VOC emission port station house monitoring system and method based on AI adaptive calibration

Abstract

The present application relates to the technical field of environmental monitoring, in particular to a VOC exhaust port station house monitoring system and method based on AI adaptive calibration, the system comprising: a signal steady-state correction module, a time drift compensation module, a drift parameter regression module, a heterogeneous weight distribution module, and a component anomaly identification module. In the present application, the rate of temperature and humidity change is used to correct signal transient deviation, the difference between the signal and the airflow rate is compared, the lag interval is identified and the compensation order is adjusted, the synchronicity of the signal time response is improved, the drift factor is constructed through the difference sequence trend, the baseline offset is corrected and the sensitivity parameter is dynamically regressed, the weight distribution is adjusted according to the probe response time sequence difference and the signal amplitude ratio, and the participation ratio change curve is optimized, the multi-channel signal fusion consistency is improved, the cross identification mechanism is constructed in combination with the direction consistency and the fluctuation frequency of the component response ratio, the abnormal identification mark is established, and the dynamic response capability to the component concentration structure offset is strengthened.

Description

Technical Field

[0001] This invention relates to the field of environmental monitoring technology, and in particular to a VOC emission station monitoring system and method based on AI adaptive calibration. Background Technology

[0002] The field of environmental monitoring technology involves technologies for the continuous monitoring, identification, and quantitative analysis of pollutants in air, water, soil, and the ecological environment. This includes environmental data acquisition, gas and particulate matter detection, pollution source tracing, automatic sampling, and remote transmission. The field utilizes physical, chemical, and biological sensing technologies to detect and identify pollutants, and combines data acquisition instruments, communication networks, and information analysis methods to construct a multi-dimensional environmental monitoring system. This system enables real-time monitoring and long-term assessment of pollution source emissions and changes in environmental quality. In recent years, with the increasing stringency of industrial emission control standards, environmental monitoring technology has gradually developed towards automation, intelligence, and networking, forming a comprehensive system integrating data acquisition, online monitoring, and information management. Among these, traditional VOC emission stations... A monitoring system is a fixed monitoring device used to monitor the concentration of volatile organic compounds (VOCs) in industrial waste gas emissions. It consists of a sampling probe, a gas sampling pipeline, a gas analysis unit, and a data transmission device. Generally, a gas sample is extracted from the exhaust pipe through the sampling probe and transported to the gas analysis unit through a heated sampling pipeline. The concentration of VOC components is measured using a photoionization detector, infrared absorption detector, or gas chromatography analyzer. The data is then transmitted to a monitoring platform for recording and display via a data acquisition device. The system relies on manual periodic introduction of standard gas for comparison and calibration to maintain the measurement accuracy of the sensors or analyzers. This method requires on-site operation, has a long cycle, and is greatly affected by factors such as ambient temperature, humidity, and equipment aging, which can lead to data instability.

[0003] Traditional VOC emission monitoring systems rely on fixed sampling and single-probe measurement. The signal acquisition process is highly sensitive to changes in temperature and humidity, and the impact of disturbances in the sampling pipeline cannot be effectively corrected, leading to fluctuations in signal stability. Furthermore, they lack dynamic adaptability to the flow rate and response characteristics of the sampled gas. When signal time drift occurs, it is impossible to accurately align the sampling time point with the actual emission behavior, resulting in data response delays and location mismatches. The calibration process relies on manual comparison with standard gases for correction, which has a long operation cycle and is susceptible to equipment aging and external interference factors, making it difficult to maintain the stability of sensor output. The lack of a real-time adjustment mechanism for probe response differences makes it impossible to form a unified data baseline when multiple probes are fused. The system also lacks the ability to sensitively identify abnormal changes in the proportions of multiple components, resulting in insufficient reliability in anomaly identification and response analysis. Summary of the Invention

[0004] To address the technical problems existing in the prior art, this invention provides a VOC emission station monitoring system and method based on AI adaptive calibration. The technical solution is as follows:

[0005] On the one hand, an AI-adaptive calibration-based VOC emission station monitoring system was provided, which includes:

[0006] The signal steady-state correction module acquires the sampling sequence of temperature and humidity data at the outlet, analyzes the offset relationship between the rate of change of temperature and humidity, evaluates the impact on the sampling flow rate and signal stability, constructs the cross-disturbance intensity, compares the ratio of the disturbance intensity to the signal amplitude, determines the correction amplitude, and generates a steady-state correction signal sequence.

[0007] The time drift compensation module uses the steady-state correction signal sequence to calculate the rate of change of the corrected signal and compare it with the rate of change of airflow speed. It analyzes the rate difference, determines the offset direction, adjusts the signal compensation sequence, and generates a time drift correction record.

[0008] The drift parameter regression module calculates the difference distribution between the measured signal and the reference concentration data in the same time series based on the time drift correction record, analyzes the degree of fluctuation and clustering of the difference series and evaluates the drift trend, constructs the drift inversion factor, adjusts the signal baseline, and generates the sensitivity drift calibration configuration.

[0009] Based on the sensitivity drift calibration configuration, the heterogeneous weight allocation module calculates the response time difference and amplitude ratio of multiple probes, compares the changing trends to determine the response order and sensitivity difference, adjusts the probes' participation in the weighting and smooths the adjustment curve, and generates a fusion record of discharge outlet monitoring data.

[0010] The component anomaly identification module analyzes the concentration response change trends of multiple components in the monitoring data based on the fusion records of the discharge outlet monitoring data, calculates the response ratio sequence between components, compares the direction and magnitude of change, identifies the continuous stage of ratio shift and analyzes the cumulative direction and consistency, establishes anomaly identification markers, and generates component structure shift monitoring results.

[0011] As a further aspect of the present invention, the steady-state correction signal sequence includes a temperature and humidity rate difference factor, a disturbance amplitude ratio coefficient, and a transient adjustment step parameter; the time drift correction record includes a rate response delay interval, a signal offset direction marker, and a time position compensation order; the sensitivity drift calibration configuration specifically includes a difference sequence compression index, a drift increase / decrease trend factor, and a signal baseline offset adjustment amount; the outlet monitoring data fusion record includes a probe time response sorting value, a signal amplitude ratio factor, and a staged weight smoothing parameter; and the component structure offset monitoring result includes a ratio change direction trend, a response offset duration segment, and a structural anomaly identification marker.

[0012] As a further aspect of the present invention, the signal steady-state correction module includes:

[0013] The temperature and humidity trend identification submodule acquires the outlet temperature and humidity data sampling sequence, analyzes the fluctuation direction of the temperature change rate and humidity change rate in the time series, evaluates the impact on gas sampling flow rate and signal stability through the offset relationship between temperature and humidity rates, and obtains the temperature and humidity influence coefficient.

[0014] The disturbance intensity assessment submodule constructs the cross disturbance intensity based on the temperature and humidity influence coefficient, compares the ratio of the cross disturbance intensity to the sampled signal amplitude, determines the correction amplitude of the signal, and establishes the disturbance correction parameters.

[0015] The signal correction and adjustment submodule calls the disturbance correction parameters to adjust the differential reduction amplitude of the signal in the transient phase, controls the correction step according to the change ratio of the disturbance intensity between consecutive samples, optimizes the correction stability between consecutive samples, and generates a steady-state correction signal sequence.

[0016] As a further aspect of the present invention, the time drift compensation module includes:

[0017] The sampling rate comparison submodule acquires the steady-state correction signal sequence, calculates the rate of change of the correction signal in the continuous sampling phase, compares it with the rate of change of airflow velocity, analyzes the direction and magnitude of the difference between the two rates, and establishes the rate difference parameter.

