A non-methane total hydrocarbon detection method, system, storage medium and program product

Abstract

A non-methane total hydrocarbon detection method, system, storage medium and program product relate to the field of testing or analyzing materials by means of determining the chemical or physical properties of materials, in which method, gas chromatography data and component data are acquired, and original total hydrocarbon concentration is calculated. Known interference components and dynamically added components are distinguished by matching CAS numbers, preset response factors of known components are respectively acquired, and theoretical response factors of added components are calculated, to obtain respective accurate concentrations. A baseline total hydrocarbon concentration deviation value caused by interference components when calculated according to a basic factor is calculated. The deviation value is deducted from the original total hydrocarbon concentration, the accurate concentrations of the interference components are compensated, and a corrected target total hydrocarbon concentration is obtained. Target methane concentration is calculated and deducted from the target total hydrocarbon concentration, to obtain non-methane total hydrocarbon concentration. The application is used for reducing the interference of dynamically changing added organic components on total hydrocarbon detection, to improve the accuracy and intelligent processing efficiency of non-methane total hydrocarbon continuous detection in a complex waste gas scene.

Description

Technical Field

[0001] This application belongs to the field of testing or analyzing materials by measuring their chemical or physical properties, and particularly relates to a method, system, storage medium, and procedure for detecting non-methane total hydrocarbons. Background Technology

[0002] Non-methane total hydrocarbons are a core indicator for monitoring industrial waste gas pollution, and the accuracy of their detection is directly related to environmental compliance and the evaluation of waste gas treatment effectiveness.

[0003] Currently, the industrial sector primarily employs a non-methane total hydrocarbon (NMR) detection technology based on comprehensive qualitative and quantitative analysis of all components. This technology typically utilizes a gas chromatography-mass spectrometry (GC-MS) system. Before detection, standard gases for all known organic components in the waste gas are pre-prepared, and standard curves are established for each component. During actual detection, this technology performs complete chromatographic separation on the collected industrial waste gas sample, identifying each independent organic component. Subsequently, the precise concentrations of each component are calculated using the pre-established standard curves. Finally, the concentrations of all non-methane organic components are summed to obtain the accurate concentration of NMR. This method, by independently calibrating and quantifying each component, avoids the problem of overestimating the total hydrocarbon concentration caused by conflating components with different response characteristics.

[0004] In the scenario of continuous emission monitoring of exhaust gases in the painting workshop of a large automobile manufacturing plant, the types of volatile organic compounds (VOCs) in the exhaust gases can change dynamically and unpredictably when different production batches are used or when different brands and models of paints and cleaning agents are switched. When new organic components not included in the existing standard curve library appear in the exhaust gases, the aforementioned related technologies will reduce the accuracy of calculating the concentration of the unknown component and even the overall non-methane total hydrocarbons due to the lack of a standard gas for real-time comparison and calibration. To maintain high-precision detection results, frequent intervention by monitoring personnel is often required in the online monitoring process, which not only increases the system's operation and maintenance costs and operational complexity but also reduces the overall automation efficiency of continuous emission monitoring. Summary of the Invention

[0005] This application provides a method, system, storage medium, and program product for detecting non-methane total hydrocarbons, which reduces the interference of dynamically changing new organic components on total hydrocarbon detection, thereby improving the accuracy and intelligent processing efficiency of continuous detection of non-methane total hydrocarbons in complex exhaust gas scenarios.

[0006] In the first aspect, this application provides a method for detecting non-methane total hydrocarbons, which involves taking the original gas chromatographic detection data and component qualitative and quantitative analysis data of the industrial waste gas sample to be tested. The original gas chromatographic detection data includes the original peak area of ​​total hydrocarbons and the original peak area of ​​methane.

[0007] The original total hydrocarbon peak area is multiplied by the preset total hydrocarbon basic response factor to obtain the original total hydrocarbon concentration;

[0008] The CAS number of each independent organic component in the qualitative and quantitative analysis data is matched with the preset multi-component calibration database to obtain known interfering components and dynamically added components.

[0009] Extract the preset hydrogen flame ionization detector response factors corresponding to each known interfering component from the multi-component calibration database;

[0010] Based on the CAS number of each dynamically added component, the effective carbon number and molecular weight of the dynamically added component are extracted from the preset chemical attribute feature library;

[0011] The theoretical response factor of each dynamically added component is calculated based on the preset reference material response factor, the preset reference material effective carbon number, the preset reference material molecular weight, the effective carbon number of the dynamically added component, and the molecular weight of the dynamically added component.

[0012] The concentration of the known interfering component is obtained by multiplying the peak area of ​​each known interfering component by its corresponding preset hydrogen flame ionization detector response factor.

[0013] The concentration of the newly added interfering component is obtained by multiplying the peak area of ​​each dynamically added component with its corresponding theoretical response factor.

[0014] The sum of the peak areas of all known interfering components and all dynamically added components is multiplied by the preset total hydrocarbon baseline response factor to obtain the baseline total hydrocarbon concentration deviation value.

[0015] The target total hydrocarbon concentration is obtained by subtracting the baseline total hydrocarbon concentration deviation from the original total hydrocarbon concentration and adding the sum of the concentrations of all known interfering components and all newly added components.

[0016] The target methane concentration is obtained by multiplying the original peak area of ​​methane in the raw gas chromatography detection data by the preset methane response factor.

[0017] The difference between the target total hydrocarbon concentration and the target methane concentration is taken as the non-methane total hydrocarbon concentration.

[0018] By adopting the above technical solution and introducing qualitative and quantitative analysis data of components, the exhaust gas components are subdivided into known interfering components and dynamically added components. For known components, a preset response factor is used; for dynamically added components, a theoretical response factor is dynamically calculated using chemical properties such as effective carbon number and molecular weight, thereby enabling targeted quantification of the true concentration of different components. Furthermore, by calculating the baseline total hydrocarbon concentration deviation, the calculation deviation caused by using a single basic response factor is extracted from the original total hydrocarbon concentration and corrected using separately calculated concentrations of each component, resulting in a target total hydrocarbon concentration that more closely reflects reality. This reduces the distortion of the response factor caused by the complex and variable composition of exhaust gas and the influx of unknown components, improving the accuracy and reliability of non-methane total hydrocarbon concentration detection results under complex industrial conditions.

[0019] In conjunction with some implementation methods of the first aspect, in some implementation methods, the CAS number of each independent organic component in the qualitative and quantitative analysis data of the components is matched with a preset multi-component calibration database to obtain known interfering components and dynamically added components, specifically including:

[0020] Extract the standard CAS number string and target retention time of each independent organic component from the qualitative and quantitative analysis data of the components;

[0021] Remove hyphens from the standard CAS number string and use a preset cryptographic hash function to perform a hash mapping on the string after removing hyphens to generate component CAS hash values;

[0022] When the CAS hash value of a component is found to match the database hash value stored in the preset multi-component calibration database, the absolute value of the retention time deviation between the target retention time and the standard retention time associated with the database hash value is calculated.

[0023] If the absolute value of the retention time deviation is less than the preset time window threshold, the independent organic component is determined to be a known interfering component;

[0024] If the absolute value of the retention time deviation is not less than the preset time window threshold or the CAS hash value of the component is inconsistent with the hash value in the database, the independent organic component is determined to be a dynamically added component.

[0025] By employing the above technical solution, and by removing hyphens from the Chemical Abstracts Service (CAS) number string and performing hash mapping, non-standardized long strings are converted into fixed-length hash values ​​for comparison. This reduces the matching failure rate caused by inconsistent string formats or input errors, and improves the computational efficiency of data retrieval and matching. Under the premise of consistent hash values, further verification of the chromatographic elution characteristics of components is achieved through a preset time window threshold. This enables the identification of isomers or retention time drift caused by column aging or fluctuations in operating conditions, reducing the probability of misclassification of components in complex waste gas matrices and improving the accuracy of classifying and identifying known interfering components and dynamically added components.

[0026] In conjunction with some implementation methods of the first aspect, in some implementation methods, the theoretical response factor of each dynamically added component is calculated based on the preset reference substance response factor, the preset reference substance effective carbon number, the preset reference substance molecular weight, the effective carbon number of the dynamically added component, and the molecular weight of the dynamically added component. Specifically, this includes:

[0027] Divide the effective carbon number of the preset reference substance by the molecular weight of the preset reference substance to obtain the effective carbon mass ratio of the reference substance;

[0028] Divide the effective carbon number of the dynamically added component by the molecular weight of the dynamically added component to obtain the effective carbon mass ratio of the target component;

[0029] Divide the effective carbon mass ratio of the reference material by the effective carbon mass ratio of the target component to obtain the ideal relative response ratio of the dynamically added component;

[0030] Calculate the absolute value of the difference between the molecular weight of the dynamically added component and the molecular weight of the preset reference substance, and divide the absolute value of the difference by the molecular weight of the preset reference substance to obtain the molecular weight deviation.

[0031] The molecular weight deviation is substituted into the preset hydrogen flame ionization detector ionization efficiency decay function for calculation to obtain the ionization efficiency compensation coefficient for dynamically added components.

[0032] The theoretical response factor of each dynamically added component is obtained by multiplying the preset reference material response factor, the ideal relative response ratio, and the ionization efficiency compensation coefficient.

[0033] By employing the above technical solution, the effective carbon mass ratio of the reference material and the dynamically added component was calculated separately, and the ideal relative response ratio was obtained accordingly. This preliminarily quantified the difference in the basic response capability of the added component and the reference material in the detector from the perspective of the effective carbon content in the molecular structure. Considering that the physicochemical process of ionization during combustion in a flame is not ideal for substances of different molecular sizes and structures, the molecular weight deviation between the dynamically added component and the reference material was further calculated, and an ionization efficiency decay function was introduced to generate a compensation coefficient. By combining the ideal relative response ratio with the ionization efficiency compensation coefficient to correct the response factor of the reference material, the theoretical bias caused by relying solely on the single carbon number estimation was reduced, and the accuracy of the theoretical response factor calculation for the dynamically added component was improved.

[0034] In conjunction with some implementations of the first aspect, in some implementations, before multiplying the peak area of ​​each dynamically added component by its corresponding theoretical response factor to obtain the concentration of the added interfering component, the method further includes:

[0035] The qualitative and quantitative analysis data of the components are traversed to extract the target retention times of each dynamically added component and each known interfering component in the original gas chromatography detection data.

[0036] Multiple components whose absolute value of the difference in target retention time is less than a preset co-efferentiation time threshold are clustered into a co-efferentiation interference component cluster;

[0037] Obtain the cluster mixing peak area of ​​the corresponding co-eluting interfering component cluster in the raw gas chromatography detection data;

[0038] Extract the relative abundance percentage of each component within the co-eluting interfering component cluster from the qualitative and quantitative analysis data of the components;

[0039] Based on the relative abundance of each component within the co-eluting interference component cluster, the preset hydrogen flame ionization detector response factor and / or theoretical response factor corresponding to each component are weighted and summed to obtain the equivalent cluster response factor.

[0040] The cluster interference concentration is obtained by multiplying the cluster mixing peak area by the equivalent cluster response factor.

