A method and system for detecting the dimensional error of a plate of a plate heat exchanger

Abstract

The present invention relates to the technical field of dimensional measurement, and specifically relates to a method and system for detecting the dimensional error of a plate of a plate heat exchanger. The present invention obtains the corrugated segmentation sequence of the corrugated area of the plate and the skeleton point cloud data, and obtains the measurement error index according to the position distribution of the point cloud data in the corrugated segmentation sequence of the skeleton point cloud data and the difference of the point cloud data in the same corrugated segmentation sequence; according to the similarity of the same corrugated segmentation sequence of the skeleton point cloud data and its reference point cloud data and the degree of dispersion of the point cloud data in the corrugated segmentation sequence, obtains the corrugation shape difference index; combines the measurement error index, the corrugation shape difference index and the height data difference of different skeleton point cloud data to detect the dimensional error of the plate. The present invention improves the reliability of detecting the dimensional error of the plate.

Description

Technical Field

[0001] The present invention relates to the technical field of dimensional measurement, and in particular to a method and system for detecting the dimensional error of the plates of a plate heat exchanger. Background Art

[0002] A plate heat exchanger is an efficient heat exchange device composed of a series of corrugated metal sheets stacked together, mainly consisting of a frame and plates. Thin rectangular channels are formed between the plates, and heat exchange is achieved by means of the plates. The performance of a plate heat exchanger largely depends on the dimensional accuracy of the plates. If there are errors in the plate dimensions, it may lead to a reduction in the working efficiency of the heat exchanger and even affect its normal operation. Therefore, detecting dimensional errors of the plates is of great significance for ensuring product quality.

[0003] The plate is a thin metal sheet with a corrugated shape; existing methods mainly detect dimensional errors based on the corrugation depth of the plate, but the texture of the plate itself has a great interference on the measurement of dimensional errors, and it is impossible to determine whether the cause of the dimensional error is normal texture or abnormal texture itself, resulting in low reliability of the dimensional error detection of the plates of a plate heat exchanger. Summary of the Invention

[0004] In order to solve the technical problem that the self-corrugation of the plate interferes with the measurement of dimensional errors, resulting in low reliability of the dimensional error detection of the plate, the purpose of the present invention is to provide a method and system for detecting the dimensional error of the plates of a plate heat exchanger, and the specific technical solutions adopted are as follows:

[0005] In a first aspect, an embodiment of the present invention provides a method for detecting the dimensional error of the plates of a plate heat exchanger, the method comprising:

[0006] Obtaining point cloud data for each scan of the plates of the plate heat exchanger, the point cloud data including height data;

[0007] Obtaining the corrugated area and the skeleton point cloud data therein; based on the corrugation type of the plate, dividing the point cloud data in the corrugation width direction where each skeleton point cloud data in the corrugated area is located, and obtaining the corrugation segmentation sequence of each skeleton point cloud data;

[0008] According to the position distribution of the point cloud data in the corrugation segmentation sequence of each skeleton point cloud data and the difference of the point cloud data in the same corrugation segmentation sequence, obtaining the measurement error index of each skeleton point cloud data;

[0009] According to the similarity of each corrugation segmentation sequence of each skeleton point cloud data and its corresponding reference point cloud data and the degree of dispersion of the point cloud data in each corrugation segmentation sequence of each skeleton point cloud data, obtaining the corrugation shape difference index between each skeleton point cloud data and the reference point cloud data;

[0010] Perform dimensional error detection on the sheet according to the ripple shape difference index and the measurement error index between each skeletal point cloud data and its corresponding point cloud data, as well as the height data difference between each skeletal point cloud data and the remaining skeletal point cloud data.

[0011] Further, the obtaining of the ripple segmentation sequence of each skeletal point cloud data includes:

[0012] For each ripple region, arbitrarily select a skeletal point cloud data within the ripple region as the example data. Perform linear fitting on the example data and the skeletal point cloud data of the adjacent sub-scans of its sub-scan. Obtain a plane passing through the example data and perpendicular to the fitting line. Arrange the point cloud data on the plane within the ripple region in sequence to obtain the ripple width sequence of the example data;

[0013] If the ripple of the sheet belongs to the first ripple type, divide the ripple width sequence into two inclined ripple sequences based on the example data;

[0014] If the ripple of the sheet belongs to the second ripple type, in the ripple width sequence, form an initial flat ripple sequence from the example data and its adjacent point cloud data; if the flat ripple sequence does not meet the candidate conditions, add the adjacent point cloud data of the point cloud data at both ends of the flat ripple sequence in the ripple width sequence to the flat ripple sequence until the updated flat ripple sequence meets the candidate conditions. Denote the flat ripple sequence when it meets the candidate conditions as the final flat ripple sequence of the example data; Arrange the point cloud data on both sides of the final flat ripple sequence in the ripple width sequence in sequence to obtain the inclined ripple sequence of the example data;

[0015] Denote the inclined ripple sequence and the final flat ripple sequence as the ripple segmentation sequence of the example data.

[0016] Further, the obtaining of the measurement error index of each skeletal point cloud data includes:

[0017] Obtain the standard height data of each point cloud data of the sheet; Calculate the difference mean value between the height data of the point cloud data in each ripple segmentation sequence of each skeletal point cloud data and the standard height data as the fitting error of the corresponding ripple segmentation sequence; Select the maximum value among the fitting errors of all ripple segmentation sequences of each skeletal point cloud data as the ripple error;

[0018] Respectively obtain the DTW value and the element number difference of the height data of the point cloud data in the two inclined ripple sequences of each skeletal point cloud data, and denote them as the height change difference index and the ripple width difference value in sequence;

[0019] Obtain the measurement error probability of each skeletal point cloud data according to the height change difference index, the ripple width difference index and the ripple error.

