An online multi-channel rapid gas chromatography analysis system and method

Abstract

The present invention discloses an online multi-channel rapid gas chromatography analysis system and method, which specifically relates to the technical field of gas chromatography analysis, and is used to solve the problems of increased baseline noise and decreased trace component detection accuracy caused by physical coupling and signal interference when multiple detectors work in parallel in the prior art; the method extracts interference signal characteristics of adjacent detection channels, combines harmonic primary and secondary sorting and recursive quantization factor analysis to dynamically generate chaotic or linear compensation signals, and suppresses common-mode interference of the target channel; adjusts the electromagnetic isolation parameters of the shielding structure in real time based on the baseline signal after noise suppression, and realizes accurate matching of impedance and interference spectrum; further separates overlapping signals in the time domain and analyzes asymmetric distortion characteristics, and eliminates system errors through normalization calibration; combines dynamic interference suppression, electromagnetic shielding optimization and signal distortion tracing, and significantly improves the anti-interference ability and analysis accuracy of multi-channel detection, which is suitable for high-throughput rapid detection scenarios of complex samples.

Description

Technical Field

[0001] The present invention relates to the technical field of gas chromatography analysis, and more particularly to an online 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 online multi-channel rapid analysis, where parallel sampling and high-speed separation are used to improve detection efficiency. In existing technologies, multi-channel gas chromatography analysis achieves high-throughput continuous analysis of complex samples by configuring multiple independent detection channels and detectors. However, the dense arrangement and high-speed operation requirements of multiple detectors lead to an enhanced coupling effect between the physical space and signal transmission between channels. In particular, in the scenario of trace component detection, the detection signal is easily interfered with by adjacent channels, affecting the reliability of the analysis results.

[0003] When multiple detectors work together in existing online multi-channel gas chromatography systems, the common-mode interference caused by physical coupling and signal interaction between detection units is difficult to effectively isolate, which directly leads to an increase in baseline noise level and distortion of effective signals, resulting in a decrease in the detection accuracy of trace components and making it difficult to meet the needs of high-sensitivity and high-reliability online rapid analysis. Summary of the Invention

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

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] An online multi-channel rapid gas chromatography analysis method comprising:

[0007] S1. Extract interference signal characteristics between adjacent detection channels in a gas chromatography system with multiple detectors working in parallel;

[0008] S2. Analyze the harmonic order based on the interference signal characteristics, and dynamically generate chaotic or linear compensation signals by combining the recursive rate and deterministic quantization factor;

[0009] S3, superimposing the compensation signal onto the baseline signal of the target detection channel to form a baseline signal after noise suppression;

[0010] S4. 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;

[0011] S5. Separating the time-domain overlapping signals of each detection channel based on the electromagnetic isolation parameter, and analyzing the asymmetric distortion characteristics of the separated time-domain overlapping signals;

[0012] S6. Normalize and calibrate the multi-channel detection results according to the asymmetric distortion characteristics, and output the corrected component concentration data.

[0013] In a preferred embodiment, when multiple detectors of a gas chromatography system are working in parallel, extracting interference signal characteristics between adjacent detection channels includes:

[0014] Perform time domain phase difference analysis on the baseline signals of adjacent detection channels to obtain time domain phase difference features;

[0015] Perform frequency domain harmonic decomposition on the time domain phase difference characteristics to extract the frequency and energy distribution weight of the harmonic components;

[0016] The frequency domain coupling coefficients of adjacent detection channels are analyzed based on the frequency and energy distribution weights of the harmonic components to generate interference signal characteristics;

[0017] The frequency domain harmonic decomposition is achieved through fast Fourier transform, and the frequency domain coupling coefficient is the product of the harmonic energy proportions of adjacent detection channels.

[0018] In a preferred embodiment, the harmonic order is analyzed based on the interference signal characteristics, and the chaotic or linear compensation signal is dynamically generated by combining the recursion rate and the deterministic quantization factor, including:

[0019] According to the frequency and energy distribution weight of the harmonic components in the interference signal characteristics, the harmonic interference of adjacent detection channels is sorted in order to determine the main interference harmonic component;

[0020] generating an initial phase reversal compensation signal based on the frequency and energy distribution weight of the main interference harmonic component;

[0021] Perform recursive graph analysis on the time domain phase difference features in the interference signal characteristics to extract the recursive rate and deterministic quantization factor;

[0022] 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 the chaotic model; otherwise, a linear compensation signal is generated based on the initial phase reversal compensation signal.

[0023] In a preferred embodiment, the compensation signal is superimposed on the baseline signal of the target detection channel to form a noise-suppressed baseline signal, including:

[0024] Select the compensation mode of time domain superposition or frequency domain filtering according to the type of compensation signal;

[0025] Perform gain calibration on the baseline signal of the target detection channel so that the dynamic range of the baseline signal matches the amplitude range of the compensation signal;

[0026] Adjust the amplitude of the superimposed baseline signal according to the sensitivity threshold of the target detection channel to ensure that the superimposed signal amplitude does not exceed the linear response range of the detector;

[0027] The adjusted baseline signal is smoothed in the time domain to eliminate the high-frequency noise components introduced in the superposition process and generate a noise-suppressed baseline signal.

[0028] In a preferred embodiment, if the compensation signal is a chaotic compensation signal, a time domain direct superposition method is adopted; if the compensation signal is a linear compensation signal, a frequency domain matched filtering followed by superposition method is adopted.

[0029] 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:

[0030] 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;

[0031] Calculate the target matching value of the shielding layer impedance based on the energy proportion of the main interference frequency band;

[0032] By adjusting the thickness of the adjustable conductive coating and the distribution density of the magnetic permeability material in the shielding layer, the errors between the real part and the imaginary part of the shielding layer impedance and the target matching value are less than a preset ratio;

[0033] 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.

[0034] In a preferred embodiment, separating the time domain overlapping signals of each detection channel based on the electromagnetic isolation parameter and analyzing the asymmetric distortion characteristics of the separated time domain overlapping signals includes:

[0035] Generate a signal separation weight matrix for each detection channel based on the real part and the imaginary part of the electromagnetic isolation parameter;

[0036] 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;

[0037] The skewness analysis of the peak shape of the independent time domain signal is performed, and the offset between the peak top position and the baseline center is calculated as the peak skewness;

[0038] 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;

[0039] The type of asymmetric distortion is determined based on the combined relationship between the peak skewness and the baseline drift slope.

