Self-adaptive window width symmetric zero area transformation LIBS peak searching method, system and device

Abstract

The invention discloses an LIBS (Laser-induced Breakdown Spectroscopy) peak searching method, system and device for adaptive window width symmetric zero area transformation, and belongs to the technical field of laser-induced breakdown spectroscopy element analysis. According to the method, firstly, a spectrum base is deducted through an SNIP method, Savitzky-Golay filtering preprocessing is carried out, then data segments are segmented, the window width is dynamically adjusted by combining the peak resolution, the full width at half peak and the horizontal distance at the maximum slope value, the resolution is improved by using a narrow window in an adjacent peak region, the noise reduction effect is enhanced by using a wide window in a noise or single peak region, and finally symmetric zero area transformation is substituted to complete peak searching. Experiments show that the peak searching accuracy rate of the method reaches 97.4%, the false drop rate is 1.75%, the error compared with an NIST database is smaller than or equal to 0.071 nm, the noise robustness is superior to that of a traditional method, adjacent peaks can be accurately recognized, noise interference can be restrained, and the method is suitable for high-precision LIBS element analysis.

Description

Technical Field

[0001] The present invention relates to the technical field of laser induced breakdown spectroscopy element analysis, and in particular to a LIBS peak-finding method, system and device with adaptive window width symmetrical zero-area transformation. Background Art

[0002] Laser-induced breakdown spectroscopy (LIBS) technology enables rapid, non-destructive analysis by detecting characteristic elemental peaks. It is widely used in various fields, but the accuracy of peak identification directly impacts analytical reliability. Existing peak-finding methods have limitations: the centroid method struggles with complex peaks, the derivative method is sensitive to noise, the wavelet transform is computationally complex, and the traditional symmetric zero-area transform method, due to its fixed window width, easily misses adjacent peaks with wide windows and is susceptible to noise interference with narrow windows. This method fails to balance noise suppression with detail preservation, resulting in insufficient peak identification accuracy. A dynamic window width adjustment mechanism based on the local characteristics of the signal is urgently needed to address this issue. Summary of the Invention

[0003] The purpose of the present invention is to provide a LIBS peak search method, system and device with adaptive window width symmetrical zero-area transformation to solve the existing problems.

[0004] The technical solution of the present invention to solve the above technical problems is as follows: In a first aspect, the present invention provides a LIBS peak finding method with adaptive window width symmetric zero-area transformation, comprising: Step S1: preprocessing the collected LIBS spectral data by full spectrum background subtraction and filtering methods; Step S2: Segment the preprocessed LIBS spectral data into several data segments and screen the number of peaks in each data segment; Step S3: Calculate peak resolution for data segments containing multiple peaks , synchronously calculate the maximum value of the half-maximum width for single-peak data segments or multi-peak data segments , the horizontal distance where the absolute value of the slope is the largest and data segment length ; Step S4: Based on peak resolution Dynamically adjust window width : when When using the narrow window calculation formula ; when Or when it is a single peak, use the wide window calculation formula ; Step S5: Set the adaptive window width Substitute the symmetrical zero-area transformation formula and search for peaks in each data segment; Step S6: If the peak search accuracy fluctuation is less than the preset threshold, the peak search result is output; otherwise, the preprocessing parameters are adjusted and steps S1 to S5 are repeated.

[0005] Further solution: The full spectrum background subtraction method in step S1 is the SNIP method, which specifically includes: S11. Transform the spectral intensity data: ,in, Indicates the Spectral intensity data of each channel, express The transformed data; S12, through the iterative formula , filter the minimum value, where express calculate iterations, and and same; S13, pass The base is obtained by inverse transformation, and the LIBS spectral data is deducted by the SNIP method; the filtering method is Savitzky-Golay filtering.

[0006] Further solution: Peak resolution as described in step S3 The calculation formula is: ,in, is the center distance between two adjacent peaks, and are the full width at half maximum of two adjacent peaks.

[0007] Further solution: In the narrow window calculation formula in step S4, is the rounding function; in the wide window calculation formula, For a round-down function, the intensity values ​​at the start and end of the data segment are both greater than a set threshold.

