Trace oxygen sampling control method

Abstract

The invention discloses a trace oxygen sampling control method which comprises the following steps: calculating a complex impedance module value, a phase angle and a time derivative thereof based on impedance real part and imaginary part data, and obtaining a double-electric-layer disturbance index through weighted fusion of an impedance change rate and a phase stability factor; according to the double-electric-layer disturbance index, the pressure fluctuation amplitude is adaptively determined, the stability of the sensor is protected, and the replacement efficiency is improved; and micro-differential-pressure modulation control parameters are generated according to the pressure fluctuation amplitude. The technical problem that a traditional fixed parameter control method cannot balance sensor protection and quick response is solved, and intelligent and accurate control over the trace oxygen sampling process is achieved.

Description

Technical Field

[0001] The present invention belongs to the field of fluid dynamics, and particularly relates to a method for controlling micro-oxygen sampling. Background Art

[0002] Micro-oxygen analysis plays a crucial role in high-end manufacturing fields such as semiconductor manufacturing, high-purity gas production, and metal heat treatment. During the semiconductor manufacturing process, even trace amounts of oxygen contamination can cause serious problems such as wafer oxidation and device performance degradation. Therefore, the oxygen content in the process gas needs to be controlled below 1 ppm. Electrochemical oxygen sensors have become the mainstream technology for micro-oxygen detection due to their high sensitivity and selectivity. However, in practical applications, how to achieve fast response while ensuring measurement accuracy has always been a technical problem in this field. Especially in an on-line monitoring system with multi-gas path switching, the replacement efficiency of the residual gas in the sensor cavity directly affects the accuracy and real-time performance of the measurement.

[0003] Existing micro-oxygen sampling control technologies mainly adopt control strategies with fixed parameters. In terms of gas path switching, a constant flow rate of carrier gas purging method is usually adopted, and the purging time is extended to ensure sufficient replacement of the gas in the cavity. Some advanced systems have introduced pulse purging technology, that is, a pulsed gas flow with a fixed frequency and intensity is applied at the initial stage of switching to accelerate the replacement of the dead zone gas. In terms of sensor protection, existing technologies mostly adopt passive protection measures of current limiting and pressure limiting, and set fixed upper limits of flow rate and pressure to avoid impact on the sensor. In terms of optimizing control parameters, the optimal purging time, pulse frequency and other parameters are mainly determined based on empirical values or off-line calibration and remain unchanged during actual operation.

[0004] However, existing technologies have obvious technical defects in practical applications. First, although the pulsed purging with a fixed intensity can accelerate gas replacement, the high-intensity pressure pulse will disturb the double-layer structure of the electrochemical sensor, resulting in baseline drift and increased noise of the sensor. This kind of disturbance is particularly prominent in micro-oxygen measurement because a slight change in the double layer will cause a measurement deviation at the ppm level. Second, the existing methods fail to monitor the electrochemical state of the sensor in real time and cannot dynamically adjust the control parameters according to the stability of the double layer, resulting in the same control strategy being adopted when the sensor is in different working states, which may cause either excessive disturbance or insufficient replacement. Summary of the Invention

[0005] The object of the invention is to provide a method for controlling micro-oxygen sampling, in order to solve at least one technical problem existing in the prior art.

[0006] Technical solution: A method for controlling micro-oxygen sampling, comprising: Obtaining the real part data and the imaginary part data of the impedance of the sensor; Based on the real part data and imaginary part data of impedance, calculate the complex impedance modulus value and the time derivative of the phase angle; Calculate the change rate of the complex impedance modulus value relative to the reference value, and calculate the phase stability factor based on the time derivative of the phase angle; Calculate the electric double layer perturbation index by weighted fusion of the change rate and the phase stability factor; Based on the electric double layer perturbation index, dynamically determine the pressure fluctuation amplitude, and the pressure fluctuation amplitude is adaptively adjusted according to the perturbation degree within a preset range; Generate a differential pressure modulation control parameter according to the pressure fluctuation amplitude for controlling the pressure modulation during the sampling process.

[0007] Advantageous effects: The present invention avoids permanent damage caused by traditional high-intensity pulses, solves the technical problem that the traditional fixed-parameter control method cannot balance sensor protection and rapid response, and realizes intelligent and precise control of the trace oxygen sampling process. Description of the Drawings

[0008] Figure 1 It is a step flow chart of a trace oxygen sampling control method provided by an embodiment of the present application.

[0009] Figure 2 It is a step flow chart of calculating the phase stability factor provided by an embodiment of the present application.