[0041] Replace the corresponding known interference component concentration and / or the newly added interference component concentration with the cluster interference concentration.

[0042] By employing the above technical solution, multiple components with similar elution times are clustered into co-elution interference component clusters by comparing target retention times. The overall mixed peak area of ​​the cluster is directly obtained, reducing the integration error caused by forced peak cutting or deconvolution. For the clustered mixed peaks, the relative abundance ratio of each component in the qualitative and quantitative analysis data is used to weight and sum the original preset or theoretical response factors of each component within the cluster, constructing an equivalent cluster response factor that reflects the overall response characteristics of the mixture. Using this equivalent cluster response factor and the cluster mixed peak area together to calculate the cluster interference concentration can more objectively restore the comprehensive concentration contribution of multiple organic compounds in the overlapping peak region, reduce the distortion of component concentration calculation caused by insufficient chromatographic separation, and improve the rationality and accuracy of the assessment of overlapping component concentrations in complex waste gas samples.

[0043] In conjunction with some implementations of the first aspect, in some implementations, based on the relative abundance ratio of each component within the co-eluting interfering component cluster, the preset hydrogen flame ionization detector response factor and / or theoretical response factor corresponding to each component are weighted and summed to obtain the equivalent cluster response factor, specifically including:

[0044] The total abundance value within a cluster is obtained by summing the relative abundance percentages of all components within the co-efferent interference component cluster.

[0045] The normalized abundance weight of each component is obtained by dividing the relative abundance percentage of each component by the total abundance value within the cluster.

[0046] For the known interfering components within the co-eluting interfering component cluster, the normalized abundance weight is multiplied by the corresponding preset hydrogen flame ionization detector response factor to obtain the first response component;

[0047] For dynamically added components within the co-efferent interference component cluster, the normalized abundance weight is multiplied by the corresponding theoretical response factor to obtain the second response component;

[0048] The equivalent cluster response factor is obtained by summing all the first response components and all the second response components within the co-efferent interference component cluster.

[0049] By employing the above technical solution, the normalized abundance weight of each component is obtained by summing and dividing the relative abundance ratios of all components within the co-eluting interfering component cluster. This reduces the interference of absolute abundance fluctuations on the proportional distribution. For known interfering components and dynamically added components, their normalized abundance weights are multiplied by the preset hydrogen flame ionization detector response factor and the theoretical response factor, and then globally accumulated to construct an equivalent cluster response factor corresponding to the true proportion of the current mixture. This allows the finally calculated equivalent cluster response factor to more accurately map the complex chemical composition differences within overlapping chromatographic peaks, improving the accuracy of co-eluting interfering component cluster concentration analysis.

[0050] In conjunction with some embodiments of the first aspect, in some embodiments, after taking the difference between the target total hydrocarbon concentration and the target methane concentration as the non-methane total hydrocarbon concentration, the method further includes:

[0051] Obtain the relative response deviation coefficients corresponding to each dynamically added component from the preset chemical property feature library;

[0052] Based on the concentration of newly added interfering components corresponding to each dynamically added component, the relative response deviation coefficient of each dynamically added component is weighted and calculated to obtain the comprehensive response deviation index of the current exhaust gas matrix.

[0053] When the comprehensive response offset index is determined to exceed the preset matrix stability threshold range, the preset total hydrocarbon basic response factor is compensated according to the comprehensive response offset index to obtain the matrix correction response factor.

[0054] The concentration of non-methane total hydrocarbons was recalculated based on the original peak area of ​​total hydrocarbons in the original gas chromatography detection data and the matrix correction response factor.

[0055] By employing the above technical solution, the relative response deviation coefficients of each dynamically added component are obtained, and weighted calculations are performed based on their concentrations to quantitatively evaluate the comprehensive response deviation index of the current exhaust gas matrix compared to the standard state. By comparing this index with a preset matrix stability threshold range, the system can autonomously identify whether the exhaust gas matrix has undergone drastic changes. When it is confirmed that the threshold range has been exceeded, the deviation index is used to specifically compensate the preset total hydrocarbon basic response factor, generating a matrix correction response factor and recalculating the non-methane total hydrocarbon concentration. This reduces the systematic measurement deviation caused by sudden changes in industrial site conditions and fluctuations in the exhaust gas matrix effect, and improves the adaptability and accuracy of the non-methane total hydrocarbon concentration detection algorithm under complex and variable operating conditions.

[0056] In conjunction with some implementation methods of the first aspect, in some implementation methods, a matrix-corrected response factor is obtained by compensating the preset total hydrocarbon basic response factor based on the comprehensive response offset index, specifically including:

[0057] The matrix offset is obtained by calculating the difference between the comprehensive response offset index and the critical boundary value of the preset matrix stability threshold range.

[0058] The matrix offset is input into a preset nonlinear smoothing compensation model to calculate the dynamic compensation coefficient. In the nonlinear smoothing compensation model, as the matrix offset increases, the growth derivative of the dynamic compensation coefficient gradually decreases.

[0059] The matrix-corrected response factor is obtained by merging the preset total hydrocarbon basic response factor with the dynamic compensation coefficient.

[0060] By employing the aforementioned technical solution, the matrix offset is obtained by calculating the difference between the comprehensive response offset index and the critical boundary value of the threshold interval, and then input into a preset nonlinear smooth compensation model with a gradually decreasing growth derivative. This model design ensures that when the matrix offset is small, the compensation coefficient maintains high sensitivity for accurate correction; while when the matrix offset increases abnormally, the growth rate of the compensation coefficient automatically converges and slows down, suppressing compensation divergence under extreme conditions, reducing the risk of overcompensation due to transient severe interference or abnormal local data fluctuations, and improving the anti-interference robustness and data output stability of the matrix correction algorithm in harsh industrial environments.

[0061] In a second aspect, embodiments of this application provide a non-methane total hydrocarbon detection system, which includes: one or more processors and a memory; the memory is coupled to one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors invoke the computer instructions to cause the system to perform the method described in the first aspect and any possible implementation thereof.

[0062] Thirdly, embodiments of this application provide a computer-readable storage medium including instructions that, when executed on a system, cause the system to perform the method described in the first aspect and any possible implementation thereof.

[0063] Fourthly, embodiments of this application provide a computer program product that, when run on a system, causes the system to execute the method described in any possible implementation of the first aspect.

[0064] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0065] 1. This application provides a method for detecting non-methane total hydrocarbons. By introducing qualitative and quantitative analysis data of components, the waste gas components are subdivided into known interfering components and dynamically added components. A preset response factor is used for known components, while a theoretical response factor is dynamically calculated using chemical properties such as effective carbon number and molecular weight for dynamically added components. This allows for targeted quantification of the true concentration of different components. Furthermore, by calculating the baseline total hydrocarbon concentration deviation, the calculation deviation caused by using a single basic response factor is extracted from the original total hydrocarbon concentration and corrected by replacing it with the separately calculated concentrations of each component, resulting in a target total hydrocarbon concentration that more closely approximates reality. This reduces the distortion of the response factor caused by the complex and variable composition of waste gas and the influx of unknown components, improving the accuracy and reliability of non-methane total hydrocarbon concentration detection results under complex industrial conditions.

[0066] 2. This application provides a method for detecting non-methane total hydrocarbons. By comparing target retention times, multiple components with similar elution times are clustered into co-elution interference component clusters, and the overall mixed peak area of ​​the cluster is directly obtained, reducing the integration error caused by forced peak cutting or deconvolution. For the clustered mixed peaks, the relative abundance ratio of each component in the qualitative and quantitative analysis data is used to weight and sum the original preset response factors or theoretical response factors of each component within the cluster, constructing an equivalent cluster response factor that reflects the overall response characteristics of the mixture. Using this equivalent cluster response factor and the cluster mixed peak area together to calculate the cluster interference concentration can more objectively restore the comprehensive concentration contribution of multiple organic compounds in the overlapping peak region, reduce the distortion of component concentration calculation caused by insufficient chromatographic separation, and improve the rationality and accuracy of the assessment of overlapping component concentrations in complex waste gas samples.

[0067] 3. This application provides a method for detecting non-methane total hydrocarbons. By obtaining the relative response deviation coefficients of each dynamically added component and weighting them with their concentrations, a comprehensive response deviation index of the current exhaust gas matrix compared to the standard state is quantitatively evaluated. By comparing this index with a preset matrix stability threshold range, the system can autonomously identify whether the exhaust gas matrix has undergone drastic changes. When it is confirmed that the threshold range has been exceeded, the deviation index is used to specifically compensate the preset total hydrocarbon basic response factor, generating a matrix correction response factor and recalculating the non-methane total hydrocarbon concentration. This reduces the systematic measurement deviation caused by sudden changes in industrial site conditions and fluctuations in the exhaust gas matrix effect, and improves the adaptability and accuracy of the non-methane total hydrocarbon concentration detection algorithm under complex and variable operating conditions. Attached Figure Description

[0068] Figure 1 This is a schematic flowchart of a non-methane total hydrocarbon detection method in an embodiment of this application.

[0069] Figure 2 This is another schematic flowchart of a non-methane total hydrocarbon detection method in the embodiments of this application.

[0070] Figure 3 This is another flowchart illustrating a method for detecting non-methane total hydrocarbons in an embodiment of this application.

[0071] Figure 4 This is a schematic diagram of the physical device structure of a non-methane total hydrocarbon detection system provided in an embodiment of this application. Detailed Implementation

[0072] The terminology used in the following embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. As used in the specification and appended claims of this application, the singular expressions “a,” “an,” “the,” “the,” “the,” and “this” are intended to include the plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in this application refers to any or all possible combinations including one or more of the listed items.

[0073] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature, and in the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more.

[0074] The following example is used in conjunction with Figure 1 The present application describes a method for detecting non-methane total hydrocarbons in an embodiment of the present application:

[0075] Please see Figure 1 This is a schematic flowchart of a non-methane total hydrocarbon detection method in an embodiment of this application.

[0076] S101. Obtain the original gas chromatography detection data and component qualitative and quantitative analysis data of the industrial waste gas sample to be tested;

[0077] The system acquires raw gas chromatographic (GC) data and qualitative and quantitative analysis data of the components from the industrial waste gas sample to be tested. The raw GC data includes the raw peak area of ​​total hydrocarbons and the raw peak area of ​​methane. The industrial waste gas sample to be tested refers to a gaseous mixture containing volatile organic compounds emitted from industrial production processes, including but not limited to waste gas from petrochemical refining, fermentation waste gas from pharmaceutical manufacturing, surface coating waste gas from automobile manufacturing, and photolithography waste gas from semiconductor manufacturing. Raw GC data refers to the converted low-level electrical signals output by the analytical instrument before filtration and complex mathematical transformations, including but not limited to baseline voltage values, retention time coordinate sequences, and digitized integrated areas. Qualitative and quantitative analysis data refers to the specific chemical substances and their relative abundance information resolved by mass spectrometry or other qualitative methods, including but not limited to mass-to-charge ratio spectra, retention indices, and relative abundance percentages. The raw peak area of ​​total hydrocarbons refers to the sum of the integrated areas representing all hydrocarbon peaks in the chromatogram. The raw peak area of ​​methane refers to the integrated area representing only methane peaks within a specific retention time interval in the chromatogram. The system establishes a communication link with the front-end analytical instrument through the underlying data interface. According to the preset sampling frequency and data format, it completely reads the above-mentioned multi-dimensional raw detection data and qualitative and quantitative analysis data into the system's running memory, providing basic data support for subsequent concentration calculation and in-depth component analysis.

