Plate size error detection method and system for plate heat exchanger

Abstract

The invention relates to the technical field of size measurement, in particular to a plate size error detection method and system for a plate heat exchanger. The method comprises the following steps: acquiring a corrugated area of a sheet and a corrugated segment sequence of skeleton point cloud data, and acquiring a measurement error index according to the difference between the position distribution of the point cloud data in the corrugated segment sequence of the skeleton point cloud data and the point cloud data in the same corrugated segment sequence; acquiring a ripple shape difference index according to the similarity of the same ripple segment sequence of the skeleton point cloud data and the contrast point cloud data thereof and the dispersion degree of the point cloud data in the ripple segment sequence; and carrying out size error detection on the sheet by combining a measurement error index, a ripple shape difference index and a height data difference of different skeleton point cloud data. According to the invention, the reliability of plate size error detection is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of dimensional measurement, and particularly 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 metal sheets with corrugated shapes, 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, it is of great significance to detect the dimensional errors of the plates to ensure the 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 the normal texture or the texture itself is abnormal, resulting in a low reliability of the dimensional error detection of the plates of the 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 a low reliability of the dimensional error detection of the plate, the object 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: In the first aspect, an embodiment of the present invention provides a method for detecting the dimensional error of the plates of a plate heat exchanger, and the method includes: Obtain the point cloud data of each scan of the plates of the plate heat exchanger, and the point cloud data includes height data; Obtain the corrugated area and the skeleton point cloud data inside it; divide the point cloud data in the corrugation width direction of each skeleton point cloud data in the corrugated area based on the corrugation type of the plate to obtain the corrugation segmentation sequence 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, obtain the measurement error index of each skeleton point cloud data; According to the similarity of each corrugation segmentation sequence between 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, obtain the corrugation shape difference index between each skeleton point cloud data and the reference point cloud data; According to the corrugation shape difference index and the measurement error index between each skeleton point cloud data and its corresponding reference point cloud data, and the height data difference between each skeleton point cloud data and the other skeleton point cloud data, perform dimensional error detection on the plate.

[0005] Further, the obtaining of the corrugation segmentation sequence of each skeleton point cloud data includes: For each corrugation region, arbitrarily 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 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 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.

[0006] Further, 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 slab; 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; Respectively obtain the DTW value and the element number difference of the height data of the point cloud data in the two inclined corrugation sequences of each skeleton point cloud data, and record them as the height change difference index and the corrugation width difference value in sequence; According to the height change difference index, the corrugation width difference index and the corrugation error, obtain the measurement error probability of each skeleton point cloud data.

[0007] Further, the obtaining of the corrugation shape difference index between each skeleton point cloud data and the reference point cloud data includes: Respectively obtain the discrete 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 between each skeleton point cloud data and each corrugation segmentation sequence of each reference point cloud data; Based on the fitting error, the discreteness index, and the DTW value of each corrugation segmentation sequence of each skeletal point cloud data, obtain the segmentation corrugation difference index of each corrugation segmentation sequence between each skeletal point cloud data and its corresponding reference point cloud data; take the sum of the segmentation corrugation difference indexes of all corrugation segmentation sequences as the corrugation shape difference index between each skeletal point cloud data and its corresponding reference point cloud data.

[0008] Further, the dimension error detection of the sheet based on the corrugation shape difference index and the measurement error index between each skeletal point cloud data and its reference point cloud data, and the height data difference between each skeletal point cloud data and the other skeletal point cloud data includes: Based on the corrugation shape difference index and the measurement error index between each skeletal point cloud data and its reference point cloud data, obtain the true dimension error index of each skeletal point cloud data; Calculate the difference between the height data of each skeletal point cloud data and the average value of the height data of all skeletal point cloud data within the corrugation region where it is located, and denote it as the corrugation height difference of each skeletal point cloud data; Based on the corrugation height difference, the corrugation error, and the true dimension error index of each skeletal point cloud data, obtain the error significance index of each skeletal 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.

[0009] Further, the obtaining of the true dimension error index of each skeletal point cloud data includes: Calculate the sum value of the measurement error indexes between each skeletal point cloud data and its corresponding 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 skeletal point cloud data and its corresponding reference point cloud data; Perform normalization processing on the sum of the local dimension error indexes between each skeletal point cloud data and all its corresponding reference point cloud data respectively, to obtain the true dimension error index of each skeletal point cloud data.