[0018] The response feature recognition submodule determines the response characteristics of the correction signal to airflow changes based on the rate difference parameter, analyzes the rate difference trend, constructs a hysteresis interval and calculates the offset of the signal change gradient within the interval, filters data whose offset direction corresponds to the compensation order, and generates hysteresis compensation index.

[0019] The compensation sequence optimization submodule calls the hysteresis compensation index to adjust the corresponding position of the signal in the time series, optimize the time correspondence of the signal before and after compensation, and generate a time drift correction record.

[0020] As a further aspect of the present invention, the drift parameter regression module includes:

[0021] Based on the time drift correction record, the differential sequence calculation submodule calculates the differential distribution between the measured signal and the reference concentration data in the same time series, analyzes the degree of fluctuation and clustering of the differential sequence in a continuous time period, and obtains the differential distribution parameters.

[0022] The compression ratio evaluation submodule calls the difference distribution parameters to calculate the compression ratio of the difference sequence within the sampling segment, and determines the drift trend by comparing the direction of change of the compression ratio, and generates the drift change coefficient.

[0023] The baseline correction and adjustment submodule constructs a drift inversion factor and corrects the signal baseline by calculating the increase or decrease of the drift trend in the time series based on the drift change coefficient, thereby generating a sensitivity drift calibration configuration.

[0024] As a further aspect of the present invention, the process of constructing the drift inversion factor and correcting the signal baseline offset is specifically as follows:

[0025] The difference sequence and signal change time gradient sequence within the corresponding time period of the time drift correction record are obtained. The product of the difference sequence and the continuous stability of the time gradient sequence is calculated as the drift judgment benchmark value. The compressed difference amplitude is compared with the drift judgment benchmark value to establish the judgment basis for the drift offset direction. The drift inversion factor is calculated by the proportional relationship between the direction judgment result and the cumulative difference within the time window.

[0026] The drift inversion factor is applied to the signal baseline data segment, and the drift start point and drift end point are used as the interpolation boundary. The baseline signal in the interpolation interval is corrected by a piecewise linear offset adjustment method, and a continuous transition region is set at the drift start point and drift end point to optimize the connection consistency between the offset segment and the non-offset segment.

[0027] As a further aspect of the present invention, the heterogeneous weight allocation module includes:

[0028] The response time comparison submodule analyzes the response time distribution of multiple probes under the same emission event based on the sensitivity drift calibration configuration, calculates the time difference and signal amplitude ratio of each probe's response signal, and establishes probe response characteristic parameters.

[0029] The weight adjustment calculation submodule calls the probe response characteristic parameters, compares the changing trend of time difference and amplitude ratio, determines the response order relationship and sensitivity difference of the probes, adjusts the participation weight of each probe, and obtains the probe weight allocation parameters.

[0030] The curve smoothing optimization submodule analyzes the correspondence between the direction of weight change and the direction of time difference change based on the probe weight allocation parameters, smooths the weight adjustment curve during the continuous sampling stage, and generates a fusion record of discharge outlet monitoring data.

[0031] As a further aspect of the present invention, the component anomaly identification module includes:

[0032] The component ratio calculation submodule integrates and records the discharge outlet monitoring data, analyzes the concentration response change trends of multiple components, calculates the response amplitude ratio sequence between each pair of components, and obtains component ratio sequence data.

[0033] The continuous offset analysis submodule calls the component proportion sequence data, compares the direction and magnitude of change of each proportion sequence in the continuous sampling stage, determines the offset characteristics of the response proportion, identifies the continuous stage of proportion change, and obtains the proportion continuous offset index.

[0034] The anomaly marker establishment submodule analyzes the cumulative degree and directional consistency of the proportional shift during the continuous phase based on the proportional continuous shift index, counts the shift direction conversion frequency of multiple time segments, establishes anomaly identification markers, and generates component structure shift monitoring results.

[0035] On the other hand, the VOC emission station monitoring method based on AI adaptive calibration, which is executed based on the aforementioned VOC emission station monitoring system based on AI adaptive calibration, includes the following steps:

[0036] S1: Obtain the sampling sequence of temperature and humidity data at the outlet, analyze the offset relationship between the rate of change of temperature and humidity, evaluate the impact on the sampling flow rate and signal stability, construct the cross-perturbation intensity, compare the ratio of perturbation intensity to signal amplitude, determine the correction amplitude, and generate a steady-state correction signal sequence.

[0037] S2: Using the steady-state correction signal sequence, calculate the rate of change of the corrected signal and compare it with the rate of change of the airflow velocity, analyze the rate difference, determine the offset direction and adjust the signal compensation sequence, and generate a time drift correction record.

[0038] S3: Based on the time drift correction record, calculate the difference distribution between the measured signal and the reference concentration data in the same time series, analyze the degree of fluctuation and clustering of the difference series and evaluate the drift trend, construct the drift inversion factor, adjust the signal baseline, and generate the sensitivity drift calibration configuration.

[0039] S4: Based on the sensitivity drift calibration configuration, calculate the response time difference and amplitude ratio of multiple probes, compare the changing trends to determine the response order and sensitivity difference, adjust the probe participation weights and smooth the adjustment curve, and generate a discharge outlet monitoring data fusion record.

[0040] S5: Based on the fusion record of the discharge outlet monitoring data, analyze the concentration response change trend of multiple components in the monitoring data, calculate the response ratio sequence between components, compare the direction and magnitude of change, identify the continuous stage of ratio shift and analyze the cumulative direction and consistency, establish anomaly identification markers, and generate component structure shift monitoring results.

[0041] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following:

[0042] By utilizing the rate of change in temperature and humidity, transient signal deviations are corrected. By comparing the differences between the signal and airflow rates, lag intervals are identified and the compensation sequence is adjusted to improve the synchronicity of the signal time response. By analyzing the trend of differential sequence changes, a drift factor is constructed to correct baseline offset and dynamically regress sensitivity parameters. The weight allocation is adjusted based on the ratio of probe response time difference to signal amplitude, and the participation ratio change curve is optimized to improve the consistency of multi-channel signal fusion. A cross-identification mechanism is constructed by combining the directional consistency of component response ratios and fluctuation frequency to establish anomaly identification markers and enhance the dynamic response capability to component concentration structure shifts. Attached Figure Description

[0043] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0044] Figure 1 This is a schematic diagram of the VOC emission station monitoring system based on AI adaptive calibration provided in an embodiment of the present invention;

[0045] Figure 2 This is a schematic diagram of the system framework of the present invention;

[0046] Figure 3 This is a flowchart of the signal steady-state correction module in this invention;

[0047] Figure 4 This is a flowchart of the time drift compensation module in this invention;

[0048] Figure 5 This is a flowchart of the drift parameter regression module in this invention;

[0049] Figure 6 This is a flowchart of the heterogeneous weight allocation module in this invention;

[0050] Figure 7 This is a flowchart of the component anomaly identification module in this invention;

[0051] Figure 8 This is a flowchart of the VOC emission station monitoring method based on AI adaptive calibration provided in an embodiment of the present invention. Detailed Implementation

[0052] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0053] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0054] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning.

