A multi-component mixed gas high-precision real-time monitoring system and method

Abstract

The application discloses a kind of multi-component mixed gas high-precision real-time monitoring system and method, specifically related to gas monitoring field, a kind of multi-component mixed gas high-precision real-time monitoring system includes: data statistics module, feature extraction module, evaluation analysis module and adjustment correction module;Data statistics module is used to real-time acquisition and summarizes the original monitoring data of multi-component mixed gas, environmental parameter data and equipment operating state data, completes the integration, classification and time series statistics of multi-source data;Feature extraction module is used to obtain each component independent effective gas characteristic parameter;Evaluation analysis module is used to combine multi-component gas characteristic parameter, preset threshold standard and gas coupling interference model.The application realizes the intelligent acquisition of multi-component mixed gas, feature separation, hierarchical research and judgment and self-adaptive error correction, and greatly improves the precision of gas monitoring under complex working conditions.

Description

Technical Field

[0001] This invention relates to the field of gas monitoring, and more specifically, to a high-precision real-time monitoring system and method for multi-component mixed gases. Background Technology

[0002] In industrial settings such as chemical production, power plant maintenance, underground utility tunnels, oil and gas storage and transportation, and industrial parks, the working environment commonly involves a mixture of various gases, including combustible gases, toxic and harmful gases, and volatile organic gases. These gases are complex in composition, exhibit frequent dynamic fluctuations in concentration, and suffer from severe spectral overlap and signal cross-interference among components. The coupling and superposition of multiple gas components easily leads to problems such as distortion in single-sensor detection, baseline drift, and large data deviations, seriously affecting the accuracy and stability of gas monitoring.

[0003] Most existing traditional gas monitoring technologies focus on simple concentration collection of single gases and lack the ability to perform time-series statistical and probability distribution analysis on multi-source monitoring data. At the same time, they are not convenient for multi-dimensional feature decomposition and heterogeneous fusion processing of mixed gas signals, and it is difficult to remove the interference features between different gas components. This results in low component identification accuracy and poor signal discrimination. They are also unable to adaptively switch between automatic judgment and manual verification mechanisms according to changes in equipment operating status and field conditions. Under special conditions such as sensor malfunction, signal disconnection, and data loss, false alarms, missed alarms, and failure to monitor are very likely to occur.

[0004] To address the aforementioned problems, a technical solution is provided. Summary of the Invention

[0005] In order to overcome the above-mentioned defects of the prior art, the embodiments of the present invention provide a high-precision real-time monitoring system and method for multi-component mixed gas to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A high-precision real-time monitoring system for multi-component mixed gas includes: a data statistics module, a feature extraction module, an evaluation and analysis module, and an adjustment and correction module;

[0008] The data statistics module is used to collect and summarize raw monitoring data, environmental parameter data and equipment operating status data of multi-component mixed gas in real time, and to complete the integration, classification and time-series statistics of multi-source data;

[0009] The feature extraction module is used to obtain independent and effective gas characteristic parameters for each component;

[0010] The assessment and analysis module is used to combine multi-component gas characteristic parameters, preset threshold standards and gas coupling interference models to complete the accurate calculation of the concentration of each gas component, the identification of operating conditions, the assessment of the degree of interference of mixed gases and the analysis of safety risk levels.

[0011] The adjustment and correction module is used to dynamically correct the detection calculation parameters of each gas component based on the evaluation and analysis results, compensate for the cross-interference error between different gases and the environmental drift error, and output the corrected real-time monitoring data.

[0012] In a preferred embodiment, the data statistics module operation specifically includes the following:

[0013] Collect spectral detection data of multi-component mixed gas at the target monitoring point and transmit the spectral detection data to the edge computing unit in real time;

[0014] Based on historical monitoring data, the distribution of concentration fluctuation trends of each gas component is predicted, and the predicted distribution probability of each gas component in different concentration ranges is obtained.

[0015] The predicted distribution entropy of the gas concentration at the target monitoring point is calculated by using the predicted distribution probability of each different concentration range.