[0040] 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 are output, including:

[0041] Selecting a corresponding calibration strategy based on the type of asymmetric distortion features;

[0042] Generate a normalized calibration coefficient for each detection channel according to the calibration strategy, and calculate the normalized calibration coefficient by the ratio of the distortion characteristic parameter to the reference parameter;

[0043] Multiply the normalized calibration coefficient by the original concentration data of the corresponding detection channel to obtain the normalized and calibrated component concentration data;

[0044] The validity of the component concentration data after normalization and calibration is verified, abnormal data beyond the preset confidence interval is eliminated, and the corrected component concentration data is output.

[0045] In a preferred embodiment, if the peak shape distortion is right-shifted, peak top position offset compensation calibration is adopted; if the baseline drift distortion is caused, baseline slope reverse compensation calibration is adopted.

[0046] In another aspect, the present invention provides an online multi-channel fast gas chromatography analysis system, comprising:

[0047] Interference feature module, used to extract interference signal features between adjacent detection channels when multiple detectors of the gas chromatography system are working in parallel;

[0048] Harmonic compensation module, used to analyze the harmonic order based on the interference signal characteristics, and dynamically generate chaotic or linear compensation signals by combining the recursion rate and deterministic quantization factor;

[0049] A noise suppression module is used to superimpose the compensation signal onto the baseline signal of the target detection channel to form a noise-suppressed baseline signal;

[0050] 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;

[0051] A distortion analysis module is used to 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;

[0052] The calibration output module is used to normalize and calibrate the multi-channel detection results according to the asymmetric distortion characteristics and output the corrected component concentration data.

[0053] Compared with the prior art, the present invention has the following beneficial effects:

[0054] 1. Through 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 harmonic primary and secondary sorting and recursive quantization factor analysis of the interference signal characteristics, the propagation paths of high-frequency noise and low-frequency coupling interference can be accurately identified, and combined with the dynamic switching strategy of chaos compensation and linear compensation, adaptive suppression schemes are effectively generated for interference signals in different frequency bands; through real-time adjustment of electromagnetic isolation parameters and spectrum matching of 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 time-domain overlapping signals, cross-layer linkage of signal processing and hardware optimization is realized, reducing the impact of common-mode interference on the detection channel, and ensuring the signal integrity and analysis reliability of trace components in complex multi-channel environments.

[0055] 2. The closed-loop noise suppression and adaptive calibration system solves the problem in traditional technologies that static parameters cannot cope with dynamic interference. The asymmetric distortion feature analysis based on the baseline signal can accurately distinguish between signal distortion caused by instrument hardware aging and transient noise, and eliminate system errors through normalized calibration of multi-channel data. The dynamic correlation mechanism between electromagnetic isolation parameters and compensation signals ensures that the physical properties of the shielding structure are strictly adapted to the real-time interference spectrum, avoiding the over-compensation or under-compensation defects of traditional fixed shielding solutions. The integration of signal processing, hardware tuning and data calibration forms an adaptive interference suppression system, which not only improves detection sensitivity but also greatly enhances the robustness and stability of the system in high-throughput, high-noise scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 This is a flow chart of an online multi-channel rapid gas chromatography analysis method of the present invention;

[0057] Figure 2 A flow chart for generating chaotic or linear compensation signals for the present invention;

[0058] Figure 3 The figure is a schematic structural diagram of an online multi-channel rapid gas chromatography analysis system of the present invention. DETAILED DESCRIPTION

[0059] The following will provide a clear and complete description of 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.

[0060] Example 1: Figure 1 The present invention provides an online multi-channel rapid gas chromatography analysis method, comprising:

[0061] S1. Extract interference signal characteristics between adjacent detection channels in a gas chromatography system with multiple detectors working in parallel;

[0062] S2. Analyze the harmonic order based on the interference signal characteristics, and dynamically generate chaotic or linear compensation signals by combining the recursive rate and deterministic quantization factor;

[0063] S3, superimposing the compensation signal onto the baseline signal of the target detection channel to form a baseline signal after noise suppression;

[0064] S4. 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;

[0065] S5. Separating the time-domain overlapping signals of each detection channel based on the electromagnetic isolation parameter, and analyzing the asymmetric distortion characteristics of the separated time-domain overlapping signals;

[0066] S6. Normalize and calibrate the multi-channel detection results according to the asymmetric distortion characteristics, and output the corrected component concentration data.

[0067] S1. Extracting interference signal features between adjacent detection channels in a gas chromatography system with multiple detectors working in parallel. This can be specifically implemented as follows:

[0068] Baseline signals are collected in real time from sensors in each detection channel of the gas chromatography system. The baseline signal acquisition frequency is set to no less than 1000 samples per second to ensure the integrity of the time domain waveform. The baseline signal represents the raw output signal of each detection channel when no sample is loaded. It is used to characterize the detector noise floor and inherent interference between channels. Baseline signals from adjacent detection channels are collected using a synchronized clock trigger to ensure time domain alignment. The synchronized clock error is controlled within 1 microsecond to avoid additional phase differences introduced by time deviations.

[0069] When performing time domain phase difference analysis on the baseline signals of adjacent detection channels, the cross-correlation function is used to calculate the phase delay of the two channel signals. The specific operation is: after aligning the baseline signals of the two channels in time series, intercept the signal segments of the same time period and calculate their cross-correlation function. The peak position of the cross-correlation function corresponds to the relative time shift of the two signals, which is converted into a time difference and used as the time domain phase difference feature. 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 feature is 5 milliseconds. The value range of the time domain phase difference feature is set according to the physical spacing of 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 milliseconds when the sampling interval is 1 millisecond), the signal is compensated by cubic spline interpolation to eliminate the impact of time deviation on subsequent analysis.

[0070] When performing frequency-domain harmonic decomposition on the time-domain phase difference feature, a fast Fourier transform (FFT) is used to convert the time-domain signal into a frequency-domain spectrum. Specifically, the time-domain phase difference feature is framed using a fixed window length of 1024 samples, with adjacent frames overlapping by 50% to minimize spectral leakage. A FFT is performed on each frame to generate a spectral energy distribution histogram. The frequency resolution of the spectral energy distribution histogram is determined by the sampling rate and window length, calculated as: sampling rate divided by window length. For example, for a sampling rate of 1000 Hz and a window length of 1024, the frequency resolution is 1000 / 1024 ≈ 0.98 Hz. The spectral amplitude is modulo-normalized to decibels (dB).