[0055] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0056] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0057] This invention provides a VOC emission station monitoring system based on AI adaptive calibration, such as... Figure 1-2 The diagram shown illustrates a VOC emission station monitoring system based on AI adaptive calibration. The system includes:

[0058] The signal steady-state correction module acquires the sampling sequence of temperature and humidity data at the outlet, analyzes the offset relationship between the rate of change of temperature and humidity, evaluates the impact on the sampling flow rate and signal stability, constructs the cross-disturbance intensity, compares the ratio of the disturbance intensity to the signal amplitude, determines the correction amplitude, and generates a steady-state correction signal sequence.

[0059] The time drift compensation module uses a steady-state correction signal sequence to calculate the rate of change of the corrected signal and compare it with the rate of change of airflow speed. It analyzes the rate difference, determines the offset direction, adjusts the signal compensation sequence, and generates a time drift correction record.

[0060] The drift parameter regression module calculates the difference distribution between the measured signal and the reference concentration data in the same time series based on the time drift correction record, analyzes the degree of fluctuation and clustering of the difference series and evaluates the drift trend, constructs the drift inversion factor, adjusts the signal baseline, and generates the sensitivity drift calibration configuration.

[0061] The heterogeneous weight allocation module is based on sensitivity drift calibration configuration. It calculates the response time difference and amplitude ratio of multiple probes, compares the changing trends to determine the response order and sensitivity difference, adjusts the probes to participate in the weight and smooths the adjustment curve, and generates a fusion record of discharge outlet monitoring data.

[0062] The component anomaly identification module is based on the fusion record of discharge outlet monitoring data. It analyzes the concentration response change trend of multiple components in the monitoring data, calculates the response ratio sequence between components, compares the direction and magnitude of change, identifies the continuous stage of ratio shift and analyzes the cumulative direction and consistency, establishes anomaly identification markers, and generates component structure shift monitoring results.

[0063] The steady-state correction signal sequence includes temperature and humidity rate difference factor, disturbance amplitude ratio coefficient, and transient adjustment step parameter. The time drift correction record includes rate response delay interval, signal offset direction mark, and time position compensation order. The sensitivity drift calibration configuration specifically includes difference sequence compression index, drift increase / decrease trend factor, and signal baseline offset adjustment amount. The outlet monitoring data fusion record includes probe time response sorting value, signal amplitude ratio factor, and stage weight smoothing parameter. The component structure offset monitoring results include ratio change direction trend, response offset duration segment, and structural anomaly identification mark.

[0064] Specifically, such as Figure 2 , 3 As shown, the signal steady-state correction module includes:

[0065] The temperature and humidity trend identification submodule acquires the outlet temperature and humidity data sampling sequence, analyzes the fluctuation direction of the temperature change rate and humidity change rate in the time series, evaluates the impact on gas sampling flow rate and signal stability through the offset relationship between temperature and humidity rates, and obtains the temperature and humidity influence coefficient.

[0066] To determine the baseline impact of temperature and humidity rate shifts on signal stability, a calibration experiment was first performed. Under standard zero-air (VOC-free) conditions, the sampling pipeline temperature was increased from 20°C to 60°C at a rate of 0.5°C / min, while the humidity was kept constant at 30%. Subsequently, the temperature was fixed at 40°C, and the humidity was increased from 10% to 50% at a rate of 1.0% / min. The baseline drift of the sensor signal during this process was recorded. The experimental data (as shown in Table 1) were used to establish the baseline impact model.

[0067] Table 1. Calibration Table of the Influence of Temperature and Humidity Changes on Signal Baseline Drift

[0068]

[0069] As shown in Table 1, the baseline influencing factors of the temperature change rate were determined through regression analysis. The baseline influence factor for the rate of humidity change is 1.80. The value is 0.40. After obtaining the actual outlet temperature and humidity data sampling sequence, for example, a sequence containing 5 consecutive time points (T1 to T5): temperature sequence =[45.0,45.5,45.2,46.0,45.8]°C; Humidity sequence =[30.0,30.2,31.0,30.5,30.8]%. First, calculate the rate of temperature change from time point T2 to T5. ,get The sequence is calculated as [N / A, +0.5, -0.3, +0.8, -0.2]°C / sample. Similarly, the rate of change of humidity is calculated. ,get The sequence is calculated as [N / A, +0.2, +0.8, -0.5, +0.3]% / sample. Next, the fluctuation direction from T2 to T5 is analyzed. T2: For positive, Positive (same direction); T3: Negative, Positive (opposite direction); T4: For positive, Negative (opposite direction); T5: Negative, The result is positive (opposite). Subsequently, its impact is assessed through the offset relationship between temperature and humidity rates, and an offset relationship function is defined. :when and same direction ( When, offset relationship (Same-direction changes exacerbate the risk of condensation); when and opposite direction ( )hour, (Anisotropic changes are relatively stable). Calculations yielded... The sequence is [N / A, 1.5, 0.8, 0.8, 0.8]. Finally, the influence coefficients of temperature and humidity are... pass Calculate (divide by 100 to convert to percentage effect). T2 point: Point T3: Point T4: T5 point: The advantage of this method is that it allows for the determination of [the desired result] through calibration experiments. and Factors, and combined with unidirectional / antidirectional changes The function makes The coefficients are calculated not based on fuzzy evaluation, but on quantized rates and calibrated physical effects, accurately reflecting the percentage of actual disturbance to the signal caused by different combinations of temperature and humidity changes.

[0070] The disturbance intensity assessment submodule constructs the cross disturbance intensity based on the temperature and humidity influence coefficient, compares the ratio of the cross disturbance intensity to the sampled signal amplitude, determines the signal correction amplitude, and establishes disturbance correction parameters.

[0071] Based on the influence coefficient of temperature and humidity The sequence is [0.0147, 0.00688, 0.01312, 0.00384], and the original sampled signals from the same time points T1 to T5 are retrieved. The sequence is [120.0, 122.0, 125.0, 123.0, 128.0]mV. First, the cross-perturbation strength is constructed. It is defined as the estimated influence of temperature and humidity on the signal amplitude at the previous stable moment, and is calculated as follows: Point T2: mV. T3 point: mV. T4 point: mV. T5 point: mV. Next, calculate the instantaneous amplitude change of the sampled signal. Point T2: mV. T3 point: mV. T4 point: mV. T5 point: mV. Then, compare the ratio of crosstalk intensity to the amplitude of the sampled signal. Point T2: Point T3: Point T4: T5 point: .in accordance with The value is used to determine the correction amplitude of the signal. To determine the interval for the correction amplitude, a perturbation superposition experiment was performed: Given a signal (…), Simulated disturbances of different intensities are superimposed on the surface. ), observe the corrected signal distortion. Experimental data shows that when At that time, disturbances dominate, and signal distortion is severe; when At that time, the disturbance and the signal are mixed; when At that time, the signal dominates. The correction level is set accordingly. : (Disturbance-dominated) (High correction) (mix), (Revised) (Signal-driven) (Low correction). Based on the example: The sequence is [1,2,1,3]. Finally, the perturbation correction parameters are established. This parameter defines the... Deduction ratio: If (high), (After deducting 90% of the disturbance). If (middle), (After deducting 50% of the disturbance). If (Low), (Deducting 10% disturbance). Based on the example, the generated disturbance correction parameters... The sequence is [0.9, 0.5, 0.9, 0.1]. The advantage of this method is that it allows for calculation... The ratio dynamically assesses the signal-to-noise ratio (SNR) of the disturbance and the actual signal changes, avoiding issues when the actual signal fluctuates significantly (e.g., at point T5). It is 5.0. Only 0.094), incorrectly applied excessively large perturbation correction (only applied a low correction of 0.1).