[0016] In a preferred embodiment, the feature extraction module specifically includes the following:

[0017] Feature detection operators in different directions are determined when extracting features from gas detection signals, and multi-directional component feature identification is performed on the gas detection signals based on the feature detection operators in each direction;

[0018] Obtain the feature calculation blocks corresponding to each sampling point in the gas detection signal of the target monitoring point;

[0019] Select a sampling point as the selected sampling point, calculate the feature calculation block of the selected sampling point and the feature detection operators in each direction, and then obtain the feature parameters of the selected sampling point;

[0020] Continue to determine the characteristic parameters of the remaining sampling points;

[0021] Based on all the characteristic parameters, a component feature map corresponding to the gas detection signal is constructed, from which the independent characteristic parameters of each gas component at the target monitoring point under the current monitoring state are identified.

[0022] In a preferred embodiment, the signal compression coefficient of the gas detection signal in the intensity channel is determined;

[0023] Based on the signal compression coefficient and the signal statistical parameters of the intensity channel, the gas detection signal is projected and compressed to obtain the intensity projection characteristics of each gas component at the target monitoring point under the current illumination conditions.

[0024] Heterogeneous feature fusion is performed on the gas detection signal based on the component characteristic parameters and the intensity projection characteristics to obtain a comprehensive feature spectrum of the multi-component mixed gas.

[0025] In a preferred embodiment, the evaluation and analysis module specifically includes the following:

[0026] Different evaluation and analysis tasks are triggered based on the gas composition information and on-site operating conditions at the target monitoring points.

[0027] Determine whether the real-time concentration values ​​of each gas component can be obtained directly through online sensing devices;

[0028] If so, trigger an automated evaluation and analysis task;

[0029] If not, retrieve the most recent historical monitoring record for that monitoring item from the current moment;

[0030] Obtain the initial concentration value corresponding to this historical monitoring record;

[0031] By combining the environmental condition change data after this historical record, the operating condition influencing factors are extracted;

[0032] Based on the initial concentration value and the operating condition influence factor, the predicted concentration value under the current condition is calculated;

[0033] If the predicted concentration value falls within the preset warning range, the manual review process will be initiated, and the standard review action flow will be loaded.

[0034] In a preferred embodiment, during the manual review process, local analysis steps are executed sequentially to obtain the first analysis value of the current step;

[0035] If the first analysis value does not reach the preset trigger threshold, then continue to execute the subsequent steps;

[0036] If the first analysis value reaches the trigger threshold, the analysis data features of the current step and the previous step are extracted to generate the first feature set;

[0037] The first feature set is compared and matched with the second feature set in the preset standard verification feature library;

[0038] If a match is successful, the execution order and verification rules of subsequent steps will be adjusted according to the standard feature library.

[0039] If the match fails, an error message will be generated based on the first analysis value to remind the reviewers to conduct a thorough check.

[0040] In a preferred embodiment, the adjustment and correction module operation specifically includes the following:

[0041] Identify the target impact types of each gas component;

[0042] Retrieve the affected experimental data records for the corresponding components;

[0043] Determine whether the target type of the operating condition influencing factor completely matches the influence type in the experimental record;

[0044] If there is a perfect match, the correction coefficient is generated directly based on the experimental records.

[0045] If the match is not perfect, then construct the affected simulation model based on the experimental records;

[0046] Input the current environmental operating condition parameters into the simulation model to obtain the simulation output results;

[0047] The simulation results were validated, and model biases were corrected.

[0048] The final correction coefficients are determined based on the verified simulation results;

[0049] The detection parameters of each gas component are compensated and corrected using correction coefficients, and real-time monitoring data is output.

[0050] A high-precision real-time monitoring method for multi-component mixed gases, used to implement the monitoring system described in any of the above-mentioned methods, includes the following steps:

[0051] Step S1: The field continuously collects spectral signals of the mixed gas, environmental parameters and equipment operating status information, completes data collection, classification and time series processing, and combines historical data to deduce the concentration change pattern and calculate the concentration distribution characteristic parameters.

[0052] Step S2 involves performing multi-dimensional feature identification on the collected gas detection signals, completing signal compression, feature fusion, and generating a comprehensive feature map, and separating the specific effective feature parameters for each gas component.

[0053] Step S3 involves calculating concentrations and assessing risks based on component characteristics, on-site conditions, and interference models. It distinguishes between automatic analysis and manual verification modes to complete the status assessment. Then, it combines the matching correction coefficients of influencing factors to compensate and optimize the detection parameters and output monitoring data.