[0078] The system can send data read commands to the registers of the gas chromatograph (GC) instrument via an industrial standard serial communication interface combined with a fieldbus protocol at fixed time intervals. Upon receiving the command, the GC instrument packages the raw GC detection data and component qualitative and quantitative analysis data stored locally into hexadecimal data frames and returns them. The system receives these data frames, verifies and parses them, and converts them into floating-point arrays and structures for storage in its local database. Alternatively, the system can establish a transmission control protocol connection with the GC instrument via an Ethernet interface deployed within an industrial LAN. After completing a single sample analysis, the GC instrument, acting as the server, proactively pushes an Extensible Markup Language (XML) file containing the raw peak areas of total hydrocarbons and methane, as well as qualitative and quantitative analysis data for each component, to the client system. The system uses a Document Object Model (DOM) parser to traverse the file, extracting all necessary numerical and textual information and loading it into memory variables.

[0079] S102. Multiply the original peak area of ​​total hydrocarbons by the preset total hydrocarbon basic response factor to obtain the original total hydrocarbon concentration;

[0080] The preset total hydrocarbon baseline response factor refers to a proportional coefficient pre-set within the system to convert the integral area of ​​the total hydrocarbon electrical signal into a standard concentration value. This coefficient is usually derived based on a single standard substance, including but not limited to propane calibration response factors, n-hexane calibration response factors, or isobutylene calibration response factors. The raw total hydrocarbon concentration refers to the estimated total hydrocarbon concentration initially calculated using only a single baseline response factor, without considering the complex component differences and matrix interference effects within the exhaust gas sample. After obtaining the raw peak area of ​​the total hydrocarbons, the system calls the arithmetic logic unit of the central processing unit to perform a floating-point multiplication operation between this area value and the preset total hydrocarbon baseline response factor stored in non-volatile memory. This transforms the dimensionless area signal into a physically meaningful concentration value, generating the raw total hydrocarbon concentration, which is then stored in the system's temporary calculation cache for subsequent correction processing.

[0081] The system can utilize the microcontroller's internal hardware multiplier module to load the original peak area of ​​total hydrocarbons and the preset total hydrocarbon baseline response factor into two independent general-purpose data registers. It then executes single-precision floating-point multiplication assembly instructions, completing the multiplication operation within a single clock cycle. The resulting original total hydrocarbon concentration is then transferred to a designated address space in static random access memory (SRAM) for storage. Alternatively, the system can utilize mathematical operation library functions in a high-level programming language. In the main thread, double-precision floating-point variables are defined to receive the original peak area of ​​total hydrocarbons and the preset total hydrocarbon baseline response factor. These two variables are multiplied by calling the multiplication operator, with an exception handling mechanism to prevent data overflow. Finally, the calculated original total hydrocarbon concentration is assigned to a global state variable for other concurrent processing threads to read and access at any time.

[0082] S103. Match the CAS number of each independent organic component in the qualitative and quantitative analysis data with the preset multi-component calibration database to obtain known interfering components and dynamically added components.

[0083] The system matches the CAS numbers of each independent organic component in the qualitative and quantitative analysis data with a pre-set multi-component calibration database to obtain known interfering components and dynamically added components. Specifically, this is achieved as follows: Extract the standard CAS number string and target retention time of each independent organic component from the qualitative and quantitative analysis data; remove hyphens from the standard CAS number string and perform a hash mapping on the string after hyphen removal using a pre-set cryptographic hash function to generate a component CAS hash value; when the component CAS hash value matches the pre-stored database hash value in the pre-set multi-component calibration database, calculate the absolute value of the retention time deviation between the target retention time and the standard retention time associated with the database hash value; if the absolute value of the retention time deviation is less than a pre-set time window threshold, the independent organic component is determined to be a known interfering component; if the absolute value of the retention time deviation is not less than the pre-set time window threshold or the component CAS hash value does not match the database hash value, the independent organic component is determined to be a dynamically added component.

[0084] Independent organic components refer to single chemical substances in exhaust gas samples that can be effectively separated by the chromatographic column and have independent chromatographic peaks, including but not limited to straight-chain alkanes, aromatic hydrocarbons, and halogenated hydrocarbons. The Chemical Abstracts Index (CAS) number is a unique numerical identification number assigned to each chemical substance. The preset multi-component calibration database refers to a structured data set built internally by the system that stores information on known chemical substances, including but not limited to relational calibration databases, non-relational calibration databases, or distributed graph databases. Known interfering components refer to substances with matching records in the preset multi-component calibration database and retention times that meet expectations. Dynamically added components refer to unknown substances with no matching records in the database or retention times that significantly deviate from expectations. The standard CAS number string refers to a standard format string containing numbers and hyphens. The target retention time refers to the x-axis time value when an independent organic component reaches its maximum peak value on the chromatogram. The preset cryptographic hash function refers to a one-way encryption algorithm used to map data of arbitrary length to a fixed-length hash value, including but not limited to Message Digest Algorithm Version 5 (CDE5) and secure hash algorithms. The component CAS hash value and the database hash value refer to fixed-length hexadecimal strings generated after hash mapping. The preset time window threshold refers to the tolerance limit used to determine whether the retention time drift is within a reasonable range. This threshold is derived from the statistical analysis of historical column aging test data, and its value is positively correlated with the stationary phase loss rate of the column. Furthermore, this threshold must be strictly greater than the single sampling cycle time of the system's underlying data acquisition board. The system extracts the standard CAS number string and the target retention time, removes hyphens, and uses a preset cryptographic hash function to generate a component CAS hash value, which is then compared with the hash value in the database. If the hash values ​​match, the absolute value of the retention time deviation between the target retention time and the standard retention time is calculated. If the absolute value of the retention time deviation is less than the preset time window threshold, the system determines it to be a known interfering component; if the absolute value of the retention time deviation is not less than the preset time window threshold, or if the hash value comparison is inconsistent, the system determines it to be a dynamically added component.

[0085] The system can use a regular expression engine to perform a global replacement operation on the standard CAS number string to remove all hyphens. Then, it calls the Message Digest Algorithm Version 5 to perform a one-way hash calculation on the processed pure numeric string to generate the component CAS hash value. Next, it sends an exact query request to the local relational database through Structured Query Language to compare the hash value. If a record is matched, it extracts the standard retention time from the database, calculates the difference between the standard retention time and the target retention time using an absolute value function, and uses conditional branch statements to determine whether the difference is less than a preset time window threshold. Thus, the component is classified into the known interfering component array or the dynamically added component array. The system can also use string slicing and concatenation functions to remove hyphens from the standard CAS number string, call the second version of the secure hash algorithm to generate high-digit component CAS hash values, and then perform a fast key-value lookup in the memory key-value database. If the lookup is successful, the associated standard retention time is extracted, and the absolute value of the nonlinear deviation between the target retention time and the standard retention time on the time axis is calculated using a dynamic time warping algorithm. The comparator logic is used to determine the relationship between the absolute value of the deviation and the preset time window threshold. Based on the judgment result, known interfering component objects are dynamically instantiated or newly added component objects are dynamically added and pushed into the corresponding processing queue.

[0086] S104. Extract the preset hydrogen flame ionization detector response factor corresponding to each known interfering component from the multi-component calibration database.

[0087] The preset hydrogen flame ionization detector response factor refers to the signal conversion coefficient that is pre-determined and stored in a database under standard test conditions for a specific known chemical substance. This coefficient reflects the detector's sensitivity to the carbon-hydrogen bond breaking and ionization of that specific substance, including but not limited to toluene-specific response factors, ethyl acetate-specific response factors, or dichloromethane-specific response factors. After classifying and determining the component properties, the system uses the unique identifier of a substance marked as a known interfering component as an index key to address and traverse the storage medium of the preset multi-component calibration database, locates the corresponding detailed parameter record line, and accurately reads the value of the preset hydrogen flame ionization detector response factor from it. This value is then loaded into the system's cache, providing the necessary multiplier parameters for subsequent quantitative calculations of the concentration of known components.

[0088] The system can utilize an open database connection interface to send data retrieval commands to a backend structured query language server using the hash values ​​of known interfering components as query conditions. Upon receiving the commands, the server scans the corresponding data table, locates the target row, extracts the floating-point value of the preset hydrogen flame ionization detector response factor, and returns this value to the system's main program via a network socket. The main program then deserializes this value and stores it in a dictionary of known component parameters. Alternatively, the system can utilize a graph database's traversal query language. Using the known interfering components as starting nodes in the graph structure, it performs a depth-first search along preset attribute relationship edges to find connected response factor attribute nodes. It directly reads the preset hydrogen flame ionization detector response factor value stored within these nodes and maps it to a contiguous array space in system memory to support subsequent high-concurrency read operations.

[0089] S105. Based on the CAS number of each dynamically added component, extract the effective carbon number and molecular weight of the dynamically added component from the preset chemical attribute feature library.

[0090] The pre-defined chemical property feature library refers to a data warehouse specifically designed to store the basic physicochemical properties of various chemical substances, either integrated internally or accessed externally. This includes, but is not limited to, locally deployed offline chemical manual databases and cloud-hosted public chemical information application interfaces. The effective carbon number refers to the equivalent number of carbon atoms within a molecule that can actually contribute to the ionization signal in a hydrogen flame ionization detector; this value is influenced by heteroatoms and the type of chemical bonds in the molecular structure. Molecular weight refers to the relative total mass of a chemical substance molecule. For substances identified as dynamically added components, the system uses their Chemical Abstracts Service (CAS) number as a unique retrieval credential to initiate a targeted query in the pre-defined chemical property feature library. It then parses the returned data packets or files, precisely extracting the effective carbon number and molecular weight values ​​corresponding to the dynamically added component. These basic physicochemical parameters are temporarily stored in the system's computational context as core raw materials for subsequently deriving the theoretical response factors of unknown substances.

[0091] The system can utilize the Hypertext Transfer Protocol (HTTP) security protocol to encapsulate the CAS number of dynamically added components within the query parameters of a Uniform Resource Locator (URL). This request is then sent to a remote national-level standard chemical property feature library server. The server returns a lightweight data exchange format file containing detailed information about the substance. The system uses its built-in parsing library to perform key-value pair parsing on this file, accurately extracting the values ​​corresponding to the effective carbon number and molecular weight fields, and converting them into double-precision floating-point data stored in local memory. Alternatively, the system can utilize the local file system interface to open a pre-downloaded and encrypted binary index file of a preset chemical property feature library stored on the hard drive. Using a binary search algorithm, it quickly locates the offset address corresponding to the CAS number in the index file, reads the contents of that address block in the data file, and after decryption and deserialization, extracts the effective carbon number and molecular weight of the dynamically added components, loading them into the system's runtime stack.

