A multi-laser sensor neighborhood self-calibration tire tread rubber profile reconstruction method and device

Abstract

The application provides a multi-laser sensor neighborhood self-calibration tire tread rubber profile reconstruction method and device, comprising the following steps: obtaining profile lines obtained by scanning a calibration block by two adjacent sensors, and determining a deviation angle and a deviation distance between the adjacent sensors according to the profile lines; obtaining local profile lines of a measured tire tread rubber obtained by scanning by a plurality of sensors, and rotating and translating the local profile lines according to the deviation angle and the deviation distance; performing Euclidean space distance calculation on the rotated and translated local profile lines, so as to obtain a best matching point set of two adjacent local profile lines; and performing weighted fusion processing on the best matching point set, so as to obtain an overall profile line of the measured tire tread rubber; the application has the beneficial effects that: the application does not need a large calibration object which is difficult to process and has high cost, and the calibration process is simple and easy to operate; the application adopts weighted fusion on the matching point set, and the accuracy of the overlapping part data is improved.

Description

Technical Field

[0001] This invention relates to the field of tread rubber profile reconstruction technology, and in particular to a method and apparatus for tread rubber profile reconstruction based on multi-laser sensor neighborhood self-calibration. Background Technology

[0002] The development of new energy vehicles has boosted China's automotive industry, thus placing demands on the tire industry for rapid delivery. Tire production is a complex, continuous process, mainly divided into five core stages. The following is an overview of each stage and its corresponding key equipment:

[0003] 1. Internal mixing: Main purpose: To mix raw materials such as rubber, carbon black, and additives to produce a rubber compound that meets performance requirements; corresponding equipment includes: internal mixer (such as a Banbury internal mixer), open mill, and rubber compound cooling line; 2. Semi-finished product preparation: Main purpose: To process the rubber compound into various tire components (tread, sidewall, cord layers, steel wire rings, etc.); corresponding equipment includes: extruder (for tread and sidewall), calender (for cord coating), cutting machine (for cutting cord and belt layers), and steel wire ring winding machine;

[0004] 3. Molding: Main purpose: To precisely assemble all semi-finished parts into an uncured "green tire" in sequence; corresponding equipment: tire forming machine (divided into two-drum one-time method, three-drum one-time method, etc., which is the "heart" equipment of tire manufacturing); 4. Vulcanization: Main purpose: To heat and pressurize the green tire to cause the rubber to cross-link (vulcanization), obtaining the final shape, pattern and performance; corresponding equipment: tire vulcanizing machine (mechanical and hydraulic types, including molds, bladders, etc.); 5. Final inspection: Main purpose: To check the appearance, uniformity, dynamic balance and internal structure of the finished tire for defects; corresponding equipment: uniformity testing machine, dynamic balance testing machine, X-ray inspection machine, appearance inspection machine.

[0005] Tire tread extruders are core equipment in the rubber industry, specifically used for producing semi-finished tire tread products. They are a type of cold-feed rubber extruder. They heat and plasticize solid rubber compound through a rotating screw, continuously extruding it through a die of a specific shape to form tread strips with the desired cross-sectional shape. The core working principle is as follows: the rubber compound (usually at room temperature) enters through the feed port and is conveyed forward by the rotating screw. During this process, the rubber compound is subjected to mechanical shearing and external heating, gradually changing from cold to hot and from hard to soft, eventually becoming a uniform viscous fluid. Sufficient pressure is built up at the die head, allowing it to be stably extruded from the die. The extruded hot tread strips then undergo cooling, shaping, and cutting processes to become semi-finished products for tire molding.

[0006] The weight, width, and height of the tread quality control points are the main factors determining whether the extruded tread compound is qualified. Currently, the pass rate is 70% to 85%. Unqualified tread compound needs to be reprocessed into tire stock before re-extrusion. If the tread compound extruded from recycled tire stock passes inspection on the second pass, it's considered a second pass; if it only passes on the third pass, it's considered a third pass. Clearly, the first-pass yield is a crucial indicator. To improve the first-pass yield of extruded tread compound, both faster delivery and energy saving and cost reduction can be achieved. To achieve this goal, existing technology involves adding a measuring device to the tread compound extruder to perform online real-time measurement and control of the tire tread compound extrusion line products, thereby improving the first-pass yield.