[0008] Further solution: Window function of the symmetric zero-area transformation in step S5 (Including the symmetric zero area window function coefficients, which are the core weight parameters of peak search calculation) satisfy and ,in, ; : The spectrum peak approximation model based on Gaussian function is as follows: ; in: : Relative position index within the window (value range is arrive , is the window width); represents a natural constant; : Full Width at Half Maximum (FWHM) of the peak, which is the width corresponding to when the peak intensity drops to half of the peak value, reflecting the width and narrowness characteristics of the peak shape; : Gaussian function normalization coefficient, ensuring Matches the standard deviation of a Gaussian distribution; : The total number of data points in the window, calculated as ( is the window width, and the total length of the left and right symmetrical windows is ); : The sum of the Gaussian function within the window range, used to Normalize to ensure The zero area constraint is satisfied.

[0009] Further solution: The symmetric zero area transformation formula in step S5 is: ,in, is the data after symmetric zero-area transformation, is the preprocessed LIBS spectral data.

[0010] Further solution: The peak position determination condition in step S5 is ,in, For the The ratio of the symmetric zero-area transform of times to its standard deviation; is the threshold, the initial value is 1; express The standard deviation of .

[0011] Further solution: Peak search accuracy in step S6 The calculation formula is ,in, is the actual number of peaks, is the number of missed peaks; the preset threshold is 0.005. Further solution: the adjusting of the preprocessing parameters in step S6 includes: modifying the number of iterations of the SNIP method and the width of the Savitzky-Golay filter.

[0012] Further solution: the peak search result is compared with the NIST database with an error of ≤0.071nm, the peak search accuracy is ≥97%, and the false detection rate is ≤2%.

[0013] In a second aspect, based on the same inventive concept, the present invention also provides a LIBS peak search system with adaptive window width symmetric zero-area transformation, comprising: A preprocessing unit is used to preprocess the LIBS spectral data using a full spectrum background subtraction method and a filtering method; A data segmentation and analysis unit is used to segment the preprocessed spectral data into several data segments and screen the number of peaks in each data segment; A window width adjustment unit for dynamically adjusting the window width based on the peak resolution, full width at half maximum, horizontal distance at the maximum slope and length of the data segment; A peak-finding calculation unit is used to substitute the adaptive window width into the symmetric zero-area transformation formula to perform peak-finding on each data segment; The result verification unit is used to determine whether the peak search accuracy fluctuation is less than a preset threshold. If so, the result is output; otherwise, the pre-processing unit is triggered to adjust the parameters and repeat the execution; In a third aspect, based on the same inventive concept, the present invention further provides a LIBS peak search device with adaptive window width symmetric zero-area transformation, comprising: Spectral acquisition module, used to obtain LIBS spectral data; a data processing module, connected to the spectrum acquisition module, and configured to execute the steps of the above method; The output module is connected to the data processing module and is used to output the peak search results.

[0014] The present invention has the following beneficial effects: This paper proposes a LIBS peak-finding method, system, and device using adaptive window width and symmetrical zero-area transformation. This method constructs a window width function with a core capability: by sensing local signal characteristics (such as peak shape changes and slope fluctuations) in real time, it uses a wider window in stable or noise-dominated regions to enhance noise suppression; and a narrower window in regions adjacent to peaks to accurately identify peak positions. Evaluations of peak detection error and algorithm robustness validate the reliability of the adaptive window width method in improving peak identification accuracy and noise immunity. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 Layout diagram of the LIBS experimental device provided in an embodiment of the present invention; Figure 2 The SNIP method provided in the embodiment of the present invention removes the background and the SG smoothing filter color image; Figure 3 A flow chart of the adaptive window width symmetric zero-area transformation method provided in an embodiment of the present invention; Figure 4Color images of peak search results using the symmetrical zero-area transform method with different window widths provided by an embodiment of the present invention; Figure a is an example image when the fixed window width is 3; Figure b is an example image when the fixed window width is 9; Figure c is an example image when the fixed window width is 15; Figure d is an example image of spectral peaks after the introduction of the adaptive window width function; Figure e is an example image when the window width of the second group of adjacent peak identification boundaries is 11; Figure f is an example image when the window width of the third group of adjacent peak identification boundaries is 5; Figure 5 This is a color map of adaptive window width visualization provided by an embodiment of the present invention.

[0016] Figure 6 Figure 1 is a noise peak color map in an embodiment of the present invention; Figure a is an example map when the window width is 3; Figure b is an example map when the window width is 9; Figure c is an example map when the window width is 15; Figure d is an example map when the window width is adaptive; Figure 7 A color chart comparing the peak-finding accuracy and false detection rate for different window widths provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0017] The principles and features of the present invention are described below with reference to the accompanying drawings. The examples given are only used to explain the present invention and are not used to limit the scope of the present invention.

[0018] Example 1 A total of 10 national standard alloy samples were used in this experiment. Table 1 lists the contents of the main elements of the samples. The selected samples were regular cylindrical in shape, with a flat and smooth surface and uniform overall shape.

