A variable speed energy-saving control system of belt conveyor based on material layer thickness detection

Abstract

The application discloses a variable-speed energy-saving control system of a belt conveyor based on material layer thickness detection and belongs to the technical field of bulk material conveying. The application comprises a multi-section material layer thickness detection array, a main controller, a variable-frequency driving unit, an upstream and downstream cooperative control unit and an energy consumption monitoring unit. The multi-section material layer thickness detection array is composed of multiple groups of laser radar sensors arranged along the length direction of the conveying belt in sequence, and real-time acquisition of material cross-section point cloud data at different positions is realized. The main controller calculates the material layer thickness distribution, the material layer uniformity coefficient and the instantaneous conveying capacity based on the point cloud data, generates and corrects the optimal belt speed instruction in combination with an energy consumption-efficiency double-target optimization model. The upstream and downstream cooperative control unit realizes predictive linkage control with an upstream feeder and a downstream conveyor. The application solves the problems of low precision, failure to consider material layer uniformity and control response lag, further reduces energy consumption, and effectively avoids material accumulation, material overflow and conveying belt deviation and other faults.

Description

Technical Field

[0001] This invention relates to the field of bulk material conveying technology, specifically to a belt conveyor speed-changing and energy-saving control system based on material layer thickness detection. Background Technology

[0002] Belt conveyors are the most widely used bulk material transport equipment in industries such as mining, ports, power, chemicals, building materials, and grain, offering advantages such as long transport distances, large transport capacity, reliable operation, and convenient maintenance. However, traditional belt conveyors generally adopt a constant speed operation mode, where the drive motor always runs at its rated speed regardless of the actual material transport volume. This operating mode can lead to serious energy waste when the material transport volume fluctuates significantly.

[0003] To address the energy consumption issue of constant-speed operation, various variable-speed energy-saving control systems based on material layer thickness detection have emerged in recent years. Existing technologies typically employ ultrasonic sensors or a single lidar sensor to detect the material layer thickness at a single point on the conveyor belt feed end. The conveying capacity is estimated based on the detected thickness, and then the conveyor belt speed is adjusted via a frequency converter. However, existing technologies suffer from the following insurmountable drawbacks: 1. Low accuracy and large error of single-point detection: Single-point detection at the feeding end cannot reflect the distribution of materials on the entire conveyor belt. The single-point detection result deviates greatly from the actual average conveying capacity, resulting in inaccurate speed regulation and a significant reduction in energy-saving effect. 2. Failure to consider the impact of material layer uniformity: The existing system only focuses on the average material layer thickness and completely ignores the uniformity of the material layer distribution. When the material layer distribution is extremely uneven, adjusting the speed according to the average material layer thickness will lead to material accumulation and overflow accidents in areas with thicker material layers, or conveyor belt deviation and slippage in areas with thinner material layers, which seriously affects the safety of system operation. 3. Severe control response lag: Traditional systems adjust speed in real time based on the currently detected material layer thickness without considering the material's transmission time on the conveyor belt. When the upstream feed rate changes abruptly, the material needs a certain amount of time to reach the detection point, and the system cannot respond in advance, resulting in speed adjustment lag. This not only affects energy-saving performance but also easily leads to safety accidents. 4. Lack of upstream and downstream coordinated control: Most existing systems only control a single conveyor independently, without establishing a linkage mechanism with the upstream feeder and downstream conveyor; when the upstream feed rate suddenly increases or the downstream conveyor malfunctions, the operating status of the conveyor cannot be adjusted in time, which can easily lead to material accumulation accidents along the entire line.

[0004] Therefore, there is an urgent need for a high-precision, high-reliability belt conveyor speed change and energy-saving control system that can solve the above problems. Summary of the Invention

[0005] To solve the above-mentioned technical problems, the technical solution provided by the present invention is as follows: The belt conveyor speed change and energy-saving control system based on material layer thickness detection according to an embodiment of the present invention includes: The multi-segment material layer thickness detection array consists of at least three sets of lidar sensors arranged along the length of the conveyor belt, located at the feed end, middle section and discharge end of the conveyor belt, respectively, for real-time acquisition of point cloud data of material cross sections at different locations; The main controller is communicatively connected to the multi-segment material layer thickness detection array, and is used to receive the point cloud data of the material cross section, calculate the material layer thickness distribution, material layer uniformity coefficient and instantaneous conveying volume, and generate the optimal belt speed command based on the energy consumption-efficiency dual-objective optimization model. The variable frequency drive unit is communicatively connected to the main controller and is used to drive the conveyor belt to run according to the optimal belt speed command. The upstream and downstream collaborative control unit is communicatively connected to the main controller and is used to realize the linkage control with the upstream feeder and the downstream conveyor; An energy consumption monitoring unit is communicatively connected to the main controller to monitor the system's energy consumption data in real time and feed the energy consumption data back to the main controller to optimize the energy consumption-efficiency dual-objective optimization model.

