A method and system for detecting a blockage of a conveyor belt

Abstract

The present application relates to a kind of methods and systems for detecting blockage, in particular to a kind of methods and systems for detecting blockage of conveying belt, solve the technical problem that the detection accuracy of existing blockage detection method is poor or cost and complexity is higher.The present application emits laser signal vertically from the top of conveying belt, can scan a face of conveying belt, compared with the practice that laser ranging radar of traditional adoption can only detect linear region, while increasing the detection area, improve the accuracy of detection result, and only use one laser signal, system is simple, reliable, low in cost.At the same time, material point cloud data is grid processing, by calculating the volume of material in each grid, the total volume of material is obtained, not only simplify the calculation of total volume of material, also improve the calculation precision, to further improve the accuracy of detection result.

Description

Technical Field

[0001] This invention relates to a method and system for detecting material blockage, specifically to a method and system for detecting material blockage in a conveyor belt. Background Technology

[0002] In industrial sectors involving material transportation, such as coal mining, power generation, and chemicals, the operational reliability and intelligent level of belt conveyors are crucial for ensuring production safety and efficiency. Material blockage refers to the abnormal accumulation of material on the conveyor belt during transport due to changes in moisture content, uneven particle size, or equipment malfunction, causing obstruction of the conveyor belt's flow channels. Material blockage not only directly causes production interruptions and significant economic losses but can also lead to equipment overload damage and even serious safety accidents due to high-risk manual clearing operations. This challenging industrial context creates an urgent need for reliable and stable material blockage detection technology.

[0003] Early material blockage detection relied primarily on mechanical devices, such as rotary paddle level switches, which triggered alarms through direct contact between the paddles and the material. While these contact sensors were simple and inexpensive, they suffered from inherent limitations, including rapid mechanical wear, susceptibility to sticky materials, poor reliability, and the ability to provide only reactive alarms. These limitations failed to meet the demands of modern industry for predictive and intelligent management. With advancements in industrial automation, material blockage detection technology has evolved towards non-contact, quantifiable, and intelligent methods. For example, laser rangefinders are installed above the conveyor belt to measure distance and determine the flow rate of the blockage. The material profile is scanned in real-time and subtracted from the empty reference profile to calculate the real-time cross-sectional area. This, combined with the conveyor belt speed integral, yields the blockage volume. However, this method is susceptible to environmental influences and relies on a calibrated empty reference profile, affecting detection reliability. To reduce blind spots, dual laser radars are used to measure the profiles on both sides of the conveyor belt, and the trapezoidal area accumulation method is employed to calculate the blockage cross-sectional area, thus determining its volume. While this method improves detection accuracy, it has high hardware costs and a complex installation and calibration process. It also requires the use of fusion algorithms, which can easily introduce new errors. Therefore, while improving detection accuracy, it also brings higher costs and system complexity. Summary of the Invention

[0004] The purpose of this invention is to solve the technical problems of poor detection accuracy or high cost and complexity of existing material blockage detection methods, and to provide a method and system for detecting material blockage in conveyor belts.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0006] A method for detecting material blockage in a conveyor belt, characterized by comprising the following steps:

[0007] Step 1: Emit a laser signal vertically from above towards the conveyor belt. When the conveyor belt is unloaded, collect the unloaded echo signal, process it, and obtain the reference point cloud data of the conveyor belt. Then, generate a reference surface based on the reference point cloud data of the conveyor belt.

[0008] Step 2: When the conveyor belt is carrying a load, the loaded echo signal is collected and processed to obtain the raw point cloud data of the material; then the raw point cloud data of the material is preprocessed to obtain the material point cloud data.

[0009] Step 3: Based on the width and speed of the conveyor belt and the frequency of the laser signal, the material point cloud data is processed into a grid. Then, combined with the reference plane, the material volume in each grid is calculated, and the total material volume is obtained by summing them up.

[0010] Step 4: Based on the total volume of the material, check for any blockages, then return to Step 2 until the conveyor belt is loaded and the blockage detection is complete.

[0011] Furthermore, step 3 specifically involves:

[0012] Step 3.1: Record the area of ​​the conveyor belt scanned by the laser signal in one scan as the calculation area, then take the width of the conveyor belt as the width of the calculation area, and calculate the length of the conveyor belt under one scan of the laser signal according to the speed of the conveyor belt and the frequency of the laser signal to obtain the length of the calculation area.

[0013] Step 3.2: Divide the computational region into multiple identical grids based on its length and width, and calculate the area of ​​each grid.