[0020] Further, obtaining the ripple shape difference index between each skeleton point cloud data and the reference point cloud data includes:

[0021] Respectively obtaining the discrete index of the height data of the point cloud data in each ripple segmentation sequence of each skeleton point cloud data, and the DTW value between each ripple segmentation sequence of each skeleton point cloud data and its each reference point cloud data;

[0022] According to the fitting error, the discrete index and the DTW value of each ripple segmentation sequence of each skeleton point cloud data, obtaining the segmented ripple difference index between each ripple segmentation sequence of each skeleton point cloud data and its each reference point cloud data; taking the sum of the segmented ripple difference indexes of all ripple segmentation sequences as the ripple shape difference index between each skeleton point cloud data and its each reference point cloud data.

[0023] Further, performing dimensional error detection on the sheet according to the ripple shape difference index and the measurement error index between each skeleton point cloud data and its reference point cloud data, and the height data difference between each skeleton point cloud data and the other skeleton point cloud data includes:

[0024] According to the ripple shape difference index and the measurement error index between each skeleton point cloud data and its reference point cloud data, obtaining the true dimensional error index of each skeleton point cloud data;

[0025] Calculating the difference between the height data of each skeleton point cloud data and the average value of the height data of all skeleton point cloud data within the ripple region where it is located, and recording it as the ripple height difference of each skeleton point cloud data;

[0026] According to the ripple height difference, the ripple error and the true dimensional error index of each skeleton point cloud data, obtaining the error significance index of each skeleton point cloud data;

[0027] Judging whether there is point cloud data with the error significance index greater than the preset error threshold. If so, the sheet has dimensional error; if not, the sheet has no dimensional error.

[0028] Further, obtaining the true dimensional error index of each skeleton point cloud data includes:

[0029] Calculating the sum value of the measurement error indexes between each skeleton point cloud data and its each reference point cloud data, and taking the product of the sum value and the ripple shape difference index as the local dimensional error index between each skeleton point cloud data and its each reference point cloud data;

[0030] Normalize the sum of the local size error metrics of each skeleton point cloud data with all its corresponding reference point cloud data to obtain the true size error metric of each skeleton point cloud data.

[0031] Further, the obtaining of the corrugated region and the skeleton point cloud data therein includes:

[0032] Select the point cloud data with the difference between the height data and the preset reference height data greater than the preset height difference threshold from the point cloud data of each scan as the corrugated point cloud data;

[0033] Take the connected domain formed by the corrugated point cloud data of all scans as the corrugated region;

[0034] Select the corrugated point cloud data corresponding to the maximum height data from the corrugated point cloud data of the same scan within each corrugated region and denote it as the skeleton point cloud data within the corresponding corrugated region.

[0035] Further, the candidate condition is that the coefficient of determination of the linear fitting of the point cloud data in the flat corrugation sequence is less than the preset fitting threshold.

[0036] Further, the reference point cloud data is the other skeleton point cloud data within the corrugated region where each skeleton point cloud data is located.

[0037] In a second aspect, another embodiment of the present invention provides a plate size error detection system for a plate heat exchanger, the system comprising:

[0038] A data acquisition module for acquiring the point cloud data of each scan of the plate of the plate heat exchanger, the point cloud data including height data;

[0039] A corrugation segmentation module for obtaining the corrugated region and the skeleton point cloud data therein; dividing the point cloud data in the corrugation width direction where each skeleton point cloud data in the corrugated region is located based on the corrugation type of the plate to obtain the corrugation segmentation sequence of each skeleton point cloud data;

[0040] A measurement error analysis module for obtaining the measurement error metric of each skeleton point cloud data according to the position distribution of the point cloud data in the corrugation segmentation sequence of each skeleton point cloud data and the difference from the point cloud data in the same corrugation segmentation sequence;

[0041] A corrugation shape analysis module for obtaining the corrugation shape difference metric between each skeleton point cloud data and the reference point cloud data according to the similarity of each corrugation segmentation sequence between each skeleton point cloud data and the reference point cloud data and the degree of dispersion of the point cloud data in each corrugation segmentation sequence of each skeleton point cloud data;

[0042] A size error detection module is used to detect the size error of the plate by using the corrugation shape difference index and the measurement error index between each skeleton point cloud data and its corresponding point cloud data, as well as the height data difference between each skeleton point cloud data and the other skeleton point cloud data.

[0043] The present invention has the following beneficial effects:

[0044] In the embodiment of the present invention, in order to accurately analyze the change in the corrugation width of the plate, a corrugation segmentation sequence of the skeleton point cloud data is obtained; the inclination similarity of the slopes on both sides of the corrugation is measured by the difference in the point cloud data in the same corrugation segmentation of the skeleton point cloud data, and combined with the position distribution of the point cloud data in the corrugation segmentation sequence presenting the abnormal degree of the corrugation where the skeleton point cloud data is located, the possibility of the corrugation where the skeleton point cloud data is located being abnormal is analyzed to obtain a measurement error index; the similarity of the corrugation segmentation sequences of the skeleton point cloud data and its corresponding point cloud data reflects the shape similarity degree of the corrugation cross-sections where the two point cloud data are located, and the discrete index of the height data of the point cloud data in the corrugation segmentation sequence reflects the shape change degree of the corrugation cross-section. By combining the two, the corrugation shape difference index between the skeleton point cloud data and the corresponding point cloud data is analyzed; the measurement error index helps to exclude abnormal data caused by measurement errors and enhance the reliability of the detection result, and the corrugation shape difference index helps to reduce the detection error caused by misjudging normal corrugations as abnormal, and combined with the consistent corrugation height, the height data difference between different skeleton point cloud data is analyzed to analyze the significant degree of the size error of the skeleton point cloud data, and then the size error of the plate is detected, enhancing the reliability of the plate size error detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0046] Figure 1 It is a flowchart of the steps of a method for detecting the size error of a plate of a plate heat exchanger provided by an embodiment of the present invention;

[0047] Figure 2 It is a system structure diagram of a system for detecting the size error of a plate of a plate heat exchanger provided by an embodiment of the present invention;

[0048] Figure 3 It is a schematic diagram of a computer device of a device for detecting the size error of a plate of a plate heat exchanger provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0049] To further elaborate on the technical means and effects adopted by the present invention to achieve the intended invention purpose, the following specifically describes, in conjunction with the accompanying drawings and preferred embodiments, a method and system for detecting the dimensional error of the plates of a plate heat exchanger according to the present invention, including its specific implementation manner, structure, features, and effects. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0050] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs.

