Intelligent speed regulation method for belt conveyor

Abstract

The invention relates to the technical field of belt conveyor control, and discloses an intelligent speed regulation method for a belt conveyor, which comprises the following steps: in a coal flow measurement area of the belt conveyor, continuously collecting coal flow data and recording the real-time length from each coal flow data point to a machine head; based on the collected data, determining the maximum value of the coal flow in the speed regulation area and a first target speed corresponding to the maximum value of the coal flow, and determining the maximum value of the coal flow in the deceleration buffer area and a second target speed corresponding to the maximum value of the coal flow; and the first target speed and the second target speed are compared with the current given speed of the belt conveyor, and a speed adjusting instruction is generated according to the comparison result to execute self-adaptive speed adjustment. According to the invention, the coal flow is detected at a section of length away from the head of the belt conveyor, so that the problem of hysteresis in speed regulation of the belt conveyor can be solved.

Description

Technical Field

[0001] This invention relates to the field of belt conveyor control technology, and specifically to an intelligent speed regulation method for belt conveyors. Background Technology

[0002] Underground belt conveyors are critical transportation equipment in coal mine production systems, and their operation directly impacts mine production efficiency and safety. Currently, most belt conveyors employ open-loop control, operating at a constant speed. Due to their large size, speed adjustments are often delayed, failing to automatically adjust speed based on real-time material flow, resulting in low operating efficiency. Especially under light or no-load conditions, continuous constant speed operation not only wastes significant amounts of electricity, increasing electricity costs, but also accelerates wear on mechanical components, shortens equipment lifespan, and may even create safety hazards, threatening safe coal mine production. This operating mode neither meets the requirements of modern intelligent mining nor aligns with industry trends towards energy conservation and emission reduction. Summary of the Invention

[0003] To address the aforementioned shortcomings in the prior art, this invention provides an intelligent speed control method for belt conveyors.

[0004] To achieve the above-mentioned objectives, the technical solution adopted by this invention is as follows: A method for intelligent speed control of a belt conveyor includes the following steps: Within the coal flow measurement area of ​​the belt conveyor, coal flow data is continuously collected and the real-time distance from the machine head to each coal flow data point is recorded. Based on the collected data, the maximum coal flow rate and its corresponding first target speed in the speed regulation zone are determined, as well as the maximum coal flow rate and its corresponding second target speed in the deceleration buffer zone are determined. The first target speed and the second target speed are compared with the current given speed of the belt conveyor, and a speed adjustment command is generated based on the comparison result to perform adaptive speed regulation.

[0005] Furthermore, generating speed adjustment commands based on the comparison results to perform adaptive speed regulation includes: If the first target speed is greater than the current given speed, then an acceleration action is performed, and the given speed is set to the first target speed; If the first target speed is less than the current given speed, and at the same time the first target speed is greater than or equal to the second target speed, then a deceleration action is performed, and the given speed is set to the first target speed.

[0006] Furthermore, the length of the coal flow measurement zone is dynamically determined based on the length of the speed regulation zone and the length of the deceleration buffer zone, specifically as follows: ; in, The length of the coal flow measurement zone. The length of the speed regulation zone, The length of the deceleration buffer zone, This is the adjustment coefficient.

[0007] Furthermore, before implementing intelligent speed regulation, a dynamic measurement step of the speed regulation range length is included, including: The belt conveyor is accelerated from its lowest speed to its highest speed, and the running length of the belt conveyor during this process is measured. This length is then set as the initial speed regulation zone length.

[0008] Furthermore, the specific steps for dynamically determining the length of the speed regulation zone include: The given speed of the belt conveyor Set to the lowest speed level; Monitoring the real-time speed of the belt conveyor When detected Then proceed to the next step; The given speed Set to the highest speed level; With a fixed collection cycle The process is repeated, and the calculations are accumulated within each data acquisition cycle. ,in The initial value is 0; The loop continues until real-time speed is detected. Once the highest speed level is reached, the accumulated values ​​are the measured length of the speed regulation zone.

[0009] Furthermore, before implementing intelligent speed regulation, a dynamic measurement step of the deceleration buffer length is included, comprising: The belt conveyor is decelerated from its highest speed to its lowest speed, and the length of the belt conveyor during this process is measured. This length is used as the initial deceleration buffer length.