[0072] The signal correction and adjustment submodule calls the disturbance correction parameters to adjust the differential reduction amplitude of the signal in the transient phase, controls the correction step according to the change ratio of the disturbance intensity between consecutive samples, optimizes the correction stability between consecutive samples, and generates a steady-state correction signal sequence.

[0073] Calling perturbation correction parameters The sequence is [0.9, 0.5, 0.9, 0.1], and the crossover perturbation strength is... The sequence is [1.764, 0.839, 1.640, 0.472]mV. First, calculate the fundamental differential reduction magnitude from time T2 to T5. Point T2: mV. T3 point: mV. T4 point: mV. T5 point: mV. Subsequently, based on The correction step is controlled by the proportion of change between consecutive samples. The proportion of change in perturbation intensity is calculated. Point T3: (52.4%). Point T4: (95.5%). Point T5: (71.2%). To determine the baseline value for step control, a stability test experiment was performed: a jump-type disturbance correction was applied to the signal acquisition system, and the oscillation period of the signal was observed. Experimental data showed that when the rate of change of the continuous correction was... When the value exceeds 40%, a full adjustment of the deduction amount will cause the output signal to oscillate continuously for 2-3 sampling cycles. Therefore, a step control reference should be set. Based on this, a step control factor is established. :like (0.4), (Full step adjustment). If (0.4), (Speed ​​limit step adjustment). Calculated based on the example. Sequence: T3 point: , Point T4: , T5 point: , Finally, through step control factors. Optimize the corrected stability between consecutive samples and calculate the final difference reduction. Point T2: mV (T2 is the first correction point, applied in full). T3 point: mV. T4 point: mV. T5 point: mV. Assuming temperature and humidity disturbance. This causes a positive signal drift, then the steady-state correction signal sequence . mV (reference point). mV. mV. mV. mV. The final generated steady-state correction signal sequence is [120.0, 120.412, 124.303, 121.977, 127.525].

[0074] Specifically, such as Figure 2 , 4 As shown, the time drift compensation module includes:

[0075] The sampling rate comparison submodule acquires the steady-state correction signal sequence, calculates the rate of change of the correction signal during the continuous sampling phase, compares it with the rate of change of airflow velocity, analyzes the direction and magnitude of the difference between the two rates, and establishes the rate difference parameter.

[0076] Obtain the steady-state correction signal sequence =[120.0,120.412,124.303,121.977,127.525]mV, and the airflow velocity sequence collected at the same time. =[1.5,1.6,1.5,1.7,1.6] m / s. First, calculate the rate of change of the correction signal at time points T2 to T5. Point T2: mV / sample. T3 point: mV / sample. T4 point: mV / sample. T5 point: mV / sample. Next, the rate of change of airflow velocity from time point T2 to T5 is calculated. Point T2: m / s / sample. Point T3: m / s / sample. T4 point: m / s / sample. T5 point: m / s / sample. To compare the two rates, they need to be normalized. Calculation Absolute value mean of the sequence .calculate Absolute value mean of the sequence Calculate the normalized signal rate. . The sequence is [0.135, 1.278, -0.764, 1.823]. The normalized airflow rate is calculated. . The sequence is [0.8, -0.8, 1.6, -0.8]. Next, the direction and magnitude of the difference between the two rates are analyzed. T2: (positive) vs (Positive), consistent direction. Amplitude difference. T3: (positive) vs (Negative), opposite direction. Amplitude difference. T4: (Negative) vs (Positive), opposite direction. Amplitude difference. T5: (positive) vs (Negative), opposite direction. Amplitude difference. Finally, rate difference parameters are established. This parameter combines the differences in direction and amplitude. The difference in direction is defined as follows: : 0 indicates the same direction, and 1 indicates the opposite direction. Sequence = [0, 1, 1, 1]. Point T2: Point T3: Point T4: T5 point: The generated rate difference parameters The sequence is [0.665, 4.156, 4.728, 5.246]. The advantage of this method is that, through... and By normalizing them separately and then comparing them, the interference from differences in dimensions and scales between the two was eliminated; and by... The calculation increases the weight of the difference in "opposite direction" by 2, so that the moments when the signal and airflow response are inconsistent (such as T3-T5) are marked significantly.

[0077] The response feature recognition submodule determines the response characteristics of the correction signal to changes in airflow based on the rate difference parameter, analyzes the rate difference trend, constructs a hysteresis interval and calculates the offset of the signal change gradient within the interval, filters data corresponding to the offset direction and compensation order, and generates hysteresis compensation index.

[0078] Based on rate difference parameters The sequence is [0.665, 4.156, 4.728, 5.246]. First, the response characteristics of the correction signal to changes in airflow are determined. To determine the criterion for response hysteresis, a calibration experiment was performed: a signal delay of 1, 2, and 3 sampling periods was artificially introduced into the sampling system, while random changes in airflow velocity were applied simultaneously. Data was collected and calculations were performed. Experimental data shows that when When the value is greater than 3.0 for two consecutive sampling periods, it indicates a lag of at least one sampling period. Therefore, a lag judgment criterion is set. Analyzing the case studies Sequence: Point T2: (Response synchronization). Point T3: (Possibly delayed). Point T4: (Delayed confirmation). Point T5: (Lag duration). Based on this, a lag interval of [T3, T5] is constructed, lasting for 3 sampling points. Next, the rate difference trend is analyzed, due to the... The continuous increase (4.156->4.728->5.246) indicates that the mismatch between the signal and the airflow is worsening. Within this lag interval [T3, T5], the offset of the signal gradient is calculated. Here, "offset" refers to the number of time-dislocation cycles of the signal. This is achieved through calculation... Sequence and Cross-correlation of sequences, finding the delay corresponding to the correlation peak. . The sequence (T3-T5) = [3.891, -2.326, 5.548] Sequence (T2-T4) (i.e.) = [0.1, -0.1, 0.2] Sequence (T1-T3) (i.e.) )=[N / A,0.1,-0.1]Calculate Time-related correlation: (T3-T5) and Pearson correlation coefficient (T2-T4). The symbols for the sequence [3.891, -2.326, 5.548] are [+, -, +]. The sequences [0.1, -0.1, 0.2] have signs of [+, -, +]. The two sequences have identical signs, indicating a strong correlation. Their correlation coefficient is calculated to be 0.998. correlation at time ( vs ): Sequence (T3-T5) symbols [+,-,+]. The sequence (T3-T5) has signs [-, +, -]. Completely opposite signs have a correlation coefficient of -0.995. Compare the differences. The correlation of values, in The time correlation reached a peak of 0.998, therefore the offset of the signal change gradient (i.e., the lag period) was determined to be 1 sampling period. Finally, data corresponding to the offset direction and compensation order were selected. The offset direction is the time lag ( The compensation order involves shifting the signal sequence forward by one period on the time axis. Based on this, a lag compensation index is generated. Its content is: {Lag period: 1, Compensation direction: Forward}.

[0079] The compensation sequence optimization submodule calls the lag compensation index to adjust the corresponding position of the signal in the time series, optimizes the time correspondence of the signal before and after compensation, and generates a time drift correction record.

