Die laser cleaning method and device based on three-dimensional vision, medium and product

Abstract

The embodiment of the invention discloses a mold laser cleaning method and device based on three-dimensional vision, a medium and a product. A specific embodiment of the method comprises the following steps: acquiring point location information of a to-be-cleaned mold in a cleaning room through a three-dimensional camera included in the laser cleaning equipment; according to the point location information, position information and contour information corresponding to the to-be-cleaned mold are generated; according to the position information and the contour information, mold size information corresponding to the to-be-cleaned mold is generated; cleaning parameter information is generated according to the mold size information; and a mechanical arm included in the laser cleaning equipment is controlled to carry a laser cleaning head to clean the to-be-cleaned mold. According to the embodiment, accurate information of the position and the contour of the mold can be automatically obtained, dependence on manpower in the cleaning process is reduced, and the cleaning efficiency and the mold cleaning effect are improved.

Description

Technical Field

[0001] Embodiments of the present disclosure relate to the field of computer technology, and more particularly to a method, apparatus, medium, and product for laser cleaning of molds based on three-dimensional vision. Background Art

[0002] Laser cleaning is an advanced surface treatment technology aimed at removing dirt, oxide layers, residues, or other contaminants on the surface of an object. Currently, when performing laser cleaning on a mold, the commonly used method is as follows: Before cleaning, manual trajectory visual calibration of the robot is performed for different types and sizes of molds, and a database is established. When cleaning, the data of the mold to be cleaned in the database needs to be manually called in advance, and the mold is cleaned according to the visual calibration trajectory in the database.

[0003] However, when using the above method, the following technical problems often exist: Manual visual calibration is required for each new mold cleaning, and manual participation is also required during the cleaning process, resulting in the inability to automatically obtain accurate information on the position and contour of the mold. The cleaning process is highly dependent on manual labor, the cleaning efficiency is low, and when the deviation between the visual calibration trajectory and the mold position is large, the mold cleaning effect is poor.

[0004] The above information disclosed in this background art section is only used to enhance the understanding of the background of the inventive concept, and thus, it may include information that does not form the prior art known to those of ordinary skill in the art in this country. Summary of the Invention

[0005] This content part of the present disclosure is used to briefly introduce the concepts, which will be described in detail in the subsequent detailed implementation part. This content part of the present disclosure is not intended to identify the key features or essential features of the claimed technical solution, nor is it intended to limit the scope of the claimed technical solution.

[0006] Some embodiments of the present disclosure propose a method for laser cleaning of molds based on three-dimensional vision, a laser cleaning device, a computer-readable medium, and a computer program product to solve one or more of the technical problems mentioned in the above background art section.

[0007] In a first aspect, some embodiments of the present disclosure provide a method for laser cleaning of molds based on three-dimensional vision, which is applied to a laser cleaning device. The method includes: obtaining the point position information of the mold to be cleaned in the cleaning chamber through a three-dimensional camera included in the above laser cleaning device; generating the position information and contour information corresponding to the mold to be cleaned according to the above point position information; generating the mold size information corresponding to the mold to be cleaned according to the above position information and the above contour information; generating cleaning parameter information according to the above mold size information; controlling a robotic arm included in the above laser cleaning device to carry a laser cleaning head to perform cleaning processing on the mold to be cleaned.

[0008] In a second aspect, some embodiments of the present disclosure provide a laser cleaning device, including: one or more processors; a three-dimensional camera configured to collect point cloud data; a robotic arm configured to move while carrying a laser cleaning head; a laser cleaning head configured to emit laser; and a storage device having stored thereon one or more programs, which when executed by the one or more processors cause the one or more processors to implement the method described in any implementation manner of the above first aspect.

[0009] In a third aspect, some embodiments of the present disclosure provide a computer-readable medium having stored thereon a computer program, wherein the program, when executed by a processor, implements the method described in any implementation manner of the above first aspect.

[0010] In a fourth aspect, some embodiments of the present disclosure provide a computer program product including a computer program, which when executed by a processor implements the method described in any implementation manner of the above first aspect.

[0011] The above-mentioned various embodiments of the present disclosure have the following beneficial effects: Through the 3D vision-based die laser cleaning method of some embodiments of the present disclosure, accurate information on the position and contour of the die can be automatically obtained, reducing the dependence on manual labor during the cleaning process, and improving the cleaning efficiency and die cleaning effect. Specifically, the reasons for the low cleaning efficiency and poor die cleaning effect are as follows: When cleaning a new die, manual visual calibration is required, and manual participation is also needed during the cleaning process, resulting in the inability to automatically obtain accurate information on the position and contour of the die. The cleaning process has a strong dependence on manual labor, the cleaning efficiency is low, and when the deviation between the calibration trajectory and the die position is large, the die cleaning effect is poor. Based on this, in the 3D vision-based die laser cleaning method of some embodiments of the present disclosure, first, the point position information of the die to be cleaned in the cleaning room is obtained through the 3D camera included in the above-mentioned laser cleaning equipment. Thus, the 3D point cloud data of the die to be cleaned can be collected by the 3D camera. Then, based on the above-mentioned point position information, the position information and contour information corresponding to the die to be cleaned are generated. Thus, the position and contour of the die to be cleaned can be automatically identified through the collected point cloud data. Next, based on the above-mentioned position information and the above-mentioned contour information, the die size information corresponding to the die to be cleaned is generated. Thus, the size of the die to be cleaned can be automatically identified. Secondly, based on the above-mentioned die size information, the cleaning parameter information is generated. Thus, the various parameters required during the cleaning process can be automatically determined. Finally, the robotic arm included in the above-mentioned laser cleaning equipment is controlled to carry the laser cleaning head to perform cleaning treatment on the die to be cleaned. Thus, through the automatically determined various parameters, the robotic arm can be controlled to drive the laser cleaning head to automatically clean the die, thereby eliminating the need for manual participation and the need to extract the calibration cleaning trajectory from the database, thus reducing the dependence on manual labor during the cleaning process and improving the cleaning efficiency and die cleaning effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In combination with the accompanying drawings and with reference to the following specific embodiments, the above and other features, advantages, and aspects of the various embodiments of the present disclosure will become more apparent. Throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic, and the elements and elements are not necessarily drawn to scale.

