Automatic control method and system for multi-mode belt conveyor

Abstract

The invention discloses an automatic control method and system for a multi-mode belt conveyor, and relates to the technical field of conveyor optimization. The method comprises the following steps: constructing a digital twin model of the belt conveyor, obtaining conveying amplitudes of the belt conveyor in different working modes, constructing a conveying amplitude curve, and obtaining amplitude influence coefficients of the different working modes on the conveying amplitudes and period influence coefficients of different conveying masses on a conveying period; obtaining an optimized conveying speed according to the actual operation data in combination with the amplitude influence coefficient, obtaining optimized operation data according to the optimized conveying speed, inputting the optimized operation data into a preset period prediction model to obtain a predicted influence coefficient, and adjusting the optimized conveying speed until the predicted influence coefficient is reduced to zero; by means of the technical scheme, the vibration amplitude of the belt conveyor can be kept within the stable floating range, and the situation that the conveying period deviates is reduced while the conveying amplitude is kept normal is facilitated.

Description

Technical Field

[0001] The invention relates to the technical field of conveyor optimization, in particular to a multi-mode belt conveyor automatic control method and system. Background Art

[0002] Automatic control of multi-mode belt conveyors is an advanced material conveying technology that combines the characteristics of automatic control and multi-mode operation. It can flexibly adjust the operating status of the conveyor according to actual needs, improve conveying efficiency and energy utilization, and automatically adjust the conveyor's operating speed, power and other parameters according to preset control strategies and real-time data;

[0003] In the prior art, there are often various variables in the operating state of the belt conveyor, and different variables have different effects on the operation of the belt conveyor. For example, different angles and speeds often produce different amplitudes, and different materials often cause the conveyor belt to idle and cause a delay in the conveying cycle. In the prior art, there is a lack of optimization means for these influencing factors, which makes it impossible to effectively and accurately control the belt conveyor. In view of the shortcomings of the prior art, the present invention provides a multi-mode belt conveyor automatic control method and system. Summary of the invention

[0004] The object of the present invention is to provide a multi-mode belt conveyor automatic control method and system.

[0005] The object of the present invention can be achieved by the following technical solution: A multi-mode belt conveyor automatic control method comprises the following steps:

[0006] Step S1: Collect the equipment information of the belt conveyor, build its digital twin model according to the equipment information, and simulate the conveying of the belt conveyor in different working modes;

[0007] Step S2: obtaining the conveying amplitude of the belt conveyor in different working modes, constructing corresponding conveying amplitude curves respectively, and obtaining the amplitude influence coefficient of different working modes on the conveying amplitude according to the conveying amplitude curves;

[0008] Step S3: Setting a standard mode, and obtaining the period influence coefficient of different conveying qualities on the conveying period under the standard mode;

[0009] Step S4: obtaining actual operation data of the belt conveyor, obtaining an optimized conveying speed according to the actual operation data combined with the amplitude influence coefficient, obtaining optimized operation data according to the optimized conveying speed, and inputting the optimized operation data into a preset cycle prediction model to obtain a prediction influence coefficient;

[0010] Step S5: Adjust the optimized conveying speed until the predicted influence coefficient drops to zero.

[0011] Furthermore, the equipment information of the belt conveyor is collected, and its digital twin model is constructed based on the equipment information. The process of conveying simulation of the belt conveyor in different working modes includes:

[0012] The equipment information refers to the structural parameters of each component of the belt conveyor. The digital twin technology is used to construct a digital twin model of the belt conveyor according to the collected equipment information;

[0013] The conveying simulation refers to adjusting the conveying angle, conveying speed, and conveying length of the belt conveyor in the digital twin model to simulate the switching process of the belt conveyor between different working modes and its operating status in different working modes;

[0014] The conveying angle refers to the angle between the conveyor belt in the belt conveyor and the horizontal line, the conveying speed refers to the moving speed of the conveyor belt in the belt conveyor, and the conveying length refers to the length of the conveyor belt in the belt conveyor.