[0007] When scanning the tread rubber profile, if the tread rubber is too far from the laser line scanning sensor, the measurement accuracy will be insufficient; if it is too close, the field of view will be insufficient, and the complete tread rubber profile cannot be scanned. To balance accuracy and field of view, multiple laser line scanning sensors are often used. When multiple laser line scanning sensors are needed to measure and generate the tread rubber profile, sensor calibration and profile reconstruction are involved. However, current calibration methods typically involve using a large reference plate to calibrate the tilt angles and distances of all laser line scanning sensors relative to the reference, and then using a smaller reference plate to calibrate the relative positions between the sensors. The disadvantages of this existing technology are: the large reference plate is difficult and costly to manufacture; the calibration process is complex; and existing profile reconstruction methods have low accuracy in extracting data from overlapping areas. Therefore, there is an urgent need for a multi-laser sensor neighborhood self-calibration tread rubber profile reconstruction method. Summary of the Invention

[0008] In view of the shortcomings of the prior art described above, the purpose of this invention is to provide a method and apparatus for reconstructing the tread rubber profile by self-calibrating the neighborhood of multiple laser sensors, in order to solve the problems of large reference plates, complex calibration process, and low accuracy in extracting data of overlapping parts in existing calibration and profile reconstruction methods.

[0009] To achieve the above and other related objectives, the present invention provides the following technical solution:

[0010] A method for reconstructing the tread rubber profile through multi-laser sensor neighborhood self-calibration includes the following steps: obtaining the profile lines obtained by scanning a calibration block using two adjacent laser line scanning sensors, wherein the calibration block is located in the overlapping area of ​​the scanning lasers of the two adjacent laser line scanning sensors; determining the deviation angle and deviation distance between the two adjacent laser line scanning sensors based on the profile lines obtained by the two adjacent laser line scanning sensors, and converting the deviation angle and deviation distance to the deviation angle and deviation distance relative to the first laser line scanning sensor, thereby completing the calibration of several laser line scanning sensors;

[0011] A number of laser line scanning sensors are used to obtain local contour lines of the tread rubber under test. These local contour lines are then rotated and translated according to the calibrated deviation angle and deviation distance. The Euclidean space distance between two adjacent local contour lines after rotation and translation is calculated, and the optimal matching point set between the two adjacent local contour lines is determined based on the calculation results. The optimal matching point set between the two adjacent local contour lines is then subjected to weighted fusion processing, and the overall contour line of the tread rubber under test is obtained based on the weighted fusion processing result.

[0012] In one embodiment of the present invention, determining the deviation angle and deviation distance between two adjacent laser line scanning sensors based on the contour lines scanned by two adjacent laser line scanning sensors includes: determining the inclination angle between the contour lines scanned by the two adjacent laser line scanning sensors and the horizontal line, and determining the deviation angle between the two adjacent laser line scanning sensors based on the difference in inclination angle between the contour lines and the horizontal line, wherein the contour lines are line segments and are all part of the overall contour line of the calibration block; rotating the two adjacent contour lines to a horizontal state according to the deviation angle, and determining the deviation distance between the two adjacent laser line scanning sensors based on the two adjacent contour lines in the horizontal state.

[0013] In one embodiment of the present invention, the contour lines scanned by two adjacent laser line scanning sensors are both line segments, and each line segment has an endpoint corresponding to the edge of the calibration block. The left endpoint of the line segment scanned by one sensor corresponds to the left edge of the calibration block, and the right endpoint of the line segment scanned by the other sensor corresponds to the right edge of the calibration block.