[0071] The method for extracting the frequency and energy distribution weights of harmonic components includes: identifying harmonic components whose energy exceeds a preset threshold from the spectrum energy distribution histogram, where the preset threshold is set to three times the average spectrum energy. The frequency value and corresponding energy value of each harmonic component are recorded. The energy distribution weight is the ratio of the energy value of each harmonic component to the total energy of the spectrum, calculated as 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 harmonic component is 500 units and the total energy is 2000 units, its energy distribution weight is 500 / 2000 × 100% = 25%. The energy distribution weight is retained to two decimal places.

[0072] When analyzing the frequency-domain coupling coefficients of adjacent detection channels based on the frequency and energy distribution weights of the harmonic components, the frequency-domain coupling coefficient is calculated by multiplying the energy distribution weights of the harmonic components of 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%, the coupling coefficient at 100 Hz is 30% × 20% = 6%. The resulting interference signal signature includes the coupling coefficient value at each frequency point and the corresponding harmonic frequency set. The coupling coefficient is stored as a percentage with one decimal place precision.

[0073] The fast Fourier transform for frequency-domain harmonic decomposition is implemented using an embedded digital signal processor that supports single-precision floating-point arithmetic and at least 1024-point complex Fourier transforms. During the generation of frequency-domain coupling coefficients, if the harmonic component of a certain frequency is not detected in either channel (energy distribution weight is 0), the coupling coefficient for that frequency is set to 0 to avoid invalid calculations.

[0074] In extreme input conditions (e.g., the complete absence of a baseline signal in a detection channel), zero-padding is performed using historical data from adjacent time windows to ensure a constant input length for the Fast Fourier Transform (FFT). The zero-padding data length matches the current window, and the historical data window is one window length forward of the current time (e.g., if the current time is the 10th second, data from the 9th second is used). The exception handling mechanism includes the following: If more than 50% of the harmonic component energy distribution weights are zero, a hardware self-test is triggered to check the sensor connection status and the gain of the signal amplification circuit. This self-test is completed by sending a test pulse signal and detecting the response waveform.

[0075] The parameter adjustment range and basis include: The preset threshold for time-domain phase difference analysis is set based on 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 0.5. The window length for frequency-domain harmonic decomposition is adjusted based on the target frequency resolution. For example, to detect low-frequency interference below 1 Hz, the window length must be greater than 1000 samples. The preset threshold for energy distribution weight is dynamically adjusted based on the noise floor. The calculation formula is: threshold = average spectral energy + 3 × standard deviation.

[0076] Figure 2 A flowchart of the present invention for generating a chaotic or linear compensation signal is provided. S2, based on the interference signal feature analysis, harmonic ordering is performed, and a chaotic or linear compensation signal is dynamically generated by combining the recursion rate and the deterministic quantization factor. Specifically, the chaotic or linear compensation signal can be implemented as follows:

[0077] When ranking the harmonic interference of adjacent detection channels based on the frequency and energy distribution weights of the harmonic components in the interference signal characteristics, the ranking rule is to prioritize the harmonic component with the highest energy distribution weight as the primary interfering harmonic component. If multiple harmonic components have the same energy distribution weight, the harmonic component with the lowest frequency is selected as the primary interfering harmonic component. For example, if the energy distribution weight of the 100Hz harmonic component of channel A is 35% and the energy distribution weight of the 150Hz harmonic component of channel B is 30%, the 100Hz harmonic component of channel A is determined to be the primary interfering harmonic component. The energy distribution weight is calculated as the percentage of the energy value of a single harmonic component to the total energy of all harmonic components. The statistical range of the total energy is the common frequency range of adjacent detection channels. The common frequency range is determined by taking the intersection of the harmonic frequency sets of adjacent detection channels. If the intersection is empty, the frequency range is expanded to adjacent frequencies with a frequency deviation of less than 5Hz. For example, if the harmonic frequency set of channel A is 100Hz, 200Hz, and 300Hz, and the harmonic frequency set of channel B is 105Hz, 200Hz, and 305Hz, then the shared frequency ranges are 200Hz (exact match) and 100Hz and 105Hz (deviation <5Hz).

[0078] When generating an initial phase reversal compensation signal based on the frequency and energy distribution weight of the main interfering harmonic component, the method for generating the initial phase reversal compensation signal includes performing a 180-degree phase reversal on the time domain waveform of the main interfering harmonic component and adjusting the amplitude of the inverted signal to be inversely proportional to the energy distribution weight of the main interfering harmonic component. For example, if the energy distribution weight of the main interfering harmonic component is 40%, the amplitude of the initial phase reversal compensation signal is adjusted to the inverse of 40% of the original signal amplitude, that is, 2.5 times the original signal amplitude. The phase reversal operation is implemented by an inverter circuit in a digital signal processor, and the amplitude adjustment is accomplished by a programmable gain amplifier, whose gain value is dynamically configured based on the energy distribution weight. The control signal for the programmable gain amplifier is generated by a microcontroller, which uses a table lookup based on the energy distribution weight to obtain the corresponding gain coefficient. The preset rule for the table lookup data is that for every 5% increase in the energy distribution weight, the gain coefficient decreases by 0.1. 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.

[0079] When performing recurrence graph analysis on the time-domain phase difference feature within the interference signal signature, the recurrence graph construction method includes: dividing the time-domain phase difference feature into multiple subsegments based on the time sequence. Each subsegment contains a fixed number of sampling points, and the subsegments have a 50% overlap ratio to minimize spectral leakage. The subsegment length is set to 1024 sampling points, which matches the window length of the frequency-domain harmonic decomposition. Phase space reconstruction is performed on each subsegment, with the embedding dimension set to 3. The time delay parameter is determined based on the first zero crossing of the autocorrelation function. For example, for a time-domain phase difference feature with a sampling rate of 1000 Hz, the autocorrelation function is calculated. When the autocorrelation function value first drops to 1 / e of the peak value (approximately 36.8%), the corresponding time delay is 10 milliseconds, and the time delay parameter is set to 10 sampling points. In the reconstructed phase space, the Euclidean distance between each state point is calculated. If the distance is less than a preset distance threshold, it is marked as a recurrence point in the recurrence graph. The distance threshold is set at 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 recursion rate is calculated by counting the proportion of recursive points to the total number of points in the recursion graph, and the determinism is calculated by counting the length of the diagonal structure in the recursion graph. The diagonal structure is defined as a line segment formed by consecutive recursive points, and the minimum line segment length is set to 5 sampling points.

