A VOCs online monitoring system

Abstract

The present invention discloses a VOCs online monitoring system, which relates to the field of waste gas physical detection technology. The VOCs online monitoring system includes: a data acquisition and processing unit, a background gas identification module, a background gas quantification module, a spectrum correction and interference elimination unit, a standard gas verification unit, and a real-time monitoring module; ultraviolet spectrum scanning is combined with a temperature influence model to accurately correct the spectrum data, effectively eliminate the interference of environmental factors on the monitoring results, and ensure the accuracy and reliability of the monitoring data. Secondly, the introduction of the background gas identification and quantification module realizes the accurate identification and concentration calculation of the background gas, provides a solid foundation for subsequent spectrum correction, and further enhances the anti-interference ability of the monitoring system. Furthermore, the use of the spectrum correction and interference elimination unit effectively eliminates the interference of background gas on the target VOCs monitoring, and significantly improves the purity and credibility of the monitoring data.

Description

Technical Field

[0001] The present invention relates to the technical field of waste gas physical detection, and in particular to a VOCs online monitoring system. Background Art

[0002] VOCs (volatile organic compounds) monitoring is an important part of environmental protection and air quality management. VOCs are widely present in the atmosphere, water bodies and soil, posing a threat to the environment and human health. VOCs emissions not only affect air quality, but may also participate in atmospheric photochemical reactions to form secondary pollutants such as ozone, exacerbating the greenhouse effect. Accurate and scientific monitoring of VOCs is the basis for preventing and controlling VOCs pollution, and helps to formulate effective environmental protection policies and measures.

[0003] For example, a composite VOCs online monitoring system disclosed in publication number CN112213405A includes a high-temperature vacuum pump, the input end of the high-temperature vacuum pump is connected to a sampling probe, the output end of the high-temperature vacuum pump is connected to a three-way joint, one end of the three-way joint is connected to a high-temperature heating box, the output end of the high-temperature heating box is connected to a chromatographic analysis system, the chromatographic analysis system includes a chromatographic analyzer, the chromatographic analyzer is connected to a vent main pipe, the other end of the three-way joint is connected to a UV analyzer, and the output end of the UV analyzer is connected to the vent main pipe. The composite VOCs online monitoring system provided by this invention integrates the UV analyzer unit into the VOCs online monitoring system, greatly reducing the user's cost and operation and maintenance difficulty.

[0004] As shown in the above technology, the existing VOCs online monitoring often uses chromatographic analyzers to perform chromatographic detection. However, in actual detection, the VOCs monitoring system has two major technical defects when dealing with complex environments: First, there is a lack of accurate correction mechanism for spectral data at different ambient temperatures, which causes the monitoring results to be greatly affected by temperature fluctuations and lack of accuracy; second, it fails to fully consider and effectively compensate for the interference of the detection equipment's own parameters on spectral detection, making the monitoring results easily affected by the equipment status, and the stability and reliability need to be improved; these defects limit the application scope and monitoring accuracy of the VOCs monitoring system. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides a VOCs online monitoring system to solve the above-mentioned problems.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: A VOCs online monitoring system, comprising:

[0007] The data acquisition and processing unit is used to set sampling parameters, record environmental parameters during sampling, perform ultraviolet spectrum scanning on the collected gas samples, and obtain an absorbance value array A(λ, T) at each wavelength, where λ represents the wavelength;

[0008] The background gas identification module is used to build a background gas characteristic spectrum database and store standard spectrum data at different temperatures; the collected spectrum data A(λ) is compared with the characteristic spectrum in the database to identify the type of background gas in the sample. At the same time, considering the influence of temperature on the spectrum data, a temperature influence model is built to correct the spectrum data at different temperatures;

[0009] The background gas quantification module is used to quantify each identified background gas and calculate the concentration of each background gas in the sample based on the slope and intercept of the standard curve and the absorbance value at the corrected characteristic wavelength.

[0010] The spectrum correction and interference elimination unit calculates the contribution of the background gas to the original spectrum according to the concentration of the background gas and its characteristic spectrum, and subtracts the contribution from the original spectrum to obtain the corrected spectrum;

[0011] The standard gas verification unit uses standard gas to verify the calibrated spectrum and the accuracy of the monitoring data; the standard gas is used regularly to check the equipment's own parameters and determine the interference items to eliminate the interference;

[0012] The real-time monitoring module is used to monitor the corrected spectrum in real time and analyze the changes in VOCs concentration.