[0010] Further, the obtaining of the corrugation region and the skeletal point cloud data inside it includes: Select, from the point cloud data scanned each time, the point cloud data with the difference between the height data and the preset reference height data greater than the preset height difference threshold as the corrugation point cloud data; Take the connected domain formed by the corrugation point cloud data scanned all times as the corrugation region; Select, from the corrugation point cloud data scanned at the same time within each corrugation region, the corrugation point cloud data corresponding to the maximum height data, and denote it as the skeletal point cloud data within the corresponding corrugation region.

[0011] Further, the to-be-selected condition is that the coefficient of determination of the linear fitting of the point cloud data at the points of the flat corrugation sequence is less than a preset fitting threshold.

[0012] Further, the reference point cloud data is other skeleton point cloud data within the corrugation region where each skeleton point cloud data is located.

[0013] In a second aspect, another embodiment of the present invention provides a system for detecting the size error of a plate of a plate heat exchanger. The system includes: A data acquisition module, configured to acquire the point cloud data of each scan of the plate of the plate heat exchanger, and the point cloud data includes height data; A corrugation segmentation module, configured to acquire the corrugation region 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 is located in the corrugation region based on the corrugation type of the plate, so as to obtain the corrugation segmentation sequence of each skeleton point cloud data; A measurement error analysis module, configured 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; A corrugation shape analysis module, configured 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 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; A size error detection module, configured to perform size 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.

[0014] The present invention has the following beneficial effects: In the embodiments 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 at the midpoints of the same corrugation segments of the skeleton point cloud data, and in combination 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 reference 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 reference 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 results, and the corrugation shape difference index helps to reduce the detection error caused by misjudging normal corrugations as abnormal, and in combination with the consistent corrugation height, the height data difference of different skeleton point cloud data is analyzed to analyze the significant degree of the dimensional error of the skeleton point cloud data, and then the dimensional error of the plate is detected to enhance the reliability of the plate dimensional error detection. Brief Description of the Drawings

[0015] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the accompanying drawings required for use in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention, and for those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0016] Figure 1 It is a flowchart of the steps of a method for detecting the dimensional error of the plate of a plate heat exchanger provided by an embodiment of the present invention; Figure 2 It is a 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; Figure 3 It is a schematic diagram of a computer device of a device for detecting the dimensional error of the plate of a plate heat exchanger provided by an embodiment of the present invention. Detailed Embodiments

[0017] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following, in combination with the accompanying drawings and preferred embodiments, details the specific embodiments, structures, features and their effects of a method and system for detecting the dimensional error of the plate of a plate heat exchanger proposed according to the present invention. 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.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the technical field to which this invention belongs.

[0019] The following specifically describes the specific solutions of a method and system for detecting the sheet size error of a plate heat exchanger provided by the present invention with reference to the accompanying drawings.

[0020] Example 1: The present invention proposes a method for detecting the sheet size error of a plate heat exchanger. Please refer to Figure 1 , which shows a flowchart of the steps of a method for detecting the sheet size error of a plate heat exchanger provided by an embodiment of the present invention. The method includes: Step S1: Obtain the point cloud data of each scan of the sheet of the plate heat exchanger. The point cloud data includes height data.

[0021] Using a laser displacement sensor, scan the upper and lower surfaces of the sheet to be detected of the plate heat exchanger through line laser, and obtain the point cloud data of each scan of each surface of the sheet , where x and y respectively represent the row data and column data of the sheet, and h represents the height data of the position of the sheet 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 of the upper surface of the sheet is used for subsequent analysis.

[0022] It should be noted that the line laser will scan along a certain direction of the sheet, generating a series of point cloud data. In order to obtain the complete three-dimensional shape of the sheet, it is usually necessary to perform multiple line laser scans on the sheet and the scanning directions of all scans are the same. The width of the point cloud data of each scan is 1. In this embodiment, the point cloud data of each scan represents each column of the sheet, and the scanning order is from left to right in the row direction of the sheet.

[0023] 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 sheet, and obtain the corrugated segmentation sequence of each skeleton point cloud data.

[0024] In order to analyze whether there is an abnormality in the corrugation of the sheet itself, obtain the corrugated area and the skeleton point cloud data of the sheet. The skeleton point cloud data reflects the overall shape and structure of the sheet. The specific method is: 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 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 in each corrugated area, and record it as the skeleton point cloud data in the corresponding corrugated area.

