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CN119090717B - A Correction and Stitching Method for EBSD Data - Google Patents

A Correction and Stitching Method for EBSD Data Download PDF

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CN119090717B
CN119090717B CN202411572748.9A CN202411572748A CN119090717B CN 119090717 B CN119090717 B CN 119090717B CN 202411572748 A CN202411572748 A CN 202411572748A CN 119090717 B CN119090717 B CN 119090717B
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CN119090717A (en
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曹耕晟
孙永辉
常松涛
沙玉辉
张芳
左良
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Northeastern University China
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Abstract

本发明提供一种EBSD数据的矫正拼接方法，涉及EBSD数据处理技术领域。该方法首先将EBSD数据转换为取向成像图，并对取向成像图进行拼接矫正获得标准图和待矫正图，匹配对齐并筛选出标准图和待矫正图的有效特征数据点，通过有效特征数据点计算待矫正图中出畸变的像素点矫正回标准位置的目标变换矩阵，并对取向成像图数据集中的每一张取向成像图中可识别的数据点进行相同的矩阵变换得到矫正的数据点，并对矫正后的数据点的格式进行标准化进而得到矫正标准化后的EBSD数据，对所有EBSD数据进行拼图，确定拼接处是否完全拼接，若有拼接痕，重新匹配特征数据点，重复这一过程直至拼接数据无拼接痕。

The invention provides a correction and splicing method for EBSD data, and relates to the technical field of EBSD data processing. The method first converts EBSD data into an orientation imaging map, splices and corrects the orientation imaging map to obtain a standard map and a map to be corrected, matches and aligns and screens out effective feature data points of the standard map and the map to be corrected, calculates a target transformation matrix for correcting distorted pixel points in the map to be corrected back to a standard position through effective feature data points, performs the same matrix transformation on identifiable data points in each orientation imaging map in the orientation imaging map data set to obtain corrected data points, and standardizes the format of the corrected data points to obtain corrected and standardized EBSD data, splices all EBSD data, determines whether the splicing is completely spliced, and if there is a splicing mark, re-matches the feature data points, and repeats this process until the spliced data has no splicing mark.

Description

Correction splicing method for EBSD data
Technical Field
The invention relates to the technical field of EBSD data processing, in particular to a correction splicing method of EBSD data.
Background
Electron back scattering diffraction (Electron Backscattered Diffraction, EBSD) is an important material characterization technique applied on Scanning Electron Microscopes (SEM). It obtains orientation and structural information of the crystal domains by analyzing diffraction patterns generated by high-energy electrons reflected from the surface of the sample. In recent years, with the revolutionary upgrade of the acquisition frequency of the detector, the analysis rate can exceed 4500pps, and the microstructure and texture of the research material can be rapidly and quantitatively counted without high beam current or sacrificing pattern resolution. This means that EBSD techniques can be free of the fence of micro-area detection, and fine characterization of the texture, grain orientation, microstructure, grain size, morphology, etc. of large area samples. Meanwhile, with the continuous and severe statistics of scientific research, the EBSD jigsaw technology is increasingly widely applied. However, in the EBSD test, in order to ensure a high spatial resolution, a dynamic focusing technique is mostly used, which means a function of automatically repairing the difference between the center and the four corners of the screen when the electron gun scans the screen. When a common electron gun focuses, a astigmatism phenomenon is generated, namely, the focal length of a pixel point in the vertical direction and the focal length of the pixel point in the horizontal direction are not uniform at corners, and the astigmatism phenomenon is most obvious at the periphery of a screen. In order to reduce this, it is necessary to dynamically compensate the electron gun so that the average number of any scan points on the screen can be clearly uniform. The focusing voltage with special waveform is periodically generated, so that the voltage of the electron beam is the lowest at the center point, and the voltage is gradually increased along with the increase of the focal length during corner scanning, and the focusing change is corrected at any time. In the dynamic correction process, the distortion of scanned data is easy to cause.