[0013] Figure 1 is a flowchart of some embodiments of the 3D vision-based die laser cleaning method according to the present disclosure;

[0014] Figure 2 is a schematic diagram of the interior of the cleaning room in the test scenario;

[0015] Figure 3 is a schematic diagram of laser cleaning being performed inside the cleaning room in the test scenario;

[0016] Figure 4It is a schematic structural diagram of a laser cleaning device suitable for implementing some embodiments of the present disclosure. Detailed implementation manners

[0017] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although some embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. On the contrary, these embodiments are provided to more thoroughly and completely understand the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not used to limit the protection scope of the present disclosure.

[0018] In addition, it should be noted that for the sake of convenience of description, only parts related to the relevant invention are shown in the drawings. Without conflict, the embodiments in the present disclosure and the features in the embodiments can be combined with each other.

[0019] It should be noted that the concepts such as "first" and "second" mentioned in the present disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order of the functions executed by these devices, modules or units or their interdependent relationships.

[0020] It should be noted that the modifications of "one" and "multiple" mentioned in the present disclosure are illustrative rather than restrictive. Those skilled in the art should understand that unless clearly specified otherwise in the context, it should be understood as "one or more".

[0021] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only for illustrative purposes and are not used to limit the scope of these messages or information.

[0022] The present disclosure will be described in detail below with reference to the drawings and in combination with the embodiments.

[0023] Figure 1 Flow 100 of some embodiments of a three-dimensional vision-based die laser cleaning method according to the present disclosure is shown. The three-dimensional vision-based die laser cleaning method includes the following steps:

[0024] Step 101, obtain the point position information of the die to be cleaned in the cleaning chamber through a three-dimensional camera included in the laser cleaning device.

[0025] In some embodiments, the execution entity (e.g., a laser cleaning device) of the mold laser cleaning method based on three-dimensional vision can obtain the point position information of the mold to be cleaned in the cleaning chamber through the three-dimensional camera included in the above-mentioned laser cleaning device. Among them, the above-mentioned laser cleaning device can be a device that cleans an object by laser. The above-mentioned laser cleaning device can include a three-dimensional camera, a robotic arm, and a laser cleaning head. The above-mentioned laser cleaning device can also include at least one of the following: a main motion mechanism, a dust suction system. The three-dimensional camera can be a depth camera and can be used to collect point cloud data. The robotic arm can be a six-axis robotic arm and can be used to carry the laser cleaning head to move. The laser cleaning head can be connected to a laser cleaning machine, and the laser cleaning head can be an output component for outputting laser. The laser cleaning machine can include a laser and a chiller. The main motion mechanism can carry the mold to be cleaned into the cleaning chamber for laser cleaning, and can also carry the cleaned mold out of the cleaning chamber. The dust suction system can be used to absorb the dust during the cleaning process, reducing pollution and secondary adhesion. The above-mentioned mold to be cleaned can be a mold with substances attached to the surface to be cleaned. For example, the mold to be cleaned can be a tire mold. The mold to be cleaned can include at least one mold component. The mold component can be a part of the mold. The above-mentioned point position information can include the three-dimensional coordinates of each collected point position.

[0026] As an example, the cleaning chamber in the test scenario can refer to Figure 2 , Figure 2 which shows the three-dimensional camera, robotic arm, main motion mechanism, and laser cleaning head in the cleaning chamber.

[0027] In some alternative implementation manners of some embodiments, the above-mentioned execution entity can obtain the point position information of the mold to be cleaned in the cleaning chamber through the three-dimensional camera included in the above-mentioned laser cleaning device by the following steps:

[0028] First step, in response to determining that the main motion mechanism included in the above-mentioned laser cleaning device stops in the cleaning chamber and the main motion mechanism carries the mold to be cleaned, collect a panoramic view of the main motion mechanism through the camera included in the above-mentioned laser cleaning device. Whether the main motion mechanism carries the mold to be cleaned can be determined by a weight sensor or an infrared sensor. Whether the main motion mechanism stops in the cleaning chamber can be determined by an infrared sensor at the stop position. The above-mentioned camera can be a two-dimensional camera.

[0029] Second step, perform background removal processing on the above-mentioned panoramic view to obtain the mold area. In practice, a panoramic view of the main motion mechanism without a mold pre-shot can be used to perform background removal processing on the panoramic view of the main motion mechanism carrying the mold to be cleaned to obtain the mold area.

[0030] Step 3: Identify the area of the mold area in the above mold area. Here, the area can be represented by the number of pixels.

[0031] Step 4: Determine the mold weight of the mold to be cleaned carried on the above main motion mechanism. The mold weight of the mold to be cleaned carried on the above main motion mechanism can be determined by a weight sensor provided on the main motion mechanism.

[0032] Step 5: Determine the area ratio by taking the ratio of the above mold area to the preset area. The preset area can be set in advance.

[0033] Step 6: Determine the weight ratio by taking the ratio of the above mold weight to the preset weight. The preset weight can be set in advance.

[0034] Step 7: Generate mold type parameter information based on the above area ratio and the above weight ratio. In practice, the weighted sum of the above area ratio and the above weight ratio can be determined as the mold type parameter information. The weighted coefficients corresponding to the above area ratio and the above weight ratio can be set in advance, and the weighted coefficient of the above area ratio is greater than the weighted coefficient of the above weight ratio. For example, the weighted coefficient of the above area ratio can be 0.8. The weighted coefficient of the above weight ratio can be 0.2.

[0035] Step 8: Determine the mold type corresponding to the above mold to be cleaned according to the above mold type parameter information. In practice, the above execution entity can, in response to determining that the above mold type parameter information is greater than or equal to the preset threshold, determine the first type as the mold type corresponding to the above mold to be cleaned. The above execution entity can, in response to determining that the above mold type parameter information is less than the preset threshold, determine the second type as the mold type corresponding to the above mold to be cleaned. The first type can represent that the mold to be cleaned is a large mold. The second type can represent that the mold to be cleaned is a small mold.

