Online multichannel rapid gas chromatographic analysis system and method

Abstract

The invention discloses an online multi-channel rapid gas chromatographic analysis system and method, particularly relates to the technical field of gas chromatographic analysis, and is used for solving the problems of baseline noise rise and trace component detection precision reduction caused by physical coupling and signal interference when multiple detectors work in parallel in the prior art. Interference signal features of adjacent detection channels are extracted, a chaotic or linear compensation signal is dynamically generated in combination with harmonic primary and secondary sorting and recursive quantization factor analysis, and common-mode interference of a target channel is suppressed. Electromagnetic isolation parameters of a shielding structure are adjusted in real time based on the baseline signal after noise suppression, and accurate matching of impedance and interference spectrum is achieved; further separating time domain overlapped signals and analyzing asymmetric distortion characteristics, and eliminating system errors through normalization calibration; dynamic interference suppression, electromagnetic shielding optimization and signal distortion traceability are combined, the anti-interference capability and analysis precision of multi-channel detection are remarkably improved, and the method is suitable for high-throughput rapid detection scenes of complex samples.

Description

Technical Field

[0001] The present invention relates to the technical field of gas chromatography analysis, and more specifically, the present invention relates to an on-line multi-channel rapid gas chromatography analysis system and method. Background Art

[0002] Gas chromatography analysis technology is widely used in the field of real-time detection of multiple pollutants, especially in on-line multi-channel rapid analysis, where the detection efficiency is improved through parallel sampling and high-speed separation. In the prior art, multi-channel gas chromatography analysis realizes high-throughput continuous analysis of complex samples by configuring multiple independent detection channels and detectors. However, the dense arrangement of multiple detectors and the requirement of high-speed operation lead to an enhanced coupling effect between physical space and signal transmission among channels. Especially in the detection scenario of trace components, the detection signal is easily interfered by adjacent channels, affecting the reliability of the analysis results.

[0003] When the existing on-line multi-channel gas chromatography system works in cooperation with multiple detectors, the common-mode interference generated by the physical coupling and signal interaction between detection units is difficult to effectively isolate, directly resulting in an increase in the baseline noise level and distortion of the effective signal, making the detection accuracy of trace components decline and it is difficult to meet the on-line rapid analysis requirements of high sensitivity and high reliability. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, embodiments of the present invention provide an on-line multi-channel rapid gas chromatography analysis system and method to solve the problems proposed in the above background art.

[0005] To achieve the above object, the present invention provides the following technical solutions: An on-line multi-channel rapid gas chromatography analysis method, comprising: S1. Extract the interference signal characteristics between adjacent detection channels in the parallel working state of multiple detectors in the gas chromatography system; S2. Analyze the primary and secondary order of harmonics based on the interference signal characteristics, and dynamically generate chaotic or linear compensation signals in combination with the recurrence rate and the deterministic quantization factor; S3. Superimpose the compensation signal onto the baseline signal of the target detection channel to form a baseline signal with noise suppression; S4. Dynamically adjust the electromagnetic isolation parameters of the detector shielding structure according to the baseline signal with noise suppression, so that the impedance of the shielding layer matches the current interference signal spectrum; S5. Separate the time-domain overlapping signals of each detection channel based on the electromagnetic isolation parameters, and analyze the asymmetric distortion characteristics of the separated time-domain overlapping signals; S6. Normalize and calibrate the multi-channel detection results according to the asymmetric distortion characteristics, and output the corrected component concentration data.

[0006] In a preferred embodiment, when the multi-detector of the gas chromatography system operates in parallel, the interference signal characteristics between adjacent detection channels are extracted, including: Perform time-domain phase difference analysis on the baseline signals of adjacent detection channels to obtain time-domain phase difference characteristics; Perform frequency-domain harmonic decomposition on the time-domain phase difference characteristics to extract the frequencies and energy distribution weights of the harmonic components; Analyze the frequency-domain coupling coefficients of adjacent detection channels based on the frequencies and energy distribution weights of the harmonic components to generate interference signal characteristics; Among them, the frequency-domain harmonic decomposition is implemented by fast Fourier transform, and the frequency-domain coupling coefficient is the product of the harmonic energy ratios of adjacent detection channels.

[0007] In a preferred embodiment, based on the interference signal characteristics, analyze the primary and secondary order of harmonics, and dynamically generate chaotic or linear compensation signals in combination with the recurrence rate and the determinism quantification factor, including: Rank the harmonic interferences of adjacent detection channels according to the frequencies and energy distribution weights of the harmonic components in the interference signal characteristics to determine the primary interference harmonic components; Generate an initial phase inversion compensation signal based on the frequencies and energy distribution weights of the primary interference harmonic components; Perform recurrence plot analysis on the time-domain phase difference characteristics in the interference signal characteristics to extract the recurrence rate and the determinism quantification factor; When the product of the recurrence rate and the determinism quantification factor exceeds the preset product threshold, generate a chaotic compensation signal synchronized with the primary interference harmonic component based on the chaotic model; otherwise, generate a linear compensation signal based on the initial phase inversion compensation signal.

[0008] In a preferred embodiment, superimpose the compensation signal onto the baseline signal of the target detection channel to form a baseline signal with noise suppression, including: Select a compensation mode of time-domain superposition or frequency-domain filtering according to the type of the compensation signal; Perform gain calibration on the baseline signal of the target detection channel to match the dynamic range of the baseline signal with the amplitude range of the compensation signal; Adjust the amplitude of the superimposed baseline signal according to the sensitivity threshold of the target detection channel to ensure that the amplitude of the superimposed signal does not exceed the linear response range of the detector; Perform time-domain smoothing processing on the adjusted baseline signal to eliminate the high-frequency noise components introduced during the superposition process and generate a baseline signal with noise suppression.

[0009] In a preferred embodiment, if the compensation signal is a chaotic compensation signal, the time-domain direct superposition method is adopted; if the compensation signal is a linear compensation signal, the method of superposition after frequency-domain matching filtering is adopted.

[0010] In a preferred embodiment, dynamically adjusting the electromagnetic isolation parameters of the detector shielding structure according to the baseline signal after noise suppression so that the shielding layer impedance matches the current interference signal spectrum includes: Extract the spectrum characteristics of the current interference signal based on the baseline signal after noise suppression, and identify the energy distribution and frequency range of the main interference frequency band; Calculate the target matching value of the shielding layer impedance according to the energy proportion of the main interference frequency band; By adjusting the thickness of the adjustable conductive coating in the shielding layer and the distribution density of the magnetic permeability material, the errors of the real part and the imaginary part of the impedance of the shielding layer and the real part and the imaginary part of the target matching value are respectively less than a preset ratio; The matching degree between the shielding layer impedance and the interference signal spectrum is monitored in real time. If the matching degree is lower than the preset matching degree, the spectrum feature extraction and parameter adjustment are re-triggered.

[0011] In a preferred embodiment, the time domain overlapping signals of each detection channel are separated based on the electromagnetic isolation parameter, and the asymmetric distortion characteristics of the separated time domain overlapping signals are analyzed, including: Generate a signal separation weight matrix for each detection channel based on the real part and the imaginary part of the electromagnetic isolation parameter; The signal separation weight matrix is ​​used to perform weighted separation on the time domain overlapping signals to obtain independent time domain signals of each channel; The skewness analysis is performed on the peak shape of the independent time domain signal, and the offset between the peak top position and the center of the baseline is calculated as the peak shape skewness; Perform trend fitting on the baseline of the independent time domain signal, and calculate the absolute value of the slope of the fitting curve as the baseline drift slope; The type of asymmetric distortion is determined based on the combined relationship between the peak skewness and the baseline drift slope.