At present, in the EBSD splicing process, in order to ensure that the obtained data can be completely and truly represented, the picture overlapping rate is generally more than 15% to obtain a reliable result, and the detection efficiency is greatly restricted. Along with the continuous expansion of the area requirement of scientific research statistics on the detection sample, the sample multiple is correspondingly reduced during detection, the difference of focal lengths in the vertical direction is further amplified by the small multiple acquisition of a charge coupled device (Charge Coupled Device, CCD) camera, the accurate splicing of the samples is difficult to be carried out under 500 times by the existing EBSD technology, meanwhile, the detected EBSD can cause the phenomenon that the samples are inconsistent in left and right length under all detection multiples due to inconsistent left and right focusing voltages, and the current splicing method has repeated data points.
Disclosure of Invention
In order to solve the technical problems, the technical scheme adopted by the invention is that the correction and splicing method of the EBSD data comprises the following steps:
Step 1, acquiring EBSD data of a target material, generating an orientation imaging diagram, establishing an orientation imaging diagram data set of the target material, and carrying out stitching correction on any orientation imaging diagram to obtain a standard diagram, wherein an uncorrected diagram corresponding to the standard diagram is a diagram to be corrected;
step 1.1, performing an EBSD jigsaw detection experiment on a target material region of interest to obtain a plurality of pieces of EBSD data, applying EBSD data processing software to obtain an orientation imaging diagram of each piece of EBSD data and orientation and position information of each identifiable pixel point in the orientation imaging diagram, establishing a mapping relation between the EBSD data points and the orientation imaging diagram, and forming an orientation imaging diagram data set;
Step 1.2, splicing and correcting any one of the orientation imaging images and the left or right adjacent orientation imaging images thereof to obtain a standard image, wherein the orientation imaging image before correction is the image to be corrected;
step 2, matching and aligning characteristic data points of the standard graph and the graph to be corrected, screening effective characteristic data points, matching geometric transformation between the standard graph and the graph to be corrected according to position information of the effective characteristic data points, and calculating a target transformation matrix;
Step 2.1, carrying out gray processing on the standard graph and the graph to be corrected, identifying characteristic data points in the standard graph and the graph to be corrected, matching the characteristic data points meeting the required quantity and quality by setting a quality threshold value of the characteristic data points, a detected spatial scale and detection precision, recording the positions of the characteristic data points, and aligning the same characteristic data points in the standard graph and the graph to be corrected;
Step 2.2, screening effective characteristic data points in the standard graph and the graph to be corrected according to the aligned characteristic data points, matching geometric transformation between the standard graph and the graph to be corrected according to the screened effective characteristic data points, and calculating a target transformation matrix for correcting distorted pixels in the graph to be corrected to a standard position;
The target transformation matrix is calculated based on matchFeatures functions, and the specific process is as follows:
Step 2.2.1, screening effective characteristic data points in a standard chart and a chart to be corrected;
Setting a standard chart and a chart to be corrected as shown in the following formula:
Wherein, the method comprises the steps of, wherein, And (3) withRespectively corresponding to the standard diagram and the diagram to be corrected,Representing the jth feature descriptor in the ith image, i=1, 2, j=1, 2 a. M;
For standard chart Each of the feature descriptors in (a)Finding feature descriptors nearest and next nearestThe distances are respectivelyAndDetermining feature descriptors using ratio testingWhether or not it is a valid feature data point involved in the subsequent calculation, if/< Threshold, consider the feature descriptorIn a standard chartIs valid, count as valid feature data points, otherwise, does not record feature descriptorsTo-be-corrected graphMeans for screening effective characteristic data point and standard chartThe screening modes are the same;
2.2.2, calculating a target change matrix by utilizing a transformation relation between coordinates of effective characteristic data points in the standard chart and coordinates of effective characteristic data points aligned with the effective characteristic data points in the standard chart in the chart to be corrected;
setting a change matrix of the orientation imaging diagram as T, and evaluating the change matrix by using a least square method, wherein the aim is to find the most accurate change matrix T so as to ensure that Further obtaining a target transformation matrix;
Wherein, And (3) withCoordinates of valid feature data points in the standard graph,And (3) withCoordinates of effective characteristic data points aligned with the effective characteristic data points of the standard graph in the graph to be corrected;