[0006] Furthermore, the multi-segment material layer thickness detection array also includes at least one set of auxiliary lidar sensors, arranged at the bends of the conveyor belt or at positions where the spacing between idlers is greater than a preset value, for detecting changes in material layer thickness at specific locations.

[0007] Furthermore, the main controller includes: The data preprocessing module is used to perform noise reduction, coordinate transformation, and background segmentation on the point cloud data collected by each group of lidar sensors. The conveying capacity calculation module is used to calculate the material cross-sectional area and instantaneous conveying capacity at each detection location based on the preprocessed point cloud data; The uniformity calculation module is used to calculate the difference in material layer thickness between different detection locations and obtain the material layer uniformity coefficient. The belt speed optimization module is used to generate an initial optimal belt speed command based on the instantaneous conveying capacity and the material layer uniformity coefficient, combined with an energy consumption-efficiency dual-objective optimization model. The belt speed correction module is used to correct the initial optimal belt speed command according to the material layer uniformity coefficient, obtain the final belt speed command, and send it to the frequency conversion drive unit. The anomaly handling module is used to issue an alarm signal and send a speed reduction command to the frequency converter drive unit when the material layer thickness is detected to exceed a first preset threshold; to send a material adjustment signal to the upstream feeder when the material layer uniformity coefficient is detected to be lower than a second preset threshold; and to send a stop command to the frequency converter drive unit when the conveyor belt idle running time is detected to exceed a third preset threshold.

[0008] Furthermore, the lidar sensor is a two-dimensional lidar with a scanning frequency of not less than 10Hz, an angular resolution of not less than 0.1°, and a scanning plane perpendicular to the running direction of the conveyor belt.

[0009] Furthermore, the uniformity calculation module uses the following formula to calculate the material layer uniformity coefficient: in, This is the material layer uniformity coefficient. To detect the number of locations, For the first Average material layer thickness at each detection location This represents the average material layer thickness at all detection locations.

[0010] Furthermore, the objective function of the energy consumption-efficiency dual-objective optimization model adopted by the belt speed optimization module is: in, The objective function value, This represents the actual power of the system. The rated power of the system, This represents the actual conveying volume. For the system's rated conveying capacity, This is the weighting coefficient, with a value range of 0.6-0.8.

[0011] Furthermore, the belt speed correction module uses the following formula to correct the initial optimal belt speed: in, The corrected belt speed. The initial belt speed is calculated based on the instantaneous conveying capacity. This is a correction factor, with a value ranging from 0.1 to 0.3. This is the uniformity coefficient of the material layer.

[0012] Furthermore, the upstream and downstream collaborative control unit is also used to predict the feed amount of the conveyor belt within a future preset time period based on the feed amount signal of the upstream feeder, and send the prediction result to the main controller so that the main controller can adjust the running speed of the conveyor belt in advance.

[0013] A method for variable speed energy-saving control of belt conveyors based on material layer thickness detection, characterized by the following steps: S1: At least three sets of lidar sensors are arranged sequentially along the length of the conveyor belt to collect point cloud data of material cross sections at different positions of the feed end, middle section and discharge end of the conveyor belt in real time; S2: Perform noise reduction, coordinate transformation, and background segmentation preprocessing on the point cloud data collected by each group of lidar sensors; S3: Based on the preprocessed point cloud data, calculate the material cross-sectional area and instantaneous conveying capacity at each detection location; S4: Calculate the difference in material layer thickness between different detection locations to obtain the material layer uniformity coefficient; S5: Based on the instantaneous conveying capacity and the material layer uniformity coefficient, an initial optimal belt speed command is generated by combining the energy consumption-efficiency dual-objective optimization model; S6: Correct the initial optimal belt speed command based on the material layer uniformity coefficient to obtain the final belt speed command; S7: Drive the conveyor belt to run through the frequency conversion drive unit according to the final belt speed command; S8: Receive the feeding amount signal and running status signal from the upstream feeder, predict the feeding amount within a preset time period based on the upstream feeding amount signal, and adjust the running speed of the conveyor belt in advance according to the prediction result; S9: Send the discharge quantity signal and operating status signal of this conveyor to the downstream conveyor so that the downstream conveyor can adjust its operating speed in advance; S10: Monitor the system's energy consumption data in real time and feed the energy consumption data back to the main controller to optimize the energy consumption-efficiency dual-objective optimization model.

