Secondary ion mass spectrum curve abnormal data restoration method
By locating anomalous data regions in secondary ion mass spectrometry curves and combining them with normal data characteristics to generate repaired data, the problems of time-consuming and inefficient operation in existing technologies are solved, achieving efficient and smooth data repair results.
Patent Information
- Application Number
- CN202511354414.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-09-22
- Publication Date
- 2026-02-03
AI Technical Summary
Existing methods for repairing abnormal data in secondary ion mass spectrometry curves are time-consuming and inefficient, and they are difficult to adapt to the noise environment of the current curve, resulting in the repaired data not being smooth enough.
By locating the abnormal data to be processed area of the SIMS curve, repair data is generated by combining the characteristics of nearby normal data, replacing the abnormal data, and using Poisson distribution or normal distribution to generate repair data, and then performing boundary smoothing processing.
It significantly improves operational efficiency, reduces the time required for repairing complex curves, and generates repaired data with characteristics consistent with normal data, avoiding errors caused by manual adjustments. The repaired data is also smoother.
Smart Images
Figure CN121460469A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of detection imaging analysis technology, and in particular to a method for repairing abnormal data in secondary ion mass spectrometry curves. Background Technology
[0002] Secondary ion mass spectrometry (SIMS) is a key technology for surface composition analysis of materials. In the analysis of SIMS curve data, the accuracy of the curve data directly affects the quantitative analysis results, while abnormal data can directly lead to deviations in concentration and depth profiles. However, during ion detection using a secondary ion mass spectrometer, some interfering factors may occur, causing abnormal data, such as instantaneous spikes in signal intensity, or a sudden drop in signal intensity to near zero or the baseline level, lasting for a very short time (a few data points) or a relatively long time (e.g., ...). Figure 1 (as shown), and then it recovered.
[0003] Therefore, before analyzing SIMS curve data, it is necessary to repair any abnormal data. Existing SIMS curve processing software provides tools for repairing abnormal data, such as... Figure 2 , Figure 3 As shown, using "drag-and-drop correction" (adjusting outliers point by point) or "layered adjustment" (manually increasing / decreasing outlier values), for complex curves containing multiple outlier segments (such as elemental distribution curves of multilayer thin films), a single repair requires adjusting dozens to hundreds of data points, which is very time-consuming and inefficient. Furthermore, manually modifying (dragging) the outlier positions can easily result in uneven dragging, requiring more time to adapt to the current curve's noise environment, which can damage the inherent noise characteristics of the data and lead to an overall less smooth curve display. Summary of the Invention
[0004] This application provides a method for repairing abnormal data in secondary ion mass spectrometry curves, which solves the problems of time-consuming operation, low efficiency, and inability to adapt well to the noise environment of the current curve in existing methods for repairing abnormal data in secondary ion mass spectrometry curves.
[0005] The method for repairing abnormal data in secondary ion mass spectrometry curves provided in this application includes the following steps.
[0006] S100, the positioning SIMS curve contains several abnormal data regions to be processed; all consecutive abnormal data in each abnormal data region to be processed form a group of abnormal data to be processed.
[0007] S200. For each abnormal data group to be processed, select a normal data region adjacent to it and obtain the data characteristics of the normal data region.
[0008] S300. Based on the location of the abnormal data group to be processed and the data characteristics of the normal data area, generate the corresponding repair data for the abnormal data group to be processed.
[0009] S400: Replace the abnormal data in the group of abnormal data to be processed with the repaired data.
[0010] The secondary ion mass spectrometry curve abnormal data repair method provided in this application embodiment can select all abnormal data for processing at once for curves with abnormal data. Furthermore, when generating repair data to replace abnormal data, it combines the data characteristics of normal data adjacent to the area to be processed by abnormal data, overcoming the problems of time-consuming processing and low processing efficiency caused by the need for single data dragging operation in the prior art. At the same time, the generated repair data has the same data characteristics as the normal data, thus possessing the inherent noise characteristics of the data, and the generated curve is smoother overall.
[0011] Preferably, step S100 specifically includes:
[0012] S110. Start the SIMS curve abnormal data repair command, obtain the left and right cursor positions in the dual cursors, and determine the curve between the left and right cursors as the abnormal data to be processed area; obtain the curve data between the left and right cursors and determine it as the abnormal data group to be processed;
[0013] S120. Continue to check if any new dual cursors have been added. If not, proceed to step S200; otherwise, repeat step S110.