Step 3, carrying out the same matrix transformation on identifiable data points in each orientation imaging diagram in the orientation imaging diagram data set by adopting a target transformation matrix to obtain corrected data points, standardizing the format of the corrected data points to obtain corrected and standardized EBSD data, carrying out jigsaw on all corrected and standardized EBSD data, determining whether splicing parts are completely spliced, returning to the step 2 to re-match characteristic data points if splicing marks exist, and recalculating the target transformation matrix until splicing data do not have splicing marks;
Step 3.1, carrying out the same matrix change on identifiable data points in each orientation imaging diagram in the orientation imaging diagram data set by utilizing the target transformation matrix obtained in the step 2.2 to obtain corrected data points and normalizing the formats of the corrected data points to obtain corrected and normalized EBSD data;
Step 3.2, deriving the EBSD data after correction standardization, and splicing the EBSD data after correction standardization;
Step 3.3, determining whether splice marks exist at the splice positions, if yes, returning to the step 2.1, adjusting a quality threshold value, a spatial scale and detection precision, and re-matching the characteristic data points with required quantity and quality;
And 3.4, re-executing the steps 2.2-3.3 until the spliced EBSD data has no splicing trace.
The beneficial effects of adopting above-mentioned technical scheme to produce lie in:
(1) When the EBSD data is processed, the program correction is carried out on the data points affected by dynamic focusing, so that the pixel-level data correction is achieved, the EBSD under the condition of small coincidence rate can be spliced, the detection cost is saved, the detection efficiency is improved when a tester is used.
(2) The invention corrects the spliced EBSD data not to be influenced by dynamic focusing distortion, has no requirement on splicing multiple in large-area splicing, can realize large-area EBSD data splicing under the condition of small view field, and widens the application range of EBSD detection.
(3) According to the method, the spliced EBSD data is corrected to have no repeated data points, so that the authenticity and accuracy of the data are improved.
Drawings
FIG. 1 is a flowchart of a method for correcting and splicing EBSD data according to an embodiment of the present invention;
FIG. 2 is a diagram of an EBSD splice using AZtec systems self-contained splicing methods according to an embodiment of the present invention;
FIG. 3 is a diagram of an EBSD splice using the corrective splice method of the present invention, provided by an embodiment of the present invention;
FIG. 4 is an orientation imaging of an EBSD measurement provided by an embodiment of the present invention;
fig. 5 is an orientation imaging diagram of a correction method according to an embodiment of the present invention.
Detailed Description
The following describes in further detail the embodiments of the present invention with reference to the drawings and examples. The following examples are illustrative of the invention and are not intended to limit the scope of the invention.
In this embodiment, after the EBSD data is obtained, the orientation imaging diagram is extracted, the standard diagram is obtained by using image processing software, and then the integral transformation matrix is obtained by using image registration technology, so as to implement correction and concatenation of EBSD data points. As shown in fig. 1, the method comprises the following steps:
Step 1, EBSD jigsaw detection experiments are applied to collect EBSD data of a target material, an orientation imaging diagram is generated, orientation and position information of identifiable pixel points of the orientation imaging diagram are determined, an orientation imaging diagram data set of the target material is established, any orientation imaging diagram is spliced and corrected by using image processing software to obtain a standard diagram, and an uncorrected diagram corresponding to the standard diagram is a diagram to be corrected;
step 1.1, performing an EBSD jigsaw detection experiment on a target material region of interest, and setting a jigsaw matrix larger than Obtaining a plurality of pieces of EBSD data, obtaining an orientation imaging diagram of each piece of EBSD data and orientation and position information of each identifiable pixel point in the orientation imaging diagram by using EBSD data processing software (crystal and Channel 5), establishing a mapping relation between the EBSD data points and the orientation imaging diagram, and forming an orientation imaging diagram data set;
step 1.2, splicing and correcting any one of the orientation imaging images and the left or right adjacent orientation imaging images thereof by using image processing software to obtain a standard image, wherein the orientation imaging image before correction is the image to be corrected;
Step 2, matching and aligning characteristic data points of the standard graph and the graph to be corrected by utilizing an image registration technology, screening effective characteristic data points, matching geometric transformation between the standard graph and the graph to be corrected according to position information of the effective characteristic data points, and calculating a target transformation matrix;
