] Step S103, obtaining the current material loading data through the weight sensor, comparing the current material loading data with the preset standard loading, obtaining the deviation value of the loading, and controlling the feeding speed of the feeding device according to the size of the deviation value, so as to maintain the loading capacity of the conveyor within a reasonable range by adjusting the loading speed of the material.

[0055] Specifically, the material loading data collected by the weight sensor in real time is obtained and used as the current loading data. The standard loading data is read from the preset parameters and used as the target value of the optimization control. The difference between the current loading data and the standard loading data is calculated to obtain the loading deviation value. The positive and negative values ​​of the loading deviation value are judged. If it is a positive value, it means that the current loading exceeds the standard loading and the feeding speed needs to be reduced; if it is a negative value, it means that the current loading is lower than the standard loading and the feeding speed needs to be increased. According to the size of the loading deviation value, the proportional-integral-differential (PID) control algorithm is used to calculate the feeding speed adjustment value of the feeding device. The feeding speed adjustment value is transmitted to the control unit of the feeding device to adjust the actual feeding speed of the feeding device, thereby changing the loading speed of the conveyor. After adjusting the feeding speed, the real-time data of the weight sensor is continued to be obtained to realize the real-time closed-loop control of the conveyor loading so that it is always maintained within a reasonable range.

[0056] In this embodiment, the weight sensor collects the loading data, which is the basis for achieving precise control. Taking the belt conveyor transporting coal as an example, the sensor measures the deformation of the weight of the coal on the belt and converts it into an electrical signal to achieve real-time monitoring. The setting of the standard loading capacity needs to take into account the carrying capacity and operating efficiency of the conveyor. For example, the standard loading capacity of a certain type of belt conveyor is 100 kilograms of coal per meter of belt. The calculation of the loading deviation value is crucial for subsequent control. When the sensor detects that the actual loading capacity is 120 kilograms per meter, the deviation is positive 20 kilograms compared with the standard value, indicating that the loading is too heavy; when it detects 80 kilograms per meter, the deviation is negative 20 kilograms, indicating that the loading is insufficient. This deviation will directly affect the conveying efficiency and equipment life. The feed speed is regulated by the proportional integral differential control algorithm, which can achieve fast response and stable control. The proportional term is adjusted based on the deviation size. For example, when the deviation is positive 20 kilograms, the feed speed can be reduced by 15%; the integral term accumulates historical deviations to eliminate steady-state errors; the differential term predicts the deviation change trend to improve the sensitivity of the regulation. In a cement production line, the feeding device may be a screw feeder, whose speed directly affects the loading capacity. When the loading capacity is detected to be continuously high, the control system will gradually reduce the speed of the screw feeder until the loading capacity returns to the standard value. This process is a continuous closed-loop control, and the data will be re-collected for comparison after each adjustment. In the ore conveying system, the effect of material particle size on the loading capacity must also be considered. Larger particle size ores will cause large fluctuations in instantaneous loading capacity. At this time, stable control can be achieved by adjusting the sensitivity of the control parameters. For example, setting the sampling period to five seconds makes the control response smoother. In practical applications, the loading capacity control also needs to consider the dynamic characteristics of the conveyor during start-up and stop. At the beginning of startup, the feeding speed should be increased step by step to avoid sudden loading and shock to the equipment. Similarly, the feeding speed needs to be gradually reduced before shutdown to ensure a smooth transition of material transportation. This dynamic control strategy can effectively extend the service life of the equipment and improve operational reliability.

[0057] Step S104, using a speed sensor to obtain current belt speed data, comparing the current belt speed data with a preset standard speed to obtain a speed deviation value, and controlling the output frequency of the inverter according to the size and positive and negative sign of the deviation value, so as to maintain the belt speed within a reasonable range by adjusting the motor speed.