[0014] Preferably, step S100 specifically includes:
[0015] S110. Initiate the SIMS curve abnormal data repair command, starting from one end of the SIMS curve, select three consecutive data points P on the curve. n P n+1 P n+2 Obtain the ion strength I corresponding to the data point. n I n+1 I n+2 Calculate the ratio of the absolute values of their ionic strength differences. When the ratio R n When it exceeds the threshold, determine P. n+2 This marks the starting point of the region containing the abnormal data to be processed.
[0016] S120, from P n+2 Continue, select three consecutive data points P m P m+1 P m+2Obtain the ion strength I corresponding to the data point. m I m+1 I m+2 Calculate the ratio of the absolute values of their ionic strength differences. When the ratio R m When it exceeds the threshold, determine P. m+2 The endpoint of the region to be processed for abnormal data is defined; the start and end points of each region to be processed for abnormal data are obtained, and the curve data between the start and end points is defined as the abnormal data group to be processed; where n and m are positive integers, and m>n;
[0017] Repeat steps S110 and S120 until all data points on the SIMS curve have been calculated, then proceed to step S130.
[0018] Step S130: Display all abnormal data pending processing areas, and receive modification and confirmation instructions, then proceed to step S200.
[0019] Preferably, step S200 specifically includes:
[0020] S210. For each group of abnormal data to be processed, determine the start position and the end position of the group of abnormal data to be processed.
[0021] S220: Receive the start and end positions of the specified normal data area, and obtain curve data of all normal data areas within the normal data area;
[0022] S230. Calculate the mean and standard deviation of the curve data in the normal data region, and determine the distribution type of the curve data in the normal data region.
[0023] Preferably, step S200 specifically includes:
[0024] S210. For each group of abnormal data to be processed, determine the start position and the end position of the group of abnormal data to be processed.
[0025] S220. From the data point preceding the starting position, a preset number of curve data points are continuously selected forward as the normal data area; and / or, from the data point following the ending position, a preset number of curve data points are continuously selected backward as the normal data area.
[0026] S230. Calculate the mean and standard deviation of the curve data in the normal data region, and determine the distribution type of the curve data in the normal data region.
[0027] Preferably, in step S210, when the area to be processed for abnormal data is [L, R], the starting position of the group of abnormal data to be processed is determined to be L and the ending position is R;
[0028] S220. Select the left normal segment [LK,L-1] and the right normal segment [R+1,R+K] as the normal data area, where K is the reference window length and R is the number of curve data points in the normal segment.
[0029] S231. Let the curve data points within the normal data region be y1, y2, ..., y3. n ;
[0030] S232. Calculate the mean of the curve data points within the normal data range:
[0031]
[0032] S233. Calculate the variance of the curve data points within the normal data range:
[0033]
[0034] S234, when u ref If u < 100, it is determined to be a Poisson distribution; when u ref If the value is ≥100, it is determined to be a normal distribution.
[0035] Preferably, step S300 specifically includes:
[0036] S310. Initialization of parameters for the region to be processed for abnormal data: Let the data points in the region to be processed for abnormal data be X1, X2, ..., X... m , m is the number of abnormal data in the abnormal data group to be processed; the repair data to be generated is:
[0037] S320. Generate repair data based on distribution type:
[0038] If the distribution is Poisson: the repaired data follows a mean of u. ref The Poisson distribution, i.e.:
[0039]
[0040] The probability mass function of the Poisson distribution is: Integer data conforming to a Poisson distribution are generated through random sampling;
[0041] If the data follows a normal distribution: the repaired data has a mean of u. ref The standard deviation is σ ref The normal distribution is:
[0042]
[0043] The probability density function of the normal distribution is: Continuous data conforming to a normal distribution is generated using the Box-Muller transformation.
[0044] Preferably, after step S320, the method further includes:
[0045] S330, Boundary Smoothing Processing: Weighted smoothing is performed on 5 data points from the beginning and 5 from the end of the repaired data.
[0046] Left boundary transition: y L For normal data points outside the left boundary of the region to be processed for abnormal data, ω k The weights increase linearly from 0.2 to 1; right boundary transition: y R For normal data points outside the right boundary of the abnormal data processing area, the weight increases linearly from 0.2 to 1.