[0014] In one embodiment of the present invention, the step of rotating two adjacent contour lines to a horizontal state according to the deviation angle, and determining the deviation distance between two adjacent laser line scanning sensors based on the two adjacent contour lines in the horizontal state, includes: rotating two adjacent contour lines to a horizontal state according to the deviation angle, and determining the distance between the endpoints of the two adjacent contour lines in the horizontal state; obtaining the length or width of the calibration block corresponding to the distance, and determining whether the distance is equal to the length or width of the calibration block; if not, the difference between the distance and the length or width of the calibration block is the deviation distance between the two adjacent laser line scanning sensors.

[0015] In one embodiment of the present invention, the step of calculating the Euclidean distance between two adjacent local contour lines after rotation and translation, and determining the optimal matching point set of the two adjacent local contour lines based on the calculation result, includes: calculating the Euclidean distance between the data points corresponding to the two adjacent local contour lines, thereby calculating the distance between the corresponding data points of the two adjacent local contour lines, and taking the corresponding data points with the smallest sum of distances between the corresponding data points of the two adjacent local contour lines as the optimal matching point set.

[0016] In one embodiment of the present invention, the step of performing weighted fusion processing on the best matching point sets of two adjacent local contour lines and obtaining the overall contour line of the tested tread rubber according to the weighted fusion processing result includes: performing weighted fusion processing according to the different weights corresponding to the best matching point sets respectively, thereby removing overlapping redundant data points, and obtaining the overall contour line of the tested tread rubber according to the weighted fusion processing result.

[0017] A multi-laser sensor neighborhood self-calibration tread rubber contour reconstruction device includes a data processing system. The data processing system includes: a data acquisition module for acquiring contour lines obtained by two adjacent laser line scanning sensors scanning a calibration block; and a sensor calibration module for determining the deviation angle and deviation distance between two adjacent laser line scanning sensors based on the contour lines obtained by the two adjacent laser line scanning sensors, and converting the deviation angle and deviation distance to deviation angle and deviation distance relative to the first laser line scanning sensor, thereby completing the calibration of several laser line scanning sensors.

[0018] The tread rubber contour reconstruction module is used to acquire local contour lines of the tested tread rubber obtained by scanning several laser line scanning sensors, and to rotate and translate the local contour lines according to the calibrated deviation angle and deviation distance; it is also used to calculate the Euclidean space distance between two adjacent local contour lines after rotation and translation, and to determine the best matching point set between the two adjacent local contour lines based on the calculation result; it is also used to perform weighted fusion processing on the best matching point set between the two adjacent local contour lines, and to obtain the overall contour line of the tested tread rubber based on the weighted fusion processing result.

[0019] In one embodiment of the present invention, it further includes a plurality of laser line scanning sensors arranged sequentially above the tread rubber to be detected and a calibration block located in the scanning laser overlap area of ​​two adjacent laser line scanning sensors; wherein, the pulse output terminal of the preceding laser line scanning sensor is connected to the scanning trigger input terminal of the following laser line scanning sensor.

[0020] As described above, the multi-laser sensor neighborhood self-calibration tread rubber contour reconstruction method and apparatus of the present invention have the following beneficial effects:

[0021] 1. This invention does not require large calibration objects that are difficult to process and costly. At the same time, the calibration process is simple and easy to operate: This invention places a small calibration block in the overlapping area of ​​the laser scanning of two adjacent laser line scanning sensors, and measures the data of the calibration block in the two sensors for calibration processing. After performing this calibration N-1 times, global calibration can be achieved. Therefore, the overall calibration process is simple and easy to operate.

[0022] 2. Weighted fusion of the matching point set improves the accuracy of overlapping data: This invention calculates the Euclidean distance between corresponding points on adjacent local contour lines to obtain the optimal matching point set, which is the overlapping point between adjacent local contour lines. Then, the optimal matching point set is weighted and fused to remove redundant data points before being spliced, thereby improving the accuracy of overlapping data. Attached Figure Description

[0023] Figure 1 The diagram shows the overall flowchart of the laser sensor neighborhood self-calibration tread rubber contour reconstruction method in the first embodiment of the present invention.

[0024] Figure 2 This is a structural diagram of the data processing system and the laser line scanning sensor in the laser sensor neighborhood self-calibration tread rubber contour reconstruction device according to the second embodiment of the present invention.