[0080] Calling the delayed compensation indicator Obtain the steady-state correction signal sequence. =[120.0(T1),120.412(T2),124.303(T3),121.977(T4),127.525(T5)]. Perform the operation to adjust the corresponding position of the signal in the time series. Based on... Indicators need to be The entire sequence is shifted forward by one sampling period. Original The value of 120.412mV at time T2 was adjusted to the effective signal value at time T1. Original The value of 124.303mV at time T3 was adjusted to the effective signal value at time T2. Original The value of 121.977mV at time T4 was adjusted to the effective signal value at time T3. Original The value of 127.525mV at time T5 was adjusted to the effective signal value at time T4. Original The value at time T1 is 120.0 mV. Since it corresponds to time T0 (data does not exist), this data point is removed from the current processing window. After adjustment, a new sequence is obtained. =[120.412,124.303,121.977,127.525], the timestamps corresponding to this sequence are [T1,T2,T3,T4]. The time correspondence of the signals before and after optimization compensation is determined. The original airflow velocity sequence... =[1.5(T1),1.6(T2),1.5(T3),1.7(T4),1.6(T5)]. The signal at time T3 before compensation. ( (Positive) corresponds to the airflow at time T3. ( (negative), the two responses are opposite. After compensation, the new signal at time T2 airflow at time T2 ( It is positive (from T1, 1.5). We re-examined. and Time correspondence (in) (Subject to) The sequence is: [120.412(T1), 124.303(T2), 121.977(T3), 127.525(T4)]. (T2-T4)=[3.891,-2.326,5.548]. The sequence is [1.5(T1), 1.6(T2), 1.5(T3), 1.7(T4)]. (T2-T4)=[0.1,-0.1,0.2]. Comparison (T2-T4) and (T2-T4): T2: (positive) vs (Positive). Same direction. T3: (Negative) vs (Negative). Same direction. T4: (positive) vs (Positive). Direction consistent. Through time compensation, the signal change rate from T2 to T4... With airflow change rate The direction achieved a perfect match. Finally, a time drift correction record was generated. This record is a dataset containing timestamps and corrected signal values. =[{T1,120.412},{T2,124.303},{T3,121.977},{T4,127.525}].

[0081] Specifically, such as Figure 2 , 5 As shown, the drift parameter regression module includes:

[0082] The differential sequence calculation submodule calculates the differential distribution between the measured signal and the reference concentration data in the same time series based on the time drift correction record, analyzes the degree of fluctuation and clustering of the differential sequence in continuous time period, and obtains the differential distribution parameters.

[0083] Records corrected based on time drift =[{T1,120.412},{T2,124.303},{T3,121.977},{T4,127.525}] (unit: mV). Obtain reference concentration data for the same time series [T1,T4]. (For example, measured by an extraction-based, condensation-dried reference method (such as GC-FID). The sequence is [50.0, 50.5, 51.0, 50.8] (unit: mg / m³). The initial sensitivity coefficient of the sensor was determined through calibration experiments during system initialization. mV / (mg / m³). First, Sequence converted to measured concentration . T1 point: mg / m³. T2 point: mg / m³. T3 point: mg / m³. T4 point: mg / m³. Next, the difference distribution between the measured signal (converted to concentration) and the reference concentration data was calculated over the time series from T1 to T4. . T1 point: mg / m³. T2 point: mg / m³. T3 point: mg / m³. T4 point: mg / m³. Differential sequences were obtained. =[0.17,1.29,-0.18,2.34]. Subsequently, the degree of volatility clustering in the differential sequence over the continuous time interval from T1 to T4 was analyzed. The degree of volatility clustering here was calculated... Standard deviation To quantify. . mg / m³. Finally, the differential distribution parameters, including the differential sequences, are obtained. =[0.17,1.29,-0.18,2.34] and the volatility clustering index .

[0084] The compression ratio evaluation submodule calls the differential distribution parameters to calculate the compression ratio of the differential sequences within the sampling segment. By comparing the direction of change of the compression ratio, it determines the drift trend and generates the drift change coefficient.

[0085] Call the differential distribution parameters, including the current sampling segment (denoted as...). ) differential sequences =[0.17,1.29,-0.18,2.34] and its fluctuation clustering degree At the same time, retrieve the previous sampling segment (denoted as...). The differential distribution parameters (corresponding to times T-3 to T0) =[-0.10,0.05,0.15,-0.02], its fluctuation clustering degree First, calculate the "compression ratio" of the differential sequences within the sampling segment. Here, "compression ratio" is defined as the standard deviation of the differential sequences. Compared with the reference concentration during this period range The ratio. (To obtain) (T1-T4) time period =[50.0,50.5,51.0,50.8], its range Get (T-3-T0) time period (Assuming the range is [49.5, 49.8, 50.2, 49.9]), its measurement range .calculate Compression ratio .calculate Compression ratio The advantage of this method is that, through and The ratio of compression ratio Normalization was achieved, reflecting the proportion of measurement error relative to the fluctuation of the true signal, and eliminating factors such as... Caused by its own drastic fluctuations Increase it. Next, by comparing the direction of change in the compression ratio, the drift trend can be determined. , . This indicates that the compression ratio (i.e., the normalized error) is at The time period increased significantly. Simultaneously, the mean of the differentially expressed sequences was analyzed. : . . ,and The value is significantly positive. Considering both the increase in compression ratio and the positive increase in the mean, the drift trend is determined to be a significant positive drift in the sensor baseline or sensitivity. Finally, the drift change coefficient is generated. This coefficient is used to quantify the rate of drift and is defined as the change in the mean difference between two time periods divided by the time period length (4 sampling points). (mg / m³) / sample. The generated drift variation coefficient. It is +0.221 (mg / m³) / sample.

[0086] The baseline correction and adjustment submodule constructs a drift inversion factor and adjusts the signal baseline by calculating the increase or decrease of the drift trend in the time series based on the drift change coefficient, and generates a sensitivity drift calibration configuration.

[0087] Based on the drift change coefficient (mg / m³) / sample, and differential sequences within the time interval from T1 to T4. =[0.17,1.29,-0.18,2.34]. Simultaneously, obtain the time drift correction records from T1 to T4. =[120.412,124.303,121.977,127.525]mV. First, obtain... Corresponding signal change time gradient sequence . (T2-T4)=[3.891,-2.326,5.548](T2=124.303-120.412=3.891;T3=121.977-124.303=-2.326;T4=127.525-121.977=5.548). According to the specific logic of the embodiment: calculate the drift determination benchmark value. . Based on the deviation magnitude of the differential sequence With respect to the continuous stability of the signal time gradient sequence The product of . Continuity and stability. Defined as Point T3: Point T4: Drift determination benchmark value Point T3: Point T4: This reference value is valid when the signal is stable ( High) and large differences ( When the value is high, the value is relatively high. Then, the magnitude of the difference after compression (referring to...) is compared. )and This logic is used to determine whether the drift occurs during a stable phase or a fluctuating phase. At point T4, , The close proximity of the two indicates that during the period of significant signal fluctuations at T4, a drift phenomenon ( This is also significant. Next, a drift inversion factor is constructed. This factor is used to correct the signal baseline. Drift trend The concentration drift (mg / m³) / sample indicates a continuous positive cumulative drift of the baseline. We convert this concentration drift to signal drift (mV). The sensitivity coefficient of the sensor is required. mV / (mg / m³). Drift inversion factor (i.e., the correction amount at each step). mV / sample. This is the drift inversion factor, representing the amount of 0.530mV that needs to be subtracted from the signal baseline at each sampling point to compensate for the drift. The signal baseline is corrected by offset adjustment: the drift start point is set to T1, and the end point to T4. A piecewise linear offset adjustment method is used to correct the baseline. The cumulative correction amount at time T1 is... mV. Cumulative correction at time T2. mV. Cumulative correction at time T3. mV. Cumulative correction at time T4. mV. To optimize the connection consistency between the offset segment and the unoffset segment (before T1), a continuous transition region is set at the drift start point T1. For example, the correction amount for T1 is not directly -0.530, but a linear interpolation from T-1 (correction amount is 0) to T1 is used, such as... In this embodiment, to simplify calculations, point T1 applies the full amount. The final generated sensitivity drift calibration configuration (actually the baseline drift calibration configuration) The cumulative correction sequence from T1 to T4 =[-0.530,-1.060,-1.590,-2.120]mV.