[0013] Preferably, the work contents of the data acquisition and processing unit include:

[0014] S1.1: Start the VOCs online monitoring system and set the sampling parameters including sampling time and flow rate;

[0015] S1.2: Collect gas samples from ambient air and record environmental parameters including sampling time, temperature T, and pressure;

[0016] S1.3: Scan the collected gas sample with ultraviolet spectrum to obtain an absorbance value array A(λ, T) at each wavelength, where λ represents the wavelength.

[0017] Preferably, the working contents of the background gas identification module include:

[0018] S2.1: Construct a background gas characteristic spectrum database and collect data at different temperatures T j The standard spectral data of various types of gases are as follows: i (λ,T j), where i represents the type of gas, and i=1, 2, ..., n; j represents the serial number of the temperature point, and j=1, 2, ..., m;

[0019] S2.2: Use correlation analysis, pattern recognition and other methods to compare A(λ) with the characteristic spectra in the database to identify the types of background gases present in the sample;

[0020] Calculate A(λ) and S i Correlation coefficient r of (λ) i , A(λ) and S i (λ) are the absorbance value array and standard spectrum data without considering the influence of temperature, using the Pearson correlation coefficient formula:

[0021] ;

[0022] in, and A(λ) and S i The average value of (λ);

[0023] S2.3: Analyze the effect of temperature on spectral data and construct a temperature effect model to predict or correct the changes in spectral data at different temperatures;

[0024] S i ′(λ, T)=S i (λ,T ref )·f(T,T ref );

[0025] Where Si′(λ, T) is the predicted or corrected spectral data, S i (λ,T ref ) is at the reference temperature T ref Spectral data under, f(T, T ref ) is the temperature correction factor.

[0026] Preferably, the working contents of the background gas quantification module include:

[0027] S3.1: For each background gas identified, perform quantitative analysis using the standard curve method;

[0028] S3.2: Based on the quantification results, calculate the concentration C of each background gas in the sample. i ;

[0029] Let K i is the slope of the standard curve of the ith background gas, b i is the intercept, then the concentration C i The calculation formula is:

[0030] ;

[0031] Among them, A′(λ i ) is A(λ) at characteristic wavelength λ i Corrected absorbance value.

[0032] Preferably, the work contents of the spectrum correction and interference elimination unit include:

[0033] S4.1: Based on the background gas concentration C i And its characteristic spectrum S i (λ), calculate the contribution B(λ) of the background gas to the original spectrum A(λ):

[0034] B(λ)= · ;

[0035] S4.2: Subtract the background contribution B(λ) from the original spectrum A(λ) to obtain the corrected spectrum A corr (λ):

[0036] A corr (λ)=A(λ)−B(λ);

[0037] Substituting the influence of temperature parameters, the spectrum formula considering temperature is:

[0038] A corr (λ,T j ) = A(λ,T j )−B(λ,T j ).

[0039] Preferably, the working contents of the standard gas verification unit include:

[0040] S5.1: Use standard gas to calibrate the spectrum A corr (λ) Conduct verification to ensure data accuracy;

[0041] S5.2: Implement quality control measures regularly, use standard gas inspection to identify interference from equipment factors, and eliminate the impact of equipment interference factors.

[0042] Preferably, the quality control measures in S5.2 include:

[0043] S5.2.1: Equipment factor identification and parameter setting:

[0044] S5.2.1.1: Analyze the factors that affect the spectral values ​​of the statistical equipment itself, such as changes in light source intensity, detector sensitivity drift, and optical component contamination or aging;

[0045] S5.2.1.2: Set parameter I for each factor analyzed t and the corresponding interference factor p t , where, suppose there are x interference factors, and t is any integer from 1 to x;

[0046] S5.2.2: Construct an initial spectral model:

[0047] S5.2.2.1: Construct an initial spectral model A based on the spectral data under ideal conditions init (λ);

[0048] S5.2.2.2: Introduce interference factors of equipment factors and construct spectral model A including interference factors model (λ, P), where P is a parameter vector containing all interference factors, that is, P = (p1, p2, ..., p x );