[0010] Furthermore, the specific steps for dynamically determining the length of the deceleration buffer zone include: Record the given speed of the belt conveyor just before it begins to decelerate. and real-time speed ; The given speed of the belt conveyor Set to the highest speed level; Monitoring the real-time speed of the belt conveyor When detected Then proceed to the next step; The given speed Set to the lowest speed level; With a fixed collection cycle The process is repeated, and the calculations are accumulated within each data acquisition cycle. ,in The initial value is 0; The loop continues until real-time speed is detected. Once the minimum speed level is reached, the accumulated values ​​are the measured deceleration buffer length.

[0011] Furthermore, the length of the deceleration buffer is predicted in real time by the neural network model based on the current operating parameters, specifically including: The current operating condition parameters are input into a pre-trained neural network model. The operating condition parameters include at least one of the following: the given speed before the deceleration action begins, the real-time speed, the initial speed when the speed changes, and the target speed. The neural network model outputs a predicted deceleration buffer length value, which is used to update the current deceleration buffer length.

[0012] Furthermore, the input variables of the neural network model also include the square of the initial velocity and the square of the target velocity.

[0013] Furthermore, based on the collected data, when determining the maximum coal flow rate and its corresponding first target speed within the speed regulation zone, and the maximum coal flow rate and its corresponding second target speed within the deceleration buffer zone, a multi-level speed regulation strategy is adopted, specifically including: When the maximum coal flow rate satisfies At that time, the corresponding lower operating speed is determined as the target speed; where This represents the maximum coal flow rate. To reduce the coal flow threshold, The threshold for low coal flow rate; When the maximum coal flow rate satisfies At that time, the corresponding low operating speed is determined as the target speed; among which, The threshold for medium coal flow rate; When the maximum coal flow rate satisfies At that time, the corresponding running speed is determined as the target speed; where, For multiple coal flow thresholds; When the maximum coal flow rate satisfies At that time, the corresponding high operating speed is determined as the target speed.

[0014] The present invention has the following beneficial effects: (1) The present invention detects the coal flow rate at a distance from the head of the belt conveyor, which can know the future changes in the coal flow rate at the head in advance and adjust the speed in advance, thus overcoming the lag problem that has always existed in the speed adjustment of the belt conveyor.

[0015] (2) To address the drawbacks of frequent speed adjustments in belt conveyors, such as accelerated equipment wear, increased failure rate, and higher energy consumption, this invention adopts a 4-level speed regulation system. Furthermore, this invention can prevent the extremely dangerous situation of the belt conveyor starting to decelerate before the acceleration action is completed or starting to accelerate before the deceleration action is completed. Ultimately, it achieves "fast rotation when there is more coal and slow rotation when there is less coal". The real-time adaptive adjustment of the main transport system speed can reduce equipment wear, extend equipment life, reduce maintenance costs, improve transportation safety, and increase production efficiency. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of a smart speed control method for a belt conveyor according to the present invention; Figure 2 This is a schematic diagram of the intelligent speed regulation of the belt conveyor according to the present invention. Detailed Implementation

[0017] The specific embodiments of the present invention are described below to enable those skilled in the art to understand the present invention. However, it should be understood that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, various changes are obvious as long as they are within the spirit and scope of the present invention as defined and determined by the appended claims. All inventions utilizing the concept of the present invention are protected.

[0018] like Figure 1 As shown in the figure, an intelligent speed regulation method for a belt conveyor provided by an embodiment of the present invention includes the following steps S1 to S3: S1. Within the coal flow measurement area of ​​the belt conveyor, continuously collect coal flow data and record the real-time distance of each coal flow data point from the machine head; In an optional embodiment of the present invention, step S1 involves first setting the given speed of the belt conveyor to its rated speed when the belt conveyor is started, and then collecting and cyclically recording the coal flow rate of the belt conveyor and the distance from this coal flow rate to the conveyor head. The specific process is as follows: (1) First, collect and record coal flow rate in a loop. Length corresponding to the distance from the machine head The specific formula is as follows: ; ; in, This represents the coal flow rate of the belt conveyor during the i-th data collection cycle. This represents the coal flow rate in the i-th collection cycle. The distance from the machine head; (2) Until the following formula is detected to be true: ; ; In an optional embodiment of the present invention, the length of the coal flow measurement zone is dynamically determined based on the length of the speed regulation zone and the length of the deceleration buffer zone, specifically: ; in, The length of the coal flow measurement zone. The length of the speed regulation zone, The length of the deceleration buffer zone, This is the adjustment coefficient.