[0047] This application also provides an electronic device, including a memory and a processor. The memory stores a computer-executable program, and the processor calls the computer-executable program in the memory to implement the above-described method for repairing abnormal data of secondary ion mass spectrometry curves.
[0048] This application embodiment also provides a storage medium, which is a computer-readable storage medium, and a computer-executable program is stored on the computer-readable storage medium. When the computer-executable program is executed by a processor, it implements the above-mentioned method for repairing abnormal data of secondary ion mass spectrometry curves.
[0049] This application also provides a computer program product for implementing curve synchronization adjustment, including code for implementing the above-described method for repairing abnormal data of secondary ion mass spectrometry curves. Attached Figure Description
[0050] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0051] Figure 1 Examples of abnormal data in the SIMS curve;
[0052] Figure 2 , Figure 3 Operation diagrams provided for existing technologies to repair abnormal data;
[0053] Figure 4 This is a schematic diagram of the abnormal data repair process for secondary ion mass spectrometry curves provided in the embodiments of this application;
[0054] Figure 5 This is a schematic diagram illustrating how dual cursors are added to determine the area of abnormal data to be processed in an embodiment of this application.
[0055] Figure 6 , Figure 7 This is a schematic diagram illustrating the effectiveness of SIMS anomaly repair in a typical scenario as described in this application. Detailed Implementation
[0056] In the following description, only certain exemplary embodiments are briefly described. As those skilled in the art will recognize, the described embodiments can be modified in various ways without departing from the spirit or scope of the invention. Therefore, the drawings and description are considered to be exemplary in nature and not restrictive.
[0057] It is important to note that terms such as "first," "second," "symmetric," and "array" are used only to distinguish between descriptive and positional descriptions and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, features specified with terms such as "first" or "symmetric" may explicitly or implicitly include one or more of that feature; similarly, when the quantity of certain features is not limited by words such as "two" or "three," it should be noted that such features also explicitly or implicitly include one or more features.
[0058] In this invention, unless otherwise explicitly specified and limited, terms such as "installation," "connection," and "fixation" should be interpreted broadly; for example, they can refer to a fixed connection, a detachable connection, or an integral molding; they can refer to a mechanical connection, a direct connection, a welding connection, or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the accompanying drawings and specific circumstances.
[0059] The technical solution of this application will now be described in detail with reference to the accompanying drawings.
[0060] like Figure 4 As shown in the embodiments of this application, a method for repairing abnormal data in secondary ion mass spectrometry curves is provided, including the following steps:
[0061] S100, the positioning SIMS curve contains several abnormal data regions to be processed; all consecutive abnormal data in each abnormal data region to be processed form a group of abnormal data to be processed.
[0062] This application provides two implementation methods for locating the abnormal data to be processed region of the SIMS curve and for determining the abnormal data group to be processed.
[0063] The first embodiment provided in this application includes the following steps to achieve the above objective, such as... Figure 5 As shown:
[0064] S110, SIMS curve abnormal data repair command, obtain the left and right cursor positions in the dual cursors, determine the curve between the left and right cursors as the abnormal data to be processed area; obtain the curve data between the left and right cursors and determine it as the abnormal data group to be processed;
[0065] S120. Continue to check if any new dual cursors have been added. If not, proceed to step S200; otherwise, repeat step S110.
[0066] In the first embodiment provided in this application, after initiating the SIMS curve abnormal data repair command, the system issues an instruction to determine the area to be processed for abnormal data via an interactive command. This prompts the operator to manually add dual cursors to specify the area to be processed. The system then monitors the dual cursor addition operation in real time and determines the starting position of the first abnormal data area to be processed by intersecting the curve with the left cursor of the first added dual cursor, and the ending position of the first abnormal data area to be processed by intersecting the curve with the right cursor. The starting and ending positions, along with the curve data between them, constitute the group of abnormal data to be processed. After locating the first abnormal data area, the system continues to monitor for new dual cursor addition operations. If any are found, the second and third abnormal data areas to be processed are determined again using the above operation. In this way, all areas containing abnormal data can be identified at once, and all abnormal data can be processed simultaneously, replacing point-by-point dragging and repair, significantly improving operational efficiency.
[0067] Simultaneously, upon detecting a cursor addition operation, the system captures the position of the intersection point between the cursor and the curve in real time and displays the physical parameters (such as sputtering depth and ion intensity) of the intersection point on the interface. The system also detects mouse movement, enabling "mouse dragging" to position the left and right cursors. Furthermore, the system detects input parameters and uses the input coordinates for precise positioning, replacing traditional point-by-point operations.