[0015] Furthermore, the process of obtaining the conveying amplitude of the belt conveyor in different working modes and constructing the corresponding conveying amplitude curves includes:

[0016] An amplitude monitoring unit is arranged on the conveyor belt of the belt conveyor, and the vibration amplitude of the conveyor belt in the running state is monitored by the amplitude monitoring unit to obtain the conveying amplitude;

[0017] By adjusting the conveying angle and conveying speed of the belt conveyor respectively, the conveying amplitude is monitored in real time during the process to obtain the conveying amplitude of the belt conveyor in different working modes;

[0018] A plurality of conveying amplitude curves are constructed according to the obtained conveying amplitude, wherein the horizontal coordinate is the conveying angle and the vertical coordinate is the conveying amplitude, and each conveying amplitude curve corresponds to a different conveying speed.

[0019] Furthermore, the process of obtaining the amplitude influence coefficient of different working modes on the transmission amplitude according to the transmission amplitude curve includes:

[0020] The conveying amplitude curves of the belt conveyor at different conveying speeds are integrated into the same coordinate system to obtain a comprehensive amplitude curve, the comprehensive amplitude curve is regarded as a scatter plot, and its regression line is constructed, and the regression line is used as a reference amplitude line;

[0021] The shortest distance between the coordinate point of each conveying amplitude curve at different conveying angles and the reference amplitude straight line is obtained, and the shortest distance is used as the amplitude influence coefficient of the corresponding amplitude angle and amplitude speed on the conveying amplitude.

[0022] Furthermore, the process of setting the standard mode and obtaining the periodic influence coefficient of different conveying qualities on the conveying period in the standard mode includes:

[0023] The intersection of the reference amplitude straight line and each conveying amplitude curve is taken as the standard point, and the conveying angle, conveying speed, and conveying amplitude of each standard point are taken as the standard mode. In the digital twin model, the belt conveyor is adjusted to different standard modes.

[0024] Under different standard modes, the standard conveying cycle when the conveying mass is zero is obtained, wherein the conveying mass refers to the total mass of the material conveyed on the conveyor belt of the belt conveyor, and the conveying cycle refers to the time interval from the conveying starting point to the conveying end point at any point on the conveyor belt;

[0025] The standard mode is kept fixed, the conveying quality is adjusted, and the adjusted conveying cycle under different conveying qualities is recorded. The difference between the adjusted conveying cycle and its standard conveying cycle is used as the period influence coefficient of the corresponding conveying quality on the conveying cycle.

[0026] Furthermore, the actual operation data of the belt conveyor is obtained, and the process of obtaining the optimized conveying speed according to the actual operation data and the amplitude influence coefficient includes:

[0027] Monitoring actual operation data of the belt conveyor in actual working state, wherein the actual operation data includes actual conveying angle, actual conveying speed, and actual conveying amplitude;

[0028] In the comprehensive amplitude curve, the theoretical conveying amplitude and amplitude influence coefficient corresponding to the actual conveying angle and the actual conveying speed are obtained, and the theoretical conveying amplitude is compared with the actual conveying amplitude to determine whether the optimized conveying amplitude is in a normal state or an abnormal state;

[0029] When it is in an abnormal state, the actual conveying angle is kept unchanged, and the actual conveying speed is adjusted until the optimized conveying amplitude returns to a normal state, and the conveying speed of the belt conveyor at this time is used as the optimized conveying speed.

[0030] Furthermore, the process of obtaining optimized operation data according to the optimized conveying speed and inputting the optimized operation data into a preset cycle prediction model to obtain a prediction influence coefficient includes:

[0031] Obtain the optimized conveying amplitude under the optimized conveying speed, and incorporate the actual conveying angle, optimized conveying speed, optimized conveying amplitude, and actual conveying quality into the optimized operation data;

[0032] Generate a cycle prediction set according to different conveying angles, conveying speeds, conveying amplitudes, conveying masses and their corresponding cycle influence coefficients, and divide the cycle prediction set into a training set and a test set;

[0033] Construct a convolutional neural network, use the conveying angle, conveying speed, conveying amplitude, and conveying quality in the training set as input data of the convolutional neural network, use the corresponding periodic influence coefficient in the training set as output data of the convolutional neural network, and train the convolutional neural network to obtain an initial convolutional neural network;

[0034] The initial convolutional neural network is model verified using the test set, and an initial convolutional neural network with a test error threshold that is less than or equal to a preset value is output as the corresponding cycle prediction model. The corresponding prediction influence coefficient is obtained through the cycle prediction model according to the optimized operation data.