[0019]

[0020] Experimental setup such as Figure 1 As shown in the figure, a high-energy pulsed Nd-YAG laser (Nimma-400, Leibao Optoelectronics, China; specifications: wavelength 1064 nm, pulse frequency 1 Hz, energy 60 mJ) was used to emit high-energy pulsed laser light. The laser beam was reflected 90 degrees by a reflector and focused by a quartz lens with a focal length of 150 mm before irradiating the alloy sample surface, inducing plasma. The optical signal emitted from the plasma was received by a signal receiver and transmitted to a spectrometer (SR-500i, Andor) via optical fiber. Simultaneously, the optical signal was converted to a digital signal by the spectrometer's integrated integrated circuit (ICCD) and transmitted to a computer for analysis and processing. Synchronization between the laser and spectrometer was achieved using a BNC575 digital delayed pulse generator (pulse delay: 1 s).

[0021] In the LIBS experiment, a wavelength range of 250nm to 900nm was selected (covering 36144 data points). By establishing a wavelength-channel address mapping relationship (250nm to 900nm corresponds to channel addresses 1 to 36144), the spectral intensity values ​​corresponding to each wavelength are directly stored using integer channel addresses as indexes. This method not only significantly improves data storage efficiency, but also, based on the continuous integer characteristics of the channel address, can quickly locate the target wavelength range, effectively supporting the efficient execution of subsequent computational analysis tasks such as spectral peak finding. This paper uses the full spectrum background subtraction (SNIP) method to process spectral data. The core principle of this method is to perform nonlinear iterative operations on the data. This operation can accurately identify and effectively process the background signal, thereby achieving more accurate identification and quantitative analysis of the peaks in the spectral data. The spectral data is transformed as follows: (1) in, Indicates the Spectral intensity data of each channel, express The transformed data.

[0022] The first Compare the transformed value of each address with the average value of its neighborhood and select the minimum value. The selection formula is as follows: (2) Where, express calculate iterations, and and Same. The base was obtained by inverse transformation, and the LIBS spectral data were subtracted using the SNIP method. Figure 2 The results show that this method can effectively suppress baseline interference (the blue line represents the original spectral data, the yellow line represents the baseline, the green line is the spectrum after removing the baseline, and the red line represents the spectrum after smoothing filtering).

[0023] SNIP is the abbreviation of "Signal-to-Noise Improvement Procedure", which is a data processing method commonly used in the fields of nuclear spectroscopy, radiation detection and spectral data processing. In order to retain the signal characteristics while reducing noise, the Savitzky-Golay (SG) filtering method is selected. The SG filtering method is a local smoothing method based on polynomial fitting, with relatively low computational complexity, and is suitable for processing large-scale data sets. Figure 2 As shown in the nested diagram in , this method provides a more reliable input for calculating the adaptive window width.

[0024] Figure 2 The SNIP method removes the baseline and the SG smoothing filter. The blue line represents the original spectral data, the yellow line represents the baseline, the green line represents the spectral data after removing the baseline, and the red line represents the spectral data after the smoothing filter.

[0025] The symmetric zero-area transform (SZAT) uses the convolution operation of the symmetric window function with the zero-area characteristic to assign different weights to each point of the spectral peak: the peak region is transformed into a positive number, and the non-peak region is transformed into a small number or a negative number. The peak searching process of the symmetric zero-area transform is as follows: Window function The window function has symmetry and its area is 0, as shown in equation 3: (3) wherein, represents the window width. As one of the core control variables of the symmetric zero-area transform, its value directly affects the transformation result.

[0026] The spectral peak region can be approximately regarded as a Gaussian function, and the window function is derived from the Gaussian function, and equation 6 shows the method of symmetric zero-area transform of spectral data.

[0027] (4) (5) (6) wherein, represents the full width at half maximum of the spectral peak, , is the data after the symmetric zero-area transform, is the preprocessed LIBS spectral data.

[0028] Equation 7 for determining the peak position is as follows: (7) wherein, represents the threshold value (the initial value is 1), is the ratio of the th symmetric zero-area transform to its standard deviation, and when is greater than the threshold value , the data is identified as a spectral peak. As can be seen from equation 7, the selection of the window width directly affects the accuracy of peak detection.