[0010] Figure 3 It is a step flow chart of calculating the electric double layer perturbation index provided by an embodiment of the present application.

[0011] Figure 4 It is a step flow chart of dynamically determining the pressure fluctuation amplitude provided by an embodiment of the present application. Detailed Embodiments

[0012] In order to enable those skilled in the art to better understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of 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.

[0013] It should be particularly noted that, for clearly showing the step flow of the present application, serial numbers are marked for each step in the specification. These serial numbers are only for the convenience of description and do not limit the execution order of the steps. In actual operation, according to the technical requirements of the specific implementation scenario, the steps can be executed in an order different from that shown in the specification, and in some cases, parallel processing between steps can also be achieved.

[0014] It is found in the research that the fixed pulse timing cannot adapt to the replacement requirements under different concentration gradients. When switching from high concentration to ultra-low concentration, the diffusion rate of the residual gas is closely related to the concentration gradient, and the existing technology ignores this dynamic characteristic, resulting in an extended response time or a decrease in measurement accuracy.

[0015] As Figure 1 shown, a micro-oxygen sampling control method is proposed, including the following steps: Obtain the real part data and imaginary part data of the impedance of the sensor; Based on the real part data and imaginary part data of the impedance, calculate the complex impedance modulus value and the time derivative of the phase angle; calculate the change rate of the complex impedance modulus value relative to the reference value, and calculate the phase stability factor based on the time derivative of the phase angle; calculate the double-layer perturbation index by weighted fusion of the change rate and the phase stability factor; Based on the double-layer perturbation index, dynamically determine the pressure fluctuation amplitude; Generate a micro-pressure difference modulation control parameter according to the pressure fluctuation amplitude.

[0016] According to one aspect of the present application, obtaining the real part data and imaginary part data of the impedance of the sensor includes: Obtain the original oxygen concentration voltage signal, real part data of the impedance, imaginary part data of the impedance, and system timestamp from the system, perform timestamp alignment correction on the multi-channel data to compensate for the acquisition delay, ensure the synchronization of the data for subsequent processing, and output a synchronized sensor data set; Perform signal preprocessing on the synchronized sensor data set: the oxygen concentration signal is processed by an adaptive Kalman filter, and the filtering parameters are adjusted in real time according to the signal change rate; the impedance data is processed by a moving window median filter (window size: 5 sampling points) to remove pulse interference, and the filtered oxygen concentration signal and purified impedance data are obtained; Convert the filtered oxygen concentration signal through a pre-stored sensor calibration coefficient matrix for three-point calibration interpolation. The calculation formula is C_O2 = Cal_Matrix[0] + Cal_Matrix[1] × V_O2_filtered + Cal_Matrix[2] × V_O2_filtered 2 , to obtain the standardized oxygen concentration signal C_O2_std, and at the same time transfer the purified impedance data to the next processing link. Where Cal_Matrix[0], Cal_Matrix[1], and Cal_Matrix[2] are the first, second, and third coefficients of the sensor calibration coefficient matrix respectively, and V_O2_filtered is the filtered oxygen concentration signal.

[0017] As Figure 2 shown, according to one aspect of the present application, calculating the phase stability factor includes: Dividing the absolute value of the time derivative of the phase angle by a preset phase change threshold to obtain a normalized phase change rate; Calculating the difference between 1 and the normalized phase change rate to obtain a phase stability factor; Wherein, the larger the absolute value of the time derivative of the phase angle, the smaller the phase stability factor, indicating that the double - layer stability is worse.

[0018] As Figure 3 shown, according to one aspect of the present application, calculating the double - layer perturbation index includes: Multiplying the absolute value of the change rate by a first weight coefficient to obtain a first weighted result; Multiplying the difference between 1 and the phase stability factor by a second weight coefficient to obtain a second weighted result; Adding the first and second weighted results to obtain the double - layer perturbation index; Wherein, the first weight coefficient is greater than the second weight coefficient, making the impedance amplitude change dominant in the perturbation evaluation.

