A method for monitoring the torsion of tubular tape based on line laser modeling technology

Abstract

This invention belongs to the field of conveyor technology and relates to a method for monitoring the torsion of tubular conveyor belts based on line laser modeling technology. This method uses line laser 3D modeling technology to model the outer contour of the cross-section of the conveyor belt in operation in real time, acquiring point cloud data of the tubular conveyor belt. Then, abnormal waveform detection and processing technology is used to detect abnormal points on the conveyor belt and calculate the corresponding coordinates. Subsequently, a point cloud reverse angle algorithm is used to convert the detection point information obtained by the abnormal waveform detection and processing technology into the required angle information, i.e., obtaining the angle data of the torsion of the tubular conveyor belt. Finally, the data is transmitted to the corresponding server in real time. This method achieves real-time, high-precision measurement of the torsion of tubular conveyor belts, fundamentally solving the industry problem of difficult torsion monitoring of tubular belt conveyors, avoiding damage to equipment caused by belt torsion, reducing economic losses, and achieving good social and economic benefits.

Description

Technical Field

[0001] This invention belongs to the field of conveyor technology, and more specifically, relates to a method for monitoring the torsion of tubular conveyor belts based on line laser modeling technology. Background Technology

[0002] During actual operation, tubular belt conveyors may experience twisting around their direction of travel as the high-speed tubular belt twists. This twisting poses a threat to the safe operation of the equipment, and in severe cases, can cause equipment damage and downtime, resulting in significant losses.

[0003] However, due to the high speed of tubular tape operation and the lack of effective monitoring methods, monitoring the torsion of tubular tape has always been a technical challenge and pain point in the industry.

[0004] Current monitoring methods typically employ single or multiple cameras to capture images. The image data is then used to analyze anomalies and calculate relevant angles. However, single-camera images cannot provide 3D information; only relative angles can be estimated using algorithms, resulting in significant and uncontrollable errors. Similarly, using multiple cameras for 3D reconstruction can also lead to decreased accuracy and larger errors due to environmental factors or uncontrollable factors in special circumstances.

[0005] In view of this, the inventors propose a method for monitoring the torsion of tubular tape based on line laser modeling technology to solve the above problems. Summary of the Invention

[0006] To address the aforementioned problems, this invention proposes a method for monitoring the torsion of tubular adhesive tape based on line laser modeling technology, comprising the following steps:

[0007] S1. Obtain point cloud data of tubular tape using laser equipment;

[0008] S2. The point cloud data obtained in step S1 is transmitted to the data processing system using the DLL dynamic library callback function. The data processing system performs corresponding data cleaning on the point cloud data and obtains valid data.

[0009] S3. The data processing system uses abnormal waveform detection and processing technology to detect the valid data obtained in step S2 and detects abnormal data.

[0010] S4. The data processing system uses the point cloud reverse angle algorithm to calculate the abnormal data obtained in step S3 and obtain the corresponding angle data, which is the angle data of the torsion of the tested tubular tape.

[0011] S5. The data processing system sends the angle data obtained in step S4 to the data server via the OPC protocol and to the remote server via the Modbus TCP protocol.

[0012] Furthermore, in step S2, the data processing system removes abnormal points in the edge data of the point cloud data by setting an effective range, thereby eliminating invalid data.

[0013] Further, in step S3, the data processing system transforms the cleaned valid data into processable data, and uses abnormal waveform detection and processing technology to convert the processable data into a data stream representing the distance between a point and the center of the circle formed by the conveyor belt. This requires calculating the distance between each point cloud data point and a specific circle center, using the following formula:

[0014]

[0015] Where x p y p This represents the x-axis and y-axis coordinates of a point p in point cloud data. c With y c The x-axis and y-axis coordinates represent the center of the circle formed by the conveyor belt of the monitored pipe conveyor. After calculating the spatial distance between each point cloud data and the center of the circle, a two-dimensional space is established using these distance formulas. Then, based on the characteristics of this space, the distance derivative between each point cloud data is calculated again to find its maximum value and its corresponding coordinate index i. The formula is as follows:

[0016]

[0017] Where d p It is the distance resulting from the formula, while Δd p This represents the integral difference between distances. Then, taking advantage of the characteristic that the fluctuation of distance changes of irregular objects in special cases lags behind the actual data changes, the previously calculated new coordinates of point i are preprocessed. The formula is as follows:

[0018]

[0019] The function f is based on the previous maximum point distance d. i Calculate the starting position I of the numerical change within a range of n before and after coordinate i, where the value of n is proportional to the length of the irregular object;

[0020] Next, the coordinates of the point cloud data in two-dimensional space are calculated. The relative position is then calculated through coordinate transformation. Based on the transformed coordinates, the inverse trigonometric function is recalculated using the center coordinates of the circle, and the corresponding angle transformation is performed. The formula is as follows:

[0021] δ=β(tan -1(x c -x I ) / y I )

[0022] Where the β function represents the transformation between the two angles, x c Let x be the x-axis coordinate of the center of the circle formed by the conveyor belt of the monitored conveyor. I y I These represent the x-axis and y-axis coordinates of the calculated starting point position I.