[0025] Figure 3 This is a structural diagram of the laser line scanning sensor and calibration block in the laser sensor neighborhood self-calibration tread rubber contour reconstruction device according to the second embodiment of the present invention.

[0026] Figure 4 The diagram shows an overlap between the beginning and end of a local contour line acquired by the sensor in this invention.

[0027] Figure 5 The diagram shows the overall outline of the tested tread rubber obtained by splicing together partial outline diagrams in this invention. Detailed Implementation

[0028] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. It should be noted that, unless otherwise specified, the following embodiments and features described herein can be combined with each other.

[0029] The first embodiment of the present invention relates to a method for reconstructing the tread rubber profile by self-calibrating the neighborhood of a multi-laser sensor, the process of which is as follows: Figure 1 As shown, the details are as follows:

[0030] Step 101: Obtain the contour lines obtained by scanning the calibration block using two adjacent laser line scanning sensors.

[0031] Specifically, the calibration block is located in the overlapping area of ​​the scanning lasers of two adjacent laser line scanning sensors.

[0032] Step 102: Determine the deviation angle and deviation distance between two adjacent laser line scanning sensors based on the contour lines scanned by the two adjacent laser line scanning sensors, and convert the deviation angle and deviation distance to the deviation angle and deviation distance relative to the first laser line scanning sensor, thereby completing the calibration of several laser line scanning sensors.

[0033] Specifically, firstly, the tilt angle between the contour lines scanned by two adjacent laser line scanning sensors and the horizontal line is determined, and the deviation angle between the two adjacent laser line scanning sensors is determined based on the difference in tilt angle between the contour lines and the horizontal line; then, the two adjacent contour lines are rotated to a horizontal state according to the deviation angle, and the deviation distance between the two adjacent laser line scanning sensors is determined based on the two adjacent contour lines in the horizontal state; it should be noted that the contour lines scanned by the two adjacent laser line scanning sensors are both line segments, and each line segment has an endpoint corresponding to the edge of the calibration block. The left endpoint of the line segment scanned by one sensor corresponds to the left edge of the calibration block, and the right endpoint of the line segment scanned by the other sensor corresponds to the right edge of the calibration block;

[0034] The process involves rotating two adjacent contour lines to a horizontal position based on a deviation angle, and determining the deviation distance between two adjacent laser line scanning sensors based on the two adjacent contour lines in the horizontal position. This includes the following steps: First, rotating two adjacent contour lines to a horizontal position based on a deviation angle, and determining the distance between the endpoints of the two adjacent contour lines in the horizontal position; then, obtaining the length or width of the calibration block corresponding to the distance, and determining whether the distance is equal to the length or width of the calibration block; if not, the difference between the distance and the length or width of the calibration block is the deviation distance between the two adjacent laser line scanning sensors; in this embodiment, the distance between the endpoints of two adjacent contour lines is equal to the width of the calibration block when there is no deviation.

[0035] For practical applications, please refer to the following details. Figure 3This scheme places a small calibration block in the overlapping area of ​​the laser scans of two adjacent laser line scan sensors, and measures the data of the calibration block in the two sensors for calibration processing. After performing this calibration N-1 times, global calibration can be achieved. In this embodiment, the specific dimensions of the small calibration block are: 120 mm long, 100 mm wide, and 15 mm high. This size is very small compared to the size of a typical calibration object required for the maximum coverage requirement of 600 mm. Furthermore, such a large calibration object would require custom markings, increasing the complexity of feature recognition.

[0036] The contour line of the calibration block scanned by the laser line scanning sensor is a line segment with two endpoints and an angle relative to the horizontal. When a calibration block is placed in the overlapping area of ​​two adjacent laser line scanning sensors, the line segment obtained by each sensor is not the entire contour line of the calibration block, and the angles of these two line segments relative to the horizontal are not necessarily the same. Calculating this angle difference yields the deviation angle between the two sensors. Each of these line segments has an endpoint corresponding to the edge of the calibration block; the left sensor corresponds to the left endpoint of its obtained line segment, and the right sensor corresponds to the right endpoint. When the two line segments are rotated to a horizontal position, the distance between the endpoints measured by the two sensors should be equal to the width of the calibration block. If this distance is not equal to the width of the calibration block, the difference between this distance and the width of the calibration block is the relative positional deviation between the two sensors. This invention aims to measure the deviation angle and deviation distance between two adjacent sensors using this small calibration block and convert the deviation angle and deviation distance to the deviation angle and deviation distance relative to the first sensor.