[0051] The following specifically describes the specific solution of a method and system for detecting the dimensional error of the plates of a plate heat exchanger provided by the present invention in conjunction with the accompanying drawings.

[0052] Embodiment 1:

[0053] The present invention provides a method for detecting the dimensional error of the plates of a plate heat exchanger. Please refer to Figure 1 , which shows the flowchart of the steps of a method for detecting the dimensional error of the plates of a plate heat exchanger provided by an embodiment of the present invention. The method includes:

[0054] Step S1: Obtain the point cloud data for each scan of the plates of the plate heat exchanger. The point cloud data includes height data.

[0055] Use a laser displacement sensor to scan the upper and lower surfaces of the plate to be detected of the plate heat exchanger through line laser, and obtain the point cloud data for each scan of each surface of the plate , where x and y respectively represent the row data and column data of the plate, and h represents the height data of the position of the plate at the row data x and column data y, that is, the distance between this position and the laser displacement sensor. Only the point cloud data obtained by scanning the upper surface of the plate is used for subsequent analysis.

[0056] It should be noted that the line laser will scan along a certain direction of the plate, generating a series of point cloud data. In order to obtain the complete three-dimensional morphology of the plate, it is usually necessary to perform multiple line laser scans on the plate and the scanning directions of all scans are the same. The width of the point cloud data for each scan is 1. In this embodiment, the point cloud data for each scan represents each column of the plate, and the scanning order is from left to right in the row direction of the plate.

[0057] Step S2: Obtain the corrugated area and the skeleton point cloud data therein; divide the point cloud data in the width direction of the corrugation where each skeleton point cloud data in the corrugated area is located based on the corrugation type of the plate, and obtain the corrugated segmentation sequence of each skeleton point cloud data.

[0058] To analyze whether there are abnormalities in the corrugations of the plate itself, the corrugated area of the plate and the skeleton point cloud data are obtained, and the skeleton point cloud data reflects the overall shape and structure of the plate. The specific method is as follows: Select the point cloud data with the difference between the height data and the preset reference height data greater than the preset height difference threshold from the point cloud data of each scan as the corrugated point cloud data; Take the connected domain composed of the corrugated point cloud data of all scans as the corrugated area; Select the corrugated point cloud data corresponding to the maximum height data from the corrugated point cloud data of the same scan within each corrugated area, and record it as the skeleton point cloud data within the corresponding corrugated area.

[0059] The method for obtaining the preset reference height data is as follows: Perform plane fitting on the point cloud data of all scans to obtain the base plane of the plate; Record the distance from the laser displacement sensor to the base plane of the plate as the preset reference height data. It should be noted that the base plane of the plate represents the planar form of the plate in an ideal state, the preset reference height data represents the height from the laser displacement sensor to the ideal planar form of the plate, and the point cloud data corresponding to the height data with a large difference from the preset reference height data represents the corrugation position.

[0060] In the embodiment of the present invention, the least squares method is selected for plane fitting, and the Chebyshev plane fitting algorithm or the like can also be selected, which will not be elaborated here.

[0061] In one implementation manner of the embodiment of the present invention, the preset reference height data is set to 0.01 mm.

[0062] There are differences in the slope types on both sides of the corrugations of different corrugation types. The slope types include flat slope and inclined slope. To improve the accuracy of corrugation abnormality analysis, the point cloud data in the corrugation width direction where the skeleton point cloud data in the corrugated area is located is segmented to obtain different slope segments, that is, the corrugation segmentation sequence.

[0063] Preferably, in some possible implementation manners of the embodiments of the present invention, the method for obtaining the corrugation segmentation sequence includes: for each corrugation region, optionally select a skeleton point cloud data within the corrugation region as the example data, perform linear fitting on the example data and the skeleton point cloud data of the adjacent sub-scans of its sub-scan, obtain a plane passing through the example data and perpendicular to the fitting line, arrange the point cloud data on the plane within the corrugation region in sequence to obtain the corrugation width sequence of the example data; if the corrugation of the slab belongs to the first corrugation type, divide the corrugation width sequence into two inclined corrugation sequences based on the example data; if the corrugation of the slab belongs to the second corrugation type, in the corrugation width sequence, form an initial flat corrugation sequence from the example data and its adjacent point cloud data; if the flat corrugation sequence does not meet the selection condition, add the adjacent point cloud data of the point cloud data at both ends of the flat corrugation sequence in the corrugation width sequence to the flat corrugation sequence until the updated flat corrugation sequence meets the selection condition, and record the flat corrugation sequence when it meets the selection condition as the final flat corrugation sequence of the example data; arrange the point cloud data on both sides of the final flat corrugation sequence in the corrugation width sequence in sequence to obtain the inclined corrugation sequence of the example data; record the inclined corrugation sequence and the final flat corrugation sequence as the corrugation segmentation sequence of the example data.

[0064] The point cloud data on the plane passing through the example data and perpendicular to the fitting line presents the change of the corrugation width of the slab. In order to make the fluid evenly distributed in the channels between the slabs, avoid too high or too low local flow velocity, and make the pressure borne by the slabs more uniform, the slab corrugations usually have symmetry. The cross-section of the slab corrugations includes triangles, sine waves, trapezoids, rectangles, etc. Based on the symmetric characteristics of the corrugations, the corrugation shapes are divided into two types, specifically: the corrugation cross-section of the first corrugation type has only one point with the maximum height, that is, the corrugation cross-section has no flat slope, such as triangular corrugations and sine wave corrugations; the corrugation cross-section of the second corrugation type has multiple points with the maximum height, that is, the corrugation cross-section has a flat slope, such as trapezoidal corrugations and rectangular corrugations.