[0023] Further, in step S4, the angle between the coordinates of the anomaly point and the center of the circle formed by the conveyor belt of the monitored conveyor is calculated using the point cloud data. The initial angle value is adjusted so that the camera's viewing angle is 0 degrees. Under a forward view, the right side of the observed laser device is positive, and the left side of the observed laser device is negative. The angle coordinates are converted again to calculate the final angle result value γ, and the formula is as follows:

[0024] γ=90-|δ|

[0025] Where δ is the angular coordinate calculated in step S3.

[0026] Compared with the prior art, the present invention has the following beneficial effects:

[0027] This method enables real-time, high-precision measurement of torsion in tubular belt conveyors. This monitoring method is applicable to both harsh weather and sunlight conditions and perfectly adapts to unhealthy equipment conditions such as pipe collapse and slight pipe expansion. It fundamentally solves the industry problem of difficult torsion monitoring of tubular belt conveyors, avoids damage to equipment caused by belt torsion, reduces economic losses, and achieves good social and economic benefits. Attached Figure Description

[0028] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0029] Figure 1 A flowchart of a method for monitoring the torsion of tubular tape based on line laser modeling technology, provided for an embodiment. Detailed Implementation

[0030] The present invention will now be clearly described with reference to the accompanying drawings and specific embodiments. This description is merely illustrative and is not intended to limit the scope of the invention. Any modifications, equivalent substitutions, or improvements made by those skilled in the art based on the embodiments of the present invention without inventive effort to obtain all other embodiments should be included within the scope of protection of the present invention.

[0031] Example

[0032] like Figure 1 This embodiment presents a method for monitoring the torsion of tubular tape based on line laser modeling technology, comprising the following steps:

[0033] S1: Acquire point cloud data.

[0034] The laser device is obtained by accessing a third-party laser system SDK, and the device is initialized and configured accordingly. The laser device is positioned directly above the tape overlap of the conveyor belt. The laser device is then activated to create a 3D model of the outer contour of the conveyor belt cross-section and acquire point cloud data. Laser detection offers relatively accurate results, and when combined with dual cameras to correct 3D coordinates, algorithms can acquire a large amount of high-precision point cloud data. This invention uses third-party laser devices and corresponding point cloud algorithm technologies, but is not limited to existing laser detection and point cloud technologies, including any new detection algorithms and point cloud generation methods.

[0035] S2: Clean up the data.

[0036] Extract point cloud data from the SDK, adjust irrelevant information in the data, and re-integrate the laser-returned data. Submit this data to the data processing system through the callback function of the DLL. Since there is a certain deviation in the edge data of point cloud data detection, it is necessary to remove invalid data by setting an effective range before processing, that is, remove abnormal points in the edge data.

[0037] S3: Regularized data.

[0038] The cleaned point cloud data is transformed into processable data, and then converted into a data stream representing the distance between each point and the center of the circle formed by the conveyor belt. This involves calculating the distance between each point cloud data point and a specific circle center (not limited to L2 paradigm, but including other Lp paradigms and infinite paradigms). The formula is as follows:

[0039]

[0040] Where x p y p This represents the x-axis and y-axis coordinates of a point p in a point cloud data. c With y c This represents the x-axis and y-axis coordinates of the center of the monitored circle. After calculating the spatial distances between each point cloud and the center using the formula, a two-dimensional space is established using these distance formulas. Then, based on the characteristics of this space, the derivative of the distance between each point is calculated again to find its maximum value and its corresponding coordinate index i. The formula is as follows:

[0041]

[0042] Where d p It is the distance resulting from the formula, while Δd p This represents the integral difference between distances; then, utilizing the characteristic that the fluctuation of distance changes of irregular objects in special cases lags behind the actual data changes, the previously calculated new coordinates of point i are preprocessed, and the formula is as follows:

[0043]

[0044] The function f is based on the previous maximum point distance d. i Calculate the starting position I of the numerical change within a range of n before and after coordinate i, where the value of n is proportional to the length of the irregular object.