[0012] In a preferred embodiment, the multi-channel detection results are normalized and calibrated according to the asymmetric distortion characteristics, and the corrected component concentration data is output, including: Selecting a corresponding calibration strategy based on the type of asymmetric distortion features; Generate a normalized calibration coefficient for each detection channel according to the calibration strategy, and the normalized calibration coefficient is calculated by the ratio of the distortion characteristic parameter to the reference parameter; Multiply the normalized calibration coefficient by the original concentration data of the corresponding detection channel to obtain the component concentration data after normalization and calibration; The validity of the component concentration data after normalization and calibration is verified, abnormal data beyond the preset credible interval is eliminated, and the corrected component concentration data is output.

[0013] In a preferred embodiment, if it is a peak-shaped right-skewed distortion, the peak position offset compensation calibration is adopted; if it is a baseline drift distortion, the baseline slope reverse compensation calibration is adopted.

[0014] On the other hand, the present invention provides an on-line multi-channel fast gas chromatography analysis system, comprising: An interference feature module, configured to extract the interference signal features between adjacent detection channels in the parallel working state of the multi-detector of the gas chromatography system; A harmonic compensation module, configured to analyze the primary and secondary order of harmonics based on the interference signal features, and dynamically generate a chaotic or linear compensation signal in combination with the recursion rate and the deterministic quantization factor; A noise suppression module, configured to superimpose the compensation signal on the baseline signal of the target detection channel to form a baseline signal after noise suppression; An electromagnetic adjustment module, configured to dynamically adjust the electromagnetic isolation parameters of the detector shielding structure according to the baseline signal after noise suppression, so that the impedance of the shielding layer matches the current interference signal spectrum; A distortion analysis module, configured to separate the time-domain overlapping signals of each detection channel based on the electromagnetic isolation parameters, and analyze the asymmetric distortion features of the separated time-domain overlapping signals; A calibration output module, configured to perform normalization calibration on the multi-channel detection results according to the asymmetric distortion features, and output the corrected component concentration data.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. Through the dynamic interference suppression and multi-dimensional signal collaborative processing mechanism, the anti-interference ability and detection accuracy of the multi-channel gas chromatography system are significantly improved; based on the primary and secondary order analysis of harmonics and the recursive quantization factor analysis of the interference signal features, the propagation paths of high-frequency noise and low-frequency coupling interference can be accurately identified, and combined with the dynamic switching strategy of chaotic compensation and linear compensation, an adaptive suppression scheme can be effectively generated for interference signals in different frequency bands; through the real-time adjustment of the electromagnetic isolation parameters and the spectrum matching of the shielding layer impedance, an adaptive anti-interference barrier is constructed at the hardware level. At the same time, combined with the distortion feature tracing and normalization calibration of the time-domain overlapping signals, the cross-layer linkage of signal processing and hardware optimization is realized, the influence of common-mode interference on the detection channels is reduced, and the signal integrity and analysis reliability of trace components in a complex multi-channel environment are ensured.

[0016] 2. Through the closed-loop noise suppression and adaptive calibration system, the problem that static parameters in traditional technologies cannot cope with dynamic interference is solved; based on the analysis of the asymmetric distortion characteristics of the baseline signal, it can accurately distinguish signal distortion caused by instrument hardware aging and instantaneous noise, and eliminate system errors through the normalization calibration of multi-channel data; the dynamic correlation mechanism between electromagnetic isolation parameters and compensation signals enables the physical characteristics of the shielding structure to be strictly adapted to the real-time interference spectrum, avoiding over-compensation or under-compensation defects of traditional fixed shielding schemes; by integrating signal processing, hardware optimization and data calibration, an adaptive interference suppression system is formed, which not only improves the detection sensitivity, but also greatly enhances the robustness and stability of the system in high-throughput and high-noise scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 is a flowchart of an online multi-channel fast gas chromatography analysis method of the present invention; Figure 2 is a flowchart of generating chaotic or linear compensation signals of the present invention; Figure 3 is a schematic structural diagram of an online multi-channel fast gas chromatography analysis system of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0018] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0019] Embodiment 1: Figure 1 An online multi-channel fast gas chromatography analysis method of the present invention is given, including: S1. Under the parallel working state of multiple detectors in the gas chromatography system, extract the interference signal characteristics between adjacent detection channels; S2. Based on the analysis of the interference signal characteristics, rank the primary and secondary harmonics, and dynamically generate chaotic or linear compensation signals in combination with the recurrence rate and the deterministic quantization factor; S3. Superimpose the compensation signal on the baseline signal of the target detection channel to form a noise-suppressed baseline signal; S4. Dynamically adjust the electromagnetic isolation parameters of the detector shielding structure according to the noise-suppressed baseline signal, so that the shielding layer impedance matches the current interference signal spectrum; S5. Separate the time-domain overlapping signals of each detection channel based on the electromagnetic isolation parameters, and analyze the asymmetric distortion characteristics of the separated time-domain overlapping signals; S6. Normalize and calibrate the multi-channel detection results according to the asymmetric distortion characteristics, and output the corrected component concentration data.

[0020] S1. Under the parallel working state of multiple detectors in the gas chromatography system, extract the interference signal characteristics between adjacent detection channels. Specifically, it can be implemented as follows: Collect the baseline signals in real time through the sensors of each detection channel in the gas chromatography system. The sampling frequency of the baseline signals is set to no less than 1000 samples per second to ensure the integrity of the time-domain waveform. The baseline signal is the original output signal of each detection channel when no sample is loaded, and is used to characterize the detector background noise and the inherent interference between channels. The baseline signals of adjacent detection channels are collected by triggering with a synchronous clock to ensure time-domain alignment. The error of the synchronous clock is controlled within 1 microsecond to avoid introducing additional phase differences due to time deviation.

[0021] When performing time-domain phase difference analysis on the time-domain phase difference characteristics, use the cross-correlation function to calculate the phase delay of the signals of the two channels. The specific operation is as follows: After aligning the baseline signals of the two channels according to the time series, intercept the signal segments in the same time period and calculate their cross-correlation function. The peak position of the cross-correlation function corresponds to the relative time shift amount of the two signals. After converting this time shift amount into a time difference, it is used as the time-domain phase difference characteristic. For example, if the sampling interval is 1 millisecond and the offset of the cross-correlation peak is 5 sampling points, the time-domain phase difference characteristic is 5 milliseconds. The value range of the time-domain phase difference characteristic is set according to the physical distance between the detection channels and the signal propagation speed. If the phase difference exceeds 50% of the sampling interval (for example, the threshold is 0.5 millisecond when the sampling interval is 1 millisecond), then perform cubic spline interpolation compensation on the signal to eliminate the influence of time deviation on subsequent analysis.

[0022] When performing frequency-domain harmonic decomposition on the time-domain phase difference characteristics, use the fast Fourier transform to convert the time-domain signal into a frequency-domain spectrum. The specific operation is as follows: Frame the time-domain phase difference characteristics according to a fixed window length. The window length is set to 1024 sampling points, and the adjacent frames overlap by 50% to reduce spectral leakage. Perform the fast Fourier transform on each frame of the signal to generate a histogram of the spectral energy distribution. The frequency resolution of the histogram of the spectral energy distribution is determined by the sampling rate and the window length. The calculation formula is that the frequency resolution is equal to the sampling rate divided by the window length. For example, when the sampling rate is 1000 Hz and the window length is 1024, the frequency resolution is 1000 / 1024≈0.98 Hz. The spectral amplitude is normalized in decibels (dB) after taking the modulus.