[0014] Step 3.3: Perform gridding processing on the material point cloud data according to the divided grid, and then calculate the height difference between the material point cloud data and the reference plane for each grid to obtain the material height in each grid;

[0015] Step 3.4: Calculate the material volume in each grid based on the material height and grid area, and sum them up to obtain the total material volume.

[0016] Further, in step 3.1, the width of the conveyor belt is 1.25m, the speed of the conveyor belt is 5m / s, and the frequency of the laser signal is 10Hz. Therefore, the length of the calculation area is 0.5m and the width is 1.25m.

[0017] In step 3.2, the size of the grid is 0.01m × 0.01m, and the number is 125 × 50.

[0018] Furthermore, in step 2, the specific method for preprocessing the raw point cloud data of the material is as follows:

[0019] The raw point cloud data of the material is subjected to voxel filtering, outlier filtering, and ROI region filtering in sequence.

[0020] Furthermore, step 1 specifically includes:

[0021] A laser signal is emitted vertically from above towards the conveyor belt. When the conveyor belt is unloaded, the unloaded echo signal is collected within 5 seconds, processed, and the average value is taken to obtain the reference point cloud data of the conveyor belt. Then, a reference surface is generated based on the reference point cloud data of the conveyor belt.

[0022] Furthermore, in step 3, after obtaining the total volume of the material, the steps also include visualizing the material point cloud data and outputting the total volume of the material.

[0023] Furthermore, in step 3, the visualization employs the Rviz visualization method.

[0024] The present invention also provides a conveyor belt blockage detection system for implementing the above-mentioned conveyor belt blockage detection method, which is characterized by including a 3D lidar, a data processing module and an application interaction module;

[0025] The 3D lidar is installed above the conveyor belt with the laser transmission direction vertically downward. Its output end is connected to the input end of the data processing module. The 3D lidar is used to emit lasers and collect the empty echo signal and loaded echo signal of the conveyor belt respectively.

[0026] The output of the data processing module is connected to the input of the application interaction module. It is used to obtain the reference point cloud data of the conveyor belt and the original point cloud data of the material based on the no-load echo signal and the loaded echo signal, respectively; to generate a reference surface based on the reference point cloud data of the conveyor belt; to preprocess the original point cloud data of the material to obtain the material point cloud data; and to calculate the total volume of the material.

[0027] The application interaction module is used to output the total volume of the material and visualize the material point cloud data.

[0028] Furthermore, it also includes a system platform, which comprises device drivers and embedded units;

[0029] The output of the device driver is connected to the control terminal of the 3D LiDAR, and the embedded unit is used to carry the data processing module and the application interaction module.

[0030] Compared with the prior art, the present invention has the following beneficial effects:

[0031] 1. The present invention provides a method for detecting material blockage in a conveyor belt. A laser signal is emitted vertically from above the conveyor belt, which can scan one surface of the conveyor belt. Compared with the traditional laser ranging radar, which can only detect linear areas, the present invention increases the detection area and improves the accuracy of the detection results. Moreover, it only uses a single laser signal, making the system simple, reliable, and low in cost.

[0032] 2. The present invention provides a method for detecting material blockage in a conveyor belt, which performs gridding processing on the material point cloud data and calculates the total material volume by calculating the material volume in each grid. This not only simplifies the calculation of the total material volume but also improves the calculation accuracy, thereby further improving the accuracy of the detection results.

[0033] 3. The present invention provides a method for detecting material blockage on a conveyor belt. It utilizes voxel filtering, outlier filtering, and ROI region filtering to preprocess the original point cloud data of the material. Voxel filtering can reduce the number of points while maintaining the overall shape of the point cloud, and quickly obtain a lightweight point cloud that can clearly reflect the outline of the blockage on the conveyor belt, which is convenient for volume estimation and improves the processing speed of subsequent algorithms. Outlier filtering can remove noise points, thereby removing erroneous points such as dust and dirt that may interfere with the calculation. After outlier filtering, the error in judging the actual blockage surface can be reduced. Attached Figure Description

[0034] Figure 1 This is a flowchart of a method according to an embodiment of the present invention;

[0035] Figure 2 This is a visual interface diagram of step 3.4 in the embodiment of the present invention;

[0036] Figure 3 This is a schematic diagram of the system structure according to an embodiment of the present invention. Detailed Implementation

[0037] To make the objectives, advantages and features of the present invention clearer, the following describes in further detail a conveyor belt blockage detection method and system proposed by the present invention in conjunction with the accompanying drawings and specific embodiments.

[0038] A method for detecting material blockage in a conveyor belt, such as Figure 1 As shown, it includes the following steps:

[0039] Step 1: Emit a laser signal vertically from above towards the conveyor belt. When the conveyor belt is unloaded, collect the unloaded echo signal within 5 seconds, process it, and take the average value to obtain the reference point cloud data of the conveyor belt. Then, generate a reference surface based on the reference point cloud data of the conveyor belt.