[0014] The advantages of this invention compared to the prior art are: 1. Multiple sets of lidar sensors are arranged along the length of the conveyor belt to detect the distribution of materials at the feed end, middle section and discharge end in real time. The average conveying volume is calculated by fusion of multi-point data, which reduces the error in conveying volume calculation and lays the foundation for precise speed regulation. 2. A quantitative calculation method for the material layer uniformity coefficient is proposed for the first time and incorporated into the belt speed optimization model. When the material layer is unevenly distributed, the belt speed is automatically reduced appropriately, which effectively avoids safety accidents such as material accumulation, material overflow and conveyor belt deviation while ensuring conveying efficiency. 3. By receiving the feed rate signal from the upstream feeder, the feed rate in the future can be predicted in advance, and the conveyor belt speed can be adjusted in advance based on the prediction results, which further improves the energy saving effect and system stability. 4. It adopts a dual-objective optimization model of energy consumption and efficiency, which can automatically adjust the weight coefficients according to the actual working conditions, so as to minimize energy consumption while ensuring conveying efficiency. 5. The built-in anomaly handling module can monitor abnormal situations such as excessive material layer thickness, uneven material layer distribution, and no-load operation in real time, and automatically take corresponding measures to improve the safety and reliability of the system. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of the overall structure of the belt conveyor speed change and energy-saving control system based on material layer thickness detection according to the present invention. Figure 2 This is a block diagram of the internal structure of the main controller of the belt conveyor speed change and energy-saving control system based on material layer thickness detection of the present invention; Figure 3 This is a flowchart of the variable speed energy-saving control method for belt conveyors based on material layer thickness detection, according to the present invention.

[0016] Figure label: 1- Multi-segment material layer thickness detection array; 11- First lidar sensor; 12- Second lidar sensor; 13- Third lidar sensor; 14- Auxiliary lidar sensor; 2-Main controller; 21-Data preprocessing module; 22-Conveyance calculation module; 23-Uniformity calculation module; 24-Belt speed optimization module; 25-Belt speed correction module; 26-Abnormal handling module; 3-Variable frequency drive unit; 4-Upstream and downstream collaborative control unit; 5-Energy consumption monitoring unit; 6-Conveyor belt; A-Infeed end; B-Middle section; C-Discharge end; 7-Upstream feeder; 8-Downstream conveyor. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0018] Example: A belt conveyor speed-changing energy-saving control system based on material layer thickness detection according to an embodiment of the present invention, such as... Figure 1 As shown, specifically, it includes a multi-segment material layer thickness detection array 1, a main controller 2, a frequency conversion drive unit 3, an upstream and downstream collaborative control unit 4, and an energy consumption monitoring unit 5.

[0019] The multi-segment material layer thickness detection array 1 is communicatively connected to the input terminal of the main controller 2. The output terminal of the main controller 2 is communicatively connected to the frequency conversion drive unit 3 and the upstream and downstream collaborative control unit 4, respectively. The output terminal of the energy consumption monitoring unit 5 is communicatively connected to the input terminal of the main controller 2.

[0020] The multi-segment material layer thickness detection array 1 consists of three sets of lidar sensors arranged sequentially along the length of the conveyor belt 6, namely the first lidar sensor 11, the second lidar sensor 12, and the third lidar sensor 13, which are respectively arranged at the feed end A, the middle section B, and the discharge end C of the conveyor belt 6. For long-distance conveyor belts with a length exceeding 100 meters, 1-2 sets of lidar sensors can be added to the middle section B to further improve the detection accuracy.

[0021] In addition, the multi-segment material layer thickness detection array 1 also includes a set of auxiliary lidar sensors 14, which are arranged at positions where the spacing between the idlers is greater than 1.5 meters, to detect changes in material layer thickness at these special positions and prevent material layer thickness detection errors caused by the sinking of the idlers; in other embodiments, the auxiliary lidar sensors 14 may also be arranged at the bends of the conveyor belt 6.