[0025] The method for obtaining the preset reference height data is: plane fitting is performed on all scanned point cloud data to obtain the base plane of the plate; the distance from the laser displacement sensor to the base plane of the plate is recorded as the preset reference height data. It should be noted that the base plane of the plate represents the plane shape of the plate in an ideal state, the preset reference height data represents the height from the laser displacement sensor to the ideal plane shape of the plate, and the point cloud data corresponding to the height data that is significantly different from the preset reference height data represents the ripple position.

[0026] The embodiment of the present invention uses the least square method for plane fitting, and may also use the Chebyshev plane fitting algorithm, etc., which will not be described in detail here.

[0027] In one implementation of the embodiment of the present invention, the preset reference height data is set to 0.01 mm.

[0028] 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. In order to improve the accuracy of corrugation anomaly analysis, the point cloud data in the corrugation width direction where the skeleton point cloud data in the corrugation area is located is segmented to obtain different slope segments, namely, the corrugation segmentation sequence.

[0029] Preferably, in some possible implementation modes of the embodiments of the present invention, the method for obtaining the ripple segmentation sequence includes: for each ripple area, a skeleton point cloud data in any ripple area is recorded as example data, a straight line fitting is performed on the skeleton point cloud data of the adjacent sub-scan of the example data and the sub-scan to which it belongs, a plane passing through the example data and perpendicular to the fitting line is obtained, and the point cloud data on the plane in the ripple area are arranged in sequence to obtain a ripple width sequence of the example data; if the ripple of the plate belongs to the first ripple type, the ripple width sequence is divided into two inclined ripple sequences based on the example data; if the ripple of the plate belongs to the second ripple type, In the ripple width sequence, the initial flat ripple sequence is composed of the example data and its adjacent point cloud data; if the flat ripple sequence does not meet the selection condition, the adjacent point cloud data of the point cloud data at both ends of the flat ripple sequence in the ripple width sequence are added to the flat ripple sequence until the updated flat ripple sequence meets the selection condition, and the flat ripple sequence that meets the selection condition is recorded as the final flat ripple sequence of the example data; the point cloud data on both sides of the final flat ripple sequence in the ripple width sequence are arranged in sequence to obtain the inclined ripple sequence of the example data; the inclined ripple sequence and the final flat ripple sequence are recorded as the ripple segmentation sequence of the example data.

[0030] The point cloud data on the plane passing through the sample data and perpendicular to the fitting line shows the variation of the plate corrugation width. To make the fluid evenly distributed in the channels between the plates, avoid too high or too low local flow velocity, and make the pressure on the plates more uniform, the plate corrugations usually have symmetry. The cross-section of the plate 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: for the first type of corrugation, there is only one point with the maximum height in the cross-section of the corrugation, that is, there is no flat slope in the cross-section of the corrugation, such as triangular corrugations and sine wave corrugations; for the second type of corrugation, there are multiple points with the maximum height in the cross-section of the corrugation, that is, there is a flat slope in the cross-section of the corrugation, such as trapezoidal corrugations and rectangular corrugations.

[0031] For the second type of corrugation, since the sample data may not be located at the middle position of the cross-section of the corrugation it belongs to, there is a large error in measuring the corrugation symmetry by dividing the cross-section of the corrugation it belongs to into two parts based on the sample data. To avoid the above error, it is necessary to extract the flat slope part of the cross-section of the corrugation. The larger the coefficient of determination of the linear fitting of the point cloud data in the flat corrugation sequence, the more likely it is that these point cloud data belong to the flat slope part of the cross-section of the corrugation of the sample data, and the greater the possibility that there are other flat slope parts in the cross-section of the corrugation. The flat slope part needs to continue to expand to both sides until the flat corrugation sequence contains an inclined slope and then stops.

[0032] The candidate condition is: 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. 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 of the embodiment of the present invention, the preset fitting threshold is set to 0.7.

[0033] As an example, assume that the corrugation width sequence of the sample data a5 is . If the corrugation of the plate belongs to the first type of corrugation, the two inclined corrugation sequences of the sample data a5 are 、 . If the corrugation of the plate belongs to the second type of corrugation, the initial flat corrugation sequence is . If J0 does not meet the preset condition, then is added to J0, and the updated flat corrugation sequence is . Assume that J1 meets the preset condition, the final flat corrugation sequence of the sample 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 plate.

[0034] It should be noted that linear fitting is performed on the skeleton point cloud data of the sample data and its adjacent previous scan and adjacent next scan of the same sub-scan; in the embodiments 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, etc.