[0037] Step 103: Obtain the local contour lines of the tread rubber being tested obtained by scanning several laser line scanning sensors, and rotate and translate the local contour lines according to the calibrated deviation angle and deviation distance.

[0038] Step 104: Calculate the Euclidean distance between two adjacent local contour lines after rotation and translation, and determine the best matching point set between the two adjacent local contour lines based on the calculation results.

[0039] Specifically, firstly, the Euclidean distance between the data points corresponding to two adjacent local contour lines is calculated, thereby calculating the distance between the corresponding data points of two adjacent local contour lines. Then, the corresponding data points with the smallest sum of distances between the corresponding data points of two adjacent local contour lines are taken as the best matching point set.

[0040] Step 105: Perform weighted fusion processing on the best matching point set of two adjacent local contour lines, and obtain the overall contour line of the tested tread rubber based on the weighted fusion processing result.

[0041] Specifically, weighted fusion processing is performed based on the different weights corresponding to the best matching point set to remove overlapping and redundant data points, and the overall contour line of the tested tread rubber is obtained based on the weighted fusion processing result.

[0042] In practical applications, when reconstructing the contour lines, the local contour lines obtained from each sensor scan are first rotated and translated according to the deviation angle and deviation distance obtained during calibration, and then they can be merged together. However, if only a simple merging is performed (placing the second local contour line after the first, and the third local contour line after the second), a lot of redundant data will be generated. Therefore, it is necessary to find the optimal matching point between adjacent sensor data for data fusion and stitching to achieve contour line reconstruction. The specific processing principle and process are as follows:

[0043] Assuming each contour line has 2000 points, and the end of the left contour line and the beginning of the right contour line have 100 overlapping points, then the 1901st point on the left contour line corresponds to the 1st point on the right contour line, the 1902nd point on the left contour line corresponds to the 2nd point on the right contour line, and so on. Therefore, the 2000th point on the left contour line corresponds to the 100th point on the right contour line. Since these corresponding points overlap, it means that calculating the distance (Euclidean distance) between these corresponding points will yield a very small result, and the sum will also be very small. However, in reality, although the number of points on each local contour line is fixed, the number of effective points participating in the splicing is not fixed, and the overlap range of the left and right local contour lines is also not determined. That is to say, the number of overlapping points is uncertain. Therefore, it is necessary to find the overlapping points, i.e., the best matching point set for the overlapping part, based on the above assumptions. The process involves iteratively probing; the point set corresponding to the smallest sum of distances found in each iteration is the optimal matching point set.

[0044] Let's take a best-matching set of 100 points as an example to illustrate the fusion and splicing process. The weight of the 1901st point on the left contour line is 100%, while the weight of the 1st point on the right contour line is 0%. The weight of the 1902nd point on the left contour line is 99%, and the weight of the 2nd point on the right contour line is 1%. The weight of the 1902nd point on the left contour line is 98%, and the weight of the 2nd point on the right contour line is 2%, and so on. The weight of the 2000th point on the left contour line is 0%, and the weight of the 100th point on the right contour line is 100%. By processing according to this weight, the 200 overlapping points are reduced to 100 points. This process is called weighted fusion.

[0045] After the above processing, the real-time contour data of the tread rubber extrusion process is obtained, which is used to achieve control and improve the first pass rate of this process. It should be noted that if a large calibration block is used and all the scanning laser lines of the sensors fall on it, you can imagine that the laser lines of the middle sensors will not be able to scan the edge of the calibration block, and therefore cannot be positioned with the adjacent sensors. This is another reason for using a small calibration block.