[0022] In this embodiment, all lidar sensors are two-dimensional lidar with a scanning frequency of 20Hz and an angular resolution of 0.05°, which can quickly and accurately collect point cloud data of the material cross-section. The lidar sensors are mounted 1.5 meters above the conveyor belt 6 by a bracket, and the scanning plane is strictly perpendicular to the running direction of the conveyor belt 6 to ensure that the collected data is the true cross-sectional contour of the material.

[0023] like Figure 2 As shown, the main controller 2 adopts a Siemens S7-1500 series PLC controller, which has a built-in data preprocessing module 21, conveying capacity calculation module 22, uniformity calculation module 23, belt speed optimization module 24, belt speed correction module 25 and anomaly handling module 26.

[0024] The data preprocessing module 21 is used to preprocess the raw point cloud data collected by each group of lidar sensors, including noise reduction, coordinate transformation and background segmentation. Noise reduction uses a statistical filtering algorithm to remove outliers caused by dust, vibration and other factors. Coordinate transformation converts the local coordinate system of the lidar sensor into the global coordinate system of the conveyor belt 6. Background segmentation removes the surface of the conveyor belt 6 and the background point cloud from the raw point cloud, retaining only the material point cloud.

[0025] The conveying capacity calculation module 22 is used to calculate the material cross-sectional area and instantaneous conveying capacity at each detection location based on the preprocessed point cloud data. The specific calculation formula is as follows: in, The cross-sectional area of ​​the material. For position The thickness of the material layer at that location, and These are the left and right boundaries of the conveyor belt. For instantaneous delivery volume, The current operating speed of the conveyor belt. This refers to the material density; the material density can be obtained through on-site calibration, and the density range for different materials is: 1.6-2.0 t / m³ for ore. 3 The sand and gravel content is 1.4-1.6 t / m³. 3 Grain consumption is 0.7-0.8 t / m³ 3 Cement concentration: 1.2-1.4 t / m³ 3 .

[0026] The uniformity calculation module 23 is used to calculate the difference in material layer thickness between different detection positions to obtain the material layer uniformity coefficient; the formula for calculating the material layer uniformity coefficient is: in, This is the material layer uniformity coefficient. To detect the number of locations, For the first Average material layer thickness at each detection location The average material layer thickness at all detection locations; material layer uniformity coefficient. The value range is 0-1. The closer it is to 1, the more uniform the material layer distribution; The closer it is to 0, the more uneven the material layer distribution.

[0027] The belt speed optimization module 24 is used to generate an initial optimal belt speed command based on the instantaneous conveying capacity and the material layer uniformity coefficient, combined with an energy consumption-efficiency dual-objective optimization model; the objective function of the energy consumption-efficiency dual-objective optimization model is: in, The objective function value, This represents the actual power of the system. The rated power of the system, This represents the actual conveying volume. For the system's rated conveying capacity, This is the weighting coefficient, with a value ranging from 0.6 to 0.8; weighting coefficient Adjustments can be made according to actual operating conditions: when energy-saving requirements are high, Use 0.7-0.8; when higher conveying efficiency is required, Take 0.6-0.7.

[0028] The belt speed correction module 25 is used to correct the initial optimal belt speed command based on the material layer uniformity coefficient to obtain the final belt speed command; the correction formula is: in, The corrected belt speed. The initial belt speed is calculated based on the instantaneous conveying capacity. The correction factor has a value range of 0.1-0.3. In this embodiment... Take 0.2; when the material layer uniformity coefficient When =0.9, the corrected belt speed is 98% of the initial belt speed; when When the value is 0.7, the corrected belt speed is 94% of the initial belt speed. The system's safe operation is ensured by appropriately reducing the belt speed.

[0029] The anomaly handling module 26 is used to monitor the system's operating status in real time and handle various abnormal situations. (1) When the thickness of the material layer at any position exceeds the first preset threshold (300 mm in this embodiment), an audible and visual alarm signal is issued, and a speed reduction command is sent to the frequency conversion drive unit 3 to reduce the belt speed to 80% of the initial belt speed; (2) When the material uniformity coefficient is detected to be lower than the second preset threshold (0.6 in this embodiment), a material adjustment signal is sent to the upstream feeder 7 to request the feeder to adjust the feeding speed so that the feeding is more uniform; (3) When the idle running time of the conveyor belt 6 exceeds the third preset threshold (5 minutes in this embodiment), a stop command is sent to the frequency conversion drive unit 3 to make the conveyor belt 6 enter the standby state and save energy.