[0023] The implementation methods for extracting the frequency and energy distribution weights of harmonic components include: identifying harmonic components with energy exceeding a preset threshold from the spectral energy distribution histogram, where the preset threshold is set to 3 times the average spectral energy. Record the frequency values and corresponding energy values of each harmonic component. The energy distribution weight is the ratio of the energy value of each harmonic component to the total spectral energy, and the calculation method is the energy of a single harmonic component divided by the sum of the energies of all harmonic components. For example, if the energy of a certain harmonic component is 500 units and the total energy is 2000 units, then its energy distribution weight is 500 / 2000×100% = 25%. The energy distribution weight is retained with two decimal places of precision.

[0024] When analyzing the frequency-domain coupling coefficient of adjacent detection channels based on the frequency and energy distribution weights of harmonic components, the calculation method of the frequency-domain coupling coefficient is: multiply the energy distribution weights of the harmonic components at the same frequency in two adjacent detection channels to obtain the coupling coefficient at that frequency. For example, if the energy distribution weight of channel A at 100 Hz is 30% and the energy distribution weight of channel B at 100 Hz is 20%, then the coupling coefficient at 100 Hz is 30%×20% = 6%. The finally generated interference signal characteristics include the coupling coefficient values at each frequency point and the corresponding harmonic frequency set. The coupling coefficient is stored in percentage form and retained with one decimal place of precision.

[0025] The fast Fourier transform for frequency-domain harmonic decomposition is implemented by an embedded digital signal processor, and the processor needs to support single-precision floating-point operations and at least 1024-point complex Fourier transform. During the generation process of the frequency-domain coupling coefficient, if the harmonic component at a certain frequency is not detected in any of the adjacent channels (the energy distribution weight is 0), then the coupling coefficient at that frequency is set to 0 to avoid invalid calculations.

[0026] In extreme input cases (such as the complete absence of the baseline signal in a certain detection channel), zero-padding processing is performed on the historical data of adjacent time windows to ensure a constant input length for the fast Fourier transform. The length of the zero-padding data is the same as the current window, and the time window of the historical data is one window length before the current time (for example, if the current is the 10th second, then the data of the 9th second is used). The exception handling mechanism includes: if the energy distribution weights of more than 50% of the harmonic components are 0, then trigger the hardware self-check program to check whether the sensor connection status and the gain of the signal amplification circuit are normal. The self-check program is completed by sending a test pulse signal and detecting the response waveform.

[0027] The parameter adjustment range and basis include: The preset threshold for time-domain phase difference analysis is set according to the maximum expected delay of the detection channel. The calculation formula is threshold = maximum channel spacing / signal propagation speed × safety factor, where the safety factor is taken as 0.5. The window length for frequency-domain harmonic decomposition is adjusted according to the target frequency resolution. For example, when detecting low-frequency interference below 1 Hz, the window length needs to be greater than 1000 sampling points. The preset threshold for the energy distribution weight is dynamically adjusted according to the noise floor. The calculation formula is threshold = average spectral energy + 3 × standard deviation.

[0028] Figure 2 The flowchart for generating chaotic or linear compensation signals according to the present invention is given. S2. Based on the interference signal characteristics, analyze the primary and secondary order of harmonics, and dynamically generate chaotic or linear compensation signals in combination with the recurrence rate and the determinism quantization factor. Specifically, it can be implemented as follows: When sorting the primary and secondary order of harmonic interference in adjacent detection channels according to the frequency and energy distribution weight of the harmonic components in the interference signal characteristics, the determination rule for the primary and secondary order is to preferentially select the harmonic component with the highest energy distribution weight as the main interference harmonic component. If there are multiple harmonic components with the same energy distribution weight, select the harmonic component with the lowest frequency as the main interference harmonic component. For example, when the energy distribution weight of the 100 Hz harmonic component in channel A is 35% and the energy distribution weight of the 150 Hz harmonic component in channel B is 30%, determine that the 100 Hz harmonic component in channel A is the main interference harmonic component. The calculation method for the energy distribution weight is the percentage of the energy value of a single harmonic component in the total energy of all harmonic components. The statistical range of the total energy is the common frequency interval of adjacent detection channels. The determination method for the common frequency interval is as follows: Take the intersection of the harmonic frequency sets of adjacent detection channels. If the intersection is empty, expand it to adjacent frequencies with a frequency deviation less than 5 Hz. For example, if the harmonic frequency set of channel A is 100 Hz, 200 Hz, 300 Hz, and the harmonic frequency set of channel B is 105 Hz, 200 Hz, 305 Hz, then the common frequency interval is 200 Hz (exact match) and 100 Hz and 105 Hz (deviation < 5 Hz).

[0029] When generating an initial phase inversion compensation signal based on the frequency and energy distribution weight of the main interference harmonic component, the generation method of the initial phase inversion compensation signal includes: performing a 180-degree phase inversion on the time-domain waveform of the main interference harmonic component, and adjusting the amplitude of the inverted signal to be inversely proportional to the energy distribution weight of the main interference harmonic component. For example, if the energy distribution weight of the main interference harmonic component is 40%, the amplitude of the initial phase inversion compensation signal is adjusted to the reciprocal multiple of 40% of the original signal amplitude, that is, 2.5 times the original signal amplitude. The phase inversion operation is implemented through an inverter circuit in the digital signal processor, and the amplitude adjustment is completed by a programmable gain amplifier, and the gain value is dynamically configured according to the energy distribution weight. The control signal of the programmable gain amplifier is generated by a microcontroller, and the microcontroller obtains the corresponding gain coefficient by looking up a table according to the energy distribution weight. The preset rule of the look-up table data is: for every 5% increase in the energy distribution weight, the gain coefficient decreases by 0.1 times. For example, when the energy distribution weight is 20%, the gain coefficient is 5 times, and when the energy distribution weight is 25%, the gain coefficient is 4 times.

[0030] When performing recurrence plot analysis on the time-domain phase difference feature in the interference signal characteristics, the construction method of the recurrence plot includes: dividing the time-domain phase difference feature into multiple sub-segments according to the time series, each sub-segment contains a fixed number of sampling points, and a 50% overlap rate is adopted between sub-segments to reduce spectral leakage. The sub-segment length is set to 1024 sampling points, which is consistent with the window length of the frequency-domain harmonic decomposition. Perform phase space reconstruction on each sub-segment, and the embedding dimension of the phase space reconstruction is set to 3, and the time delay parameter is determined according to the first zero crossing of the autocorrelation function. For example, for the time-domain phase difference feature with a sampling rate of 1000 Hz, calculate its autocorrelation function. When the autocorrelation function value first drops to 1 / e (about 36.8%) of the peak value, the corresponding time delay is 10 milliseconds, then the time delay parameter is set to 10 sampling points. In the reconstructed phase space, calculate the Euclidean distance between each state point. If the distance is less than the preset distance threshold, it is marked as a recurrence point in the recurrence plot. The distance threshold is set as 1.5 times the standard deviation of the time-domain phase difference feature to ensure robustness under noise interference. For example, if the standard deviation of the time-domain phase difference feature is 0.2 milliseconds, the distance threshold is set to 0.3 milliseconds. The recurrence rate is calculated by statistically analyzing the proportion of recurrence points in the total number of points in the recurrence plot, and the determinism is calculated by statistically analyzing the proportion of the length of the diagonal structure in the recurrence plot. The diagonal structure is defined as a line segment formed by consecutive recurrence points, and the minimum line segment length is set to 5 sampling points.

[0031] When the product of the recursion rate and the deterministic quantization factor exceeds a preset product threshold, a chaotic compensation signal synchronized with the main interference harmonic component is generated based on a chaotic model. The construction method of the chaotic model is to use the Lorenz system equation, and by adjusting the parameters of the Lorenz system, the frequency of its output signal is made consistent with the frequency of the main interference harmonic component. For example, if the frequency of the main interference harmonic component is 100 Hz, the parameter σ of the Lorenz system is set to the dimensionless value corresponding to 100 Hz, and a chaotic signal is generated through numerical iteration.