[0019] Specifically, based on the purified impedance data {Z_real_clean, Z_imag_clean}, calculating the complex impedance modulus |Z| = sqrt(Z_real_clean 2 + Z_imag_clean 2) and the phase angle φ = arctan(Z_imag_clean / Z_real_clean). Calculate the time derivative dφ / dt of the phase angle through a numerical differentiation algorithm, and construct an impedance feature vector Z_feature = [|Z|, φ, dφ / dt] that includes the impedance magnitude, phase information, and phase change rate. Where Z_real_clean is the purified real part data of the impedance, Z_imag_clean is the purified imaginary part data of the impedance, t is time, and d is the differentiation symbol. Aiming at the problem that traditional impedance analysis methods ignore phase stability, an improved double-layer electric potential perturbation detection algorithm is developed: calculate the impedance change rate ΔZ_rate = (|Z|_current - |Z|_baseline) / |Z|_baseline within a sliding window, where |Z|_current is the complex impedance modulus value at the current moment, and |Z|_baseline is the reference impedance modulus value; then introduce a phase stability factor Phase_stability = 1 - |dφ / dt| / φ_threshold, where φ_threshold is a preset threshold, and |dφ / dt| / φ_threshold is the normalized phase change rate. Finally, calculate the comprehensive perturbation index Disturbance_Index = α×|ΔZ_rate| + β×(1 - Phase_stability) through weighted fusion, where α = 0.6 and β = 0.4 are weight coefficients determined by optimizing a large amount of experimental data, and output the double-layer electric potential perturbation index Disturbance_Index.

[0020] As Figure 4 shown, according to one aspect of the present application, dynamically determining the pressure fluctuation amplitude includes: Obtaining the time derivative of the oxygen concentration signal as the concentration gradient data; Determining the sensor stability state according to the double-layer electric potential perturbation index and setting a corresponding state correction factor; Based on the concentration gradient data, calculating a gradient response factor for responding to the concentration change rate; Calculating a perturbation compensation factor according to the double-layer electric potential perturbation index, where the larger the double-layer electric potential perturbation index, the smaller the perturbation compensation factor; Multiplying the preset basic pressure difference value by the state correction factor, the gradient response factor, and the perturbation compensation factor to obtain the pressure fluctuation amplitude.

[0021] According to one aspect of the present application, determining the sensor stability state according to the double-layer electric potential perturbation index includes: When the double-layer electric potential perturbation index is less than the first threshold, it is determined to be in a stable state; When the double - layer electric potential disturbance index is greater than or equal to the first threshold and less than the second threshold, it is determined as the slight disturbance state; When the double - layer electric potential disturbance index is greater than or equal to the second threshold, it is determined as the significant disturbance state; Among them, the second threshold is 2 to 4 times the first threshold, and the state correction factors corresponding to different states decrease in turn.

[0022] Specifically, a three - level threshold determination is performed on the double - layer electric potential disturbance index Disturbance_Index: when Disturbance_Index < 0.05, it is determined as the stable state (Status = 1); when 0.05 ≤ Disturbance_Index < 0.15, it is slight disturbance (Status = 2); when Disturbance_Index ≥ 0.15, it is significant disturbance (Status = 3), and the sensor stability status code Sensor_Status is output.

[0023] According to one aspect of the present application, calculating the gradient response factor includes: Dividing the absolute value of the concentration gradient data by a preset concentration change threshold to obtain a normalized gradient value; Multiplying the normalized gradient value by a gradient response coefficient and then adding 1 to obtain an initial response factor; Setting an upper limit value for the initial response factor to obtain the gradient response factor; Among them, the faster the oxygen concentration changes, the larger the gradient response factor, so that the pressure fluctuation amplitude increases accordingly to accelerate the replacement process.

[0024] According to one aspect of the present application, calculating the disturbance compensation factor includes: Multiplying the double - layer electric potential disturbance index by a compensation coefficient to obtain a compensation amount; Calculating the difference between 1 and the compensation amount to obtain the disturbance compensation factor; Among them, the compensation coefficient is less than 1, so that the larger the double - layer electric potential disturbance index, the smaller the disturbance compensation factor, thereby reducing the pressure fluctuation amplitude to protect the sensor stability.

[0025] According to one aspect of the present application, calculating the gradient response factor based on the concentration gradient data further includes introducing dynamic compensation for the concentration change acceleration: Performing numerical differentiation on the time derivative of the oxygen concentration signal to obtain the second - order derivative of the oxygen concentration; Dividing the absolute value of the second - order derivative by a preset acceleration threshold to obtain a normalized acceleration value; Calculating a change acceleration factor according to the normalized acceleration value. When the absolute value of the second - order derivative is greater than zero, the change acceleration factor is greater than 1; Multiply the gradient response factor by the change acceleration factor to obtain an enhanced gradient response factor that takes into account the concentration change trend.