[0049] S5.2.3: Standard gas inspection and data analysis:

[0050] S5.2.3.1: Select multiple standard gases with representative spectral characteristics and concentration ranges covering actual detection requirements;

[0051] S5.2.3.2: Use the equipment to perform spectral scanning on the standard gas and record the actual spectral data A meas (λ);

[0052] S5.2.3.3: Analyze actual spectral data A meas (λ) and standard spectral data S i (λ), calculate the error ϵ(λ);

[0053] ϵ(λ)=A meas (λ)− S i (λ);

[0054] S5.2.4: Intelligent adjustment of interference factors:

[0055] S5.2.4.1: Design an optimization algorithm (e.g., gradient descent, genetic algorithm, etc.) to minimize the error ϵ(λ) and adjust the interference factor P;

[0056] S5.2.4.2: Iterate the optimization process until the error meets a preset threshold or the maximum number of iterations is reached;

[0057] S5.2.4.3: Record the optimized interference factor P opt , and update the spectral model to A corr (λ) = A model (λ,Popt ).

[0058] Preferably, after the spectral model is updated, S5.2.5 is performed: further verification of the updated spectral model is performed, specifically including:

[0059] S5.2.5.1: Verify the optimized spectral model Acorr(λ) using new or non-trained standard gases;

[0060] S5.2.5.2: Analyze the error between the validation results and the standard value to ensure the accuracy and reliability of the model;

[0061] S5.2.5.3: If the verification results are not satisfactory, return to S5.2.3 and add more standard gases for training.

[0062] Preferably, unsatisfactory verification result in S5.2.5.3 means that the error in S5.2.5.2 exceeds a preset value, and an error threshold is set in advance. When the calculated error is greater than the error threshold, return to S5.2.3 and add more standard gases for training.

[0063] Preferably, the device is also provided with a temperature control system for adjusting to different temperatures for gas spectrum detection.

[0064] The present invention provides a VOCs online monitoring system. Compared with the prior art, it has the following beneficial effects:

[0065] 1. The VOCs online monitoring system uses ultraviolet spectral scanning combined with a temperature influence model to accurately correct spectral data, effectively eliminating the interference of environmental factors on the monitoring results, and ensuring the accuracy and reliability of the monitoring data. Secondly, the introduction of the background gas identification and quantification module realizes the accurate identification and concentration calculation of background gases, provides a solid foundation for subsequent spectral correction, and further enhances the anti-interference ability of the monitoring system. Furthermore, the use of spectral correction and interference elimination units effectively eliminates the interference of background gases on target VOCs monitoring, significantly improving the purity and credibility of monitoring data. Finally, the real-time monitoring module ensures the continuity and timeliness of the monitoring process, and provides strong data support for environmental protection and pollution control.

[0066] 2. The VOCs online monitoring system builds a database of background gas characteristic spectra, considers the impact of temperature on spectral data, builds a temperature impact model, and achieves accurate correction of spectral data, significantly enhancing the system's adaptability under different environmental conditions and ensuring the accuracy of monitoring results; at the same time, the system can automatically adjust the comparison and verification of spectral data at different temperatures, and uses the impact of temperature on gas spectral values ​​to make the spectral values ​​close to the gas at the original temperature change differently with the change of temperature, thereby more accurately distinguishing gas categories. This innovation not only improves the monitoring accuracy, but also broadens the application scope of the system, enabling it to maintain efficient and accurate monitoring performance under complex and changing environmental conditions.

[0067] 3. The VOCs online monitoring system takes into account that the detection equipment itself will affect the various parameters of spectral detection, sets interference factors for each parameter, uses standard gas detection to determine the degree of interference of various interference items, and then adjusts the interference factors to compensate for the interference, further corrects the spectral model, and achieves a refined correction of the spectral data; and after the correction, it further uses other standard gases for verification and training, and sets thresholds for cyclic training, which not only ensures that the model can cover a wide range of detection scenarios, but also continuously optimizes the interference factors through intelligent adjustment algorithms until the error is minimized, effectively eliminating the interference caused by equipment factors. This method not only improves the accuracy of spectral detection, but also enhances the adaptability and stability of the model, providing more reliable technical support for environmental monitoring, chemical analysis and other fields. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 This is a general system principle block diagram of a first embodiment of the present invention;

