Method and device for automatically removing scattering noise of 3D fluorescence spectrum and readable storage medium

Abstract

The invention provides a 3D fluorescence spectrum scattering noise automatic removal method and device and a readable storage medium thereof, and belongs to the technical field of spectrum data analysis and optical signal processing. The method aims at solving the problems that in the prior art, when scattering noise is removed through a traditional method, signal strength is reduced, useful information is lost, cost is high, universality is poor, and operation is complex. The method comprises the steps of data acquisition, preprocessing, scattering noise recognition and removal, data output and the like, and scattering noise is automatically removed through an image recognition algorithm and a specific mathematical model. The method is suitable for 3D fluorescence spectrum data processing, can improve data accuracy, and is widely applied to spectrum data analysis in the fields of environmental monitoring, biomedicine, material science and the like.

Description

Technical Field

[0001] The present invention relates to the technical field of spectral data analysis and optical signal processing, and particularly to a method, apparatus and readable storage medium for automatically removing scattered noise from 3D fluorescence spectra, which integrates unsupervised machine learning algorithms (DBSCAN) and statistical anomaly detection (Z-score), and is applicable to spectral data preprocessing in fields such as environmental monitoring, biomedicine, and chemical analysis. Background Art

[0002] In the field of fluorescence spectral analysis, 3D fluorescence spectroscopy technology is widely used because it can provide rich information on the structure and properties of substances, and plays an important role in many fields such as environmental monitoring, biomedicine, and materials science. However, in the actual application process, 3D fluorescence spectral data is often severely interfered by scattered noise, which poses a great challenge to the accurate interpretation and subsequent analysis of spectral data.

[0003] Traditional methods have many deficiencies in dealing with scattered noise. For example, although some hardware-based filtering techniques can suppress noise to a certain extent, they usually reduce the overall intensity of the signal, resulting in the loss of useful information. Moreover, these hardware devices are often costly, increasing the economic burden of spectral analysis and limiting their application in some scenarios with limited resources. In addition, although some software algorithms perform well under specific conditions, they have many assumptions about the characteristics of spectral data, and often require a large number of manual parameter adjustments in actual applications, which not only increases the complexity of operation, but also places high requirements on the professional knowledge and experience of operators. Moreover, the generality of these algorithms is poor, and it is often difficult to achieve an ideal denoising effect when facing different types of spectral data and application scenarios.

[0004] With the continuous development of spectral analysis technology and the increasing growth of application requirements, the market's demand for a technical solution that can efficiently, accurately and automatically remove scattered noise from 3D fluorescence spectra is becoming increasingly urgent.

[0005] Therefore, developing a method for removing scattered noise that requires no manual intervention, is efficient and accurate, and has strong adaptability is a key technical challenge in this field. Summary of the Invention

[0006] Embodiments of the present invention provide a method, apparatus and readable storage medium for automatically removing scattered noise from 3D fluorescence spectra, aiming at the problems existing in the current technology in the processing of 3D fluorescence spectral scattered noise, where traditional methods rely on manual operations, have low efficiency and unreliable results, while deep learning methods face problems such as high data annotation costs and insufficient generalization ability.

[0007] The core technology of the present invention mainly constructs a two-layer noise recognition mechanism by combining the unsupervised machine learning algorithm DBSCAN and the statistical anomaly detection method Z-score, realizing the automatic recognition, elimination of scattered noise in 3D fluorescence spectra, and spectral surface reconstruction, without manual data annotation, significantly improving the automation level and accuracy of noise processing.

[0008] In a first aspect, the present invention provides a method for automatically removing scattered noise from 3D fluorescence spectra, the method comprising the following steps: Preprocess the original 3D fluorescence spectrum data to construct a three-dimensional data structure of excitation wavelength, emission wavelength, and fluorescence intensity; Based on the spatial density distribution of the three-dimensional data structure, use the DBSCAN clustering algorithm to identify potential scattered noise points; Based on statistical distribution characteristics, use the Z-score method to perform a secondary verification on the clustering results to determine the final noise point set; Interpolate and reconstruct the spectral data after removing noise to restore the continuity of the spectral surface; Evaluate the denoising effect through quantitative indicators.