[0032] When generating the compensation signal based on the chaotic model, the differential equations of the Lorenz system are described as dx / dt = σ(y - x), dy / dt = x(ρ - z) - y, dz / dt = xy - βz, where: σ is the Prandtl number of the system, which is used to characterize the ratio of heat conduction to momentum conduction. In this embodiment, it can be converted into a dimensionless value through the ratio of the actual frequency to the reference frequency (for example, when the main interference frequency is 100 Hz, σ = 1); ρ is the normalized Rayleigh number, which reflects the ratio of buoyancy to viscous force in hydrodynamics. In this embodiment, it can be fixed at 28 to ensure that the system is in a chaotic state; β is the geometric constraint factor, which is used to describe the influence of the system spatial scale. In this embodiment, it can be set to 8 / 3 to match the characteristics of the standard Lorenz attractor; x is the intensity variable of the convective motion, which corresponds to the instantaneous amplitude fluctuation of the compensation signal in this embodiment; y is the coupling variable of the temperature gradient and the fluid velocity, which is mapped as the phase modulation factor of the compensation signal in this embodiment; z is the non-linear deviation variable of the vertical temperature distribution, which is used to control the intensity of the chaotic characteristics of the compensation signal in this embodiment; t is the independent time variable, which represents the continuous time variable of the system evolution.

[0033] σ is determined by the mapping relationship between the main interference frequency and the reference frequency (reference frequency = sampling rate / 10), and ρ and β adopt the standard values of the classical chaotic model to ensure predictable dynamic behavior. For example, when the sampling rate is 1000 Hz, the reference frequency is 100 Hz. If the main interference frequency is 100 Hz, then σ = 1, and at this time, the frequency of the chaotic signal generated by the system is strictly synchronized with the main interference.

[0034] The amplitude of the chaotic compensation signal is dynamically adjusted according to the energy distribution weight of the main interference harmonic component. The adjustment rule is that the maximum amplitude of the chaotic signal is equal to 1.2 times the amplitude value of the main interference harmonic component. For example, if the amplitude of the main interference harmonic component is 5 volts, then the maximum amplitude of the chaotic compensation signal is 6 volts. The method for verifying the synchronization between the chaotic compensation signal and the main interference harmonic component is to calculate the peak value of their cross-correlation function. If the peak value exceeds a preset threshold (for example, peak value > 0.8), it is considered that the synchronization is successful. The calculation window length of the cross-correlation function is 1024 points, and the step is 512 points to balance the calculation efficiency and accuracy.

[0035] When the product of the recursion rate and the deterministic quantization factor does not exceed the preset product threshold, a linear compensation signal is generated based on the initial phase inversion compensation signal. The method for generating the linear compensation signal includes: performing frequency-domain filtering on the initial phase inversion compensation signal to retain the frequencies of the main interference harmonic components and their third harmonic components. For example, if the main interference harmonic component is 100 Hz, the passband is set to 95 Hz to 105 Hz (fundamental frequency) and 295 Hz to 305 Hz (third harmonic). The filtered signal is adjusted to the target amplitude through a linear gain amplifier, and the setting rule for the target amplitude is inversely proportional to the energy distribution weight of the main interference harmonic component. For example, if the energy distribution weight of the main interference harmonic component is 30%, the target amplitude is 3.33 times the amplitude of the original signal. The frequency-domain filtering is implemented using a finite impulse response filter, and the passband width of the filter is set to ±5 Hz to cover frequency fluctuations. The filter order is set to 64, and the stopband attenuation is not less than 40 dB to ensure the out-of-band noise suppression effect.

[0036] The preset product threshold is set based on the statistical results of experimental data. By testing the effectiveness of the chaotic compensation signal in different noise environments, the optimal range of the product threshold is determined to be 0.4 to 0.6. For example, in a laboratory environment, when the product threshold is set to 0.5, the chaotic compensation has the best suppression effect on nonlinear interference. The dynamic adjustment method of the product threshold is to correct it in real time according to the environmental noise level, and the correction coefficient is the ratio of the current environmental noise energy to the reference noise energy, and the reference noise energy is obtained through the no-load test in the system initialization stage. The duration of the no-load test is 30 seconds, and the average noise energy without sample injection is recorded as the reference value during this period. For example, if the current environmental noise energy is twice the reference value, the correction coefficient is 2, and the product threshold is adjusted to 0.5×2 = 1.0.

[0037] The exception handling mechanism includes: if an invalid phase space reconstruction is detected during the recurrence plot analysis (such as the embedding dimension or time delay parameter exceeding the reasonable range), switch to the default parameter mode, with the default embedding dimension being 2 and the default time delay being 10% of the sampling interval. For example, when the sampling rate is 1000 Hz, the default time delay is 1 millisecond (10%×10 milliseconds). If the chaotic model synchronization fails (the peak value of the cross-correlation function does not reach the threshold), enable the linear compensation signal as an alternative solution and record the fault code for subsequent diagnosis. The encoding rule for the fault code is: code 1 represents an exception in the recurrence plot parameters, code 2 represents a chaotic synchronization failure, and code 3 represents an invalid energy distribution weight. The handling method in extreme input cases is: when the energy distribution weight of the main interference harmonic component is less than 5%, it is determined as an invalid interference, skip the compensation signal generation step, and directly output the original detection signal. The determination of invalid interference requires that the energy distribution weight <5% is satisfied in three consecutive time windows (each window has 1024 points) to avoid misjudgment of instantaneous noise.

[0038] In this step S2, by combining the primary and secondary sorting of harmonics with the dynamic generation of recursive quantization factors, the problem of poor adaptability of a single compensation mode to complex interference in traditional multi-channel gas chromatography is solved. The primary and secondary sorting based on harmonic energy weights can accurately locate the main interference source and avoid the residue of secondary harmonics. The recursive quantization factor analyzes the non-linear characteristics of the time-domain phase difference to distinguish linear and chaotic interferences, and thus adaptively selects the phase inversion or chaotic compensation strategy. Compared with the existing technology that only relies on fixed thresholds or frequency-domain filtering, the accuracy of interference suppression and environmental adaptability are improved through dynamic parameter fusion. Especially in non-stationary noise and multi-modal interference scenarios, the baseline noise can be significantly reduced and the signal-to-noise ratio of trace component detection can be increased, ensuring the consistency of multi-channel data.

[0039] S3. Superimpose the compensation signal onto the baseline signal of the target detection channel to form a baseline signal with noise suppression, which can be specifically implemented as follows: When selecting the compensation mode of time-domain superposition or frequency-domain filtering according to the type of the compensation signal, if the compensation signal is a chaotic compensation signal, the time-domain direct superposition method is adopted. The specific operation is as follows: Align the chaotic compensation signal and the baseline signal of the target detection channel according to the time series, and the alignment basis is the synchronous clock mark of the chaotic compensation signal and the baseline signal, and the time error of the synchronous clock mark is controlled within 1 microsecond. The time alignment method is to intercept the signal segments of the same time period. Taking the starting sampling point of the chaotic compensation signal as the reference, perform interpolation compensation on the baseline signal to ensure that the time axes are completely matched. If the compensation signal is a linear compensation signal, the superposition method after frequency-domain matching filtering is adopted. The specific operation is as follows: Perform a fast Fourier transform on the linear compensation signal, extract its frequency-domain amplitude spectrum, retain the frequency band components consistent with the frequencies of the main interference harmonic components, and the frequency band range is the frequency value of the main interference harmonic components ±5Hz. After setting the amplitudes of the remaining frequency band components to zero, perform an inverse fast Fourier transform to generate a filtered linear compensation signal, and then superimpose it with the baseline signal. The frequency band retention rule for frequency-domain matching filtering is: If the frequency of the main interference harmonic component is 100Hz, the passband is set to 95Hz to 105Hz.