[0035] Further, the process of adjusting the optimized conveying speed until the predicted influence coefficient is reduced to zero includes:

[0036] The optimized conveying speed is continuously adjusted so that the predicted influence coefficient drops to zero. The conveying speed at this time is marked as the adjusted conveying speed. Based on the adjusted conveying speed, steps S4 and S5 are repeated until the optimized conveying amplitude is normal and the predicted influence coefficient drops to zero.

[0037] Multi-mode belt conveyor automatic control system, including the following modules:

[0038] The data acquisition module is used to collect the equipment information of the belt conveyor, build its digital twin model based on the equipment information, and simulate the conveying of the belt conveyor in different working modes;

[0039] The first influence module is used to obtain the conveying amplitude of the belt conveyor in different working modes, construct corresponding conveying amplitude curves respectively, and obtain the amplitude influence coefficient of different working modes on the conveying amplitude according to the conveying amplitude curve;

[0040] The second impact module is used to set a standard mode and obtain the period impact coefficient of different conveying qualities on the conveying period under the standard mode;

[0041] The first optimization module is used to obtain the actual operation data of the belt conveyor, obtain the optimized conveying speed according to the actual operation data combined with the amplitude influence coefficient, obtain the optimized operation data according to the optimized conveying speed, and input the optimized operation data into a preset period prediction model to obtain the prediction influence coefficient;

[0042] The second optimization module adjusts the optimized conveying speed until the predicted influence coefficient drops to zero.

[0043] Compared with the prior art, the present invention has the following beneficial effects:

[0044] 1. The present invention monitors the conveying amplitude of the belt conveyor in different working modes and constructs corresponding conveying amplitude curves, integrates all conveying amplitude curves to obtain a reference amplitude straight line, and takes the shortest distance between each coordinate point and the reference amplitude straight line as its amplitude influence coefficient, which can reflect the influence degree of different conveying angles and conveying speeds on the conveying amplitude. The influence degree is used as a judgment standard. When the difference between the theoretical conveying amplitude and the actual conveying amplitude exceeds its influence degree, it can be judged that the actual conveying amplitude is abnormal and adjusted, which is conducive to keeping the vibration amplitude of the belt conveyor within a stable floating range and realizing effective control and optimization of its working conditions;

[0045] 2. The present invention constructs a digital twin model of the belt conveyor, and simulates the periodic influence coefficient of different conveying qualities on the conveying cycle under the standard mode. Based on this, a periodic prediction model that can predict the periodic influence coefficient is constructed. The corresponding predicted influence coefficient can be obtained according to various actual data, and the conveying speed is adjusted with the goal of reducing the predicted influence coefficient to zero. This is beneficial to reducing the deviation of the conveying cycle while maintaining the normal conveying amplitude. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0047] like Figure 1 As shown, the multi-mode belt conveyor automatic control method comprises the following steps:

[0048] Step S1: Collect the equipment information of the belt conveyor, build its digital twin model according to the equipment information, and simulate the conveying of the belt conveyor in different working modes;

[0049] Step S2: obtaining the conveying amplitude of the belt conveyor in different working modes, constructing corresponding conveying amplitude curves respectively, and obtaining the amplitude influence coefficient of different working modes on the conveying amplitude according to the conveying amplitude curves;

[0050] Step S3: Setting a standard mode, and obtaining the period influence coefficient of different conveying qualities on the conveying period under the standard mode;

[0051] Step S4: obtaining actual operation data of the belt conveyor, obtaining an optimized conveying speed according to the actual operation data combined with the amplitude influence coefficient, obtaining optimized operation data according to the optimized conveying speed, and inputting the optimized operation data into a preset cycle prediction model to obtain a prediction influence coefficient;

[0052] Step S5: Adjust the optimized conveying speed until the predicted influence coefficient drops to zero.