Step 2.1, carrying out gray processing on a standard chart and a chart to be corrected by utilizing image processing software, identifying characteristic data points in the standard chart and the chart to be corrected by utilizing a picture registration technology, matching the characteristic data points meeting the number and the quality of the standard chart and the chart to be corrected by setting a quality threshold (MetricThreshold) of the characteristic data points, a detected spatial scale (NumOctaves) and a detection precision (NumOctaves), recording the positions of the characteristic data points, and aligning the same characteristic data points in the standard chart and the chart to be corrected;
Step 2.2, screening effective characteristic data points in the standard graph and the graph to be corrected according to the aligned characteristic data points, matching geometric transformation between the standard graph and the graph to be corrected according to the screened effective characteristic data points, and calculating a target transformation matrix for correcting distorted pixels in the graph to be corrected to a standard position;
The target transformation matrix is calculated based on matchFeatures functions, and the specific process is as follows:
Step 2.2.1, screening effective characteristic data points in a standard chart and a chart to be corrected;
Setting a standard chart and a chart to be corrected as shown in the following formula:
Wherein, the method comprises the steps of, wherein, And (3) withRespectively corresponding to the standard diagram and the diagram to be corrected,Representing a j-th feature descriptor in the i-th image;
For standard chart Each of the feature descriptors in (a)Finding feature descriptors nearest and next nearestThe distances are respectivelyAndTo improve the robustness of the matching, fuzzy matching is eliminated, and a Ratio Test (Ratio Test) is used to determine the feature descriptorsWhether it is a valid feature data point participating in the subsequent calculation, if/< Threshold, consider the feature descriptorIn a standard chartIs valid, count as valid feature data points, otherwise, does not record feature descriptorsTo-be-corrected graphThe method for screening the effective characteristic data points is the same;
2.2.2, calculating a target change matrix by utilizing a transformation relation between coordinates of effective characteristic data points in the standard chart and coordinates of effective characteristic data points aligned with the effective characteristic data points in the standard chart in the chart to be corrected;
setting a change matrix of an orientation imaging diagram as T, wherein T is a three-dimensional numerical matrix, and evaluating the change matrix by using a least square method, wherein the aim is to find the most accurate change matrix T so as to ensure that Further obtaining a target transformation matrix;
Wherein, And (3) withCoordinates of valid feature data points in the standard graph,And (3) withCoordinates of effective characteristic data points aligned with the effective characteristic data points of the standard graph in the graph to be corrected;
step 3, carrying out the same matrix transformation on identifiable data points in each orientation imaging diagram in the orientation imaging diagram data set by adopting a target transformation matrix to obtain corrected data points, standardizing the format of the corrected data points to obtain corrected and standardized EBSD data, carrying out jigsaw on all corrected and standardized EBSD data by utilizing a jigsaw program (MTEX), determining whether splicing parts are completely spliced by utilizing image processing software, if splicing marks exist, returning to the step 2 to re-match characteristic data points, executing the step 2-3, and repeating the process until splicing data do not have splicing marks;
Step 3.1, carrying out the same matrix change on identifiable data points in each orientation imaging diagram in the orientation imaging diagram data set by utilizing the target transformation matrix obtained in the step 2.2 to obtain corrected data points and normalizing the formats of the corrected data points to obtain corrected and normalized EBSD data;
step 3.2, exporting the EBSD data after correction and standardization by utilizing an output function in MTEX programs, and splicing the EBSD data after correction and standardization by utilizing an automatic jigsaw function of MTEX programs;
Step 3.3, determining whether a splicing mark exists at a splicing position by utilizing image processing software, if so, returning to the step 2.1, adjusting a quality threshold (MetricThreshold), a spatial scale (NumOctaves) and detection accuracy (NumOctaves), and re-matching the characteristic data points with required quantity and quality;
Step 3.4, re-executing the steps 2.2-3.3 until the spliced EBSD data has no splicing trace;
In this embodiment, comparing the AZtec system self-contained splicing method with the EBSD splicing diagram obtained by the EBSD data correction splicing method of the present invention, see fig. 2 and fig. 3, under the condition that the test multiple is 100 times, many repeated data points exist in the spliced picture obtained by the AZtec system self-contained splicing method in fig. 2, a splicing seam will be left in the picture, and a step-like morphology will be left below the picture, and the repeated data points in the spliced picture obtained by the method of the present invention in fig. 3 are corrected, and no splicing mark and step morphology are generated.