[0036] Step 9: Determine the target 3D camera to be activated according to the above mold type. Among them, the 3D cameras applicable to each mold type can be pre-configured and set in the above cleaning room. For example, the 3D camera applicable to the first type can be a 3D camera with a large field of view, high precision, and long working distance, and can include but are not limited to: area array 3D cameras, ToF (Time-of-Flight) cameras, laser triangulation cameras. The 3D camera applicable to the second type can be a 3D camera with a relatively small field of view, precision, and working distance, and can include but are not limited to: line scan 3D cameras, structured light 3D cameras. In practice, one 3D camera can be set for each type in the cleaning room.

[0037] Step 10: Activate the above target 3D camera.

[0038] The eleventh step is to initialize the camera parameter information of the above-mentioned target 3D camera. Among them, the above-mentioned camera parameter information includes gain, exposure time, scanning resolution, and frame rate. In practice, the camera parameter information of the above-mentioned target 3D camera can be initialized by using preset gain, exposure time, scanning resolution, and frame rate.

[0039] The twelfth step is to control the above-mentioned target 3D camera to scan the mold to be cleaned on the main motion mechanism from at least one perspective to obtain point position information.

[0040] The thirteenth step is to adjust the gain and / or exposure time included in the camera parameter information of the above-mentioned target 3D camera in response to detecting that the noise information of the collected point position information meets the preset noise condition. The noise information can be the standard deviation of the distances between the collected individual point positions and adjacent point positions. The preset noise condition can be that the noise information is greater than the preset standard deviation. In practice, the gain included in the camera parameter information of the above-mentioned target 3D camera can be reduced by a preset gain value and / or the exposure time included in the camera parameter information of the above-mentioned target 3D camera can be reduced by a preset duration.

[0041] The fourteenth step is to adjust the scanning resolution included in the camera parameter information of the above-mentioned target 3D camera in response to detecting that the point cloud resolution of the collected point position information meets the preset resolution condition. The preset resolution condition can be that the average value of the distances between the collected individual point positions and adjacent point positions is less than the above-mentioned scanning resolution. In practice, the scanning resolution included in the camera parameter information of the above-mentioned target 3D camera can be increased by a preset value.

[0042] The above first step - fourteenth step, as an inventive point of the embodiment of the present disclosure, solves the technical problem of "when the sizes of different molds vary greatly, the performance of a single 3D camera cannot adapt to different types of molds. For example, a large mold may contain complex geometric structures (such as grooves, holes, etc.). If the field of view is insufficient, some areas may not be scanned, which will affect the subsequent cleaning path planning. If a low - resolution camera is used to scan a small mold, details may be lost, resulting in poor quality of the collected point cloud data". The factors that lead to poor quality of the collected point cloud data are often as follows: when the sizes of different molds vary greatly, the performance of a single 3D camera cannot adapt to different types of molds. For example, a large mold may contain complex geometric structures (such as grooves, holes, etc.). If the field of view is insufficient, some areas may not be scanned, which will affect the subsequent cleaning path planning. If a low - resolution camera is used to scan a small mold, details may be lost, resulting in poor quality of the collected point cloud data. If the above factors are solved, the effect of improving the quality of the collected point cloud data can be achieved. To achieve this effect, the present disclosure uses corresponding types of 3D cameras for different types of molds, so as to generate high - quality point cloud data according to the mold model characteristics, improving the integrity, resolution and accuracy of the point cloud data. It can also effectively improve the scanning efficiency of the point cloud data.

[0043] Step 102, generate the position information and contour information of the corresponding mold to be cleaned according to the point position information.

[0044] In some embodiments, the above - mentioned execution entity can generate the position information and contour information of the corresponding mold to be cleaned according to the above - mentioned point position information. The above - mentioned position information can represent the position of the mold to be cleaned. The contour information can represent the contour of at least one area in the mold to be cleaned.

[0045] In some optional implementation manners of some embodiments, the above - mentioned execution entity can generate the position information and contour information of the corresponding mold to be cleaned according to the above - mentioned point position information through the following steps:

[0046] First step, for each point position in the above - mentioned point position information, perform the following first loop step:

[0047] The first sub - step, determine the adjacent point positions corresponding to the above - mentioned point position to obtain each adjacent point position. Among them, the adjacent point position can be a point position whose distance from the above - mentioned point position is less than a preset distance. The adjacent point position can be a predetermined number of point positions with the closest distance to the above - mentioned point position.

[0048] The second sub - step, generate the point - to - point distance between the above - mentioned point position and each of the above - mentioned adjacent point positions to obtain each point - to - point distance. Here, the point - to - point distance can be the Euclidean distance.

[0049] The third sub-step is to determine the average of the distances between the above-mentioned points as the average point distance.

[0050] The second step is to generate the mean and standard deviation of the average point distances based on the determined average point distances. In practice, the above-mentioned execution entity can determine the mean of the above-mentioned average point distances as the mean of the average point distances, and can determine the standard deviation of the above-mentioned average point distances as the standard deviation of the average point distances.

[0051] The third step is to perform the following second loop step for each point in the point information:

[0052] The first sub-step is to determine the number of adjacent points corresponding to the above-mentioned point as the adjacent number.

[0053] The second sub-step is to determine the absolute value of the difference between the average point distance corresponding to the above-mentioned point and the mean of the average point distances as the distance deviation.

[0054] The third sub-step is to determine the product of the standard deviation of the average point distance and a preset coefficient as the comparison information. For example, the value range of the above-mentioned preset coefficient can be between 1 and 3, specifically depending on the proportion of point clouds to be filtered out. The larger the preset coefficient, the smaller the filtered proportion, and the smaller the preset coefficient, the larger the filtered proportion.

