Method and system for detecting concentration of trace metal ions

Abstract

The invention relates to the technical field of spectral analysis, and discloses a method and a system for detecting the concentration of trace metal ions. The method comprises the following steps: acquiring original spectral response data of an electronic smoke sample; carrying out signal separation by utilizing a frequency domain feature extraction technology to obtain an initial spectrum data set; performing background noise filtering and baseline drift correction on the initial data to obtain a refined spectrum data set; constructing a concentration-wavelength mapping relation through absorption peak positioning and characteristic wave band screening on the basis of the refined spectrum data set; carrying out continuous time sequence sampling on the samples based on the mapping relation to generate a concentration dynamic change data set; analyzing a concentration fluctuation rule and an abnormal point in the dynamic data, and generating concentration risk assessment data; and finally outputting a time sequence detection file. According to the method, high-precision dynamic monitoring and intelligent risk assessment of trace metal ion concentration are realized, interference is effectively inhibited, and sensitivity and early warning capability are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of spectral analysis, and in particular to a method and system for detecting trace metal ion concentration. Background Art

[0002] As an emerging consumer product, e-cigarettes are attracting increasing public attention for their safety, particularly regarding potential health and environmental risks, which have become a critical public health issue. The potential presence of trace amounts of hazardous substances in e-cigarette smoke, such as metal ions, poses a significant threat to human health. Researching and monitoring the concentrations of these substances has become crucial for ensuring user safety.

[0003] In one existing technology, electronic smoke particles within a fixed period of time are collected through a filter membrane. After the sample is pre-processed, the total metal ion concentration is measured once using atomic absorption spectroscopy, and a test report is output based on the static data.

[0004] However, single offline sampling cannot capture the continuous changes in concentration over time, leading to inaccurate exposure risk assessments in actual use scenarios. Furthermore, the time between sampling and laboratory analysis is too long, making it difficult to provide real-time risk warnings. Therefore, existing technologies have a limited ability to capture the temporal fluctuations in metal ion concentrations in real time. Summary of the Invention

[0005] The present invention relates to the field of spectral analysis technology, and in particular to a method and system for detecting trace metal ion concentrations, in order to solve the problem that the prior art has a low ability to capture the temporal fluctuation characteristics of metal ion concentrations in real time.

[0006] In a first aspect, in order to solve the above technical problems, the present invention provides a method for detecting trace metal ion concentration, comprising: Obtaining raw spectral response data of electronic smoke samples; Preprocessing the raw spectral response data to obtain a refined spectral data set; performing concentration gradient calibration on the refined spectral data set to obtain stable mapping relationship data between metal ion concentration and wavelength; Based on the stable mapping relationship data, the samples are continuously spectrally scanned at different time points to obtain a dynamic change data set of metal ion concentration fluctuations over time; According to the dynamic change data set, combined with the historical record of signal intensity quantification, the concentration fluctuation law is analyzed to obtain the time series trend characteristic data of the metal ion concentration; According to the time series trend characteristic data, the concentration exceeding risk point is determined to obtain the abnormal fluctuation determination result; Based on the abnormal fluctuation determination results, continuously adjust the risk warning threshold deviation range to determine the final concentration risk assessment data; Based on the final concentration risk assessment data, a dynamic detection report for the metal ion concentration in the electronic smoke is generated to obtain a time series detection file.

[0007] In an optional implementation, obtaining the original spectral response data includes: The initial spectrum of the electronic smoke sample is collected by a spectrum scanning device to obtain the original spectrum response data within the wavelength range of metal ion absorption.

[0008] In an optional embodiment, the preprocessing of the original spectral response data to obtain a refined spectral data set includes: Performing denoising on the original spectral response data to obtain a denoised first spectral data set; Correcting and adjusting the baseline drift in the first spectral data set to obtain a corrected second spectral data set; performing smoothing repair on the second spectral data set to obtain a third spectral data set; The third spectral data set is matched and classified with a preset characteristic interval template to determine a final refined spectral data set.

[0009] In an optional embodiment, performing concentration gradient calibration on the refined spectral data set to obtain stable mapping relationship data between metal ion concentration and wavelength includes: performing signal enhancement adjustment on the refined spectral data set to obtain a first signal set; Extracting characteristic bands from the first signal set and performing calibration to obtain a second signal set; performing hierarchical processing on the signal strengths of the second signal set to obtain a third signal set; The wavelength ranges and concentration gradients in the third signal set are sorted, and the core data are classified to obtain the stable mapping relationship data.

[0010] In an optional embodiment, the method of performing continuous spectral scanning on sample data at different time points based on the stable mapping relationship data to obtain a dynamic change data set of metal ion concentration fluctuations over time includes: extracting spectral signal data from the sample data based on the stable mapping relationship data to obtain a first spectral signal set; performing calibration processing on the first spectral signal set to determine a calibrated second spectral signal set; According to the second set of spectral signals, the spectral signal data is processed in segments, wave characteristic data is obtained and classified, and a third set of spectral signals is obtained. The concentration fluctuations and time changes in the third set of spectral signals are integrated and matched to determine the dynamic change data set.

[0011] In an alternative embodiment, according to the dynamic change data set, the concentration fluctuation law is analyzed in combination with the historical record of signal intensity quantification, and the time trend characteristic data of metal ion concentration is obtained, including: The concentration fluctuation data in the dynamic change data set is processed in segments to obtain an adjusted first fluctuation data set. The first fluctuation data set is smoothed and corrected to obtain a second fluctuation data set. The second fluctuation data set is classified to obtain a third fluctuation data set. The third fluctuation data set is extracted to determine the time trend characteristic data of metal ions.

[0012] In an alternative embodiment, the time trend characteristic data is used to determine concentration over-limit risk points to obtain an abnormal fluctuation determination result, including: The time trend characteristic data is processed in layers and labeled to obtain a first fluctuation segment set. The non-linear deviation in the first fluctuation segment set is corrected and adjusted to obtain a second fluctuation segment set. The second fluctuation segment set is classified to obtain a third fluctuation segment set. The third fluctuation segment set is identified and processed for abnormal fluctuation to obtain an abnormal fluctuation determination result.

[0013] In an alternative embodiment, according to the abnormal fluctuation determination result, the threshold range of the risk prompt is continuously adjusted to determine the final concentration risk assessment data, including: According to the abnormal fluctuation determination result, the time segment of the metal ion concentration fluctuation is extracted and classified to obtain an initial fluctuation data set. The initial fluctuation data set is suppressed and real-time calibrated to determine a fluctuation law data set. If the concentration value in the fluctuation law data set exceeds the preset concentration threshold, the risk prompt threshold is dynamically adjusted to obtain an adjusted risk prompt threshold. According to the adjusted risk prompt threshold, the metal ion concentration risk is comprehensively mapped with the fluctuation law data set to obtain concentration risk assessment data.

[0014] In an optional embodiment, generating a dynamic detection report for the metal ion concentration in the electronic smoke based on the final concentration risk assessment data to obtain a time series detection file includes: Segmentally processing and dynamically refreshing the concentration risk assessment data to obtain a preliminary concentration fluctuation data set; Performing absorption peak location and identification on the preliminary concentration fluctuation data set to obtain abnormal point distribution characteristic information; Adjusting the concentration gradient calibration according to the abnormal point distribution characteristic information to generate concentration risk grading data; Generate a dynamic detection report based on the concentration risk classification data and obtain a time series detection file; The dynamic detection report includes the fluctuation pattern of metal ion concentration in electronic smoke over time, the distribution of abnormal points located by absorption peaks, and risk interval information calibrated by concentration gradients.