[0053] It should be further explained that, in the specific implementation process, the equipment information of the belt conveyor is collected, and its digital twin model is constructed based on the equipment information. The process of conveying simulation of the belt conveyor in different working modes includes:

[0054] The equipment information refers to the structural parameters of each component of the belt conveyor, including the conveyor belt, driving device, roller, roller, tensioning device, frame, funnel, and material guide chute. The digital twin model of the belt conveyor is constructed according to the collected equipment information using digital twin technology;

[0055] The belt conveyor is multi-mode, having a variety of different working modes, such as horizontal mode, inclined mode, low-speed mode, high-speed mode, short-distance mode, long-distance mode, and other working modes formed by combining these modes;

[0056] The conveying simulation refers to adjusting the conveying angle, conveying speed, and conveying length of the belt conveyor in the constructed digital twin model to simulate the switching process of the belt conveyor between different working modes and its operating status in different working modes;

[0057] The conveying angle refers to the angle between the conveyor belt in the belt conveyor and the horizontal line, the conveying speed refers to the moving speed of the conveyor belt in the belt conveyor, and the conveying length refers to the length of the conveyor belt in the belt conveyor.

[0058] It should be further explained that, in the specific implementation process, the process of obtaining the conveying amplitude of the belt conveyor in different working modes and constructing the corresponding conveying amplitude curves includes:

[0059] Taking any belt conveyor as an example, for a single belt conveyor, its conveying length is fixed, and an amplitude monitoring unit is arranged on its conveyor belt, and the vibration amplitude of the belt conveyor in operation is monitored by the amplitude monitoring unit and marked as the conveying amplitude;

[0060] Under the condition that the conveying length remains unchanged, the conveying angle and conveying speed of the belt conveyor are adjusted respectively, and the conveying amplitude is monitored in real time during the process to obtain the conveying amplitude of the belt conveyor in different working modes;

[0061] For example, the conveying speed is adjusted to a and remains unchanged, and the conveying angle is continuously adjusted from small to large, and the conveying amplitude corresponding to different conveying angles when the conveying speed of the belt conveyor is a is obtained, and a conveying amplitude curve is constructed with the horizontal coordinate being the conveying angle and the vertical coordinate being the conveying amplitude;

[0062] By analogy, the conveying speed is adjusted to b and remains unchanged, and the conveying angle is continuously adjusted from small to large to obtain the conveying amplitude corresponding to different conveying angles when the conveying speed of the belt conveyor is b, and a conveying amplitude curve is constructed with the horizontal axis being the conveying angle and the vertical axis being the conveying amplitude.

[0063] It should be further explained that, in a specific implementation process, the process of obtaining the amplitude influence coefficient of different working modes on the transmission amplitude according to the transmission amplitude curve includes:

[0064] Integrate the conveying amplitude curves of the belt conveyor at different conveying speeds into the same coordinate system to obtain a comprehensive amplitude curve, wherein the abscissa of the comprehensive amplitude curve is the conveying angle and the ordinate is the conveying amplitude, and the comprehensive amplitude curve contains multiple conveying amplitude curves, each of which corresponds to a different conveying speed;

[0065] Since there are multiple turning points on each transmission amplitude curve, the comprehensive amplitude curve can be regarded as a scatter plot, and its regression line is constructed within the comprehensive amplitude curve, and the constructed regression line is marked as the reference amplitude line;

[0066] The shortest distance between the coordinate point of each conveying amplitude curve at different conveying angles and the reference amplitude straight line is obtained, and the obtained shortest distance is marked as the amplitude influence coefficient of the corresponding amplitude angle and amplitude speed on the conveying amplitude.

[0067] It should be further explained that, in the specific implementation process, the process of setting the standard mode and obtaining the periodic influence coefficient of different conveying qualities on the conveying cycle in the standard mode includes:

[0068] The intersection of the reference amplitude straight line and each conveying amplitude curve is marked as a standard point, and the conveying angle, conveying speed, and conveying amplitude corresponding to each standard point is marked as a standard mode. In the digital twin model, the belt conveyor is adjusted to the standard mode;

[0069] The conveying mass refers to the total mass of materials conveyed on the conveyor belt of the belt conveyor, and the conveying cycle refers to the time interval from the conveying starting point to the conveying end point at any point on the conveyor belt. In the standard mode, the conveying cycle when the conveying mass is zero is obtained and marked as the standard conveying cycle;

[0070] In the digital twin model, any standard mode is kept fixed, the conveying quality is adjusted in order from small to large, and the conveying cycles corresponding to different conveying qualities are recorded and marked as adjusted conveying cycles;

[0071] The adjusted conveying cycles corresponding to different conveying qualities under different standard modes are obtained, and the difference between each adjusted conveying cycle and its standard conveying cycle is marked as the period influence coefficient of the corresponding conveying quality on the conveying cycle.

