Conveyor speed control method and system, storage medium, and computer program product

Abstract

This disclosure specifically relates to the field of conveyor technology, and more specifically to a speed control method and system for conveyors, a storage medium, a computer program product, and electronic equipment. The method includes: acquiring first transport volume data corresponding to the current monitoring cycle of the conveyor; determining a first belt speed by combining the conveyor's carrying area and the first transport volume data; determining a second belt speed based on the first belt speed according to the conveyor's efficiency attribute data, and updating the conveyor's target belt speed according to the second belt speed; wherein the efficiency attribute data represents the relationship between the conveyor's operating power and conveying efficiency. This solution enables precise frequency conversion control of the conveyor belt speed.

Description

Technical Field

[0001] This disclosure relates to the field of conveyor technology, specifically to a conveyor speed control method, a conveyor speed control system, a storage medium, a computer program product, and an electronic device. Background Technology

[0002] Currently, belt conveyors are widely used in industries such as power, metallurgy, and ports, and there is a growing trend towards larger capacity and longer distance transport. Traditional conveyor control generally employs a constant speed operation mode, meaning that regardless of the amount of material coming from upstream, the conveyor always operates at a fixed speed, leading to significant energy waste. Some solutions utilize automatic speed control devices on certain sections of the conveyor to regulate speed, but these solutions still have drawbacks. For example, the control logic is simple, and the calculation process uses completely assumed parameters, often resulting in theoretical calculations that deviate from actual conditions. Alternatively, frequent speed adjustments during actual control can lead to belt speed instability and oscillations, resulting in reduced reliability and poor energy-saving performance.

[0003] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0004] This disclosure provides a speed control method for a conveyor, a speed control system for a conveyor, a storage medium, a computer program product, and an electronic device, which can effectively overcome the defects existing in the prior art.

[0005] Other features and advantages of this disclosure will become apparent from the following detailed description, or may be learned in part by practice of this disclosure.

[0006] According to a first aspect of this disclosure, a speed control method for a conveyor is provided, the method comprising:

[0007] Obtain the first transport volume data corresponding to the current monitoring cycle of the conveyor;

[0008] Based on the conveyor's carrying area and the first conveying volume data, the first belt speed is determined;

[0009] Based on the efficiency attribute data of the conveyor, a second belt speed is determined based on the first belt speed, and the target belt speed of the conveyor is updated based on the second belt speed; wherein, the efficiency attribute data is used to represent the relationship between the operating power and conveying efficiency of the conveyor.

[0010] In some exemplary embodiments, the method further includes:

[0011] Obtain the target random number within the preset data range;

[0012] The third belt speed is determined by combining the target random number with the second belt speed, and the target belt speed of the conveyor is updated according to the third belt speed.

[0013] In some exemplary embodiments, after obtaining the first transport volume data corresponding to the current monitoring cycle of the conveyor, the method further includes:

[0014] Collect the current environmental parameters corresponding to the current monitoring period;

[0015] The first transport volume data and the current environmental parameters are used as input parameters. The input parameters are processed using a frequency conversion control model to obtain the fourth belt speed, and the target belt speed of the conveyor is updated according to the fourth belt speed.

[0016] In some exemplary embodiments, the method further includes:

[0017] A sample data is constructed by collecting historical data corresponding to a preset number of monitoring cycles; wherein, the sample data includes: environmental parameters, power parameters, operating speed parameters, and transport capacity parameters; the operating speed parameters include a second belt speed or a third belt speed;

[0018] Initialize the neural network model and initialize its hyperparameters;

[0019] The sample data is used to perform forward feedback training on the neural network model to obtain a frequency conversion control model based on the neural network.

[0020] In some exemplary embodiments, after configuring the target belt speed of the conveyor, the method further includes:

[0021] Determine the second transport volume data corresponding to the target belt speed;

[0022] If the second transport volume data and the first transport volume data belong to the same transport volume range, then the target belt speed of the conveyor will not be updated; or

[0023] When the second transport volume data and the first transport volume data belong to different transport volume ranges, the target belt speed of the conveyor is updated according to the maximum transport volume value of the transport volume range corresponding to the second transport volume data.

[0024] In some exemplary embodiments, when the second traffic volume data and the first traffic volume data belong to different traffic volume ranges, the method further includes:

[0025] Determine the target delay parameters; wherein, the target delay parameters include: basic delay and / or additional delay;

[0026] After the duration of the target delay parameter is executed, the target bandwidth is then executed.

[0027] In some exemplary embodiments, the method further includes:

[0028] When the second traffic volume is less than the first traffic volume, the target delay parameter is configured to include a basic delay and an additional delay; or...

[0029] When the second transport volume data is greater than the first transport volume data, the target delay parameter is configured to include a basic delay.

[0030] In some exemplary embodiments, determining the additional delay in the target delay parameter includes:

[0031] Obtain the prior traffic volume data corresponding to at least one prior monitoring period;

[0032] Based on the prior traffic volume data and the traffic volume change trend of the first traffic volume data, the traffic volume change time corresponding to the traffic volume change trend is estimated, and the traffic volume change time is configured as the additional delay.