[0019] In this embodiment, when the belt conveyor is started for the first time, the belt conveyor speed control system needs to measure... The formula is as follows: ; in: This represents a coefficient greater than 1. Because when the acceleration and deceleration strategy of the belt conveyor is modified... and The value of will change, so a coefficient greater than 1 needs to be introduced. Leave a certain margin, so that when and When the flow rate increases, there is no need to adjust the position of the coal flow detector.

[0020] In an optional embodiment of the present invention, a dynamic measurement step of the speed regulation zone length is included before performing intelligent speed regulation, comprising: The belt conveyor is accelerated from its lowest speed to its highest speed, and the running length of the belt conveyor during this process is measured. This length is then set as the initial speed regulation zone length.

[0021] The specific steps for dynamically determining the length of the speed regulation zone include: The given speed of the belt conveyor Set to the lowest speed level; Monitoring the real-time speed of the belt conveyor When detected Then proceed to the next step; The given speed Set to the highest speed level; With a fixed collection cycle The process is repeated, and the calculations are accumulated within each data acquisition cycle. ,in The initial value is 0; The loop continues until real-time speed is detected. Once the highest speed level is reached, the accumulated values ​​are the measured length of the speed regulation zone.

[0022] In this embodiment, when the coal flow rate at the machine head increases, the belt conveyor needs to accelerate. However, when the increased coal flow rate at the machine head is detected, due to the inertia of the belt conveyor, further acceleration is too late. To overcome the drawback of the belt conveyor's speed regulation lag, a certain distance from the belt conveyor head is used for acceleration. (like Figure 2 From the middle of the machine head When the detected coal flow rate increases by one level, the belt conveyor speed will start to accelerate, ensuring that when the detected coal flow rate increases by one level, the speed of the belt conveyor has increased to the corresponding speed level when it reaches the head of the machine.

[0023] When the acceleration / deceleration strategy is modified, the belt conveyor speed control system needs to be relearned and remeasured. The method is as follows: (1) First, set the given speed of the belt conveyor to the lowest speed level and execute the following formula: ; in, This indicates the given speed of the belt conveyor; (2) After the following formula is detected to be true, proceed to the next step: ; in, This indicates the real-time speed detected by the belt conveyor during the current data collection cycle. (3) Then set the given speed of the belt conveyor to the highest speed level and execute the following formula: ; (4) Repeat the following formula until the formula in step ⑤ is true: ; in, This indicates the data collection cycle; note the sequence within this loop. The initial value is 0; (5) The loop ends after the following formula is detected to be true. Test complete: ; In an optional embodiment of the present invention, a dynamic measurement step of the deceleration buffer length is included before intelligent speed regulation, comprising: The belt conveyor is decelerated from its highest speed to its lowest speed, and the length of the belt conveyor during this process is measured. This length is used as the initial deceleration buffer length.

[0024] The specific steps for dynamically determining the length of the deceleration buffer zone include: Record the given speed of the belt conveyor just before it begins to decelerate. and real-time speed ; The given speed of the belt conveyor Set to the highest speed level; Monitoring the real-time speed of the belt conveyor When detected Then proceed to the next step; The given speed Set to the lowest speed level; With a fixed collection cycle The process is repeated, and the calculations are accumulated within each data acquisition cycle. ,in The initial value is 0; The loop continues until real-time speed is detected. Once the minimum speed level is reached, the accumulated values ​​are the measured deceleration buffer length.