[0072] It should be further explained that, in the specific implementation process, the process of obtaining the actual operation data of the belt conveyor and obtaining the optimized conveying speed based on the actual operation data and the amplitude influence coefficient includes:

[0073] Monitoring actual operation data of the belt conveyor in actual working state, wherein the actual operation data includes actual conveying angle, actual conveying speed, and actual conveying amplitude;

[0074] In the comprehensive amplitude curve, obtain the theoretical conveying amplitude Z corresponding to the actual conveying angle and actual conveying speed L and amplitude influence coefficient Z y , the theoretical transmission amplitude Z L The actual transmission amplitude Z S For comparison, if |Z S -Z L |<Z y , then the optimized transmission amplitude is judged to be in normal state and no other operations are performed on it;

[0075] If | Z S -Z L |≥Z y , then the optimized conveying amplitude is judged to be in an abnormal state, the actual conveying angle is kept unchanged, the actual conveying speed is adjusted until the optimized conveying amplitude is in a normal state, and the conveying speed of the belt conveyor at this time is marked as the optimized conveying speed.

[0076] It should be further explained that, in the specific implementation process, the process of obtaining the optimized operation data according to the optimized conveying speed and inputting the optimized operation data into the preset cycle prediction model to obtain the prediction influence coefficient includes:

[0077] Obtain the optimized conveying amplitude under the optimized conveying speed, and incorporate the actual conveying angle, optimized conveying speed, optimized conveying amplitude, and actual conveying quality into the optimized operation data;

[0078] The preset process of the cycle prediction model is as follows:

[0079] According to different conveying angles, conveying speeds, conveying amplitudes, conveying masses and their corresponding cycle influence coefficients, a cycle prediction set is generated, and the obtained cycle prediction set is divided into a training set and a test set;

[0080] Construct a convolutional neural network, use the conveying angle, conveying speed, conveying amplitude, and conveying quality in the training set as input data of the convolutional neural network, use the corresponding periodic influence coefficient in the training set as output data of the convolutional neural network, and train the convolutional neural network to obtain an initial convolutional neural network;

[0081] The initial convolutional neural network is model verified using the test set, and the initial convolutional neural network with a test error threshold value less than or equal to the preset value is output as the corresponding cycle prediction model;

[0082] The optimized operation data is input into the cycle prediction model, and the cycle prediction model outputs the corresponding prediction influence coefficient according to the actual conveying angle, optimized conveying speed, optimized conveying amplitude and actual conveying quality in the optimized operation data.

[0083] It should be further explained that, in the specific implementation process, the process of adjusting the optimized conveying speed until the predicted influence coefficient is reduced to zero includes:

[0084] Since the actual conveying angle remains unchanged and the actual conveying quality changes dynamically, it is necessary to continuously adjust the optimized conveying speed so that the predicted influence coefficient is reduced to zero, and the conveying speed at this time is marked as the adjusted conveying speed;

[0085] During this process, the optimized conveying amplitude will also change accordingly. Based on the adjusted conveying speed, steps S4 and S5 are repeated until the optimized conveying amplitude is in a normal state and the predicted influence coefficient drops to zero.