[0040] Step 2: When the conveyor belt is carrying a load, the loaded echo signal is collected and processed to obtain the raw point cloud data of the material; then the raw point cloud data of the material is subjected to voxel filtering, outlier filtering and ROI region filtering in sequence to obtain the material point cloud data.

[0041] Step 3: Based on the width and speed of the conveyor belt, and the frequency of the laser signal, the material point cloud data is meshed. Then, combined with the reference plane, the material volume in each mesh is calculated, and finally, the total material volume is obtained by summing them up. Specifically:

[0042] Step 3.1: Record the area of ​​the conveyor belt scanned by the laser signal in one pass as the calculation area. Then, use the width of the conveyor belt as the width of the calculation area. Based on the conveyor belt's speed and the laser signal frequency, calculate the length of the conveyor belt during one laser signal scan to obtain the length of the calculation area. In this embodiment, the conveyor belt width is 1.25m, the conveyor belt speed is 5m / s, and the laser signal frequency is 10Hz. Therefore, the size of the calculation area is 1.25m × 0.5m.

[0043] Step 3.2: Divide the calculation region into multiple identical grids based on its length and width, and calculate the grid area. In this embodiment, the grid size is set to 0.01m × 0.01m, therefore, the grid area is 10. -4 m 2 The quantity is 125 x 50.

[0044] Step 3.3: Perform gridding processing on the material point cloud data according to the divided grid. Then, for each grid, calculate the height difference between the material point cloud data and the reference plane to obtain the material height in each grid.

[0045] Step 3.4: Calculate the material volume in each grid based on the material height and grid area, and sum them up to obtain the total material volume. Then, use the Rviz visualization method to visualize the material point cloud data, and output the total material volume in the visualization interface. The visualization interface is shown below. Figure 2 As shown.

[0046] Step 4: Based on the total volume of the material, check for any blockages, then return to Step 2 until the conveyor belt is loaded and the blockage detection is complete.

[0047] In this embodiment, when calculating the volume of the blocked material, the conveyor belt area is defined by a grid. The width of the conveyor belt is 1.25m, the movement speed is 5m / s, and a 10Hz laser signal is used to complete one scan. Therefore, the belt moves 0.5m during each scan. Thus, when calculating the material volume, the calculation area is mathematically abstracted into 125×50 grids, each with a length and width of 0.01m. The height of each grid is obtained by subtracting its height from the reference plane, and then multiplying by 10. -4 m 2 The material volume of the grid can be obtained. By accumulating all grids, the total material volume of this measurement can be obtained, thereby detecting whether there is a blockage and obtaining the blockage volume.

[0048] Meanwhile, during the detection process, the information collected when the conveyor belt is unloaded serves as the detection benchmark. To avoid interference from equipment vibration or foreign objects in subsequent measurement results, which could lead to errors in the obtained material blockage volume, this embodiment obtains the conveyor belt benchmark point cloud data by averaging the unloaded echo signals collected within 5 seconds. Based on this, a benchmark surface is obtained, which is then removed when measuring the material volume later.

[0049] This embodiment also provides a conveyor belt blockage detection system to implement the above-mentioned conveyor belt blockage detection method, such as... Figure 3 As shown, the system includes a 3D LiDAR, a data processing module, an application interaction module, and a system platform. The 3D LiDAR is mounted above the conveyor belt with its laser transmission direction vertically downwards. Its output is connected to the input of the data processing module. The 3D LiDAR emits laser light and collects both the empty and loaded echo signals from the conveyor belt. The output of the data processing module is connected to the input of the application interaction module. It is used to obtain the conveyor belt reference point cloud data and the material's original point cloud data based on the empty and loaded echo signals, generate a reference surface based on the conveyor belt reference point cloud data, preprocess the material's original point cloud data to obtain the material's point cloud data, calculate the total material volume, and detect any blockages. The application interaction module outputs the detection results and the total material volume, and visualizes the material point cloud data. The system platform includes a device driver and an embedded unit. The output of the device driver is connected to the control terminal of the 3D LiDAR, and the embedded unit houses the data processing module and the application interaction module.

[0050] In this embodiment, the 3D LiDAR serves as the system's sensing organ, its core function being to actively emit laser beams and receive echo signals. As the system's brain, the data processing module executes the core algorithm. Based on the echo signals, when the conveyor belt is empty, it learns and stores a reference surface representing the empty conveyor belt surface. When the conveyor belt is loaded, it compares the real-time acquired raw material point cloud data with the reference surface, processes the point cloud data through three filtering steps, then meshes the conveyor belt to calculate the total material volume, thereby detecting whether there is material blockage. The material status is presented to the user through Rviz visualization via the application interaction module. The embedded unit of the system platform provides the operating environment and computing resources for the entire system.