[0030] The variable frequency drive unit 3 includes an ABB ACS880 series frequency converter and a Y-series three-phase asynchronous motor. The frequency converter receives the final belt speed command sent by the main controller 2, adjusts the output frequency, thereby changing the speed of the motor and realizing stepless adjustment of the speed of the conveyor belt 6. The speed adjustment range of the frequency converter is 0.2-2.0m / s, and the speed adjustment accuracy is ±0.01m / s.

[0031] The upstream and downstream collaborative control unit 4 uses an industrial Ethernet communication module to establish a communication connection with the control systems of the upstream feeder 7 and the downstream conveyor 8. The upstream and downstream collaborative control unit 4 receives the real-time feeding amount signal and operating status signal of the upstream feeder 7. Based on the upstream feeding amount signal, it uses a time series prediction algorithm to predict the feeding amount of the conveyor belt 6 in the next 30 seconds and sends the prediction result to the main controller 2, so that the main controller 2 can adjust the running speed of the conveyor belt 6 in advance. It also sends the real-time discharge amount signal and operating status signal of the downstream conveyor 8, so that the downstream conveyor 8 can adjust its running speed in advance, thereby achieving energy saving in the entire line.

[0032] The energy consumption monitoring unit 5 includes a current transformer, a voltage transformer, and an intelligent power meter, which can collect data such as the three-phase current, three-phase voltage, active power, reactive power, and energy consumption of the system in real time, and send these data to the main controller 2. The main controller 2 continuously optimizes the parameters of the energy consumption-efficiency dual-objective optimization model based on the energy consumption data, so that the model can adapt to different operating conditions.

[0033] This embodiment provides a variable speed energy-saving control method for belt conveyors based on material layer thickness detection, such as... Figure 3 As shown, it includes the following steps: S1: Using three sets of lidar sensors arranged sequentially along the length of conveyor belt 6, the material cross-section point cloud data at different positions of the feed end A, middle section B and discharge end C of conveyor belt 6 are collected in real time at a frequency of 20Hz. S2: Preprocess the raw point cloud data collected by each group of lidar sensors: First, use a statistical filtering algorithm to remove outliers, then convert the point cloud data from the lidar coordinate system to the conveyor belt 6 coordinate system, and finally use a background segmentation algorithm to remove the conveyor belt 6 and background point cloud, keeping only the material point cloud. S3: Based on the preprocessed point cloud data, the material cross-sectional area at each detection position is calculated using the integral method. Then, the instantaneous conveying capacity at each detection position is calculated based on the material cross-sectional area, the current speed of the conveyor belt 6, and the material density. Finally, the average value of the instantaneous conveying capacity at the three detection positions is taken to obtain the actual conveying capacity of the system. S4: Calculate the average material layer thickness at the three detection locations, and then calculate the material layer uniformity coefficient according to the formula. ; S5: Based on the actual conveying capacity and the material layer uniformity coefficient, substitute them into the energy consumption-efficiency dual-objective optimization model to obtain the initial optimal belt speed command. .

[0034] S6: Based on the material layer uniformity coefficient For the initial optimal belt speed Make corrections to obtain the final belt speed command. ; S7: Main controller 2 will send the final speed command. The command is sent to the frequency converter drive unit 3, which adjusts the frequency converter output frequency according to the command and drives the conveyor belt 6 to run at the corrected speed. S8: The upstream and downstream collaborative control unit 4 receives the real-time feeding amount signal and running status signal from the upstream feeder 7, uses the ARIMA time series prediction algorithm to predict the feeding amount in the next 30 seconds, and sends the prediction result to the main controller 2; the main controller 2 adjusts the running speed of the conveyor belt 6 in advance according to the prediction result to realize predictive control. S9: The upstream and downstream collaborative control unit 4 sends the real-time discharge quantity signal and operating status signal of the downstream conveyor 8 to the downstream conveyor 8, so that the downstream conveyor 8 can adjust its operating speed in advance and achieve energy saving in the whole line. S10: Energy consumption monitoring unit 5 monitors the system's energy consumption data in real time and feeds the data back to main controller 2; main controller 2 uses an online learning algorithm to continuously optimize the weight coefficients of the energy consumption-efficiency dual-objective optimization model. and correction factor This ensures that the system is always in an optimal operating state.