[0029] When using the above-mentioned symmetric zero-area transformation method, selecting a wide window can smooth the spectral data over a larger range and suppress the interference of noise on the analysis results, but it may ignore local significant features in the data. In particular, when processing multiple peaks that are close in position but not completely overlapping (referred to as "adjacent peaks" in this article), a wide window often causes adjacent peaks to be missed. On the contrary, a narrow window provides higher resolution and can better display adjacent peaks, but it is also more likely to over-respond to noise, and the effect of suppressing noise is relatively limited. Therefore, when selecting a window width, When performing noise reduction, a balance needs to be struck between accuracy and noise suppression according to the analysis goal.

[0030] To achieve the above objectives, this paper proposes an adaptive window width calculation method. First, the peak resolution needs to be calculated as the adaptive window width evaluation criterion. Peak resolution can help evaluate the degree of peak separation and be used to preliminarily determine whether there are adjacent peaks within the target range. The calculation formula is (8) in, and Respectively represent the full width at half maximum (FWHM) of two adjacent peaks (in the same target range), Represents the center distance between two adjacent peaks.

[0031] The calculation formula of adaptive window width is: when the peak resolution of two adjacent peaks is When the peaks are close to each other, a narrow window should be used to improve the resolution. Or when the data segment contains only a single peak, a wide window is preferred to reduce noise interference and improve anti-interference ability.

[0032] (9) Where, is the length of the data segment, which is obtained by filtering the spectral line data processed by the SNIP method and SG filtering (the start and end of the data segment are greater than the set threshold). is the horizontal distance (x-axis span) corresponding to the point where the absolute value of the slope (of the curve in this data segment) is the maximum. is the maximum full width at half maximum in the data segment, d / FWHM is called the slope peak width adjustment factor, is the floor function.

[0033] The flow chart of adaptive window width symmetric zero area transformation is as follows Figure 3 As shown: The first step of the process is to remove defective data and pre-process the LIBS experimental data using the SNIP method and SG filtering method. The processed data is divided into several data segments, and the number of peaks in each data segment is screened: when there are multiple peaks, the peak resolution is calculated. When the peak resolution is less than or equal to 2, a narrow window is adaptively matched; when there is only a single peak in the data segment or the peak resolution is greater than 2, a wide window is matched. The adaptive window width is adjusted. Substitute the symmetric zero-area transform into the peak-finding result. If the fluctuation in peak-finding accuracy is less than 0.5%, output the result. Otherwise, continue to modify the number of iterations in the SNIP algorithm and the filter width in the SG algorithm to optimize the data and repeat the above steps.

[0034] When multiple spectral peaks are closely adjacent in the wavelength dimension, the signal interference between adjacent peaks will cause the boundary contour of the target peak to be distorted. In actual analysis, spectral peaks often exhibit asymmetric characteristics (such as tailing or protrusion). Such asymmetry will form a compound interference effect in the overlapping area, making it difficult to accurately locate the peak center. To address this key technical problem, this paper selects three groups of adjacent peaks as research objects. Figure 4 As shown in the figure, the first group of adjacent peaks are located between 1110 and 1165. The traversal method is used to set the window width parameter range from 1 to 15, and each parameter value is substituted into the symmetric zero area transformation method for experimental analysis. The results show that Figure 4 (b) There is a deviation in the positioning of the peak center of channel 1139. Figure 4 In (c), the peak at channel 1139 is completely lost, and the peak at channel 1122 is significantly deviated. Figure 4 (d) After introducing the adaptive window width function, the three spectral peaks are precisely located. Compared with the fixed window width, the adaptive window width has a significant advantage in the adjacent peak identification process.

[0035] It is worth noting that under this adjacent peak, when the window width When <9, the symmetric zero-area algorithm can accurately locate the three spectral peaks, such as Figure 4 As shown in Figure a. When ≥9, the algorithm cannot accurately identify adjacent peaks.

[0036] Similarly, Figure 4 Middle e (nearby peaks between 1320-1360) and Figure 4 The two groups of adjacent peaks in the middle f (the adjacent peaks between channel addresses 19080-19140) require window widths of ≤11 and Accurate identification can be achieved when the peak shape characteristics of the three groups of adjacent peaks are significantly different. Figure 4 The peak spacing and boundary definition of a are relatively moderate. Figure 4 The peak centers of e are extremely narrow and have fuzzy boundaries, while Figure 4The peak height difference in the middle f is significant. Analysis shows that the range of window width parameters significantly affects the accuracy of adjacent peak identification. The introduction of the adaptive window width function significantly improves peak finding efficiency by dynamically adjusting the window width without manually selecting peak shape parameters.