[0080] 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 interfering harmonic component is generated based on the chaotic model. The chaotic model is constructed using the Lorenz system equation. By adjusting the parameters of the Lorenz system, the frequency of the output signal is aligned with the frequency of the main interfering harmonic component. For example, if the frequency of the main interfering harmonic component is 100 Hz, the Lorenz system parameter σ is set to the dimensionless value corresponding to 100 Hz, and the chaotic signal is generated through numerical iteration.

[0081] When generating compensation signals based on the chaotic model, the differential equations of the Lorenz system are described as dx / dt=σ(yx),dy / dt=x(ρ-z)-y, dz / dt=xy-βz, where: σ is the system's Prandtl number, which is used to characterize the ratio of heat conduction to momentum conduction. In this embodiment, it can be converted into a dimensionless value by the ratio of the actual frequency to the reference frequency (for example, a main interference frequency of 100 Hz corresponds to σ=1); ρ is the normalized Rayleigh number, which reflects the ratio of buoyancy to viscous force in fluid dynamics. In this embodiment, it can be fixed to 28 to ensure that the system is in a chaotic state; β is a geometric constraint factor, which is used to describe the influence of the spatial scale of the system. In this embodiment, it can be set to 8 / 3 to match the standard Lorenz attractor characteristics; 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 to the phase modulation factor of the compensation signal in this embodiment; z is the nonlinear 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 time independent variable, which represents the continuous time variable of the system evolution.

[0082] σ is determined by mapping the main interference frequency to the reference frequency (reference frequency = sampling rate / 10). ρ and β use standard values from classical chaos models to ensure predictable dynamic behavior. For example, if the sampling rate is 1000Hz and the reference frequency is 100Hz, then σ = 1 if the main interference frequency is 100Hz. In this case, the frequency of the chaotic signal generated by the system is strictly synchronized with the main interference.

[0083] The amplitude of the chaotic compensation signal is dynamically adjusted based on the energy distribution weight of the main interfering harmonic component. The adjustment rule is that the maximum amplitude of the chaotic signal is equal to 1.2 times the amplitude of the main interfering harmonic component. For example, if the amplitude of the main interfering harmonic component is 5 volts, the maximum amplitude of the chaotic compensation signal is 6 volts. The synchronization between the chaotic compensation signal and the main interfering harmonic component is verified by calculating the peak value of the cross-correlation function between the two. If the peak value exceeds a preset threshold (for example, peak value > 0.8), synchronization is considered successful. The calculation window length of the cross-correlation function is 1024 points, with a step size of 512 points, to balance computational efficiency and accuracy.

[0084] When the product of the recursion rate and the deterministic quantization factor does not exceed a preset product threshold, a linear compensation signal is generated based on the initial phase-reversal compensation signal. This linear compensation signal generation method includes frequency-domain filtering the initial phase-reversal compensation signal to retain the frequency of the main interfering harmonic component and its third harmonic component. For example, if the main interfering 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 then adjusted to a target amplitude using a linear gain amplifier. The target amplitude is set inversely proportional to the energy distribution weight of the main interfering harmonic component. For example, if the energy distribution weight of the main interfering harmonic component is 30%, the target amplitude is 3.33 times the original signal amplitude. Frequency-domain filtering is implemented using a finite-length unit impulse response filter with a passband width of ±5 Hz to cover frequency fluctuations. The filter order is set to 64, and the stopband attenuation is at least 40 dB to ensure effective out-of-band noise suppression.

[0085] The preset product threshold is set based on 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 make real-time corrections based on the ambient noise level. The correction coefficient is the ratio of the current ambient noise energy to the baseline noise energy. The baseline noise energy is obtained through a no-load test during the system initialization phase. The no-load test lasts for 30 seconds, during which the average noise energy when no sample is injected is recorded as the baseline value. For example, if the current ambient noise energy is twice the baseline value, the correction coefficient is 2, and the product threshold is adjusted to 0.5×2=1.0.

[0086] The exception handling mechanism includes the following: If invalid phase space reconstruction is detected during recurrence graph analysis (e.g., if the embedding dimension or time delay parameters are outside the acceptable range), the system switches to default parameter mode, with a default embedding dimension of 2 and a default time delay of 10% of the sampling interval. For example, at a sampling rate of 1000 Hz, the default time delay is 1 millisecond (10% × 10 milliseconds). If chaotic model synchronization fails (the cross-correlation function peak does not reach the threshold), a linear compensation signal is activated as a backup, and a fault code is recorded for subsequent diagnosis. The fault code encoding rule is: Code 1 indicates a recurrence graph parameter error, Code 2 indicates chaotic synchronization failure, and Code 3 indicates an invalid energy distribution weight. For extreme input conditions, if the energy distribution weight of the main interference harmonic component is less than 5%, it is considered invalid interference, the compensation signal generation step is skipped, and the original detection signal is directly output. Invalid interference is determined when the energy distribution weight is less than 5% in three consecutive time windows (each with 1024 points) to avoid misjudgment due to transient noise.

[0087] This step S2 solves the problem of poor adaptability of a single compensation mode to complex interference in traditional multi-channel gas chromatography by combining the harmonic primary and secondary sorting with the recursive quantization factor to dynamically generate a compensation signal. The primary and secondary sorting based on the harmonic energy weight can accurately locate the main interference source and avoid the residual of secondary harmonics; the recursive quantization factor distinguishes between linear and chaotic interference by analyzing the nonlinear characteristics of the time domain phase difference, thereby adaptively selecting a phase reversal or chaos compensation strategy. Compared with the existing technology that only relies on fixed thresholds or frequency domain filtering, the accuracy and environmental adaptability of interference suppression are improved through dynamic parameter fusion, especially in non-stationary noise and multimodal interference scenarios, which can significantly reduce baseline noise and improve the signal-to-noise ratio of trace component detection, ensuring the consistency of multi-channel data.