[0092] S106. Calculate the theoretical response factor of each dynamically added component based on the preset reference material response factor, the preset reference material effective carbon number, the preset reference material molecular weight, the effective carbon number of the dynamically added component, and the molecular weight of the dynamically added component.

[0093] The system calculates the theoretical response factor of each dynamically added component based on the preset reference material response factor, the preset reference material effective carbon number, the preset reference material molecular weight, the effective carbon number of the dynamically added component, and the dynamic added component molecular weight. Specifically, this is achieved as follows: Divide the preset reference material effective carbon number by the preset reference material molecular weight to obtain the effective carbon mass ratio of the reference material; divide the effective carbon number of the dynamically added component by the dynamic added component molecular weight to obtain the effective carbon mass ratio of the target component; divide the reference material effective carbon mass ratio by the target component effective carbon mass ratio to obtain the ideal relative response ratio of the dynamically added component; calculate the absolute value of the difference between the molecular weight of the dynamically added component and the preset reference material molecular weight, and divide the absolute value of the difference by the preset reference material molecular weight to obtain the molecular weight deviation; substitute the molecular weight deviation into the preset hydrogen flame ionization detector ionization efficiency decay function for calculation to obtain the ionization efficiency compensation coefficient for the dynamically added component; multiply the preset reference material response factor, the ideal relative response ratio, and the ionization efficiency compensation coefficient to obtain the theoretical response factor of each dynamically added component.

[0094] In a preferred embodiment of this application, the preset ionization efficiency decay function of the hydrogen flame ionization detector can be constructed using the following exponential decay model:

[0095] f(ΔM_rel) =α* exp(-β*ΔM_rel) +γ

[0096] Where f(ΔM_rel) represents the calculated ionization efficiency compensation coefficient for dynamically added components;

[0097] ΔM_rel represents the molecular weight deviation, i.e., |M_target - M_ref| / M_ref, where M_target is the molecular weight of the dynamically added component and M_ref is the molecular weight of the preset reference substance;

[0098] α, β, and γ are all preset empirical constants, which are obtained by regression analysis and fitting of actual response data of a large number of known organic compounds with different molecular weights under specific hydrogen flame ionization detector operating conditions (such as hydrogen / air flow ratio, carrier gas flow rate, combustion chamber temperature, etc.).

[0099] The constant α is usually related to the detector’s baseline sensitivity, β reflects the rate of decay of ionization efficiency for every unit increase in molecular weight, and γ represents the minimum baseline ionization efficiency that can be maintained even when the molecular weight is very large.

[0100] The preset reference material response factor refers to the response coefficient of the chemical substance used as a global reference standard. The preset reference material's effective carbon number and molecular weight refer to the inherent physicochemical properties of the reference standard material. The effective carbon mass ratio of the reference material is the ratio of its effective carbon number to its molecular weight. The effective carbon mass ratio of the target component is the ratio of the effective carbon number to its molecular weight of the dynamically added component. The ideal relative response ratio is the quotient of the effective carbon mass ratio of the reference material and the effective carbon mass ratio of the target component. The molecular weight deviation is the ratio of the absolute value of the difference between the molecular weight of the dynamically added component and the molecular weight of the preset reference material to the molecular weight of the preset reference material. The ionization efficiency decay function of the hydrogen flame ionization detector refers to a mathematical model describing the decrease in ionization efficiency caused by incomplete combustion of macromolecules or complex molecules in a flame, including but not limited to exponential decay models and polynomial fitting models. The ionization efficiency compensation coefficient is the correction parameter calculated through the decay function. The theoretical response factor is the virtual response coefficient derived for dynamically added components. The system divides the effective carbon number of the preset reference material by its molecular weight to obtain the effective carbon mass ratio of the reference material, and divides the effective carbon number of the dynamically added component by its molecular weight to obtain the effective carbon mass ratio of the target component. The system divides the effective carbon mass ratio of the reference material by the effective carbon mass ratio of the target component to obtain the ideal relative response ratio. The system calculates the absolute value of the difference between the molecular weight of the dynamically added component and the preset reference material, and divides this value by the preset reference material's molecular weight to obtain the molecular weight deviation. The system substitutes the molecular weight deviation into the ionization efficiency decay function of the hydrogen flame ionization detector to obtain the ionization efficiency compensation coefficient. The system multiplies the preset reference material response factor, the ideal relative response ratio, and the ionization efficiency compensation coefficient to obtain the theoretical response factor.

[0101] The system can utilize the floating-point arithmetic unit in the central processing unit to sequentially execute division instructions to calculate the effective carbon mass ratio of the reference material and the effective carbon mass ratio of the target component, and then execute division instructions again to obtain the ideal relative response ratio. Subsequently, it calls the absolute value function in the standard mathematical library to calculate the molecular weight difference, and obtains the molecular weight deviation through division instructions. Then, it inputs the deviation as an independent variable into a pre-written exponential decay function code block, and calculates the ionization efficiency compensation coefficient through a Taylor series expansion approximation algorithm. Finally, it executes a series of multiplication instructions to multiply the preset reference material response factor, the ideal relative response ratio, and the ionization efficiency compensation coefficient to obtain the theoretical response factor. The system can also utilize the parallel computing architecture of a graphics processor to construct a one-dimensional tensor from the effective carbon number and molecular weight of multiple dynamically added components. Tensor division operations are used to batch generate the effective carbon mass ratio tensor and ideal relative response ratio tensor of the target components. Tensor subtraction, absolute value operations, and division are used to batch calculate the molecular weight deviation tensor. Subsequently, the deviation tensor is input into a lightweight neural network decay model based on a multilayer perceptron architecture. Matrix multiplication and forward propagation of activation functions are used to output the ionization efficiency compensation coefficient tensor. This neural network model is trained using gradient descent based on a large amount of real ionization efficiency data of known compounds, enabling it to accurately fit complex nonlinear decay patterns. Finally, through element-wise tensor multiplication operations, the theoretical response factor tensor of all dynamically added components is calculated in parallel at once.

[0102] S107. Multiply the peak area of ​​each known interfering component by its corresponding preset hydrogen flame ionization detector response factor to obtain the concentration of the known interfering component.

[0103] The peak area of ​​a component refers to the area value obtained after baseline subtraction and integration of the chromatographic peak belonging to a specific known interfering component in the raw gas chromatography detection data, including but not limited to the integrated area after vertical cutting and the integrated area after tangent removal. The concentration of a known interfering component refers to the true concentration value obtained after accurately converting its peak area using the specific response factor of that substance. The system traverses the list of known interfering components, extracts the peak area of ​​each component on the chromatogram one by one, and retrieves the corresponding preset hydrogen flame ionization detector response factor from the parameter dictionary in the system memory. The underlying calculation module is then called to perform a multiplication operation on these two values, thereby converting the area signal into a specific concentration physical quantity, and the calculated concentration of the known interfering component is recorded one by one in the system's result data table.

[0104] The system can traverse a linked list of known interfering components using a loop control structure. In each iteration, it extracts the component peak area variable and the preset hydrogen flame ionization detector response factor variable of the current node, pushes these two variables onto the operation stack, calls the arithmetic logic unit to execute a scalar multiplication instruction, pops the operation result from the stack and assigns it to the known interfering component concentration attribute of the current node, until the linked list traversal is complete. The system can also utilize vectorized programming techniques to extract the component peak areas of all known interfering components and construct an area row vector, construct a factor column vector for the corresponding preset hydrogen flame ionization detector response factors, call the linear algebra library to perform vector dot product multiplication operations, and leverage the single instruction multiple data (SID) instruction set of the underlying hardware to accelerate the calculation process. It directly outputs a result vector containing the concentrations of all known interfering components and maps it to the corresponding fields in the result database.

[0105] S108. Multiply the peak area of ​​each dynamically added component by its corresponding theoretical response factor to obtain the concentration of the newly added interfering component.

[0106] The concentration of newly added interfering components refers to the estimated concentration value obtained by converting the peak area of ​​unknown or unexpected substances appearing in the exhaust gas sample using the theoretical response factor dynamically derived by the system. The system traverses the set of dynamically added components, extracts the peak area of ​​each dynamically added component obtained by integrating it in the original gas chromatography detection data, and reads the theoretical response factor corresponding to the component calculated in the previous steps from the temporary calculation buffer. The system control and calculation core performs a floating-point multiplication operation on these two data points to complete the quantitative conversion from chromatographic peak area to the concentration of newly added interfering components, and appends the generated concentration data of newly added interfering components to the system's comprehensive analysis result set.

[0107] The system can utilize a multi-threaded concurrent processing mechanism, allocating an independent computation thread for each dynamically added component. Within each thread, the component's peak area and theoretical response factor are independently read, and the multiplication operation is performed using the microprocessor's multiplication register to obtain the concentration of the new interfering component. Subsequently, a mutex lock mechanism is used to safely write the calculation result to a shared global result array, ensuring data consistency during multi-threaded writes. The system can also utilize a streaming data processing framework, packaging the peak area and theoretical response factor of dynamically added components into data tuples and injecting them into the processing pipeline. Upon receiving the tuple, the mapping operator in the pipeline extracts the two values ​​in real time and performs a multiplication operation to generate a new data tuple containing the concentration of the new interfering component. This new tuple is then passed to the downstream persistence operator, which writes the concentration data in batches to the time-series database.

[0108] S109. Multiply the sum of the peak areas of all known interfering components and all dynamically added components by the preset total hydrocarbon basic response factor to obtain the baseline total hydrocarbon concentration deviation value.

[0109] The baseline total hydrocarbon concentration deviation refers to the overall concentration calculation error caused by the incorrect application of a uniform preset total hydrocarbon baseline response factor to the peak areas of all interfering components (including known and newly added components) during the calculation of the original total hydrocarbon concentration. The system allocates an accumulator space in memory, iterates through all known interfering components and dynamically added components recorded by the system, and accumulates their peak areas one by one into the accumulator to obtain a total area sum. Then, the system extracts the preset total hydrocarbon baseline response factor, multiplies the total area sum in the accumulator with this baseline response factor, and calculates the spurious concentration value contributed by this part of the interfering components under the traditional single response factor calculation mode, i.e., the baseline total hydrocarbon concentration deviation value, and temporarily stores it for subsequent baseline subtraction operations.

[0110] The system can utilize a software-level loop accumulation algorithm to initialize a double-precision floating-point total area variable to zero. It then iterates through two consecutive loop structures, traversing the known interfering component array and the dynamically added component array respectively. In each loop, the peak area of ​​the current component is added to the total area variable. After the loop ends, a mathematical operation module is called to multiply the total area variable by a preset total hydrocarbon baseline response factor, obtaining the baseline total hydrocarbon concentration deviation value and storing it in a specified memory address. Alternatively, the system can utilize a hardware accumulation tree structure in a field-programmable gate array (FPGA) to input all component peak area data in parallel to the leaf nodes of the accumulation tree. Within a few clock cycles, a hardware-level adder network quickly summarizes the component peak areas to obtain the sum. This sum is then input to the input port of a hardware multiplier module and multiplied by a preset total hydrocarbon baseline response factor stored in a register, directly outputting the digital signal of the baseline total hydrocarbon concentration deviation value at the hardware level.