[0033] In some exemplary embodiments, estimating the time taken for the change in traffic volume corresponding to the traffic volume change trend based on the traffic volume change trend of the prior traffic volume data and the first traffic volume data includes:

[0034] Collect historical transport volume data and corresponding time information for each historical monitoring period, and construct sample data based on the historical transport volume data and corresponding time information;

[0035] The sample data was used to train a delay estimation model based on a neural network;

[0036] Configure the first traffic volume data and the prior traffic volume data corresponding to the at least one prior monitoring period as input data and input them into the delay estimation model to obtain the traffic volume change time output by the delay estimation model.

[0037] In some exemplary embodiments, the method further includes:

[0038] Data statistics are performed based on historical transport volume data for each historical monitoring period, and the probability distribution of transport volume is determined based on the statistical results.

[0039] The traffic volume intervals are divided based on the aforementioned traffic volume probability distribution.

[0040] According to a second aspect of this disclosure, a speed control system for a conveyor is provided, comprising:

[0041] The transport volume detection module is used to obtain the first transport volume data corresponding to the current monitoring cycle of the conveyor;

[0042] The belt speed calculation module is used to determine the first belt speed by combining the bearing area of ​​the conveyor and the first conveying volume data;

[0043] The belt speed control module is used to determine a second belt speed based on the first belt speed according to the efficiency attribute data of the conveyor, and to update the target belt speed of the conveyor according to the second belt speed; wherein, the efficiency attribute data is used to represent the relationship between the operating power and the conveying efficiency of the conveyor.

[0044] According to a third aspect of this disclosure, a storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the speed control method for the conveyor described above.

[0045] According to a fourth aspect of this disclosure, an electronic device is provided, comprising:

[0046] Processor; and

[0047] Memory for storing the executable instructions of the processor;

[0048] The processor is configured to implement the navigation deception signal processing method described above when executing the executable instructions.

[0049] According to a fifth aspect of this disclosure, a computer program product is provided, on which a computer program is stored, which, when executed by a processor, implements the speed control method for the conveyor described above.

[0050] The conveyor speed control method provided in the embodiments of this disclosure uses real-time monitoring of the first conveying volume data of the current monitoring cycle and calculates the corresponding first belt speed based on the first conveying volume data. It can fine-tune the first belt speed according to the efficiency attribute data of the conveyor itself, thereby enabling real-time output of a more accurate belt speed and achieving precise frequency conversion control of the conveyor belt speed.

[0051] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0052] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure. It is obvious that the drawings described below are merely some embodiments of this disclosure, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0053] Figure 1 This schematic diagram illustrates a speed control method for a conveyor according to an exemplary embodiment of the present disclosure;

[0054] Figure 2 The schematic diagram illustrates the architecture of a control system according to an exemplary embodiment of the present disclosure;

[0055] Figure 3 This diagram schematically illustrates a variation in the energy consumption performance of an electric motor, as per an exemplary embodiment of this disclosure.

[0056] Figure 4 The schematic diagram illustrates a method for regulating a second belt speed according to an exemplary embodiment of the present disclosure;

[0057] Figure 5 The diagram illustrates a variable frequency control model control method according to an exemplary embodiment of the present disclosure.

[0058] Figure 6 This schematic diagram illustrates a method for calculating the target belt speed of a conveyor using a frequency conversion control model in an exemplary embodiment of the present disclosure.

[0059] Figure 7 A schematic diagram illustrating a traffic volume variation curve in an exemplary embodiment of the present disclosure is shown.

[0060] Figure 8 This diagram illustrates a traffic volume zone division result in an exemplary embodiment of the present disclosure.

[0061] Figure 9 This schematic diagram illustrates the composition of a speed control system for a conveyor according to an exemplary embodiment of the present disclosure.

[0062] Figure 10 This schematic diagram illustrates the composition of an electronic device according to an exemplary embodiment of the present disclosure. Detailed Implementation

[0063] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that this disclosure will be more comprehensive and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0064] Furthermore, the accompanying drawings are merely illustrative of this disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0065] To address the shortcomings and deficiencies of existing technologies, this exemplary embodiment provides a speed control method for a conveyor, which can be applied to real-time frequency conversion control of the operating speed of a belt conveyor. (Reference) Figure 1 As shown, the speed control method may specifically include:

[0066] Step S11: Obtain the first transport volume data corresponding to the current monitoring cycle of the conveyor;

[0067] Step S12: Determine the first belt speed by combining the bearing area of ​​the conveyor and the first conveying volume data;

[0068] Step S13: Based on the efficiency attribute data of the conveyor, determine the second belt speed based on the first belt speed, and update the target belt speed of the conveyor according to the second belt speed; wherein, the efficiency attribute data is used to represent the relationship between the operating power and conveying efficiency of the conveyor.

[0069] The conveyor speed control method provided in this example embodiment can be applied to belt conveyors or other types of conveyors. By acquiring the first conveying volume data of the current monitoring cycle of the conveyor in real time and combining it with the carrying area of ​​the conveyor, its first belt speed can be determined. Based on the efficiency attribute data of the conveyor, a second belt speed can be calculated based on the first belt speed and configured as the target belt speed of the conveyor. This allows for fine-tuning of the first belt speed based on the efficiency attribute data of the conveyor itself, thereby enabling more accurate real-time output of the belt speed and achieving precise frequency conversion control of the conveyor belt speed.