[0086] The embodiment of the present invention also includes: a multi-mode belt conveyor automatic control system, including the following modules:

[0087] The data acquisition module is used to collect the equipment information of the belt conveyor, build its digital twin model based on the equipment information, and simulate the conveying of the belt conveyor in different working modes;

[0088] The first influence module is used to obtain the conveying amplitude of the belt conveyor in different working modes, construct corresponding conveying amplitude curves respectively, and obtain the amplitude influence coefficient of different working modes on the conveying amplitude according to the conveying amplitude curve;

[0089] The second impact module is used to set a standard mode and obtain the period impact coefficient of different conveying qualities on the conveying period under the standard mode;

[0090] The first optimization module is used to obtain the actual operation data of the belt conveyor, obtain the optimized conveying speed according to the actual operation data combined with the amplitude influence coefficient, obtain the optimized operation data according to the optimized conveying speed, and input the optimized operation data into a preset period prediction model to obtain the prediction influence coefficient;

[0091] The second optimization module adjusts the optimized conveying speed until the predicted influence coefficient drops to zero.

[0092] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A multi-mode belt conveyor automatic control method, characterized in that: The following steps are involved: Step S1: Collect the equipment information of the belt conveyor, build its digital twin model according to the equipment information, and simulate the conveying of the belt conveyor in different working modes; Step S2: obtaining the conveying amplitude of the belt conveyor in different working modes, constructing corresponding conveying amplitude curves respectively, and obtaining the amplitude influence coefficient of different working modes on the conveying amplitude according to the conveying amplitude curves; Step S3: Setting a standard mode, and obtaining the period influence coefficient of different conveying qualities on the conveying period under the standard mode; Step S4: obtaining actual operation data of the belt conveyor, obtaining an optimized conveying speed according to the actual operation data combined with the amplitude influence coefficient, obtaining optimized operation data according to the optimized conveying speed, and inputting the optimized operation data into a preset cycle prediction model to obtain a prediction influence coefficient; Step S5: Adjust the optimized conveying speed until the predicted influence coefficient drops to zero.

2. The multi-mode belt conveyor automatic control method according to claim 1, characterized in that: The process of building a digital twin model based on equipment information and performing transportation simulation on it includes: The equipment information refers to the structural parameters of each component of the belt conveyor. The digital twin technology is used to construct a digital twin model of the belt conveyor according to the collected equipment information; The conveying simulation refers to adjusting the conveying angle, conveying speed, and conveying length of the belt conveyor in the digital twin model to simulate the switching process of the belt conveyor between different working modes and its operating status in different working modes; The conveying angle refers to the angle between the conveyor belt in the belt conveyor and the horizontal line, the conveying speed refers to the moving speed of the conveyor belt in the belt conveyor, and the conveying length refers to the length of the conveyor belt in the belt conveyor.

3. The multi-mode belt conveyor automatic control method according to claim 2, characterized in that: The process of obtaining the conveying amplitude of the belt conveyor and constructing the conveying amplitude curve includes: An amplitude monitoring unit is arranged on the conveyor belt of the belt conveyor, and the vibration amplitude of the conveyor belt in the running state is monitored by the amplitude monitoring unit to obtain the conveying amplitude; By adjusting the conveying angle and conveying speed of the belt conveyor respectively, the conveying amplitude is monitored in real time during the process to obtain the conveying amplitude of the belt conveyor in different working modes; A plurality of conveying amplitude curves are constructed according to the obtained conveying amplitude, wherein the horizontal coordinate is the conveying angle and the vertical coordinate is the conveying amplitude, and each conveying amplitude curve corresponds to a different conveying speed.

4. The multi-mode belt conveyor automatic control method according to claim 3, characterized in that: The process of obtaining the amplitude influence coefficient according to the conveying amplitude curve includes: The conveying amplitude curves of the belt conveyor at different conveying speeds are integrated into the same coordinate system to obtain a comprehensive amplitude curve, the comprehensive amplitude curve is regarded as a scatter plot, and its regression line is constructed, and the regression line is used as a reference amplitude line; The shortest distance between the coordinate point of each conveying amplitude curve at different conveying angles and the reference amplitude straight line is obtained, and the shortest distance is used as the amplitude influence coefficient of the corresponding amplitude angle and amplitude speed on the conveying amplitude.