[0111] S110. Subtract the baseline total hydrocarbon concentration deviation from the original total hydrocarbon concentration, and add the sum of the concentrations of all known interfering components and all newly added components to obtain the target total hydrocarbon concentration.

[0112] The target total hydrocarbon concentration refers to the final concentration value that accurately reflects the actual total hydrocarbon content in the industrial waste gas sample after baseline error stripping and precise concentration backfilling correction for each component. The system first calls the subtraction operation logic to read the original total hydrocarbon concentration from the system's temporary buffer and subtracts the baseline total hydrocarbon concentration deviation value calculated in the previous steps, thereby eliminating the erroneous concentration portion caused by the misuse of response factors. Subsequently, the system calls the accumulation operation logic to sum the concentrations of all known interfering components and the newly added interfering components, and adds this summation result to the difference from the above subtraction operation, completing the precise compensation for the true concentration of each interfering component, finally generating the target total hydrocarbon concentration, and updating it in the system's core monitoring indicator register.

[0113] The system can utilize the arithmetic logic unit (ALU) of the central processing unit to execute multiple machine instructions sequentially. First, the original total hydrocarbon concentration is loaded into an accumulator register. A subtraction instruction is then executed to deduct the baseline total hydrocarbon concentration deviation. Subsequently, a loop structure sequentially reads the concentrations of each known interfering component and the newly added component, executing addition instructions to accumulate them into the accumulator register. After the loop completes, the value in the accumulator register is the target total hydrocarbon concentration, which the system writes back to main memory. Alternatively, the system can utilize a data flow graph calculation engine to construct a directed acyclic graph containing subtraction nodes and multi-input addition nodes. The original total hydrocarbon concentration and the baseline total hydrocarbon concentration deviation are input into the subtraction node, while the concentrations of all known interfering components and the newly added component are input into the multi-input addition node. The outputs of the subtraction node and the multi-input addition node are then combined into a final merge addition node. The calculation engine automatically schedules node execution according to the topology, and the target total hydrocarbon concentration is finally obtained at the output of the merge addition node.

[0114] S111. Multiply the original peak area of ​​methane in the original gas chromatography detection data by the preset methane response factor to obtain the target methane concentration;

[0115] The preset methane response factor refers to the signal conversion coefficient specifically measured and calibrated for methane under standard conditions. This coefficient accurately converts the integral area of ​​the methane electrical signal into a methane concentration value. The target methane concentration refers to the actual methane content in the exhaust gas sample obtained after calculation using the dedicated response factor. The system accurately extracts the original methane peak area representing methane from the raw gas chromatography detection data and retrieves the preset methane response factor from the system's core parameter library. The system's control and calculation unit performs independent multiplication operations on these two specific values, avoiding cross-interference with other complex hydrocarbons, thereby calculating a high-precision target methane concentration, which is then stored as a key subtraction factor for calculating the total non-methane hydrocarbon concentration.

[0116] The system can utilize a specific condition-triggered mechanism in the main control program. When methane characteristic retention time stamps are detected during the parsing of raw gas chromatography detection data, a high-priority hardware interrupt is immediately triggered. The raw methane peak area is extracted in the interrupt service routine and multiplied directly with a preset methane response factor stored in read-only memory. After obtaining the target methane concentration, the interrupt is exited and the main program resumes execution. Alternatively, the system can utilize an independent data processing microservice within the edge computing gateway. This microservice specifically subscribes to methane channel data in the raw gas chromatography detection data stream. Once it receives a raw methane peak area data packet, its lightweight script engine immediately executes the multiplication logic, multiplying the raw methane peak area by the preset methane response factor to obtain the target methane concentration. This concentration value is then published to other integrated computing modules in the system via a message queue.

[0117] S112. The difference between the target total hydrocarbon concentration and the target methane concentration is taken as the non-methane total hydrocarbon concentration.

[0118] Non-methane total hydrocarbon concentration refers to the total concentration of all volatile hydrocarbons other than methane in an industrial waste gas sample. This indicator is a core parameter for assessing the degree of industrial waste gas pollution and environmental compliance. After obtaining the finely corrected target total hydrocarbon concentration and the independently calculated target methane concentration, the system calls the final indicator synthesis module to perform a simple subtraction operation, accurately subtracting the target methane concentration from the target total hydrocarbon concentration, thereby removing the background contribution of methane and obtaining the pure non-methane total hydrocarbon concentration. The system then formats this non-methane total hydrocarbon concentration and outputs it to the system's human-machine interface for real-time display, or uploads it to the superior environmental monitoring platform via industrial communication protocols.

[0119] The system can utilize the arithmetic logic unit (ALU) within the microprocessor to execute basic subtraction instructions. Using the target total hydrocarbon concentration stored at a specific memory address as the minuend and the target methane concentration as the subtrahend, it calculates the difference. After calculating the difference, a boundary check function is called to verify if the difference is negative. If it is non-negative, it is assigned to the non-methane total hydrocarbon concentration variable, and the rendering function from the graphical user interface library is called to display it in a designated data frame on the industrial touchscreen. The system can also utilize the mathematical operation function block in the programmable logic controller (PLC) to map the target total hydrocarbon concentration and target methane concentration to two analog input registers. A subtraction function block is configured to read the values ​​from these two registers, subtract them, and output the calculated non-methane total hydrocarbon concentration to an analog output register. Subsequently, a digital-to-analog converter (DAC) converts this into a standard 4-20 mA current signal, which is then transmitted to the plant's distributed control system for unified recording and alarm monitoring.

[0120] In the above embodiments, by introducing qualitative and quantitative analysis data of components, the exhaust gas components are subdivided into known interfering components and dynamically added components. A preset response factor is used for known components, while a theoretical response factor is dynamically calculated using chemical properties such as effective carbon number and molecular weight for dynamically added components. This allows for targeted quantification of the true concentration of different components. Furthermore, by calculating the baseline total hydrocarbon concentration deviation, the calculation deviation caused by using a single basic response factor is extracted from the original total hydrocarbon concentration and corrected using the separately calculated concentrations of each component, resulting in a target total hydrocarbon concentration that more closely reflects reality. This reduces the distortion of the response factor caused by the complex and variable composition of exhaust gas and the influx of unknown components, improving the accuracy and reliability of non-methane total hydrocarbon concentration detection results under complex industrial conditions.

[0121] In the above embodiments, although a refined correction of the target total hydrocarbon concentration was achieved by calculating the concentrations of known interfering components and dynamically added components separately, in actual gas chromatography analysis, complex waste gas samples often exhibit the phenomenon of multiple components eluting within a very short time, leading to overlapping chromatographic peaks. In such cases, it is difficult to accurately separate and obtain the independent peak area of ​​a single component. To address the local quantitative error problem caused by the aforementioned overlapping chromatographic peaks and further improve the accurate assessment of the concentration of overlapping components in complex waste gas samples, this application provides another method for detecting non-methane total hydrocarbons based on the above embodiments. The following is a detailed explanation... Figure 2 Another method for detecting non-methane total hydrocarbons in the embodiments of this application is described below:

[0122] Please see Figure 2 This is another flowchart illustrating a method for detecting non-methane total hydrocarbons in an embodiment of this application.

[0123] S201. Traverse the qualitative and quantitative analysis data of the components and extract the target retention times of each dynamically added component and each known interfering component in the original gas chromatography detection data.

[0124] Since the previous steps have already subdivided and tagged the complex components in the exhaust gas sample, the system now needs to perform in-depth analysis of this categorized data. Following a specific data structure access order, the system examines each record in the qualitative and quantitative analysis dataset, identifying and filtering out chemical entries that are clearly determined to be dynamically added components and known interfering components. After identifying these target entries, the system further analyzes the mapping relationship between the raw gas chromatography detection data and the qualitative and quantitative analysis data, accurately retrieving the target retention time value corresponding to each target component during column separation. The target retention time, as a key time coordinate characterizing the properties of substances in chromatographic analysis, is essentially the time span from sample injection to the generation of the maximum response signal in the detector. The system structures all extracted target retention times, constructing a basic data view containing component identification and time coordinate mapping relationships. This view provides a fundamental time dimension reference for subsequent assessment of whether co-elution phenomena exist between components.

[0125] The system can utilize the iterator interface in object-oriented programming to access the collection of qualitative and quantitative analysis data objects residing in the system's main memory one by one. During traversal, the system calls the type acquisition method for each component object and uses branch selection logic to determine whether it belongs to a dynamically added component or a known interfering component. Once a match is confirmed, the system extracts the target retention time as a double-precision floating-point number by calling the retention time read function encapsulated within the object, and stores it along with the component's Chemical Abstracts Index (CAI) number into a pre-instantiated hash map to support constant-time lookup.

[0126] The system can also utilize mapping operators in the distributed data processing framework to load massive amounts of component qualitative and quantitative analysis data in parallel into the memory of multiple worker nodes. Each worker node's mapping operator independently executes filtering logic, using regular expressions or bitmask matching techniques to filter out component records for the target classification, and then extracts the target retention time data from specific byte offsets in the records. After processing by each worker node, the extracted target retention time data is aggregated to the master node using a reduction operator. The master node integrates this data into a globally ordered array of time coordinate sequences, which can then be directly accessed by downstream clustering algorithm modules.

[0127] S202. Cluster multiple components whose absolute value of the difference between target retention times is less than a preset co-escape time threshold into a co-escape interference component cluster.

[0128] The preset co-elution time threshold refers to the time difference limit set by the system to determine whether two or more adjacent elution peaks severely overlap. This threshold is derived from theoretical calculations of column resolution and statistical analysis of historical data from complex waste gas samples. Its value is strictly smaller than the aforementioned preset time window threshold for determining component identity, because co-elution determination requires a more stringent peak overlap. A co-elution interference cluster refers to a collection of multiple chemical components whose chromatographic peaks overlap due to extremely close elution times, including but not limited to two-component overlapping clusters and multi-component complex overlapping clusters. After extracting the target retention times of all target components, the system calculates the absolute value of the difference between the target retention times of different components pairwise. The system compares this absolute value with the preset co-elution time threshold. When the absolute values ​​of the time differences between multiple components are all within this threshold range, the system determines that the elution processes of these components in the chromatographic column are highly synchronized. Logically, the system groupes and binds these substances together to form a unified co-elution interference cluster object, treating it as a whole for subsequent parameter extraction and calculation.

[0129] The system utilizes a density-based spatial clustering algorithm to cluster components. The system treats the target retention time of each component as a data point in a one-dimensional space, and sets a preset co-escape time threshold as the neighborhood radius parameter of the algorithm. The system iterates through all data points, calculating the Euclidean distance between any two points, i.e., the absolute value of the difference in target retention times. When the number of data points in the neighborhood of a given data point reaches the preset minimum cluster size, the system marks it as a core point and assigns all components within its neighborhood, along with that core point, to the same co-escape interference component cluster. The algorithm continuously expands the clusters until all components meeting the conditions have been assigned, outputting multiple independent cluster sets.