[0015] In a second aspect, the present invention provides a device for detecting trace metal ion concentration, comprising: The original spectrum data acquisition module obtains the original spectrum response data of the electronic smoke sample; A spectral data preprocessing module, which preprocesses the raw spectral response data to obtain a refined spectral data set; a concentration gradient calibration module, performing concentration gradient calibration on the refined spectral data set to obtain stable mapping relationship data between metal ion concentration and wavelength; A dynamic concentration monitoring module, based on the stable mapping relationship data, continuously scans the spectra of samples at different time points to obtain a dynamic change data set of metal ion concentration fluctuations over time; A time series trend analysis module analyzes the concentration fluctuation pattern based on the dynamic change data set and the historical record of signal intensity quantification to obtain the time series trend characteristic data of the metal ion concentration; The module for determining the risk of exceeding the standard is used to determine the risk point of exceeding the standard concentration based on the time series trend characteristic data and obtain the abnormal fluctuation determination result; A risk assessment optimization module continuously adjusts the risk warning threshold range based on the abnormal fluctuation determination result to determine the final concentration risk assessment data; The dynamic detection report generation module generates a dynamic detection report for the metal ion concentration in the electronic smoke according to the final concentration risk assessment data, and obtains a time series detection file.

[0016] In a third aspect, the present invention also provides an electronic device comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor implements any one of the above-described trace metal ion concentration methods when executing the computer program.

[0017] In a fourth aspect, the present invention also provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute any one of the trace metal ion concentration methods described above.

[0018] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention uses frequency domain feature extraction technology to perform signal separation on the initial spectrum, which can effectively distinguish the target metal ion signal from background interference of specific frequencies. Background noise filtering directly removes irrelevant high-frequency noise, and baseline drift correction eliminates low-frequency baseline offset caused by external factors, significantly improving the anti-interference ability of trace metal ion concentration detection, thereby extracting purer and more reliable core spectral information, laying a solid foundation for subsequent precise analysis.

[0019] 2. The present invention uses a spectral analysis method to locate the absorption peak and screen the characteristic band, combined with signal intensity quantification and signal enhancement technology, to accurately identify and amplify trace signal characteristics, and to establish a stable mapping relationship between metal ion concentration and a specific wavelength range, thereby establishing a reliable quantitative benchmark. Continuous spectral scanning is performed on samples at different time points, and the target area is locked using the established wavelength mapping relationship to directly obtain a spectral response sequence reflecting the concentration change, solving the problems of unstable concentration-wavelength correspondence and weak dynamic change capture ability in the prior art. This allows a substantial breakthrough in the dynamic monitoring accuracy of trace metal ion concentrations, and can accurately reflect the true situation of concentration fluctuations over time in real time.

[0020] 3. This invention uses time series processing to analyze concentration fluctuations, identifying periodic characteristics and potential anomaly patterns. It also compares historical data with benchmarks and applies nonlinear bias correction to improve the accuracy of outlier identification. It also generates risk warning information and, combined with a time-series update mechanism and real-time calibration for dynamic datasets, continuously and dynamically adjusts the risk warning threshold range to ensure the adaptability of early warnings. Ultimately, this creates a closed-loop risk identification, assessment, and early warning mechanism, significantly improving the risk assessment capabilities and practical value of time-series metal ion concentration detection archives, and providing strong support for timely early warning and decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 1 is a schematic flow chart of a trace metal ion concentration method provided by an embodiment of the present invention; Figure 2 It is a structural schematic diagram of a trace metal ion concentration monitoring system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0022] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0023] Reference Figure 1 The first embodiment of the present invention provides a method for determining the concentration of trace metal ions, comprising the following steps: S11, obtaining the original spectral response data of the electronic smoke sample; S12, preprocessing the original spectral response data to obtain a refined spectral data set; S13, performing concentration gradient calibration on the refined spectral data set to obtain stable mapping relationship data between metal ion concentration and wavelength; S14, based on the stable mapping relationship data, continuously scanning the spectra of the samples at different time points to obtain a dynamic change data set of metal ion concentration fluctuations over time; S15, analyzing concentration fluctuation patterns based on the dynamically changing data set and combining historical records of signal intensity quantification to obtain time series trend characteristic data of metal ion concentration; S16, judging the concentration exceeding risk point based on the time series trend characteristic data, and obtaining an abnormal fluctuation determination result; S17, continuously adjusting the risk warning threshold deviation range based on the abnormal fluctuation determination result to determine the final concentration risk assessment data; S18, generating a dynamic detection report for the metal ion concentration in the electronic smoke based on the final concentration risk assessment data, and obtaining a time series detection file.

[0024] In step S11, the original spectral response data of the electronic smoke sample is obtained, including: The initial spectrum of the electronic smoke sample is collected by a spectrum scanning device to obtain the original spectrum response data within the wavelength range of metal ion absorption.

[0025] It should be noted that a spectral scanning device is an instrument capable of measuring the absorption, emission, or scattering intensity of light of different wavelengths or frequencies. It directs light from a light source through an electronic smoke sample. A spectroscopic system then disperses the transmitted, emitted, or scattered light by wavelength. Finally, a detector measures the light intensity at each discrete wavelength. Raw spectral response data refers to the initial data set containing all measurement information that is directly output by the spectral scanning device after completing the above acquisition process, without any processing or correction.

[0026] In step S12, the raw spectral response data is preprocessed to obtain a refined spectral data set, including: S121, performing denoising processing on the original spectral response data to obtain a denoised first spectral data set; S122, correcting and adjusting the baseline drift in the first spectral data set to obtain a corrected second spectral data set; S123, performing smoothing repair on the second spectral data set to obtain a third spectral data set; S124: Match and classify the third spectral data set with a preset characteristic interval template to determine a final refined spectral data set.

[0027] In step S121 , the original spectral response data is subjected to denoising processing to obtain a first spectral data set after denoising.

[0028] It should be noted that the denoising process performed on the acquired raw spectral response data involves filtering out background noise from the spectral data and screening the noise signal. If the detected noise component exceeds a preset interference frequency threshold, it is removed to obtain a denoised first spectral data set. The interference frequency threshold refers to the critical value boundary used to distinguish noise components from valid signal components.

[0029] In step S122 , the baseline drift in the first spectral data set is corrected and adjusted to obtain a corrected second spectral data set.

[0030] It should be noted that baseline drift refers to the slow, irregular upward and downward shift of the overall baseline of the spectral response data during spectral signal acquisition. This is typically caused by long-term instrument operation or changes in ambient temperature. This embodiment uses a preset instrument baseline standard value as the correction standard for baseline drift in the first spectral data set. This standard value is set by performing multiple scans using a blank control and obtaining an average value during the spectrometer initialization phase. By comparing the baseline standard value, the baseline drift in the first spectral data set is linearly interpolated and adjusted to determine the corrected second spectral data set.

[0031] In a feasible embodiment, there is a signal frequency component in the first spectral data set at around 50 Hz, and its baseline shows an obvious overall upward drift, which is judged to be a power supply interference signal. After correcting and adjusting the baseline drift in the signal data, a second spectral data set containing preliminary characteristics of metal ions is obtained, which significantly improves the purity of the data and helps to more accurately identify the target signal.

[0032] In step S123 , smoothing and repairing is performed on the second spectral data set to obtain a third spectral data set.