[0009] Furthermore, the preprocessing step includes: converting the original spectral data into a three-dimensional matrix and eliminating instrument response differences through Z-score normalization. The formula is:

[0010] where, is the global mean of fluorescence intensity, representing the central tendency of the data; is the standard deviation of fluorescence intensity, representing the degree of data dispersion; represents the excitation wavelength and the emission wavelength the fluorescence intensity value at the intersection point; is the standard score.

[0011] Furthermore, the DBSCAN clustering algorithm includes: Reconstruct the standardized three-dimensional data points into a spatial point set, and identify the noise point regions with sparse density by setting the neighborhood radius and the minimum number of samples.

[0012] Furthermore, the initial value of the neighborhood radius is 0.1, and the initial value of the minimum number of samples is 5, and they can be adaptively adjusted according to data characteristics.

[0013] Furthermore, the Z-score secondary verification includes: Calculate the absolute value of the Z-score of each data point, and determine the points with absolute values greater than the preset threshold as statistically abnormal points. The preset threshold is 3.

[0014] Furthermore, bicubic spline interpolation is used for interpolation reconstruction, and piecewise interpolation is performed along the excitation wavelength axis and the emission wavelength axis to ensure smooth transition of the spectral surface.

[0015] Furthermore, the quantization index is the signal-to-noise ratio, and the denoising effect is evaluated by calculating the ratio of the signal energy to the noise energy.

[0016] In a second aspect, the present invention provides a 3D fluorescence spectrum scattering noise automatic removal device, comprising: A data preprocessing module: used to load the original spectral data and perform normalization processing to construct a three-dimensional data structure; A noise detection module: integrating the DBSCAN clustering algorithm and the Z-score verification algorithm to identify and mark the scattering noise points; An interpolation filling module: based on the cubic spline interpolation algorithm, performing surface reconstruction on the spectral data after removing the noise; A verification output module: evaluating the denoising effect through the signal-to-noise ratio index and outputting the optimized spectral data.

[0017] In a third aspect, the present invention provides an electronic device, comprising a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute the above-mentioned 3D fluorescence spectrum scattering noise automatic removal method.

[0018] In a fourth aspect, the present invention provides a readable storage medium, in which a computer program is stored, and the computer program includes program codes for controlling a process to execute the process, and the process includes the 3D fluorescence spectrum scattering noise automatic removal method according to the above.

[0019] The main contributions and innovations of the present invention are as follows: 1. High-precision removal of scattering noise: By adopting an advanced image recognition algorithm and a specific mathematical model, the scattering noise in the 3D fluorescence spectrum data can be more accurately identified and removed. Compared with traditional methods, the damage to the spectral data is smaller, the useful information is effectively retained, and the accuracy and reliability of the data are improved.

[0020] 2. No need for complex parameter adjustment: Overcoming the defect that traditional software algorithms require a large number of manual parameter adjustments, the automatic removal of scattering noise is realized, the dependence on the professional knowledge and experience of operators is reduced, the spectral data analysis is made more simple and easy, and the needs of a wider range of users can be met.

[0021] 3. Good versatility: The method of the present invention is applicable to various types of 3D fluorescence spectrum data and different application scenarios, such as environmental monitoring, biomedicine, materials science and other fields, with strong versatility and wide applicability, and has more advantages compared with the prior art.

[0022] 4. Reduce hardware costs: Compared with traditional hardware-based filtering techniques, the present invention does not require additional purchase of expensive hardware devices, can effectively reduce the economic cost of spectral analysis, and make the spectral analysis technology more economical and popularizable.

[0023] 5. Improve analysis efficiency: The method of automatically removing scattered noise can quickly process a large amount of 3D fluorescence spectral data, reduce the time of manual operation and data processing, improve the efficiency of spectral data analysis, and help to accelerate the research and application process in related fields.