[0040] When performing gain calibration on the baseline signal of the target detection channel, the operation of gain calibration is as follows: Adjust the amplitude of the baseline signal through a programmable gain amplifier so that its dynamic range matches the amplitude range of the compensation signal. The determination rule for dynamic range matching is that the maximum amplitude of the baseline signal does not exceed ±10% of the maximum amplitude of the compensation signal. For example, if the maximum amplitude of the compensation signal is 5 volts, then the maximum amplitude of the baseline signal is calibrated to be between 4.5 volts and 5.5 volts. The gain coefficient of the programmable gain amplifier is dynamically calculated by the microcontroller according to the amplitude parameter of the compensation signal. The calculation method is that the gain coefficient is equal to the amplitude of the compensation signal divided by the amplitude of the baseline signal. For example, if the amplitude of the compensation signal is 5 volts and the amplitude of the baseline signal is 4 volts, then the gain coefficient is 5 / 4 = 1.25. The calculation result of the gain coefficient is retained with two decimal places of precision and is output to the control port of the programmable gain amplifier through a digital-to-analog converter.

[0041] When adjusting the amplitude of the superimposed baseline signal according to the sensitivity threshold of the target detection channel, the setting basis of the sensitivity threshold is the upper limit value of the linear response range of the detector. The adjustment rule is to limit the amplitude of the superimposed signal within 90% of the upper limit of the linear response range. For example, if the upper limit of the linear response range of the detector is 10 volts, then the amplitude of the superimposed signal shall not exceed 9 volts. The amplitude adjustment is achieved through a digital attenuator. The attenuation coefficient of the attenuator is calculated in real time according to the ratio of the target amplitude to the current amplitude. For example, if the current amplitude of the superimposed signal is 10 volts and the target amplitude is 9 volts, then the attenuation coefficient is set to 0.9. The control signal of the digital attenuator is generated by the microcontroller. The microcontroller real-time collects the amplitude value of the superimposed signal through an analog-to-digital converter and dynamically adjusts the attenuation coefficient according to the sensitivity threshold. The adjustment frequency is 1000 times per second to ensure real-time performance.

[0042] When performing time-domain smoothing processing on the adjusted baseline signal, the time-domain smoothing processing is implemented using a moving average filter. The window length of the filter is set to 1 / 10 of the sampling rate. For example, when the sampling rate is 1000 Hz, the window length is 100 sampling points. The output of the moving average filter is the arithmetic mean of all sampling points within the window. When calculating, outliers exceeding ±3 times the standard deviation are ignored. The determination method for outliers is as follows: Calculate the mean and standard deviation of the sampling points within the window. If the absolute difference between the value of a certain sampling point and the mean exceeds 3 times the standard deviation, then it is determined as an outlier and excluded from the calculation. The signal after smoothing processing needs to evaluate whether the high-frequency noise component meets the preset threshold through the standard deviation. If the standard deviation exceeds the threshold, the smoothing processing is repeated until the noise component is lower than the threshold. The setting method of the standard deviation threshold is the standard deviation of the initial noise floor multiplied by a safety factor. The safety factor ranges from 0.5 to 0.8. The standard deviation of the initial noise floor is obtained through an idle test during the system initialization phase. The test duration is 30 seconds, and the standard deviation of the data in the last 20 seconds is taken as the reference value.

[0043] In extreme input cases (such as when the amplitude of the compensation signal exceeds the detector range), the exception handling mechanism includes: If during the gain calibration process, it is detected that the amplitude of the baseline signal exceeds ±20% of the compensation signal amplitude range, then switch to the backup gain coefficient, which is set according to the average value of the historical calibration records. The historical calibration records store the gain coefficients of the most recent 100 calibrations, and the backup gain coefficient is the arithmetic mean of them. If the retained band energy of the compensation signal after frequency-domain matched filtering is less than 10% of the total energy, then it is determined that the filtering has failed, and the original baseline signal without superposition is directly output. The determination of filtering failure requires that the retained band energy < 10% be satisfied in three consecutive time windows (each window has 1024 points) to avoid misjudgment due to instantaneous interference.

[0044] S4. Dynamically adjust the electromagnetic isolation parameters of the detector shielding structure according to the baseline signal after noise suppression, so that the impedance of the shielding layer matches the current interference signal spectrum. Specifically, it can be implemented as follows: When extracting the spectral characteristics of the current interference signal based on the baseline signal after noise suppression, the fast Fourier transform is used to convert the time-domain signal into a frequency-domain energy distribution. The method for extracting spectral characteristics is as follows: The baseline signal after noise suppression is framed in windows of 1024 sampling points, with 512 sampling points overlapping between adjacent frames to reduce spectral leakage. The fast Fourier transform is performed on each frame of the signal to generate a spectral amplitude distribution map. The identification rule for the main interference frequency band is: Select a continuous frequency band with an energy ratio exceeding 60% of the total energy in the spectral amplitude distribution map as the main interference frequency band. For example, if the total spectral energy is 1000 units, the energy of the main interference frequency band is 650 units and the frequency range is from 95 Hz to 105 Hz, then this frequency band is determined as the main interference frequency band. The frequency resolution of the spectral amplitude distribution map is 0.98 Hz (sampling rate 1000 Hz / 1024 points), and the amplitude unit is decibel (dB), which is normalized after taking the logarithm calculation.

[0045] When calculating the target matching value of the shielding layer impedance according to the energy proportion of the main interference frequency band, the real part and the imaginary part of the target matching value respectively correspond to the average resistance and reactance components of the main interference frequency band. The calculation method of the real part is the average resistance value of the main interference frequency band multiplied by the energy proportion coefficient, and the energy proportion coefficient is the ratio of the total energy to the energy of the main interference frequency band. For example, if the energy proportion of the main interference frequency band is 65%, the energy proportion coefficient is 100 / 65≈1.54. If the average resistance value of the main interference frequency band is 30 ohms, the target real part is 30×1.54≈46.2 ohms. The calculation method of the imaginary part is the average reactance value of the main interference frequency band multiplied by the energy proportion coefficient, and the phase compensation factor is superimposed. The phase compensation factor is determined according to the physical distance between the detector and the shielding structure. For every 1 cm increase in the distance, the phase compensation factor increases by 0.1. For example, when the distance is 5 cm, the phase compensation factor is 0.5. If the average reactance value is -20 ohms, the target imaginary part is -20×1.54 + 0.5≈-30.3 ohms.

[0046] When performing time-domain smoothing processing on the adjusted baseline signal, the time-domain smoothing processing is implemented by a moving average filter, and the window length of the filter is set to 1 / 10 of the sampling rate. For example, when the sampling rate is 1000 Hz, the window length is 100 sampling points. The output of the moving average filter is the arithmetic mean of all sampling points within the window, and the outliers beyond ±3 times the standard deviation are ignored during the calculation. The determination method of outliers is as follows: calculate the average value and standard deviation of the sampling points within the window. If the absolute difference between the value of a certain sampling point and the average value exceeds 3 times the standard deviation, it is determined as an outlier and excluded from the calculation. The high-frequency noise component of the smoothed signal needs to be evaluated through the standard deviation to see if it meets the preset threshold. If the standard deviation exceeds the threshold, the smoothing process is repeated until the noise component is lower than the threshold. The setting method of the standard deviation threshold is the standard deviation of the initial noise floor multiplied by the safety factor, and the safety factor ranges from 0.5 to 0.8. The standard deviation of the initial noise floor is obtained through the no-load test in the system initialization stage. The test duration is 30 seconds, and the standard deviation of the data in the last 20 seconds is taken as the reference value.