[0065] For the second corrugation type, since the example data may not be located at the middle position of the cross-section of the corrugation it is in, there is a large error in measuring the corrugation symmetry by dividing the two parts of the cross-section of the corrugation it is in based on the example data. To avoid the above error, it is necessary to extract the flat slope part of the corrugation cross-section. The larger the determination coefficient of the linear fitting of the point cloud data in the flat corrugation sequence, the more it indicates that these point cloud data belong to the flat slope part of the corrugation cross-section of the example data, and the greater the possibility that there are other flat slope parts in the corrugation cross-section. The flat slope part needs to continue to expand to both sides until the flat corrugation sequence contains an inclined slope and then stops.

[0066] The conditions to be selected are as follows: the coefficient of determination for linear fitting of the point cloud data in the flat corrugation sequence is less than a preset fitting threshold. Among them, the coefficient of determination is a well-known technology to those skilled in the art and will not be elaborated here. In one implementation manner of the embodiment of the present invention, the preset fitting threshold is set to 0.7.

[0067] As an example, assume that the corrugation width sequence of the example data a5 is . If the corrugation of the sheet belongs to the first corrugation type, the two inclined corrugation sequences of the example data a5 are 、 . If the corrugation of the sheet belongs to the second corrugation type, the initial flat corrugation sequence , if J0 does not meet the preset conditions, then is added to J0, and the updated flat corrugation sequence . Assume that J1 meets the preset conditions. The final flat corrugation sequence of the example data a5 is J1, and the two inclined corrugation sequences are 、 . The height data of the point cloud data in the inclined corrugation sequence gradually decreases and approaches the base plane of the sheet.

[0068] It should be noted that linear fitting is performed on the example data and the skeleton point cloud data of the adjacent previous scan and the adjacent next scan of its sub-scan; in the embodiment of the present invention, the least squares method is selected for linear fitting, and the gradient descent method and the Gauss-Newton method can also be selected.

[0069] Step S3: Obtain the measurement error index of each skeleton point cloud data according to the position distribution of the point cloud data in the corrugation segmentation sequence of each skeleton point cloud data and the difference of the point cloud data in the same corrugation segmentation sequence.

[0070] Since the sheet corrugation has symmetry, the inclination of the slopes on both sides of the corrugation should be relatively consistent. The similarity of the inclination of the slopes on both sides of the corrugation is measured by the difference of the point cloud data at the midpoint of the same corrugation segmentation of the skeleton point cloud data; the position distribution of the point cloud data in the corrugation segmentation sequence of the skeleton point cloud data can specifically present whether there is an abnormality in the corrugation where the skeleton point cloud data is located; combining the above factors to analyze the possibility of an abnormality in the corrugation where the skeleton point cloud data is located, the measurement error index is obtained.

[0071] Preferably, in some possible implementation manners of the embodiments of the present invention, the method for obtaining the measurement error probability includes: obtaining the standard height data of each point cloud data of the sheet; calculating the mean difference between the height data of the point cloud data in each corrugation segmentation sequence of each skeleton point cloud data and the standard height data as the fitting error corresponding to the corrugation segmentation sequence; selecting the maximum value among the fitting errors of all the corrugation segmentation sequences of each skeleton point cloud data as the corrugation error; respectively obtaining the DTW value and the difference in the number of elements of the height data of the point cloud data in the two inclined corrugation sequences of each skeleton point cloud data, and recording them as the height change difference index and the corrugation width difference value in sequence; obtaining the measurement error probability of each skeleton point cloud data according to the height change difference index, the corrugation width difference index, and the corrugation error. Among them, the dynamic time warping (DTW) algorithm is a well-known technology to those skilled in the art and will not be elaborated here.

[0072] The standard height data is the normal height data of the standard sheet without dimensional errors. If the fitting error is larger, it indicates that the coincidence degree between the point cloud data in the corrugation segmentation sequence and the corrugation shape at the corresponding position of the standard sheet is lower, and the possibility of abnormality in the corrugation where the point cloud data is located is greater, then the possibility of measurement error in the skeleton point cloud data is greater. Since the sheet corrugation has symmetry, the inclination conditions of the slopes on both sides of the corrugation should be relatively consistent; the inclined corrugation sequence represents the inclination slope of each side of the corrugation. The DTW value and the absolute value of the difference in the number of elements of the height data of the point cloud data in the two inclined corrugation sequences of the skeleton point cloud data are used to measure the inclination similarity degree of the inclination slopes on both sides of the corrugation where the skeleton point cloud data is located. The larger the two values are, the lower the inclination similarity degree of the slopes on both sides is, and the greater the possibility of measurement error in the skeleton point cloud data is. Therefore, the height change difference index, the corrugation width difference index, and the corrugation error are all positively correlated with the measurement error probability. In the embodiments of the present invention, the product of the height change difference index, the corrugation width difference index, and the corrugation error of each skeleton point cloud data is normalized to obtain the measurement error probability of the corresponding skeleton point cloud data.

[0073] In the embodiments of the present invention, the correlation relationship between the height change difference index, the corrugation width difference index, the corrugation error, and the measurement error probability can also be constructed through other basic mathematical operations, which will not be limited and elaborated here.

[0074] It should be noted that in the embodiments of the present invention, the Norm function is used for normalization processing. In the embodiments of the present invention, other normalization methods can also be selected, such as function transformation, maximum-minimum normalization, etc. The normalization methods will not be limited here.