[0033] It should be noted that smoothing and repairing is the core operation for correcting the waveform distortion caused by low-frequency interference in spectral data. For the second spectral data set, it is necessary to obtain the signal components related to the low-frequency interference, and first repair the waveform distortion in the signal components. If it is detected that the interference peak signal intensity exceeds the critical intensity of the valid signal, it is eliminated and suppressed to obtain the third spectral data set after smoothing and repairing.

[0034] In one possible implementation, when processing low-frequency interference in the second data set, the waveform distortion caused by the low-frequency interference can be smoothed and repaired. Low-frequency interference may originate from vibrations within the instrument or slight fluctuations in the external environment. When the signal intensity of a certain interference peak is detected to be 0.3 absorbance units, exceeding the critical intensity of 0.1 absorbance units for a valid signal, and waveform distortion occurs, this portion of the signal is eliminated and suppressed, resulting in a smoothed and repaired third data set. This processing method can make the data curve smoother, facilitating subsequent feature extraction.

[0035] In step S124 , the third spectral data set is matched and classified with a preset characteristic interval template to determine a final refined spectral data set.

[0036] It should be noted that matching and classification involves extracting core information related to metal ion concentration from the third spectral data set, including the peak position, peak shape similarity, and half-peak width ratio of the data signal waveform. This information is then matched against a preset characteristic interval template, and the signal data that matches the template features is integrated and classified again. The preset characteristic interval template is a predefined key parameter that serves as a framework for identifying the wavelength range of the target metal ion's characteristic absorption peak.

[0037] In one embodiment, there may be multiple interference sources in the sample. The characteristic interval template is preset in advance with an absorption peak wavelength range of 248 to 252 nanometers, and the target characteristic absorption wavelength of metal ions is 250 nanometers. The process can be gradually advanced from background noise filtering to feature information extraction. If the detected signal has an obvious response within the characteristic interval template, the relevant information will be integrated and classified to determine the final refined data set.

[0038] In step S13, the refined spectral data set is subjected to concentration gradient calibration to obtain stable mapping relationship data between metal ion concentration and wavelength, including: S131, performing signal enhancement adjustment on the refined spectral data set to obtain a first signal set; S132, extracting characteristic bands from the first signal set and performing calibration to obtain a second signal set; S133, performing hierarchical processing on the signal strengths of the second signal set to obtain a third signal set; S134 , sorting the wavelength ranges and concentration gradients in the third signal set, classifying the core data, and obtaining the stable mapping relationship data.

[0039] In step S131 , signal enhancement adjustment is performed on the refined spectral data set to obtain a first signal set.

[0040] It should be noted that when processing the refined spectral data set, it is necessary to identify and preliminarily scan the signal features related to the absorption peak. If the signal peak intensity is lower than the critical intensity of the valid signal, the signal is enhanced and adjusted. By amplifying the weak signal intensity and suppressing noise amplification, the enhanced first signal set is obtained.

[0041] In one implementation, signal features associated with absorption peaks are identified and initially scanned, yielding a signal peak intensity of 0.2 absorbance units. While the critical intensity range for a valid signal is 0.5 to 1.0 absorbance units, the signal intensity is low, potentially affecting the accuracy of subsequent analysis. This signal is then amplified, for example by amplifying weak signal intensities and adjusting them to within 0.6 absorbance units, forming an enhanced first signal set. This approach helps ensure signal readability and provides a more reliable data foundation for subsequent steps.

[0042] In step S132, characteristic bands in the first signal set are extracted and calibrated to obtain a second signal set.

[0043] It should be noted that calibrating the characteristic wavelength bands in the first signal set forcibly aligns the original signal wavelength distribution to within a preset interval standard. The characteristic wavelength bands of the first signal set are first extracted and processed to obtain waveform data related to the trace signal. Data that does not meet the preset interval standard is normalized to determine the calibrated second signal set. The preset interval standard is the core criterion for defining the effective distribution range of the trace signal and is essentially a wavelength boundary specification for the characteristic wavelength band of the target metal ion.

[0044] In one embodiment, the preset range is 200 to 300 nanometers, while the detected waveform data distribution range is 180 to 320 nanometers, exceeding the preset range. In this case, characteristic wavelength bands are extracted from the signal set and normalized and calibrated to adjust the distribution range to meet the range, thus forming a second signal set. This processing method can unify the data distribution characteristics, facilitating subsequent feature comparison and analysis.

[0045] In step S133, the signal strengths of the second signal set are classified to obtain a third signal set.

[0046] It should be noted that the signal strengths can be quantitatively classified based on the second signal set to obtain a signal distribution feature related to the concentration gradient, and the distribution feature is compared with a pre-established mapping template to determine the signal strength level corresponding to the wavelength interval to obtain the classified third signal set. The concentration gradient refers to a discretized concentration scale established by signal strength classification, and its essence is a technical means for mapping continuous spectral signal strength to discrete concentration intervals. The pre-established mapping template is a quantitative conversion model that accurately correlates signal strength with concentration gradient, and its essence is a discretized scale of spectral response-concentration relationship.

[0047] In an implementation, the signal strength is classified into three levels, namely low, medium and high, corresponding to concentration gradients of 1, 5 and 10 micrograms per liter respectively. Through the pre-established mapping template, the signal strength can be corresponded to the wavelength interval, for example, the signal strength at a wavelength of 250 nanometers belongs to the high level, corresponding to a concentration of 10 micrograms per liter, and finally forming the classified third signal set. This classification method helps to clearly show the relationship between signal and concentration.

[0048] In step S134, the wavelength interval and the concentration gradient in the third signal set are sorted to classify core data to obtain the stable mapping relationship data.

[0049] It should be noted that the sorting of the wavelength interval and the concentration gradient in the third signal set needs to traverse each data point in the third signal set, each data point contains wavelength and signal strength at that point, and the signal strength value at a specific wavelength is associated with the concentration gradient of the measured substance. The sorted data is distributed to the pre-defined "core response feature" wavelength interval according to its wavelength value, for example, a signal wavelength of 250 nm has a significantly enhanced signal strength, and this enhancement is related to high concentration of the substance, which is classified as a high concentration response area; the signal strength is relatively weak, which is classified as a low concentration response area. Data points belonging to the same wavelength interval are considered to share the same concentration response feature, which completes the classification of core data. The core data refers to the data points in the key wavelength interval, such as the high and low concentration response areas mentioned above, which can most clearly and stably reflect the concentration change trend of the substance.

[0050] In one implementation, the data of the third signal set can first be sorted out to find the correspondence between the wavelength range and the concentration gradient. Let the signal intensity at the signal wavelength of 250 nanometers correspond to the high concentration response area. The signal intensity of the data points falling within this wavelength range mainly reflects the high concentration level. At 260 nanometers, it corresponds to the low concentration response area. The signal intensity of the data points falling within this wavelength range mainly reflects the low concentration. Data points belonging to the same wavelength range are considered to share the same concentration response characteristics, which completes the classification of the core data and ultimately determines the mapping relationship data set. This classification process can simplify complex data into clear correspondences, providing convenience for subsequent applications.

[0051] It should be noted that in the above steps, signal enhancement and data normalization may require parameter adjustment based on the specific instrument characteristics. The signal enhancement ratio will vary under different light source intensities, and the operator can determine the optimal enhancement factor through multiple tests. This flexibility allows for adaptability to various detection environments and ensures data stability.

[0052] In step S14, based on the stable mapping relationship data, the samples at different time points are continuously spectrally scanned to obtain a dynamic change data set of metal ion concentration fluctuations over time, including: S141, extracting spectral signal data from the sample data based on the stable mapping relationship data to obtain a first spectral signal set; S142, performing calibration processing on the first spectral signal set to determine a calibrated second spectral signal set; S143, performing segmentation processing on the spectral signal data according to the second spectral signal set, obtaining and classifying fluctuation characteristic data, and obtaining a third spectral signal set; S144 , integrating and matching the concentration fluctuations and time changes in the third spectral signal set to determine the dynamic change data set.