[0024] Details of one or more embodiments of the present invention are set forth in the following drawings and description to make other features, objects, and advantages of the present invention more concise and understandable. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The drawings described herein are used to provide a further understanding of the present invention, and constitute a part of the present invention. The schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings: Figure 1 is a flowchart of a method for automatically removing scattered noise from 3D fluorescence spectra according to an embodiment of the present invention; Figure 2 is an unprocessed three-dimensional fluorescence image according to an embodiment of the present invention, where the highlighted area is the scattering area; Figure 3 is a comparison diagram between the traditional method and the method of the present invention according to an embodiment of the present invention; Figure 4 is a comparison diagram between the traditional method and the method of the present invention according to an embodiment of the present invention.

[0026] Figure 5 is a comparison diagram of the noise removal performance between the method of the present invention and the traditional algorithm according to an embodiment of the present invention, where the first picture is the original unprocessed picture; Figure 6 is a schematic diagram of the hardware structure of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0027] Here, exemplary embodiments will be described in detail, and examples are shown in the drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with one or more embodiments of the present specification. On the contrary, they are merely examples of devices and methods consistent with some aspects of one or more embodiments of the present specification as detailed in the appended claims.

[0028] It should be noted that: in other embodiments, the steps of the corresponding method are not necessarily executed in the order shown and described in this specification. In some other embodiments, the steps included in the method may be more or less than those described in this specification. In addition, a single step described in this specification may be decomposed into multiple steps for description in other embodiments; and multiple steps described in this specification may also be combined into a single step for description in other embodiments.

[0029] In the prior art for 3D fluorescence spectrum scattering noise processing, traditional methods rely on manual operation, have low efficiency and unreliable results, while deep learning methods face problems such as high data annotation costs and insufficient generalization ability.

[0030] Based on this, the present invention combines the unsupervised machine learning algorithm DBSCAN with the statistical anomaly detection method Z-score to solve the problems existing in the prior art.

[0031] Embodiment 1 The present invention aims to propose an automatic method for removing 3D fluorescence spectrum scattering noise. Specifically, referring to Figure 1 , the method includes the following steps: Step 1: Acquisition and digital preprocessing of 3D fluorescence spectrum data. For the problem of high-dimensional spectral features in water pollution detection, first collect 3D fluorescence spectrum data and convert it into a digital matrix form, that is, a three-dimensional data structure composed of excitation wavelength (Ex), emission wavelength (Em), and fluorescence intensity. The data structure ensures the unity and automation of subsequent algorithm processing.

[0032] As Figure 2 shown is the unprocessed three-dimensional fluorescence map (the highlighted area is the scattering area). It can be seen that the unprocessed scattered fluorescence map does not show the contour structure, which is used to compare the effects before and after removing scattering.

[0033] Therefore, in this embodiment, Step 1 specifically includes: Step 101: Read the original 3D fluorescence spectrum as a structured three-dimensional matrix in the form of:

[0034] where i and j respectively represent the discrete sampling point numbers of the excitation wavelength and the emission wavelength; represents the excitation wavelength and the emission wavelength at the intersection point of the fluorescence intensity value, reflecting the luminescence characteristics of the substance under this Ex / Em combination.

[0035] Step 102: Preprocess the instrument response difference in the original data through Z-score standardization:

[0036] where is the global mean of the fluorescence intensity, representing the central tendency of the data; is the standard deviation of the fluorescence intensity, representing the degree of data dispersion; represents the excitation wavelength and the emission wavelength the fluorescence intensity value at the intersection point; is the standard score; this step ensures the comparability of spectral data among different batches and samples and unifies the dimension.

[0037] Eliminating the instrument response difference through Z - score standardization is the basis for the present invention to achieve "automation, high precision, and cross - scenario applicability" for denoising. Its technical value lies not only in solving the data scale problem, but also in providing a unified mathematical space for the organic combination of subsequent unsupervised learning and statistical methods.

[0038] Step 2: DBSCAN clustering anomaly detection based on spatial reconstruction. On the basis of fully considering the spatial distribution characteristics of the fluorescence intensity, a three - dimensional point set composed of (Ex, Em, Intensity) is constructed, and the density clustering algorithm DBSCAN is introduced to identify the scattered noise points with abnormal density. Compared with the traditional denoising strategy based only on intensity threshold, this method combines spatial position and intensity information, realizes the structured detection of complex background scattering patterns, and reflects the targeted improvement and domain - specific adaptation of the technology in the water environment spectral data scenario.