[0075] In an embodiment of the present invention, the same method as that for the sheet to be detected in step S1 is adopted for the standard sheet to obtain the point cloud data of the standard sheet, and the reference point cloud of each point cloud data of the sheet to be detected is selected from the point cloud data of the standard sheet. The row data and column data of each point cloud data of the sheet to be detected are respectively equal to those of its reference point cloud; the height data of the reference point cloud of each point cloud data of the sheet to be detected is used as the standard height data corresponding to the point cloud data.

[0076] It should be noted that the standard sheet and the sheet to be detected are sheets of the same corrugation type and the same size produced in the same batch, and there are no abnormalities in the corrugations of the standard sheet, so it can be considered that there are no dimensional errors in the standard sheet. During the acquisition of the point cloud data of the standard sheet, the distance from the laser displacement sensor to the standard sheet is equal to the distance from the laser displacement sensor to the sheet to be detected; the point cloud data of the standard sheet and the sheet to be detected can be considered to be in the same three-dimensional coordinate system.

[0077] Step S4: Obtain the corrugation shape difference index between each skeleton point cloud data and the reference point cloud data according to the similarity of each corrugation segmentation sequence between each skeleton point cloud data and its reference point cloud data and the degree of dispersion of the point cloud data in each corrugation segmentation sequence of each skeleton point cloud data.

[0078] Within the same corrugation area, the corrugation skeleton structure should be relatively consistent. In order to more accurately analyze the corrugation abnormalities of the skeleton point cloud data, other skeleton point cloud data within the corrugation area where each skeleton point cloud data is located is recorded as its reference point cloud data.

[0079] The similarity of the corrugation segmentation sequences between the skeleton point cloud data and its reference point cloud data reflects the shape similarity degree of the corrugation cross-sections where the two point cloud data are located, and the dispersion index of the height data of the point cloud data in the corrugation segmentation sequence reflects the shape change degree of the corrugation cross-section. By combining the two to analyze the cross-section shape difference of the corrugation where the skeleton point cloud data and the reference point cloud data are located, the corrugation shape difference index is obtained.

[0080] Preferably, in some possible implementation manners of the embodiment of the present invention, the method for obtaining the corrugation shape difference index includes: respectively obtaining the dispersion index of the height data of the point cloud data in each corrugation segmentation sequence of each skeleton point cloud data, and the DTW value of each corrugation segmentation sequence between each skeleton point cloud data and each of its reference point cloud data; obtaining the segmented corrugation difference index of each corrugation segmentation sequence between each skeleton point cloud data and each of its reference point cloud data according to the fitting error, dispersion index and DTW value of each corrugation segmentation sequence of each skeleton point cloud data; taking the sum of the segmented corrugation difference indexes of all corrugation segmentation sequences as the corrugation shape difference index between each skeleton point cloud data and each of its reference point cloud data.

[0081] The discrete index of the height data of the point cloud data in the corrugated segmentation sequence reflects the degree of shape change of the corrugated cross-section. The smaller the discrete index, the smaller the degree of shape change of the corrugated cross-section, and the smaller the difference in corrugated shape. The DTW value of each corrugated segmentation sequence of the skeleton point cloud data and its corresponding control point cloud data reflects the similarity of the corrugated cross-sections where the two point cloud data are located. If the DTW value is larger, it means that the similarity of the corrugated cross-sections where the two point cloud data are located is smaller, and the difference in the cross-sectional shapes of the corrugations where the two point cloud data are located is larger. If the fitting error is larger, the coincidence degree of the point cloud data in the corrugated segmentation sequence and the corrugated shape at the corresponding position on the standard plate is lower, indicating that the authenticity of the corrugation where the point cloud data in the corrugated segmentation sequence is located is smaller, and the difference in the cross-sectional shapes of the corrugations where the skeleton point cloud data and the control point cloud data are located is larger. Therefore, the fitting error, the discrete index, and the DTW value are all positively correlated with the segmented corrugation difference index.

[0082] In the embodiment of the present invention, the product of the fitting error, the discrete index, and the DTW value of each corrugated segmentation sequence of each skeleton point cloud data is used as the segmented corrugation difference index of each corrugated segmentation sequence of each skeleton point cloud data and its corresponding control point cloud data. In the embodiment of the present invention, the correlation relationship between the discrete index, the DTW value, the fitting error, and the segmented corrugation difference index can also be constructed through other basic mathematical operations, which will not be limited and elaborated here.

[0083] Indicators such as standard deviation, variance, and interquartile range can all reflect the degree of dispersion of a set of data. In this embodiment, the discrete index is variance, and variance can also be replaced with indicators such as standard deviation or interquartile range.

[0084] In a specific implementation manner of the embodiment of the present invention, the corrugated shape difference index is expressed by the formula:

[0085]

[0086] In the formula, is the corrugated shape difference index of each skeleton point cloud data and its i-th corresponding control point cloud data; N is the total number of corrugated segmentation sequences of each skeleton point cloud data; is the discrete index of the height data of the point cloud data in the n-th corrugated segmentation sequence of each skeleton point cloud data; is the n-th corrugated segmentation sequence of each skeleton point cloud data; is the n-th corrugated segmentation sequence of the i-th corresponding control point cloud data of each skeleton point cloud data; is the DTW value of the n-th corrugated segmentation sequence of each skeleton point cloud data and its i-th corresponding control point cloud data; is the fitting error of the n-th corrugated segmentation sequence of each skeleton point cloud data; It is the segmentation ripple difference index between each skeleton point cloud data and the nth ripple segmentation sequence of its ith control point cloud data.

[0087] It should be noted that the inclined ripple sequences at the front end and the rear end of the ripple width sequence of the skeleton point cloud data are sequentially recorded as the 1st and 2nd ripple segmentation sequences of the skeleton point cloud data; if there is a final flat ripple sequence in the skeleton point cloud data, the final flat ripple sequence is recorded as the 3rd ripple segmentation sequence.