[0054] The technical effects and advantages of the high-precision real-time monitoring system and method for multi-component mixed gas of this invention are as follows:

[0055] 1. By collecting real-time edge data, predicting historical trends, and calculating concentration distribution entropy, the severity of gas concentration fluctuations can be quantified, enabling early prediction of on-site gas condition changes and significantly improving the system's environmental adaptability. It can accurately separate overlapping spectral signals of multi-component mixed gases, effectively removing invalid interference information caused by illumination clutter, baseline drift, and component crosstalk, and constructing single-component feature maps to achieve synchronous, high-precision, and independent identification and detection of multi-component gases.

[0056] 2. When the equipment is in normal operation, it can efficiently complete automated monitoring. When there are abnormal data or equipment failures, it can complete risk prediction and anomaly identification through historical extrapolation and feature matching, which greatly improves the reliability of gas monitoring in complex industrial scenarios. For different types of environmental interference and cross-interference characteristics of different gases, it can differentiate between standard and unconventional operating conditions to generate correction coefficients. Through model simulation verification, it can eliminate detection drift error, achieve high-precision calibration in all scenarios and all weather conditions, and significantly improve the detection accuracy in long-term operation. Attached Figure Description

[0057] Figure 1 This is a schematic diagram of the structure of a high-precision real-time monitoring system for multi-component mixed gas according to the present invention.

[0058] Figure 2 This is a flowchart illustrating a high-precision real-time monitoring method for multi-component mixed gases according to the present invention. Detailed Implementation

[0059] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0060] Example 1

[0061] Figure 1 The present invention provides a high-precision real-time monitoring system for multi-component mixed gases, comprising: a data statistics module, a feature extraction module, an evaluation and analysis module, and an adjustment and correction module;

[0062] The data statistics module is used to collect and summarize raw monitoring data, environmental parameter data and equipment operating status data of multi-component mixed gas in real time, and to complete the integration, classification and time-series statistics of multi-source data;

[0063] The feature extraction module is used to obtain independent and effective gas characteristic parameters for each component;

[0064] The assessment and analysis module is used to combine multi-component gas characteristic parameters, preset threshold standards and gas coupling interference models to complete the accurate calculation of the concentration of each gas component, the identification of operating conditions, the assessment of the degree of interference of mixed gases and the analysis of safety risk levels.

[0065] The adjustment and correction module is used to dynamically correct the detection calculation parameters of each gas component based on the evaluation and analysis results, compensate for the cross-interference error between different gases and the environmental drift error, and output the corrected real-time monitoring data.

[0066] The data statistics module operation specifically includes the following:

[0067] Collect spectral detection data of multi-component mixed gas at the target monitoring point and transmit the spectral detection data to the edge computing unit in real time;

[0068] Based on historical monitoring data, the distribution of concentration fluctuation trends of each gas component is predicted, and the predicted distribution probability of each gas component in different concentration ranges is obtained.

[0069] The predicted distribution entropy of the gas concentration at the target monitoring point is calculated by using the predicted distribution probability of each different concentration range.

[0070] It enables real-time processing of gas spectral data locally, reducing signal delay and loss caused by long-distance transmission; it predicts concentration change trends based on historical data and quantifies the overall fluctuation of gas concentration by combining distribution entropy, thus accurately characterizing the on-site gas state.

[0071] In multi-point gas monitoring scenarios in chemical industrial parks, the equipment continuously collects spectral data of combustible and toxic gases at monitoring points within the park and uploads them synchronously to the edge computing unit. The system retrieves historical monitoring data from the past seven days, predicts the probability of concentration distribution of various gases at different times, and then calculates the concentration distribution entropy of the corresponding points based on the probability values, thereby intuitively determining whether the gas concentration in the area is stable with slight fluctuations or undergoes drastic changes.

[0072] The feature extraction module operation specifically includes the following:

[0073] Feature detection operators in different directions are determined when extracting features from gas detection signals, and multi-directional component feature identification is performed on the gas detection signals based on the feature detection operators in each direction;

[0074] Obtain the feature calculation blocks corresponding to each sampling point in the gas detection signal of the target monitoring point;

[0075] Select a sampling point as the selected sampling point, calculate the feature calculation block of the selected sampling point and the feature detection operators in each direction, and then obtain the feature parameters of the selected sampling point;

[0076] Continue to determine the characteristic parameters of the remaining sampling points;

[0077] Based on all the characteristic parameters, a component feature map corresponding to the gas detection signal is constructed, from which the independent characteristic parameters of each gas component at the target monitoring point under the current monitoring state are identified.