[0037] Figure 4 Symmetrical zero-area transform peak detection results with different window widths. All are set to 1, (a) fixed window width =3; (b) fixed window width =9; (c) fixed window width =15; (d) adaptive window width; (e) the second set of adjacent peak recognition limits =11; (f) The third group of adjacent peak identification boundaries = 5. The blue line represents the spectral data, the yellow line represents the transformed data with a fixed window width, the red line represents the transformed data with an adaptive window width, and the red dot represents the center point of the detected peak.

[0038] Figure 5 This is a visualization of the processing results of the adaptive window width method in LIBS data. As shown in the figure, the length of the data segment in the two dotted boxes is Both are 47. Adaptive window width matching of No. 1 =17, No. 2 adaptive window width matching =4. This difference reflects the algorithm's adaptive adjustment to complex peak shapes. This adjustment eliminates the need to filter the window width based on spectral peaks, significantly reducing computational redundancy and significantly improving the practical efficiency of the symmetric zero-area transform method.

[0039] Figure 5 Adaptive window width visualization. The X-axis represents the channel address, the Y-axis in the upper half represents the spectral line intensity, and the height of the column in the lower half represents the window width. Value, the width of the column indicates the length of the data segment .

[0040] The amplitude of noise peaks is usually small and appears randomly, which may be mistaken for the characteristic signal peaks of elements, interfering with element feature recognition. Compared with the fixed window width method, the adaptive window width has potential advantages in suppressing noise. To verify this feature, this paper selects 500 data points of background noise peaks ( Figure 6 ) for comparative testing. Figure 6 Figure a uses =3, the noise peak is over-identified. As the window width increases ( Figure 6 Middle B =9, Figure 6 Figure C =15), the number of detected peaks is reduced from 19 to 10. Compared with the fixed window width, Figure 6 The noise peak was not identified when the adaptive window width was used. The internal reason for this phenomenon can be summarized as follows: the noise peak has an abnormal characteristic, i.e., the "horizontal distance at the maximum absolute value of the slope (formula 9)" is significantly smaller; the peak shape is severely distorted, resulting in an abnormal increase in the "deformed FWHM". Both of these factors cause the slope peak width adjustment factor in formula 9 to approach 0, and thus the value of formula 9 also approaches 0. Experimental results show that the adaptive window width algorithm avoids identifying noise peaks by dynamically adjusting the window width parameter. Figure 6 In the middle: (a) fixed window width = 3; (b) fixed window width = 9; (c) fixed window width = 15; (d) adaptive window width = 0; the adaptive window width symmetric zero area transformation method was used to process the LIBS spectral data. In order to evaluate the effects of adaptive window width and fixed window width on the peak detection of the symmetric zero area transformation method, the following experiments were performed. In the experiment, true peaks (TP), missed peaks (FN), and false detected peaks (FP) were defined. True peaks: actual peaks in the signal that should be detected. Missed peaks: peaks that should be detected but were not detected due to algorithm limitations or improper parameter settings. False detected peaks: peaks that were incorrectly detected as peaks, but were not actually true peaks in the signal. In this paper, the peak detection accuracy (PDA) and false discovery rate (FDR) were defined as follows: (10) (11) The wavelength range was selected to be 457.063 nm to 570.148 nm, which avoids the main distribution bands of common interference sources (such as strong infrared / ultraviolet components in sunlight, laboratory light noise). According to the data analysis in Table 2, the window width has a significant effect on the peak detection results. As the window width gradually increases, the number of missed peaks increases from 44 to 96, while the number of false detected peaks gradually decreases from 87 to 18. In terms of the key indicator of peak detection accuracy, the adaptive window width method shows a significant advantage. As shown in FIG. 6 (the ordinate is the window width Figure 7 value, the X-axis: percentage value, the positive and negative directions correspond to PDR (blue, positive direction) and FDR (red, negative direction), respectively), the peak detection accuracy of the adaptive window width algorithm is as high as 97.4% (fixed window width ​​​=15 is only 58.44%), with significant performance improvement; at the same time, its false detection rate is further reduced to 1.75%. Adaptive window width improves peak detection accuracy by dynamically adjusting the symmetrical zero-area transform;

[0041] Figure 7 Peak search accuracy and false detection rate for different window widths. The vertical axis is the window width. value; To fully evaluate the performance of the adaptive window width symmetric zero-area transform (SZAT) peak search algorithm, the following two tests were performed: (1) Benchmark comparison test: The experimental data of Fe-Cr-Mn-Ni alloy were compared with the NIST database. Table 3 shows the comparison results of the peak positions identified by the proposed algorithm and the NIST database. As can be seen from Table 3, the peak search results of the proposed algorithm are basically consistent with the data in the NIST database, with an error of less than 0.071nm.