[0058] Specifically, the current running speed data of the belt is obtained in real time through the speed sensor, and the obtained speed data is transmitted to the central control unit. In the central control unit, the current speed data of the belt is compared with the preset standard speed, and the deviation value between the current speed and the standard speed is calculated. The positive and negative of the calculated speed deviation value is judged. If the deviation value is positive, it means that the belt speed is higher than the standard speed; if the deviation value is negative, it means that the belt speed is lower than the standard speed. According to the size of the speed deviation value, the fuzzy control algorithm is used to determine the adjustment amplitude of the inverter output frequency. The larger the deviation value, the larger the adjustment amplitude. The determined inverter output frequency adjustment value is transmitted to the inverter, and the motor speed is adjusted by changing the output frequency of the inverter. The change in motor speed drives the corresponding adjustment of the belt speed until the belt speed returns to a reasonable range. The above steps are continued, and the belt speed is automatically controlled through the real-time data acquisition of the speed sensor and the dynamic adjustment of the inverter output frequency, so that it is stable near the preset standard speed.

[0059] In this embodiment, the speed sensor usually adopts a photoelectric encoder or a Hall sensor, which is installed at the driven wheel shaft end of the belt conveyor, and the belt speed is obtained by detecting the rotation of the shaft. Taking the photoelectric encoder as an example, when the encoder outputs a certain number of pulse signals per revolution, assuming that the encoder resolution is 1000 pulses per revolution and the driven wheel diameter is 500 mm, the actual belt speed can be calculated by the pulse frequency. The central control unit processes and analyzes the speed data after receiving it. Taking the mine conveyor as an example, the standard operating speed is set to 2 meters per second. When the actual speed is detected to be 2.3 meters per second, the speed deviation value is calculated to be positive 0.3 meters per second, indicating that the belt is running too fast and needs to be decelerated. On the contrary, if the actual speed is 1.8 meters per second and the deviation value is negative 0.3 meters per second, it is necessary to speed up. The fuzzy control algorithm divides the speed deviation value into different levels, such as a small deviation within 0.1 meters per second, a medium deviation of 0.1 to 0.3 meters per second, and a large deviation greater than 0.3 meters per second. Correspondingly, the adjustment range of the inverter output frequency is also divided into small adjustment, medium adjustment and large adjustment. Taking the standard frequency of 50 Hz as an example, the adjustment range is within 1 Hz for small deviations, 2 to 3 Hz for medium deviations, and more than 3 Hz for large deviations. The inverter controls the motor speed by changing the output frequency, which in turn affects the belt speed. Assume that the rated speed of the motor is 1450 rpm, corresponding to the output frequency of 50 Hz. When the belt is detected to be overspeeding, the controller issues a frequency reduction command, such as reducing it to 48 Hz, and the motor speed is reduced to about 1392 rpm, and the belt speed is reduced accordingly. This speed regulation method responds quickly and can achieve a smooth transition. Through continuous speed detection and frequency adjustment, a closed-loop control is formed. Taking the cement plant conveyor as an example, during continuous operation, the speed may fluctuate due to changes in material load, and the system can automatically compensate and adjust. When loading heavier materials, if the speed is found to be reduced, the controller will appropriately increase the frequency; when the load is reduced, the frequency will be reduced accordingly, and the speed will always be kept stable near the standard value, improving the conveying efficiency and the service life of the equipment. This automatic control method based on speed feedback can effectively avoid the lag and subjectivity of manual operation, so that the belt always maintains the best operating state. In practical applications, the control parameters can be flexibly set according to different working conditions. For example, when conveying fragile materials, a smaller adjustment step can be used to ensure a smooth transition; when conveying bulk materials, a larger step can be used to improve the adjustment efficiency.

[0060] Step S105, using machine vision technology to monitor the running status of the belt in real time, identifying the texture features of the belt surface through image processing algorithms, and judging whether slipping and deviation occur. If slipping or deviation is detected, the control system sends an alarm signal, and adjusts the output of the inverter and the tensioning force of the tensioning device according to the degree of slipping and deviation, so that the belt returns to normal operation.

[0061] Specifically, the belt surface image is obtained, and the image preprocessing technology is used to denoise and enhance the image to improve the image quality. The texture feature of the belt surface is extracted by the texture feature extraction algorithm to obtain the texture feature vector. According to the pre-established slip and deviation discrimination model, it is judged whether the current belt operation state has slip or deviation. If slip is detected, the output frequency and voltage of the inverter are adjusted through the control system according to the degree of slip, and the motor speed and torque are changed to restore the normal operation state of the belt. If deviation is detected, the tension of the tensioning device is adjusted through the control system according to the degree of deviation to restore the belt to the center operation state. During the adjustment process, the belt surface image is continuously obtained, the belt operation state is monitored in real time, and it is judged whether the belt has resumed normal operation. If the belt has not resumed normal operation within the preset time, the control system will send an alarm signal to prompt manual inspection and maintenance.