[0025] In this embodiment, when the coal flow rate at the machine head decreases, the belt conveyor needs to decelerate. However, if it decelerates prematurely, it is very likely to cause coal pile-up. Therefore, the belt conveyor does not need to decelerate in advance. However, in order to prevent the belt conveyor from starting to accelerate before the deceleration action is completed, which is extremely dangerous, a belt conveyor deceleration buffer zone is required. ( Figure 2 Middle position Arrive at the location (length).

[0026] When the acceleration / deceleration strategy is modified, the belt conveyor speed control system needs to be relearned and remeasured. The method is as follows: (1) Record the given value of the previous acquisition cycle and the real-time speed of the belt conveyor when it performs deceleration; execute the following formula: ; ; in, This represents the given value from the previous data acquisition cycle when the belt conveyor performs a deceleration action; This indicates the real-time speed of the belt conveyor in the previous data collection cycle when the belt conveyor performs a deceleration action; (2) Set the given speed of the belt conveyor to the highest speed level and execute the following formula: ; (3) After the following formula is detected to be true, proceed to the next step: ; (4) Then set the given speed of the belt conveyor to the lowest speed level and execute the following formula: ; (5) Repeat the following formulas until the next formula is true: ; in: This indicates the length of the belt conveyor's travel when it performs a deceleration action from its highest speed to its lowest speed. Note the time elapsed in this cycle. The initial value is 0; (6) The loop ends after the following formula is found to be true: ; At a certain distance from the head of the belt conveyor The coal flow rate is measured starting from the location, such as... Figure 2 The middle machine head is in position The length of the belt conveyor is used as the basis for speed adjustment.

[0027] In an optional embodiment of the present invention, the length of the deceleration buffer is predicted in real time by a neural network model based on current operating parameters, specifically including: The current operating condition parameters are input into a pre-trained neural network model. The operating condition parameters include at least one of the following: the given speed before the deceleration action begins, the real-time speed, the initial speed when the speed changes, and the target speed. The neural network model outputs a predicted deceleration buffer length value, which is used to update the current deceleration buffer length.

[0028] The input variables of the neural network model also include the square of the initial velocity and the square of the target velocity.

[0029] This embodiment executes the following formula as training. A sample from a neural network: ; ; ; ; ; in, This indicates that the belt conveyor is performing a deceleration action. The initial speed of the belt conveyor when the value changes; This indicates that the belt conveyor is performing a deceleration action. The ending speed of the belt conveyor when the value changes; Record , , , , , , As training A sample of a neural network model; In this embodiment, the given value from the previous acquisition cycle when the belt conveyor performs a deceleration action is selected. The speed of the belt conveyor in the previous data acquisition cycle when the belt conveyor performs deceleration is selected as the first feature value. As a second characteristic value; when the belt conveyor performs a deceleration operation. When the value changes, select the initial speed of the belt conveyor. The third characteristic value is the end speed of the belt conveyor. The initial speed of the belt conveyor is the fourth characteristic value. square The fifth characteristic value is the end speed of the belt conveyor. square The sixth feature value serves as the input variable to the BP neural network; the output variable is the deceleration operation performed by the belt conveyor. When the value changes, the length of the belt conveyor traveled until the deceleration action is completed. ; The training method for neural network models is to record... , , , , , , As a sample for training a neural network model; when the belt conveyor acceleration and deceleration strategy is modified, it is necessary to first... As The value is run for a period of time, during which sample data is continuously collected. When 100 data points are collected, these data are used as the training set. The elements in the training set are normalized, and the preprocessed training set is trained using the BP neural network algorithm to establish a BP neural network model for outputting the length of the deceleration buffer of the belt conveyor. The steps include: (1) After the belt conveyor acceleration and deceleration strategy is modified, As The value is run for a period of time, during which sample data is continuously collected. 100 data points are collected as the training set, and the elements in the training set are normalized using the following formula: ; Where x is any element in the training set; (2) Determine the structure of the neural network; The number of hidden layer nodes is calculated using a formula: Where y is the number of hidden layer nodes, m is the number of input layer nodes (7 nodes), n is the number of output layer nodes, and a is a constant, typically 1 to 10. The final data of the hidden layer is determined through specific experiments during implementation. The number of output layer nodes is 6, and 1 output layer node is used to indicate the length O of the belt conveyor deceleration buffer zone. (3) Calculate the input and output values ​​of each neuron in the hidden layer; Calculate the weighted sum of the output values ​​of the elements in the previous layer for the j-th cell in the current layer, and use it as the input value for that cell; ; ; in This represents the weight connecting the i-th neuron in the input layer and the j-th neuron in the hidden layer. This represents the input of the i-th node in the input layer. This represents the bias of the j-th neuron in the hidden layer. This represents the activation function of the hidden layer. This represents the input value of the j-th node in the hidden layer. This is represented as the output value of the j-th node in the hidden layer; Calculate the input and output values ​​of each neuron in the output layer; ; ; In the formula, This represents the weight connecting the j-th neuron in the hidden layer. represents the output layer bias; z represents the input value of the output layer; and O represents the output value of the output layer.