[0026] Specifically, perform gradient analysis on the standardized oxygen concentration signal C_O2_std, and calculate the first derivative dC / dt = (C_O2_std(t) - C_O2_std(t-Δt)) / Δt and the second derivative d 2 C / dt 2 = (dC / dt(t) -dC / dt(t-Δt)) / Δt by numerical differentiation, and construct the concentration gradient feature Gradient_Feature = [dC / dt, d 2 C / dt 2 , |d 2 C / dt 2 |] that reflects the concentration change trend, where Δt is the time interval. Based on the problem that the traditional fixed pressure difference method has significant differences in effects under different sensor states, construct an adaptive pressure difference algorithm with multi-factor coupling: set the base pressure difference P_base = 0.8% as the sensor protection benchmark, determine the state correction factor Status_Factor according to the sensor stability status code Sensor_Status, and the corresponding coefficients for stable, slightly disturbed, and significantly disturbed states are 1.0, 0.7, and 0.4 respectively, Status_Factor ={1.0, 0.7, 0.4}, corresponding to Status = {1, 2, 3}; calculate the gradient response factor Gradient_Factor = min(2.0, 1.0 + 0.5 × |dC / dt| / C_threshold) to respond to the concentration change rate, where C_threshold is the concentration change threshold and 0.5 is the gradient response coefficient; introduce the disturbance compensation factor Disturbance_Factor = 1.0 - 0.3×Disturbance_Index, where Disturbance_Index is the double-layer disturbance index and 0.3 is the compensation coefficient. Finally, the pressure difference amplitude P_amplitude = P_base × Status_Factor × Gradient_Factor ×Disturbance_Factor, and limit it within the range of 0.5% to 2.0%, P_amplitude = max(0.5%, min(2.0%,P_amplitude)), where P_base is the base pressure difference value, and output the optimal pressure fluctuation amplitude P_amplitude.

[0027] According to one aspect of the present application, generating the micro-pressure difference modulation control parameter according to the pressure fluctuation amplitude includes: Calculate the initial pulse interval based on the absolute value of the concentration gradient data. The larger the concentration gradient data, the smaller the initial pulse interval. Based on the initial pulse interval, generate a pulse time series using a non-uniform interval increasing rule, where the interval of the nth pulse is the initial pulse interval multiplied by (1 + αn). β , where α and β are preset increasing coefficients. Use the pressure fluctuation amplitude as the initial pulse intensity, and the intensity of each subsequent pulse decays according to an exponential law while maintaining a minimum intensity threshold. Combine each time point in the pulse time series with the corresponding pulse intensity to form the micro-pressure difference modulation control parameter.

[0028] According to one aspect of the present application, the intensity of each subsequent pulse decays according to an exponential law, including: Use the pressure fluctuation amplitude as the intensity of the first pulse. For the nth pulse, its intensity is equal to the sum of the pressure fluctuation amplitude multiplied by the exponential decay term and the pressure fluctuation amplitude multiplied by the holding coefficient. Among them, the exponential decay term decreases exponentially with the pulse sequence number, and the holding coefficient ensures that the pulse intensity is not lower than a preset ratio of the initial intensity.

[0029] According to one aspect of the present application, calculating the initial pulse interval includes: Add 1 to the absolute value of the concentration gradient data to obtain the first normalized gradient value. Divide the preset reference interval value by the first normalized gradient value to obtain the dynamic adjustment factor. Compare the dynamic adjustment factor with the minimum interval threshold, and take the larger value as the initial pulse interval.

[0030] Specifically, develop a non-uniform pulse sequence generation algorithm to replace the traditional uniform method: calculate the initial pulse interval Interval_initial = max(1ms, 50ms / (1 + |dC / dt|)) according to the concentration gradient feature Gradient_Feature, and use the increasing rule Interval(n) = Interval_initial × (1 + 0.2n). 0.8Generate a pulse time series, where n is the pulse sequence number and 0.8 is the empirically optimized exponent. The sequence termination condition is: cumulative time ≥ 0.8 × T_required. When the cumulative time reaches 0.8 times the predicted replacement time T_required, terminate the sequence generation and output the pulse interval sequence Pulse_Sequence. Construct an adaptive intensity decay strategy: set the initial pulse intensity as the optimal pressure fluctuation amplitude P_amplitude, and the subsequent pulse intensity decays according to the rule Intensity(n) = P_amplitude × exp(-0.15n) + 0.3 × P_amplitude, where 0.15 is the decay rate and 0.3 is the minimum intensity retention factor. Combine the time series and intensity data to generate the differential pressure modulation parameter Control_Params and the pulse control command Pulse_Commands for the system to perform the pressure modulation operation.