[0039] In this embodiment, step 2 specifically includes: Step 201: Perform spatial reconstruction on the standardized fluorescence data points, and represent each data point as:

[0040] to form a three - dimensional point set , where .

[0041] Step 202: Use the DBSCAN (Density - Based Spatial Clustering of Applications with Noise) algorithm for clustering to identify the density - sparse regions, that is, potential scattered noise points.

[0042] Step 203: Select the neighborhood radius: the empirical initial value is 0.1, which is adjusted according to the density distribution of the data feature space.

[0043] Step 204: The minimum number of samples : Generally set to 5 or select the optimal value through grid search.

[0044] Step 205: For each point, calculate the number of its neighbor points within the ε radius.

[0045] Step 206: If the number of neighbors of a certain point, it is defined as a "core point", otherwise it is a "boundary point" or a "noise point".

[0046] The set of noise points is denoted as .

[0047] This step can effectively identify abnormal detection benchmarks such as isolated scattered points (such as noise, Raman scattering peaks).

[0048] Step 3: Statistical screening and misjudgment correction mechanism based on Z-score. Aiming at the possible misjudgment problems of the DBSCAN algorithm in identifying boundary noise points, the Z-score statistical anomaly rejection method is introduced as a secondary verification mechanism to screen potential remaining abnormal points. By calculating the Z-score value and setting a reasonable threshold, the robustness and stability of the detection are enhanced from the level of statistical significance. This "dual-mechanism combination" strategy is an algorithmic sequential innovation for the problem that weak-intensity perturbations in the water background spectrum are easily mis-identified.

[0049] In this embodiment, Step 3 specifically includes: Step 301: Based on the Z-score algorithm, perform secondary verification on the noise judgment result: Step 302: For each point , calculate the absolute value of its Z-score. If it satisfies:

[0050] Then it is considered an outlier in the statistical sense, and an abnormal point set is constructed.

[0051] Step 303: The final set of noise points is the intersection of the two results:

[0052] This mechanism can effectively eliminate misjudgments caused by data sparsity or numerical errors, ensuring the accuracy and robustness of noise identification.

[0053] Step 4: Spectral surface reconstruction by bidirectional cubic spline interpolation. In the sparse region after scattering removal, the bidirectional cubic spline (2D cubic spline) method is used for surface reconstruction. During the reconstruction process, interpolation is performed along the excitation wavelength axis and the emission wavelength axis respectively to ensure a natural transition of the spectral surface in terms of physical continuity and mathematical smoothness. This method is superior to linear interpolation or uniaxial interpolation and is especially suitable for complex water quality sample backgrounds that require high-precision restoration of local signal continuity.

[0054] In this embodiment, step 4 specifically includes: Step 401: For the standardized spatial region, perform supplementation using bicubic spline interpolation (2D cubic spline interpolation): Step 402: Perform interpolation on each wavelength slice of each band (along the EM axis); Step 403: Perform interpolation on each slice of each band (along the EX axis); Step 404: The reconstruction process ensures that each segment of the spectrum is continuously connected to the next segment, ensuring smooth transitions in smoothness and curvature. On the basis of retaining the original data form of the reconstructed spectral surface, the signal stability in the local area is effectively restored, which is suitable for data analysis and feature extraction in subsequent intervals.

[0055] Step 5: A quantization evaluation mechanism based on SNR. To comprehensively evaluate the denoising effect, the signal-to-noise ratio (SNR) is introduced as a quantization index to evaluate the model performance from the dimension of information retention. This evaluation strategy strengthens the engineering verifiability and domain adaptation ability of the method, provides an objective evaluation basis for subsequent modeling and feature extraction, and reflects the systematic advantages of this method in the practical application of water quality monitoring.

[0056] In this embodiment, step 5 specifically includes: Step 501: Evaluate the denoising quality through quantization indicators. For example, use the signal-to-noise ratio (SNR) to evaluate the denoising quality:

[0057] Among them, the signal-to-noise ratio (Signal-to-Noise Ratio, SNR) is the core index in the field of signal processing for measuring the ratio of the effective component to the noise in the signal; A is the signal energy; B is the noise energy.