[0088] Step S5: Perform dimensional error detection on the sheet according to the ripple shape difference index and the measurement error index between each skeleton point cloud data and its control point cloud data, and the height data difference between each skeleton point cloud data and the other skeleton point cloud data.

[0089] The ripple shape difference index and the measurement error index reflect the authenticity of the dimensional error of the skeleton point cloud data; the ripple height of the sheet usually remains consistent. By the height data difference between each skeleton point cloud data and the other skeleton point cloud data, the significant degree of the dimensional error of the skeleton point cloud data is measured to detect the dimensional error.

[0090] According to the ripple shape difference index and the measurement error index between each skeleton point cloud data and its control point cloud data, obtain the true index of the dimensional error of each skeleton point cloud data. The specific obtaining method is: calculate the sum value of the measurement error indexes between each skeleton point cloud data and each of its control point cloud data, and take the product of the sum value and the ripple shape difference index as the local dimensional error index between each skeleton point cloud data and each of its control point cloud data; perform normalization processing on the cumulative sum of the local dimensional error indexes between each skeleton point cloud data and all its control point cloud data respectively to obtain the true index of the dimensional error of each skeleton point cloud data.

[0091] If the difference in the ripple shapes between the skeleton point cloud data and its control point cloud data is greater, the ripple error at the position of the skeleton point cloud data is greater; if the measurement error index of the control point cloud data of the skeleton point cloud data is greater and the interference of the dimensional error of the control point cloud data on the dimensional error detection of the ripple cross-section where the skeleton point cloud data is located is more serious, then the authenticity of the dimensional error of the skeleton point cloud data is higher and the true index of the dimensional error is larger.

[0092] In the embodiment of the present invention, the obtaining method of the error significant index is: calculate the difference between the height data of each skeleton point cloud data and the average value of the height data of all skeleton point cloud data in the ripple region where it is located, which is recorded as the ripple height difference of each skeleton point cloud data; obtain the error significant index of each skeleton point cloud data according to the ripple height difference, ripple error and true index of the dimensional error of each skeleton point cloud data.

[0093] The corrugation height of the plate is usually kept consistent. The greater the difference in corrugation height, the greater the difference between the height of the corrugation where the skeleton point cloud data is located and the overall height of the plate corrugation, the greater the dimensional error of the skeleton point cloud data, and the greater the error significant index. If the true indices of corrugation error and dimensional error are greater, it indicates a greater likelihood of abnormality in the corrugation where the skeleton point cloud data is located, a greater dimensional error of the skeleton point cloud data, and a greater error significant index. Therefore, the difference in corrugation height, corrugation error, and the true index of dimensional error are all positively correlated with the error significant index. In a specific implementation of the embodiment of the present invention, the error significant index is expressed by the formula:

[0094]

[0095] In the formula, WX is the error significant index of each skeleton point cloud data; h is the height data of each skeleton point cloud data; is the average value of the height data of all skeleton point cloud data within the corrugation area where each skeleton point cloud data is located; is the difference in corrugation height of each skeleton point cloud data; BW is the corrugation error of each skeleton point cloud data; C is the true index of dimensional error of each skeleton point cloud data. is the absolute value function; Norm is the normalization function.

[0096] Determine whether there is point cloud data with an error significant index greater than the preset error threshold. If so, the plate has a dimensional error, and the position of the dimensional abnormality of the plate to be detected is the point cloud data with an error significant index greater than the preset error threshold; if not, the plate has no dimensional error.

[0097] In an implementation of the embodiment of the present invention, the preset error threshold is set to 0.8.

[0098] The dimensional error detection of the upper surface of the plate to be detected is completed through steps S2 to S5, and the same method is used for the lower surface of the plate to be detected for dimensional error detection.

[0099] So far, the present invention is completed.

[0100] Embodiment 2:

[0101] The present invention provides a system for detecting the dimensional error of the plate of a plate heat exchanger. Please refer to Figure 2 , which shows the system structure diagram of a system for detecting the dimensional error of the plate of a plate heat exchanger provided by an embodiment of the present invention. The system includes:

[0102] A data acquisition module 610, configured to obtain the point cloud data of each scan of the plate of the plate heat exchanger, and the point cloud data includes height data;

[0103] The corrugation segmentation module 620 is used to obtain the corrugated area and the skeleton point cloud data therein; based on the corrugation type of the slab, divide the point cloud data in the width direction of the corrugation of each skeleton point cloud data in the corrugated area to obtain the corrugation segmentation sequence of each skeleton point cloud data;

[0104] The measurement error analysis module 630 is used to obtain the measurement error index of each skeleton point cloud data according to the position distribution of the point cloud data in the corrugation segmentation sequence of each skeleton point cloud data and the difference of the point cloud data in the same corrugation segmentation sequence;

[0105] The corrugation shape analysis module 640 is used to obtain the corrugation shape difference index between each skeleton point cloud data and the reference point cloud data according to the similarity of each corrugation segmentation sequence of each skeleton point cloud data and the reference point cloud data and the degree of dispersion of the point cloud data in each corrugation segmentation sequence of each skeleton point cloud data;

[0106] The dimensional error detection module 650 is used to perform dimensional error detection on the slab according to the corrugation shape difference index and the measurement error index between each skeleton point cloud data and the reference point cloud data, and the height data difference between each skeleton point cloud data and the other skeleton point cloud data.

[0107] It should be noted that: for the device provided in the above embodiment, only the above division of each functional module is used for illustration. In practical applications, the above functions can be allocated to different functional modules according to needs, that is, the internal structure of the computer device is divided into different functional modules to complete all or part of the functions described above. In addition, the slab dimensional error detection system of a plate heat exchanger and the embodiment of the slab dimensional error detection method of a plate heat exchanger provided in the above embodiment belong to the same concept. The specific implementation process can be seen in the method embodiment and will not be elaborated here.