[0053] In step S141 , spectral signal data in the sample data is extracted based on the stable mapping relationship data to obtain a first spectral signal set.

[0054] It should be noted that during the electronic smoke sample collection process, sample data acquired at different time points is continuously scanned and processed to extract spectral signal data related to the wavelength range. Signal data with intensities below a preset dynamic signal threshold is then enhanced to produce an adjusted first spectral signal set. The preset dynamic signal threshold is a critical criterion for determining the minimum intensity of a valid signal; its essence is the intensity boundary that distinguishes a recognizable signal from background noise.

[0055] In one embodiment, a specific scanning frequency can be set to analyze samples collected at different time points one by one. If the scanning frequency is set to once per minute, the collected spectral signal data will show some fluctuation within the wavelength range of 200 to 300 nanometers. If the signal intensity is only 0.15 absorbance units, which is lower than the minimum signal intensity of 0.3 absorbance units, signal enhancement can be performed to adjust the intensity to 0.35 absorbance units to form the first spectral signal set. This approach ensures signal identifiability and lays the foundation for subsequent processing.

[0056] In step S142 , calibration processing is performed on the first spectral signal set to determine a calibrated second spectral signal set.

[0057] It should be noted that calibration is a core step in resolving signal distribution shifts caused by wavelength drift. Its essence is to align the measured spectrum to the standard wavelength range through data transformation to ensure the accuracy of subsequent analysis. The first spectral signal set is divided into signal sub-ranges, classified into target positioning areas (containing absorption peaks and matching standard features); offset calibration areas (where the signal is significantly non-zero but outside the standard range); and invalid noise areas (where the signal intensity is close to the baseline). The target positioning area corresponding to the absorption peak is then identified, and data that does not conform to the standard wavelength range is normalized and calibrated to determine the calibrated second spectral signal set.

[0058] In one implementation, the signal data in the first spectral signal set is divided into multiple sub-ranges. The standard wavelength range is set at 240 to 260 nanometers, but the actual signal distribution range is 230 to 270 nanometers, exceeding the standard wavelength range. After normalization, the signal distribution outside the target region is adjusted to meet the required range, forming the second spectral signal set. This process helps unify data standards and facilitates subsequent feature extraction.

[0059] In step S143, based on the second spectral signal set, the spectral signal data is segmented to obtain and classify the fluctuation characteristic data to obtain a third spectral signal set.

[0060] It should be noted that the segmented processing of spectral signal data is divided according to the time dimension. First, time series analysis is performed, and time periods are divided according to preset key time nodes or signal mutation points to obtain fluctuation characteristic data related to the response sequence. After obtaining the fluctuation characteristic data, classification is performed based on the association rules between the fluctuation characteristics and changes in metal ion concentration. For example, fluctuation characteristics with high amplitude and steep slope, and sudden decrease or increase in concentration changes are classified as mutation response type. Fluctuation characteristic data is a set of key indicators extracted from the time dimension segmentation processing that reflects the dynamic change law of metal ion concentration. Its essence is the process of converting the original spectral signal into time series characteristics that can be quantified and analyzed.

[0061] In one implementation, the signals in the second spectral signal set exhibit significant fluctuations at the 5th and 10th minutes of detection. Feature extraction and classification reveal that these fluctuations are closely correlated with changes in metal ion concentration. The spectral signal data is segmented to obtain the fluctuation characteristics at these critical time points and then categorized in relation to changes in metal ion concentration. For example, fluctuations characterized by high amplitude and steep slope, with a sudden decrease or increase in concentration, are classified as a sudden response type; fluctuations characterized by periodic oscillations and stable amplitude, with periodic concentration fluctuations, are classified as a resonant response type. This ultimately forms the third spectral signal set. This classification method clearly reflects the connection between temporal changes and concentration fluctuations.

[0062] In step S144 , the concentration fluctuations and time changes in the third spectral signal set are integrated and matched to determine the dynamic change data set.

[0063] It should be noted that the dynamic change dataset is a structured matrix that stores the quantitative relationship between the temporal evolution of metal ion concentrations. Its essence is to transform spectral signals into a traceable spatiotemporal evolution archive. The dynamic relationship between concentration fluctuations and temporal changes in the third spectral signal set is integrated and processed to construct a three-dimensional relationship between time, concentration, and fluctuation characteristics. The core data is then matched to ensure that the concentration trend is compatible with the fluctuation characteristic type, and the fluctuation characteristic parameters are mathematically correlated with the concentration change amount, thus determining the final dynamic change dataset.

[0064] In one implementation, data integration of the third spectral signal set can organize the dynamic relationship between concentration fluctuations and time changes into structured data. For example, if the concentration value was 2 μg / L at the 5th minute and rose to 8 μg / L at the 10th minute, the concentration trend was matched with the fluctuation feature type to determine a dynamically changing data set. This organization method helps reveal the underlying patterns of change in the data and supports subsequent research.

[0065] In step S15, the concentration fluctuation pattern is analyzed based on the dynamic change data set and the historical record of signal intensity quantification to obtain the time series trend characteristic data of the metal ion concentration, including: S151, sorting the concentration fluctuation data in the dynamically changing data set into sections to obtain an adjusted first fluctuation data set; S152, performing smoothing correction on the first fluctuation data set to obtain a second fluctuation data set; S153, classifying the second fluctuation data set to obtain a third fluctuation data set; S154 , extracting core data points of periodic changes from the third fluctuation data set to determine time series trend characteristic data of the metal ions.

[0066] In step S151 , the concentration fluctuation data in the dynamically changing data set is segmented and sorted to obtain an adjusted first fluctuation data set.

[0067] It should be noted that the segmentation of concentration fluctuation data is performed by identifying key nodes of the fluctuation pattern, rather than simply dividing the time. The dynamically changing data set is segmented and sorted. First, the fluctuation segment data related to the periodic characteristics are obtained. If the signal intensity of the segment data is lower than the fluctuation segment intensity threshold, signal enhancement adjustment is performed to obtain the adjusted first fluctuation data set. The fluctuation segment intensity threshold is the data quality threshold used to screen valid periodic fluctuation segments. Its essence is to distinguish the intensity boundary between analyzable fluctuations and background noise.

[0068] In one implementation, the data collection spans 30 minutes. The concentration fluctuation data in the dynamic change dataset is segmented and organized into 5-minute time periods. The signal intensity of a particular data segment is found to be only 0.12 absorbance units, below the fluctuation segment intensity threshold of 0.2 absorbance units. In this case, the signal intensity is adjusted to 0.25 absorbance units, resulting in the adjusted first fluctuation dataset. This adjustment helps ensure data readability and provides a reliable foundation for subsequent analysis.

[0069] In step S152, smoothing correction is performed on the first fluctuation data set to obtain a second fluctuation data set.

[0070] It should be noted that smoothing correction is a signal repair technology that suppresses abnormal fluctuations based on historical data benchmarks. Its core goal is to preserve the true dynamic trend by eliminating unrealistic concentration mutations. After comparing the first fluctuation dataset, the distribution characteristics corresponding to the abnormal fluctuations are extracted in combination with the signal intensity quantification data in the historical records. If the fluctuation amplitude of the distribution characteristics meets the abnormal fluctuation standard, the data is smoothed using cubic spline interpolation to determine the calibrated second fluctuation dataset.