[0069] Figure 2 This is a principle block diagram of a standard gas verification unit according to a first embodiment of the present invention;

[0070] Figure 3 It is a schematic diagram of a flow chart of a second embodiment of the present invention;

[0071] Figure 4 This is a logic flow diagram of a third embodiment of the present invention. DETAILED DESCRIPTION

[0072] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0073] The present invention provides the following three technical solutions:

[0074] Figure 1-Figure 2 The first embodiment is shown: a VOCs online monitoring system, comprising:

[0075] The data acquisition and processing unit is used to set sampling parameters, record environmental parameters during sampling, perform ultraviolet spectrum scanning on the collected gas samples, and obtain an absorbance value array A(λ, T) at each wavelength, where λ represents the wavelength;

[0076] The background gas identification module is used to build a background gas characteristic spectrum database and store standard spectrum data at different temperatures; the collected spectrum data A(λ) is compared with the characteristic spectrum in the database to identify the type of background gas in the sample. At the same time, considering the influence of temperature on the spectrum data, a temperature influence model is built to correct the spectrum data at different temperatures;

[0077] The background gas quantification module is used to quantify each identified background gas and calculate the concentration of each background gas in the sample based on the slope and intercept of the standard curve and the absorbance value at the corrected characteristic wavelength.

[0078] The spectrum correction and interference elimination unit calculates the contribution of the background gas to the original spectrum according to the concentration of the background gas and its characteristic spectrum, and subtracts the contribution from the original spectrum to obtain the corrected spectrum;

[0079] The standard gas verification unit uses standard gas to verify the calibrated spectrum and the accuracy of the monitoring data; the standard gas is used regularly to check the equipment's own parameters and determine the interference items to eliminate the interference;

[0080] The real-time monitoring module is used to monitor the corrected spectrum in real time and analyze the changes in VOCs concentration.

[0081] The ultraviolet spectrum scanning combined with the temperature influence model is used to accurately correct the spectral data, effectively eliminating the interference of environmental factors on the monitoring results, and ensuring the accuracy and reliability of the monitoring data. Secondly, the introduction of the background gas identification and quantification module realizes the accurate identification and concentration calculation of the background gas, provides a solid foundation for subsequent spectral correction, and further enhances the anti-interference ability of the monitoring system. Furthermore, the use of the spectral correction and interference elimination unit effectively eliminates the interference of background gas on the target VOCs monitoring, and significantly improves the purity and credibility of the monitoring data. Finally, the real-time monitoring module ensures the continuity and timeliness of the monitoring process, and provides strong data support for environmental protection and pollution control.

[0082] Figure 3The second embodiment is shown, and the main difference from the first embodiment is that the work content of the data acquisition and processing unit includes:

[0083] S1.1: Start the VOCs online monitoring system and set the sampling parameters including sampling time and flow rate;

[0084] S1.2: Collect gas samples from ambient air and record environmental parameters including sampling time, temperature T, and pressure;

[0085] S1.3: Scan the collected gas sample with ultraviolet spectrum to obtain an absorbance value array A(λ, T) at each wavelength, where λ represents the wavelength.

[0086] The work content of the background gas identification module includes:

[0087] S2.1: Construct a background gas characteristic spectrum database and collect data at different temperatures T j The standard spectral data of various types of gases are as follows: i (λ,T j ), where i represents the type of gas, and i=1, 2, ..., n; j represents the serial number of the temperature point, and j=1, 2, ..., m;

[0088] S2.2: Use correlation analysis, pattern recognition and other methods to compare A(λ) with the characteristic spectra in the database to identify the types of background gases present in the sample;

[0089] Calculate A(λ) and S i Correlation coefficient r of (λ) i , A(λ) and S i (λ) are the absorbance value array and standard spectrum data without considering the influence of temperature, using the Pearson correlation coefficient formula:

[0090] ;

[0091] in, and A(λ) and S i The average value of (λ);

[0092] S2.3: The device is also provided with a temperature control system for adjusting to different temperatures for gas spectrum detection, analyzing the influence of temperature on spectral data, and constructing a temperature influence model for predicting or correcting changes in spectral data at different temperatures;

[0093] S i ′(λ, T)=S i (λ,T ref )·f(T,Tref );

[0094] Where Si′(λ, T) is the predicted or corrected spectral data, S i (λ,T ref ) is at the reference temperature T ref Spectral data under, f(T, T ref ) is the temperature correction factor.