[0046] Furthermore, after obtaining the real-time contour data, the area below the contour is calculated by integrating the measured contour. Multiplying the area by the stretching speed and the tire material density yields the instantaneous weight. With the instantaneous weight, the weight of 1 meter of tread rubber can be calculated, which is known in the industry as meter weight. Thus, the weight of one strip of tread rubber can be calculated, which is known in the industry as strip weight. If the strip weight is too heavy, the width is too large, or the height of the quality control point is too high, the stretching speed should be increased, and vice versa, thereby improving the first-pass yield of the extruded tread rubber.

[0047] Furthermore, the complete technical solution provided by the present invention includes: 1. Using multiple PHM2000 series laser line scanning sensors manufactured by the company arranged above the tread rubber to be detected;

[0048] 2. The sensor uses an external level trigger for scanning. After scanning, the sensor outputs a low-level pulse. If there are three sensors, connect the output pulse of sensor 1 to the scan trigger input of sensor 2, connect the output pulse of sensor 2 to the scan trigger input of sensor 3, and connect the output pulse of sensor 3 to the scan trigger input of sensor 1, forming a loop. This connection method ensures that only one sensor scans at a time, thus eliminating crosstalk from other laser sensors. The start or stop of the system scan is controlled by sending a start or stop command to sensor 1.

[0049] 3. N sensors undergo N-1 calibration cycles between adjacent sensors (the calibration block is placed in the laser overlap area of ​​two adjacent sensors for measurement and calculation). The goal of calibration is to measure the deviation angle between the two sensors (the principle is as described above). Taking sensor number one as a base, i.e., assuming sensor number one is horizontal, the data from sensor number two is rotated to be horizontal, and then the depth distance deviation between sensor number two and sensor number one is calculated. Additionally, edge feature points in the data of each sensor are identified, and combined with the width of the calibration block, the horizontal coordinates of sensor number two in the coordinate system of sensor number one are calculated (the principle is as described above).

[0050] 4. Perform pairwise calibration on the remaining sensors, calculate the angular deviation between the sensors, convert the deviation into the angular deviation with respect to sensor number one, rotate the sensor data to the horizontal, calculate the deviation of the depth direction distance relative to sensor number one, and calculate the horizontal coordinate relative to the previous sensor in the same way as above, and then convert it into the horizontal direction coordinate in the coordinate system of sensor number one. This completes the calibration process (the principle is as described above).

[0051] 5. Taking three sensors as an example, the process involves processing the tail of the data from sensor one to extract the header of the data from sensor two, and processing the tail of the data from sensor two to extract the header of the data from sensor three (the principle is as described above); please refer to [link to relevant documentation] for details. Figure 4 The positions of the three curves in the figure were roughly adjusted manually and then precisely obtained using the aforementioned method.

[0052] 6. Calculate the Euclidean spatial distance for the overlapping portions of the sensor data above to determine the optimal set of matching points (the principle is as described above); then perform weighted fusion processing on the matching point sets to remove duplicates, and finally generate a complete curve (the principle is as described above). Please refer to [link to documentation] for details. Figure 5 Alternatively, the previously generated curve can be resampled to make the horizontal interval between adjacent points equal.

[0053] It should also be noted that there are other alternative solutions to the technical solution of this patent that can also achieve the purpose of the invention: 1. The present invention aims to propose a device for contour line reconstruction, which first uses manual coarse adjustment of the position of data in the chart, and then uses a small-sized, low-cost calibration block to achieve global calibration through a simple method to determine the precise position of the data in the chart. The device that uses the method of calculating Euclidean space distance to match overlapping point sets, and then performs weighted fusion and resampling to reconstruct the curve during sensor data reconstruction should all be within the scope of protection; 2. The present invention is applicable to online detection applications in the detection scenarios shown in the above solution block diagram, including applications in subdivided industries, but is not limited to these applications.