[0042] (2) Noise robustness verification: Gaussian noise is added to the experimental data. Gaussian noise is one of the common noises in LIBS. Gaussian noise, calculate the average error, after ten experiments, take the average value. As the value of = 0.05 when 0.6081pm gradually increases to = 0.5. However, compared with the centroid method (14.0961pm→14.8143pm), Gaussian product function method (2.0653pm→2.5034pm), and derivative method (2.4781pm→3.0286pm), the proposed method has the smallest average error, demonstrating its great potential in applications requiring high precision.

[0043] This study addresses the limitations of the traditional symmetric zero-area transform (SZAT) peak-finding algorithm in complex LIBS spectral analysis and innovatively proposes AWSAZC. This algorithm dynamically analyzes local statistical characteristics of spectral lines (such as line slope, full width at half maximum, and peak spacing distribution) and incorporates dynamic peak resolution assessment to construct an adaptive window width adjustment framework, enabling efficient analysis of adjacent peaks and noise peaks.

[0044] Table 4 shows a systematic validation of the adaptive window width symmetric zero-area transform method on an iron-chromium-manganese-nickel alloy sample. Experimental results demonstrate significant advantages in peak detection using the adaptive window width symmetric zero-area transform method. Comparisons with the NIST standard database reveal an error within 0.071 nm, improving peak detection accuracy to 97.4% and reducing the false detection rate to 1.75%. The proposed algorithm maintains robustness under Gaussian noise interference of varying intensities, demonstrating its applicability in practical analytical scenarios. Future research will focus on developing effective physical models or statistical criteria to reliably distinguish weak peaks from noise peaks.

[0045]

[0046] Example 2 For example, a LIBS spectral analysis system built by a research institute is designed to analyze the elemental composition of ore samples. The specific steps for implementing the LIBS peak-finding system based on adaptive window width symmetric zero-area transformation are as follows: Preprocessing unit: SNIP method implementation: self-developed spectral data processing software is used to load the collected LIBS spectral data file (.spc format). The software uses the SNIP method formula to process the spectral intensity data. Perform the transformation: For example, when processing a batch of iron ore spectrum data, the number of iterations is set to 5 times, using the formula: ; The minimum value was screened and inversely transformed to obtain the base, and the base signal was successfully subtracted, making the peak signal in the spectrum more prominent.

[0047] Savitzky-Golay filtering implementation: The Savitzky-Golay filtering algorithm was invoked within the same software, with a filter window width of 11 points and a polynomial order of 3. This filtering effectively suppressed high-frequency noise in the spectral data while preserving key features such as peak sharpness and full width at half maximum, providing a high-quality data foundation for subsequent data processing.

[0048] Data segmentation and analysis unit: Data Segmentation: A data processing script written in Python segments the preprocessed spectral data into several segments based on an intensity threshold (set to 500 counts). For example, when analyzing a spectrum containing characteristic peaks of multiple elements, five segments were successfully segmented, each containing clear peak information or background regions.

[0049] Peak Count Screening: Each data segment is analyzed using a custom peak identification algorithm. This algorithm identifies the number of peaks within a data segment based on changes in peak slope and intensity. Of the five data segments mentioned above, two were accurately identified as single-peaked and three as multi-peaked, providing a basis for subsequent targeted parameter calculations.

[0050] Window width adjustment unit: Parameter calculation: For multi-peak data segments, write a Python function to calculate the peak resolution For example, in a data segment containing adjacent characteristic peaks of iron and magnesium, the center distance between the two adjacent peaks is measured. 2.5nm, full width at half maximum of the two peaks 0.8nm, is 0.7nm, substitute into the formula: , calculated At the same time, the numpy library function is used to calculate the maximum value of the half-maximum full width of the data segment The horizontal distance where the absolute value of the slope is the maximum is 0.8nm 10 data points and data segment length There are 100 data points.

[0051] Window width determination: Based on the calculated Value, when (As calculated above, 1.67), using the narrow window calculation formula , calculate: , (in actual application, it can be appropriately scaled according to the data scale, such as taking 1 / 10 as 62).

[0052] for Or single peak data segment, use wide window calculation formula , if a single-peak data segment is 1.2nm, then (It can also be adjusted according to actual conditions, such as 10 for 10 times magnification).