[0030] (4) Calculate the error between the neural network prediction and the actual value using mean squared error: The output layer data needs to be within the error range. Therefore, the network model determines whether to end the calculation and output the final data after judging the error. If the error meets the requirements, the model calculation ends by outputting the data; if the error does not meet the requirements, the weight correction amount of each layer needs to be calculated in reverse and the weights are corrected. The mean squared error is used to calculate the error between the neural network prediction result and the actual value (loss function): ; In the formula, Indicates the error value. This represents the actual value of the k-th sample in the output layer. This represents the predicted value corresponding to the k-th sample in the output layer. In this invention, the number of nodes in the output layer is 1. (5) Determine whether the error meets the accuracy requirements: If the error does not meet the requirements, calculate the gradient of each weight and bias with respect to the loss function and backpropagate it back into the network; based on the gradient calculated by backpropagation, update the weights and biases of the neural network using gradient descent.

[0031] Calculate the gradient of each weight and bias with respect to the loss function. The formula for calculating the gradient is: ; in, This represents the gradient of the weights from the j-th node in the hidden layer to the output layer with respect to the loss function. This represents the actual value of the k-th sample in the output layer. This represents the predicted value corresponding to the k-th sample in the output layer. This represents the output value of the j-th node in the hidden layer; ; in, This represents the gradient of the output layer bias with respect to the loss function. This represents the actual value of the k-th sample in the output layer. This represents the predicted value corresponding to the k-th sample in the output layer; ; in, This represents the gradient of the weights from the i-th node in the input layer to the j-th node in the hidden layer with respect to the loss function. This represents the actual value of the k-th sample in the output layer. This represents the predicted value corresponding to the k-th sample in the output layer. This represents the derivative of the activation function. This represents the input of the i-th node in the input layer; ; in, This represents the gradient of the bias of the j-th neuron in the hidden layer with respect to the loss function. This represents the actual value of the k-th sample in the output layer. This represents the predicted value corresponding to the k-th sample in the output layer. This represents the derivative of the activation function; When updating the weights and biases of a neural network using gradient descent, the formula for calculating the weight adjustment is as follows: ; in, This represents the weight adjustment amount for the j-th node in the hidden layer. Indicates the learning rate; ; in, This represents the weight adjustment amount for the i-th neuron in the input layer and the j-th neuron in the hidden layer. Indicates the learning rate; When updating the weights and biases of a neural network using gradient descent, the formula for calculating the bias correction is as follows: ; in, This represents the output layer bias correction amount. Indicates the learning rate; ; in, The bias correction amount of the j-th node in the hidden layer. Indicates the learning rate; Return to step (3) and repeat the above steps until the error meets the set precision.

[0032] S2. Based on the collected data, determine the maximum coal flow rate and its corresponding first target speed in the speed regulation zone, and determine the maximum coal flow rate and its corresponding second target speed in the deceleration buffer zone. In an optional embodiment of the present invention, when step S2 determines the maximum coal flow rate and its corresponding first target speed in the speed regulation zone and the maximum coal flow rate and its corresponding second target speed in the deceleration buffer zone based on the collected data, a multi-level speed regulation strategy is adopted, specifically including: When the maximum coal flow rate satisfies At that time, the corresponding lower operating speed is determined as the target speed; where This represents the maximum coal flow rate. To reduce the coal flow threshold, The threshold for low coal flow rate; When the maximum coal flow rate satisfies At that time, the corresponding low operating speed is determined as the target speed; among which, The threshold for medium coal flow rate; When the maximum coal flow rate satisfies At that time, the corresponding running speed is determined as the target speed; where, For multiple coal flow thresholds; When the maximum coal flow rate satisfies At that time, the corresponding high operating speed is determined as the target speed.