[0054] The second embodiment of the present invention relates to a multi-laser sensor neighborhood self-calibration tread rubber contour reconstruction device. Please refer to [link to relevant documentation]. Figure 2 and Figure 3 This includes a data processing system, which comprises:

[0055] The data acquisition module is used to acquire the contour lines obtained by two adjacent laser line scanning sensors scanning the calibration block;

[0056] The sensor calibration module is used to determine the deviation angle and deviation distance between two adjacent laser line scanning sensors based on the contour lines scanned by two adjacent laser line scanning sensors, and convert the deviation angle and deviation distance to the deviation angle and deviation distance relative to the first laser line scanning sensor, thereby completing the calibration of several laser line scanning sensors;

[0057] The tread rubber contour reconstruction module is used to acquire local contour lines of the tested tread rubber obtained by scanning several laser line scanning sensors, and rotate and translate these local contour lines according to the calibrated deviation angle and deviation distance; it is also used to calculate the Euclidean space distance between two adjacent local contour lines after rotation and translation, and determine the best matching point set between the two adjacent local contour lines based on the calculation results; it is also used to perform weighted fusion processing on the best matching point set between the two adjacent local contour lines, and obtain the overall contour line of the tested tread rubber based on the weighted fusion processing result.

[0058] It also includes several laser line scanning sensors arranged sequentially above the tread rubber being tested, and a calibration block located in the overlapping area of ​​the scanning lasers of two adjacent laser line scanning sensors; wherein, the pulse output terminal of the preceding laser line scanning sensor is connected to the scanning trigger input terminal of the following laser line scanning sensor.

[0059] It is not difficult to see that this embodiment is a system implementation corresponding to the first embodiment, and this embodiment can be implemented in conjunction with the first embodiment. The relevant technical details mentioned in the first embodiment are still valid in this embodiment, and will not be repeated here to reduce repetition. Accordingly, the relevant technical details mentioned in this embodiment can also be applied to the first embodiment.

[0060] It is worth mentioning that all modules involved in this embodiment are logical modules. In practical applications, a logical unit can be a physical unit, a part of a physical unit, or a combination of multiple physical units. Furthermore, to highlight the innovative aspects of this invention, this embodiment does not introduce units that are not closely related to solving the technical problem proposed by this invention; however, this does not mean that other units are absent from this embodiment.

[0061] In summary, this invention proposes a small-sized calibration block that achieves global calibration through N-1 pairwise calibrations between sensors. This invention also proposes a combined method to find the overlapping parts of adjacent sensor data, then process the data, and finally reconstruct the tread rubber profile.

[0062] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the invention. All equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in this invention should still be covered by the claims of this invention.

Claims

1. A method for reconstructing the tread rubber profile using multi-laser sensor neighborhood self-calibration, characterized in that, Includes the following steps: The contour line obtained by scanning a calibration block by two adjacent laser line scanning sensors is acquired, wherein the calibration block is located in the overlapping area of ​​the scanning lasers of the two adjacent laser line scanning sensors; The deviation angle and deviation distance between two adjacent laser line scanning sensors are determined based on the contour lines scanned by two adjacent laser line scanning sensors, and the deviation angle and deviation distance are converted to the deviation angle and deviation distance relative to the first laser line scanning sensor, thereby completing the calibration of several laser line scanning sensors; A partial contour line of the tread rubber under test is obtained by scanning with several laser line scanning sensors, and the partial contour line is rotated and translated according to the calibrated deviation angle and deviation distance; The Euclidean distance between two adjacent local contour lines after rotation and translation is calculated, and the best matching point set between the two adjacent local contour lines is determined based on the calculation results. The best matching point set of two adjacent local contour lines is weighted and fused, and the overall contour line of the tested tread rubber is obtained based on the weighted fusion result.

2. The method for reconstructing the tread rubber profile by multi-laser sensor neighborhood self-calibration according to claim 1, characterized in that: The step of determining the deviation angle and deviation distance between two adjacent laser line scanning sensors based on the contour lines scanned by the two adjacent laser line scanning sensors includes: Determine the inclination angle between the contour line obtained by two adjacent laser line scanning sensors and the horizontal line, and determine the deviation angle between the two adjacent laser line scanning sensors based on the difference in inclination angle between the contour line and the horizontal line, wherein the contour line is a line segment and is a part of the overall contour line of the calibration block; The two adjacent contour lines are rotated to a horizontal state according to the deviation angle, and the deviation distance between the two adjacent laser line scanning sensors is determined based on the two adjacent contour lines in the horizontal state.