[0055] The fourth sub-step is to, in response to determining that the above-mentioned distance deviation is greater than the above-mentioned comparison information and the above-mentioned adjacent number is less than the preset number, remove the above-mentioned point from the point information to update the point information, and then perform the above-mentioned second loop step again using the updated point information. The above-mentioned preset number can be less than the above-mentioned predetermined number. Thus, the collected point information can be filtered from two dimensions: the distance from adjacent points and the number of adjacent points, to remove abnormal points.

[0056] The fourth step is to generate the position information and contour information corresponding to the mold to be cleaned based on the updated point information.

[0057] In some optional implementation manners of some embodiments, the above-mentioned execution entity can generate the position information and contour information corresponding to the mold to be cleaned based on the updated point information through the following steps:

[0058] The first step is to separate the mold point information of the mold to be cleaned from the updated point information. In practice, the above-mentioned execution entity can use a region growing algorithm or a clustering algorithm (such as DBSCAN) to separate the point cloud corresponding to the overall contour area of the mold to be cleaned from the background to obtain the mold point information.

[0059] The second step is to extract each abscissa, each ordinate, and each vertical coordinate from the above-mentioned mold point information respectively.

[0060] Step 3: Determine the mean value of each of the above abscissas as the abscissa center.

[0061] Step 4: Determine the mean value of each of the above ordinates as the ordinate center.

[0062] Step 5: Determine the mean value of each of the above applicates as the applicate center.

[0063] Step 6: Determine the above abscissa center, the above ordinate center, and the above applicate center as the position information corresponding to the mold to be cleaned.

[0064] In some optional implementation manners of some embodiments, the above execution subject may generate the position information and contour information corresponding to the mold to be cleaned according to the updated point position information through the following steps:

[0065] Step 1: Generate a triangular mesh according to each point position in the above mold point position information. In practice, the above execution subject may construct a Delaunay triangulation with each point position in the above mold point position information as vertices to obtain a triangular mesh.

[0066] Step 2: For each triangular region in the above triangular mesh, determine the circumradius of the above triangular region. In practice, the above execution subject may determine the area of the above triangular region and the side lengths of the three sides. Then, the product of the squares of the three side lengths can be determined. Next, the ratio of the square root of the above product to the product of the above area and a target value can be determined as the circumradius. The target value can be 4.

[0067] Step 3: Extract the triangular regions from the above triangular mesh whose corresponding circumradii satisfy a preset radius condition as the retained triangular regions to obtain each retained triangular region. The above preset radius condition may be that the circumradius is less than a preset radius length.

[0068] Step 4: Determine the boundary edges in each of the above retained triangular regions as the contour edges to obtain each contour edge. The boundary edge may be an edge that is not shared with other triangular regions.

[0069] Step 5: Determine the vertex point positions corresponding to each of the above contour edges as the initial contour point set. Thus, a more accurate contour can be extracted.

[0070] Step 6: For each point position in the above mold point position information, perform the following steps:

[0071] The first sub-step: Determine the adjacent point positions corresponding to the above point position to obtain an adjacent point position set.

[0072] The second sub-step is to construct a first covariance matrix based on the above adjacent point sets and the above points. The constructed first covariance matrix can be a 3×3 symmetric matrix. For example, the first covariance matrix can be constructed by the following formula: C represents the first covariance matrix. p c represents the three-dimensional coordinates of the above points. p j represents the three-dimensional coordinates of an adjacent point. N represents the number of each adjacent point included in the adjacent point set.

[0073] The third sub-step is to generate each first eigenvalue and the corresponding first eigenvector based on the above first covariance matrix. In practice, the above execution entity can solve the eigenvalues and eigenvectors of the above first covariance matrix to obtain each first eigenvalue and the corresponding first eigenvector. Each first eigenvalue corresponds to a first eigenvector.

[0074] The fourth sub-step is to determine the first eigenvector corresponding to the first eigenvalue that satisfies the preset numerical condition among the above first eigenvalues as the normal vector corresponding to the above points. Among them, the above preset numerical condition can be that the first eigenvalue is the smallest.

[0075] The seventh step is to perform the following steps for every two adjacent points in the above mold point information:

[0076] The first sub-step is to determine the included angle between the normal vectors corresponding to the above two points.

[0077] The second sub-step is to determine whether the above two points exist in the initial contour point set in response to determining that the above included angle satisfies the preset included angle condition. Among them, the above preset included angle condition can be that the included angle is less than the preset angle. For example, the preset angle can be 30°.

[0078] The third sub-step is to add the point among the above two points that does not exist in the initial contour point set to the initial contour point set to update the initial contour point set in response to determining that there is a point among the above two points that does not exist in the initial contour point set. Thus, the contour boundary can be refined using the normal vectors of the points.

[0079] The eighth step is to perform a smoothing process on each initial contour point in the updated initial contour point set to obtain the contour information corresponding to the above mold to be cleaned. In practice, interpolation or a low-pass filter can be used to process each initial contour point in the updated initial contour point set to obtain the contour information corresponding to the above mold to be cleaned. The contour information can include each point representing the overall contour. Thus, the serrated or irregular situations in the contour can be reduced through the smoothing process. Thereby, high-precision contour information can be obtained.

[0080] Step 103: Generate mold dimension information corresponding to the mold to be cleaned based on the position information and the contour information.

[0081] In some embodiments, the above-mentioned execution entity may generate mold dimension information corresponding to the mold to be cleaned based on the above-mentioned position information and the above-mentioned contour information.

[0082] In some optional implementation manners of some embodiments, the above-mentioned execution entity may generate mold dimension information corresponding to the mold to be cleaned based on the above-mentioned position information and the above-mentioned contour information through the following steps:

[0083] First step: Respectively extract the maximum and minimum values corresponding to the horizontal axis, vertical axis, and vertical axis from the above-mentioned contour information to obtain the horizontal axis maximum value, horizontal axis minimum value, vertical axis maximum value, vertical axis minimum value, vertical axis maximum value, and vertical axis minimum value.