[0095] The work content of the background gas quantification module includes:

[0096] S3.1: For each background gas identified, perform quantitative analysis using the standard curve method;

[0097] S3.2: Based on the quantification results, calculate the concentration C of each background gas in the sample. i ;

[0098] Let K i is the slope of the standard curve of the ith background gas, b i is the intercept, then the concentration C i The calculation formula is:

[0099] ;

[0100] Among them, A′(λ i ) is A(λ) at characteristic wavelength λ i Corrected absorbance value.

[0101] The work contents of the spectrum correction and interference elimination unit include:

[0102] S4.1: Based on the background gas concentration C i And its characteristic spectrum S i (λ), calculate the contribution B(λ) of the background gas to the original spectrum A(λ):

[0103] B(λ)= · ;

[0104] S4.2: Subtract the background contribution B(λ) from the original spectrum A(λ) to obtain the corrected spectrum A corr (λ):

[0105] A corr (λ)=A(λ)−B(λ);

[0106] Substituting the influence of temperature parameters, the spectrum formula considering temperature is:

[0107] A corr (λ,T j) = A(λ,T j )−B(λ,T j ).

[0108] The work contents of the standard gas verification unit include:

[0109] S5.1: Use standard gas to calibrate the spectrum A corr (λ) Conduct verification to ensure data accuracy;

[0110] S5.2: Implement quality control measures regularly, use standard gas inspection to identify interference from equipment factors, and eliminate the impact of equipment interference factors.

[0111] By building a database of background gas characteristic spectra and considering the impact of temperature on spectral data, a temperature impact model was constructed to achieve accurate correction of spectral data, significantly enhancing the system's adaptability under different environmental conditions and ensuring the accuracy of monitoring results; at the same time, the system can adjust the comparison and verification of spectral data at different temperatures by itself, and use the impact of temperature on gas spectral values ​​to make the spectral values ​​close to the gas at the original temperature change differently with the change of temperature, thereby more accurately distinguishing gas categories. This innovation not only improves monitoring accuracy, but also broadens the application scope of the system, enabling it to maintain efficient and accurate monitoring performance under complex and changing environmental conditions.

[0112] Figure 4 A third embodiment is shown, which differs from the second embodiment mainly in that the quality control measures in S5.2 include:

[0113] S5.2.1: Equipment factor identification and parameter setting:

[0114] S5.2.1.1: Analyze the factors that affect the spectral values ​​of the statistical equipment itself, such as changes in light source intensity, detector sensitivity drift, and optical component contamination or aging;

[0115] S5.2.1.2: Set parameter I for each factor analyzed t and the corresponding interference factor p t , where, suppose there are x interference factors, and t is any integer from 1 to x;

[0116] S5.2.2: Construct an initial spectral model:

[0117] S5.2.2.1: Construct an initial spectral model A based on the spectral data under ideal conditions init (λ);

[0118] S5.2.2.2: Introduce interference factors of equipment factors and construct spectral model A including interference factors model(λ, P), where P is a parameter vector containing all interference factors, that is, P = (p1, p2, ..., p x );

[0119] S5.2.3: Standard gas inspection and data analysis:

[0120] S5.2.3.1: Select multiple standard gases with representative spectral characteristics and concentration ranges covering actual detection requirements;

[0121] S5.2.3.2: Use the equipment to perform spectral scanning on the standard gas and record the actual spectral data A meas (λ);

[0122] S5.2.3.3: Analyze actual spectral data A meas (λ) and standard spectral data S i (λ), calculate the error ϵ(λ);

[0123] ϵ(λ)=A meas (λ)− S i (λ);

[0124] S5.2.4: Intelligent adjustment of interference factors:

[0125] S5.2.4.1: Design an optimization algorithm (e.g., gradient descent, genetic algorithm, etc.) to minimize the error ϵ(λ) and adjust the interference factor P;

[0126] S5.2.4.2: Iterate the optimization process until the error meets a preset threshold or the maximum number of iterations is reached;