3. The method for reconstructing the tread rubber profile by multi-laser sensor neighborhood self-calibration according to claim 2, characterized in that: The contour lines obtained by the two adjacent laser line scanning sensors are both line segments, and each line segment has an endpoint that corresponds to the edge of the calibration block. The left endpoint of the line segment obtained by one sensor corresponds to the left edge of the calibration block, and the right endpoint of the line segment obtained by the other sensor corresponds to the right edge of the calibration block.

4. The method for reconstructing the tread rubber profile by multi-laser sensor neighborhood self-calibration according to claim 3, characterized in that: The step of rotating two adjacent contour lines to a horizontal state according to the deviation angle, and determining the deviation distance between two adjacent laser line scanning sensors based on the two adjacent contour lines in the horizontal state, includes: Two adjacent contour lines are rotated to a horizontal state according to the deviation angle, and the distance between the endpoints of the two adjacent contour lines in the horizontal state is determined. Obtain the length or width of the calibration block corresponding to the distance, and determine whether the distance is equal to the length or width of the calibration block; if not, the difference between the distance and the length or width of the calibration block is the deviation distance between two adjacent laser line scanning sensors.

5. The method for reconstructing the tread rubber profile by multi-laser sensor neighborhood self-calibration according to claim 1, characterized in that: The step of calculating the Euclidean distance between two adjacent local contour lines after rotation and translation, and determining the optimal matching point set for the two adjacent local contour lines based on the calculation results, includes: The Euclidean distance between the data points corresponding to two adjacent local contour lines is calculated, thereby calculating the distance between the corresponding data points of two adjacent local contour lines. The corresponding data points with the smallest sum of distances between the corresponding data points of two adjacent local contour lines are taken as the optimal matching point set.

6. The method for reconstructing the tread rubber profile by multi-laser sensor neighborhood self-calibration according to claim 1, characterized in that: The step of performing weighted fusion processing on the best matching point sets of two adjacent local contour lines, and obtaining the overall contour line of the tested tread rubber based on the weighted fusion processing result, includes: The optimal matching point set is weighted and fused according to its corresponding weights to remove overlapping and redundant data points, and the overall contour line of the tested tread rubber is obtained based on the weighted fusion result.

7. A multi-laser sensor neighborhood self-calibration tread rubber contour reconstruction device, characterized in that: Includes a data processing system, the data processing system comprising: The data acquisition module is used to acquire the contour lines obtained by two adjacent laser line scanning sensors scanning the calibration block; The sensor calibration module is used to determine the deviation angle and deviation distance between two adjacent laser line scanning sensors based on the contour lines scanned by two adjacent laser line scanning sensors, and convert the deviation angle and deviation distance into deviation angle and deviation distance relative to the first laser line scanning sensor, thereby completing the calibration of several laser line scanning sensors; The tread rubber contour reconstruction module is used to acquire local contour lines of the tested tread rubber obtained by scanning several laser line scanning sensors, and to rotate and translate the local contour lines according to the calibrated deviation angle and deviation distance; it is also used to calculate the Euclidean space distance between two adjacent local contour lines after rotation and translation, and to determine the best matching point set between the two adjacent local contour lines based on the calculation result; it is also used to perform weighted fusion processing on the best matching point set between the two adjacent local contour lines, and to obtain the overall contour line of the tested tread rubber based on the weighted fusion processing result.

8. The multi-laser sensor neighborhood self-calibration tread rubber contour reconstruction device according to claim 7, characterized in that: It also includes several laser line scanning sensors arranged sequentially above the tread rubber being tested, and a calibration block located in the overlapping area of ​​the scanning lasers of two adjacent laser line scanning sensors; wherein, the pulse output terminal of the preceding laser line scanning sensor is connected to the scanning trigger input terminal of the following laser line scanning sensor.

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