[0031] According to one aspect of the present application, before generating the pulse time series, it further includes predicting the replacement completion time based on the pressure fluctuation amplitude to determine the generation length of the pulse time series: Calculate the differential pressure acceleration factor based on the ratio of the pressure fluctuation amplitude to the reference differential pressure; Multiply the base decay coefficient by the differential pressure acceleration factor to obtain the enhanced decay coefficient; Based on the exponential decay model and the enhanced decay coefficient, calculate the predicted replacement time required to reduce the residual concentration to a preset ratio of the initial concentration; Multiply the predicted replacement time by a preset coefficient as the generation length threshold of the pulse time series.

[0032] Specifically, aiming at the limitation that the traditional fixed timing method cannot adapt to different replacement scenarios, establish a residual concentration prediction model based on diffusion kinetics: adopt the exponential decay basic model C_residual(t) = C_initial × exp(-λt), where C_initial is the initial concentration and λ is the decay coefficient; calculate the differential pressure acceleration factor Acceleration_Factor = 1 + γ × P_amplitude / 2.0% in combination with the optimal pressure fluctuation amplitude P_amplitude; the enhanced decay coefficient λ_enhanced = λ_base × Acceleration_Factor, where γ = 1.5 is the acceleration coefficient and λ_base = 0.1s -1 is the base decay coefficient, and predict the required predicted replacement time T_required through T_required = -ln(Target_ratio) / λ_enhanced, where Target_ratio = 0.05 means reducing the residual concentration to below 5%.

[0033] According to one aspect of the present application, the preset range of the pressure fluctuation amplitude is 0.5% to 2.0%; The first weight coefficient is 0.5 to 0.7, and the second weight coefficient is 0.3 to 0.5; The first threshold is 0.03 to 0.08, and the second threshold is 0.12 to 0.20; The compensation coefficient is 0.2 to 0.4; Among them, the parameter range is determined based on the balance between the sensor protection requirement and the replacement efficiency.

[0034] In summary, the present application discloses a method for controlling micro-oxygen sampling, including calculating the complex impedance modulus value, phase angle and their time derivatives based on the real and imaginary part data of the impedance, obtaining the double-layer perturbation index by weighted fusion of the impedance change rate and the phase stability factor; adaptively determining the pressure fluctuation amplitude according to the perturbation index and the oxygen concentration gradient to protect both the sensor stability and improve the replacement efficiency; adopting a non-equidistant increasing pulse timing, the initial interval is dynamically calculated according to the concentration gradient, and the pulse intensity decays according to an exponential law; by establishing a residual concentration prediction model based on diffusion kinetics, calculating the enhanced attenuation coefficient according to the pressure fluctuation amplitude, and accurately predicting the replacement completion time. It solves the technical problem that the traditional fixed-parameter control method cannot balance sensor protection and fast response, and realizes the intelligent and precise control of the micro-oxygen sampling process.

[0035] In a specific embodiment of the present application, it is described by taking the monitoring of the oxygen content in the protective gas during the steel metallurgy process as an application scenario. In the metal heat treatment process protected by high-purity nitrogen, it is necessary to control the oxygen content below 0.5 ppm, and the system uses an electrochemical oxygen sensor for on-line monitoring. The specific process of the micro-oxygen sampling control method is as follows: Step 1: Sensor data preprocessing and status identification.

[0036] 1.1. During the process of switching from standard gas (oxygen content 10 ppm) to high-purity nitrogen (target oxygen content < 0.5 ppm), the system collects sensor data every 100 milliseconds. At the 2nd second after switching, the original data obtained is as follows: oxygen concentration voltage signal V_O2 = 0.825 V (acquisition timestamp T1 = 2.000 s), real part of impedance Z_real = 1250 Ω (acquisition timestamp T2 = 2.002 s), imaginary part of impedance Z_imag = -320 Ω (acquisition timestamp T3 = 2.002 s). Due to a 2 ms delay in the impedance data, timestamp alignment correction is performed to align all data to T = 2.000 s, and the synchronized sensor data set {V_O2(2.0) = 0.825 V, Z_real(2.0) = 1250 Ω, Z_imag(2.0) = -320 Ω}_sync is output.

[0037] 1.2. Adaptive Kalman filtering is applied to the oxygen concentration signal. The current signal change rate is 0.015 V / s, and the filter automatically adjusts the process noise covariance Q = 0.001×(1 + 10×0.015) = 0.00115. After filtering, V_O2_filtered = 0.824 V. Median filtering with 5 points is used for the impedance data. Processing the sequence [1248, 1249, 1250, 1251, 1252] Ω, the output is Z_real_clean = 1250 Ω; processing the sequence [-318, -319, -320, -321, -322] Ω, the output is Z_imag_clean = -320 Ω.