[0068] In the first embodiment provided in this application, all abnormal data areas to be processed can be determined at once through simple human-computer interaction, and information can be promptly fed back to the operator through human-computer interaction, facilitating flexible operation and accurate positioning by the operator.
[0069] The second embodiment provided in this application includes the following steps to achieve the above objective:
[0070] S110. Initiate the SIMS curve abnormal data repair command. Starting from one end of the SIMS curve, such as the starting end, sequentially select three consecutive data points P on the curve. n P n+1 P n+2 Obtain the ion strength I corresponding to the data point. n I n+1 I n+2 Calculate the ratio of the absolute values of their ionic strength differences. When the ratio R n When it exceeds the threshold, determine P. n+2 This marks the starting point of the region containing the abnormal data to be processed.
[0071] S120. Once the starting point of the abnormal data is determined, start from the starting point of the abnormal data, that is, from P. n+2 Continue, selecting three consecutive data points P in sequence. m P m+1 P m+2 Obtain the ion strength I corresponding to the data point. m I m+1 I m+2 Calculate the ratio of the absolute values of their ionic strength differences. When the ratio R m When it exceeds the threshold, determine P. m+2 The endpoint of the region to be processed for abnormal data is defined; the start and end points of each region to be processed for abnormal data are obtained, and the curve data between the start and end points is defined as the abnormal data group to be processed; where n and m are positive integers, and m>n;
[0072] Repeat steps S110 and S120 until all data points on the SIMS curve have been calculated, then proceed to step S130.
[0073] Step S130: Display all abnormal data pending processing areas, and receive modification and confirmation instructions, then proceed to step S200.
[0074] Although the SIMS curve constructed from normal data exhibits variations, it retains a degree of continuity, with relatively small fluctuations in ion intensity. Therefore, the ratio of the absolute values of the ion intensity differences between three consecutive data points on the SIMS curve is generally between 0.5 and 3. However, at locations where abnormal data occurs, the ion intensity changes abruptly, showing a significant increase or decrease compared to the ion intensity of adjacent normal data points. Consequently, the ratio of the absolute values of the ion intensity differences between three consecutive data points on the SIMS curve will be significantly higher or lower than the normal ratio. By appropriately setting thresholds, automatic detection can identify the regions and groups of abnormal data to be processed within the SIMS curve. Since there are rare cases where the first or second data point on the SIMS curve is abnormal, or where an abnormal data point exists on a normal SIMS curve, this application includes an operator modification and confirmation step after automatically locating the regions to be processed for abnormal data. When the aforementioned special cases are not identified or located, corrections can be made manually. For example, incorrectly selected areas to be processed can be deleted, incorrectly selected areas can be added, or the starting point of the SIMS curve can be redefined as the starting point for initiating the SIMS curve abnormal data repair command, thereby avoiding the appearance of abnormal data from the outset. In the second embodiment provided in this application, the system can automatically determine the areas to be processed and the groups of abnormal data to be processed, further improving processing efficiency.
[0075] S200. For each abnormal data group to be processed, select a normal data region adjacent to it and obtain the data characteristics of the normal data region.
[0076] This application provides two implementation methods for selecting normal data regions for each region of abnormal data to be processed, which are described below:
[0077] In the first embodiment provided by this application, step S200 specifically includes:
[0078] S210. For each group of abnormal data to be processed, determine the start position and the end position of the group of abnormal data to be processed.
[0079] S220: Receive the start and end positions of the specified normal data area, and obtain curve data of all normal data areas within the normal data area;
[0080] S230. Calculate the mean and standard deviation of the curve data in the normal data region, and determine the distribution type of the curve data in the normal data region.
[0081] In the first embodiment provided by this application, the normal data area is mainly determined through human-computer interaction by prompting the user to select it. Once the abnormal data to be processed area is determined, each abnormal data to be processed area can be obtained, thus obtaining each group of abnormal data to be processed. Then, for the first group of abnormal data to be processed, the operator is prompted to determine the starting position of the normal data area, and the system detects whether the user determines the starting position of the normal data by clicking a data point on the SIMS curve with the mouse or by inputting a value. The system continues to detect whether the user determines the ending position of the normal data by clicking another data point on the SIMS curve with the mouse or by inputting a second value. The continuous data on the SIMS curve between the starting and ending positions is determined as the normal data area. In a further improved preferred embodiment provided by this application, when it is detected that the user has specified a normal data area for each group of abnormal data to be processed, it is also detected whether the normal data area is a continuous data area on the SIMS curve, that is, whether there is an abnormal data to be processed area between the starting and ending positions. If so, an error is determined, and the user is prompted to re-specify, while the reason for the error is given.