Comparing the orientation imaging diagram measured in the step 1 with the orientation imaging diagram corrected by the method, and referring to fig. 4 and 5, it can be seen from fig. 4 that the distortion is inconsistent due to the influence of dynamic focusing on the left and right sides of the orientation imaging diagram, and it can be seen from fig. 5 that the distortion is corrected.
The method of the invention is adopted to process the EBSD data, and the program correction is carried out on the data points affected by dynamic focusing, thereby achieving the data correction at the pixel level, realizing the splicing of the EBSD under the condition of small coincidence rate, and greatly saving the detection cost. In the present stage, in order to eliminate the influence of dynamic focusing, the overlapping rate of the test data can only be increased, and the overlapping rate suggested by AZtec system is 20%. In addition, the method avoids the influence of dynamic focusing on the test result, so that the test can be performed under the condition that the minimum field of view is 25 times, the efficiency is improved by nearly 400 times under the condition of 500 times of the field of view proposed by AZtec test systems, and the application range of EBSD detection is greatly widened.
It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and that those skilled in the art will understand that they can make modifications to the technical solutions described in the above-mentioned embodiments or make equivalent substitutions of some or all of the technical features, without departing from the essence of the corresponding technical solutions from the scope of the invention defined by the claims.

Claims (6)

1.一种EBSD数据的矫正拼接方法，其特征在于：包括以下步骤：1. A correction and splicing method for EBSD data, characterized in that it comprises the following steps: 步骤1：采集目标材料的EBSD数据并生成取向成像图，建立目标材料的取向成像图数据集，对任意一张取向成像图进行拼接矫正获得一张标准图，与之对应的未矫正图片为待矫正图；Step 1: Collect EBSD data of the target material and generate an orientation imaging map, establish an orientation imaging map data set of the target material, stitch and correct any orientation imaging map to obtain a standard map, and the corresponding uncorrected image is the image to be corrected; 步骤2：对标准图和待矫正图进行特征数据点匹配对齐，并筛选出有效特征数据点，根据有效特征数据点位置信息对标准图和待矫正图之间的几何变换进行匹配，计算目标变换矩阵；Step 2: Match and align the feature data points of the standard image and the image to be corrected, and filter out the valid feature data points. Match the geometric transformation between the standard image and the image to be corrected according to the position information of the valid feature data points, and calculate the target transformation matrix. 步骤2.1：对标准图和待矫正图进行灰度处理，识别出标准图和待矫正图中的特征数据点，通过设置特征数据点的质量阈值、检测的空间尺度和检测精度匹配出符合要求数量和质量的特征数据点，记录特征数据点的位置，对齐标准图和待矫正图中相同的特征数据点；Step 2.1: grayscale the standard image and the image to be corrected, identify the characteristic data points in the standard image and the image to be corrected, match the characteristic data points that meet the required quantity and quality by setting the quality threshold of the characteristic data points, the spatial scale of detection, and the detection accuracy, record the positions of the characteristic data points, and align the same characteristic data points in the standard image and the image to be corrected; 