[0051] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the present invention.

Claims

1. A method for detecting material blockage in a conveyor belt, characterized in that, Includes the following steps: Step 1: Emit a laser signal vertically from above towards the conveyor belt. When the conveyor belt is unloaded, collect the unloaded echo signal, process it, and obtain the reference point cloud data of the conveyor belt. Then, generate a reference surface based on the reference point cloud data of the conveyor belt. Step 2: When the conveyor belt is carrying a load, the loaded echo signal is collected and processed to obtain the raw point cloud data of the material; then the raw point cloud data of the material is preprocessed to obtain the material point cloud data. Step 3: Based on the width and speed of the conveyor belt and the frequency of the laser signal, the material point cloud data is processed into a grid. Then, combined with the reference plane, the material volume in each grid is calculated, and the total material volume is obtained by summing them up. Step 4: Based on the total volume of the material, check for any blockages, then return to Step 2 until the conveyor belt is loaded and the blockage detection is complete.

2. The method for detecting material blockage in a conveyor belt according to claim 1, characterized in that, Step 3 specifically involves: Step 3.1: Record the area of ​​the conveyor belt scanned by the laser signal in one scan as the calculation area, then take the width of the conveyor belt as the width of the calculation area, and calculate the length of the conveyor belt under one scan of the laser signal according to the speed of the conveyor belt and the frequency of the laser signal to obtain the length of the calculation area. Step 3.2: Divide the computational region into multiple identical grids based on its length and width, and calculate the area of ​​each grid. Step 3.3: Perform gridding processing on the material point cloud data according to the divided grid, and then calculate the height difference between the material point cloud data and the reference plane for each grid to obtain the material height in each grid; Step 3.4: Calculate the material volume in each grid based on the material height and grid area, and sum them up to obtain the total material volume.

3. The method for detecting material blockage in a conveyor belt according to claim 2, characterized in that: In step 3.1, the width of the conveyor belt is 1.25m, the speed of the conveyor belt is 5m / s, and the frequency of the laser signal is 10Hz. Therefore, the length of the calculation area is 0.5m and the width is 1.25m. In step 3.2, the size of the grid is 0.01m × 0.01m, and the number is 125 × 50.

4. A method for detecting material blockage in a conveyor belt according to any one of claims 1-3, characterized in that, In step 2, the specific method for preprocessing the raw point cloud data of the material is as follows: The raw point cloud data of the material is subjected to voxel filtering, outlier filtering, and ROI region filtering in sequence.

5. The method for detecting material blockage in a conveyor belt according to claim 4, characterized in that, Step 1 is as follows: A laser signal is emitted vertically from above towards the conveyor belt. When the conveyor belt is unloaded, the unloaded echo signal is collected within 5 seconds, processed, and the average value is taken to obtain the reference point cloud data of the conveyor belt. Then, a reference surface is generated based on the reference point cloud data of the conveyor belt.

6. The method for detecting material blockage in a conveyor belt according to claim 5, characterized in that: Step 3, after obtaining the total volume of the material, also includes visualizing the material point cloud data and outputting the total volume of the material.

7. The method for detecting material blockage in a conveyor belt according to claim 6, characterized in that: In step 3, the visualization is performed using the Rviz visualization method.

8. A conveyor belt blockage detection system, used to implement the conveyor belt blockage detection method according to any one of claims 1-7, characterized in that: Includes 3D LiDAR, data processing module, and application interaction module; The 3D lidar is installed above the conveyor belt with the laser transmission direction vertically downward. Its output end is connected to the input end of the data processing module. The 3D lidar is used to emit lasers and collect the empty echo signal and loaded echo signal of the conveyor belt respectively. The output of the data processing module is connected to the input of the application interaction module. It is used to obtain the reference point cloud data of the conveyor belt and the original point cloud data of the material based on the no-load echo signal and the loaded echo signal, respectively; to generate a reference surface based on the reference point cloud data of the conveyor belt; to preprocess the original point cloud data of the material to obtain the material point cloud data; and to calculate the total volume of the material. The application interaction module is used to output the total volume of the material and visualize the material point cloud data.

9. The conveyor belt blockage detection system according to claim 8, characterized in that: It also includes a system platform, which comprises device drivers and embedded units; The output of the device driver is connected to the control terminal of the 3D LiDAR, and the embedded unit is used to carry the data processing module and the application interaction module.

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