[0035] 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 ripple segmentation sequence of each skeleton point cloud data and the difference of the point cloud data in the same ripple segmentation sequence.

[0036] Since the plate ripple has symmetry, the inclination of the slopes on both sides of the ripple should be relatively consistent. The similarity of the inclination of the slopes on both sides of the ripple is measured by the difference of the point cloud data at the midpoint of the same ripple segmentation of the skeleton point cloud data; the position distribution of the point cloud data in the ripple segmentation sequence of the skeleton point cloud data can specifically present whether there is an abnormality in the ripple where the skeleton point cloud data is located; combining the above factors to analyze the possibility of an abnormality in the ripple where the skeleton point cloud data is located, and obtaining the measurement error index.

[0037] 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 plate; calculating the difference mean of the height data of the point cloud data in each ripple segmentation sequence of each skeleton point cloud data and the standard height data as the fitting error of the corresponding ripple segmentation sequence; selecting the maximum value among the fitting errors of all the ripple segmentation sequences of each skeleton point cloud data as the ripple error; respectively obtaining 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 skeleton point cloud data, and sequentially recording them as the height change difference index and the ripple width difference value; obtaining the measurement error probability of each skeleton point cloud data according to the height change difference index, the ripple width difference index and the ripple 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.

[0038] The standard height data is the normal height data of a standard plate without dimensional errors. The greater the fitting error, the lower the coincidence degree between the point cloud data in the corrugation segmentation sequence and the corrugation shape at the corresponding position on the standard plate, and the greater the possibility that the corrugation where the point cloud data is located is abnormal, and the greater the possibility that the measurement error of the skeleton point cloud data occurs. Due to the symmetry of the plate corrugations, the inclination of the slopes on both sides of the corrugation should be relatively consistent; the inclined corrugation sequence represents the inclination slope on each side of the corrugation. The DTW value of the height data of the point cloud data in the two inclined corrugation sequences of the skeleton point cloud data and the absolute value of the difference in the number of elements are used to measure the similarity of the inclination of the slopes on both sides of the corrugation where the skeleton point cloud data is located. The larger the two values, the lower the similarity of the inclination of the slopes on both sides, and the greater the possibility that the measurement error of the skeleton point cloud data occurs. 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 embodiment 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.

[0039] In the embodiment 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.

[0040] It should be noted that in the embodiment of the present invention, the Norm function is used for normalization processing. In the embodiment of the present invention, other normalization methods can also be selected, such as function transformation, maximum-minimum normalization and other normalization methods, which will not be limited here.

[0041] In the embodiment of the present invention, the same method as that of the plate to be detected in step S1 is adopted for the standard plate to obtain the point cloud data of the standard plate. The reference point cloud of each point cloud data of the plate to be detected is selected from the point cloud data of the standard plate. The row data and column data of each point cloud data of the plate 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 plate to be detected is used as the standard height data of the corresponding point cloud data.

[0042] It should be noted that the standard plate and the plate to be detected are of the same corrugation type and the same size produced in the same batch. There is no abnormality in the corrugations of the standard plate, and it can be considered that the standard plate has no dimensional errors. During the acquisition of the point cloud data of the standard plate, the distance from the laser displacement sensor to the standard plate is equal to the distance from the laser displacement sensor to the plate to be detected; the point cloud data of the standard plate and the plate to be detected can be considered to be in the same three-dimensional coordinate system.

[0043] Step S4: Obtain the corrugation shape difference index between each skeletal point cloud data and the reference point cloud data according to the similarity of each corrugation segmentation sequence between the skeletal 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 skeletal point cloud data.

[0044] The corrugation skeleton structures within the same corrugation region should be relatively consistent. To more accurately analyze the corrugation anomalies in the skeletal point cloud data, the other skeletal point cloud data within the corrugation region where each skeletal point cloud data is located is denoted as its reference point cloud data.

[0045] The similarity of the corrugation segmentation sequences between the skeletal point cloud data and its reference point cloud data reflects the shape similarity of the corrugation cross-sections where the two point cloud data are located. The dispersion index of the height data of the point cloud data in the corrugation segmentation sequence reflects the degree of shape change of the corrugation cross-section. By combining the two to analyze the cross-section shape difference of the corrugations where the skeletal point cloud data and the reference point cloud data are located, the corrugation shape difference index is obtained.