[0088] Specifically, such as Figure 2 , 6 As shown, the heterogeneous weight allocation module includes:

[0089] The response time comparison submodule analyzes the response time distribution of multiple probes under the same emission event based on the sensitivity drift calibration configuration, calculates the time difference and signal amplitude ratio of each probe's response signal, and establishes probe response characteristic parameters.

[0090] Baseline drift calibration configuration =[-0.530,-1.060,-1.590,-2.120]mV. This configuration is applied to multi-probe signals during the time period T1 to T4. Assume the system is equipped with three heterogeneous probes (A, B, C). Probe A's... Sequence = [120.412, 124.303, 121.977, 127.525] mV. Probe B's... Sequence = [121.000, 124.000, 121.500, 126.800] mV. Probe C's... The sequence is [120.800, 124.800, 122.500, 128.000]mV. First, [the sequence is...] The final calibration signal was obtained by applying the calibration to three probes. . =[119.882,123.243,120.387,125.405]. =[120.470,122.940,119.910,124.680]. =[120.270,123.740,120.910,125.880]. Next, the response of multiple probes under the same emission event (concentration rise from T3 to T4) is analyzed. Response time distribution analysis: To obtain accurate response times, higher resolution data is needed. In this embodiment, by analyzing high-frequency sampled data (hypothetically) between T3 and T4, the signal values ​​from T3 (…) of each probe are obtained. The signal value rose to T4. 10%-90% response time Probe A: s. Probe B: s. Probe C: s. Calculate the signal amplitude ratio: signal amplitude . mV. mV. mV. Calculate the total amplitude. mV. Calculate the signal amplitude ratio. : . . Calculate the time difference: using the fastest probe C (1.1s) as the reference. s. s. s. s. Establish probe response characteristic parameters, which include response time, time difference, and amplitude ratio: .

[0091] The weight adjustment calculation submodule calls the probe response characteristic parameters, compares the changing trends of time difference and amplitude ratio, determines the response order relationship and sensitivity difference of the probes, adjusts the participation weight of each probe, and obtains the probe weight allocation parameters.

[0092] Calling probe response characteristic parameters , =[1.2,1.8,1.1]s. (Amplitude ratio) = [0.340, 0.323, 0.337]. (Time difference) = [0.1, 0.7, 0.0] s. First, compare the changing trend of the time difference and amplitude ratio. This step is used to identify the quality of the probe. Probe A: Lower (0.1s), Relatively high (0.340). Probe B: Very high (0.7s), Low (0.323). Probe C: Minimum (0.0s), High (0.337). Determine the response order and sensitivity differences of the probes: Response order (from fastest to slowest): C>A>B. Sensitivity differences (from highest to lowest): A>C>B. Adjust the participation weight of each probe. The weight settings should ensure a fast response time. Low) and high sensitivity ( Higher-resolution probes are assigned higher weights. To establish the baseline parameters for weight calculation, 100 standard gas step experiments were performed. Probe response time was analyzed using multivariate regression. and amplitude The impact on the accuracy of the fused data (RMSE compared to the standard value). The analysis results show that when the weights... and Proportional to, with The RMSE is minimized when it is inversely proportional to the square root of the value. Therefore, the probe quality factor is set... Calculate the quality factor of each probe. : . . Calculate the total quality factor. Through normalization To calculate probe weight allocation parameters . . . (Check: 0.3559 + 0.2759 + 0.3682 = 1.0000). The advantage of this method is that the weights... Calculation It is based on experimental data verification. The use of this approach balances the importance of response time, preventing excessive weight oscillations caused by small differences in response time (such as A and C). Ultimately, the resulting (original) probe weight assignment parameters... The sequence is [0.3559, 0.2759, 0.3682].

[0093] The curve smoothing optimization submodule analyzes the correspondence between the direction of weight change and the direction of time difference change based on the probe weight allocation parameters, smooths the weight adjustment curve during the continuous sampling stage, and generates a fusion record of discharge outlet monitoring data.

[0094] Based on the (original) probe weight allocation parameters of the current event (T3-T4) =[0.3559,0.2759,0.3682]. Retrieve the smoothed weights of the previous emission event (assuming it occurs at time T2). =[0.3500,0.3000,0.3500]. Analyze the correspondence between the direction of weight change and the direction of time difference change: direction of weight change ( ): (A weight increases slightly.) (B weight decreases.) (C weight increased). Time difference (Event T4) = [A:0.1, B:0.7, C:0.0] s. Time difference of probe B. Its weight is 0.7s (slowest response). The corresponding decrease is (-0.0241). Time difference of probe C. Its weight is 0.0s (fastest response). The weight is increased accordingly by (+0.0182). This correspondence (slower response means lower weight) is as expected. The weight adjustment curve for the continuous sampling phase is smoothed using exponential smoothing. ,in Reference Smoothing coefficient Settings: To confirm Simulation experiments were conducted. Abrupt and gradual changes in probe performance were introduced into the simulated signal. The experiments showed that when… At this time, the system can stably adapt to sudden changes within a set of events (approximately 3-4 sampling periods), while effectively filtering out glitches (gradual changes) in individual events. Therefore, setting... Calculate the smoothed weights at time T4. : . . (Check the sum: 0.3518 + 0.2928 + 0.3555 = 1.0001, which is approximately 1). This yields the final smoothing weights at time T4. =[0.3518,0.2928,0.3555]. Finally, a fusion record of discharge outlet monitoring data is generated. . Retrieve T1 to T4 sequence: =[119.882,123.243,120.387,125.405]. =[120.470,122.940,119.910,124.680]. =[120.270,123.740,120.910,125.880]. The smoothing weights from time T2 are used for times T1 to T3 (non-event periods). =[0.35,0.30,0.35]. mV. mV. mV. The smoothing weights at time T4 (during the event) are used. =[0.3518,0.2928,0.3555]. mV. Generated discharge outlet monitoring data fusion record. The sequence is [120.195, 123.326, 120.427, 125.379].

[0095] Specifically, such as Figure 2 , 7 As shown, the component anomaly identification module includes:

[0096] The component ratio calculation submodule is based on the fusion record of discharge outlet monitoring data, analyzes the concentration response change trend of multiple components, calculates the response amplitude ratio sequence between each pair of components, and obtains component ratio sequence data.

[0097] Based on the fusion record of discharge outlet monitoring data To identify component anomalies, the system retrieves data from another sensor array (or chromatographic analysis module), which provides synchronized, separated component concentrations. The following data (T1 to T10) are calibrated component concentrations (mg / m³).

[0098] Table 2 Component Concentration Monitoring Data

[0099]

[0100] As shown in Table 2, toluene (T1 to T10) was obtained. ) and xylene ( The concentration data of the two components were analyzed. First, the concentration response trends of various components were analyzed. From T1 to T6, the concentrations of the two components fluctuated slightly around their respective baselines (30.0 and 15.0). Starting from T7, The concentration rose sharply from 30.0 to 80.0, while The concentrations were maintained at a baseline level of 15.1–15.3. Next, the response amplitude ratio sequence between each pair of components was calculated. In this embodiment, the concentration ratio of toluene to xylene was calculated. T1: T2: T3: T4: T5: T6: T7: T8: T9: T10: Ultimately, the obtained component proportion sequence data [2.000, 2.007, 2.000, 2.000, 2.000, 2.000, 2.980, 3.947, 4.902, 5.333].