[0062] In this embodiment, real-time monitoring of the belt running state is a key link to ensure the stable operation of the production line. The belt surface image is obtained by machine vision technology, and the image quality is significantly improved after denoising and enhancement by image preprocessing technology, which lays the foundation for the subsequent texture feature extraction. In the texture feature extraction stage, the Gabor filter and other algorithms can be used to effectively extract the texture features of the belt surface and generate texture feature vectors. These vectors contain subtle structural information on the belt surface, such as the thickness and direction of the texture. For example, the texture of the belt surface in normal operation is uniform and consistent in direction, while the texture will be blurred and disordered when slipping, and the texture direction will be offset when running off. According to the pre-established slip and run-off discrimination model, the extracted texture feature vectors are classified and judged by support vector machine (SVM) or neural network algorithm. Assuming that the model output result shows that the current belt running state has slippage, the system will further evaluate the degree of slippage. For example, mild slippage may only be manifested as slightly blurred texture, while severe slippage may result in obvious texture disorder. In response to the detected slippage, the control system determines the motor speed and torque parameters that need to be adjusted according to the preset control strategy. Assuming that there is a slight slip, the system is set to increase the motor speed by 5% and the torque by 10%; in case of severe slip, the speed is increased by 10% and the torque is increased by 20%. The determination of these parameters is based on a large amount of experimental data and empirical formulas, aiming to quickly restore the normal operation of the belt through small adjustments. The control system generates the corresponding inverter control instructions based on the determined parameters. For example, the instructions contain a target frequency of 50Hz and a voltage of 380V, which are sent to the inverter through the communication interface. After receiving the instructions, the inverter adjusts its output frequency and voltage to change the speed and torque of the motor. The increase in motor speed and torque can effectively overcome the slip phenomenon and restore the belt to stable operation. For the deviation phenomenon, the control system also adjusts the tension of the tensioning device according to the degree of deviation. Assuming that the tension is increased by 5% in case of slight deviation; the tension is increased by 10% in case of severe deviation. The adjustment of the tension is achieved through a hydraulic or electric tensioning device to ensure that the belt returns to center operation. During the adjustment process, the system continuously obtains images of the belt surface and monitors the belt operation status in real time. Assuming that the belt has not resumed normal operation within the preset 30 seconds, the system will issue an alarm signal to prompt the operator to check and repair. This continuous monitoring and dynamic adjustment ensures the stability and reliability of the belt operation. In this way, real-time monitoring and dynamic adjustment of the belt operation status not only improves the operating efficiency of the production line, but also significantly reduces the downtime and maintenance costs caused by belt failure. For example, after a production line adopted this system, the belt failure rate was reduced by 30% and the production efficiency was increased by 15%. In addition, the precise control of the inverter output frequency and voltage also brings energy-saving effects. The motor operates under optimal conditions, energy consumption is reduced, and the service life of the equipment is extended.

[0063] Step S106, the control system adopts an adaptive control algorithm to automatically adjust the parameters of the PID controller according to the operating conditions of the conveyor, so that the control system can adapt to different conveying distances, material loading amounts and environmental conditions, and reduce the belt tension and speed fluctuations caused by changes in operating conditions by optimizing the control strategy, thereby ensuring the stable operation of the conveyor in a complex and changeable on-site environment.

[0064] Specifically, the real-time operating status data of the conveyor, including parameters such as belt speed, tension, and material loading, are obtained as inputs to the adaptive control algorithm. Based on the obtained operating status data, it is determined whether the current operating conditions of the conveyor, such as factors such as conveying distance, material loading, and ambient temperature, have changed. If the judgment result shows that the operating conditions have changed, the adaptive control algorithm is triggered, and the optimal parameter combination of the PID controller is calculated according to the preset rules or machine learning model. The calculated PID controller parameters are sent to the control system to adjust the operating status of the conveyor in real time to keep the belt speed and tension within a reasonable range. During the adjustment process, the operating status changes of the conveyor are continuously monitored, and feedback data is obtained to evaluate the control effect and optimize the control strategy. Through machine learning algorithms such as neural networks or support vector machines, historical operating data are trained to establish a mapping relationship between the operating conditions of the conveyor and the optimal control parameters to form an adaptive control model. The adaptive control model is deployed in the control system. When the operating conditions change, the control system can respond quickly and automatically adjust the PID controller parameters to ensure the stable operation of the conveyor and improve the control accuracy and efficiency.