[0084] Second step: Determine the difference between the above-mentioned horizontal axis maximum value and the above-mentioned horizontal axis minimum value as the first length.

[0085] Third step: Determine the difference between the above-mentioned vertical axis maximum value and the above-mentioned vertical axis minimum value as the first width.

[0086] Fourth step: Determine the difference between the above-mentioned vertical axis maximum value and the above-mentioned vertical axis minimum value as the first height.

[0087] Fifth step: Determine the above-mentioned first length, the above-mentioned first width, and the above-mentioned first height as the basic mold dimensions.

[0088] Sixth step: Generate mold dimension information based on the above-mentioned basic mold dimensions.

[0089] In some optional implementation manners of some embodiments, the above-mentioned execution entity may generate mold dimension information based on the above-mentioned basic mold dimensions through the following steps:

[0090] First step: Divide the local area from the above-mentioned mold point information to obtain local point information corresponding to at least one local area. In practice, the above-mentioned execution entity may use a clustering algorithm (such as DBSCAN) or a region segmentation method based on the normal vector to divide the mold point information into local point information corresponding to different local areas. For example, the groove area and the plane area can be separated.

[0091] Second step: For the local point information corresponding to each local area, perform the following steps:

[0092] The first sub-step is to perform geometric fitting on the above local point information to obtain a geometric fitting result. In practice, for a planar region, the least squares method can be used to fit the plane equation as the geometric fitting result. For a cylindrical hole region, the RANSAC algorithm can be used to fit the cylinder model to obtain the cylinder formula as the geometric fitting result.

[0093] The second sub-step is to determine whether the above geometric fitting result meets the preset error condition according to the above local point information. In practice, the above execution entity can input each point included in the above local point information into the geometric fitting result to obtain the fitted points. Then, the distance between the above point and the above fitted point can be determined as the point error. After that, the mean value of the determined point errors can be determined as the fitting error. Finally, in response to determining that the above fitting error is less than the preset error value, it can be determined that the above geometric fitting result meets the preset error condition. In response to determining that the above fitting error is greater than or equal to the preset error value, it can be determined that the above geometric fitting result does not meet the preset error condition.

[0094] The third sub-step is to generate local die dimensions according to the above geometric fitting result in response to determining that the above geometric fitting result meets the preset error condition. For a planar region, the length, width, and area of the above planar region can be determined as the local die dimensions. For a hole region, the diameter and depth in the geometric fitting result can be determined as the local die dimensions.

[0095] The fourth sub-step is to generate a local point cloud centroid according to the above local point information in response to determining that the above geometric fitting result does not meet the preset error condition. When the above geometric fitting result does not meet the preset error condition, it can indicate that the local region is an irregular region. The local point cloud centroid can be the mean value of each point included in the local point information.

[0096] The fifth sub-step is to construct a second covariance matrix according to the above local point cloud centroid and the above local point information. Here, the method of generating the second covariance matrix can refer to the method of generating the first covariance matrix, which will not be elaborated here.

[0097] The sixth sub-step is to generate each second eigenvalue and the corresponding second eigenvectors according to the above second covariance matrix. In practice, the above execution entity can solve the eigenvalues and eigenvectors of the above second covariance matrix to obtain each second eigenvalue and the corresponding second eigenvectors. The number of each second eigenvalue can be 3. The number of each second eigenvector can be 3.

[0098] The seventh sub-step is to determine the second eigenvector corresponding to the second eigenvalue that satisfies the first numerical condition among the above-mentioned various second eigenvalues as the first principal axis direction. Among them, the above-mentioned first numerical condition can be that the second eigenvalue is the maximum value among the above-mentioned various second eigenvalues. The first principal axis direction can represent the direction with the largest variance of the point cloud distribution and is usually used to describe the length direction or the main extension direction of the mold.

[0099] The eighth sub-step is to determine the second eigenvector corresponding to the second eigenvalue that satisfies the second numerical condition among the above-mentioned various second eigenvalues as the second principal axis direction. Among them, the above-mentioned second numerical condition can be that the second eigenvalue is the intermediate value among the above-mentioned various second eigenvalues. The second principal axis direction can represent the direction orthogonal to the first principal axis direction and with the second largest variance, and is usually used to describe the width direction of the mold.

[0100] The ninth sub-step is to determine the second eigenvector corresponding to the second eigenvalue that satisfies the third numerical condition among the above-mentioned various second eigenvalues as the third principal axis direction. Among them, the above-mentioned second numerical condition can be that the second eigenvalue is the minimum value among the above-mentioned various second eigenvalues. The third principal axis direction can represent the direction orthogonal to both the first principal axis direction and the second principal axis direction and with the smallest variance, and is usually used to describe the height direction of the mold or the plane normal vector direction.

[0101] The tenth sub-step is to project each point in the above-mentioned local point information onto the above-mentioned first principal axis direction, the above-mentioned second principal axis direction, and the above-mentioned third principal axis direction respectively to obtain the first projected local point information, the second projected local point information, and the third projected local point information. Here, the first projected local point information, the second projected local point information, and the third projected local point information can respectively include the lengths of the projected points in the principal axis direction.

[0102] The eleventh sub-step is to generate the first principal axis length according to the maximum value and the minimum value included in the above-mentioned first projected local point information. In practice, the difference between the maximum value and the minimum value included in the above-mentioned first projected local point information can be determined as the first principal axis length.

[0103] The twelfth sub-step is to generate the second principal axis length according to the maximum value and the minimum value included in the above-mentioned second projected local point information. In practice, the difference between the maximum value and the minimum value included in the above-mentioned second projected local point information can be determined as the second principal axis length.

[0104] The thirteenth sub-step is to generate the third principal axis length according to the maximum value and the minimum value included in the above-mentioned third projected local point information. In practice, the difference between the maximum value and the minimum value included in the above-mentioned third projected local point information can be determined as the third principal axis length.

[0105] The fourteenth sub-step is to determine the above-mentioned first main axis length, second main axis length, and the above-mentioned third main axis length as the local mold dimensions.