[0071] In one implementation, the first fluctuation dataset was combined with signal intensity quantification data from historical records to extract the distribution characteristics of abnormal fluctuations. Historical records show that normal fluctuations range from 0.1 to 0.3 absorbance units, but a certain fluctuation range in the current data reached 0.5 absorbance units, clearly exceeding this range. The abnormal fluctuations in the first fluctuation dataset were smoothed and corrected, and the fluctuation range was reduced to 0.28 absorbance units, forming the second fluctuation dataset. This correction method can reduce noise interference in the data and improve the stability of the analysis.

[0072] In step S154, core data points of periodic changes are extracted from the third fluctuation data set to determine time series trend characteristic data of the metal ions.

[0073] It's important to note that the core data points of cyclical changes are the extreme points in the cyclical evolution trajectory. The time-series trend characteristic data mathematically describe the concentration evolution and are also the key output for dynamically monitoring fluctuations in metal ion concentrations in electronic smoke. When integrating and mapping the correlation between abnormal fluctuations and cyclical characteristics in the third fluctuation dataset, we first extract the cyclic extreme points, identify abnormal fluctuations, and calculate the coupling parameters of the correlation between abnormal fluctuations and cyclical characteristics. Finally, we extract the core data points relevant to trend determination to determine the final time-series trend characteristic data for metal ion concentrations.

[0074] In one implementation, an abnormal fluctuation occurs at the 15th minute, corresponding to a sudden increase in concentration from 3 micrograms per liter to 10 micrograms per liter. Core data points of periodic variation are extracted from the third fluctuation dataset, correlated with the abnormal fluctuation, and a coupling parameter linking the abnormal fluctuation with the periodic characteristics is calculated. This fluctuation point is identified as a key node in the trend change, ultimately generating characteristic time-series trend data for metal ion concentration. This mapping method intuitively reflects the inherent connection between fluctuations and trends, laying the foundation for further research.

[0075] In one embodiment, core data points with periodic variations are extracted from the third fluctuation dataset. To address the difficulty of correcting abnormal fluctuations, multiple time period comparisons can be used to assist in identification. For example, if a fluctuation is not apparent within a single time period, but data changes from preceding and subsequent time periods reveal a correlation with a sudden concentration change, this multi-angle analysis approach can improve the comprehensiveness of anomaly identification and provide support for the construction of final trend feature data.

[0076] In step S16, the concentration exceeding risk point is determined based on the time series trend characteristic data, and an abnormal fluctuation determination result is obtained, including: S161, performing hierarchical processing and labeling on the time series trend feature data to obtain a first fluctuation segment set; S162, correcting and adjusting the nonlinear deviation in the first fluctuation segment set to obtain a second fluctuation segment set; S163, classifying and sorting the second wave segment set to obtain a third wave segment set; S164, performing abnormal fluctuation identification and processing on the third fluctuation segment set to obtain an abnormal fluctuation determination result.

[0077] In step S161, the time series trend feature data is hierarchically processed and labeled to obtain a first fluctuation segment set.

[0078] It should be noted that when stratifying the temporal trend characteristic data of metal ion concentration, the concentration value detection results are divided and screened according to different levels, focusing on data segments that exceed the concentration stratification threshold. These data segments are initially marked based on the concentration change trend to obtain the first set of marked fluctuation segments. Among them, the concentration stratification threshold is the key criterion for metal ion concentration safety risk classification. Its essence is to define the concentration boundary between normal fluctuation and abnormal release, and the selection of the concentration stratification threshold may be adjusted depending on different experimental conditions.

[0079] In one implementation, the time-series trend feature data is stratified, with a preset concentration threshold of 5 μg / L. In a particular experiment, if the concentration reaches 8 μg / L within a certain time period, clearly exceeding the range, this segment can be marked as an abnormal segment and included in the first set of fluctuation segments. This stratification helps quickly identify potential problem areas and provides a clear starting point for subsequent analysis.

[0080] In step S162, the nonlinear deviation in the first fluctuation segment set is corrected and adjusted to obtain a second fluctuation segment set.

[0081] It should be noted that nonlinear deviation refers to abnormal distortions in metal ion concentration fluctuations that do not conform to physical or chemical linear laws. Its essence is the non-proportional distortion between the signal response and the concentration change. The first set of fluctuation segments will be compared with the baseline data in combination with the historical concentration gradient records to extract the characteristic distribution related to the identification of abnormal fluctuations. If the fluctuation amplitude of this characteristic distribution exceeds the nonlinear deviation threshold, the nonlinear deviation is adjusted to determine the adjusted second set of fluctuation segments. The nonlinear deviation threshold is a dynamic boundary used to determine whether the concentration fluctuation amplitude is abnormal. Its essence is a reasonable fluctuation range based on historical data statistics.

[0082] In one implementation, historical records show that normal concentration fluctuations range from 1 to 3 micrograms per liter, but the fluctuations in the current first set of fluctuation segments reach 6 micrograms per liter, exceeding the nonlinear deviation threshold. In this case, the nonlinear deviations in the first set of fluctuation segments are corrected, for example, to a value close to the historical baseline, forming a second set of fluctuation segments. This comparison and correction method effectively identifies abnormal features in the data, laying the foundation for further classification.

[0083] In step S163, the second fluctuation segment set is classified and sorted to obtain a third fluctuation segment set.

[0084] It should be noted that the core of the second fluctuation segment set's classification and organization of the corrected data is to intelligently classify outliers through spatiotemporal density clustering and risk weight analysis. This method obtains potential outliers associated with the distribution of risk points. If the distribution density of these outliers exceeds the high-risk area density threshold, the area is marked as a high-risk area, resulting in the classified third fluctuation segment set. The high-risk area density threshold is a critical criterion for determining the spatial distribution density of risk points. Its essence is a safety margin for abnormal frequency based on historical failure data statistics.

[0085] In one implementation, the second fluctuation segment set categorizes and organizes the corrected data. If the density of outliers within a certain time period is significantly higher than the high-risk area density threshold of once every 10 minutes, the outliers are marked as high-risk areas, forming the third fluctuation segment set. This categorization and organization method can intuitively reflect the risk distribution and provide an important reference for subsequent comprehensive processing.

[0086] In step S164, abnormal fluctuations are identified and processed on the third fluctuation segment set to obtain an abnormal fluctuation determination result.

[0087] It should be noted that the identification and processing of abnormal fluctuations in the third fluctuation segment set requires the comprehensive correlation between signal intensity, gradient and pattern and the judgment of exceeding the standard risk, and the generation of the final risk distribution map in combination with the concentration change trend to obtain the abnormal fluctuation judgment result.

[0088] In one implementation, abnormal fluctuation identification and processing were performed on the third set of fluctuation segments. A sudden increase in concentration from 4 micrograms per liter to 9 micrograms per liter at a certain point in time, consistent with a historical abnormal fluctuation trend, was identified as a potential concentration exceeding the standard risk point, resulting in an abnormal fluctuation determination result. This integrated processing clearly demonstrates the correlation between concentration changes and risk points, providing a visual basis for subsequent research.

[0089] In step S17, based on the abnormal fluctuation determination result, the risk warning threshold deviation range is continuously adjusted to determine the final concentration risk assessment data, including: S171, based on the abnormal fluctuation determination result, extracting time series segments related to the metal ion concentration fluctuation and classifying and arranging them to obtain an initial fluctuation data set; S172, performing suppression processing and real-time calibration on the initial fluctuation data set to determine a fluctuation regularity data set; S173, if the concentration value in the fluctuation pattern data set exceeds a preset concentration threshold, dynamically adjusting the risk warning threshold to obtain an adjusted risk warning threshold; S174, based on the adjusted risk warning threshold, comprehensively map the metal ion concentration risk and the fluctuation pattern data set to obtain concentration risk assessment data.