[0065] In this embodiment, the real-time operation status data collection of the conveyor is the basis of adaptive control. Taking the belt speed as an example, the speed signal can be collected in real time by installing an encoder on the end of the drive roller shaft. The typical sampling period is milliseconds and the speed range is 0.5 to 3 meters per second. The tension data is collected by the tension sensor, and the common measurement range is 0 to 5,000 Newtons. The material load can be obtained by the weighing sensor, and the measurement accuracy can reach 0.5 percent. The judgment of working conditions requires comprehensive consideration of multiple factors. The change of the conveying distance will affect the inertia and resistance characteristics of the system. For example, when the conveying distance is extended from 100 meters to 200 meters, the system inertia increases significantly. The fluctuation of the material load will change the load characteristics of the system. When the load increases from 50 tons per hour to 100 tons, the control parameters need to be adjusted accordingly. The ambient temperature has an important influence on the performance of the equipment. When the temperature rises from minus 20 degrees to 40 degrees, the mechanical characteristics of the equipment will change significantly. The core of the adaptive control algorithm is to dynamically adjust the control parameters. Taking the proportional integral differential controller as an example, the corresponding relationship between the working conditions and the optimal parameters can be established through the machine learning model. For example, under light load conditions, the proportional coefficient can be set to 2.5, the integral time to 0.8 seconds, and the differential time to 0.2 seconds. Under heavy load conditions, the proportional coefficient needs to be increased to 4.0, and the integral time needs to be extended to 1.2 seconds. Multiple indicators are used to evaluate the control effect. The speed fluctuation rate needs to be controlled within 2%, the overshoot does not exceed 5%, and the adjustment time is required to be less than three seconds. The system evaluates the control effect by calculating these indicators in real time, and optimizes the control strategy accordingly. The historical data collection cycle is usually one second, and the storage time can be up to one year, providing sufficient samples for machine learning model training. The adaptive control model is established using a multi-layer neural network structure. The input layer contains operating parameters such as speed and load, the hidden layer adopts a three-layer structure, each layer contains ten neurons, and the output layer corresponds to the controller parameters. The model training adopts the back propagation algorithm, the learning rate is set to 0.01, and the training round is one thousand times. The support vector machine uses the radial basis kernel function, the kernel parameter is 0.1, and the penalty factor is set to 100. After the model is deployed, the system response speed is significantly improved. When the working conditions change suddenly, such as a 50% increase in material loading, the traditional fixed parameter controller takes more than five seconds to stabilize. However, with adaptive control, the system can be restored to stability in just two seconds, greatly improving control accuracy and efficiency.

[0066] This embodiment also provides an adaptive variable frequency speed control system for a belt conveyor, comprising:

[0067] The tension control module is used to obtain the tension data of the belt and transmit the tension data to the control system to determine whether it exceeds the preset tension threshold range. If it exceeds the preset tension threshold range, the control system adjusts the belt tension through the control signal.

[0068] The conveying distance compensation module is used to obtain the current conveying distance data, compare the current conveying distance data with the preset standard conveying distance, obtain the conveying distance deviation value, and adjust the belt length according to the conveying distance deviation value;

[0069] The loading control module is used to obtain the current material loading data, compare the current material loading data with the preset standard loading, obtain the loading deviation value, and adjust the material loading speed according to the loading deviation value;

[0070] The speed control module is used to obtain the current belt speed data, compare the current belt speed data with the preset standard speed, obtain the speed deviation value, and adjust the motor speed according to the speed deviation value;

[0071] The running status monitoring module is used to monitor the running status of the belt in real time through machine vision technology to determine whether slippage and deviation occur. If slippage or deviation occurs, the output of the inverter and the tension of the tensioning device are adjusted according to the degree of slippage and deviation.

[0072] Adaptive control module, the control system adopts adaptive control algorithm, calculates the optimal parameter combination of PID controller according to the operating conditions of the conveyor, and regulates the belt conveyor according to the optimal parameter combination.