[0127] S5.2.4.3: Record the optimized interference factor P opt , and update the spectral model to A corr (λ) = A model (λ,P opt );

[0128] Taking the change of light source intensity as an example, assuming that the initial interference factor p t =1, the intensity of the light source u is weakened in the actual measurement, resulting in overall low spectral data; by adjusting p through the optimization algorithm t to a value greater than 1 (such as p t，oPt =1.1), the corrected spectral data is:

[0129] A corr (λ) = A meas (λ)·p t，oPt ;

[0130] In this way, the influence of changes in light source intensity on spectral data can be compensated to a certain extent; similar methods can be used to adjust and correct other equipment factors.

[0131] After updating the spectral model, proceed to S5.2.5: further verify the updated spectral model, including:

[0132] S5.2.5.1: Verify the optimized spectral model Acorr(λ) using new or non-trained standard gases;

[0133] S5.2.5.2: Analyze the error between the validation results and the standard value to ensure the accuracy and reliability of the model;

[0134] S5.2.5.3: If the verification results are not satisfactory, return to S5.2.3 and add more standard gases for training;

[0135] Unsatisfactory verification results in S5.2.5.3 means that the error in S5.2.5.2 exceeds the preset value. The error threshold is set in advance. When the calculated error is greater than the error threshold, return to S5.2.3 and add more standard gases for training.

[0136] Considering that the detection equipment itself will affect the various parameters of spectral detection, and setting interference factors for each parameter, standard gas detection is used to determine the interference degree of various interference items, and then the interference is compensated by adjusting the interference factors, and the spectral model is further corrected to achieve a refined correction of the spectral data; and after the correction, other standard gases are further used for verification and training, and thresholds are set for cyclic training, which not only ensures that the model can cover a wide range of detection scenarios, but also continuously optimizes the interference factors through intelligent adjustment algorithms until the error is minimized, effectively eliminating the interference caused by equipment factors. This method not only improves the accuracy of spectral detection, but also enhances the adaptability and stability of the model, providing more reliable technical support for environmental monitoring, chemical analysis and other fields.