[0070] The following will describe in more detail each step of the speed control method for the conveyor in this exemplary embodiment, with reference to the accompanying drawings and embodiments.

[0071] For example, refer to Figure 2As shown, a material flow detection component 105 can be installed at the outlet of the tail guide chute of the conveyor 101 to obtain the material conveying volume of the conveyor in real time. For example, the material flow detection component can be a detection component based on lidar and / or vision inspection technology. This disclosure does not impose any special limitations on the form and detection method of the material flow detection component. The conveyor 105 can also be connected to a speed sensor 104 to collect the operating belt speed of the conveyor in real time. In addition, a temperature sensor 102 and a humidity sensor 103 can be installed around the conveyor 101 to detect the temperature and humidity of the environment around the conveyor belt. Each sensor and detection component can transmit the detection data to the control terminal 106 in real time via wired and / or wireless means; or, each sensor and detection component can send the current detection data to the main controller of the conveyor 101, and then send it to the control terminal 106 through the main controller. The control terminal can calculate based on the currently received data, output the belt speed information of the conveyor belt, and send the corresponding belt speed update command to the main controller of the conveyor belt, thereby realizing frequency conversion control of the conveyor belt speed. The control terminal 106 can be a laptop, desktop computer, or a smart mobile terminal device such as a mobile phone or tablet, or a backend server with certain computing capabilities. Alternatively, in some exemplary embodiments, the control terminal 106 can be a control terminal integrated with the conveyor.

[0072] In step S11, the first transport volume data corresponding to the current monitoring cycle of the conveyor is obtained.

[0073] In this example implementation, as Figure 2 The system architecture shown allows the control terminal to acquire the first conveying volume data by receiving real-time detection data from the material flow detection component 105. This first conveying volume data can be the material flow rate at the outlet of the conveyor's tail guide chute. The current monitoring period can be one second, three seconds, or five seconds, and the first conveying volume data can be the real-time material flow rate of the conveyor, thereby enabling real-time detection of changes in the conveyor's current conveying volume and triggering frequency conversion control of the conveyor belt speed. Alternatively, the monitoring period can also be 10 seconds, 15 seconds, 30 seconds, or one minute.

[0074] In step S12, the first belt speed is determined by combining the carrying area of ​​the conveyor and the first transport volume data.

[0075] In this example implementation, the material flow rate Q of the conveyor is equal to the belt speed v multiplied by the carrying area S. The carrying area S of the conveyor can be determined based on the width of the conveyor belt and the conveyor's transport length. Since the carrying area S is a constant for the conveyor itself, the minimum belt speed v required to carry the current material load can be calculated based on the above formula; this is the first belt speed. This first belt speed can be configured as the lower limit of the operating speed corresponding to the current real-time transport volume Q.

[0076] In step S13, a second belt speed is determined based on the first belt speed according to the efficiency attribute data of the conveyor, and the target belt speed of the conveyor is updated according to the second belt speed; wherein, the efficiency attribute data is used to represent the relationship between the operating power and the conveying efficiency of the conveyor.

[0077] In this example implementation, the conveyor's efficiency attribute data is used to represent the relationship between the conveyor's operating power and conveying efficiency. For example, refer to... Figure 3 As shown, the relationship curve between the conveyor motor speed and efficiency can be used as efficiency attribute data. Specifically, the relationship curve between the conveyor motor speed and efficiency can be obtained in advance. For example... Figure 3 The diagram shows the operating characteristics of the conveyor motor, where η is the efficiency curve.

[0078] Specifically, a belt speed parameter adjustment range can be pre-configured. For example, based on the actual conditions such as the type of material being conveyed, the site type, and the conveyor's running length, the belt speed parameter adjustment range can be pre-configured to be 0–0.5 m / s.

[0079] For the currently acquired first belt speed, calculations can be performed in increments of +0 to 0.5 m / s. For example, based on the motor speed and efficiency curve, the speed with the lowest overall energy consumption, i.e., the second belt speed, is calculated in speed increments of 0.1 m / s. This second belt speed is then configured as the target speed and output. The conveyor belt speed is controlled using this second belt speed, thus achieving the updating and frequency conversion control of the conveyor belt speed. This completes the frequency conversion control of the belt speed for the current detection cycle.

[0080] Considering that the lowest operating speed of the conveyor belt does not necessarily correspond to the lowest energy consumption, after obtaining the first belt speed, the second belt speed with the lowest energy consumption within a certain speed range is determined by referring to the power consumption attribute data of the conveyor belt. This allows for a more accurate determination of the second belt speed with the lowest energy consumption.

[0081] In some exemplary embodiments, after completing one belt speed update in the current detection cycle, the next detection cycle can be started after a fixed interval t, and the real-time conveyor volume data can be acquired again.

[0082] In this example implementation, refer to Figure 4 As shown, after obtaining the second belt speed, the speed control method may further include:

[0083] Step S21: Obtain the target random number within the preset data range;

[0084] Step S22: Combine the target random number with the second belt speed to determine the third belt speed, and update the target belt speed of the conveyor according to the third belt speed.