[0047] In extreme input cases (such as the compensation signal amplitude exceeding the detector range), the exception handling mechanism includes: if it is detected during the gain calibration process that the baseline signal amplitude exceeds ±20% of the compensation signal amplitude range, switch to the backup gain coefficient, and the backup gain coefficient is set according to the average value of the historical calibration records. The historical calibration records save the gain coefficients of the most recent 100 calibrations, and the backup gain coefficient takes its arithmetic mean. If the retained band energy of the compensation signal after frequency-domain matched filtering is lower than 10% of the total energy, it is determined that the filtering fails, and the un-superimposed original baseline signal is directly output. The determination of filtering failure needs to satisfy that the retained band energy <10% in three consecutive time windows (each window has 1024 points) to avoid misjudgment of instantaneous interference.

[0048] S4. Dynamically adjust the electromagnetic isolation parameters of the detector shielding structure according to the baseline signal after noise suppression, so that the impedance of the shielding layer matches the current interference signal spectrum. Specifically, it can be implemented as follows: When extracting the spectral characteristics of the current interference signal based on the baseline signal after noise suppression, the fast Fourier transform is used to convert the time-domain signal into a frequency-domain energy distribution. The method for extracting spectral characteristics is as follows: The baseline signal after noise suppression is framed in windows of 1024 sampling points, with 512 sampling points overlapping between adjacent frames to reduce spectral leakage. The fast Fourier transform is performed on each frame of the signal to generate a spectral amplitude distribution map. The identification rule for the main interference frequency band is: Select a continuous frequency band with an energy proportion exceeding 60% of the total energy in the spectral amplitude distribution map as the main interference frequency band. For example, if the total spectral energy is 1000 units, the energy of the main interference frequency band is 650 units and the frequency range is from 95 Hz to 105 Hz, then this frequency band is determined as the main interference frequency band. The frequency resolution of the spectral amplitude distribution map is 0.98 Hz (sampling rate 1000 Hz / 1024 points), and the amplitude unit is decibel (dB), which is standardized after taking the logarithm calculation.

[0049] When calculating the target matching value of the shielding layer impedance according to the energy proportion of the main interference frequency band, the real part and the imaginary part of the target matching value respectively correspond to the average resistance and reactance components of the main interference frequency band. The calculation method for the real part is the average resistance value of the main interference frequency band multiplied by the energy proportion coefficient, and the energy proportion coefficient is the ratio of the total energy to the energy of the main interference frequency band. For example, if the energy proportion of the main interference frequency band is 65%, then the energy proportion coefficient is 100 / 65 ≈ 1.54. If the average resistance value of the main interference frequency band is 30 ohms, then the target real part is 30 × 1.54 ≈ 46.2 ohms. The calculation method for the imaginary part is the average reactance value of the main interference frequency band multiplied by the energy proportion coefficient, and a phase compensation factor is superimposed. The phase compensation factor is determined according to the physical distance between the detector and the shielding structure. For every 1 cm increase in the distance, the phase compensation factor increases by 0.1. For example, when the distance is 5 cm, the phase compensation factor is 0.5. If the average reactance value is -20 ohms, then the target imaginary part is -20 × 1.54 + 0.5 ≈ -30.3 ohms.

[0050] When performing skewness analysis on the peak shape of an independent time-domain signal, the calculation method for the peak shape skewness is as follows: Determine the peak top position (the point with the maximum amplitude) and the baseline center position (the midpoint between the peak start point and the end point) of the chromatographic peak, and calculate the proportion of the offset between the two to the peak width. The peak width is defined as the width at 10% of the peak height, and the peak height is the difference between the peak top amplitude and the baseline amplitude. For example, if the peak top is at the 500th sampling point, the baseline center is at the 450th sampling point, and the peak width is 100 sampling points, then the offset is 50 sampling points, and the skewness is 50 / 100 = 0.5. The peak height calculation method is the peak top amplitude of 5 volts minus the baseline amplitude of 0.5 volts, resulting in a peak height of 4.5 volts.

[0051] When performing trend fitting on the baseline of an independent time-domain signal, the least squares method is used for linear regression of the baseline region. The baseline region is selected as the 100 sampling points before the peak start and the 100 sampling points after the peak end. The absolute value of the slope of the fitting curve is used as the baseline drift slope, and the calculation method is the amplitude change of the fitting line within a unit time. For example, if the fitting line equation is u = 0.2v + 10, the slope is 0.2, and the baseline drift slope is 0.2. Here, v is the time independent variable (unit: second or sampling point number), and u is the baseline signal amplitude dependent variable (unit: volt or normalized amplitude). If there is non-linear fluctuation in the baseline region, the baseline is divided into sub-segments of 50 sampling points each, and after fitting respectively, the maximum value of the absolute value of the slope of each segment is taken as the final slope.

[0052] When determining the type of asymmetric distortion based on the combined relationship between the peak shape skewness and the baseline drift slope, the determination rule is: if the peak shape skewness is greater than 0.3 and the baseline drift slope is less than 0.1, it is determined as right-skewed peak distortion; if the peak shape skewness is less than -0.3 and the baseline drift slope is greater than 0.5, it is determined as baseline drift distortion; in other cases, it is determined as compound distortion. The setting basis of the thresholds 0.3, 0.1, and 0.5 is the statistical distribution of historical experimental data, and the optimal discrimination point is determined through receiver operating characteristic curve analysis. For example, in 100 groups of experimental data, the average skewness of right-skewed peak distortion is 0.4 ± 0.1, and the average slope of baseline drift distortion is 0.6 ± 0.2. The threshold setting can maximize the classification accuracy.

[0053] In extreme input cases (such as when the weight value of a certain channel in the time-domain overlapping signal is lower than 0.1), the abnormal handling mechanism includes: if the weight value of a certain channel in the weight matrix is lower than 0.1, it is determined that the signal of this channel is invalid, the separation step is skipped and it is marked as a noise channel; if the baseline drift slope exceeds the detector range (such as slope > 10 V / s), the signal re-acquisition process is triggered, the current data is discarded and the sampling is restarted. The marking information of the noise channel is stored in the log file, and the log file contains the timestamp, channel number, and abnormal type code. The interval time of signal re-acquisition is set to at least 5 seconds to avoid system overload caused by high-frequency triggering.

[0054] This step S5 dynamically generates a signal separation weight matrix through electromagnetic isolation parameters, solves the problem of poor adaptability of traditional fixed weights in multi-channel signal separation, and combines the joint analysis of peak shape skewness and baseline drift slope to accurately identify the type of asymmetric distortion of chromatographic peaks. Compared with the existing technology that only relies on single peak shape or baseline characteristics, through the dynamic optimization of the weight matrix and the fusion of multi-dimensional distortion characteristics, the separation accuracy and the reliability of distortion detection are significantly improved. Deeply coupling electromagnetic parameters with signal processing and using physical shielding characteristics to optimize signal separation logic can effectively suppress crosstalk between channels and reduce the false positive rate.

[0055] S6. Normalize and calibrate the multi-channel detection results according to the asymmetric distortion characteristics, and output the corrected component concentration data. Specifically, it can be implemented as follows: When selecting the corresponding calibration strategy based on the type of asymmetric distortion characteristics, if the distortion type is right-skewed peak distortion, the specific operation of the peak position offset compensation calibration is as follows: Calculate the offset of the peak position relative to the baseline center according to the peak skewness, convert the offset into a time compensation value, and perform time-domain translation on the chromatographic peak through an interpolation algorithm to align the peak top with the baseline center. For example, if the peak top offset is 50 sampling points and the sampling interval is 1 millisecond, the time compensation value is 50 milliseconds, and the cubic spline interpolation is used for the interpolation algorithm. If the distortion type is baseline drift distortion, the operation of the baseline slope reverse compensation calibration is as follows: Generate a reverse compensation signal according to the baseline drift slope, and the amplitude change rate of the compensation signal is equal to the absolute value of the baseline slope and in the opposite direction. The baseline drift is cancelled by superimposing the compensation signal. For example, if the baseline slope is 0.2 V / s, the slope of the compensation signal is -0.2 V / s. The selection basis of the calibration strategy is the distortion type determined in step S5, ensuring strict association with the upstream logic.