[0088] S3, superimposing the compensation signal onto the baseline signal of the target detection channel to form a baseline signal after noise suppression, which can be specifically implemented as follows:

[0089] When selecting a compensation mode of time domain superposition or frequency domain filtering based on the compensation signal type, if the compensation signal is a chaotic compensation signal, a direct time domain superposition method is used. Specifically, the chaotic compensation signal is time-series aligned with the baseline signal of the target detection channel. The alignment is based on the synchronization clock marks of the chaotic compensation signal and the baseline signal, and the time error of the synchronization clock marks is controlled within 1 microsecond. The time alignment method is to intercept signal segments of the same time period and interpolate the baseline signal based on the starting sampling point of the chaotic compensation signal to ensure a perfect time axis match. If the compensation signal is a linear compensation signal, a frequency domain matched filtering and superposition method is used. Specifically, the linear compensation signal is fast Fourier transformed to extract its frequency domain amplitude spectrum. The frequency band components that coincide with the frequency of the main interfering harmonic component are retained within the frequency range of the main interfering harmonic component ±5 Hz. The amplitudes of the remaining frequency band components are reset to zero and then inverse fast Fourier transformed to generate a filtered linear compensation signal, which is then superimposed with the baseline signal. The frequency band preservation rule of the frequency domain matched filter is: if the frequency of the main interference harmonic component is 100 Hz, the passband is set to 95 Hz to 105 Hz.

[0090] When performing gain calibration on the baseline signal of the target detection channel, the gain calibration operation is as follows: the amplitude of the baseline signal is adjusted through the programmable gain amplifier so that its dynamic range matches the amplitude range of the compensation signal. The dynamic range matching is determined by the maximum amplitude of the baseline signal not exceeding ±10% of the maximum amplitude of the compensation signal. For example, if the maximum amplitude of the compensation signal is 5 volts, the maximum amplitude of the baseline signal is calibrated to between 4.5 volts and 5.5 volts. The gain factor of the programmable gain amplifier is dynamically calculated by the microcontroller based on the amplitude parameters of the compensation signal. The gain factor is calculated as the compensation signal amplitude divided by the baseline signal amplitude. For example, if the compensation signal amplitude is 5 volts and the baseline signal amplitude is 4 volts, the gain factor is 5 / 4 = 1.25. The gain factor calculation result is retained to two decimal places of precision and output to the control port of the programmable gain amplifier through the digital-to-analog converter.

[0091] When adjusting the amplitude of the superimposed baseline signal based on the sensitivity threshold of the target detection channel, the sensitivity threshold is set based on the upper limit of the detector's linear response range. The adjustment rule is to limit the amplitude of the superimposed signal to within 90% of the upper limit of the linear response range. For example, if the upper limit of the detector's linear response range is 10 volts, the amplitude of the superimposed signal must not exceed 9 volts. Amplitude adjustment is achieved through a digital attenuator, and the attenuation coefficient of the attenuator is calculated in real time based on the ratio of the target amplitude to the current amplitude. For example, if the current superimposed signal amplitude is 10 volts and the target amplitude is 9 volts, the attenuation coefficient is set to 0.9. The control signal of the digital attenuator is generated by a microcontroller, which uses an analog-to-digital converter to acquire the amplitude value of the superimposed signal in real time and dynamically adjusts the attenuation coefficient based on the sensitivity threshold. The adjustment frequency is 1000 times per second to ensure real-time performance.

[0092] When time-domain smoothing the adjusted baseline signal, a moving average filter is used, with the filter window length set to 1 / 10 of the sampling rate. For example, at a sampling rate of 1000 Hz, the window length is 100 samples. The output of the moving average filter is the arithmetic mean of all sampling points within the window, ignoring outliers that exceed ±3 standard deviations. Outliers are identified by calculating the mean and standard deviation of the sampling points within the window. If the absolute difference between a sampling point and the mean exceeds 3 standard deviations, the point is considered an outlier and excluded from the calculation. The smoothed signal is then evaluated using the standard deviation to determine whether the high-frequency noise component meets a preset threshold. If the standard deviation exceeds the threshold, smoothing is repeated until the noise component falls below the threshold. The standard deviation threshold is set by multiplying the standard deviation of the initial noise floor by a safety factor ranging from 0.5 to 0.8. The standard deviation of the initial noise floor is obtained during a 30-second no-load test during system initialization. The standard deviation of the last 20 seconds of data is used as the baseline value.

[0093] In extreme input conditions (for example, the compensation signal amplitude exceeds the detector range), the exception handling mechanism includes the following: If the baseline signal amplitude is detected to exceed ±20% of the compensation signal amplitude range during the gain calibration process, the system switches to the backup gain coefficient, which is set based on the average value of the historical calibration records. The historical calibration records store the gain coefficients of the last 100 calibrations, and the backup gain coefficient takes the arithmetic mean. If the retained frequency band energy of the compensation signal after frequency domain matching filtering is less than 10% of the total energy, the filter is deemed to have failed, and the original baseline signal without superposition is directly output. The determination of filter failure requires that the retained frequency band energy is <10% in three consecutive time windows (1024 points in each window) to avoid misjudgment of transient interference.

[0094] S4. 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. Specifically, it can be implemented as follows:

[0095] When extracting the spectral features of the current interference signal based on the noise-suppressed baseline signal, a fast Fourier transform (FFT) is used to convert the time-domain signal into a frequency-domain energy distribution. The spectral features are extracted by dividing the noise-suppressed baseline signal into windows of 1024 sampling points, overlapping adjacent frames by 512 sampling points to reduce spectral leakage. A FFT is then performed on each frame to generate a spectral amplitude distribution map. The primary interference frequency band is identified by selecting a continuous frequency band with an energy contribution exceeding 60% of the total energy in the spectral amplitude distribution map as the primary interference frequency band. For example, if the total spectral energy is 1000 units, the energy of the primary interference frequency band is 650 units, and the frequency range is 95Hz to 105Hz, then this band is identified as the primary interference frequency band. The frequency resolution of the spectral amplitude distribution map is 0.98Hz (sampling rate 1000Hz / 1024 points), and the amplitude is expressed in decibels (dB), normalized by taking the logarithm.