步骤2.2：根据对齐的特征数据点，筛选标准图和待矫正图中的有效特征数据点，并根据筛选出的有效特征数据点对标准图和待矫正图之间的几何变换进行匹配，计算待矫正图中出畸变的像素点矫正回标准位置的目标变换矩阵；Step 2.2: Based on the aligned feature data points, screen the valid feature data points in the standard image and the image to be corrected, match the geometric transformation between the standard image and the image to be corrected based on the screened valid feature data points, and calculate the target transformation matrix for correcting the distorted pixels in the image to be corrected back to the standard position; 步骤3：采用目标变换矩阵对取向成像图数据集中的每一张取向成像图中可识别的数据点进行相同的矩阵变换得到矫正的数据点，并对矫正后的数据点的格式进行标准化进而得到矫正标准化后的EBSD数据，对所有矫正标准化后的EBSD数据进行拼图，确定拼接处是否完全拼接，若有拼接痕，则返回步骤2重新匹配特征数据点，重新计算目标变换矩阵，直至拼接数据无拼接痕。Step 3: Use the target transformation matrix to perform the same matrix transformation on each identifiable data point in the orientation imaging image data set to obtain the corrected data point, and standardize the format of the corrected data point to obtain the corrected and standardized EBSD data. Puzzle all the corrected and standardized EBSD data to determine whether the splicing is completely spliced. If there is a splicing mark, return to step 2 to re-match the feature data points and recalculate the target transformation matrix until the spliced data has no splicing marks. 2.根据权利要求1所述的一种EBSD数据的矫正拼接方法，其特征在于：所述步骤1的具体方法为：2. The correction and splicing method of EBSD data according to claim 1, characterized in that: the specific method of step 1 is: 步骤1.1：对目标材料感兴趣区域进行EBSD拼图检测实验，获得多张EBSD数据，应用EBSD数据处理软件获得每张EBSD数据的取向成像图及取向成像图中各可识别像素点的取向和位置信息，建立EBSD数据点与取向成像图的映射关系并形成取向成像图数据集；Step 1.1: Perform EBSD mosaic detection experiments on the target material area of interest to obtain multiple EBSD data, use EBSD data processing software to obtain the orientation image of each EBSD data and the orientation and position information of each identifiable pixel in the orientation image, establish the mapping relationship between EBSD data points and orientation image, and form an orientation image data set; 步骤1.2：对任意一张取向成像图及其左侧或右侧相邻取向成像图进行拼接矫正处理，获取标准图，未矫正前的取向成像图为待矫正图。Step 1.2: Perform stitching and correction processing on any orientation imaging image and its adjacent orientation imaging image on the left or right to obtain a standard image. The orientation imaging image before correction is the image to be corrected. 3.根据权利要求1所述的一种EBSD数据的矫正拼接方法，其特征在于：所述步骤2.2的目标变换矩阵基于matchFeatures函数计算得到，具体过程为：3. The correction and splicing method of EBSD data according to claim 1 is characterized in that: the target transformation matrix of step 2.2 is calculated based on the matchFeatures function, and the specific process is: 步骤2.2.1：筛选标准图和待矫正图中的有效特征数据点；Step 2.2.1: Screen the valid feature data points in the standard image and the image to be corrected; 步骤2.2.2：利用标准图中有效特征数据点的坐标和待矫正图中与标准图有效特征数据点对齐的有效特征数据点的坐标之间的变换关系计算目标变化矩阵。Step 2.2.2: Calculate the target change matrix using the transformation relationship between the coordinates of the valid feature data points in the standard image and the coordinates of the valid feature data points in the image to be corrected that are aligned with the valid feature data points in the standard image. 4.