[0056] When generating the normalization calibration coefficients for each detection channel according to the calibration strategy, the calculation method of the normalization calibration coefficient is as follows: Take the ratio of the distortion characteristic parameter to the reference parameter as the calibration coefficient. For right-skewed peak distortion, the reference parameter is the preset standard peak top offset of the system (for example, the standard offset is 10 sampling points), and the calibration coefficient is the ratio of the actual offset to the standard offset. For example, if the actual offset is 50 sampling points, the calibration coefficient is 50 / 10 = 5.0. For baseline drift distortion, the reference parameter is the maximum baseline slope allowed by the system (for example, the maximum slope is 0.5 V / s), and the calibration coefficient is the ratio of the actual slope to the maximum slope. For example, if the actual slope is 0.2 V / s, the calibration coefficient is 0.2 / 0.5 = 0.4. The calibration coefficient is reserved to two decimal places and mapped to the preset compensation gain value through a look-up table method. The look-up table data is stored in a non-volatile memory, and the storage format is a CSV file, including fields such as distortion type, calibration coefficient, and timestamp.

[0057] When multiplying the normalization calibration coefficient by the original concentration data of the corresponding detection channel, the original concentration data is calculated by the chromatographic peak area integration method, and the unit of the peak area is volt × second. The calculation formula for the corrected component concentration data is: Corrected concentration = Original concentration × Calibration coefficient. For example, if the original concentration is 100 ppm and the calibration coefficient is 0.4, the corrected concentration is 100 × 0.4 = 40 ppm. The multiplication operation is implemented by a floating-point multiplier in the digital signal processor. The input data format is 32-bit floating-point numbers, and the output data retains three significant figures. The calculation error of the floating-point multiplier is controlled within ±0.1% to ensure data accuracy.

[0058] When performing validity verification on the normalized and calibrated component concentration data, the preset confidence interval is set based on the statistical distribution of historical experimental data. The upper limit of the confidence interval is 3 times the standard deviation of the historical data average, and the lower limit is 0.5 times the standard deviation of the historical data average. For example, if the historical data average is 50 ppm and the standard deviation is 5 ppm, then the confidence interval is 50 - 3×5 = 35 ppm to 50 + 3×5 = 65 ppm. If the calibrated data exceeds this interval, it is determined as abnormal data and excluded. The processing method for abnormal data is to record it in the log file and trigger the data re-sampling process, and the re-sampling interval time is set to at least 30 seconds. The log file contains the timestamp, channel number, abnormal value, and calibration coefficient, and the storage format is a CSV file with the file encoding being UTF-8, and each line record is separated by a line break character.

[0059] In extreme input cases (such as when the calibration coefficient exceeds the system preset range), the abnormal handling mechanism includes: if the calibration coefficient is greater than 10 or less than 0.1, it is determined that the coefficient fails, and switch to the backup calibration coefficient (the backup coefficient is the moving average of the historical calibration coefficient); if there is still abnormal data after three consecutive data re-samplings, trigger the hardware self-check program to check the detector sensitivity and the signal path connection status. The self-check program determines a hardware failure when the distortion degree of the response waveform exceeds 5% by sending a standard test signal (1 kHz sine wave, amplitude 1 V) and detecting the integrity of the response waveform. The hardware self-check result is recorded in the system log and a status indicator light is used to prompt the user.

[0060] The technical solution of the embodiment of the present invention breaks through the limitation of single-dimensional interference suppression in traditional multi-channel gas chromatography analysis through the synergistic effect of multiple steps. The prior art usually relies on fixed threshold filtering or static shielding parameters and is difficult to cope with dynamic interference coupling and non-stationary noise. In this embodiment, first, the electromagnetic isolation parameter is combined with non-linear dynamics analysis (the S2 recursive quantization factor and the S4 shielding parameter are linked), and through chaotic compensation and cross-layer optimization of the hardware impedance, the problem of residual interference caused by the disconnection between frequency domain filtering and physical shielding is solved; second, the multi-dimensional traceability of asymmetric distortion of time-domain overlapping signals is introduced (the combination of the S5 peak skewness and the baseline drift slope is used for determination), breaking through the traditional single criterion mode that only relies on peak symmetry or baseline smoothness, and can accurately distinguish the distortion types caused by hardware aging and instantaneous interference; finally, a dynamic feedback closed loop is established (S3 noise suppression → S4 shielding adjustment → S6 calibration verification), and the inefficiency of manual experience-based parameter tuning is avoided through parameter self-adaptability. The chaotic compensation generation requires synchronization with the main interference harmonic frequency (S2), and the shielding layer impedance needs to match the interference spectrum in real time (S4). The combination of the two can simultaneously suppress conducted and radiated interference, while the traditional scheme is generally processed step by step. The deep integration of electromagnetics, non-linear signal processing, and chromatographic analysis forms a cross-domain collaborative interference suppression system.

[0061] Example 2: Figure 3 The structural schematic diagram of an on-line multi-channel fast gas chromatography analysis system according to the present invention is given. An on-line multi-channel fast gas chromatography analysis system includes: An interference feature module, which is used to extract the interference signal features between adjacent detection channels in the parallel working state of multiple detectors in the gas chromatography system; A harmonic compensation module, which is used to analyze the primary and secondary order of harmonics based on the interference signal features, and dynamically generate chaotic or linear compensation signals in combination with the recursion rate and the deterministic quantization factor; A noise suppression module, which is used to superimpose the compensation signal onto the baseline signal of the target detection channel to form a baseline signal after noise suppression; An electromagnetic adjustment module, which is used to dynamically adjust the electromagnetic isolation parameters of the detector shielding structure according to the baseline signal after noise suppression, so that the impedance of the shielding layer matches the current interference signal spectrum; A distortion analysis module, which is used to separate the time-domain overlapping signals of each detection channel based on the electromagnetic isolation parameters, and analyze the asymmetric distortion features of the separated time-domain overlapping signals; A calibration output module, which is used to perform normalization calibration on the multi-channel detection results according to the asymmetric distortion features, and output the corrected component concentration data.

[0062] The calculations involved in the embodiments are all dimensionless numerical calculations. The preset parameters and threshold selections in the calculations are set by those skilled in the art according to the actual situation.

[0063] It should be noted that the present invention can be deployed on the device itself to achieve embedded applications, or can be run on a PC or other terminals with a user interface, so as to meet various hardware environments and usage requirements.

[0064] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (such as infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that includes one or more collections of available media. The available media can be magnetic media (such as floppy disks, hard disks, magnetic tapes), optical media (such as DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

[0065] Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working processes of the systems, devices, and modules described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0066] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the modules is only a logical function division, and there can be other division methods in actual implementation. For example, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the couplings, direct couplings, or communication connections shown or discussed with each other can be through some interfaces, and the indirect couplings or communication connections of the devices or modules can be in electrical, mechanical, or other forms.

[0067] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules. They can be located in one place or distributed to multiple network modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0068] In addition, in each embodiment of the present application, each functional module can be integrated into a processing module, or each module can exist physically alone, or two or more modules can be integrated into one module.

[0069] If the above-mentioned function is implemented in the form of a software functional module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs.