[0106] The third step is to combine the above-mentioned basic mold dimensions and the determined local mold dimensions into mold dimension information.

[0107] The above first step to the third step are an inventive point of an embodiment of the present disclosure, which solves the technical problem that "there will be separate local areas inside the overall contour of the mold. If only the external overall contour is used to determine the mold dimensions, the accuracy is poor, and the shape of the mold is not all regular shapes. When determining the dimensions of an irregular-shaped mold, only the geometric fitting method is not applicable, resulting in poor accuracy of the determined mold dimensions, and thus poor effect of mold cleaning". The factors that lead to poor effect of mold cleaning are often as follows: there will be separate local areas inside the overall contour of the mold. If only the external overall contour is used to determine the mold dimensions, the accuracy is poor, and the shape of the mold is not all regular shapes. When determining the dimensions of an irregular-shaped mold, only the geometric fitting method is not applicable, resulting in poor accuracy of the determined mold dimensions, and thus poor effect of mold cleaning. If the above factors are solved, the effect of mold cleaning can be improved. To achieve this effect, the present disclosure comprehensively determines the dimensions of the overall contour of the mold and the dimensions of the local areas, and adopts different dimension determination methods for regular areas and irregular areas respectively. Thus, the comprehensiveness and accuracy of the determined mold dimension information can be improved, and the effect of mold cleaning is improved.

[0108] Step 104 is to generate cleaning parameter information according to the mold dimension information.

[0109] In some embodiments, the above-mentioned execution subject can generate cleaning parameter information according to the above-mentioned mold dimension information.

[0110] In some optional implementation manners of some embodiments, the above-mentioned execution subject can generate cleaning parameter information according to the above-mentioned mold dimension information through the following steps:

[0111] The first step is to determine the mold material and dirt type of the mold to be cleaned. The mold material and dirt type can be determined in advance or identified through computer vision technology.

[0112] The second step is to determine the first laser power according to the above-mentioned mold material. In practice, the material coefficient corresponding to the above-mentioned mold material can be determined first. The material coefficient of each mold material can be set in advance. For example, the material coefficient corresponding to metal can be 1.2, and the material coefficient corresponding to plastic can be 0.8. Then, the product of the material coefficient and the basic laser power can be determined as the first laser power. The basic laser power can be set in advance.

[0113] In the third step, determine the second laser power according to the above-mentioned dirt type. In practice, the dirt type coefficient corresponding to the above-mentioned dirt type can be determined first. The dirt type coefficient for each dirt type can be preset. Then, the product of the dirt type coefficient and the above-mentioned basic laser power can be determined as the second laser power.

[0114] In the fourth step, generate the laser power according to the above-mentioned first laser power and the above-mentioned second laser power. In practice, the above-mentioned first laser power and the above-mentioned second laser power can be weighted and summed to obtain the laser power. The weight coefficients corresponding to the above-mentioned first laser power and the above-mentioned second laser power can be preset.

[0115] In the fifth step, generate the cleaning speed according to the above-mentioned mold size information. In practice, the product of the length and width in the basic mold size included in the mold size information can be determined as the product value. Then, the ratio of the above-mentioned product value to the surface area of the mold to be cleaned can be determined as the initial size coefficient. Then, the square root of the initial size coefficient can be determined as the size coefficient. Finally, the product of the size coefficient and the basic speed can be determined as the basic cleaning speed. The basic speed can be preset. For each local area, the product of the basic cleaning speed and the deceleration factor can be determined as the local cleaning speed. The deceleration factor can be a preset value less than 1. For example, the deceleration factor can be 0.5. When there is no local area, the basic cleaning speed can be determined as the cleaning speed. When there are local areas, the obtained basic cleaning speed and each local cleaning speed can be determined as the cleaning speed.

[0116] In the sixth step, for each point within the above-mentioned contour information, perform the following steps:

[0117] In the first sub-step, determine the laser direction corresponding to the above-mentioned point according to the normal vector of the above-mentioned point. In practice, the direction collinear with the normal vector of the above-mentioned point and facing the mold to be cleaned can be determined as the laser direction corresponding to the above-mentioned point.

[0118] In the second sub-step, generate the laser focus depth corresponding to the above-mentioned point according to the vertical coordinate of the above-mentioned point. In practice, the sum of the vertical coordinate and the focal length offset of the laser cleaning head can be determined as the laser focus depth corresponding to the above-mentioned point.

[0119] In the third sub-step, determine the above-mentioned laser direction and the above-mentioned laser focus depth as the point cleaning parameters corresponding to the above-mentioned point.

[0120] Step 7: Generate cleaning path information based on the above mold size information. In practice, for a planar area, Zigzag scanning or spiral scanning can be used for path planning to obtain the area cleaning path. For a hole area, a circular scanning path can be generated around the hole to obtain the area cleaning path. For each of the obtained area cleaning paths, they can be sorted in descending order according to the corresponding planar area to obtain a sequence of area cleaning paths as the cleaning path information.

[0121] Step 8: Determine the above laser power, the above cleaning speed, the determined cleaning parameters for each point, and the above cleaning path information as the cleaning parameter information. Thus, the laser cleaning parameters can be set more accurately according to the mold size. These parameters can not only ensure the cleaning effect, but also improve the cleaning efficiency and reduce energy consumption.

[0122] Step 105: Control the robotic arm included in the laser cleaning device to carry the laser cleaning head to perform cleaning on the mold to be cleaned.

[0123] In some embodiments, the above execution subject can control the robotic arm included in the laser cleaning device to carry the laser cleaning head to perform cleaning on the mold to be cleaned. In practice, the above execution subject can control the robotic arm to carry the above laser cleaning head to move according to the above cleaning speed and the above cleaning path information, emit laser according to the above laser power, and emit laser according to the laser direction and focus parameters corresponding to each point when passing through each point. As an example, the scenario of performing cleaning in a cleaning room under a test scenario can be referred to Figure 3 , Figure 3 shows a scenario of laser cleaning a tire mold.