[0053] Peak finding calculation unit: Window function construction: Write a function in Python to construct a window function for symmetric zero-area transformation Taking the Gaussian function as an example, according to the formula ( is the full width at half maximum of the spectrum peak, here we take the corresponding data segment calculated ) Calculate the Gaussian function value, and then according to: , build a window function.

[0054] Peak search calculation: the adaptive window width Substitute into the symmetric zero area transformation formula , using Python's numpy library for efficient matrix operations. Peak search calculations are performed on each data segment to identify the peak positions. For example, in a complex multi-peak data segment, three peak positions were accurately identified and compared with the peak positions of a known standard spectrum, with an acceptable error.

[0055] Result verification unit: Accuracy calculation: Write a Python script to calculate the peak search accuracy By comparing with the known standard spectrum database, the number of true peaks is counted. and the number of missed peaks For example, when analyzing 100 spectral data segments, determine There are 95 For 3, we can calculate: .

[0056] Threshold judgment and adjustment: The calculated Compare with the preset threshold of 0.005. If the fluctuation is less than the threshold, as calculated in this If the PDA fluctuation compared to the last calculation is 0.003, the peak search results are output to a result file (.txt format). If not, adjust the number of SNIP iterations (for example, from 5 to 7) and the width of the Savitzky-Golay filter (for example, from 11 points to 13 points), and re-trigger the preprocessing unit until the conditions are met.

[0057] Example 3 The LIBS peak-finding device with adaptive window width and symmetrical zero-area transformation includes: Spectral acquisition module: Laser induced components: A high-energy Nd-YAG laser is used, with a wavelength of 1064nm, a pulse width of 5ns, and a repetition frequency of 10Hz. The laser beam emitted by the laser is reflected by a set of high-precision mirrors (reflectivity>99%) and then focused on the surface of the metal sample through a focusing lens with a focal length of 75mm. For example, when testing aluminum alloy samples, the laser energy density reaches , effectively inducing plasma on the sample surface.

[0058] Signal receiving assembly: A large-diameter quartz optical fiber (200μm core diameter) efficiently transmits the optical signal emitted by the plasma to the spectrometer. One end of the fiber is positioned close to the sample surface at a 45° angle to optimize optical signal collection efficiency.

[0059] Spectrometer and ICCD: The spectrometer used has a wavelength range of 200nm-1000nm and a resolution of 0.05nm. The ICCD (enhanced charge-coupled device) has an adjustable gain range of 1-1000x, and the exposure time can be precisely controlled from 1μs to 100ms. During the detection process, the ICCD gain was set to 200x and the exposure time to 10μs based on the sample's emission light intensity to ensure the acquisition of clear spectral data.

[0060] Data processing module: Hardware Configuration: A high-performance industrial computer serves as the data processing core, equipped with an Intel Xeon E5 processor (8 cores, 3.0 GHz), 16 GB of DDR4 memory, and a 512 GB SSD hard drive. The computer is connected to the spectrometer and ICCD via a USB 3.0 interface, enabling high-speed data transmission.

[0061] Software Operation: Install our proprietary LIBS data processing software on your computer. This software, developed in C++, integrates the aforementioned method steps. Upon startup, the software automatically identifies the connected spectral acquisition device, receives the acquired spectral data in real time, and performs preprocessing, data segmentation, window width adjustment, peak finding calculations, and result verification. For example, when processing spectral data from a batch of stainless steel samples, the software completes all data processing within one minute and outputs accurate peak finding results.

[0062] Output Module: Results display: Connecting a computer to a 24-inch LCD monitor via the HDMI port displays peak-finding results to the operator in the form of visual charts (peak positions and corresponding elements are marked on the spectrum) and data tables (peak position, intensity, and corresponding elements). The operator can intuitively understand the elemental composition and content of the sample.

[0063] Data Storage: Peak search results are stored in a designated folder on the computer's hard drive. Network sharing can also be used to transfer the data to an internal company server for easy management and analysis. For example, within a month, the device tested 500 batches of metal samples, and all peak search results were fully stored, providing strong data support for product quality control.

[0064] Synchronous control module: Hardware connection: The synchronization control module is implemented using a programmable logic controller (PLC) and is connected to the laser controller in the laser induction module and the spectrometer control unit in the spectrum acquisition module through the RS485 communication interface.

[0065] Synchronous Control: A control program is written in the PLC to set a 500ns delay between laser triggering and spectrometer acquisition, ensuring that the spectrometer acquires data when the plasma's optical signal is strongest. For example, during continuous testing, the PLC maintains stable synchronization between the laser and spectrometer, ensuring accurate and consistent data from each test.