[0101] The continuous offset analysis submodule calls the component proportion sequence data, compares the direction and magnitude of change of each proportion sequence during the continuous sampling phase, determines the offset characteristics of the response proportion, identifies the continuous phase of proportion change, and obtains the proportion continuous offset index.

[0102] Call component proportion sequence data (T1-T10)=[2.000,2.007,2.000,2.000,2.000,2.000,2.980,3.947,4.902,5.333]. To determine the deviation characteristics of the response ratio, a ratio baseline needs to be established first. The toluene / xylene concentration ratio is determined by statistically analyzing component data under historical normal operating conditions (e.g., the first 72 hours). Stable distribution Within the range. Based on this, a proportional benchmark is set. Set the offset tolerance threshold Compare the direction and magnitude of change of each scaling sequence during the continuous sampling phase. Calculate the scaling offset. . (T1-T6) are all in Within, less than (0.100). . . . Determine the offset characteristics of the response ratio. During T1-T6, The proportions are normal. In T7, A positive shift occurs. At T8, The positive offset continues and its amplitude increases. At T9, The positive offset continues and its amplitude increases. At T10, The positive offset is persistent and its amplitude is increasing. The duration of the proportional change is identified. Historical data is analyzed to determine the criteria for identifying the "duration phase." The analysis shows that instantaneous proportional offsets caused by sampling or measurement fluctuations typically recover within 1-2 sampling periods. Therefore, a minimum duration is set. One sampling period. In this example, starting from time T7, four consecutive sampling points (T7, T8, T9, T10) are used. All greater than . .because This confirms that T7 to T10 constitutes a valid proportional sustained offset phase. Ultimately, the obtained proportional sustained offset index... This is a data structure that records the start time, duration, and offset direction of this phase: .

[0103] The anomaly marker establishment submodule analyzes the cumulative degree and directional consistency of proportional shift during the continuous phase based on the proportional continuous shift index, counts the frequency of shift direction conversion in multiple time segments, establishes anomaly identification markers, and generates component structure shift monitoring results.

[0104] Based on the proportional persistent offset index and the offset sequence of this stage. (T7-T10)=[+0.980,+1.947,+2.902,+3.333]. First, analyze the cumulative degree of proportional shift during the sustained phase. Cumulative degree... By calculating the offset within this stage It is quantified by the sum of all elements. The analysis direction is consistent; in stages T7 to T10, all offsets... All values ​​are positive ([+,+,+,+]), indicating 100% consistency in the offset direction. The frequency of offset direction transitions across multiple time intervals was statistically analyzed. During this duration [T7, T10], the offset direction (positive) remained unchanged, therefore the transition frequency was... Establish anomaly identification markers. To establish markers, judgment thresholds need to be set. Through statistical analysis of historically confirmed abnormal emission events (such as process failures and abnormal material feeding), the following judgment criteria are established: 1. Duration length threshold. (Sampling period). 2. Cumulative offset threshold (quantitative) 3. Direction conversion threshold (i.e., zero conversions are allowed). When and and At that time, an anomaly marker is established. Substitute the data from stages T7-T10 of this example into the judgment: 1. . determination: The conditions are met. 2. . determination: The conditions are met. 3. . determination: The conditions are met. Since all three conditions are met, the system establishes an anomaly identification marker at time T10. (Indicating a high-level anomaly). This result indicates that the severe imbalance in the toluene / xylene ratio (from 2.0:1 to 5.3:1) occurring between T7 and T10 is a genuine, persistent, cumulative, and non-random fluctuation anomaly. Finally, a component structure shift monitoring result is generated, which includes timestamps, anomaly markers, and relevant evidence (such as...). , Data records: .

[0105] Please see Figure 8 The AI-adaptive calibration-based VOC emission station monitoring method is implemented based on the aforementioned AI-adaptive calibration-based VOC emission station monitoring system, and includes the following steps:

[0106] S1: Obtain the sampling sequence of temperature and humidity data at the outlet, analyze the offset relationship between the rate of change of temperature and humidity, evaluate the impact on the sampling flow rate and signal stability, construct the cross-perturbation intensity, compare the ratio of perturbation intensity to signal amplitude, determine the correction amplitude, and generate a steady-state correction signal sequence.

[0107] S2: Using the steady-state correction signal sequence, calculate the rate of change of the corrected signal and compare it with the rate of change of airflow velocity. Analyze the rate difference, determine the offset direction, adjust the signal compensation sequence, and generate a time drift correction record.

[0108] S3: Based on time drift correction records, calculate the difference distribution between the measured signal and the reference concentration data in the same time series, analyze the degree of fluctuation and clustering of the difference series and evaluate the drift trend, construct the drift inversion factor, adjust the signal baseline, and generate the sensitivity drift calibration configuration.

[0109] S4: Based on the sensitivity drift calibration configuration, calculate the response time difference and amplitude ratio of multiple probes, compare the changing trends to determine the response order and sensitivity difference, adjust the probe participation weights and smooth the adjustment curve, and generate a fusion record of discharge outlet monitoring data.

[0110] S5: Based on the fusion record of discharge outlet monitoring data, analyze the concentration response change trend of multiple components in the monitoring data, calculate the response ratio sequence between components, compare the direction and magnitude of change, identify the continuous stage of ratio shift and analyze the cumulative direction and consistency, establish anomaly identification markers, and generate component structure shift monitoring results.

[0111] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A VOC emission station monitoring system based on AI adaptive calibration, characterized in that, The system includes: The signal steady-state correction module acquires the sampling sequence of temperature and humidity data at the outlet, analyzes the offset relationship between the rate of change of temperature and humidity, evaluates the impact on the sampling flow rate and signal stability, constructs the cross-disturbance intensity, compares the ratio of the disturbance intensity to the signal amplitude, determines the correction amplitude, and generates a steady-state correction signal sequence. The time drift compensation module uses the steady-state correction signal sequence to calculate the rate of change of the corrected signal and compare it with the rate of change of airflow speed. It analyzes the rate difference, determines the offset direction, adjusts the signal compensation sequence, and generates a time drift correction record. The drift parameter regression module calculates the difference distribution between the measured signal and the reference concentration data in the same time series based on the time drift correction record, analyzes the degree of fluctuation and clustering of the difference series and evaluates the drift trend, constructs the drift inversion factor, adjusts the signal baseline, and generates the sensitivity drift calibration configuration. Based on the sensitivity drift calibration configuration, the heterogeneous weight allocation module calculates the response time difference and amplitude ratio of multiple probes, compares the changing trends to determine the response order and sensitivity difference, adjusts the probes' participation in the weighting and smooths the adjustment curve, and generates a fusion record of discharge outlet monitoring data.

2. The VOC emission station monitoring system based on AI adaptive calibration according to claim 1, characterized in that, The steady-state correction signal sequence includes a temperature and humidity rate difference factor, a disturbance amplitude ratio coefficient, and a transient adjustment step parameter. The time drift correction record includes a rate response delay interval, a signal offset direction marker, and a time position compensation order. The sensitivity drift calibration configuration specifically includes a difference sequence compression index, a drift increase / decrease trend factor, and a signal baseline offset adjustment amount. The outlet monitoring data fusion record includes a probe time response sorting value, a signal amplitude ratio factor, and a staged weight smoothing parameter.