[0035] The following is an application example of the belt conveyor speed change and energy-saving control system based on material layer thickness detection: A port ore conveyor belt has a rated belt speed of 2m / s, a rated conveying capacity of 1000t / h, and a rated system power of 200kW. After adopting the system of the present invention, laser radar sensors are installed at the feed end A, the middle section B, and the discharge end C of the conveyor belt, and a set of auxiliary laser radar sensors 14 are installed at the turning point.

[0036] (1) Operating condition 1: Stable feeding condition When the feed rate of the upstream feeder 7 stabilizes at 500 t / h, the system detects that the average material layer thickness at three locations is 150 mm, 148 mm, and 152 mm, respectively, and calculates the material layer uniformity coefficient. =0.99; Based on the energy consumption-efficiency dual-objective optimization model, the initial optimal belt speed is 1.2 m / s; Due to the extremely high uniformity coefficient of the material layer, the belt speed remains 1.2 m / s after correction; (2) Condition 2: Sudden change in feed rate When the feed rate of the upstream feeder 7 suddenly increases from 500t / h to 800t / h, the upstream and downstream coordination control unit 4 receives the feed rate signal from the upstream feeder 7 and predicts that the feed rate of the conveyor belt 6 will increase to 800t / h within the next 25 seconds. Therefore, it adjusts the speed of the conveyor belt 6 from 1.2m / s to 1.8m / s in advance. When the material reaches the feed end A of the conveyor belt 6, the system is already at a suitable operating speed, avoiding the occurrence of material accumulation accidents. (3) Working condition 3: Uneven material layer distribution When the system detects average material layer thicknesses of 210mm, 110mm, and 180mm at three locations, respectively, the material layer uniformity coefficient is calculated. =0.72; The initial belt speed calculated based on the instantaneous conveying capacity is 1.5m / s. According to the belt speed correction formula, the corrected belt speed is 1.5×[1-0.2×(1-0.72)]=1.416m / s; After appropriately reducing the belt speed, both the conveying efficiency and the material overflow phenomenon in the thick material layer are guaranteed.

[0037] The present invention and its embodiments have been described above. This description is not restrictive, and the accompanying drawings are only one embodiment of the present invention; the actual structure is not limited thereto. In conclusion, if those skilled in the art are inspired by this description and design similar structures and embodiments without departing from the spirit of the invention, such designs should fall within the protection scope of the present invention.

Claims

1. A variable speed energy-saving control system for a belt conveyor based on material layer thickness detection, characterized in that, include: The multi-segment material layer thickness detection array (1) consists of at least three sets of lidar sensors arranged along the length of the conveyor belt (6), located at the feed end (A), middle section (B) and discharge end (C) of the conveyor belt (6), respectively, for real-time acquisition of material cross-section point cloud data at different locations; The main controller (2) is connected to the multi-segment material layer thickness detection array (1) to receive the material cross-section point cloud data, calculate the material layer thickness distribution, material layer uniformity coefficient and instantaneous conveying volume, and generate the optimal belt speed command based on the energy consumption-efficiency dual-objective optimization model. The variable frequency drive unit (3) is connected in communication with the main controller (2) and is used to drive the conveyor belt (6) to run according to the optimal belt speed command; The upstream and downstream collaborative control unit (4) is connected in communication with the main controller (2) to realize the linkage control with the upstream feeder (7) and the downstream conveyor (8); The energy consumption monitoring unit (5) is connected to the main controller (2) for real-time monitoring of the system's energy consumption data and feeding the energy consumption data back to the main controller (2) to optimize the energy consumption-efficiency dual-objective optimization model.

2. The belt conveyor speed change and energy-saving control system based on material layer thickness detection according to claim 1, characterized in that, The multi-segment material layer thickness detection array (1) also includes at least one set of auxiliary lidar sensors (14), which are arranged at the bends of the conveyor belt (6) or at positions where the spacing between idlers is greater than a preset value, for detecting changes in material layer thickness at special positions.