[0124] The above embodiments of the present disclosure have the following beneficial effects: Through the three-dimensional vision-based mold laser cleaning method of some embodiments of the present disclosure, accurate information on the position and contour of the mold can be automatically obtained, reducing the dependence on manual labor during the cleaning process and improving the cleaning efficiency and the mold cleaning effect. Specifically, the reasons for the low cleaning efficiency and poor mold cleaning effect are as follows: When cleaning a new mold, manual visual calibration is required, and manual participation is also needed during the cleaning process, resulting in the inability to automatically obtain accurate information on the position and contour of the mold. The cleaning process has a strong dependence on manual labor, the cleaning efficiency is low, and when the deviation between the calibration trajectory and the mold position is large, the mold cleaning effect is poor. Based on this, in the three-dimensional vision-based mold laser cleaning method of some embodiments of the present disclosure, first, the three-dimensional camera included in the above laser cleaning device is used to obtain the point position information of the mold to be cleaned in the cleaning room. Thus, the three-dimensional point cloud data of the mold to be cleaned can be collected by the three-dimensional camera. Then, based on the above point position information, the position information and contour information corresponding to the mold to be cleaned are generated. Thus, the position and contour of the mold to be cleaned can be automatically identified through the collected point cloud data. Next, based on the above position information and the above contour information, the mold size information corresponding to the mold to be cleaned is generated. Thus, the size of the mold to be cleaned can be automatically identified. Secondly, based on the above mold size information, the cleaning parameter information is generated. Thus, the various parameters required during the cleaning process can be automatically determined. Finally, the robotic arm included in the above laser cleaning device is controlled to carry the laser cleaning head to perform cleaning processing on the mold to be cleaned. Thus, through the automatically determined various parameters, the robotic arm can be controlled to drive the laser cleaning head to automatically clean the mold, thereby eliminating the need for manual participation and the need to extract the calibration cleaning trajectory from the database, thus reducing the dependence on manual labor during the cleaning process and improving the cleaning efficiency and the mold cleaning effect.

[0125] Reference is made below to Figure 4 , which shows a schematic structural diagram of a laser cleaning device 400 suitable for use in implementing some embodiments of the present disclosure. Figure 4 The laser cleaning device shown is merely an example and should not impose any limitation on the functions and usage scope of the embodiments of the present disclosure.

[0126] As Figure 4 shown, the laser cleaning device 400 may include a processing device 401 (such as a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 402 or the program loaded from the storage device 408 into the random access memory (RAM) 403. In the RAM 403, various programs and data required for the operation of the laser cleaning device 400 are also stored. The processing device 401, the ROM 402, and the RAM 403 are connected to each other through a bus 404. The input / output (I / O) interface 405 is also connected to the bus 404.

[0127] Typically, the following devices can be connected to the I / O interface 405: an input device 406 including, for example, a touch screen, a touchpad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 407 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 408 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 409. The communication device 409 can allow the laser cleaning device 400 to communicate with other devices wirelessly or wiredly to exchange data. Although Figure 4 the laser cleaning device 400 with various devices is shown, it should be understood that it is not required to implement or have all the shown devices. More or fewer devices can be alternatively implemented or had. Figure 4 Each block shown in

[0128] can represent one device or, as needed, multiple devices.

[0129] It should be noted that the computer-readable media described in some embodiments of the present disclosure may be a computer-readable signal medium, a computer-readable storage medium, or any combination of the two. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In some embodiments of the present disclosure, the computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In some embodiments of the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries computer-readable program code. Such a propagated data signal may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer-readable signal medium may also be any computer-readable medium other than the computer-readable storage medium, which can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium may be transmitted by any appropriate medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0130] In some embodiments, the client and the server may communicate using any currently known or future-developed network protocol such as HTTP (HyperText Transfer Protocol), and may be interconnected with digital data communication in any form or medium (e.g., a communication network). Examples of communication networks include local area networks ("LAN"), wide area networks ("WAN"), the Internet (e.g., the Internet), and end-to-end networks (e.g., ad hoc end-to-end networks), as well as any currently known or future-developed networks.

[0131] The above computer-readable medium may be included in the above laser cleaning device; or it may exist independently without being assembled into the laser cleaning device. The above computer-readable medium carries one or more programs, and when the above one or more programs are executed by the laser cleaning device, the laser cleaning device is caused to: obtain the point position information of the mold to be cleaned in the cleaning chamber through the three-dimensional camera included in the laser cleaning device; generate the position information and contour information corresponding to the mold to be cleaned according to the above point position information; generate the mold size information corresponding to the mold to be cleaned according to the above position information and the above contour information; generate the cleaning parameter information according to the above mold size information; and control the robotic arm included in the laser cleaning device to carry the laser cleaning head to perform cleaning processing on the mold to be cleaned.

[0132] Computer program code for performing the operations of some embodiments of the present disclosure may be written in one or more programming languages or combinations thereof. The above programming languages include object-oriented programming languages - such as Java, Smalltalk, C++; and also include conventional procedural programming languages - such as the "C" language or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, executed as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network - including a local area network (LAN) or a wide area network (WAN) - or may be connected to an external computer (e.g., by using an Internet service provider to connect through the Internet).

[0133] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagram may represent a module, a program segment, or a part of code that contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, may be implemented by a dedicated hardware-based system for performing the specified functions or operations, or may be implemented by a combination of dedicated hardware and computer instructions.

[0134] The functions described above in this document can be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that can be used include: Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), Systems on Chip (SOCs), Complex Programmable Logic Devices (CPLDs), and so on.

[0135] Some embodiments of the present disclosure also provide a computer program product, including a computer program, which when executed by a processor, implements any one of the above-described three-dimensional vision-based die laser cleaning methods.

[0136] The above description is only some preferred embodiments of the present disclosure and an explanation of the applied technical principles. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present disclosure is not limited to the technical solutions formed by the specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above inventive concept. For example, technical solutions formed by mutually replacing the above features with technical features having similar functions (but not limited to) disclosed in the embodiments of the present disclosure.