[0045] Next, the coordinates of the point cloud data in two-dimensional space need to be calculated. The relative position is calculated through coordinate transformation. Based on the transformed coordinates, the inverse trigonometric function is recalculated using the center coordinates of the circle, and the corresponding angle transformation is performed. The formula is as follows:

[0046] δ=β(tan -1 (x c -x I ) / y I )

[0047] The main function of the β function is to transform between two perspectives, x c Let x be the x-coordinate of the center of the circle being tested. I y I These represent the x-axis and y-axis coordinates of the calculated starting point position I.

[0048] S4: Process data.

[0049] The angle between the anomaly's coordinates and the center of the circle is calculated using the point cloud data. The initial angle value is adjusted so that the camera's field of view is 0 degrees. From a forward view, the right side of the observed laser device is positive, and the left side is negative. The angle coordinates are then recalculated to obtain the final angle value γ. The formula is as follows:

[0050] γ=90-|δ|

[0051] Where δ is the previously calculated angular coordinate.

[0052] S5: Transmit data.

[0053] The data processing system synchronously processes point cloud data based on the DLL callback function call frequency, and also needs to synchronously update the external data results. Based on the OPC protocol, it provides OPC services, stores the result values ​​at a fixed location on the server, and provides access to other OPC client programs. While establishing the OPC service program, a Modbus TCP client process also needs to be established to perform periodic updates to the Modbus TCP remote server.

[0054] The embodiments of the present invention have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments.

Claims

1. A method for monitoring the torsion of tubular adhesive tape based on line laser modeling technology, characterized in that, Includes the following steps: S1. Obtain point cloud data of tubular tape using laser equipment; S2. The point cloud data obtained in step S1 is transmitted to the data processing system using the DLL dynamic library callback function. The data processing system performs corresponding data cleaning on the point cloud data to obtain valid data. The data processing system removes abnormal points in the edge data of the point cloud data by setting a valid range, thereby removing invalid data. S3. The data processing system uses abnormal waveform detection and processing technology to detect the valid data obtained in step S2 and detects abnormal data. The data processing system transforms the cleaned, valid data into processable data. Then, using anomaly waveform detection and processing technology, it converts this processable data into a data stream representing the distance between points and the centers of the circles formed by the conveyor belt. This requires calculating the distance between each point cloud data point and a specific circle center, using the following formula: in , This represents the x-axis and y-axis coordinates of a point p in point cloud data. and The x-axis and y-axis coordinates represent the center of the circle formed by the conveyor belt of the monitored pipe conveyor. After calculating the spatial distance between each point cloud data and the center of the circle, a two-dimensional space is established using these distance formulas. Then, based on the characteristics of this space, the distance derivative between each point cloud data is calculated again to find its maximum value and its corresponding coordinate index i. The formula is as follows: in It is the distance resulting from the formula, and This represents the integral difference between distances. Then, taking advantage of the characteristic that the fluctuation of distance changes of irregular objects in special cases lags behind the actual data changes, the previously calculated new coordinates of point i are preprocessed. The formula is as follows: Where the function Based on the previous maximum point distance Calculate the starting position I of the numerical change within a range of n before and after coordinate i, where the value of n is proportional to the length of the irregular object; Next, the coordinates of the point cloud data in two-dimensional space are calculated. The relative position is then calculated through coordinate transformation. Based on the transformed coordinates, the inverse trigonometric function is recalculated using the center coordinates of the circle, and the corresponding angle transformation is performed. The formula is as follows: in The function represents the transformation between two perspectives. Let x be the x-axis coordinate of the center of the circle formed by the conveyor belt of the monitored conveyor. These represent the x-axis and y-axis coordinates of the calculated starting point position I. S4. The data processing system uses the point cloud reverse angle algorithm to calculate the abnormal data obtained in step S3 and obtain the corresponding angle data, which is the angle data of the torsion of the tested tubular tape. Using the point cloud data of the anomaly point, the angle between this coordinate and the center of the circle formed by the conveyor belt of the monitored conveyor is calculated. The initial angle value is adjusted so that the camera's viewing angle is 0 degrees. In a forward view, the right side of the observed laser device is positive, and the left side of the observed laser device is negative. The angle coordinates are then converted again to obtain the final angle result. The formula is as follows: in These are the angular coordinates calculated in step S3; S5. The data processing system sends the angle data obtained in step S4 to the data server via the OPC protocol and to the remote server via the Modbus TCP protocol.

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