[0058] As Figure 3 and 4 shown, traditional methods usually adopt the method of fixed region cutting, which easily leads to some fluorescence peaks being mis-cut, affecting the integrity and analysis effect of the data. The present invention has better universality, can flexibly cut the scattering region for different samples, and at the same time ensure that the connection between the cut region and the normal region is smoother and more natural, avoiding mutation phenomena, thereby improving the data quality and the accuracy of subsequent analysis.

[0059] As Figure 5 shown, in order to verify the technical effect of the present invention, the effectiveness of the present invention will be verified by algorithm comparison. Traditional manual denoising methods rely on subjective judgment (such as "the degree of noise removal is visible to the naked eye"), lacking a quantization standard; while the present invention converts the denoising effect into a specific value through the SNR formula, for example: The SNR before denoising is relatively low (such as Figure 5 1.1013 dB in the comparison), indicating that noise dominates; The SNR after denoising is significantly improved (such as Figure 5 7.999 dB in ), proving that the proportion of effective signals increases and the noise is effectively suppressed.

[0060] It can be seen that the method provided by the present invention significantly improves the overall performance of noise removal in this field. According to the technical indicators, the SNR is significantly improved compared with the traditional algorithm. Under the action of the new algorithm, it can ensure that the data signal is not lost to the greatest extent while removing the noise signal.

[0061] In the present invention, the SNR is used for: Parameter tuning: By maximizing the SNR value, optimize the neighborhood radius (ε), minimum number of samples (MinPts), or Z-score threshold of DBSCAN; Scene adaptation: For different types of spectral data (such as water bodies, biological samples), verify the universality of the algorithm with the SNR as an index.

[0062] In summary, the SNR formula serves as the quantization core of the denoising effect in the present invention. It not only provides an objective technical verification means but also constructs a complete technical chain of "algorithm optimization - effect evaluation - scene adaptation". The present invention innovatively integrates physical optical characteristics and machine learning decision models, and realizes multi-dimensional cross-verification of abnormal regions through a three-level collaborative filtering architecture (physical noise reduction - spatial clustering - statistical verification).

[0063] The following is a supplementary explanation of the professional terms not detailed in the present invention, which is explained in combination with the well-known meanings in the field: 1. DBSCAN (Density-Based Spatial Clustering of Applications with Noise) Full name: Density-Based Spatial Clustering of Applications with Noise Meaning: An unsupervised machine learning algorithm that identifies clustering clusters and noise points through the local density of data points. The core idea is that if the density of data points in a certain area exceeds a set threshold (defined by the neighborhood radius ε and the minimum number of samples MinPts), it is regarded as an effective signal cluster where the "core points" are located; otherwise, points with sparse density are judged as noise points.

[0064] Application in the present invention: Identify scattered noise points with abnormal density in the three-dimensional spectral space through DBSCAN (such as isolated Raman scattering peaks).

[0065] 2. Z-score (Standard Score) Meaning: An index in statistics used to measure the degree of deviation of a single data point from the mean of a data set. The calculation formula is:

[0066] where X is the data point value, is the mean, is the standard deviation. The larger the absolute value of the Z-score, the more the data point deviates from the mean.

[0067] Application of the present invention: Set a threshold (such as |Z|>3) to identify outliers in a statistical sense, as a secondary verification of the DBSCAN clustering result, and eliminate misjudged noise points.

[0068] 3. Rayleigh Scattering Meaning: Elastic scattering that occurs when light interacts with matter molecules. The wavelength of the scattered light is the same as that of the incident light. In fluorescence spectroscopy, Rayleigh scattering appears as a high-intensity narrow peak along the excitation wavelength axis and is one of the main sources of noise.

[0069] Scenario of the present invention: As a typical scattering noise in 3D fluorescence spectroscopy, it is identified and eliminated through the collaborative mechanism of DBSCAN and Z-score.

[0070] 4. Raman Scattering Meaning: Inelastic scattering that occurs when light interacts with matter molecules. The wavelength of the scattered light is shifted due to the difference in molecular vibration energy levels. In fluorescence spectroscopy, Raman scattering appears as a broad peak with a fixed offset from the excitation wavelength, interfering with the extraction of effective signals.