[0096] When calculating the target shield impedance matching value based on the energy contribution of the main interference frequency band, the real and imaginary parts of the target matching value correspond to the average resistance and reactance components of the main interference frequency band, respectively. The real part is calculated by multiplying the average resistance value of the main interference frequency band by the energy contribution coefficient, which is the ratio of the total energy to the energy of the main interference frequency band. For example, if the energy contribution of the main interference frequency band is 65%, the energy contribution 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 imaginary part is calculated by multiplying the average reactance value of the main interference frequency band by the energy contribution coefficient and adding a phase compensation factor. The phase compensation factor is determined by the physical distance between the detector and the shielding structure; for every 1 cm increase in distance, the phase compensation factor increases by 0.1. For example, for a 5 cm distance, 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.

[0097] When time-domain smoothing the adjusted baseline signal, a moving average filter is used, with the filter window length set to 1 / 10 of the sampling rate. For example, at a sampling rate of 1000 Hz, the window length is 100 samples. The output of the moving average filter is the arithmetic mean of all sampling points within the window, ignoring outliers that exceed ±3 standard deviations. Outliers are identified by calculating the mean and standard deviation of the sampling points within the window. If the absolute difference between a sampling point and the mean exceeds 3 standard deviations, the point is considered an outlier and excluded from the calculation. The smoothed signal is then evaluated using the standard deviation to determine whether the high-frequency noise component meets a preset threshold. If the standard deviation exceeds the threshold, smoothing is repeated until the noise component falls below the threshold. The standard deviation threshold is set by multiplying the standard deviation of the initial noise floor by a safety factor ranging from 0.5 to 0.8. The standard deviation of the initial noise floor is obtained during a 30-second no-load test during system initialization. The standard deviation of the last 20 seconds of data is used as the baseline value.

[0098] In extreme input conditions (for example, the compensation signal amplitude exceeds the detector range), the exception handling mechanism includes the following: If the baseline signal amplitude is detected to exceed ±20% of the compensation signal amplitude range during the gain calibration process, the system switches to the backup gain coefficient, which is set based on the average value of the historical calibration records. The historical calibration records store the gain coefficients of the last 100 calibrations, and the backup gain coefficient takes the arithmetic mean. If the retained frequency band energy of the compensation signal after frequency domain matching filtering is less than 10% of the total energy, the filter is deemed to have failed, and the original baseline signal without superposition is directly output. The determination of filter failure requires that the retained frequency band energy is <10% in three consecutive time windows (1024 points in each window) to avoid misjudgment of transient interference.

[0099] S4. 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. Specifically, it can be implemented as follows:

[0100] When extracting the spectral features of the current interference signal based on the noise-suppressed baseline signal, a fast Fourier transform (FFT) is used to convert the time-domain signal into a frequency-domain energy distribution. The spectral features are extracted by dividing the noise-suppressed baseline signal into windows of 1024 sampling points, overlapping adjacent frames by 512 sampling points to reduce spectral leakage. A FFT is then performed on each frame to generate a spectral amplitude distribution map. The primary interference frequency band is identified by selecting a continuous frequency band with an energy contribution exceeding 60% of the total energy in the spectral amplitude distribution map as the primary interference frequency band. For example, if the total spectral energy is 1000 units, the energy of the primary interference frequency band is 650 units, and the frequency range is 95Hz to 105Hz, then this band is identified as the primary interference frequency band. The frequency resolution of the spectral amplitude distribution map is 0.98Hz (sampling rate 1000Hz / 1024 points), and the amplitude is expressed in decibels (dB), normalized by taking the logarithm.

[0101] When calculating the target shield impedance matching value based on the energy contribution of the main interference frequency band, the real and imaginary parts of the target matching value correspond to the average resistance and reactance components of the main interference frequency band, respectively. The real part is calculated by multiplying the average resistance value of the main interference frequency band by the energy contribution coefficient, which is the ratio of the total energy to the energy of the main interference frequency band. For example, if the energy contribution of the main interference frequency band is 65%, the energy contribution 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 imaginary part is calculated by multiplying the average reactance value of the main interference frequency band by the energy contribution coefficient and adding a phase compensation factor. The phase compensation factor is determined by the physical distance between the detector and the shielding structure; for every 1 cm increase in distance, the phase compensation factor increases by 0.1. For example, for a 5 cm distance, 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.

[0102] When performing skew analysis on the peak shape of an independent time-domain signal, the peak skewness is calculated by determining the peak apex (maximum amplitude) and the baseline center (midpoint between the peak start and end points) of the chromatographic peak and calculating the offset between them as a percentage of the peak width. Peak width is defined as the width at 10% of the peak height, while peak height is the difference between the peak apex amplitude and the baseline amplitude. For example, if the peak apex is at the 500th sample point, the baseline center is at the 450th sample point, and the peak width is 100 sample points, then the offset is 50 sample points, and the skewness is 50 / 100 = 0.5. The peak height is calculated as the peak amplitude of 5 volts minus the baseline amplitude of 0.5 volts, resulting in a peak height of 4.5 volts.

[0103] When performing trend fitting on the baseline of an independent time-domain signal, the baseline trend fitting method uses the least squares method to perform linear regression on the baseline region. The baseline region is selected from the 100 sampling points before the peak onset and the 100 sampling points after the peak end. The absolute value of the slope of the fitted curve is used as the baseline drift slope, calculated as the amplitude change of the fitted line per unit time. For example, if the fitted line equation is u = 0.2v + 10, the slope is 0.2, and the baseline drift slope is 0.2. Where v is the independent time variable (unit: seconds or sampling point number), and u is the dependent baseline signal amplitude variable (unit: volts or normalized amplitude). If the baseline region exhibits nonlinear fluctuations, the baseline is segmented into 50 sampling points each. After fitting, the maximum absolute value of the slope of each segment is taken as the final slope.

[0104] The asymmetric distortion type is determined based on the combined relationship between peak skewness and baseline drift slope. The following rules apply: if the peak skewness is greater than 0.3 and the baseline drift slope is less than 0.1, it is considered a right-shifted peak distortion; if the peak skewness is less than -0.3 and the baseline drift slope is greater than 0.5, it is considered a baseline drift distortion; all other cases are considered a combined distortion. The thresholds of 0.3, 0.1, and 0.5 were set based on the statistical distribution of historical experimental data, with the optimal distinction determined through receiver operating characteristic curve analysis. For example, in 100 sets of experimental data, the mean skewness of the right-shifted peak distortion was 0.4±0.1, and the mean slope of the baseline drift distortion was 0.6±0.2. This threshold setting maximizes classification accuracy.