[0130] The system can also utilize a sliding window aggregation algorithm for one-dimensional time series. First, the system sorts all components in ascending order according to the target retention time, constructing an ordered time series array. Then, the system initializes a sliding time window with a width equal to a preset co-escape time threshold, sliding it forward from the beginning of the array. During the sliding process, the system monitors the number of components contained within the window in real time. If two or more components exist simultaneously within the window, the system extracts a segment of data within that window, instantiates these components into a co-escape interference component cluster object, and stores the generated cluster object reference in the system's global cluster management list.

[0131] S203. Obtain the cluster mixing peak area of ​​the corresponding co-eluting interfering component cluster in the original gas chromatography detection data;

[0132] The cluster mixed peak area refers to the total area of ​​a broadened chromatographic peak or multiple envelope peak that is not completely separated and forms when all components within a co-eluted interfering component cluster elute together in the chromatogram of the original gas chromatography detection data. This area represents the total electrical signal response generated by all overlapping substances within the cluster on a flame ionization detector. After clustering the co-eluted interfering component clusters, the system determines the start and end elution times of the mixed peak on the chromatogram based on the target retention time distribution range of each component within the cluster. Using this time interval as an index, the system returns to the original gas chromatography detection data for reverse positioning, locking the continuous voltage signal data points within this time period. The system treats the overlapping region as an indivisible whole, directly obtaining the total integrated area within this time interval, which is defined as the cluster mixed peak area.

[0133] The system can utilize the application programming interface provided by the underlying chromatography workstation to encapsulate the start and end retention times of the co-eluting interfering component clusters into a time interval parameter object. This object is then invoked via an inter-process communication mechanism to call the chromatography workstation's integration engine. Upon receiving the instruction, the integration engine locates the time interval in the digital spectrum of the raw gas chromatography detection data. It then performs numerical integration on the discrete signal points within the interval using either the trapezoidal rule or Simpson's rule to calculate the total area of ​​the envelope peak. The calculated cluster mixed peak area value is returned to the system's main program via a callback function, which binds it to the corresponding co-eluting interfering component cluster object.

[0134] S204. Extract the relative abundance percentage of each component within the co-eluting interference component cluster from the qualitative and quantitative analysis data of the components.

[0135] Relative abundance percentage refers to the percentage or fractional proportion of the characteristic ion signal intensity or quantitative ion peak area of ​​a specific chemical component within the total signal intensity of all components in the same co-eluting interfering component cluster, as determined by qualitative and quantitative analysis methods such as mass spectrometry (MS). Specifically, the relative abundance percentage refers to the relative proportion of the raw mass spectrum peak areas acquired by the mass spectrometer (MS) for each component's characteristic ion. This value reflects the relative content relationship of each component in the mixture, including but not limited to the relative area percentage in the total ion chromatogram and the relative intensity percentage of channels with specific mass-to-charge ratios. After acquiring the cluster mixed peak area, the system iterates through each component within the co-eluting interfering component cluster, using the component's unique identifier to initiate a targeted search within the component's qualitative and quantitative analysis data. The system parses the detailed analysis report or data structure returned by the search and accurately extracts the relative abundance percentage value given by the qualitative analysis instrument for that component. The system temporarily stores these extracted percentage values ​​in the context of the co-efferent interference component cluster, as a weight allocation parameter for subsequent proportional synthesis of equivalent response factors, supporting numerical mapping from the overall mixed signal to the characteristics of internal components.

[0136] The system can utilize an Extensible Markup Language (EXPLAIN) parser to read qualitative and quantitative analysis data files generated by the mass spectrometer workstation. The system traverses the document object model tree to locate the node matching the Chemical Abstracts Service (CASS) index number of each component within the co-eluting interference cluster. It then reads the text content of the child node named "Relative Abundance Percentage" under that node and converts it into a double-precision floating-point number. The system maintains a hash table in memory for this co-eluting interference cluster, storing the component identifier as the key and the extracted relative abundance percentage as the value, so that subsequent calculation modules can read it with constant time complexity.

[0137] The system can also utilize a lightweight data exchange format parsing library to process qualitative and quantitative analysis data streams received in real time via message queues. The system deserializes the received strings into nested dictionary objects and extracts the relative abundance percentage field values ​​at a specific level of the dictionary based on the identifiers of each component within the co-emission interference component cluster. After extraction, the system executes data verification logic to check if the sum of the relative abundance percentages of all components within the cluster is close to 100%. If there is a slight deviation, automatic normalization is performed. Finally, the verified relative abundance percentage data is updated in the corresponding cluster entity attributes in the system state machine.

[0138] S205. Based on the relative abundance ratio of each component in the co-eluting interference component cluster, the preset hydrogen flame ionization detector response factor and / or theoretical response factor corresponding to each component are weighted and summed to obtain the equivalent cluster response factor.

[0139] The system calculates the equivalent cluster response factor by weighting and summing the preset hydrogen flame ionization detector response factors and / or theoretical response factors for each component based on the relative abundance ratio of each component within the co-eluting interference component cluster. Specifically, this can be achieved as follows: The relative abundance ratios of all components within the co-eluting interference component cluster are summed to obtain the total abundance value within the cluster; the relative abundance ratio of each component is divided by the total abundance value within the cluster to obtain the normalized abundance weight of each component; for known interference components within the co-eluting interference component cluster, the normalized abundance weight is multiplied by the corresponding preset hydrogen flame ionization detector response factor to obtain the first response component; for dynamically added components within the co-eluting interference component cluster, the normalized abundance weight is multiplied by the corresponding theoretical response factor to obtain the second response component; and all first response components and all second response components within the co-eluting interference component cluster are summed to obtain the equivalent cluster response factor.

[0140] The total abundance value within a cluster refers to the arithmetic sum of the relative abundance percentages of all components within the cluster that co-efferentize interfering components. The normalized abundance weight is a dimensionless proportionality coefficient obtained by dividing the relative abundance percentage of a single component by the total abundance value within the cluster. The first response component is the response contribution value obtained by scaling the preset hydrogen flame ionization detector response factor for known interfering components within the cluster using their normalized abundance weights. The second response component is the response contribution value obtained by scaling the theoretical response factor for dynamically added components within the cluster using their normalized abundance weights. The equivalent cluster response factor is a virtual response coefficient constructed by comprehensively summing all the first and second response components within the cluster. The system sums the relative abundance percentages of all components within the cluster to obtain the total abundance value, and then divides the relative abundance percentage of each component by this total value to calculate the normalized abundance weight of each component. The system determines the component category. For known interfering components, it multiplies their normalized abundance weights by a preset hydrogen flame ionization detector response factor to generate a first response component. For dynamically added components, it multiplies their normalized abundance weights by a theoretical response factor to generate a second response component. The system sums all the first and second response components to derive the equivalent cluster response factor of the co-efferentiating interfering component cluster.

[0141] The system can initialize an accumulator register to zero and iterate through the components within a cluster, accumulating the relative abundance percentages of each component to obtain the total abundance value within the cluster. A second loop then begins, using division instructions to calculate the normalized abundance weight of each component. When calculating the response components, the system uses conditional branch instructions to determine the component type. If it is a known interfering component, a preset hydrogen flame ionization detector response factor is read from memory and multiplied with the weights to obtain the first response component. If it is a dynamically added component, the theoretical response factor is read and multiplied with the weights to obtain the second response component. The system accumulates the generated components in real time into another result register. After the loop ends, the value in this register is the equivalent cluster response factor, which the system writes to main memory.

[0142] The system can also construct a one-dimensional tensor from the relative abundance proportions of all components within a cluster, and directly output the scalar value of the total abundance within the cluster by calling the tensor summation function. Utilizing a tensor broadcasting mechanism, the system divides the relative abundance proportion tensor by the total abundance value scalar to calculate the normalized abundance weight tensor in parallel. Based on the component type, the system constructs two mask tensors, extracting weight subsets of known interfering components and dynamically added components, respectively. These are then multiplied element-wise by the tensor with the preset response factor tensor and the theoretical response factor tensor, generating the first and second response component tensors in parallel. Finally, the system calls a global tensor summation and reduction operation to merge and accumulate all elements in the two component tensors, calculating the equivalent cluster response factor.

[0143] S206. Multiply the cluster mixing peak area by the equivalent cluster response factor to obtain the cluster interference concentration;

[0144] Cluster interference concentration refers to the comprehensive concentration of co-eluting interfering component clusters that cannot be separated in the chromatogram. This concentration is calculated quantitatively using the overall cluster mixed peak area and the proportionally synthesized equivalent cluster response factor, resulting in a numerical value representing the total concentration contribution of all interfering substances within the overlapping region. After successfully constructing the equivalent cluster response factor and obtaining the cluster mixed peak area, the system calls the underlying multiplication logic to multiply these two macroscopic parameters representing the overall characteristics.

[0145] S207. Replace the corresponding known interference component concentration and / or the newly added interference component concentration with the cluster interference concentration.

[0146] In the co-efferentiation scenario, the system uses the cluster interference concentration calculated based on the overall mixed peak area and equivalent response factor for subsequent processing. The system iterates through all generated co-efferentiation interference component clusters and extracts their corresponding cluster interference concentrations. In the global concentration correction data table, the system locates the original independent concentration records of all known interference components and dynamically added components within the cluster. The system performs data overwrite or logical masking operations to invalidate these individual component concentration data and uses the cluster interference concentration as a unified overall concentration compensation term in the calculation formula. Through this substitution operation, when finally calculating the target total hydrocarbon concentration, the system incorporates the concentration contribution of the overlapping region into the corrected calculation model of the total hydrocarbon concentration in the form of a unified comprehensive concentration compensation term, thus improving the calculation chain for non-methane total hydrocarbon concentration.

[0147] In the above embodiments, by comparing target retention times, multiple components with similar elution times are clustered into co-elution interfering component clusters, and the overall mixed peak area of ​​the cluster is directly obtained, reducing the integration error caused by forced peak cutting or deconvolution. For the clustered mixed peaks, the relative abundance ratio of each component in the qualitative and quantitative analysis data is used to weight and sum the original preset response factors or theoretical response factors of each component within the cluster, constructing an equivalent cluster response factor that can reflect the overall response characteristics of the mixture. Using this equivalent cluster response factor and the cluster mixed peak area together to calculate the cluster interference concentration can more objectively restore the comprehensive concentration contribution of multiple organic compounds in the overlapping peak region, reduce the distortion of component concentration calculation caused by insufficient chromatographic separation, and improve the rationality and accuracy of the assessment of overlapping component concentrations in complex waste gas samples.

[0148] Through the clustering and equivalent calculation of co-eluting interfering component clusters in the above embodiments, this application effectively overcomes the distortion in the calculation of local component concentrations caused by insufficient chromatographic resolution. However, in actual industrial emission processes, exhaust gas not only suffers from overlapping interference of local components, but its overall matrix composition also often changes significantly due to fluctuations in operating conditions. This global matrix effect directly affects the overall response state of the detector, thereby introducing systematic measurement bias. To address the fluctuations in exhaust gas matrix effect caused by sudden changes in industrial operating conditions and to further improve the global adaptive capability of the detection algorithm under complex and variable operating conditions, this application provides another method for detecting non-methane total hydrocarbons based on the above embodiments. The following is a combination of... Figure 3 Another method for detecting non-methane total hydrocarbons in the embodiments of this application is described below:

[0149] Please see Figure 3 This is another flowchart illustrating a method for detecting non-methane total hydrocarbons in an embodiment of this application.