[0137] Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0138] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0139] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A VOCs online monitoring system, characterized in that: include: The data acquisition and processing unit is used to set sampling parameters, record environmental parameters during sampling, perform ultraviolet spectrum scanning on the collected gas samples, and obtain the absorbance value array A(λ, T) at each wavelength, where λ represents the wavelength; The background gas identification module is used to build a background gas characteristic spectrum database and store standard spectrum data at different temperatures; the collected spectrum data A(λ) is compared with the characteristic spectrum in the database to identify the type of background gas in the sample. At the same time, considering the influence of temperature on the spectrum data, a temperature influence model is built to correct the spectrum data at different temperatures; The background gas quantification module is used to perform quantitative analysis on each identified background gas and calculate the concentration of each background gas in the sample based on the slope and intercept of the standard curve and the absorbance value at the corrected characteristic wavelength; The spectrum correction and interference elimination unit calculates the contribution of the background gas to the original spectrum according to the concentration of the background gas and its characteristic spectrum, and subtracts the contribution from the original spectrum to obtain the corrected spectrum; The standard gas verification unit uses standard gas to verify the calibrated spectrum and the accuracy of the monitoring data; the standard gas is used regularly to check the equipment's own parameters and determine the interference items to eliminate the interference; Real-time monitoring module, used to monitor the corrected spectrum in real time and analyze the changes in VOCs concentration; The work contents of the data acquisition and processing unit include: S1.1: Start the VOCs online monitoring system and set the sampling parameters including sampling time and flow rate; S1.2: Collect gas samples from ambient air and record environmental parameters including sampling time, temperature T, and pressure; S1.3: Scan the collected gas sample with ultraviolet spectrum to obtain an absorbance value array A(λ, T) at each wavelength, where λ represents the wavelength; The working contents of the background gas identification module include: S2.1: Construct a background gas characteristic spectrum database and collect data at different temperatures T j The standard spectral data of various types of gases are as follows: i (λ,T j ), where i represents the type of gas, and i=1, 2, ..., n; j represents the serial number of the temperature point, and j=1, 2, ..., m; S2.2: using correlation analysis or pattern recognition method, compare A(λ) with the characteristic spectrum in the database to identify the type of background gas present in the sample; Calculate A(λ) and S i Correlation coefficient r of (λ) i , A(λ) and S i (λ) are the absorbance value array and standard spectrum data without considering the influence of temperature, using the Pearson correlation coefficient formula: in, and A(λ) and S i The average value of (λ); S2.3: Analyze the effect of temperature on spectral data and construct a temperature effect model to predict or correct the changes in spectral data at different temperatures; S i ′(λ,T)=S i (λ,T ref )·f(T,T ref ); Where Si′(λ, T) is the predicted or corrected spectral data, S i (λ,T ref ) is at the reference temperature T ref Spectral data under, f(T, T ref ) is the temperature correction factor; The working contents of the background gas quantification module include: S3.1: For each background gas identified, perform quantitative analysis using the standard curve method; S3.2: Based on the quantification results, calculate the concentration C of each background gas in the sample. i ; Let K i is the slope of the standard curve of the ith background gas, b i is the intercept, then the concentration C i The calculation formula is: Among them, A′(λ i ) is A(λ) at characteristic wavelength λ i Corrected absorbance value under ; The work contents of the spectrum correction and interference elimination unit include: S4.1: Based on the background gas concentration C i And its characteristic spectrum S i (λ), calculate the contribution B(λ) of the background gas to the original spectrum A(λ): S4.2: Subtract the background contribution B(λ) from the original spectrum A(λ) to obtain the corrected spectrum A corr (λ): A corr (λ)=A(λ)B(λ); Substituting the influence of temperature parameters, the spectrum formula considering temperature is: A corr (λ, T j )=A(λ,T j )B(λ,T j ); The work contents of the standard gas verification unit include: S5.1: Use standard gas to calibrate the spectrum A corr (λ) Conduct verification to ensure data accuracy; S5.2: Regularly implement quality control measures, use standard gas inspection to identify interference from equipment factors, and eliminate the impact of equipment interference factors; Quality control measures in S5.2 include: S6.1: Equipment factor identification and parameter setting: S6.1.1: Analyze the factors that affect the spectral values ​​of the statistical equipment itself; S6.1.2: Set parameter I for each factor analyzed t and the corresponding interference factor p t , where, suppose there are x interference factors, and t is any integer from 1 to x; S6.2: Construct initial spectral model: S6.2.1: Construct an initial spectral model A based on the spectral data under ideal conditions init (λ); S6.2.2: Introduce interference factors of equipment factors and construct spectral model A including interference factors model (λ, P), where P is a parameter vector containing all interference factors, that is, P = (p1, p2, ..., p x ); S6.3: Standard gas inspection and data analysis: S6.3.1: Select multiple standard gases with representative spectral characteristics and concentration ranges covering actual detection requirements; S6.3.2: Use the equipment to perform spectral scanning on the standard gas and record the actual spectral data A meas (λ); S6.3.3: Analyze actual spectral data A meas (λ) and standard spectral data S i The difference between (λ) and the calculated error (λ); (λ)=A meas (λ)S i (l); S6.4: Intelligent adjustment of interference factors: S6.4.1: Design an optimization algorithm to adjust the interference factor P with the goal of minimizing the error (λ); S6.4.2: Iterate the optimization process until the error meets a preset threshold or the maximum number of iterations is reached; S6.4.3: Record the optimized interference factor P opt , and update the spectral model to A corr (λ)=A model (λ,P opt ).

2. A VOCs online monitoring system according to claim 1, characterized in that: After updating the spectral model, further verification of the updated spectral model is performed, including: S6.5.1: Verify the optimized spectral model Acorr(λ) using new or non-trained standard gases; S6.5.2: Analyze the error between the verification result and the certified value; S6.5.3: If the verification results are not satisfactory, return to S6.3 and add more standard gases for training.

3. A VOCs online monitoring system according to claim 2, characterized in that: Unsatisfactory verification results in S6.5.3 means that the error in S6.5.2 exceeds the preset value. The error threshold is set in advance. When the calculated error is greater than the error threshold, return to S6.3 and add more standard gases for training.

4. A VOCs online monitoring system according to claim 1, characterized in that: The device is also equipped with a temperature control system for adjusting to different temperatures for gas spectrum detection.

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