[0038] 1.3. Using the pre-stored sensor calibration coefficient matrix Cal_Matrix = [2.3, 12.5, 0.08], through three-point calibration interpolation conversion: C_O2 = 2.3 + 12.5×0.824 + 0.08×0.824 2 = 2.3 + 10.3 + 0.054 = 7.95 ppm, the standardized oxygen concentration signal C_O2_std = 7.95 ppm is obtained, and at the same time, the purified impedance data {Z_real_clean = 1250 Ω, Z_imag_clean = -320 Ω} is transmitted.

[0039] Step 2. Real-time monitoring of the stability of the electric double layer.

[0040] 2.1. Calculate the complex impedance modulus based on the purified impedance data: |Z| = sqrt(Z_real 2 + Z_imag 2 ) = sqrt(1250 2+ (-320) 2 ) = sqrt(1664900) = 1290.3Ω. Calculate the phase angle: φ = arctan(Z_imag / Z_real) = arctan(-320 / 1250) = -0.2506 radians. Through numerical differentiation, using the phase angle φ_prev = -0.2485 radians at the previous moment (t = 1.9s), calculate the time derivative of the phase angle: dφ / dt = (φ_current - φ_prev) / Δt = (-0.2506 - (-0.2485)) / 0.1 = -0.021 radians per second. Construct the impedance feature vector Z_feature = [1290.3Ω, -0.2506rad, -0.021rad / s]. Where φ_current is the phase angle at the current moment.

[0041] 2.2. Calculate the reference impedance modulus |Z|_baseline = 1285Ω within the sliding window (the past 10 sampling points), and the impedance change rate: ΔZ_rate = (|Z|_current - |Z|_baseline) / |Z|_baseline = (1290.3 - 1285) / 1285 = 0.00412. Introduce the phase stability factor, set the phase change threshold φ_threshold = 0.05rad / s, and the normalized phase change rate is: |dφ / dt| / φ_threshold = 0.021 / 0.05 = 0.42. The phase stability factor Phase_stability = 1 - 0.42 = 0.58. Calculate the comprehensive disturbance index through weighted fusion (the first weight coefficient is optimized to be 0.6 and the second weight coefficient is 0.4 through experiments): Disturbance_Index = α×|ΔZ_rate| + β×(1 - Phase_stability) = 0.6×|0.00412| + 0.4×(1 - 0.58) = 0.00247 + 0.168 = 0.171.

[0042] 2.3. Perform a three-level threshold determination on the double-layer disturbance index 0.171. The first threshold is set to 0.05, and the second threshold is set to 0.15. Since 0.171 > 0.15, it is determined to be in a significant disturbance state, and the sensor stability status code Sensor_Status = 3 is output, corresponding to the status correction factor Status_Factor = 0.4.

[0043] Step three. Calculate the dynamic differential pressure modulation parameters.

[0044] 3.1. Perform gradient analysis on the standardized oxygen concentration signal. First derivative: dC / dt = (7.95 - 7.79) / 0.1 = 1.6 ppm / s (where 7.79 ppm is the concentration at t = 1.9 s). Second derivative: d 2 C / dt 2 = (1.6 - 1.45) / 0.1 = 1.5 ppm / s 2 (where 1.45 ppm / s is the first derivative at t = 1.9 s). Construct the concentration gradient feature Gradient_Feature = [1.6 ppm / s, 1.5 ppm / s 2 , 1.5 ppm / s 2 .

[0045] 3.2. Set the base pressure difference P_base = 0.8%. According to Sensor_Status = 3 (significant disturbance), determine the status correction factor Status_Factor = 0.4. Calculate the gradient response factor. Let C_threshold = 2.0 ppm / s, the normalized gradient value is 1.6 / 2.0 = 0.8, and the gradient response factor Gradient_Factor = min(2.0, 1.0 + 0.5×|1.6| / 2.0) = min(2.0, 1.4) = 1.4. Introduce the disturbance compensation factor: the compensation coefficient is taken as 0.3, and the disturbance compensation factor Disturbance_Factor = 1.0 - 0.3×0.171 = 0.949. With the base pressure difference P_base = 0.8%, the final pressure difference amplitude P_amplitude = P_base × Status_Factor × Gradient_Factor × Disturbance_Factor = 0.8%×0.4×1.4×0.949 = 0.425%. Since it is lower than the minimum value of 0.5%, the optimal pressure fluctuation amplitude P_amplitude = 0.5% is output.