[0082] Furthermore, in this embodiment of the application, when selecting a normal data region for an area of abnormal data to be processed, the user can specify two normal data regions to be merged into the normal data region of the corresponding area of abnormal data to be processed. For example, a normal data region can be specified on both sides of an area of abnormal data to be processed, and the two regions can be merged to form the normal data region of the corresponding area of abnormal data to be processed.
[0083] Once the normal data region corresponding to each group of outlier data to be processed is determined, the mean and standard deviation of the curve data in the normal data region corresponding to each outlier data region can be calculated, and the distribution type of the curve data in the normal data region can be determined. The distribution types of the curves mainly include Poisson distribution and normal distribution.
[0084] In the second embodiment provided in this application, step S200 specifically includes:
[0085] S210. For each group of abnormal data to be processed, determine the start position and the end position of the group of abnormal data to be processed.
[0086] S220. From the data point preceding the starting position, a preset number of curve data points are continuously selected as the normal data area; and / or, from the data point following the ending position, a preset number of curve data points are continuously selected as the normal data area.
[0087] S230. Calculate the mean and standard deviation of the curve data in the normal data region, and determine the distribution type of the curve data in the normal data region.
[0088] The most significant improvement of the second embodiment provided in this application compared to the first embodiment lies in the method of determining the normal data region. In the preferred embodiment provided in this application, after determining the abnormal data groups to be processed, the system automatically determines the corresponding normal data region for each abnormal data group. That is, the system automatically selects a data region along the SIMS curve from the start or end position of each abnormal data group as the normal data region. Alternatively, it can select a normal data region forward and backward from the start or end position of each abnormal data region to be processed, and the normal data at both ends are combined to form the normal data region of the corresponding abnormal data region to be processed. The data region of the SIMS curve selected in this way has a better correlation with the data of the abnormal data region to be processed. The selected normal data region can be selected according to a preset number. In a preferred embodiment provided in this application, the number of normal data in the selected normal data region is 1.5 times the number of abnormal data in its corresponding abnormal data group, or the same as the number of abnormal data in its corresponding abnormal data group. When the number of consecutive normal data is less than the number of abnormal data in the corresponding abnormal data group to be processed, all consecutive normal data can be selected as the normal data region.
[0089] In this embodiment of the application, taking [L, R] as an example, the step S200 is explained as follows:
[0090] S210. Determine the starting position of the abnormal data group to be processed as L and the ending position as R.
[0091] S220. Select the left normal segment [LK,L-1] and the right normal segment [R+1,R+K] as the normal data area. K is the reference window length, which can be set to 50 data points or 1.5 times the length of the abnormal interval, or the same number as the abnormal area, taking the larger value.
[0092] S231. Let the curve data points in the normal data area be y1, y2, ..., yn; n is the total quantity in the normal data area.
[0093] S232. Calculate the mean value of the curve data points within the normal data region to reflect the normal ion intensity level:
[0094] S233. Calculate the variance of the curve data points within the normal data area to reflect the normal noise level:
[0095]
[0096] S234. Distribution type determination: Based on the physical characteristics of the SIMS curve data, select either Poisson distribution or normal distribution.
[0097] When u ref <100 indicates a low count region, which is determined to be a Poisson distribution; this conforms to the discrete characteristics of ion counting.
[0098] When u ref ≥100 indicates a high count region, which is determined to be a normal distribution, approximating the central limit theorem.
[0099] S300. Based on the location of the abnormal data group to be processed and the data characteristics of the normal data area, generate the corresponding repair data for the abnormal data group to be processed.
[0100] In this embodiment of the application, the specific method for generating repair data is as follows:
[0101] S310. Initialization of parameters for the region to be processed for abnormal data: Let the data points in the region to be processed for abnormal data be X1, X2, ..., X... m , m is the number of abnormal data in the abnormal data group to be processed; the repair data to be generated is:
[0102] S320. Generate repair data based on distribution type:
[0103] If the distribution is Poisson (low count region): the repaired data follows a mean of u. ref The Poisson distribution, i.e.:
[0104]
[0105] The probability mass function of the Poisson distribution is: Integer data conforming to a Poisson distribution is generated through random sampling.