[0150] S301. Obtain the relative response deviation coefficients corresponding to each dynamically added component from the preset chemical property feature library;

[0151] The preset chemical attribute feature library refers to a data set internally or externally mounted for persistently storing the physicochemical properties of various chemical substances and detector response characteristic parameters, including but not limited to relational databases, document databases, or distributed key-value storage systems. The relative response deviation coefficient is a quantitative indicator of the degree of deviation of the electrical signal response capability of a specific dynamically added component under the current detector physicochemical conditions from that of a standard calibration material or under ideal conditions. This coefficient reflects the inherent influence of the substance's own structure on the detector's ionization efficiency. After identifying each dynamically added component in the exhaust gas sample, the system needs to obtain the potential impact parameters of these components on the detector response. The system establishes a data connection with the preset chemical attribute feature library using the unique chemical identifier of each dynamically added component as the search keyword. The system performs precise matching in the feature library's index structure to locate the record row or data node containing the target component's attribute. The system extracts the corresponding relative response deviation coefficient value from this record and loads it into the current running memory context, binding it with the corresponding dynamically added component object to provide basic characteristic parameter input for subsequent quantification of the overall changes in the exhaust gas matrix.

[0152] S302. Based on the concentration of newly added interfering components corresponding to each dynamically added component, the relative response deviation coefficient of each dynamically added component is weighted and calculated to obtain the comprehensive response deviation index of the current exhaust gas matrix.

[0153] The concentration of newly added interfering components refers to the specific content value calculated in the previous processing flow for volatile organic compounds that unexpectedly appear in the exhaust gas sample, representing the absolute or relative amount of the substance in the current sample. The comprehensive response deviation index is a macroscopic dimensionless index constructed by the system through mathematical operations. It is used to comprehensively characterize the degree to which the overall response state of the detector deviates from the standard baseline due to the combined effect of all dynamically added components in the current exhaust gas sample, including but not limited to weighted deviation mean, cumulative deviation total, etc. After obtaining the relative response deviation coefficients of each dynamically added component and their corresponding newly added interfering component concentrations, the system enters the data fusion stage. The system uses the concentration of newly added interfering components as the core indicator to measure the weight of the component's influence on the overall exhaust gas matrix. The system iterates through all dynamically added components, correlating the relative response deviation coefficient of each component with its corresponding newly added interfering component concentration. The system scales up or down the deviation coefficients according to the concentration, and then performs global summation or weighted averaging of the calculation results for all components.

[0154] The system utilizes the arithmetic logic unit (ALU) of the central processing unit to execute loop accumulation instructions to perform weighted calculations. The system initializes a double-precision floating-point accumulator variable in memory and sets it to zero. The system iterates through an array containing all dynamically added component objects. In each loop iteration, the system calls the object's attribute acquisition method to read the concentration of the newly added interfering component and its relative response deviation coefficient. The system sends a multiplication instruction to the ALU to calculate the product of the two values ​​as the weighted offset component for that component, and then sends an addition instruction to accumulate this component into the accumulator variable. After the loop completes, the final value in the accumulator is the overall response offset index.

[0155] The system can also perform vectorized dot product calculations in memory using a matrix operation library. The system extracts the concentrations of all dynamically added interfering components in a fixed order, constructing a one-dimensional concentration row vector; simultaneously, it extracts the corresponding relative response deviation coefficients in the same order, constructing a one-dimensional deviation column vector. The system calls a vector dot product function optimized by a basic linear algebra subroutine at the underlying level, directly performing an inner product operation on these two one-dimensional vectors. This inner product operation processes multiple elements in parallel using single-instruction multiple-data stream technology at the underlying level, and the final output is a single scalar result, which is the comprehensive response offset index of the current exhaust gas matrix.

[0156] S303. When it is determined that the comprehensive response offset index exceeds the preset matrix stability threshold range, the preset total hydrocarbon basic response factor is compensated according to the comprehensive response offset index to obtain the matrix correction response factor.

[0157] When the overall response offset index exceeds the preset matrix stability threshold range, the system compensates the preset total hydrocarbon basic response factor based on the overall response offset index to obtain the matrix-corrected response factor. Specifically, this can be achieved as follows: calculate the difference between the overall response offset index and the critical boundary value of the preset matrix stability threshold range to obtain the matrix offset; input the matrix offset into the preset nonlinear smoothing compensation model to calculate the dynamic compensation coefficient. In the nonlinear smoothing compensation model, as the matrix offset increases, the growth derivative of the dynamic compensation coefficient gradually decreases; and fuse the preset total hydrocarbon basic response factor with the dynamic compensation coefficient to obtain the matrix-corrected response factor.

[0158] The preset matrix stability threshold range refers to the upper and lower limits of values ​​set by the system to determine whether changes in the exhaust gas matrix are within the tolerable error range. This range is derived from the statistical distribution characteristics of historical standard gas test data, and its critical boundary value is directly compared with the comprehensive response offset index. The preset total hydrocarbon basic response factor refers to the initial conversion coefficient set by the detector for the target hydrocarbons under standard environmental and pure carrier gas conditions. The matrix correction response factor refers to the updated conversion coefficient adapted to the current complex exhaust gas matrix state after algorithm compensation. The critical boundary value refers to the maximum or minimum value within the preset matrix stability threshold range. The matrix offset refers to the absolute difference between the comprehensive response offset index and the critical boundary value. The preset nonlinear smoothing compensation model refers to the system's built-in mathematical mapping logic used to convert linear matrix offsets into nonlinear dynamic compensation coefficients, including but not limited to logarithmic decay models and exponential smoothing models. The dynamic compensation coefficient refers to the multiplicative or additive correction parameters used to directly adjust the basic response factor. The system determines whether the comprehensive response offset index falls within the preset matrix stability threshold range. If it exceeds this range, the system calculates the difference between the index and the breached critical boundary value to obtain the matrix offset. The system inputs the matrix offset into a preset nonlinear smoothing compensation model. This model ensures sufficient compensation when the offset is small, while limiting the infinite growth of the compensation coefficient when the offset is extremely large, i.e., the growth derivative gradually decreases. The system mathematically fuses the calculated dynamic compensation coefficient with a preset total hydrocarbon base response factor to output a matrix-corrected response factor.

[0159] The system can utilize its built-in mathematical function library to perform algebraic operations to implement this refinement technique. The system executes a subtraction instruction to calculate the absolute value of the difference between the comprehensive response offset exponent and the critical boundary value, storing this as the matrix offset in a register. The system then calls the natural logarithm or square root function from the mathematical function library to construct a preset nonlinear smoothing compensation model. The system adds a constant bias to the matrix offset and passes it as an input parameter to the logarithmic function. The mathematical properties of the logarithmic function inherently guarantee that the growth rate of its output value, i.e., the first derivative, strictly decreases monotonically as the input value increases. The system obtains the output value of the logarithmic function as the dynamic compensation coefficient. Finally, it calls a multiplication instruction to multiply the preset total hydrocarbon basic response factor by the dynamic compensation coefficient to obtain the matrix-corrected response factor and overwrites the original memory address.

[0160] The system can also utilize a pre-trained multilayer perceptron neural network as a preset nonlinear smoothing compensation model. The multilayer perceptron possesses fitting capabilities guaranteed by a general approximation theorem and can learn complex nonlinear mapping relationships. The system uses the matrix offset as the activation value of the input layer neurons, and performs multiplication and addition operations with the weight matrix of the hidden layer through the network's forward propagation algorithm. The hidden layer neurons employ hyperbolic tangent or logistic sigmoid activation functions. The derivatives of these activation functions gradually approach zero as the absolute value of the input increases, thus achieving the nonlinear smoothing characteristic of gradually decreasing derivatives at the network structure level. The system obtains the values ​​of the output layer neurons as dynamic compensation coefficients, and then uses additive or multiplicative logic to fuse them with a preset total hydrocarbon baseline response factor to generate a matrix-corrected response factor.

[0161] S304. Recalculate the total non-methane hydrocarbon concentration based on the original peak area of ​​total hydrocarbons and the matrix correction response factor in the original gas chromatography detection data.

[0162] The raw peak area of ​​total hydrocarbons (THH) refers to the sum of the original integrated areas representing the overall response intensity of all carbon-containing organic compounds in the detector output signal of the gas chromatograph, without any baseline subtraction or component stripping. The non-methane total hydrocarbon concentration refers to the total concentration of all volatile hydrocarbons other than methane in the exhaust gas sample; it is the target detection result that the system ultimately outputs. After completing a global evaluation and correction of the detector response state, the system proceeds to the final concentration calculation stage. The system reads the pre-integrated raw peak area value of TTH from the memory block or temporary file of the raw gas chromatographic detection data. The system extracts the matrix correction response factor generated in the previous step, which already includes nonlinear compensation information for the current exhaust gas matrix abrupt effect. The system uses the raw peak area of ​​TTH and the matrix correction response factor as core input parameters and substitutes them into the system's preset total hydrocarbon concentration calculation formula. Through direct calculation of these two parameters, the system converts the macroscopic electrical signal area into a concentration value calibrated by the matrix effect, completes the recalculation of the non-methane total hydrocarbon concentration, and persistently saves the result or outputs it to the human-computer interface.

[0163] The system can utilize the double-precision floating-point arithmetic module in the main control program to perform the final concentration calculation. The system extracts the original peak area variable and matrix correction response factor variable from the global state manager. The system calls the underlying multiplication interface to multiply the original peak area of ​​total hydrocarbons by the matrix correction response factor to obtain the corrected total hydrocarbon concentration. Subsequently, the system reads the pre-calculated methane independent concentration value from another memory address, performs a subtraction operation to deduct the methane concentration from the corrected total hydrocarbon concentration, and obtains the final non-methane total hydrocarbon concentration. The system formats this result as a string with a specific number of significant digits and writes it to a local comma-separated value log file.

[0164] The system can also utilize a custom-designed digital signal processing hard core within a field-programmable gate array (FPGA) to perform this calculation. The system converts the original peak area of ​​total hydrocarbons and the matrix correction response factor into a binary floating-point format conforming to the IEEE standard, and sends this data to the input buffer of the FPGA via a high-speed serial bus. Driven by a hardware clock, the floating-point multipliers and subtractors configured within the array execute multiplication and subtraction operations sequentially according to a preset pipeline timing, calculating the binary result of the non-methane total hydrocarbon concentration within nanoseconds. The system then retrieves this result to the host's physical memory via direct memory access (DMI) for use by upper-layer data visualization microservices.