[0085] For example, a preset data range for generating random number i can be pre-configured; for instance, the value range of random number i can be configured to be 0 to 0.5. Specifically, after obtaining the second belt speed, or when calculating the second belt speed, a random number generation task can be triggered. The terminal device executes the task and generates the current target random number in the preset data range. The target random number i is added to the second belt speed to obtain the final third belt speed, and the third belt speed is updated to the final target belt speed.

[0086] In this example implementation, data from each detection cycle can also be collected, and a frequency converter control model for calculating the belt speed can be trained. Specifically, refer to... Figure 5 As shown, the above method may further include:

[0087] Step S31: Collect historical data corresponding to a preset number of monitoring cycles to construct sample data; wherein, the sample data includes: environmental parameters, power parameters, operating speed parameters, and transport capacity parameters; the operating speed parameters include a second belt speed or a third belt speed;

[0088] Step S32: Initialize the neural network model and initialize the model hyperparameters;

[0089] Step S33: Use the sample data to perform forward feedback training on the neural network model to obtain a frequency conversion control model based on the neural network.

[0090] Specifically, a certain number of historical data periods can be pre-collected to construct sample data. This historical data for each monitoring period can include environmental parameters, power parameters, operating speed parameters, and transport capacity parameters. The operating speed parameter can be the second or third belt speed of the conveyor within that monitoring period. Considering the impact of temperature and humidity changes on the conveyor's operating power in different working environments, environmental parameters such as temperature and humidity within the monitoring period can be used as sample data. For example, 500, 800, 1000, or other quantities of historical data can be collected to construct samples. After constructing the sample data, an initial model based on a Convolutional Neural Network (CNN) can be built. The neural network model is initialized, hyperparameters and a loss function are set, and the temperature, humidity, conveyor motor power, and transport capacity from the sample data are input into the model to obtain the predicted transport speed. This predicted speed is then compared with the collected transport speed parameters to calculate the loss between the predicted result and the actual value. The model's hyperparameters are then updated via backpropagation. Training ends when the set number of iterations is reached, resulting in a neural network-based variable frequency control model.

[0091] In this example implementation, after obtaining the first transport volume data corresponding to the current monitoring cycle of the conveyor, the target belt speed of the conveyor can also be calculated using the trained frequency conversion control model. (Reference) Figure 6 As shown, the method further includes:

[0092] Step S61: Collect the current environmental parameters corresponding to the current monitoring period;

[0093] Step S62: The first transport volume data and the current environmental parameters are used as input parameters. The input parameters are processed using a frequency conversion control model to obtain the fourth belt speed, and the target belt speed of the conveyor is updated according to the fourth belt speed.

[0094] Specifically, within each monitoring cycle, the current environmental parameters corresponding to the environment in which the transport aircraft is located can also be detected. These current environmental parameters can include current temperature and humidity data. The first transport volume data, temperature data, humidity data, and the corresponding current belt speed can be used as input data. This input data is fed into a trained variable frequency control model, and the model outputs a fourth belt speed. This fourth belt speed can then be configured as the target belt speed, enabling belt speed updates for the conveyor and achieving variable frequency control of the conveyor belt speed based on the minimum operating speed.

[0095] In this example implementation, after obtaining the target belt speed based on the second, third, or fourth belt speed, a determination can be made based on the transport volume data to determine whether to update the target belt speed. Specifically, refer to... Figure 7As shown, after configuring the target belt speed of the conveyor, the method further includes:

[0096] Step S71: Determine the second transport volume data corresponding to the target belt speed;

[0097] Step S72: When the second transport volume data and the first transport volume data belong to the same transport volume range, the target transport speed of the conveyor is not updated; or

[0098] Step S73: When the second transport volume data and the first transport volume data belong to different transport volume intervals, update the target belt speed of the conveyor according to the maximum transport volume value of the transport volume interval corresponding to the second transport volume data.

[0099] Specifically, after calculating and determining the target belt speed to be updated based on the second, third, or fourth belt speed, the second conveying capacity data can be calculated based on the current target belt speed, combined with the conveyor belt size and bearing area. The second conveying capacity data is then compared with the first conveying capacity data to determine if they are within the same conveying capacity range. If the second and first conveying capacity data are within the same range, it means that although the conveying capacity has changed, the current change can be ignored, and there is no need to update the conveyor's target speed. The conveyor speed can be maintained constant for the current conveying capacity, thus achieving intelligent frequency conversion control of the belt conveyor.

[0100] Alternatively, if the second transport volume data and the first transport volume data both belong to the same transport volume range, the maximum transport volume of the transport volume range in which the second transport volume data is located can be used to recalculate the target belt speed and update the current transport speed of the conveyor.

[0101] For example, the first belt speed can be calculated using the maximum transport capacity, and the corresponding second belt speed can be calculated based on the first belt speed, with the second belt speed serving as the target belt speed. Alternatively, a third belt speed can be calculated based on the second belt speed, and this third belt speed can be used as the target belt speed. Alternatively, the maximum transport capacity, temperature, humidity, and current belt speed can be input into a trained variable frequency control model, and the model's output value can be used as the target belt speed.