5. The multi-mode belt conveyor automatic control method according to claim 4, characterized in that: The process of setting the standard mode and obtaining the period influence coefficient in the standard mode includes: The intersection of the reference amplitude straight line and each conveying amplitude curve is taken as the standard point, and the conveying angle, conveying speed, and conveying amplitude of each standard point are taken as the standard mode. In the digital twin model, the belt conveyor is adjusted to different standard modes. Under different standard modes, the standard conveying cycle when the conveying mass is zero is obtained, wherein the conveying mass refers to the total mass of the material conveyed on the conveyor belt of the belt conveyor, and the conveying cycle refers to the time interval from the conveying starting point to the conveying end point at any point on the conveyor belt; The standard mode is kept fixed, the conveying quality is adjusted, and the adjusted conveying cycle under different conveying qualities is recorded. The difference between the adjusted conveying cycle and its standard conveying cycle is used as the period influence coefficient of the corresponding conveying quality on the conveying cycle.

6. The multi-mode belt conveyor automatic control method according to claim 5, characterized in that: The process of optimizing the conveying speed based on actual operation data combined with the amplitude influence coefficient includes: Monitoring actual operation data of the belt conveyor in actual working state, wherein the actual operation data includes actual conveying angle, actual conveying speed, and actual conveying amplitude; In the comprehensive amplitude curve, the theoretical conveying amplitude and amplitude influence coefficient corresponding to the actual conveying angle and the actual conveying speed are obtained, and the theoretical conveying amplitude is compared with the actual conveying amplitude to determine whether the optimized conveying amplitude is in a normal state or an abnormal state; When it is in an abnormal state, the actual conveying angle is kept unchanged, and the actual conveying speed is adjusted until the optimized conveying amplitude returns to a normal state, and the conveying speed of the belt conveyor at this time is used as the optimized conveying speed.

7. The multi-mode belt conveyor automatic control method according to claim 6, characterized in that: The process of obtaining the optimization run data and inputting it into the period forecasting model to obtain the forecast impact coefficients includes: Obtain the optimized conveying amplitude under the optimized conveying speed, and incorporate the actual conveying angle, optimized conveying speed, optimized conveying amplitude, and actual conveying quality into the optimized operation data; Generate a cycle prediction set according to different conveying angles, conveying speeds, conveying amplitudes, conveying masses and their corresponding cycle influence coefficients, and divide the cycle prediction set into a training set and a test set; Construct a convolutional neural network, use the conveying angle, conveying speed, conveying amplitude, and conveying quality in the training set as input data of the convolutional neural network, use the corresponding periodic influence coefficient in the training set as output data of the convolutional neural network, and train the convolutional neural network to obtain an initial convolutional neural network; The test set is used to verify the model of the initial convolutional neural network, and the initial convolutional neural network with a test error threshold less than or equal to a preset value is output as the corresponding cycle prediction model. The corresponding prediction influence coefficient is obtained according to the optimized operation data through the cycle prediction model.

8. The multi-mode belt conveyor automatic control method according to claim 7, characterized in that: The process of adjusting the optimized conveying speed until the predicted influence coefficient drops to zero includes: The optimized conveying speed is continuously adjusted so that the predicted influence coefficient drops to zero. The conveying speed at this time is marked as the adjusted conveying speed. Based on the adjusted conveying speed, steps S4 and S5 are repeated until the optimized conveying amplitude is normal and the predicted influence coefficient drops to zero.

9. A multi-mode belt conveyor automatic control system, which is used to implement the multi-mode belt conveyor automatic control method according to any one of claims 1 to 8, characterized in that: The system comprises: The data acquisition module is used to collect the equipment information of the belt conveyor, build its digital twin model based on the equipment information, and simulate the conveying of the belt conveyor in different working modes; The first influence module is used to obtain the conveying amplitude of the belt conveyor in different working modes, construct corresponding conveying amplitude curves respectively, and obtain the amplitude influence coefficient of different working modes on the conveying amplitude according to the conveying amplitude curve; The second impact module is used to set a standard mode and obtain the period impact coefficient of different conveying qualities on the conveying period under the standard mode; The first optimization module is used to obtain the actual operation data of the belt conveyor, obtain the optimized conveying speed according to the actual operation data combined with the amplitude influence coefficient, obtain the optimized operation data according to the optimized conveying speed, and input the optimized operation data into a preset period prediction model to obtain the prediction influence coefficient; The second optimization module adjusts the optimized conveying speed until the predicted influence coefficient drops to zero.

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