3. The VOC emission station monitoring system based on AI adaptive calibration according to claim 1, characterized in that, The signal steady-state correction module includes: The temperature and humidity trend identification submodule acquires the outlet temperature and humidity data sampling sequence, analyzes the fluctuation direction of the temperature change rate and humidity change rate in the time series, evaluates the impact on gas sampling flow rate and signal stability through the offset relationship between temperature and humidity rates, and obtains the temperature and humidity influence coefficient. The disturbance intensity assessment submodule constructs the cross disturbance intensity based on the temperature and humidity influence coefficient, compares the ratio of the cross disturbance intensity to the sampled signal amplitude, determines the correction amplitude of the signal, and establishes the disturbance correction parameters. The signal correction and adjustment submodule calls the disturbance correction parameters to adjust the differential reduction amplitude of the signal in the transient phase, controls the correction step according to the change ratio of the disturbance intensity between consecutive samples, optimizes the correction stability between consecutive samples, and generates a steady-state correction signal sequence.

4. The VOC emission station monitoring system based on AI adaptive calibration according to claim 3, characterized in that, The time drift compensation module includes: The sampling rate comparison submodule acquires the steady-state correction signal sequence, calculates the rate of change of the correction signal in the continuous sampling phase, compares it with the rate of change of airflow velocity, analyzes the direction and magnitude of the difference between the two rates, and establishes the rate difference parameter. The response feature recognition submodule determines the response characteristics of the correction signal to airflow changes based on the rate difference parameter, analyzes the rate difference trend, constructs a hysteresis interval and calculates the offset of the signal change gradient within the interval, filters data whose offset direction corresponds to the compensation order, and generates hysteresis compensation index. The compensation sequence optimization submodule calls the hysteresis compensation index to adjust the corresponding position of the signal in the time series, optimize the time correspondence of the signal before and after compensation, and generate a time drift correction record.

5. The VOC emission station monitoring system based on AI adaptive calibration according to claim 4, characterized in that, The drift parameter regression module includes: Based on the time drift correction record, the differential sequence calculation submodule calculates the differential distribution between the measured signal and the reference concentration data in the same time series, analyzes the degree of fluctuation and clustering of the differential sequence in a continuous time period, and obtains the differential distribution parameters. The compression ratio evaluation submodule calls the difference distribution parameters to calculate the compression ratio of the difference sequence within the sampling segment, and determines the drift trend by comparing the direction of change of the compression ratio, and generates the drift change coefficient. The baseline correction and adjustment submodule constructs a drift inversion factor and corrects the signal baseline by calculating the increase or decrease of the drift trend in the time series based on the drift change coefficient, thereby generating a sensitivity drift calibration configuration.

6. The VOC emission station monitoring system based on AI adaptive calibration according to claim 5, characterized in that, The process of constructing the drift inversion factor and correcting the signal baseline offset is as follows: The difference sequence and signal change time gradient sequence within the corresponding time period of the time drift correction record are obtained. The product of the difference sequence and the continuous stability of the time gradient sequence is calculated as the drift judgment benchmark value. The compressed difference amplitude is compared with the drift judgment benchmark value to establish the judgment basis for the drift offset direction. The drift inversion factor is calculated by the proportional relationship between the direction judgment result and the cumulative difference within the time window. The drift inversion factor is applied to the signal baseline data segment, and the drift start point and drift end point are used as the interpolation boundary. The baseline signal in the interpolation interval is corrected by a piecewise linear offset adjustment method, and a continuous transition region is set at the drift start point and drift end point to optimize the connection consistency between the offset segment and the non-offset segment.

7. The VOC emission station monitoring system based on AI adaptive calibration according to claim 5, characterized in that, The heterogeneous weight allocation module includes: The response time comparison submodule analyzes the response time distribution of multiple probes under the same emission event based on the sensitivity drift calibration configuration, calculates the time difference and signal amplitude ratio of each probe's response signal, and establishes probe response characteristic parameters. The weight adjustment calculation submodule calls the probe response characteristic parameters, compares the changing trend of time difference and amplitude ratio, determines the response order relationship and sensitivity difference of the probes, adjusts the participation weight of each probe, and obtains the probe weight allocation parameters. The curve smoothing optimization submodule analyzes the correspondence between the direction of weight change and the direction of time difference change based on the probe weight allocation parameters, smooths the weight adjustment curve during the continuous sampling stage, and generates a fusion record of discharge outlet monitoring data.

8. The VOC emission station monitoring system based on AI adaptive calibration according to claim 1, characterized in that, The system also includes: The component anomaly identification module is based on the fusion record of the discharge outlet monitoring data, analyzes the concentration response change trend of multiple components in the monitoring data, calculates the response ratio sequence between components, compares the change direction and magnitude, identifies the continuous stage of ratio shift and analyzes the cumulative direction and consistency, establishes anomaly identification markers, and generates component structure shift monitoring results. The component structure shift monitoring results include the trend of proportional change direction, the continuous segment of response shift, and structural anomaly identification markers.

9. The VOC emission station monitoring system based on AI adaptive calibration according to claim 8, characterized in that, The component anomaly identification module includes: The component ratio calculation submodule integrates and records the discharge outlet monitoring data, analyzes the concentration response change trends of multiple components, calculates the response amplitude ratio sequence between each pair of components, and obtains component ratio sequence data. The continuous offset analysis submodule calls the component proportion sequence data, compares the direction and magnitude of change of each proportion sequence in the continuous sampling stage, determines the offset characteristics of the response proportion, identifies the continuous stage of proportion change, and obtains the proportion continuous offset index. The anomaly marker establishment submodule analyzes the cumulative degree and directional consistency of the proportional shift during the continuous phase based on the proportional continuous shift index, counts the shift direction conversion frequency of multiple time segments, establishes anomaly identification markers, and generates component structure shift monitoring results.

10. A monitoring method for VOC emission outlets based on AI adaptive calibration, characterized in that, The VOC emission station monitoring system based on AI adaptive calibration according to any one of claims 1-9 includes the following steps: S1: Obtain the sampling sequence of temperature and humidity data at the outlet, analyze the offset relationship between the rate of change of temperature and humidity, evaluate the impact on the sampling flow rate and signal stability, construct the cross-perturbation intensity, compare the ratio of perturbation intensity to signal amplitude, determine the correction amplitude, and generate a steady-state correction signal sequence. S2: Using the steady-state correction signal sequence, calculate the rate of change of the corrected signal and compare it with the rate of change of the airflow velocity, analyze the rate difference, determine the offset direction and adjust the signal compensation sequence, and generate a time drift correction record. S3: Based on the time drift correction record, calculate the difference distribution between the measured signal and the reference concentration data in the same time series, analyze the degree of fluctuation and clustering of the difference series and evaluate the drift trend, construct the drift inversion factor, adjust the signal baseline, and generate the sensitivity drift calibration configuration. S4: Based on the sensitivity drift calibration configuration, calculate the response time difference and amplitude ratio of multiple probes, compare the changing trends to determine the response order and sensitivity difference, adjust the probe participation weights and smooth the adjustment curve, and generate a discharge outlet monitoring data fusion record. S5: Based on the fusion record of the discharge outlet monitoring data, analyze the concentration response change trend of multiple components in the monitoring data, calculate the response ratio sequence between components, compare the direction and magnitude of change, identify the continuous stage of ratio shift and analyze the cumulative direction and consistency, establish anomaly identification markers, and generate component structure shift monitoring results.

Images