[0046] Preferably, in some possible implementation manners of the embodiments 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 skeletal point cloud data, and the DTW value of each corrugation segmentation sequence between each skeletal point cloud data and each of its reference point cloud data; obtaining the segmentation corrugation difference index of each corrugation segmentation sequence between each skeletal 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 skeletal point cloud data; taking the sum of the segmentation corrugation difference indexes of all corrugation segmentation sequences as the corrugation shape difference index between each skeletal point cloud data and each of its reference point cloud data.

[0047] The dispersion index of the height data of the point cloud data in the corrugation segmentation sequence reflects the degree of shape change of the corrugation cross-section. The smaller the dispersion index, the smaller the degree of shape change of the corrugation cross-section, and the smaller the corrugation shape difference. The DTW value of each corrugation segmentation sequence between the skeletal point cloud data and its reference point cloud data reflects the similarity of the corrugation cross-sections where the two point cloud data are located. The larger the DTW value, the smaller the similarity of the corrugation cross-sections where the two point cloud data are located, and the greater the cross-section shape difference of the corrugations where the two point cloud data are located. If the fitting error is larger, the coincidence degree of the point cloud data in the corrugation segmentation sequence with the corrugation shape at the corresponding position on the standard sheet is lower, indicating that the authenticity of the corrugation where the point cloud data in the corrugation segmentation sequence is located is smaller, and the greater the cross-section shape difference of the corrugations where the skeletal point cloud data and the reference point cloud data are located. Therefore, the fitting error, dispersion index and DTW value are all positively correlated with the segmentation corrugation difference index.

[0048] In the embodiments of the present invention, the product of the fitting error, the discrete index and the DTW value of each corrugation segmentation sequence of each skeleton point cloud data is used as the segmentation corrugation difference index between each skeleton point cloud data and each corrugation segmentation sequence of each of its corresponding reference point cloud data. In the embodiments of the present invention, the correlation relationships between the discrete index, the DTW value, the fitting error and the segmentation corrugation difference index can also be constructed through other basic mathematical operations, which are not limited and elaborated herein.

[0049] Indicators such as standard deviation, variance and interquartile range can all reflect the dispersion degree of a set of data. In this embodiment, the discrete index is variance, and variance can also be replaced by indicators such as standard deviation or interquartile range.

[0050] In a specific implementation manner of the embodiments of the present invention, the corrugation shape difference index is expressed by the formula: In the formula, is the corrugation shape difference index between each skeleton point cloud data and its i-th corresponding reference point cloud data; N is the total number of corrugation 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 corrugation segmentation sequence of each skeleton point cloud data; is the n-th corrugation segmentation sequence of each skeleton point cloud data; is the n-th corrugation segmentation sequence of the i-th corresponding reference point cloud data of each skeleton point cloud data; is the DTW value between the n-th corrugation segmentation sequence of each skeleton point cloud data and its i-th corresponding reference point cloud data; is the fitting error of the n-th corrugation segmentation sequence of each skeleton point cloud data; is the segmentation corrugation difference index between the n-th corrugation segmentation sequence of each skeleton point cloud data and its i-th corresponding reference point cloud data.

[0051] It should be noted that the inclined corrugation sequences at the front end and the rear end of the corrugation width sequence of the skeleton point cloud data are sequentially recorded as the 1st and 2nd corrugation segmentation sequences of the skeleton point cloud data; if there is a final flat corrugation sequence in the skeleton point cloud data, the final flat corrugation sequence is recorded as the 3rd corrugation segmentation sequence.

[0052] Step S5: Perform dimensional error detection on the sheet according to the corrugation shape difference index and the measurement error index between each skeleton point cloud data and its corresponding reference point cloud data, and the height data difference between each skeleton point cloud data and the other skeleton point cloud data.

[0053] The corrugation shape difference index and the measurement error index reflect the authenticity of the dimensional error of the skeleton point cloud data; the corrugation height of the sheet is usually consistent, and the significance of the dimensional error of the skeleton point cloud data is measured by the height data difference between each skeleton point cloud data and the other skeleton point cloud data, and the dimensional error is detected.