[0108] Embodiment 3:

[0109] Figure 3 This is a schematic diagram of a computer device for a slab dimensional error detection device of a plate heat exchanger provided by an embodiment of the present invention. Exemplarily, as Figure 3 shown, the computer device includes: a memory 701, a processor 702, and a computer program 703 stored in the memory 701 and running on the processor 702. Among them, when the processor 702 executes the computer program 703, the computer device can execute any one of the slab dimensional error detection methods of the plate heat exchanger introduced above.

[0110] In addition, an embodiment of the present application also protects a device, which may include a memory and a processor. Among them, executable program code is stored in the memory, and the processor is used to call and execute the executable program code to execute a method for detecting the plate size error of a plate heat exchanger provided by an embodiment of the present application.

[0111] In this embodiment, the device can be divided into functional modules according to the above method example. For example, each functional module can be corresponded, or two or more functions can be integrated into one processing module. The above integrated module can be implemented in the form of hardware. It should be noted that the division of modules in this embodiment is illustrative, only a logical function division, and there can be other division methods in actual implementation.

[0112] It should be understood that the device provided in this embodiment is used to execute the above method for detecting the plate size error of a plate heat exchanger, so the same effect as the above implementation method can be achieved.

[0113] In the case of adopting an integrated unit, the device may include a processing module and a storage module. Among them, when the device is applied to a device, the processing module can be used to control and manage the actions of the device. The storage module can be used to support the device to execute mutual program code, etc.

[0114] Among them, the processing module can be a processor or a controller, which can implement or execute various exemplary logic blocks, modules and circuits included in combination with the disclosure of the present application. The processor can also be a combination that realizes computing functions, such as including a combination of one or more microprocessors, a combination of digital signal processing (DSP) and a microprocessor, etc. The storage module can be a memory.

[0115] It should be noted that the above sequence of embodiments of the present invention is only for description and does not represent the advantages or disadvantages of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0116] Each embodiment in this specification is described in a progressive manner. The same or similar parts among the embodiments can be referred to each other, and the key points of each embodiment are the differences from other embodiments.

[0117] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for detecting the dimensional error of the plates of a plate heat exchanger, characterized in that, The method includes: Obtaining point cloud data for each scan of the plates of a plate heat exchanger, where the point cloud data includes height data; Obtaining the corrugated region and the skeleton point cloud data therein; dividing the point cloud data in the width direction of the corrugation where each skeleton point cloud data in the corrugated region is located based on the corrugation type of the plate to obtain the corrugation segmentation sequence of each skeleton point cloud data; Obtaining the measurement error index of each skeleton point cloud data according to the position distribution of the point cloud data in the corrugation segmentation sequence of each skeleton point cloud data and the difference of the point cloud data in the same corrugation segmentation sequence; Obtaining the corrugation shape difference index between each skeleton point cloud data and the reference point cloud data according to the similarity of each corrugation segmentation sequence between each skeleton point cloud data and its reference point cloud data and the degree of dispersion of the point cloud data in each corrugation segmentation sequence of each skeleton point cloud data; the reference point cloud data is other skeleton point cloud data in the corrugated region where each skeleton point cloud data is located; Performing dimensional error detection on the plate according to the corrugation shape difference index and the measurement error index between each skeleton point cloud data and its reference point cloud data, and the height data difference between each skeleton point cloud data and the other skeleton point cloud data; The obtaining of the corrugation segmentation sequence of each skeleton point cloud data includes: For each corrugated region, optionally select a skeleton point cloud data in the corrugated region as the example data, perform linear fitting on the example data and the skeleton point cloud data of the adjacent scan of its sub-scan, obtain a plane passing through the example data and perpendicular to the fitting line, arrange the point cloud data on the plane in the corrugated region in sequence to obtain the corrugation width sequence of the example data; If the corrugation of the plate belongs to the first corrugation type, divide the corrugation width sequence into two inclined corrugation sequences based on the example data; If the corrugation of the plate belongs to the second corrugation type, in the corrugation width sequence, form an initial flat corrugation sequence from the example data and its adjacent point cloud data; if the flat corrugation sequence does not meet the selection condition, add the adjacent point cloud data of the point cloud data at both ends of the flat corrugation sequence in the corrugation width sequence to the flat corrugation sequence until the updated flat corrugation sequence meets the selection condition, and record the flat corrugation sequence when it meets the selection condition as the final flat corrugation sequence of the example data; respectively arrange the point cloud data on both sides of the final flat corrugation sequence in the corrugation width sequence in sequence to obtain the inclined corrugation sequence of the example data; Record the inclined corrugation sequence and the final flat corrugation sequence as the corrugation segmentation sequence of the example data; The obtaining of the corrugated region and the skeleton point cloud data therein includes: Selecting the point cloud data with the difference between the height data and the preset reference height data greater than the preset height difference threshold from the point cloud data of each scan as the corrugated point cloud data; Taking the connected domain formed by the corrugated point cloud data of all sub-scans as the corrugated region; Selecting the corrugated point cloud data corresponding to the maximum height data from the corrugated point cloud data of the same scan in each corrugated region and recording it as the skeleton point cloud data in the corresponding corrugated region.

2. The method for detecting the size error of the plate of a plate heat exchanger according to claim 1, characterized in that, The obtaining of the measurement error index of each skeleton point cloud data includes: Obtain the standard height data of each point cloud data of the sheet; calculate the mean difference between the height data of the point cloud data in each corrugation segmentation sequence of each skeleton point cloud data and the standard height data as the fitting error of the corresponding corrugation segmentation sequence; select the maximum value among the fitting errors of all corrugation segmentation sequences of each skeleton point cloud data as the corrugation error. Obtain the DTW value and the difference in the number of elements of the height data of the point cloud data in the two inclined corrugation sequences of each skeleton point cloud data respectively, and record them as the height change difference index and the corrugation width difference value in sequence. Obtain the measurement error probability of each skeleton point cloud data according to the height change difference index, the corrugation width difference index and the corrugation error.