[0090] In step S171, based on the abnormal fluctuation determination result, time series segments related to the metal ion concentration fluctuation are extracted and classified to obtain an initial fluctuation data set.

[0091] It should be noted that classification and organization are based on a comprehensive approach to categorizing time series segments based on fluctuation intensity, waveform pattern, and environmental coupling. In the field of spectral analysis of electronic smoke samples, after determining abnormal fluctuations in metal ion concentration, the relevant data is processed and analyzed. Time series segments related to the metal ion concentration fluctuations are obtained from the dynamic dataset and classified. For example, fluctuations with an intensity greater than 200% of the baseline, a pulsed waveform, and a strong correlation with voltage are classified as equipment failures; fluctuations with moderate periodic intensity, a regular sinusoidal waveform, and synchronization with equipment vibration are classified as periodic oscillations. After completing the classification and organization of the time series segments, the initial fluctuation dataset is obtained.

[0092] In one implementation, concentration data within 24 hours is selected, and it is found that the concentration value fluctuates frequently within a certain 6-hour period, triggering a dynamic threshold of metal ion concentration. Time series fragments related to the metal ion concentration fluctuation are obtained from the dynamic data set, and are classified and sorted according to their fluctuation intensity, waveform pattern, and environmental coupling. Such fragments are classified as part of the initial fluctuation data set, laying the foundation for subsequent classification and sorting.

[0093] In step S172, the initial fluctuation data set is subjected to suppression processing and real-time calibration to determine a fluctuation regularity data set.

[0094] It should be noted that the purpose of suppression processing is to eliminate low-frequency interference from background noise or environmental factors during device operation. Low-frequency interference in the signal is identified through Fourier transform spectrum analysis, filtered using a zero-phase Butterworth high-pass filter, and the time domain signal is reconstructed to obtain a fluctuating data signal with the interference removed. Real-time calibration is achieved by generating a reference benchmark, detecting deviations, calculating calibration values, and dynamically compensating the signal in real time to obtain the true fluctuation characteristics and determine the fluctuation pattern dataset.

[0095] In one implementation, there are periodic low-frequency fluctuations in a certain initial fluctuation data set. The low-frequency interference in the signal is identified through Fourier transform spectrum analysis, suppressed, filtered using a zero-phase Butterworth high-pass filter, and the time domain signal is re-established to obtain a fluctuation data signal with the interference filtered out. At this time, the data curve becomes smoother. At the same time, the fluctuation data signal with the interference filtered out is calibrated in real time to eliminate system deviations, retain the true fluctuation characteristics, and ensure that the calibrated fluctuation pattern data set is closer to the actual concentration changes. For example, if the device reading is found to be 0.5 micrograms per liter higher during a calibration, the system will automatically adjust the data value downward to form a more accurate fluctuation pattern data set.

[0096] In step S173, if the concentration value in the fluctuation pattern data set exceeds the preset concentration threshold, the risk warning threshold is dynamically adjusted to obtain the adjusted risk warning threshold.

[0097] It should be noted that the preset concentration threshold refers to a fixed safety boundary value set during system initialization, which is used to determine whether the environmental monitoring data is abnormal. The risk warning threshold is an intelligent boundary value used to dynamically determine the risk level of metal ion concentration. Its essence is an adaptive safety red line that integrates environmental parameters and historical data. The essence of dynamic adjustment is to adaptively correct the safety boundary based on real-time monitoring data and environmental changes. First, data points that exceed the risk warning threshold are identified. Combined with the environment (such as temperature, pH value, flow rate) and historical data, it is determined whether the anomaly is an occasional interference or a trend change. Finally, the risk determination threshold is updated in real time to make it closer to the current environmental conditions.

[0098] In one implementation, if the concentration value in a calibrated fluctuation pattern dataset exceeds a preset threshold range—for example, if the preset threshold is 5 micrograms per liter and a certain segment of data reaches 7 micrograms per liter, indicating that the concentration value in the fluctuation pattern dataset clearly exceeds the preset concentration threshold—then a data re-comparison is required. The system then combines environmental and historical data to determine whether the anomaly is a sporadic interference or a trend change. Finally, the risk assessment threshold is updated in real time to better reflect current environmental conditions, resulting in an adjusted risk warning threshold. This dynamic adjustment can better adapt to changing experimental conditions.

[0099] In step S174, based on the adjusted risk warning threshold, the metal ion concentration risk is comprehensively mapped with the fluctuation pattern data set to obtain concentration risk assessment data.

[0100] It should be noted that the comprehensive mapping of metal ion concentration risks and fluctuation pattern data sets is a multi-dimensional risk modeling process. Its essence is to convert the original concentration value into a risk assessment result with practical warning significance through the deep coupling of dynamic thresholds and data fluctuation characteristics.

[0101] In one implementation, if the concentration value remains stable at 5.2 micrograms per liter over a certain period of time, slightly above the original threshold, the metal ion concentration risk is comprehensively mapped against the fluctuation pattern dataset. Combined with the fluctuation pattern, it is found that the change trend is gentle and does not show mutation characteristics. The system may assess it as a low-risk area. Conversely, if a certain segment of data jumps from 4 micrograms per liter to 6 micrograms per liter in a short period of time and the fluctuation trend is abnormal, it may be marked as high-risk. This comprehensive mapping method can intuitively reflect the correlation between concentration changes and potential risks.

[0102] In the above embodiment, when classifying and arranging the initial fluctuation data set, multi-dimensional division can be performed based on the fluctuation frequency and amplitude. For example, data with a fluctuation frequency higher than 2 times per hour and an amplitude greater than 1 microgram per liter are classified as a high-concern group, while other data are classified as a low-concern group. This hierarchical approach makes it easier to focus on key problem areas during subsequent analysis and improves processing efficiency. Overall, the close connection of the above links ensures a smooth process from data extraction to risk assessment, providing strong support for the accurate identification of abnormal metal ion concentrations.

[0103] In step S18, a dynamic detection report for the metal ion concentration in the electronic smoke is generated based on the final concentration risk assessment data, and a time series detection file is obtained, including: S181, performing segmented processing and dynamic refreshing on the concentration risk assessment data to obtain a preliminary concentration fluctuation data set; S182, performing absorption peak location identification on the preliminary concentration fluctuation data set to obtain abnormal point distribution characteristic information; S183, adjusting the concentration gradient calibration according to the abnormal point distribution characteristic information to generate concentration risk grading data; S184: Generate a dynamic detection report based on the concentration risk grading data to obtain a time series detection file.

[0104] In step S181, the concentration risk assessment data is segmented and dynamically updated to obtain a preliminary concentration fluctuation data set.

[0105] It should be noted that the concentration risk assessment data needs to be segmented according to changes in the time dimension. The fluctuation patterns of the data within the time period are recorded, including the starting and ending concentrations, concentration change trends, and maximum fluctuations. Dynamic refresh refers to re-capturing new data when the system clock reaches the top of the hour, rolling updates within the time period, re-analyzing the updated segments, and refreshing the dynamic dataset to obtain a preliminary concentration fluctuation dataset.