[0106] Mean ion intensity μ of the normal data region of the false reference ref =5 (low count scenario, follows a Poisson distribution), calculate the probability that the repaired data is "3":
[0107] The probability of data repair = 3:
[0108] If the data follows a normal distribution: the repaired data has a mean of u. ref The standard deviation is σ ref The normal distribution is:
[0109]
[0110] The probability density function of the normal distribution is: Continuous data conforming to a normal distribution is generated using the Box-Muller transformation.
[0111] After generating the repaired data, a preferred embodiment of this application further includes a boundary smoothing step to prevent abrupt changes between the repaired data and normal data outside the abnormal data processing area.
[0112] S330, Boundary Smoothing Processing: Weighted smoothing is performed on 5 data points from the beginning and 5 from the end of the repaired data.
[0113] Left boundary transition: y L For normal data points outside the left boundary of the region to be processed for abnormal data, ω k The weights are linearly increased from 0.2 to 1.
[0114] Right boundary transition: y R For normal data points outside the right boundary of the abnormal data processing area, the weight increases linearly from 0.2 to 1.
[0115] S400: Replace the abnormal data in the group of abnormal data to be processed with the repaired data.
[0116] The secondary ion mass spectrometry curve abnormal data repair method provided in this application embodiment has significantly improved operation efficiency: batch interval repair replaces point-by-point operation, reducing the repair time of complex curves by more than 70%; better preservation of physical meaning: the repaired data is consistent with the noise distribution of the surrounding normal area, avoiding subjective errors of manual adjustment; more flexible operation: it supports parallel processing of multiple sets of cursors, adapting to complex curve repair scenarios with "multiple abnormal segments".
[0117] The method for repairing abnormal data in secondary ion mass spectrometry curves provided in this application, such as... Figure 6 , Figure 7 As shown, its effectiveness is verified through a typical SIMS anomaly repair scenario:
[0118] Efficiency test: When processing a curve with 5 abnormal segments, the traditional point-by-point repair method takes 15 minutes, while this method only takes 3 minutes, improving efficiency by 80%.
[0119] Distribution matching test: The distribution deviation (such as standard deviation difference) between the repaired data and the surrounding normal area is ≤5%, which is far better than the deviation of more than 30% of the traditional method;
[0120] Visual verification: The repaired segment and the normal segment are connected naturally without any "abrupt jump", which is consistent with the physical noise characteristics of SIMS data.
[0121] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various variations or substitutions within the technical scope disclosed in the present invention, and these should all be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.
Claims
1. A method for repairing abnormal data in secondary ion mass spectrometry curves, characterized in that, Includes the following steps, S100, the positioning SIMS curve contains several abnormal data regions to be processed; all consecutive abnormal data in each abnormal data region to be processed form a group of abnormal data to be processed. S200. For each abnormal data group to be processed, select the normal data area adjacent to it and obtain the data characteristics of the normal data area. S300. Based on the location of the abnormal data group to be processed and the data characteristics of the normal data area, generate the corresponding repair data for the abnormal data group to be processed. S400: Replace the abnormal data in the group of abnormal data to be processed with the repaired data.
2. The method for repairing abnormal data in secondary ion mass spectrometry curves as described in claim 1, characterized in that, Step S100 specifically includes: S110. Start the SIMS curve abnormal data repair command, obtain the left and right cursor positions in the dual cursors, and determine the curve between the left and right cursors as the abnormal data to be processed area; obtain the curve data between the left and right cursors and determine it as the abnormal data group to be processed; S120. Continue to check if any new dual cursors have been added. If not, proceed to step S200; otherwise, repeat step S110.