Claims

1. A mold laser cleaning method based on 3D vision, applied to a laser cleaning device, comprising: Obtaining the point position information of the mold to be cleaned in the cleaning chamber through a 3D camera included in the laser cleaning device; Generating the position information and contour information corresponding to the mold to be cleaned according to the point position information; Generating the mold size information corresponding to the mold to be cleaned according to the position information and the contour information; Generating cleaning parameter information according to the mold size information; Controlling a robotic arm included in the laser cleaning device to carry a laser cleaning head to perform cleaning treatment on the mold to be cleaned.

2. The method according to claim 1, wherein The generating the position information and contour information corresponding to the mold to be cleaned according to the point position information includes: For each point position in the point position information, performing the following first loop step: Determining the adjacent point positions corresponding to the point position to obtain each adjacent point position; Generating the point position distances between the point position and each adjacent point position to obtain each point position distance; Determining the average of the point position distances as the average point position distance; Generating the average value and standard deviation of the average point position distances according to the determined average point position distances; For each point position in the point position information, performing the following second loop step: Determining the number of adjacent point positions corresponding to the point position as the adjacent number; Determining the absolute value of the difference between the average point position distance corresponding to the point position and the average value of the average point position distances as the distance deviation; Determining the product of the standard deviation of the average point position distances and a preset coefficient as the comparison information; In response to determining that the distance deviation is greater than the comparison information and the adjacent number is less than a preset number, removing the point position from the point position information to update the point position information, and using the updated point position information to perform the second loop step again; Generating the position information and contour information corresponding to the mold to be cleaned according to the updated point position information.

3. The method according to claim 2, wherein, The generating the position information and contour information corresponding to the mold to be cleaned according to the updated point position information includes: Separating the mold point position information of the mold to be cleaned from the updated point position information; Respectively extracting each abscissa, each ordinate, and each vertical coordinate from the mold point position information; Determining the average value of the abscissas as the abscissa center; Determining the average value of the ordinates as the ordinate center; Determining the average value of the vertical coordinates as the vertical coordinate center; Determining the abscissa center, the ordinate center, and the vertical coordinate center as the position information corresponding to the mold to be cleaned.

4. The method according to claim 2, wherein, The generating the position information and contour information corresponding to the mold to be cleaned according to the updated point position information includes: Generating a triangular mesh according to each point position in the mold point position information; For each triangular region in the triangular mesh, determining the circumradius of the triangular region; Extracting the triangular regions whose corresponding circumradii in the triangular mesh meet a preset radius condition as the retained triangular regions to obtain each retained triangular region; Determining the boundary edges in each retained triangular region as the contour edges to obtain each contour edge; Determining the vertex point positions corresponding to each contour edge as the initial contour point set; For each point in the die point information, perform the following steps: Determine the adjacent points corresponding to the point to obtain a set of adjacent points; Construct a first covariance matrix based on the set of adjacent points and the point; Generate respective first eigenvalues and corresponding first eigenvectors according to the first covariance matrix; Determine the eigenvector corresponding to the first eigenvalue that satisfies the preset numerical condition among the respective first eigenvalues as the normal vector corresponding to the point; For every two adjacent points in the die point information, perform the following steps: Determine the angle between the normal vectors corresponding to the two points; In response to determining that the angle satisfies the preset angle condition, determine whether the two points exist in the initial contour point set; In response to determining that there is a point among the two points that does not exist in the initial contour point set, add the point that does not exist in the initial contour point set among the two points to the initial contour point set to update the initial contour point set; Smooth each initial contour point in the updated initial contour point set to obtain the contour information corresponding to the die to be cleaned.

5. The method according to claim 4, wherein The generating die size information corresponding to the die to be cleaned according to the position information and the contour information includes: Respectively extract the maximum and minimum values corresponding to the horizontal axis, vertical axis, and vertical axis from the contour information to obtain the horizontal axis maximum value, horizontal axis minimum value, vertical axis maximum value, vertical axis minimum value, vertical axis maximum value, and vertical axis minimum value; Determine the difference between the horizontal axis maximum value and the horizontal axis minimum value as the first length; Determine the difference between the vertical axis maximum value and the vertical axis minimum value as the first width; Determine the difference between the vertical axis maximum value and the vertical axis minimum value as the first height; Determine the first length, the first width, and the first height as the basic die size; Generate die size information according to the basic die size.

6. The method according to claim 1, wherein The generating cleaning parameter information according to the die size information includes: Determine the die material and the dirt type of the die to be cleaned; Determine the first laser power according to the die material; Determine the second laser power according to the dirt type; Generate a laser power according to the first laser power and the second laser power; Generate a cleaning speed according to the die size information; For each point within the contour information, perform the following steps: Determine the laser direction corresponding to the point according to the normal vector of the point; Generate the laser focus depth corresponding to the point according to the vertical coordinate of the point; Determine the laser direction and the laser focus depth as the point cleaning parameters corresponding to the point; Generate cleaning path information according to the die size information; Determine the laser power, the cleaning speed, the determined point cleaning parameters, and the cleaning path information as the cleaning parameter information.

7. The method according to claim 6, wherein The controlling the robotic arm included in the laser cleaning device to carry the laser cleaning head to perform a cleaning process on the die to be cleaned includes: Control the robotic arm to move the laser cleaning head according to the cleaning speed and the cleaning path information, emit laser according to the laser power, and when passing through each point, emit laser according to the laser direction and focus parameters corresponding to the point.

8. A laser cleaning device, comprising: One or more processors; A three-dimensional camera configured to collect point cloud data; A robotic arm configured to move a laser cleaning head; A laser cleaning head configured to emit laser; A storage device having stored thereon one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1-7.

9. A computer-readable medium having a computer program stored thereon, wherein, When the computer program is executed by a processor, the method according to any one of claims 1-7 is implemented.

10. A computer program product, comprising a computer program which, when executed by a processor, implements the method according to any one of claims 1-7.

Images