[0071] Scenario of the present invention: Belongs to the "density-sparse outliers" that are mainly identified by the DBSCAN algorithm and are eliminated through double detection of spatial density and statistical distribution.

[0072] 5. Cubic Spline Interpolation Meaning: A piecewise interpolation method that fits data points through cubic polynomial functions to ensure the continuity of the first and second derivatives between adjacent segments, thus achieving a smooth transition.

[0073] Application of the present invention: Bidirectional cubic spline interpolation (along the excitation wavelength axis and the emission wavelength axis) is used to fill the gaps in the denoised spectral data, restore the surface continuity, and avoid mutations or distortions that may be introduced by linear interpolation.

[0074] 6. Signal-to-Noise Ratio (SNR) Meaning: A quantitative index that measures the ratio of the effective component to the noise in a signal. The calculation formula is:

[0075] The unit is decibel (dB), and the larger the value, the higher the signal quality; A is the signal energy; B is the noise energy.

[0076] Application of the present invention: As the core index for evaluating the denoising effect, by calculating the ratio of signal energy to noise energy, objectively verify the ability of the algorithm to suppress scattered noise and signal fidelity.

[0077] 7. Core Point / Border Point / Noise Point - DBSCAN algorithm terms: Core Point: A point that contains at least MinPts data points within the neighborhood radius ε, belonging to the core region of a high-density signal cluster; Border Point: A point that is located within the neighborhood of a core point but has less than MinPts data points in its own neighborhood, belonging to the edge of the signal cluster; Noise Point: A point that is neither a core point nor a border point, belonging to the low-density noise region.

[0078] Application of the present invention: By determining the type of data points (core point / noise point), accurately distinguish effective signals from scattered noise.

[0079] 8. 3D Fluorescence Spectroscopy: A spectroscopic technique that can obtain information such as the structure, composition, and properties of a substance by detecting and analyzing the fluorescence emission of the substance in three-dimensional space. It usually includes three dimensions: excitation light wavelength, emission light wavelength, and fluorescence intensity, and can more comprehensively reflect the optical properties of the substance, and is widely used in environmental monitoring, biomedicine, materials science and other fields.

[0080] 9. Scattered Noise: During the spectral measurement process, the non-fluorescent signals generated due to the interaction between light and matter will interfere with the accurate measurement of the fluorescence spectrum, and this interference signal is called scattered noise. Common scattered noises include Rayleigh scattering and Mie scattering, etc., which will cause problems such as an increase in the background signal of spectral data and spectral line broadening, affecting the accuracy and reliability of spectral analysis.

[0081] Embodiment 2 Based on the same concept, the present invention also proposes a 3D fluorescence spectrum scattered noise automatic removal device, including: Data Preprocessing Module: Used to load the original spectral data and perform normalization processing to construct a three-dimensional data structure; Among them, this module first preprocesses the input spectral data to eliminate the deviation caused by inconsistent data scales or other external factors, and provides a standardized input for subsequent de-scattering processing.

[0082] Noise detection module: Integrates the DBSCAN clustering algorithm and the Z-score verification algorithm to identify and mark scattered noise points; Among them, this module uses the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm to perform clustering analysis on the preprocessed data. By analyzing the local density distribution of the data in the three-dimensional spectral space, it effectively identifies and removes low-density noise point regions, realizing the preliminary identification and automatic removal of scattered noise. This algorithm has the advantages of not requiring the specification of the number of clusters and being sensitive to irregular distributions, and is suitable for the identification of scattered anomalies under complex backgrounds. On the basis of preliminary denoising, to further remove potential residual anomaly points, a statistical detection method based on Z-score is introduced. This module quantitatively identifies and removes outliers in the spectral matrix by measuring the degree to which the data deviates from the overall mean, supplements possible omissions in the density clustering method in regions with blurred boundaries, and improves the accuracy and integrity of overall noise removal.

[0083] Interpolation filling module: Based on the cubic spline interpolation algorithm, performs surface reconstruction on the spectral data after noise removal; Among them, this module uses the cubic spline interpolation technique to reconstruct and smooth the missing regions caused by denoising, restoring the continuity and structural consistency of the spectral surface. This module ensures that the denoising process does not introduce new mutations or distortions, while improving the visualization effect of the spectral data and the stability of subsequent analysis.