[0054] According to the corrugation shape difference index and the measurement error index of each skeleton point cloud data and its corresponding reference point cloud data, the true dimensional error index of each skeleton point cloud data is obtained. The specific obtaining method is: calculate the sum value of the measurement error indexes of each skeleton point cloud data and its each corresponding reference point cloud data, and take the product of the sum value and the corrugation shape difference index as the local dimensional error index of each skeleton point cloud data and its each corresponding reference point cloud data; perform normalization processing on the cumulative sum of the local dimensional error indexes of each skeleton point cloud data and all its corresponding reference point cloud data respectively, and obtain the true dimensional error index of each skeleton point cloud data.

[0055] If the difference in the corrugation shape between the skeleton point cloud data and its corresponding reference point cloud data is greater, the corrugation error at the position of the skeleton point cloud data is greater; if the measurement error index of the corresponding reference point cloud data of the skeleton point cloud data is greater, and the dimensional error of the reference point cloud data interferes more seriously with the detection of the dimensional error of the corrugation cross-section where the skeleton point cloud data is located, then the authenticity of the dimensional error of the skeleton point cloud data is higher, and the true dimensional error index is greater.

[0056] In the embodiment of the present invention, the method for obtaining the error significance 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 corrugation area where it is located, and record it as the corrugation height difference of each skeleton point cloud data; according to the corrugation height difference, corrugation error and true dimensional error index of each skeleton point cloud data, obtain the error significance index of each skeleton point cloud data.

[0057] The corrugation height of the sheet is usually consistent. If the corrugation height difference is greater, it means that the difference between the height of the corrugation where the skeleton point cloud data is located and the overall height of the sheet corrugation is greater, then the dimensional error of the skeleton point cloud data is greater, and the error significance index is greater. If the corrugation error and the true dimensional error index are greater, it means that the possibility of the corrugation where the skeleton point cloud data is located being abnormal is greater, the dimensional error of the skeleton point cloud data is greater, and the error significance index is greater. Therefore, the corrugation height difference, corrugation error and true dimensional error index are all positively correlated with the error significance index. In a specific implementation manner of the embodiment of the present invention, the error significance index is expressed by the formula: In the formula, WX is the error significance 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 in the corrugation area where each skeleton point cloud data is located; Δh is the ripple height difference of each skeleton point cloud data; BW is the ripple error of each skeleton point cloud data; C is the true index of the dimensional error of each skeleton point cloud data. abs is the absolute value function; Norm is the normalization function.

[0058] Determine whether there is point cloud data with a significant error index greater than the preset error threshold. If so, the sheet has a dimensional error, and the abnormal dimensional position of the sheet to be detected is the point cloud data with a significant error index greater than the preset error threshold; otherwise, the sheet has no dimensional error.

[0059] In one implementation of the embodiment of the present invention, the preset error threshold is set to 0.8.

[0060] The dimensional error detection of the upper surface of the sheet to be detected is completed through steps S2 to S5, and the same method as the upper surface is used for the dimensional error detection of the lower surface of the sheet to be detected.

[0061] So far, the present invention is completed.

[0062] Embodiment 2: The present invention provides a system for detecting the dimensional error of a 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 a plate of a plate heat exchanger provided by an embodiment of the present invention. The system includes: A data acquisition module 610, configured to acquire point cloud data for each scan of the plate of the plate heat exchanger, and the point cloud data includes height data; A ripple segmentation module 620, configured to acquire a ripple region and the skeleton point cloud data therein; divide the point cloud data in the ripple width direction of each skeleton point cloud data in the ripple region based on the ripple type of the plate to obtain a ripple segmentation sequence of each skeleton point cloud data; A measurement error analysis module 630, configured to obtain a measurement error index of each skeleton point cloud data according to the position distribution of the point cloud data in the ripple segmentation sequence of each skeleton point cloud data and the difference of the point cloud data in the same ripple segmentation sequence; A ripple shape analysis module 640, configured to obtain a ripple shape difference index between each skeleton point cloud data and the reference point cloud data according to the similarity of each ripple 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 ripple segmentation sequence of each skeleton point cloud data; A dimensional error detection module 650, configured to perform dimensional error detection on the plate according to the ripple shape difference index and the measurement error index of 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.

[0063] It should be noted that: For the device provided in the above embodiments, only the division of the above functional modules is used for illustration. In actual applications, the above functions can be allocated to different functional modules as needed, that is, the internal structure of the computer device can be divided into different functional modules to complete all or part of the functions described above. In addition, the embodiments of a plate heat exchanger plate size error detection system and a plate heat exchanger plate size error detection method provided in the above embodiments belong to the same concept. For the specific implementation process, please refer to the method embodiments and will not be elaborated here.