[0102] By comparing and judging the second transport volume data corresponding to the target belt speed to be updated with the current first transport volume data, a decision can be made on whether to update the belt speed based on the changes in transport volume data. This effectively avoids repetitive, minor speed adjustment operations. When recalculating the target belt speed based on the maximum transport volume within the transport volume range to which the second transport volume data belongs, the transport speed can be effectively estimated and expanded within a certain range, thereby accommodating situations where the transport volume may continue to increase, and thus avoiding repeated speed adjustment operations. This achieves reasonable variable frequency speed control.

[0103] For example, the aforementioned traffic volume range can be pre-configured by statistically analyzing traffic volume data from historical monitoring periods. Specifically, the method may further include:

[0104] Step S71: Perform data statistics based on historical transport volume data for each historical monitoring period, and determine the transport volume probability distribution based on the data statistics results;

[0105] Step S72: Divide the traffic volume interval based on the traffic volume probability distribution.

[0106] Specifically, historical traffic volume data for monitoring periods can be collected in advance. For daily monitoring periods, this can be done minute-by-minute, calculating the traffic volume per minute; based on the minute-by-minute traffic volume statistics, the actual hourly traffic volume can be calculated. For example, refer to... Figure 7 As shown, with the actual transport volume as the vertical axis and time as the horizontal axis, the hourly transport volume variation curve within a day is represented by B. For multiple days, corresponding transport volume variation curves B1, B2, B3, ... can be generated. Curve B can then be transformed from the time domain to the frequency domain, for example, by using 50 tons as a increment to convert it into the probability of occurrence for each transport volume, and then storing this relationship in a list A1, A2, A3, ... on a daily basis; for example... Figure 4 The traffic volume range is shown.

[0107] When cyclical factors in transportation are not considered, the newly added transport volume probability Ai can be added to the existing transport volume probability A, and the sum can be divided by the number of days to obtain the latest updated transport volume data A. When cyclical factors need to be considered, monthly transport volume probabilities A can be generated. This is useful if a company's production has distinct peak and off-peak seasons throughout the year; the data A is then aggregated into 12 different transport volume probability change statistics for each month, using different A data for each month, thereby improving control accuracy.

[0108] To define the automatically adjusted transport volume range, based on the transport volume probability data A, the transport volume range is divided into segments using significant inflection points on the probability curve. A significant inflection point is defined as a point where the probability change exceeds a preset threshold. If there is no significant change in transport volume throughout the entire range, it indicates that the probabilities of different transport volumes are essentially the same; in this case, an equal distribution method is used to set the transport volume range. Transport volume segments with a cumulative lowest probability of occurrence of less than 10% are merged into a single segment. For example, refer to... Figure 8 As shown. If the volume of transport does not change by more than 20% within the entire interval, it means that the probability of occurrence of various types of transport volume is basically the same. In this case, the method of equal distribution is used to set 10 transport volume intervals.

[0109] In this example implementation, to avoid frequent speed adjustments caused by occasional fluctuations in transport volume, a delay measurement for the variable frequency speed control action can be set. Specifically, when the second transport volume data and the first transport volume data belong to different transport volume ranges, the method further includes:

[0110] Determine the target delay parameters; wherein the target delay parameters include: basic delay and / or additional delay; after the duration of the target delay parameters, execute the target bandwidth.

[0111] For example, the method further includes:

[0112] When the second traffic volume is less than the first traffic volume, the target delay parameter is configured to include a basic delay and an additional delay; or...

[0113] When the second transport volume data is greater than the first transport volume data, the target delay parameter is configured to include a basic delay.

[0114] Specifically, the target delay parameter can include a basic delay T1 and an additional delay T2. The basic delay is pre-configured based on the conveyor's equipment performance attributes, the type of material being conveyed, and the environment; for example, it might be set to 3-5 seconds. The additional delay can be determined based on an estimate of the transport volume trend and can be expressed as the time from the current transport volume state to the next transport volume state. If the previous monitoring cycle involved a large transport volume, and the current cycle will transition to a smaller, slower transport volume in another transport volume range, the target delay parameter is determined based on the basic delay T1 plus the additional delay T2, and the frequency conversion speed reduction measure is implemented after this delay. If the previous monitoring cycle involved a small transport volume, and the next cycle will transition to a larger, faster transport volume in another transport volume range, the speed is adjusted after only the basic delay T1 to avoid overloading the conveyor due to delayed acceleration.

[0115] By configuring delay measurement, frequent speed adjustments caused by occasional fluctuations in traffic volume can be effectively avoided, thus achieving precise control of variable frequency speed regulation.

[0116] In this example implementation, determining the additional delay in the target delay parameter includes: acquiring prior traffic volume data corresponding to at least one prior monitoring period; estimating the traffic volume change time corresponding to the traffic volume change trend based on the traffic volume change trend of the prior traffic volume data and the first traffic volume data, and configuring the traffic volume change time as the additional delay.

[0117] In this example implementation, estimating the time required for the change in freight volume corresponding to the freight volume change trend based on the freight volume change trend of the prior freight volume data and the first freight volume data includes:

[0118] Step S81: Collect historical transport volume data and corresponding time information for each historical monitoring period, and construct sample data based on the historical transport volume data and corresponding time information.