[0046] Step Four: Adaptive Pulse Timing Optimization.

[0047] 4.1. The exponential decay basic model C_residual(t) = C_initial×exp(-λt) is adopted, where C_initial = 7.95 ppm. Calculate the enhanced decay coefficient: λ_enhanced = λ_base×(1 + 1.5×P_amplitude / 2.0%) = 0.1×(1 + 1.5×0.5% / 2.0%) = 0.1×1.375 = 0.1375 / s, and the basic decay coefficient λ_base = 0.1 / s; Predict the time required to reduce the residual concentration to below 5%: T_required = -ln(0.05) / 0.1375 = 2.996 / 0.1375 = 21.8 s.

[0048] 4.2. Calculate the initial pulse interval according to the concentration gradient characteristics: The absolute value of the oxygen concentration gradient |dC / dt| = 1.6 ppm / s, and the normalized gradient value 1 + |dC / dt| is 1.6 + 1 = 2.6. The reference interval value is set to 50 ms, and the dynamic adjustment factor is 50 / 2.6 = 19.2 ms. Comparing with the minimum interval threshold of 1 ms, take the larger value. Therefore, Interval_initial = max(1 ms, 50 ms / (1 + |1.6|)) = max(1 ms, 19.2 ms) = 19.2 ms. An increasing rule is adopted to generate the pulse time series. Let α = 0.2 and β = 0.8. The interval of the nth pulse is: Interval(n) = 19.2 × (1 + 0.2n) 0.8 . The specific sequence is: The 1st pulse: t = 0 ms, Interval(1) = 19.2 ms; The 2nd pulse: t = 19.2 ms, Interval(2) = 19.2×(1 + 0.2×2) 0.8 = 22.3 ms; The 3rd pulse: t = 41.5 ms, Interval(3) =19.2×(1 + 0.2×3) 0.8 = 25.2 ms. Terminate when the cumulative time reaches 0.8×21.8 = 17.4 s, and output the pulse interval sequence Pulse_Sequence.

[0049] 4.3. Set the initial pulse intensity to P_amplitude = 0.5%. Calculate the subsequent pulse intensities: Intensity(1)= 0.5%; Intensity(2) = 0.5%×exp(-0.15×1) + 0.3×0.5% = 0.431%; Intensity(3) =0.5%×exp(-0.15×2) + 0.3×0.5% = 0.370%; Combine the time series and intensity data to generate the differential pressure modulation parameters Control_Params = {(0ms, 0.5%), (19.2ms, 0.431%), (41.5ms, 0.370%)...} and the pulse control commands Pulse_Commands.

[0050] When further optimizing the control effect, calculate the second derivative of the oxygen concentration: d 2 C / dt 2 = (1.6 - 1.45) / 0.1 =1.5ppm / s 2 . Set the acceleration threshold to 2.0ppm / s 2 , and the normalized acceleration value is 1.5 / 2.0 = 0.75. The variable acceleration factor is 1 + 0.3×0.75 = 1.225. The enhanced gradient response factor is 1.4×1.225 = 1.715.

[0051] This embodiment shortens the response time of the system, reduces the baseline drift of the sensor. Especially in the state of significant disturbance, it effectively protects the sensor by reducing the amplitude of pressure fluctuation, and avoids the permanent damage caused by traditional high-intensity pulses. The actual operation data shows that after continuous operation for 1000 hours, the retention rate of the sensor sensitivity has been improved, fully verifying the superiority of this embodiment in balancing fast response and sensor protection.

[0052] The preferred embodiments of the present invention have been described in detail above. However, the present invention is not limited to the specific details in the above embodiments. Within the scope of the technical concept of the present invention, various equivalent transformations can be made to the technical solutions of the present invention, and these equivalent transformations all fall within the protection scope of the present invention.

Claims

1. A method for controlling micro-oxygen sampling, characterized in that, Including: Obtain the real part data and imaginary part data of the impedance of the sensor; calculate the complex impedance modulus value and the time derivative of the phase angle accordingly; Calculate the change rate of the complex impedance modulus value with respect to the reference value, and calculate the phase stability factor based on the time derivative of the phase angle; Calculate the electric double layer perturbation index by weighted fusion of the change rate and the phase stability factor; Dynamically determine the pressure fluctuation amplitude based on the electric double layer perturbation index; Generate the micro-pressure difference modulation control parameter according to the pressure fluctuation amplitude.