[0165] In the above embodiments, by obtaining the relative response deviation coefficients of each dynamically added component and weighting them with their concentrations, the comprehensive response deviation index of the current exhaust gas matrix compared to the standard state is quantitatively evaluated. By comparing this index with a preset matrix stability threshold range, the system can autonomously identify whether the exhaust gas matrix has undergone drastic changes. When it is confirmed that the threshold range has been exceeded, the deviation index is used to specifically compensate the preset total hydrocarbon basic response factor, generate a matrix correction response factor, and recalculate the non-methane total hydrocarbon concentration. This reduces the systematic measurement deviation caused by sudden changes in industrial site conditions and fluctuations in the exhaust gas matrix effect, and improves the adaptability and accuracy of the non-methane total hydrocarbon concentration detection algorithm under complex and variable conditions.

[0166] The system in the embodiments of this invention is described below from the perspective of hardware processing. Please refer to [link / reference needed]. Figure 4 This is a schematic diagram of the physical device structure of a non-methane total hydrocarbon detection system provided in an embodiment of this application.

[0167] It should be noted that, Figure 4 The structure of the system shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0168] like Figure 4 As shown, the system includes a Central Processing Unit (CPU) 401, which can perform various appropriate actions and processes based on a program stored in Read-Only Memory (ROM) 402 or a program loaded from storage portion 408 into Random Access Memory (RAM) 403, such as executing the methods described in the above embodiments. The RAM 403 also stores various programs and data required for system operation. The CPU 401, ROM 402, and RAM 403 are interconnected via a bus 404. An Input / Output (I / O) interface 405 is also connected to the bus 404.

[0169] The following components are connected to I / O interface 405: input section 406 including a camera, infrared sensor, etc.; output section 407 including a liquid crystal display (LCD) and speakers, etc.; storage section 408 including a hard disk, etc.; and communication section 409 including a network interface card such as a LAN (Local Area Network) card and a modem, etc. Communication section 409 performs communication processing via a network such as the Internet. Drive 410 is also connected to I / O interface 405 as needed. Removable media 411, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 410 as needed so that computer programs read from it can be installed into storage section 408 as needed.

[0170] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing computer programs for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 409, and / or installed from removable medium 411. When the computer program is executed by central processing unit (CPU) 401, it performs the various functions defined in the present invention.

[0171] It should be noted that the computer-readable medium shown in the embodiments of the present invention can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In the present invention, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, wherein a computer-readable computer program is carried. The transmitted data signal can take many forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof.

[0172] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. Each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0173] In another aspect, the present invention also provides a computer-readable storage medium, which may be included in the system described in the above embodiments; or it may exist independently and not assembled into the system. The storage medium carries one or more computer programs that, when executed by a processor of a system, cause the system to implement the methods provided in the above embodiments.

[0174] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit it. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

[0175] As used in the above embodiments, depending on the context, the term "when..." can be interpreted as "if...", "after...", "in response to determining...", or "in response to detecting...". Similarly, depending on the context, the phrase "when determining..." or "if (the stated condition or event) is interpreted as "if determining...", "in response to determining...", "when (the stated condition or event) is detected", or "in response to detecting (the stated condition or event)".

[0176] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state drive), etc.

[0177] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This program can be stored in a computer-readable storage medium, and when executed, it can include the processes described in the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM or random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A method for detecting non-methane total hydrocarbons, characterized in that, include: Obtain raw gas chromatographic detection data and component qualitative and quantitative analysis data of the industrial waste gas sample to be tested. The raw gas chromatographic detection data includes the raw peak area of ​​total hydrocarbons and the raw peak area of ​​methane. The original total hydrocarbon peak area is multiplied by the preset total hydrocarbon basic response factor to obtain the original total hydrocarbon concentration; The CAS number of each independent organic component in the qualitative and quantitative analysis data of the components is matched with a preset multi-component calibration database to obtain known interfering components and dynamically added components. Extract the preset hydrogen flame ionization detector response factor corresponding to each of the known interfering components from the multi-component calibration database; Based on the Chemical Abstracts Index (CAS) number of each dynamically added component, the effective carbon number and molecular weight of the dynamically added component are extracted from a preset chemical property feature library. The theoretical response factor of each of the dynamically added components is calculated based on the preset reference material response factor, the preset reference material effective carbon number, the preset reference material molecular weight, the effective carbon number of the dynamically added component, and the molecular weight of the dynamically added component. The concentration of the known interfering component is obtained by multiplying the peak area of ​​each known interfering component by its corresponding preset hydrogen flame ionization detector response factor. The concentration of the newly added interfering component is obtained by multiplying the peak area of ​​each dynamically added component by its corresponding theoretical response factor. The sum of the peak areas of all known interfering components and all dynamically added components is multiplied by the preset total hydrocarbon baseline response factor to obtain the baseline total hydrocarbon concentration deviation value. The target total hydrocarbon concentration is obtained by subtracting the baseline total hydrocarbon concentration deviation from the original total hydrocarbon concentration and adding the sum of the concentrations of all known interfering components and all newly added interfering components. The target methane concentration is obtained by multiplying the original peak area of ​​methane in the original gas chromatography detection data by a preset methane response factor. The difference between the target total hydrocarbon concentration and the target methane concentration is taken as the non-methane total hydrocarbon concentration.

2. The method according to claim 1, characterized in that, The step of matching the CAS numbers of each independent organic component in the qualitative and quantitative analysis data with a preset multi-component calibration database to obtain known interfering components and dynamically added components specifically includes: Extract the standard CAS number string and target retention time of each independent organic component from the qualitative and quantitative analysis data of the components; Remove hyphens from the standard CAS number string, and use a preset cryptographic hash function to perform a hash mapping on the string after removing hyphens to generate component CAS hash values; When it is determined that the CAS hash value of the component matches the database hash value pre-stored in the preset multi-component calibration database, the absolute value of the retention time deviation between the target retention time and the standard retention time associated with the database hash value is calculated; If the absolute value of the retention time deviation is less than a preset time window threshold, the independent organic component is determined to be a known interfering component; If the absolute value of the retention time deviation is not less than the preset time window threshold or the CAS hash value of the component is inconsistent with the hash value of the database, the independent organic component is determined to be a dynamically added component.

3. The method according to claim 1, characterized in that, The step of calculating the theoretical response factor of each dynamically added component based on the preset reference substance response factor, the preset reference substance effective carbon number, the preset reference substance molecular weight, the effective carbon number of the dynamically added component, and the molecular weight of the dynamically added component specifically includes: Divide the effective carbon number of the preset reference substance by the molecular weight of the preset reference substance to obtain the effective carbon mass ratio of the reference substance; Divide the effective carbon number of the dynamically added component by the molecular weight of the dynamically added component to obtain the effective carbon mass ratio of the target component; Divide the effective carbon mass ratio of the reference material by the effective carbon mass ratio of the target component to obtain the ideal relative response ratio of the dynamically added component; Calculate the absolute value of the difference between the molecular weight of the dynamically added component and the molecular weight of the preset reference substance, and divide the absolute value of the difference by the molecular weight of the preset reference substance to obtain the molecular weight deviation. The molecular weight deviation is substituted into the preset hydrogen flame ionization detector ionization efficiency decay function for calculation to obtain the ionization efficiency compensation coefficient for the dynamically added component. The theoretical response factor of each of the dynamically added components is obtained by multiplying the preset reference material response factor, the ideal relative response ratio, and the ionization efficiency compensation coefficient.

4. The method according to claim 1, characterized in that, Before multiplying the peak area of ​​each dynamically added component by its corresponding theoretical response factor to obtain the concentration of the added interfering component, the method further includes: The qualitative and quantitative analysis data of the components are traversed, and the target retention times of each of the dynamically added components and each of the known interfering components in the original gas chromatography detection data are extracted. Multiple components whose absolute value of the difference in target retention time is less than a preset co-escape time threshold are clustered into a co-escape interference component cluster; Obtain the cluster mixed peak area corresponding to the co-eluting interfering component cluster in the original gas chromatography detection data; The relative abundance percentage of each component within the co-eluting interfering component cluster is extracted from the qualitative and quantitative analysis data of the components. Based on the relative abundance ratio of each component within the co-eluting interference component cluster, the preset hydrogen flame ionization detector response factor and / or the theoretical response factor corresponding to each component are weighted and summed to obtain the equivalent cluster response factor. The cluster interference concentration is obtained by multiplying the cluster mixing peak area by the equivalent cluster response factor. Replace the corresponding known interference component concentration and / or the newly added interference component concentration with the cluster interference concentration.

5. The method according to claim 4, characterized in that, The step of weighted summing of the preset hydrogen flame ionization detector response factor and / or the theoretical response factor corresponding to each component based on the relative abundance ratio of each component within the co-eluting interference component cluster to obtain the equivalent cluster response factor specifically includes: The relative abundance percentages of all components within the co-efferentiation interference component cluster are summed to obtain the total abundance value within the cluster. The normalized abundance weight of each component is obtained by dividing the relative abundance percentage of each component by the total abundance value within the cluster. For the known interfering components within the co-eluting interfering component cluster, the normalized abundance weight is multiplied by the corresponding preset hydrogen flame ionization detector response factor to obtain the first response component; For the dynamically added components within the co-efferent interference component cluster, the normalized abundance weight is multiplied by the corresponding theoretical response factor to obtain the second response component; The equivalent cluster response factor is obtained by summing all the first response components and all the second response components within the co-efferent interference component cluster.

6. The method according to claim 1, characterized in that, After taking the difference between the target total hydrocarbon concentration and the target methane concentration as the non-methane total hydrocarbon concentration, the method further includes: Obtain the relative response deviation coefficients corresponding to each of the dynamically added components from the preset chemical property feature library; Based on the concentration of the newly added interfering component corresponding to each of the dynamically added components, the relative response deviation coefficients of each of the dynamically added components are weighted and calculated to obtain the comprehensive response offset index of the current exhaust gas matrix. If the comprehensive response offset index is determined to exceed the preset matrix stability threshold range, the preset total hydrocarbon basic response factor is compensated according to the comprehensive response offset index to obtain the matrix correction response factor. The concentration of non-methane total hydrocarbons is recalculated based on the original peak area of ​​total hydrocarbons in the original gas chromatography detection data and the matrix correction response factor.

7. The method according to claim 6, characterized in that, The step of compensating the preset total hydrocarbon basic response factor based on the comprehensive response offset index to obtain the matrix-corrected response factor specifically includes: The matrix offset is obtained by calculating the difference between the comprehensive response offset index and the critical boundary value of the preset matrix stability threshold range. The matrix offset is input into a preset nonlinear smoothing compensation model to calculate the dynamic compensation coefficient. In the nonlinear smoothing compensation model, as the matrix offset increases, the growth derivative of the dynamic compensation coefficient gradually decreases. The matrix-corrected response factor is obtained by fusing the preset total hydrocarbon basic response factor with the dynamic compensation coefficient.

8. A non-methane total hydrocarbon detection system, characterized in that, The system includes: One or more processors and a memory; the memory is coupled to the one or more processors, the memory being used to store computer program code, the computer program code including computer instructions, the one or more processors invoking the computer instructions to cause the system to perform the method as described in any one of claims 1-7.

9. A computer-readable storage medium comprising instructions, characterized in that, When the instructions are executed on the system, the system performs the method as described in any one of claims 1-7.

10. A computer program product, characterized in that, When the computer program product is run on the system, the system performs the method as described in any one of claims 1-7.

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