3. A method for detecting the dimensional error of the plate of a plate heat exchanger according to claim 2, characterized in that The obtaining of the corrugation shape difference index between each skeleton point cloud data and the reference point cloud data includes: Obtain the discrete index of the height data of the point cloud data in each corrugation segmentation sequence of each skeleton point cloud data respectively, and the DTW value between each skeleton point cloud data and each corrugation segmentation sequence of each of its reference point cloud data. Obtain the segmented corrugation difference index between each skeleton point cloud data and each corrugation segmentation sequence of each of its reference point cloud data according to the fitting error, the discrete index and the DTW value of each corrugation segmentation sequence of each skeleton point cloud data; take the sum of the segmented corrugation difference indexes of all corrugation segmentation sequences as the corrugation shape difference index between each skeleton point cloud data and each of its reference point cloud data.

4. A method for detecting the dimensional error of the plate of a plate heat exchanger according to claim 2, characterized in that, The dimension error detection of the sheet according to the corrugation shape difference index and the measurement error index between each skeleton point cloud data and its reference point cloud data, and the height data difference between each skeleton point cloud data and the other skeleton point cloud data includes: Obtain the true dimension error index of each skeleton point cloud data according to the corrugation shape difference index and the measurement error index between each skeleton point cloud data and its reference point cloud data. Calculate the difference between the height data of each skeleton point cloud data and the mean value of the height data of all skeleton point cloud data in the corrugation area where it is located, and record it as the corrugation height difference of each skeleton point cloud data. Obtain the error significance index of each skeleton point cloud data according to the corrugation height difference, the corrugation error and the true dimension error index of each skeleton point cloud data. Judge whether there is point cloud data with the error significance index greater than the preset error threshold. If so, the sheet has a dimension error; if not, the sheet does not have a dimension error.

5. A method for detecting the dimensional error of a plate of a plate heat exchanger according to claim 4, characterized in that, The obtaining of the true dimension error index of each skeleton point cloud data includes: Calculate the sum value of the measurement error indexes between each skeleton point cloud data and each of its reference point cloud data, and take the product of the sum value and the corrugation shape difference index as the local dimension error index between each skeleton point cloud data and each of its reference point cloud data. Perform normalization processing on the sum of the local dimension error indexes between each skeleton point cloud data and all of its reference point cloud data respectively to obtain the true dimension error index of each skeleton point cloud data.

6. A method for detecting the dimensional error of the plates of a plate heat exchanger according to claim 1, characterized in that, The condition to be selected is that the coefficient of determination of the linear fitting of the point cloud data in the flat corrugation sequence is less than the preset fitting threshold.

7. A plate size error detection system for a plate heat exchanger, characterized in that, The system includes: A data acquisition module for obtaining point cloud data for each scan of the plates of a plate heat exchanger, the point cloud data including height data; A corrugation segmentation module for obtaining corrugated regions and the skeleton point cloud data therein; dividing the point cloud data in the corrugation width direction of each skeleton point cloud data in the corrugated region based on the corrugation type of the plate to obtain the corrugation segmentation sequence of each skeleton point cloud data; A measurement error analysis module for obtaining the measurement error index of each skeleton point cloud data according to the position distribution of the point cloud data in the corrugation segmentation sequence of each skeleton point cloud data and the difference of the point cloud data in the same corrugation segmentation sequence; A corrugation shape analysis module for obtaining the corrugation shape difference index between each skeleton point cloud data and the reference point cloud data according to the similarity of each corrugation segmentation sequence between each skeleton point cloud data and its reference point cloud data and the degree of dispersion of the point cloud data in each corrugation segmentation sequence of each skeleton point cloud data; the reference point cloud data is other skeleton point cloud data in the corrugated region where each skeleton point cloud data is located; A dimensional error detection module for performing dimensional error detection on the plate according to the corrugation shape difference index and the measurement error index between each skeleton point cloud data and its reference point cloud data, and the height data difference between each skeleton point cloud data and the other skeleton point cloud data; The obtaining of the corrugation segmentation sequence of each skeleton point cloud data includes: For each corrugated region, arbitrarily select a skeleton point cloud data in the corrugated region as the sample data, perform linear fitting on the sample data and the skeleton point cloud data of the adjacent scans of its sub-scan, obtain a plane passing through the sample data and perpendicular to the fitting line, arrange the point cloud data on the plane in the corrugated region in sequence to obtain the corrugation width sequence of the sample data; If the corrugation of the plate belongs to the first corrugation type, divide the corrugation width sequence into two inclined corrugation sequences based on the sample data; If the corrugation of the plate belongs to the second corrugation type, in the corrugation width sequence, an initial flat corrugation sequence is formed by the sample data and its adjacent point cloud data; if the flat corrugation sequence does not meet the selection conditions, add the adjacent point cloud data of the point cloud data at both ends of the flat corrugation sequence in the corrugation width sequence to the flat corrugation sequence until the updated flat corrugation sequence meets the selection conditions, and record the flat corrugation sequence when it meets the selection conditions as the final flat corrugation sequence of the sample data; arrange the point cloud data on both sides of the final flat corrugation sequence in the corrugation width sequence in sequence to obtain the inclined corrugation sequence of the sample data; Record the inclined corrugation sequence and the final flat corrugation sequence as the corrugation segmentation sequence of the sample data; The obtaining of the corrugated region and the skeleton point cloud data therein includes: Select the point cloud data with the difference between the height data and the preset reference height data greater than the preset height difference threshold from the point cloud data of each scan as the corrugated point cloud data; Take the connected domain formed by the corrugated point cloud data of all sub-scans as the corrugated region; Select the point cloud data corresponding to the maximum height data from the ripple point cloud data within the same scan in each ripple area, which is denoted as the skeleton point cloud data within the corresponding ripple area.

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