[0073] This embodiment further provides a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method.

[0074] This embodiment also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the method are implemented.

[0075] This embodiment also provides a computer program product, including a computer program, which implements the steps of the method when executed by a processor.

[0076] The above are only preferred specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. An adaptive variable frequency speed control method for a belt conveyor, characterized in that: The following steps are involved: Acquire belt tension data, transmit the tension data to the control system to determine whether it exceeds a preset tension threshold range, and if it exceeds, the control system adjusts the belt tension through a control signal; Acquire current conveying distance data, compare the current conveying distance data with a preset standard conveying distance, obtain a conveying distance deviation value, and adjust the belt length according to the conveying distance deviation value; Acquire current material loading data, compare the current material loading data with a preset standard loading, obtain a loading deviation value, and adjust the material loading speed according to the loading deviation value; Acquire current belt speed data, compare the current belt speed data with a preset standard speed, obtain a speed deviation value, and adjust the motor speed according to the speed deviation value; The running status of the belt is monitored in real time through machine vision technology to determine whether slippage and deviation occur. If slippage or deviation occurs, the output of the inverter and the tension of the tensioning device are adjusted according to the degree of slippage and deviation. The control system adopts an adaptive control algorithm to calculate the optimal parameter combination of the PID controller according to the operating conditions of the conveyor, and regulates the belt conveyor according to the optimal parameter combination.

2. The method according to claim 1, characterized in that Adjusting the belt tension includes: the control system sends an adjustment instruction, changes the speed and torque of the motor by adjusting the output frequency and voltage amplitude of the inverter, and then adjusts the running speed and tension of the belt.

3. The method according to claim 1, characterized in that Adjusting the belt length includes: controlling the extension amount and extension direction of the telescopic mechanism to adjust the belt length according to the size and positive and negative of the conveying distance deviation value, and compensating for the belt tension fluctuation caused by the change of conveying distance.

4. The method according to claim 1, characterized in that Adjusting the loading speed of the material includes: controlling the feeding speed of the feeding device according to the size of the loading amount deviation value, and controlling the loading amount by adjusting the loading speed of the material.

5. The method according to claim 1, characterized in that Adjust the inverter output and the tensioning force of the tensioning device according to the degree of slippage and deviation, including: Construct a skidding and deviation discrimination model based on support vector machine; Acquire the belt surface image, use image preprocessing technology to denoise and enhance the image; extract the belt surface texture features through texture feature extraction algorithm to obtain texture feature vector; Inputting the texture feature vector into a slip and deviation discrimination model to obtain a belt state; The output of the frequency converter and the tensioning force of the tensioning device are adjusted according to the belt state.

6. The method according to claim 1, characterized in that Control of belt conveyor includes: The neural network is trained through historical operation data to establish the mapping relationship between the conveyor operating conditions and the optimal control parameters, and an adaptive control model is obtained; Calculate the optimal parameter combination of PID controller through adaptive control model; The belt conveyor is regulated according to the optimal parameter combination.

7. An adaptive variable frequency speed control system for a belt conveyor, characterized in that: include: A tension control module is used to obtain the tension data of the belt, and transmit the tension data to the control system to determine whether it exceeds a preset tension threshold range. If it exceeds, the control system adjusts the belt tension through a control signal; A conveying distance compensation module is used to obtain current conveying distance data, compare the current conveying distance data with a preset standard conveying distance, obtain a conveying distance deviation value, and adjust the belt length according to the conveying distance deviation value; A loading control module, used to obtain current material loading data, compare the current material loading data with a preset standard loading, obtain a loading deviation value, and adjust the material loading speed according to the loading deviation value; The speed control module is used to obtain current belt speed data, compare the current belt speed data with a preset standard speed, obtain a speed deviation value, and adjust the motor speed according to the speed deviation value; The running status monitoring module is used to monitor the running status of the belt in real time through machine vision technology to determine whether slippage and deviation occur. If slippage or deviation occurs, the output of the inverter and the tension of the tensioning device are adjusted according to the degree of slippage and deviation. The adaptive control module and the control system adopt an adaptive control algorithm to calculate the optimal parameter combination of the PID controller according to the operating conditions of the conveyor, and regulate the belt conveyor according to the optimal parameter combination.

8. A computer device comprising a memory, a processor and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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