[0105] In extreme input conditions (for example, when the weight of a channel in a time-domain overlapping signal is less than 0.1), the exception handling mechanism includes the following: If the weight of a channel in the weight matrix is less than 0.1, the channel signal is deemed invalid, the separation step is skipped, and the channel is marked as a noise channel; if the baseline drift slope exceeds the detector range (for example, the slope is >10 V / s), the signal reacquisition process is triggered, the current data is discarded, and sampling is restarted. The marking information of the noise channel is stored in a log file, which contains a timestamp, channel number, and exception type code. The interval between signal reacquisition is set to at least 5 seconds to avoid system overload caused by high-frequency triggering.

[0106] This step S5 dynamically generates a signal separation weight matrix using electromagnetic isolation parameters, addressing the poor adaptability of traditional fixed weights in multi-channel signal separation. Combined with the combined analysis of peak skewness and baseline drift slope, it can accurately identify the type of asymmetric distortion in chromatographic peaks. Compared to existing technologies that rely solely on single peak shape or baseline features, the dynamic optimization of the weight matrix and the fusion of multi-dimensional distortion features significantly improve separation accuracy and the reliability of distortion detection. By deeply coupling electromagnetic parameters with signal processing and utilizing physical shielding properties to optimize signal separation logic, it effectively suppresses crosstalk between channels and reduces the error rate.

[0107] S6. Normalize and calibrate the multi-channel detection results according to the asymmetric distortion characteristics, and output the corrected component concentration data. The specific implementation is as follows:

[0108] When selecting a calibration strategy based on the type of asymmetric distortion, if the distortion type is right-skewed peak shape distortion, the peak top offset compensation calibration is performed as follows: the peak top offset relative to the baseline center is calculated based on the peak skewness, the offset is converted into a time offset value, and the chromatographic peak is time-shifted using 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 offset value is 50 milliseconds, and the interpolation algorithm uses cubic spline interpolation. If the distortion type is baseline drift, the baseline slope reverse compensation calibration is performed as follows: a reverse compensation signal is generated based on the baseline drift slope. The amplitude change rate of the compensation signal is equal to the absolute value of the baseline slope and has an opposite direction. The baseline drift is offset 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 calibration strategy is selected based on the distortion type determined in step S5 to ensure strict correlation with the upstream logic.

[0109] When generating normalized calibration coefficients for each detection channel based on the calibration strategy, the coefficients are calculated by taking the ratio of the distortion characteristic parameter to the reference parameter as the calibration coefficient. For right-skew peak distortion, the reference parameter is the system's preset standard peak top offset (for example, a standard offset of 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 system's maximum allowable baseline slope (for example, a maximum slope of 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 coefficients retain two decimal places of precision and are mapped to preset compensation gain values using a lookup table. The lookup table data is stored in non-volatile memory as a CSV file containing fields for distortion type, calibration coefficient, and timestamp.

[0110] When the normalized calibration factor is multiplied by the raw concentration data for the corresponding detection channel, the raw concentration data is calculated using the chromatographic peak area integration method, with peak area units expressed in volts × seconds. The formula for calculating the calibrated component concentration data is: Calibrated concentration = Raw concentration × Calibration factor. For example, if the raw concentration is 100 ppm and the calibration factor is 0.4, the calibrated concentration is 100 × 0.4 = 40 ppm. The multiplication operation is performed by the floating-point multiplier in the digital signal processor. The input data format is 32-bit floating-point, and the output data retains three significant digits. The calculation error of the floating-point multiplier is controlled within ±0.1%, ensuring data accuracy.

[0111] When performing validity verification on the component concentration data after normalization and calibration, the preset credible interval is set based on the statistical distribution of historical experimental data. The upper limit of the credible interval is 3 times the standard deviation of the historical data mean, and the lower limit is 0.5 times the standard deviation of the historical data mean. For example, if the historical data mean is 50ppm and the standard deviation is 5ppm, the credible interval is 50-3×5=35ppm to 50+3×5=65ppm. If the calibrated data exceeds this interval, it is judged as abnormal data and eliminated. Abnormal data is processed by recording it in a log file and triggering the data re-sampling process. The re-sampling interval is set to at least 30 seconds. The log file contains timestamps, channel numbers, abnormal values and calibration coefficients. The storage format is CSV file, the file encoding is UTF-8, and each line of record is separated by a line break.

[0112] In extreme input conditions (e.g., when the calibration coefficient exceeds the system's preset range), the following exception handling mechanisms are implemented: If the calibration coefficient is greater than 10 or less than 0.1, the coefficient is deemed invalid and the system switches to the backup calibration coefficient (which is a sliding average of the historical calibration coefficients). If abnormal data persists after three consecutive resamples, a hardware self-test is triggered to check the detector sensitivity and signal path connection status. The self-test procedure sends a standard test signal (1kHz sine wave, 1V amplitude) and tests the integrity of the response waveform. A hardware failure is identified when the response waveform distortion exceeds 5%. The hardware self-test results are recorded in the system log and the user is notified via a status indicator.

[0113] The technical solution of the present invention overcomes the limitations of single-dimensional interference suppression in traditional multi-channel gas chromatography analysis through the synergistic effect of multiple steps. Existing technologies typically rely on fixed-threshold filtering or static shielding parameters, making them difficult to address dynamic interference coupling and nonstationary noise. This embodiment first combines electromagnetic isolation parameters with nonlinear dynamic analysis (linking the S2 recursive quantization factor with the S4 shielding parameter). Through chaos compensation and cross-layer optimization of hardware impedance, this solution addresses the residual interference caused by the disconnect between frequency-domain filtering and physical shielding. Secondly, it introduces multi-dimensional tracing of asymmetric distortion in overlapping time-domain signals (combining S5 peak skewness and baseline drift slope), breaking away from the traditional single-criteria approach of peak symmetry or baseline stationarity and accurately distinguishing distortion types caused by hardware aging and transient interference. Finally, a dynamic feedback loop (S3 noise suppression → S4 shielding adjustment → S6 calibration verification) is established, eliminating the inefficiency of manual parameter adjustment through parameter adaptability. Chaotic compensation generation requires synchronization with the main interference harmonic frequency (S2), and the shield impedance must be matched to the interference spectrum (S4) in real time. This combination simultaneously suppresses both conducted and radiated interference, unlike traditional approaches, which typically involve separate steps. By deeply integrating electromagnetics, nonlinear signal processing, and chromatographic analysis, a cross-domain, collaborative interference suppression system is formed.