[0064] Embodiment 3: Figure 3 The following is a schematic diagram of a computer device of a plate heat exchanger plate size error detection device 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. When the processor 702 executes the computer program 703, the computer device can execute any of the plate heat exchanger plate size error detection methods introduced above.

[0065] In addition, an embodiment of the present application also protects a device, which may include a memory and a processor. Among them, an executable program code is stored in the memory, and the processor is used to call and execute the executable program code to execute a plate heat exchanger plate size error detection method provided by an embodiment of the present application.

[0066] In this embodiment, the device can be divided into functional modules according to the above method examples. For example, it can correspond to each functional module, 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 may be other division methods in actual implementation.

[0067] It should be understood that the device provided in this embodiment is used to execute the above plate heat exchanger plate size error detection method, so the same effect as the above implementation method can be achieved.

[0068] 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 codes, etc.

[0069] Among them, the processing module can be a processor or a controller, which can implement or execute various exemplary logical blocks, modules, and circuits included in the disclosure of the present application. The processor can also be a combination that implements 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.

[0070] It should be noted that the above sequence of the embodiments of the present invention is only for description and does not represent the superiority or inferiority of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0071] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other. Each embodiment focuses on the differences from other embodiments.

[0072] 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 principles of the present invention shall be included in the protection scope of the present invention.

Claims

1. A plate size error detection method for a plate heat exchanger, characterized in that: The method includes: Obtaining point cloud data of each scan of the plate of the plate heat exchanger, wherein the point cloud data includes height data; Obtain the skeleton point cloud data of the corrugated area and its interior; divide the point cloud data of each skeleton point cloud data in the corrugated width direction in the corrugated area based on the corrugation type of the plate, and obtain the corrugation segmentation sequence of each skeleton point cloud data; According to the position distribution of the point cloud data in the ripple segmentation sequence of each skeleton point cloud data and the difference between the point cloud data in the same ripple segmentation sequence, the measurement error index of each skeleton point cloud data is obtained; According to the similarity between each ripple segmentation sequence of each skeleton point cloud data and its reference point cloud data and the degree of discreteness of the point cloud data in each ripple segmentation sequence of each skeleton point cloud data, the ripple shape difference index between each skeleton point cloud data and the reference point cloud data is obtained; The plate is subjected to size error detection based on the corrugation shape difference index and the measurement error index between each skeleton point cloud data and its comparison point cloud data, and the height data difference between each skeleton point cloud data and the remaining skeleton point cloud data.

2. A plate size error detection method for a plate heat exchanger according to claim 1, characterized in that: The step of obtaining a ripple segmentation sequence of each skeleton point cloud data comprises: For each ripple region, a skeleton point cloud data in the ripple region is randomly selected and recorded as sample data, and a straight line fitting is performed between the sample data and the skeleton point cloud data of the adjacent sub-scan to which it belongs, and a plane passing through the sample data and perpendicular to the fitting line is obtained, and the point cloud data in the ripple region on the plane are sequentially arranged to obtain a ripple width sequence of the sample data; If the corrugation of the plate belongs to the first corrugation type, the corrugation width sequence is divided into two inclined corrugation sequences based on the example data; If the corrugation of the plate belongs to the second corrugation type, then in the corrugation width sequence, the sample data and its adjacent point cloud data constitute an initial flat corrugation sequence; if the flat corrugation sequence does not meet the selection condition, the adjacent point cloud data of the point cloud data at both ends of the flat corrugation sequence in the corrugation width sequence are added to the flat corrugation sequence until the updated flat corrugation sequence meets the selection condition, and the flat corrugation sequence that meets the selection condition is recorded as the final flat corrugation sequence of the sample data; the point cloud data at both sides of the final flat corrugation sequence in the corrugation width sequence are arranged in sequence to obtain the inclined corrugation sequence of the sample data; The inclined ripple sequence and the final flat ripple sequence are recorded as a ripple segment sequence of example data.

3. A plate size error detection method for a plate heat exchanger according to claim 2, characterized in that: The step of obtaining the measurement error index of each skeleton point cloud data includes: Obtain standard height data of each point cloud data of the plate; calculate the mean difference between the height data of the point cloud data in each ripple segmentation sequence of each skeleton point cloud data and the standard height data as the fitting error of the corresponding ripple segmentation sequence; select the maximum value of the fitting errors of all ripple segmentation sequences of each skeleton point cloud data as the ripple error; 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 skeleton point cloud data respectively, and record them as the height change difference index and the ripple width difference value respectively; The measurement error probability of each skeleton point cloud data is obtained according to the height change difference index, the corrugation width difference index and the corrugation error.