[0033] This embodiment finds the maximum value of coal flow in the speed regulation zone and the corresponding speed, as well as the maximum value of coal flow in the buffer zone and the corresponding speed, based on the recorded coal flow rate and the distance from this coal flow rate to the machine head. The specific process is as follows: (1) In Figure 2 Location Collect and record coal flow rate at the location Length corresponding to the distance from the machine head : ; ; Where n represents the distance from the nose to the... Location, and the number of collection cycles included; (2) Execute the following determination formula: ; ; ; ; ; ; Where m represents the number of data collection cycles that the belt conveyor at the machine head position has completed in this data collection cycle; h represents the number of data collection cycles that the belt conveyor at the machine head position has completed. Location, the number of acquisition cycles included; k represents the distance from the camera head to... Location, the number of acquisition cycles included; h represents the current acquisition cycle. The number of data collection cycles completed by the location belt conveyor. (3) If the above formula is true, then execute the following formula: ; ; ; ; ; (4) Locate the machine head Location, maximum coal flow rate and arrive The location and maximum coal flow rate are determined by the following formula: ; ; in, Indicates the nose to Location, maximum coal flow rate; express arrive Location, maximum coal flow rate; (5) In harsh underground environments, to avoid damage to the motor caused by frequent adjustments to the belt conveyor speed, real-time speed regulation based on instantaneous coal flow rate is not advisable. Therefore, a four-level speed regulation system based on coal flow rate is designed. This system can smoothly transition the operating speed within a certain period of time, thereby reducing the impact of frequent speed adjustments on the motor and mechanical equipment. According to different coal flow rates, the system corresponds to four belt operating speeds: high speed, medium speed, medium-low speed, and low speed, as shown in Table 1.

[0034] Table 1. Four-level speed regulation system

[0035] Based on the 4-level speed control system table, find and corresponding speed and .

[0036] S3. Compare the first target speed and the second target speed with the current given speed of the belt conveyor, and generate a speed adjustment command based on the comparison result to perform adaptive speed regulation.

[0037] In an optional embodiment of the present invention, step S3, generating a speed adjustment command based on the comparison result to perform adaptive speed regulation, includes: If the first target speed is greater than the current given speed, then an acceleration action is performed, and the given speed is set to the first target speed; If the first target speed is less than the current given speed, and at the same time the first target speed is greater than or equal to the second target speed, then a deceleration action is performed, and the given speed is set to the first target speed.

[0038] This embodiment compares the speed corresponding to the maximum coal flow rate in the speed regulation zone, the speed corresponding to the maximum coal flow rate in the buffer zone, and the given speed of the belt conveyor to perform speed regulation. The specific process is as follows: (1) If the speed corresponding to the maximum coal flow rate in the speed regulation zone is greater than the given speed of the belt conveyor, that is The belt conveyor will then perform a speed-up action, executing the following formula: ; (2) If and The belt conveyor will then perform a speed reduction action, executing the following formula: ; This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0039] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0040] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0041] Specific embodiments have been used to illustrate the principles and implementation methods of this invention. The descriptions of the embodiments above are only for the purpose of helping to understand the method and core ideas of this invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this invention. Therefore, the content of this specification should not be construed as a limitation of this invention.

[0042] Those skilled in the art will recognize that the embodiments described herein are intended to help the reader understand the principles of the invention, and should be understood that the scope of protection of the invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific modifications and combinations based on the technical teachings disclosed in this invention without departing from the spirit of the invention, and these modifications and combinations are still within the scope of protection of this invention.