[0070] The above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in the present application, and all of them should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0071] Finally: The above is only the preferred embodiment of the present invention and is not used to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An online multi-channel fast gas chromatography analysis method, characterized in that, Including: S1. Extract the interference signal characteristics between adjacent detection channels in the multi-detector parallel working state of the gas chromatography system; S2. Analyze the primary and secondary order of harmonics based on the interference signal characteristics, and dynamically generate chaotic or linear compensation signals in combination with the recurrence rate and the deterministic quantization factor; S3. Superimpose the compensation signal onto the baseline signal of the target detection channel to form a baseline signal after noise suppression; S4. Dynamically adjust the electromagnetic isolation parameters of the detector shielding structure according to the baseline signal after noise suppression, so that the impedance of the shielding layer matches the current interference signal spectrum; S5. Separate the time-domain overlapping signals of each detection channel based on the electromagnetic isolation parameters, and analyze the asymmetric distortion characteristics of the separated time-domain overlapping signals; S6. Normalize and calibrate the multi-channel detection results according to the asymmetric distortion characteristics, and output the corrected component concentration data.

2. The on-line multi-channel fast gas chromatography analysis method according to claim 1, wherein In the multi-detector parallel working state of the gas chromatography system, extracting the interference signal characteristics between adjacent detection channels includes: Perform time-domain phase difference analysis on the baseline signals of adjacent detection channels to obtain time-domain phase difference characteristics; Perform frequency-domain harmonic decomposition on the time-domain phase difference characteristics, and extract the frequencies and energy distribution weights of the harmonic components; Analyze the frequency-domain coupling coefficients of adjacent detection channels based on the frequencies and energy distribution weights of the harmonic components to generate interference signal characteristics; Among them, the frequency-domain harmonic decomposition is realized by fast Fourier transform, and the frequency-domain coupling coefficient is the product of the harmonic energy ratios of adjacent detection channels.

3. An on-line multi-channel rapid gas chromatography analysis method according to claim 1, characterized in that, Analyzing the primary and secondary order of harmonics based on the interference signal characteristics, and dynamically generating chaotic or linear compensation signals in combination with the recurrence rate and the deterministic quantization factor includes: Rank the harmonic interferences of adjacent detection channels according to the frequencies and energy distribution weights of the harmonic components in the interference signal characteristics to determine the main interference harmonic components; Generate an initial phase inversion compensation signal based on the frequencies and energy distribution weights of the main interference harmonic components; Perform recurrence plot analysis on the time-domain phase difference characteristics in the interference signal characteristics, and extract the recurrence rate and the deterministic quantization factor; When the product of the recurrence rate and the deterministic quantization factor exceeds the preset product threshold, generate a chaotic compensation signal synchronized with the main interference harmonic component based on the chaotic model; otherwise, generate a linear compensation signal based on the initial phase inversion compensation signal.

4. An on-line multi-channel fast gas chromatography analysis method according to claim 1, characterized in that, Superimposing the compensation signal onto the baseline signal of the target detection channel to form a baseline signal after noise suppression includes: Select a compensation mode of time-domain superposition or frequency-domain filtering according to the type of the compensation signal; Perform gain calibration on the baseline signal of the target detection channel to make the dynamic range of the baseline signal match the amplitude range of the compensation signal; Adjust the amplitude of the superimposed baseline signal according to the sensitivity threshold of the target detection channel to ensure that the amplitude of the superimposed signal does not exceed the linear response range of the detector; Perform time-domain smoothing processing on the adjusted baseline signal to eliminate the high-frequency noise components introduced during the superposition process, and generate a baseline signal after noise suppression.

5. An on-line multi-channel fast gas chromatography analysis method according to claim 4, characterized in that, If the compensation signal is a chaotic compensation signal, the time-domain direct superposition method is adopted; if the compensation signal is a linear compensation signal, the frequency-domain matching filtering and then superposition method is adopted.

6. An on-line multi-channel fast gas chromatography analysis method according to claim 1, characterized in that Dynamically adjust the electromagnetic isolation parameters of the detector shielding structure according to the baseline signal after noise suppression, so that the shielding layer impedance matches the current interference signal spectrum, including: Extract the spectrum characteristics of the current interference signal based on the baseline signal after noise suppression, and identify the energy distribution and frequency range of the main interference frequency band; Calculate the target matching value of the shielding layer impedance according to the energy proportion of the main interference frequency band; By adjusting the thickness of the adjustable conductive coating in the shielding layer and the distribution density of the magnetic permeability material, the errors of the real part and the imaginary part of the shielding layer impedance with the real part and the imaginary part of the target matching value are respectively less than a preset ratio; The matching degree between the shielding layer impedance and the interference signal spectrum is monitored in real time. If the matching degree is lower than the preset matching degree, the spectrum feature extraction and parameter adjustment are re-triggered.

7. An on-line multi-channel fast gas chromatography analysis method according to claim 1, characterized in that, Separate the time domain overlapping signals of each detection channel based on electromagnetic isolation parameters, and analyze the asymmetric distortion characteristics of the separated time domain overlapping signals, including: Generate a signal separation weight matrix for each detection channel based on the real part and the imaginary part of the electromagnetic isolation parameter; The signal separation weight matrix is ​​used to perform weighted separation on the time domain overlapping signals to obtain independent time domain signals of each channel; The skewness analysis is performed on the peak shape of the independent time domain signal, and the offset between the peak top position and the center of the baseline is calculated as the peak shape skewness; Perform trend fitting on the baseline of the independent time domain signal, and calculate the absolute value of the slope of the fitting curve as the baseline drift slope; The type of asymmetric distortion is determined based on the combined relationship between the peak skewness and the baseline drift slope.

8. An on-line multi-channel fast gas chromatography analysis method according to claim 1, characterized in that, The multi-channel detection results are normalized and calibrated according to the asymmetric distortion characteristics, and the corrected component concentration data is output, including: Selecting a corresponding calibration strategy based on the type of asymmetric distortion features; Generate a normalized calibration coefficient for each detection channel according to the calibration strategy, and the normalized calibration coefficient is calculated by the ratio of the distortion characteristic parameter to the reference parameter; Multiply the normalized calibration coefficient by the original concentration data of the corresponding detection channel to obtain the component concentration data after normalization and calibration; The validity of the component concentration data after normalization and calibration is verified, abnormal data beyond the preset credible interval is eliminated, and the corrected component concentration data is output.

9. An on-line multi-channel fast gas chromatography analysis method according to claim 8, characterized in that, If the peak shape is right-biased, the peak position offset compensation calibration is used; if the baseline drift distortion is caused, the baseline slope reverse compensation calibration is used.

10. An on-line multi-channel fast gas chromatography analysis system for implementing an on-line multi-channel fast gas chromatography analysis method according to any one of claims 1-9, characterized in that, include: The interference feature module is used to extract the interference signal features between adjacent detection channels when multiple detectors of the gas chromatography system are working in parallel; The harmonic compensation module is used to analyze the harmonic order based on the interference signal characteristics and dynamically generate chaotic or linear compensation signals by combining the recursive rate and the deterministic quantization factor; A noise suppression module, used for superimposing the compensation signal onto the baseline signal of the target detection channel to form a baseline signal after noise suppression; An electromagnetic adjustment module is used to dynamically adjust the electromagnetic isolation parameters of the detector shielding structure according to the baseline signal after noise suppression, so that the shielding layer impedance matches the current interference signal spectrum; A distortion analysis module, used to separate the time domain overlapping signals of each detection channel based on the electromagnetic isolation parameters, and analyze the asymmetric distortion characteristics of the separated time domain overlapping signals; Calibration output module, which is used to perform normalization calibration on the multi-channel detection results according to the asymmetric distortion characteristics and output the corrected component concentration data.

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