[0119] Step S82: Train a delay estimation model based on a neural network using the sample data;

[0120] Step S83: Configure the first traffic volume data and the prior traffic volume data corresponding to the at least one prior monitoring period as input data and input them into the delay estimation model to obtain the traffic volume change time output by the delay estimation model.

[0121] Specifically, a delay estimation model for calculating additional delays can be pre-trained. Based on the conveyor's operational volume change data, the model learns the trend of volume changes to complete the training of the delay estimation model. For the first volume data of the current monitoring cycle, when determining to update the target belt speed, the first volume data, along with historical volume data from one, two, or more previous monitoring cycles, can be input into the model. The delay estimation model outputs a prediction of the current volume trend, estimating the time required to jump from the current volume state to the first volume state, which is then used as the additional delay. Here, the current volume state can be the volume the conveyor is currently transporting, and the first volume state can be the volume to be transported identified in the current monitoring cycle, i.e., the amount of material to be transported by the conveyor obtained from the outlet of the conveyor's tail chute.

[0122] The conveyor speed control method provided in this embodiment can utilize historical monitoring cycle transport volume data collected through a material flow detection component. Based on the actual transport volume from 0:00 to 24:00 each day, a daily transport volume change curve is statistically analyzed. For example, the transport volume on a certain day may vary within the range of 0 to 2000 t / h. The transport volume is then converted from the time domain to the frequency domain, with 50 tons as a statistical unit, converting it into the probability of occurrence for each type of transport volume. This relationship list is then saved on a daily basis. Using the transport volume probability as a data sample, transport volume intervals are divided using significant probability curve inflection points as segmentation points. For classes with a cumulative lowest probability of occurrence of less than 10%, these classes are merged into a large speed range. If the transport volume does not exceed a 20% change range throughout the entire interval, it indicates that the probability of occurrence for various different transport volumes is basically the same. In this case, an equal distribution method is used to set 10 transport volume intervals. For the current monitoring cycle, the real-time transport volume is acquired using detection equipment as the first transport volume data. Based on this first transport volume data, the lower limit of the operating speed corresponding to the real-time transport volume Q is calculated, i.e., the first belt speed. Then, within the speed range of 0–0.5 m / s, the speed with the lowest comprehensive energy consumption is calculated based on the motor speed and efficiency curve, i.e., the second belt speed. Based on the second belt speed, a system-generated random number is added to obtain the final third belt speed, which is used as the final output target belt speed. The final target belt speed, along with corresponding parameters such as conveyor transport volume Q, motor power N, ambient temperature T, and humidity W, can be recorded to construct sample data and train the frequency converter control model. After accumulating a predetermined number of iterations, the real-time transport volume Q, ambient temperature T, and ambient humidity W acquired for the current monitoring cycle can be input into the frequency converter control model, along with the target belt speed predicted by the model. The second transport volume data is determined based on the target belt speed and compared with the first transport volume data. If they are within the same transport volume range, the belt speed is not updated. Alternatively, if they do not belong to the same transport volume range, the target belt speed v is calculated based on the maximum transport volume of the transport volume partition where the second transport volume data is located. 环 And after determining the delay, adjust the actual operating speed of the conveyor to v. 环 This method utilizes a neural network-based variable frequency speed control model to incorporate multiple factors that may affect operational efficiency into the calculation, achieving a comprehensive consideration of multi-dimensional factors. By designing a speed control method that calculates capacity ranges and delays, the capacity range is set through time-domain and frequency-domain changes in historical data. Through the judgment of capacity ranges and the calculation of delay times, stable control of the variable frequency is achieved, avoiding excessive oscillations caused by frequent speed adjustments.

[0123] It should be noted that the above figures are merely illustrative of the processes included in the method according to exemplary embodiments of the present invention, and are not intended to be limiting. It is readily understood that the processes shown in the above figures do not indicate or limit the temporal order of these processes. Furthermore, it is readily understood that these processes may, for example, be executed synchronously or asynchronously in multiple modules.

[0124] Further reference Figure 9 As shown, this example embodiment also provides a conveyor speed control system 90, which includes: a conveying capacity detection module 901, a belt speed calculation module 902, and a belt speed adjustment module 903. Wherein,

[0125] The transport volume detection module 901 is used to acquire the first transport volume data corresponding to the current monitoring cycle of the conveyor.

[0126] The belt speed calculation module 902 is used to determine the first belt speed by combining the bearing area of ​​the conveyor and the first conveying volume data;

[0127] The belt speed control module 903 is used to determine a second belt speed based on the first belt speed according to the efficiency attribute data of the conveyor, and update the target belt speed of the conveyor according to the second belt speed; wherein, the efficiency attribute data is used to represent the relationship between the operating power and the conveying efficiency of the conveyor.

[0128] In some exemplary embodiments, the belt speed control module 903 can also be used to obtain a target random number within a preset data range; combine the target random number with the second belt speed to determine a third belt speed; and update the target belt speed of the conveyor according to the third belt speed.