[0084] Verification and output module: Evaluates the denoising effect through the signal-to-noise ratio index and outputs the optimized spectral data.

[0085] Embodiment 3 This embodiment also provides an electronic device, referring to Figure 6 , including a memory 404 and a processor 402. The memory 404 stores a computer program, and the processor 402 is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0086] Specifically, the above-mentioned processor 402 may include a central processing unit (CPU), or a specific integrated circuit (Application Specific Integrated Circuit, abbreviated as ASIC), or may be configured with one or more integrated circuits implementing the embodiments of the present invention.

[0087] Among them, the memory 404 may include a mass storage 404 for data or instructions. By way of example and not limitation, the memory 404 may include a hard disk drive (HDD), a floppy disk drive, a solid state drive (SSD), a flash memory, an optical disc, a magneto-optical disc, a magnetic tape, or a universal serial bus (USB) drive, or a combination of two or more of these. In appropriate cases, the memory 404 may include removable or non-removable (or fixed) media. In appropriate cases, the memory 404 may be internal or external to the data processing device. In a particular embodiment, the memory 404 is non-volatile memory. In a particular embodiment, the memory 404 includes a read-only memory (ROM) and a random access memory (RAM). In appropriate cases, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically alterable ROM (EAROM), or a flash memory, or a combination of two or more of these. In appropriate cases, the RAM may be a static random access memory (SRAM) or a dynamic random access memory (DRAM), where the DRAM may be a fast page mode dynamic random access memory (FPMDRAM), an extended date out dynamic random access memory (EDODRAM), a synchronous dynamic random access memory (SDRAM), etc.

[0088] The memory 404 can be used to store or cache various data files required for processing and / or communication, as well as possible computer program instructions executed by the processor 402.

[0089] By reading and executing the computer program instructions stored in the memory 404, the processor 402 implements any one of the 3D fluorescence spectrum scattering noise automatic removal methods in the above embodiments.

[0090] Optionally, the above electronic device may further include a transmission device 406 and an input / output device 408. Among them, the transmission device 406 is connected to the above processor 402, and the input / output device 408 is connected to the above processor 402.

[0091] The transmission device 406 can be used to receive or send data via a network. Specific examples of the above network may include wired or wireless networks provided by the communication provider of the electronic device. In one example, the transmission device includes a Network Interface Controller (NIC), which can be connected to other network devices through a base station and thus communicate with the Internet. In one example, the transmission device 406 can be a Radio Frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0092] The input / output device 408 is used to input or output information.

[0093] Embodiment 4 This embodiment also provides a readable storage medium, in which a computer program is stored. The computer program includes program code for controlling a process to execute the process, and the process includes the 3D fluorescence spectrum scattering noise automatic removal method according to Embodiment 1.

[0094] It should be noted that the specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementation manners, and are not described herein again.

[0095] Generally, various embodiments can be implemented in hardware or dedicated circuits, software, logic, or any combination thereof. Some aspects of the present invention can be implemented in hardware, while other aspects can be implemented by firmware or software executed by a controller, a microprocessor, or other computing devices, but the present invention is not limited thereto. Although various aspects of the present invention can be shown and described as block diagrams, flowcharts, or using some other graphical representations, it should be understood that, as a non-limiting example, the blocks, devices, systems, technologies, or methods described herein can be implemented in hardware, software, firmware, dedicated circuits or logic, general hardware or a controller, or other computing devices, or some combination thereof.

[0096] Embodiments of the present invention can be implemented by computer software, which is executable by a data processor of a mobile device, such as in a processor entity, or by hardware, or by a combination of software and hardware. A computer software or program (also referred to as a program product), including software routines, applets, and / or macros, can be stored in any device-readable data storage medium, and they include program instructions for performing specific tasks. The computer program product can include one or more computer-executable components that are configured to perform the embodiments when the program runs. The one or more computer-executable components can be at least one software code or a part thereof. Additionally, in this regard, it should be noted that any box in the logical flow, as Figure 1 shown in [reference], can represent a program step, or interconnected logic circuits, boxes, and functions, or a combination of program steps and logic circuits, boxes, and functions. The software can be stored on physical media such as memory chips or storage blocks implemented within a processor, magnetic media such as hard disks or floppy disks, and optical media such as, for example, DVDs and their data variants, CDs. The physical media are non-transitory media.