[0114] Example 2: Figure 3 The present invention provides a structural schematic diagram of an online multi-channel fast gas chromatography analysis system, which includes:

[0115] Interference feature module, used to extract interference signal features between adjacent detection channels when multiple detectors of the gas chromatography system are working in parallel;

[0116] Harmonic compensation module, used to analyze the harmonic order based on the interference signal characteristics, and dynamically generate chaotic or linear compensation signals by combining the recursion rate and deterministic quantization factor;

[0117] A noise suppression module is used to superimpose the compensation signal onto the baseline signal of the target detection channel to form a noise-suppressed baseline signal;

[0118] 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;

[0119] A distortion analysis module is used to 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;

[0120] The calibration output module is used to normalize and calibrate the multi-channel detection results according to the asymmetric distortion characteristics and output the corrected component concentration data.

[0121] The calculations involved in the embodiments are all dimensionless numerical calculations, and the preset parameters and thresholds in the calculations are set by those skilled in the art according to actual conditions.

[0122] It should be noted that the present invention can be deployed on the device itself to implement embedded applications, and can also be run on a PC or other terminal with a user interface, thereby meeting various hardware environments and usage requirements.

[0123] The above embodiments can be implemented in whole or in part via software, hardware, firmware, or any other combination. 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 comprises one or more computer instructions or computer programs. When loaded or executed on a computer, the processes or functions described in the embodiments of this application are fully or partially performed. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. 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 means (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium accessible by a computer or a data storage device such as a server or data center that contains a collection of one or more available media. The available medium can be magnetic media (e.g., floppy disks, hard disks, tapes), optical media (e.g., DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

[0124] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and modules described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0125] In the several embodiments provided in this 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 schematic. For example, the division of the modules is only a logical function division. In actual implementation, there may be other division methods, such as 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 mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0126] The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules, and may be located in one place or distributed across multiple network modules. Some or all of the modules may be selected to achieve the purpose of this embodiment according to actual needs.

[0127] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0128] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the 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 enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0129] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

[0130] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. An online multi-channel rapid gas chromatography analysis method, characterized in that: include: S1. Extract interference signal characteristics between adjacent detection channels in a gas chromatography system with multiple detectors working in parallel; S2. Analyze the harmonic order based on the interference signal characteristics, and dynamically generate chaotic or linear compensation signals by combining the recursive rate and deterministic quantization factor, including: According to the frequency and energy distribution weight of the harmonic components in the interference signal characteristics, the harmonic interference of adjacent detection channels is sorted in order to determine the main interference harmonic component; generating an initial phase reversal compensation signal based on the frequency and energy distribution weight of the main interference harmonic component; Perform recursive graph analysis on the time-domain phase difference features in the interference signal characteristics to extract the recursive rate and deterministic quantization factor; 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 the chaotic model; otherwise, a linear compensation signal is generated based on the initial phase reversal compensation signal; S3, superimposing 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 based on 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 based on the energy proportion of the main interference frequency band; By adjusting the thickness of the adjustable conductive coating and the distribution density of the magnetic permeability material in the shielding layer, the errors between the real part and the imaginary part of the shielding layer impedance and the target matching value are less than a preset ratio; Real-time monitoring of the matching degree between the shielding layer impedance and the interference signal spectrum. If the matching degree is lower than the preset matching degree, the spectrum feature extraction and parameter adjustment will be re-triggered. S5. Separating the time-domain overlapping signals of each detection channel based on the electromagnetic isolation parameters, and analyzing 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 of the peak shape of the independent time domain signal is performed, and the offset between the peak top position and the baseline center is calculated as the peak 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 peak skewness and baseline drift slope; S6. Normalize and calibrate the multi-channel detection results according to the asymmetric distortion characteristics, and output the corrected component concentration data.

2. An online multi-channel fast gas chromatography analysis method according to claim 1, characterized in that: When multiple detectors of a gas chromatography system are working 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 features; Perform frequency domain harmonic decomposition on the time domain phase difference characteristics to extract the frequency and energy distribution weight of the harmonic components; The frequency domain coupling coefficients of adjacent detection channels are analyzed based on the frequency and energy distribution weights of the harmonic components to generate interference signal characteristics; The frequency domain harmonic decomposition is achieved through fast Fourier transform, and the frequency domain coupling coefficient is the product of the harmonic energy proportions of adjacent detection channels.

3. The online multi-channel rapid gas chromatography analysis method according to claim 1, characterized in that: The compensation signal is added to the baseline signal of the target detection channel to form a noise-suppressed baseline signal, including: Select the compensation mode of time domain superposition or frequency domain filtering according to the type of compensation signal; Perform gain calibration on the baseline signal of the target detection channel so that the dynamic range of the baseline signal matches 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 superimposed signal amplitude does not exceed the linear response range of the detector; The adjusted baseline signal is smoothed in the time domain to eliminate the high-frequency noise components introduced in the superposition process and generate a noise-suppressed baseline signal.

4. The online multi-channel rapid gas chromatography analysis method according to claim 3, characterized in that: If the compensation signal is a chaotic compensation signal, a time domain direct superposition method is adopted; if the compensation signal is a linear compensation signal, a frequency domain matched filtering and superposition method is adopted.

5. The online 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 calculate the normalized calibration coefficient 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 normalized and calibrated component concentration data; The validity of the component concentration data after normalization and calibration is verified, abnormal data beyond the preset confidence interval is eliminated, and the corrected component concentration data is output.

6. The online multi-channel rapid gas chromatography analysis method according to claim 5, 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.

7. An online multi-channel fast gas chromatography analysis system for implementing the online multi-channel fast gas chromatography analysis method according to any one of claims 1 to 6, characterized in that: include: Interference feature module, used to extract interference signal features between adjacent detection channels when multiple detectors of the gas chromatography system are working in parallel; Harmonic compensation module, used to analyze the harmonic order based on the interference signal characteristics, and dynamically generate chaotic or linear compensation signals by combining the recursion rate and deterministic quantization factor; A noise suppression module is used to superimpose the compensation signal onto the baseline signal of the target detection channel to form a noise-suppressed baseline signal; 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 is used to 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; The calibration output module is used to normalize and calibrate the multi-channel detection results according to the asymmetric distortion characteristics and output the corrected component concentration data.

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