[0129] In some exemplary embodiments, the belt speed control module 903 can also be used to: after acquiring the first transport volume data corresponding to the current monitoring cycle of the conveyor, collect the current environmental parameters corresponding to the current monitoring cycle; use the first transport volume data and the current environmental parameters as input parameters, use the frequency conversion control model to process the input parameters to obtain the fourth belt speed, and update the target belt speed of the conveyor according to the fourth belt speed.

[0130] In some exemplary embodiments, the system 90 further includes: a frequency conversion control model training module, used to collect historical data corresponding to a preset number of monitoring cycles to construct sample data; wherein, the sample data includes: environmental parameters, power parameters, operating speed parameters, and transport capacity parameters; the operating speed parameters include a second belt speed or a third belt speed; initialize the neural network model and initialize the model hyperparameters; use the sample data to perform forward feedback training on the neural network model to obtain a frequency conversion control model based on the neural network.

[0131] In some exemplary embodiments, the system 90 further includes: a capacity range determination module, configured to determine second capacity data corresponding to the target belt speed after configuring the target belt speed of the conveyor; when the second capacity data and the first capacity data belong to the same capacity range, the target belt speed of the conveyor is not updated; or when the second capacity data and the first capacity data belong to different capacity ranges, the target belt speed of the conveyor is updated according to the maximum capacity of the capacity range corresponding to the second capacity data.

[0132] In some exemplary embodiments, the system 90 further includes: a delay calculation module, used to determine a target delay parameter when the second transport volume data and the first transport volume data belong to different transport volume intervals; wherein the target delay parameter includes: a basic delay and / or an additional delay; and after the duration of the target delay parameter is executed, the target belt speed is executed.

[0133] In some exemplary embodiments, the delay calculation module can also be used to configure the target delay parameter to include a basic delay and an additional delay when the second transport volume data is less than the first transport volume data; or, when the second transport volume data is greater than the first transport volume data, configure the target delay parameter to include a basic delay.

[0134] In some exemplary embodiments, the delay calculation module can also be used to determine the additional delay in the target delay parameter, obtain the prior traffic volume data corresponding to at least one prior monitoring period, estimate the traffic volume change time corresponding to the traffic volume change trend based on the traffic volume change trend of the prior traffic volume data and the first traffic volume data, and configure the traffic volume change time as the additional delay.

[0135] In some exemplary embodiments, the system 90 further includes: a delay estimation model processing module, used to collect historical traffic volume data corresponding to each historical monitoring period, as well as corresponding time information, and construct sample data based on the historical traffic volume data and corresponding time information; train a delay estimation model based on a neural network using the sample data; configure the first traffic volume data and the prior traffic volume data corresponding to the at least one prior monitoring period as input data and input them into the delay estimation model to obtain the traffic volume change time output by the delay estimation model.

[0136] In some exemplary embodiments, the system 90 further includes: a capacity interval division module, used to perform data statistics based on historical capacity data of each historical monitoring period, and determine the capacity probability distribution based on the data statistics results; and divide the capacity interval based on the capacity probability distribution.

[0137] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to embodiments of this disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0138] Figure 10 A schematic diagram of an electronic device suitable for implementing embodiments of the present invention is shown.

[0139] It should be noted that, Figure 10 The illustrated electronic device 1000 is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments disclosed herein.

[0140] like Figure 10 As shown, the electronic device 1000 includes a Central Processing Unit (CPU) 1001, which can perform various appropriate actions and processes based on programs stored in Read-Only Memory (ROM) 1002 or programs loaded from Storage Unit 1008 into Random Access Memory (RAM) 1003. The RAM 1003 also stores various programs and data required for system operation. The CPU 1001, ROM 1002, and RAM 1003 are interconnected via a bus 1004. An Input / Output (I / O) interface 1005 is also connected to the bus 1004.

[0141] The following components are connected to I / O interface 1005: an input section 1006 including a keyboard, mouse, etc.; an output section 1007 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 1008 including a hard disk, etc.; and a communication section 1009 including a network interface card such as a LAN (Local Area Network) card, modem, etc. The communication section 1009 performs communication processing via a network such as the Internet. A drive 1010 is also connected to I / O interface 1005 as needed. Removable media 1011, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 1010 as needed so that computer programs read from them can be installed into storage section 1008 as needed.

[0142] In particular, according to embodiments of the present invention, the processes described below with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a storage medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 1009, and / or installed from removable medium 1011. When the computer program is executed by central processing unit (CPU) 1001, it performs various functions defined in the system of this application.

[0143] Specifically, the aforementioned electronic devices can be smart mobile electronic devices such as mobile phones, tablets, or laptops. Alternatively, the aforementioned electronic devices can also be smart electronic devices such as desktop computers.

[0144] It should be noted that the storage medium shown in the embodiments of the present invention can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In the present invention, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, wherein computer-readable program code is carried. Such transmitted data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The computer-readable signal medium can also be any storage medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the storage medium can be transmitted using any suitable medium, including but not limited to wireless, wired, etc., or any suitable combination thereof.

[0145] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0146] The units described in the embodiments of the present invention can be implemented in software or hardware, and the described units can also be located in a processor. The names of these units do not necessarily limit the specific unit itself.