[0106] In one implementation, the concentration risk assessment data is segmented, with 48 consecutive hours of concentration data divided into six-hour time periods, resulting in eight time-segment data records. Within each segment, the system analyzes the concentration trend. For example, if the concentration value gradually increases from 3.2 μg / L to 4.8 μg / L within a particular segment, this trend will be recorded as part of a fluctuation pattern. This segmentation approach helps capture short-term changes and lays the foundation for subsequent analysis.

[0107] In another implementation, the real-time refresh of the dynamic data update mechanism can be understood as the system continuously updating the initial concentration fluctuation dataset based on newly collected data. Every hour, the system automatically obtains the latest concentration reading and integrates it with the previous six hours of data to form an updated fluctuation dataset. If the latest data point shows a sudden increase in concentration to 5.1 micrograms per liter, the system will flag it as a potential anomaly, triggering further analysis. This real-time refresh ensures the timeliness of the data.

[0108] In step S182, absorption peak location and identification are performed on the preliminary concentration fluctuation data set to obtain abnormal point distribution feature information.

[0109] It should be noted that absorption peak location identification achieves precise feature extraction through the combined use of multi-order derivatives and adaptive threshold segmentation. This is primarily intended to extract key feature information from a preliminary concentration fluctuation dataset. Signal extraction is performed on this preliminary concentration fluctuation dataset, and absorption peak location identification is performed. Feature information about the distribution of outliers is obtained from this dataset, and the distribution patterns of these outliers over time are determined.

[0110] In one implementation, the system discovered multiple absorption peaks in the concentration data through spectral analysis. One peak corresponded to a concentration of 6.3 micrograms per liter, significantly higher than surrounding data points. By locating these peaks, the system was able to identify the distribution patterns of outliers, such as whether they were concentrated within a specific time period. This identification method helps pinpoint potential problem areas.

[0111] In step S183, the concentration gradient calibration is adjusted according to the abnormal point distribution characteristic information to generate concentration risk grading data.

[0112] It should be noted that concentration gradient calibration is to calibrate the concentration value through dynamic compensation of environmental parameters, and to perform risk classification on the calibrated concentration based on dynamic thresholds to locate high-risk intervals. If the characteristic information of the abnormal point distribution exceeds the preset risk interval judgment threshold, the concentration gradient calibration is compared and adjusted, and the concentration risk classification data is generated in combination with the risk interval information to determine the potential high-risk interval. Among them, the risk interval judgment threshold refers to the dynamic judgment boundary set by the system for the abnormal point distribution characteristics. Its essence is a multi-dimensional abnormal behavior quantification standard.

[0113] In one implementation, if the distribution of outliers exceeds the risk interval threshold (for example, a threshold of 5.0 μg / L), and multiple data points exceed this value within a certain time period, the system will perform a concentration gradient calibration adjustment. By comparing historical data, if it is found that the current ambient temperature is too high, which may cause the reading to deviate, the system will adjust the concentration value downward by 0.3 μg / L, forming an adjusted data set. This adjustment method can reduce the impact of external interference on the data.

[0114] In step S184, a dynamic detection report is generated based on the concentration risk grading data to obtain a time series detection file.

[0115] It should be noted that the dynamic detection report will record in detail the fluctuation patterns, abnormal distribution and risk ranges within a certain time period. When generating concentration risk classification data, the system will classify the data based on the risk range information, integrate it into trace metal detection information, generate a dynamic detection report, and obtain a complete time-series detection file.

[0116] In summary, the present invention discloses a method and system for detecting trace metal ion concentrations. The method comprises: obtaining raw spectral response data from electronic smoke samples; performing signal separation using frequency domain feature extraction techniques to obtain an initial spectral dataset; filtering the initial data for background noise and baseline drift correction to obtain a refined spectral dataset; constructing a concentration-wavelength mapping relationship based on the refined spectral dataset by locating absorption peaks and screening characteristic bands; continuously sampling samples in a time series based on the mapping relationship to generate a concentration dynamic change dataset; analyzing concentration fluctuation patterns and anomalies in the dynamic data to generate concentration risk assessment data; and finally outputting a time series detection profile. This method enables high-precision dynamic monitoring and intelligent risk assessment of trace metal ion concentrations, effectively suppressing interference, and improving sensitivity and early warning capabilities.

[0117] Reference Figure 2 A second embodiment of the present invention provides a system for detecting trace metal ion concentrations, comprising: The original spectrum data acquisition module obtains the original spectrum response data of the electronic smoke sample; A spectral data preprocessing module, which preprocesses the raw spectral response data to obtain a refined spectral data set; a concentration gradient calibration module, performing concentration gradient calibration on the refined spectral data set to obtain stable mapping relationship data between metal ion concentration and wavelength; A dynamic concentration monitoring module, based on the stable mapping relationship data, continuously scans the spectra of samples at different time points to obtain a dynamic change data set of metal ion concentration fluctuations over time; A time series trend analysis module analyzes the concentration fluctuation pattern based on the dynamic change data set and the historical record of signal intensity quantification to obtain the time series trend characteristic data of the metal ion concentration; The module for determining the risk of exceeding the standard is used to determine the risk point of exceeding the standard concentration based on the time series trend characteristic data and obtain the abnormal fluctuation determination result; A risk assessment optimization module continuously adjusts the risk warning threshold range based on the abnormal fluctuation determination result to determine the final concentration risk assessment data; The dynamic detection report generation module generates a dynamic detection report for the metal ion concentration in the electronic smoke according to the final concentration risk assessment data, and obtains a time series detection file.

[0118] It should be noted that the trace metal ion concentration detection device provided in an embodiment of the present invention is used to execute all process steps of the trace metal ion concentration detection method of the above embodiment. The working principles and beneficial effects of the two correspond one to one, so they will not be repeated here.

[0119] An embodiment of the present invention further provides an electronic device. The electronic device includes: a processor, a memory, and a computer program stored in the memory and executable on the processor, such as a system for detecting trace metal ion concentrations. When the processor executes the computer program, the steps of the above-mentioned methods for detecting trace metal ion concentrations are implemented, such as Figure 1 Alternatively, when the processor executes the computer program, the functions of the modules in the above-mentioned device embodiments are realized, such as the original spectrum data acquisition module.

[0120] Exemplarily, the computer program may be divided into one or more modules, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules may be a series of computer program instruction segments capable of implementing specific functions, and the instruction segments are used to describe the execution process of the computer program in the electronic device.

[0121] The electronic device may be a computing device such as a desktop computer, notebook, PDA, or smart tablet. The electronic device may include, but is not limited to, a processor and memory. Those skilled in the art will appreciate that the aforementioned components are merely examples of electronic devices and do not constitute a limitation of the electronic device. The electronic device may include more or fewer components than those described above, or a combination of certain components, or different components. For example, the electronic device may also include input / output devices, network access devices, buses, and the like.

[0122] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor. The processor is the control center of the electronic device and connects various parts of the entire electronic device using various interfaces and lines.

[0123] The memory can be used to store the computer programs and / or modules. The processor implements the various functions of the electronic device by running or executing the computer programs and / or modules stored in the memory and accessing the data stored in the memory. The memory may primarily include a program storage area and a data storage area. The program storage area may store an operating system and at least one application required for a function (such as a sound playback function or an image playback function); the data storage area may store data generated based on the use of the mobile phone (such as audio data, a phone book, etc.). Furthermore, the memory may include high-speed random access memory and non-volatile memory, such as a hard disk, internal memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one disk storage device, a flash memory device, or other volatile solid-state storage device.