根据权利要求3所述的一种EBSD数据的矫正拼接方法，其特征在于：所述步骤2.2.1的具体方法为：4. The correction and splicing method of EBSD data according to claim 3 is characterized in that: the specific method of step 2.2.1 is: 设定标准图和待矫正图如下公式所示：The standard image and the image to be corrected are set as shown in the following formula: F1＝{f1,1,f1,2,…f1,m}F 1 ={f 1,1 ,f 1,2 ,…f 1,m } F2＝{f2,1,f2,2,…f2,m}F 2 ={f 2,1 ,f 2,2 ,…f 2,m } 其中，F1与F2分别对应标准图和待矫正图，fi,j表示第i个图像中的第j个特征描述子，i＝1、2，j＝1、2、3……m；Among them, F1 and F2 correspond to the standard image and the image to be corrected respectively, fi ,j represents the jth feature descriptor in the i-th image, i = 1, 2, j = 1, 2, 3...m; 对标准图F1中的每个特征描述子f1,j找到距离最近和次近的特征描述子f1,t,f1,k，其距离分别为d1和d2，使用比值测试(Ratio Test)确定特征描述子f1,j是否为参与后续计算的有效特征数据点，如果d1/d2<阈值，则认为特征描述子f1,j在标准图F1中是有效的，计为有效特征数据点，反之则不记录特征描述子f1,j；待矫正图F2中筛选有效特征数据点的方式与标准图F1的筛选方式相同。For each feature descriptor f 1,j in the standard graph F 1 , find the closest and second closest feature descriptors f 1,t and f 1,k , whose distances are d 1 and d 2 respectively. Use the ratio test to determine whether the feature descriptor f 1,j is a valid feature data point for subsequent calculations. If d 1 /d 2 <threshold, the feature descriptor f 1,j is considered valid in the standard graph F 1 and is counted as a valid feature data point. Otherwise, the feature descriptor f 1,j is not recorded. The method for screening valid feature data points in the graph to be corrected F 2 is the same as that of the standard graph F 1 . 5.根据权利要求4所述的一种EBSD数据的矫正拼接方法，其特征在于：所述步骤2.2.2的具体方法为：5. The correction and splicing method of EBSD data according to claim 4 is characterized in that: the specific method of step 2.2.2 is: 设定取向成像图的变化矩阵为T，使用最小二乘法评估变化矩阵，目标是找到最准确的变换矩阵T使得进而得到目标变换矩阵；Set the change matrix of the orientation imaging map to T, and use the least squares method to evaluate the change matrix. The goal is to find the most accurate transformation matrix T so that Then the target transformation matrix is obtained; 其中，x1,h与y1,h为标准图中有效特征数据点的坐标，x2,h与y2,h为待矫正图中与标准图有效特征数据点对齐的有效特征数据点的坐标。Among them, x1 ,h and y1 ,h are the coordinates of the valid feature data points in the standard image, and x2 ,h and y2 ,h are the coordinates of the valid feature data points in the image to be corrected that are aligned with the valid feature data points in the standard image. 6.根据权利要求5所述的一种EBSD数据的矫正拼接方法，其特征在于：所述步骤3的具体方法为：6. The correction and splicing method of EBSD data according to claim 5, characterized in that: the specific method of step 3 is: 步骤3.1：利用步骤2.2得到的目标变换矩阵，对取向成像图数据集中的每一张取向成像图中可识别的数据点进行相同的矩阵变化，得到矫正的数据点并对矫正后的数据点的格式进行标准化，得到矫正标准化后的EBSD数据；Step 3.1: Using the target transformation matrix obtained in step 2.2, perform the same matrix transformation on the identifiable data points in each orientation imaging image in the orientation imaging image data set to obtain the corrected data points and standardize the format of the corrected data points to obtain the corrected and standardized EBSD data; 步骤3.2：导出矫正标准化后的EBSD数据，并对矫正标准化后的EBSD数据点进行拼接；Step 3.2: Export the corrected and standardized EBSD data and stitch the corrected and standardized EBSD data points; 步骤3.3：确定拼接处是否有拼接痕，若有拼接痕，则返回步骤2.1，调整质量阈值、空间尺度和检测精度，重新匹配要求数量和质量的特征数据点；Step 3.3: Determine whether there are any splicing marks at the splicing point. If there are splicing marks, return to step 2.1, adjust the quality threshold, spatial scale and detection accuracy, and re-match the required number and quality of feature data points; 步骤3.4：再重新执行步骤2.2～步骤3.3，直至拼接的EBSD数据无拼接痕迹。Step 3.4: Re-execute steps 2.2 to 3.3 until there are no traces of splicing in the spliced EBSD data.
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