[0147] It should be noted that, as another aspect, this application also provides a storage medium, which may be included in an electronic device or may exist independently without being assembled into the electronic device. The aforementioned storage medium carries one or more programs, which, when executed by an electronic device, cause the electronic device to perform the methods described in the following embodiments. For example, the electronic device may perform... Figure 1 The steps of the speed control method for the conveyor shown are as follows.

[0148] In one embodiment, this application provides a computer program product including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0149] Furthermore, the above figures are merely illustrative of the processes included in the method according to exemplary embodiments of the present invention, and are not intended to be limiting. It is readily understood that the processes shown in the above figures do not indicate or limit the temporal order of these processes. Additionally, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0150] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and embodiments are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the claims.

[0151] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. A speed control method for a conveyor, characterized in that, The method includes: Obtain the first transport volume data corresponding to the current monitoring cycle of the conveyor; Based on the conveyor's carrying area and the first conveying volume data, the first belt speed is determined; Based on the efficiency attribute data of the conveyor, a second belt speed is determined based on the first belt speed, and the target belt speed of the conveyor is updated based on the second belt speed; wherein, the efficiency attribute data is used to represent the relationship between the operating power and conveying efficiency of the conveyor; Alternatively, obtain a target random number within a preset data range; The third belt speed is determined by combining the target random number with the second belt speed, and the target belt speed of the conveyor is updated according to the third belt speed; After configuring the target belt speed of the conveyor, the method further includes: Determine the second transport volume data corresponding to the target belt speed; wherein, the second transport volume data is calculated based on the current target belt speed, combined with the size of the conveyor belt and the carrying area; If the second transport volume data and the first transport volume data belong to the same transport volume range, then the target belt speed of the conveyor will not be updated; or When the second transport volume data and the first transport volume data belong to different transport volume ranges, the target belt speed of the conveyor is updated according to the maximum transport volume value of the transport volume range corresponding to the second transport volume data.

2. The method according to claim 1, characterized in that, When the second traffic volume data and the first traffic volume data belong to different traffic volume ranges, the method further includes: Determine the target delay parameters; wherein, the target delay parameters include: basic delay and / or additional delay; After the duration of the target delay parameter is executed, the target bandwidth is then executed.

3. The method according to claim 2, characterized in that, The method further includes: When the second traffic volume is less than the first traffic volume, the target delay parameter is configured to include a basic delay and an additional delay; or... When the second transport volume data is greater than the first transport volume data, the target delay parameter is configured to include a basic delay.

4. The method according to claim 2, characterized in that, Determining the additional delay in the target delay parameter includes: Obtain the prior traffic volume data corresponding to at least one prior monitoring period; Based on the prior traffic volume data and the traffic volume change trend of the first traffic volume data, the traffic volume change time corresponding to the traffic volume change trend is estimated, and the traffic volume change time is configured as the additional delay.

5. The method according to claim 4, characterized in that, The step of estimating the time consumed by the change in transportation volume corresponding to the transportation volume change trend based on the prior transportation volume data and the first transportation volume data includes: Collect historical transport volume data and corresponding time information for each historical monitoring period, and construct sample data based on the historical transport volume data and corresponding time information; The sample data was used to train a delay estimation model based on a neural network; Configure the first traffic volume data and the prior traffic volume data corresponding to the at least one prior monitoring period as input data and input them into the delay estimation model to obtain the traffic volume change time output by the delay estimation model.

6. The method according to claim 1, characterized in that, The method further includes: Data statistics are performed based on historical transport volume data for each historical monitoring period, and the probability distribution of transport volume is determined based on the statistical results. The traffic volume intervals are divided based on the aforementioned traffic volume probability distribution.

7. A speed control system for a conveyor, characterized in that, The system includes: The transport volume detection module is used to obtain the first transport volume data corresponding to the current monitoring cycle of the conveyor; The belt speed calculation module is used to determine the first belt speed by combining the bearing area of ​​the conveyor and the first conveying volume data; The belt speed control module is used to determine a second belt speed based on the first belt speed according to the efficiency attribute data of the conveyor, and to update the target belt speed of the conveyor according to the second belt speed; wherein, the efficiency attribute data is used to represent the relationship between the operating power and the conveying efficiency of the conveyor; Alternatively, obtain a target random number within a preset data range; The third belt speed is determined by combining the target random number with the second belt speed, and the target belt speed of the conveyor is updated according to the third belt speed; After configuring the target belt speed of the conveyor, the belt speed control module is also used for: Determine the second transport volume data corresponding to the target belt speed; wherein, the second transport volume data is calculated based on the current target belt speed, combined with the size of the conveyor belt and the carrying area; If the second transport volume data and the first transport volume data belong to the same transport volume range, then the target belt speed of the conveyor will not be updated; or When the second transport volume data and the first transport volume data belong to different transport volume ranges, the target belt speed of the conveyor is updated according to the maximum transport volume value of the transport volume range corresponding to the second transport volume data.

8. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the speed control method for the conveyor as described in any one of claims 1 to 6.

9. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the speed control method for the conveyor according to any one of claims 1 to 6.

10. An electronic device, characterized in that, include: processor; as well as Memory for storing the executable instructions of the processor; The processor is configured to execute the speed control method of the conveyor according to any one of claims 1 to 6 by executing the executable instructions.

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