3. The method for repairing abnormal data in secondary ion mass spectrometry curves as described in claim 1, characterized in that, Step S100 specifically includes: S110. Initiate the SIMS curve abnormal data repair command, starting from one end of the SIMS curve, select three consecutive data points P on the curve. n P n+1 P n+2 Obtain the ion strength I corresponding to the data point. n I n+1 I n+2 Calculate the ratio of the absolute values of their ionic strength differences. When the ratio R n When it exceeds the threshold, determine P. n+2 This marks the starting point of the region containing the abnormal data to be processed. S120, from P n+2 Continue, select three consecutive data points P m P m+1 P m+2 Obtain the ion strength I corresponding to the data point. m I m+1 I m+2 Calculate the ratio of the absolute values of their ionic strength differences. When the ratio R m When it exceeds the threshold, determine P. m+2 The endpoint of the region to be processed for abnormal data is defined; the start and end points of each region to be processed for abnormal data are obtained, and the curve data between the start and end points is defined as the abnormal data group to be processed; where n and m are positive integers, and m>n; Repeat steps S110 and S120 until all data points on the SIMS curve have been calculated, then proceed to step S130. Step S130: Display all abnormal data pending processing areas, and receive modification and confirmation instructions, then proceed to step S200.
4. The method for repairing abnormal data in secondary ion mass spectrometry curves as described in claim 2 or 3, characterized in that, The specific steps of S200 are as follows: S210. For each group of abnormal data to be processed, determine the start position and the end position of the group of abnormal data to be processed. S220: Receive the specified normal data area and obtain curve data of all normal data areas within the normal data area; S230. Calculate the mean and standard deviation of the curve data in the normal data region, and determine the distribution type of the curve data in the normal data region.
5. The method for repairing abnormal data in secondary ion mass spectrometry curves as described in claim 2 or 3, characterized in that, The specific steps of S200 are as follows: S210. For each group of abnormal data to be processed, determine the start position and the end position of the group of abnormal data to be processed. S220. From the data point preceding the starting position, a preset number of curve data points are continuously selected forward as the normal data area; and / or, from the data point following the ending position, a preset number of curve data points are continuously selected backward as the normal data area. S230. Calculate the mean and standard deviation of the curve data in the normal data region, and determine the distribution type of the curve data in the normal data region.
6. The method for repairing abnormal data in secondary ion mass spectrometry curves as described in claim 5, characterized in that: In step S210, when the area to be processed for abnormal data is [L, R], the starting position of the group of abnormal data to be processed is determined to be L and the ending position is R. S220. Select the left normal segment [LK,L-1] and the right normal segment [R+1,R+K] as the normal data area, where K is the reference window length and R is the number of curve data points in the normal segment. S231. Let the curve data points within the normal data region be y1, y2, ..., y3. n ; S232. Calculate the mean of the curve data points within the normal data range: S233. Calculate the variance of the curve data points within the normal data range: S234, when u ref If u < 100, it is determined to be a Poisson distribution; when u ref If the value is ≥100, it is determined to be a normal distribution.
7. The method for repairing abnormal data in secondary ion mass spectrometry curves as described in claim 6, characterized in that, Step S300 specifically includes: S310. Initialization of parameters for the region to be processed for abnormal data: Let the data points in the region to be processed for abnormal data be X1, X2, ..., X... m , m is the number of abnormal data in the abnormal data group to be processed; the repair data to be generated is: S320. Generate repair data based on distribution type: If the distribution is Poisson: the repaired data follows a mean of u. ref The Poisson distribution, i.e.: The probability mass function of the Poisson distribution is: Integer data conforming to a Poisson distribution are generated through random sampling; If the data follows a normal distribution: the repaired data has a mean of u. ref The standard deviation is σ ref The normal distribution is: The probability density function of the normal distribution is: Continuous data conforming to a normal distribution is generated using the Box-Muller transformation.
8. The method for repairing abnormal data in secondary ion mass spectrometry curves as described in claim 7, characterized in that, Following step S320, the method further includes: S330, Boundary Smoothing Processing: Weighted smoothing is performed on 5 data points from the beginning and 5 from the end of the repaired data. Left boundary transition: y L For normal data points outside the left boundary of the region to be processed for abnormal data, ω k The weights are linearly increased from 0.2 to 1. Right boundary transition: y R For normal data points outside the right boundary of the abnormal data processing area, the weight increases linearly from 0.2 to 1.
9. An electronic device, characterized in that, The device includes a memory and a processor, wherein the memory stores a computer-executable program, and the processor calls the computer-executable program in the memory to implement the secondary ion mass spectrometry curve abnormal data repair method as described in any one of claims 1 to 8.
10. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer-executable program is stored on the computer-readable storage medium. When the computer-executable program is executed by a processor, it implements the method for repairing abnormal data of secondary ion mass spectrometry curves as described in any one of claims 1 to 8.