[0124] If the module / unit integrated into the electronic device is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention can implement all or part of the process steps in the above-mentioned method embodiments by using a computer program to instruct the relevant hardware. The computer program can be stored in a computer-readable storage medium. When executed by a processor, the computer program can implement the steps of each of the above-mentioned method embodiments. The computer program includes computer program code, which can be in source code form, object code form, executable file, or some intermediate form. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal, and software distribution medium. It should be noted that the content of the computer-readable medium can be appropriately increased or decreased based on the requirements of legislation and patent practice in a jurisdiction. For example, in some jurisdictions, based on legislation and patent practice, computer-readable media does not include electric carrier signals and telecommunication signals.

[0125] It should be noted that the device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed across multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment. In addition, in the drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art can understand and implement the present invention without inventive effort.

[0126] The specific embodiments described above further illustrate the objectives, technical solutions, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. A method for detecting trace metal ion concentration, characterized in that: include: Obtaining raw spectral response data of electronic smoke samples; Preprocessing the raw spectral response data to obtain a refined spectral data set; performing concentration gradient calibration on the refined spectral data set to obtain stable mapping relationship data between metal ion concentration and wavelength; Based on the stable mapping relationship data, the samples at different time points are continuously spectrally scanned to obtain a dynamic change data set of metal ion concentration fluctuations over time; According to the dynamic change data set, combined with the historical record of signal intensity quantification, the concentration fluctuation law is analyzed to obtain the time series trend characteristic data of the metal ion concentration; According to the time series trend characteristic data, the concentration exceeding risk point is determined to obtain the abnormal fluctuation determination result; Based on the abnormal fluctuation determination results, continuously adjust the risk warning threshold deviation range to determine the final concentration risk assessment data; Based on the final concentration risk assessment data, a dynamic detection report for the metal ion concentration in the electronic smoke is generated to obtain a time series detection file.

2. The method for detecting trace metal ion concentration according to claim 1, wherein The obtaining of original spectral response data of the electronic smoke sample includes: The initial spectrum of the electronic smoke sample is collected by a spectrum scanning device to obtain the original spectrum response data within the wavelength range of metal ion absorption.

3. The method for detecting trace metal ion concentration according to claim 1, wherein The preprocessing of the original spectral response data to obtain a refined spectral data set includes: Performing denoising on the original spectral response data to obtain a denoised first spectral data set; Correcting and adjusting the baseline drift in the first spectral data set to obtain a corrected second spectral data set; performing smoothing repair on the second spectral data set to obtain a third spectral data set; The third spectral data set is matched and classified with a preset characteristic interval template to determine a final refined spectral data set.

4. The method for detecting trace metal ion concentration according to claim 1, wherein The step of performing concentration gradient calibration on the refined spectral data set to obtain stable mapping relationship data between metal ion concentration and wavelength includes: performing signal enhancement adjustment on the refined spectral data set to obtain a first signal set; Extracting characteristic bands from the first signal set and performing calibration to obtain a second signal set; performing hierarchical processing on the signal strengths of the second signal set to obtain a third signal set; The wavelength ranges and concentration gradients in the third signal set are sorted, and the core data are classified to obtain the stable mapping relationship data.

5. The method for detecting trace metal ion concentration according to claim 1, wherein Based on the stable mapping relationship data, continuous spectral scanning is performed on the sample data at different time points to obtain a dynamic change data set of metal ion concentration fluctuations over time, including: extracting spectral signal data from the sample data based on the stable mapping relationship data to obtain a first spectral signal set; performing calibration processing on the first spectral signal set to determine a calibrated second spectral signal set; According to the second spectral signal set, the spectral signal data is segmented to obtain and classify the fluctuation characteristic data to obtain a third spectral signal set; The concentration fluctuations and time changes in the third spectral signal set are integrated and matched to determine the dynamic change data set.

6. The method for detecting trace metal ion concentration according to claim 1, wherein The concentration fluctuation law is analyzed based on the dynamic change data set and combined with the historical record of signal intensity quantification to obtain the time series trend characteristic data of the metal ion concentration, including: Segmentally sorting the concentration fluctuation data in the dynamically changing data set to obtain an adjusted first fluctuation data set; performing smoothing correction on the first fluctuation data set to obtain a second fluctuation data set; performing classification processing on the second fluctuation data set to obtain a third fluctuation data set; Core data points of periodic changes are extracted from the third fluctuation data set to determine time series trend characteristic data of the metal ions.

7. The method for detecting trace metal ion concentration according to claim 1, wherein The time series trend characteristic data is used to determine the risk point of concentration exceeding the standard and obtain the abnormal fluctuation determination result, including: Performing layered processing and labeling on the time series trend feature data to obtain a first fluctuation segment set; Correcting and adjusting the nonlinear deviation in the first fluctuation segment set to obtain a second fluctuation segment set; Classifying and arranging the second wave segment set to obtain a third wave segment set; Abnormal fluctuations are identified and processed on the third fluctuation segment set to obtain an abnormal fluctuation determination result.

8. The method for detecting trace metal ion concentration according to claim 1, wherein The method of continuously adjusting the risk warning threshold range based on the abnormal fluctuation determination result to determine the final concentration risk assessment data includes: According to the abnormal fluctuation determination result, extracting time series segments related to the metal ion concentration fluctuation and classifying and arranging them to obtain an initial fluctuation data set; performing suppression processing and real-time calibration on the initial fluctuation data set to determine a fluctuation regularity data set; If the concentration value in the fluctuation pattern data set exceeds the preset concentration threshold, the risk warning threshold is dynamically adjusted to obtain the adjusted risk warning threshold; According to the adjusted risk warning threshold, the metal ion concentration risk is comprehensively mapped with the fluctuation pattern data set to obtain concentration risk assessment data.

9. The method for detecting trace metal ion concentration according to claim 1, wherein The method generates a dynamic detection report for the metal ion concentration in the electronic smoke based on the final concentration risk assessment data, and obtains a time series detection file, including: Segmentally processing and dynamically refreshing the concentration risk assessment data to obtain a preliminary concentration fluctuation data set; Performing absorption peak location and identification on the preliminary concentration fluctuation data set to obtain abnormal point distribution characteristic information; Adjusting the concentration gradient calibration according to the abnormal point distribution characteristic information to generate concentration risk grading data; Based on the concentration risk grading data, a dynamic detection report is generated to obtain a time-series detection file; the dynamic detection report includes the fluctuation pattern of the metal ion concentration in the electronic smoke over time, the distribution of abnormal points located by absorption peaks, and risk interval information calibrated by concentration gradients.

10. A detection system for trace metal ion concentration, characterized in that: include: The original spectrum data acquisition module obtains the original spectrum response data of the electronic smoke sample; A spectral data preprocessing module preprocesses the raw spectral response data to obtain a refined spectral data set; a concentration gradient calibration module, performing concentration gradient calibration on the refined spectral data set to obtain stable mapping relationship data between metal ion concentration and wavelength; A dynamic concentration monitoring module, based on the stable mapping relationship data, continuously scans the spectra of samples at different time points to obtain a dynamic change data set of metal ion concentration fluctuations over time; A time series trend analysis module analyzes the concentration fluctuation pattern based on the dynamic change data set and the historical record of signal intensity quantification to obtain the time series trend characteristic data of the metal ion concentration; The module for determining the risk of exceeding the standard is used to determine the risk point of exceeding the standard concentration based on the time series trend characteristic data and obtain the abnormal fluctuation determination result; A risk assessment optimization module continuously adjusts the risk warning threshold range based on the abnormal fluctuation determination result to determine the final concentration risk assessment data; The dynamic detection report generation module generates a dynamic detection report for the metal ion concentration in the electronic smoke according to the final concentration risk assessment data, and obtains a time series detection file.

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