2. The method according to claim 1, wherein Calculating the phase stability factor includes: Divide the absolute value of the time derivative of the phase angle by the preset phase change threshold to obtain the normalized phase change rate; Calculate the difference between 1 and the normalized phase change rate to obtain the phase stability factor; Wherein, the larger the absolute value of the time derivative of the phase angle, the smaller the phase stability factor, indicating that the stability of the electric double layer is worse.

3. The method according to claim 1 or 2, characterized in that, Calculating the electric double layer perturbation index includes: Multiply the absolute value of the change rate by the first weight coefficient to obtain the first weighted result; Multiply the difference between 1 and the phase stability factor by the second weight coefficient to obtain the second weighted result; Add the first and second weighted results to obtain the electric double layer perturbation index; Wherein, the first weight coefficient is greater than the second weight coefficient.

4. The method according to claim 1, wherein Dynamically determining the pressure fluctuation amplitude includes: Obtain the time derivative of the oxygen concentration signal as the concentration gradient data; Determine the sensor stability state according to the electric double layer perturbation index, and set the corresponding state correction factor; Calculate the gradient response factor based on the concentration gradient data; Calculate the perturbation compensation factor according to the electric double layer perturbation index, and the larger the electric double layer perturbation index, the smaller the perturbation compensation factor; Multiply the preset basic pressure difference value by the state correction factor, the gradient response factor and the perturbation compensation factor to obtain the pressure fluctuation amplitude.

5. The method according to claim 4, characterized in that Generating the micro-pressure difference modulation control parameter according to the pressure fluctuation amplitude includes: Calculate the initial pulse interval based on the absolute value of the concentration gradient data. The larger the concentration gradient data, the smaller the initial pulse interval. Accordingly, generate a pulse time series using a non-equal interval increasing rule, where the interval of the nth pulse is the initial pulse interval multiplied by (1 + αn). β , where α and β are preset increasing coefficients; Use the pressure fluctuation amplitude as the initial pulse intensity, and the subsequent pulse intensities decay according to the exponential law, while maintaining the minimum intensity threshold; Combine each time point in the pulse time series with the corresponding pulse intensity to form the micro-pressure difference modulation control parameter.

6. The method according to claim 4, wherein Determining the sensor stability state according to the electric double layer perturbation index includes: When the electric double layer perturbation index is less than the first threshold, it is determined to be in a stable state; When the electric double layer perturbation index is greater than or equal to the first threshold and less than the second threshold, it is determined to be in a slightly perturbed state; When the electric double layer perturbation index is greater than or equal to the second threshold, it is determined to be in a significantly perturbed state; Wherein, the second threshold is 2 to 4 times the first threshold, and the state correction factors corresponding to different states decrease in turn.

7. The method according to claim 4, wherein Calculating the gradient response factor includes: Divide the absolute value of the concentration gradient data by the preset concentration change threshold to obtain the normalized gradient value; Multiply the normalized gradient value by the gradient response coefficient and then add 1 to obtain the initial response factor; Set the upper limit value for the initial response factor to obtain the gradient response factor; Wherein, the faster the oxygen concentration changes, the larger the gradient response factor, so that the pressure fluctuation amplitude increases accordingly.

8. The method according to claim 4, characterized in that Calculating the perturbation compensation factor includes: Multiply the electric double layer perturbation index by the compensation coefficient to obtain the compensation amount; Calculate the difference between 1 and the compensation amount to obtain the perturbation compensation factor; Wherein, the compensation coefficient is less than 1, so that the larger the electric double layer perturbation index, the smaller the perturbation compensation factor.

9. The method according to claim 5, characterized in that The subsequent pulse intensities decay according to an exponential law, including: Taking the pressure fluctuation amplitude as the intensity of the first pulse; For the nth pulse, its intensity is equal to the sum of the pressure fluctuation amplitude multiplied by the exponential decay term and the pressure fluctuation amplitude multiplied by the holding coefficient; Among them, the exponential decay term decreases exponentially with the pulse sequence number, and the holding coefficient ensures that the pulse intensity is not lower than a preset ratio of the initial intensity.

10. The method according to claim 5, wherein Calculating the initial pulse interval includes: Adding 1 to the absolute value of the concentration gradient data to obtain the first normalized gradient value; Dividing the preset reference interval value by the first normalized gradient value to obtain the dynamic adjustment factor; Comparing the dynamic adjustment factor with the minimum interval threshold, and taking the larger value as the initial pulse interval.

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