Claims

1. A method of intelligent speed control of a belt conveyor, characterized by, The method comprises the following steps: continuously collecting coal flow data in the coal flow measurement area of the belt conveyor and recording the real-time length of each coal flow data point from the head; based on the collected data, determining the maximum coal flow in the speed regulation area and the corresponding first target speed, and determining the maximum coal flow in the deceleration buffer area and the corresponding second target speed; comparing the first target speed and the second target speed with the current given speed of the belt conveyor, and generating a speed adjustment instruction according to the comparison result to perform adaptive speed regulation.

2. The intelligent speed regulation method of a belt conveyor according to claim 1, characterized in that, According to the comparison result, the speed adjustment instruction is generated to perform adaptive speed regulation, which includes: if the first target speed is greater than the current given speed, perform the speed-up action and set the given speed to the first target speed; if the first target speed is less than the current given speed, and at the same time the first target speed is greater than or equal to the second target speed, perform the speed-down action and set the given speed to the first target speed.

3. The method of claim 1, wherein, The length of the coal flow measurement area is dynamically determined according to the length of the speed regulation area and the length of the deceleration buffer area, specifically: ； wherein, is the length of the coal flow measurement zone, is the length of the speed regulation zone, is the length of the deceleration buffer zone, is the adjustment coefficient.

4. The method of claim 1, wherein, Before performing intelligent speed regulation, a dynamic determination step of the length of the speed regulation area is included, which includes: accelerate the belt conveyor from the lowest speed to the highest speed, and determine the running length of the belt conveyor during this process, and set this length as the initial length of the speed regulation area.

5. The intelligent speed regulation method of a belt conveyor according to claim 1 or 4, characterized in that, The dynamic determination step of the length of the speed regulation area specifically includes: Setting a given speed of a belt conveyor to the lowest speed level; Monitoring real-time speed of a belt conveyor When detecting the next step is performed; setting the given speed to the highest speed level; with a fixed acquisition period is performed, accumulating the calculation in each acquisition period wherein the initial value of is 0; Continuously cycle until real-time speed is monitored Upon reaching the highest speed level, the accumulated length of the measured speed zone is obtained.

6. The method of claim 1, wherein, Before performing intelligent speed regulation, a dynamic determination step of the length of the deceleration buffer area is included, which includes: decelerate the belt conveyor from the highest speed to the lowest speed, and determine the running length of the belt conveyor during this process, and set this length as the initial length of the deceleration buffer area.

7. The intelligent speed regulation method of a belt conveyor according to claim 1 or 6, characterized in that, The dynamic determination step of the length of the deceleration buffer area specifically includes: the given speed at the moment before the belt conveyor starts to perform the deceleration action and real-time speed ; Setting a given speed of a belt conveyor set to the highest speed level; Monitoring real-time speed of a belt conveyor When detecting the next step is performed; setting the given speed to the lowest speed level; with a fixed acquisition period is performed cyclically, with an accumulated calculation in each acquisition period wherein the initial value of is 0; Continuously loop until real-time speed is monitored At the lowest speed level, the accumulated result is the determined deceleration cushion length.

8. The method of claim 1, wherein, The length of the deceleration buffer area is predicted in real time by a neural network model according to the current operating parameters, specifically including: input the current operating condition parameters into a pre-trained neural network model, the operating condition parameters including at least one of the following: given speed before the start of the speed-down action, real-time speed, initial speed and target speed when the speed changes; the neural network model outputs the predicted deceleration buffer area length value, which is used to update the current deceleration buffer area length.

9. The method of claim 8, wherein, The input variables of the neural network model also include the square value of the initial speed and the square value of the target speed.

10. The method of claim 1, wherein, When determining the maximum coal flow in the speed regulation area and the corresponding first target speed, and determining the maximum coal flow in the deceleration buffer area and the corresponding second target speed based on the collected data, a multi-stage speed regulation strategy is adopted, specifically including: when the coal flow maximum satisfies a corresponding lower operating speed is determined to be the target speed; wherein is the coal flow maximum, is the less coal flow threshold, is the less coal flow threshold; When the coal flow maximum value meets , the corresponding low running speed is determined as the target speed; wherein, is a medium coal flow threshold value; When the coal flow maximum value satisfies , the corresponding medium running speed is determined as the target speed; wherein, is a multiple coal flow threshold value. When the coal flow maximum value satisfies , the corresponding high operating speed is determined as the target speed.

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