[0097] Those skilled in the art should understand that the technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered to be within the scope described in this specification.

[0098] The above embodiments merely represent several implementation manners of the present invention, and their descriptions are relatively specific and detailed. However, they should not be construed as limiting the scope of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the appended claims.

Claims

1. An automatic removal method for 3D fluorescence spectrum scattering noise, characterized in that, It includes the following steps: Preprocess the original 3D fluorescence spectral data to construct a three-dimensional data structure of excitation wavelength, emission wavelength, and fluorescence intensity; Based on the spatial density distribution of the three-dimensional data structure, use the DBSCAN clustering algorithm to identify potential scattered noise points; Based on the statistical distribution characteristics, use the Z-score method to perform a secondary verification on the clustering results to determine the final set of noise points; Interpolate and reconstruct the spectral data after removing noise to restore the continuity of the spectral surface; Evaluate the denoising effect through quantitative indicators.

2. The automatic removal method of 3D fluorescence spectrum scattering noise according to claim 1, characterized in that The preprocessing step includes: converting the original spectral data into a three-dimensional matrix and eliminating the instrument response difference through Z-score normalization. The formula is: ； Among them, is the global mean of the fluorescence intensity, representing the central tendency of the data; is the standard deviation of the fluorescence intensity, representing the degree of data dispersion; represents the excitation wavelength and the emission wavelength at the intersection of the fluorescence intensity value; is the standard score.

3. The automatic removal method for 3D fluorescence spectrum scattering noise according to claim 1, wherein, The DBSCAN clustering algorithm includes: Reconstruct the standardized three-dimensional data points into a spatial point set, and identify the noise point regions with sparse density by setting the neighborhood radius and the minimum number of samples.

4. The automatic removal method of 3D fluorescence spectrum scattering noise according to claim 3, characterized in that, The initial value of the neighborhood radius is 0.1, and the initial value of the minimum number of samples is 5, and they can be adaptively adjusted according to the data characteristics.

5. The automatic removal method of 3D fluorescence spectrum scattering noise according to claim 1, characterized in that The Z-score secondary verification includes: Calculate the absolute value of the Z-score of each data point, and determine the points with absolute value greater than the preset threshold as statistically abnormal points. The preset threshold is 3.

6. The automatic removal method for 3D fluorescence spectrum scattering noise according to claim 1, wherein The interpolation reconstruction uses bicubic spline interpolation, and performs piecewise interpolation along the excitation wavelength axis and the emission wavelength axis to ensure smooth transition of the spectral surface.

7. A method for automatically removing scattering noise from 3D fluorescence spectra according to any one of claims 1-6, characterized in that, The quantitative indicator is the signal-to-noise ratio, and the denoising effect is evaluated by calculating the ratio of the signal energy to the noise energy.

8. An automatic removal device for 3D fluorescence spectrum scattering noise, characterized in that, It includes: Data preprocessing module: used to load the original spectral data and perform normalization processing to construct a three-dimensional data structure; Noise detection module: integrating the DBSCAN clustering algorithm and the Z-score verification algorithm to identify and mark scattered noise points; Interpolation filling module: based on the cubic spline interpolation algorithm, perform surface reconstruction on the spectral data after removing noise; Verification output module: evaluate the denoising effect through the signal-to-noise ratio index and output the optimized spectral data.

9. An electronic device, comprising a memory and a processor, characterized in that, The memory stores a computer program, and the processor is set to run the computer program to execute the automatic removal method of 3D fluorescence spectral scattered noise according to any one of claims 1 to 7.

10. A readable storage medium, characterized in that, The readable storage medium stores a computer program, and the computer program includes program codes for controlling a process to execute the process, and the process includes the automatic removal method of 3D fluorescence spectral scattered noise according to any one of claims 1 to 7.

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