4. A plate size error detection method for a plate heat exchanger according to claim 3, characterized in that: The step of obtaining the corrugation shape difference index between each skeleton point cloud data and the reference point cloud data includes: 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 of each ripple segmentation sequence of each skeleton point cloud data and each of its comparison point cloud data; According to the fitting error, the discrete index and the DTW value of each ripple segmentation sequence of each skeleton point cloud data, the segmented ripple difference index of each ripple segmentation sequence of each skeleton point cloud data and each of its reference point cloud data is obtained; the cumulative sum of the segmented ripple difference indexes of all ripple segmentation sequences is used as the ripple shape difference index of each skeleton point cloud data and each of its reference point cloud data.

5. The plate size error detection method of a plate heat exchanger according to claim 3 is characterized in that: The dimensional error detection of the plate is performed 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 remaining skeleton point cloud data, including: According to the corrugation shape difference index of each skeleton point cloud data and its comparison point cloud data and the measurement error index, a true index of the size error of each skeleton point cloud data is obtained; Calculate the difference between the height data of each skeleton point cloud data and the mean height data of all skeleton point cloud data in the ripple area where it is located, and record it as the ripple height difference of each skeleton point cloud data; Obtaining an error significance index for each skeleton point cloud data according to the corrugation height difference, the corrugation error and the true index of the size error of each skeleton point cloud data; It is determined whether there is point cloud data whose error significance index is greater than a preset error threshold. If so, the plate has a size error; otherwise, the plate does not have a size error.

6. A plate size error detection method for a plate heat exchanger according to claim 5, characterized in that: The step of obtaining the true index of the size error of each skeleton point cloud data includes: Calculating the sum of the measurement error indexes of each skeleton point cloud data and each of its comparison point cloud data, and multiplying the sum by the corrugation shape difference index as the local size error index of each skeleton point cloud data and each of its comparison point cloud data; Each skeleton point cloud data is normalized with the accumulated sum of the local size error indicators of all its comparison point cloud data to obtain a true size error indicator for each skeleton point cloud data.

7. The plate size error detection method of a plate heat exchanger according to claim 1, characterized in that: The step of obtaining the corrugated area and the skeleton point cloud data therein comprises: Selecting point cloud data whose height data differs from preset reference height data by more than a preset height difference threshold from the point cloud data of each scan as ripple point cloud data; The connected domain formed by the ripple point cloud data of all scans is taken as the ripple area; The ripple point cloud data corresponding to the maximum height data is selected from the ripple point cloud data in each ripple area within the same scan, and recorded as the skeleton point cloud data in the corresponding ripple area.

8. The plate size error detection method of a plate heat exchanger according to claim 2, characterized in that: The candidate condition is: the determination coefficient of the straight line fitting of the point cloud data in the flattened corrugation sequence is less than a preset fitting threshold.

9. The plate size error detection method of a plate heat exchanger according to claim 1, characterized in that: The comparison point cloud data are other skeleton point cloud data within the ripple area where each skeleton point cloud data is located.

10. A plate size error detection system for a plate heat exchanger, characterized in that: The system includes: A data acquisition module is used to obtain point cloud data of each scan of the plate of the plate heat exchanger, and the point cloud data includes height data; The corrugation segmentation module is used to obtain the corrugation area and the skeleton point cloud data inside it; based on the corrugation type of the plate, the point cloud data of each skeleton point cloud data in the corrugation area in the corrugation width direction is divided to obtain the corrugation segmentation sequence of each skeleton point cloud data; A measurement error analysis module is used to obtain a measurement error index for each skeleton point cloud data according to the position distribution of the point cloud data in the ripple segmentation sequence of each skeleton point cloud data and the difference between the point cloud data in the same ripple segmentation sequence; A corrugation shape analysis module is used to obtain a corrugation shape difference index between each skeleton point cloud data and the reference point cloud data according to the similarity between each corrugation segment sequence of each skeleton point cloud data and its reference point cloud data and the degree of discreteness of the point cloud data in each corrugation segment sequence of each skeleton point cloud data; The size error detection module is used to perform size 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 control point cloud data, as well as the height data difference between each skeleton point cloud data and the remaining skeleton point cloud data.

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