[0066] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. The LIBS peak search method with adaptive window width symmetric zero area transformation is characterized by: include: Step S1: preprocessing the collected LIBS spectral data by full spectrum background subtraction and filtering methods; Step S2: Segment the preprocessed LIBS spectral data into several data segments and screen the number of peaks in each data segment; Step S3: Calculate peak resolution for data segments containing multiple peaks , synchronously calculate the maximum value of the half-maximum width for multi-peak data segments , the horizontal distance where the absolute value of the slope is the largest and data segment length ; Step S4: Based on peak resolution Dynamically adjust window width : when When using the narrow window calculation formula ; when When using the wide window calculation formula ; In the narrow window calculation formula, is the rounding function; in the wide window calculation formula, For a round-down function, both the starting and ending intensity values ​​of the data segment are greater than a set threshold; Step S5: Set the window width Substitute the symmetrical zero-area transformation formula and search for peaks in each data segment; Step S6: If the peak search accuracy fluctuation is less than the preset threshold, the peak search result is output; otherwise, the preprocessing parameters are adjusted and steps S1 to S5 are repeated.

2. The LIBS peak search method of the adaptive window width symmetrical zero area transformation according to claim 1, characterized in that: The full spectrum background subtraction method in step S1 is the SNIP method, which specifically includes: S11. Transform the spectral intensity data: ,in, Indicates the Spectral intensity data of each channel, express The transformed data; S12, through the iterative formula , filter the minimum value, where express calculate iterations, and and same; S13, pass The base is obtained by inverse transformation, and the LIBS spectral data is deducted by the SNIP method; the filtering method is Savitzky-Golay filtering.

3. The LIBS peak search method of the adaptive window width symmetrical zero area transformation according to claim 1, characterized in that, Peak resolution in step S3 The calculation formula is: ,in, is the center distance between two adjacent peaks, and are the full width at half maximum of two adjacent peaks.

4. The LIBS peak search method of the adaptive window width symmetrical zero area transformation according to claim 1, characterized in that Window function of the symmetric zero-area transform in step S5 satisfy and ; in, ; in, It represents the spectrum peak approximation model based on Gaussian function. The specific formula is: ； in: Indicates the full width at half maximum of the spectrum peak; represents the Gaussian function standardization coefficient; Represents the total number of data points in the window. The specific formula is: ; Represents the sum of the Gaussian functions within the window range.

5. The LIBS peak search method of the adaptive window width symmetrical zero area transformation according to claim 4 is characterized in that, The symmetric zero-area transformation formula in step S5 is: ,in, is the data after symmetric zero-area transformation, is the preprocessed LIBS spectral data.

6. The LIBS peak search method of adaptive window width symmetric zero area transformation according to claim 5, characterized in that: The peak position determination condition in step S5 is ,in, For the The ratio of the symmetric zero-area transform of times to its standard deviation; is the threshold, the initial value is 1; express The standard deviation of .

7. The LIBS peak search method of adaptive window width symmetric zero area transformation according to claim 1, characterized in that: Peak search accuracy in step S6 The calculation formula is ,in, is the actual number of peaks, is the number of missed peaks; the preset threshold is 0.

005.

8. The LIBS peak search method of adaptive window width symmetric zero area transformation according to claim 2, characterized in that: The adjustment of the preprocessing parameters in step S6 includes: modifying the number of iterations of the SNIP method and the width of the Savitzky-Golay filter.

9. A LIBS peak search system using the adaptive window width symmetrical zero-area transformation method according to any one of claims 1 to 8, characterized in that: include: A preprocessing unit is used to preprocess the LIBS spectral data using a full spectrum background subtraction method and a filtering method; A data segmentation and analysis unit is used to segment the preprocessed spectral data into several data segments and screen the number of peaks in each data segment; A window width adjustment unit for dynamically adjusting the window width based on the peak resolution, full width at half maximum, horizontal distance at the maximum slope and length of the data segment; A peak-finding calculation unit is used to substitute the adaptive window width into the symmetric zero-area transformation formula to perform peak-finding on each data segment; The result verification unit is used to determine whether the peak search accuracy fluctuation is less than a preset threshold. If so, the result is output; otherwise, the preprocessing unit is triggered to adjust the parameters and repeat the execution.

10. LIBS peak search device with adaptive window width symmetrical zero area transformation, characterized in that: include: Spectral acquisition module, used to obtain LIBS spectral data; a data processing module, connected to the spectrum acquisition module, and configured to perform the steps of the method according to any one of claims 1 to 8; The output module is connected to the data processing module and is used to output the peak search results.

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