3. The belt conveyor speed change and energy-saving control system based on material layer thickness detection according to claim 1, characterized in that, The main controller (2) includes: The data preprocessing module (21) is used to perform noise reduction, coordinate transformation and background segmentation on the point cloud data collected by each group of lidar sensors; The conveying capacity calculation module (22) is used to calculate the material cross-sectional area and instantaneous conveying capacity at each detection location based on the preprocessed point cloud data; The uniformity calculation module (23) is used to calculate the difference in material layer thickness between different detection positions and obtain the material layer uniformity coefficient. The belt speed optimization module (24) is used to generate an initial optimal belt speed command based on the instantaneous conveying capacity and the material layer uniformity coefficient, combined with the energy consumption-efficiency dual-objective optimization model. The belt speed correction module (25) is used to correct the initial optimal belt speed command according to the material layer uniformity coefficient, obtain the final belt speed command and send it to the frequency conversion drive unit (3). The abnormality handling module (26) is used to issue an alarm signal and send a speed reduction command to the frequency conversion drive unit (3) when the material layer thickness is detected to exceed the first preset threshold; send a material adjustment signal to the upstream feeder (7) when the material layer uniformity coefficient is detected to be lower than the second preset threshold; and send a stop command to the frequency conversion drive unit (3) when the idle running time of the conveyor belt (6) is detected to exceed the third preset threshold.

4. The belt conveyor speed change and energy-saving control system based on material layer thickness detection according to claim 1, characterized in that, The lidar sensor is a two-dimensional lidar with a scanning frequency of not less than 10Hz, an angular resolution of not less than 0.1°, and a scanning plane perpendicular to the running direction of the conveyor belt (6).

5. The belt conveyor speed change and energy-saving control system based on material layer thickness detection according to claim 3, characterized in that, The uniformity calculation module (23) uses the following formula to calculate the uniformity coefficient of the material layer: in, This is the material layer uniformity coefficient. To detect the number of locations, For the first Average material layer thickness at each detection location This represents the average material layer thickness at all detection locations.

6. The belt conveyor speed change and energy-saving control system based on material layer thickness detection according to claim 3, characterized in that, The objective function of the energy consumption-efficiency dual-objective optimization model adopted by the belt speed optimization module (24) is: in, The objective function value, This represents the actual power of the system. The rated power of the system, This represents the actual conveying volume. For the system's rated conveying capacity, This is the weighting coefficient, with a value range of 0.6-0.

8.

7. The belt conveyor speed change and energy-saving control system based on material layer thickness detection according to claim 3, characterized in that, The belt speed correction module (25) corrects the initial optimal belt speed using the following formula: in, The corrected belt speed. The initial belt speed is calculated based on the instantaneous conveying capacity. This is a correction factor, with a value ranging from 0.1 to 0.

3. This is the uniformity coefficient of the material layer.

8. The belt conveyor speed change and energy-saving control system based on material layer thickness detection according to claim 1, characterized in that, The upstream and downstream collaborative control unit (4) is also used to predict the feed amount of the conveyor belt (6) within a future preset time period based on the feed amount signal of the upstream feeder (7), and send the prediction result to the main controller (2) so that the main controller (2) can adjust the running speed of the conveyor belt (6) in advance.

9. A method for energy-saving speed control of a belt conveyor based on material layer thickness detection, characterized in that, Includes the following steps: S1: At least three sets of lidar sensors are arranged sequentially along the length of the conveyor belt (6) to collect point cloud data of material cross sections at different positions of the feed end (A), middle section (B) and discharge end (C) of the conveyor belt (6) in real time; S2: Perform noise reduction, coordinate transformation, and background segmentation preprocessing on the point cloud data collected by each group of lidar sensors; S3: Based on the preprocessed point cloud data, calculate the material cross-sectional area and instantaneous conveying capacity at each detection location; S4: Calculate the difference in material layer thickness between different detection locations to obtain the material layer uniformity coefficient; S5: Based on the instantaneous conveying capacity and the material layer uniformity coefficient, an initial optimal belt speed command is generated by combining the energy consumption-efficiency dual-objective optimization model; S6: Correct the initial optimal belt speed command based on the material layer uniformity coefficient to obtain the final belt speed command; S7: Drive the conveyor belt (6) through the frequency conversion drive unit (3) according to the final belt speed command; S8: Receive the feeding amount signal and running status signal from the upstream feeder (7), predict the feeding amount within a preset time period based on the upstream feeding amount signal, and adjust the running speed of the conveyor belt (6) in advance according to the prediction result. S9: Send the discharge quantity signal and running status signal of this conveyor to the downstream conveyor (8) so that the downstream conveyor (8) can adjust its running speed in advance; S10: Monitor the energy consumption data of the system in real time and feed the energy consumption data back to the main controller (2) to optimize the energy consumption-efficiency dual-objective optimization model.

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