[0078] Determine the signal compression coefficient of the gas detection signal in the intensity channel;

[0079] Based on the signal compression coefficient and the signal statistical parameters of the intensity channel, the gas detection signal is projected and compressed to obtain the intensity projection characteristics of each gas component at the target monitoring point under the current illumination conditions.

[0080] Heterogeneous feature fusion is performed on the gas detection signal based on the component characteristic parameters and the intensity projection characteristics to obtain a comprehensive feature spectrum of the multi-component mixed gas.

[0081] By using multi-directional operators to achieve full-domain signal recognition, extracting features through point-by-point operations and building component feature maps, the unique signals of each component in the mixed gas can be accurately separated. Combined with signal compression and projection processing to obtain intensity features, and then completing heterogeneous feature fusion, external interference such as illumination and clutter is effectively eliminated, solving the problem of overlapping and difficult-to-distinguish signals of multi-component gas, and greatly improving the accuracy of feature recognition.

[0082] For monitoring scenarios involving a mixture of methane and hydrogen sulfide gases within a factory area, the system invokes a multi-directional feature detection operator to perform full-domain identification of the collected spectral signals, divides the calculation blocks corresponding to each sampling point, and performs point-by-point calculations to generate component feature maps for each of the two types of gases. The system also calculates the signal compression coefficient based on the on-site lighting environment, performs projection compression on the original signal to obtain intensity projection features, and finally fuses the two types of features to form a comprehensive map, quickly distinguishing the independent features of different gases.

[0083] The evaluation and analysis module includes the following components:

[0084] Different evaluation and analysis tasks are triggered based on the gas composition information and on-site operating conditions at the target monitoring points.

[0085] Determine whether the real-time concentration values ​​of each gas component can be obtained directly through online sensing devices;

[0086] If so, trigger an automated evaluation and analysis task;

[0087] If not, retrieve the most recent historical monitoring record for that monitoring item from the current moment;

[0088] Obtain the initial concentration value corresponding to this historical monitoring record;

[0089] By combining the environmental condition change data after this historical record, the operating condition influencing factors are extracted;

[0090] Based on the initial concentration value and the operating condition influence factor, the predicted concentration value under the current condition is calculated;

[0091] If the predicted concentration value falls within the preset warning range, the manual review process will be initiated, and the standard review action flow will be loaded.

[0092] During manual review, local analysis steps are executed sequentially to obtain the first analysis value of the current step;

[0093] If the first analysis value does not reach the preset trigger threshold, then continue to execute the subsequent steps;

[0094] If the first analysis value reaches the trigger threshold, the analysis data features of the current step and the previous step are extracted to generate the first feature set;

[0095] The first feature set is compared and matched with the second feature set in the preset standard verification feature library;

[0096] If a match is successful, the execution order and verification rules of subsequent steps will be adjusted according to the standard feature library.

[0097] If the match fails, an error message will be generated based on the first analysis value to remind the reviewers to conduct a thorough check.

[0098] It distinguishes between two working modes: automatic analysis and manual verification, and can flexibly switch the analysis process to adapt to different monitoring conditions. It calculates concentration values ​​based on historical data and environmental factors, identifies potential risks of exceeding standards in advance, and optimizes the analysis process and identifies abnormal data through feature comparison in the manual verification stage, ensuring the operational efficiency of routine monitoring and improving the overall reliability of gas monitoring.

[0099] In underground utility tunnel gas monitoring scenarios, the system assigns analysis tasks based on information such as gas type, temperature, humidity, and ventilation status within the tunnel. When the sensors are working normally, the automatic analysis process is directly initiated to calculate the gas concentration. When a sensor experiences an abnormal signal and cannot directly read data, the system retrieves past concentration data for that location and estimates the current concentration by combining it with operating factors such as ventilation, temperature, and humidity. If the estimated value is close to the safety warning line, manual verification is initiated and a standardized analysis process is loaded. During the verification process, data characteristics are compared with the built-in standard feature library in real time. If a match is found, the verification steps are automatically optimized; if an anomaly is found, an alarm message is promptly pushed to remind maintenance personnel to investigate and handle the situation.

[0100] The adjustment and correction module operation specifically includes the following:

[0101] Identify the target impact types of each gas component;

[0102] Retrieve the affected experimental data records for the corresponding components;

[0103] Determine whether the target type of the operating condition influencing factor completely matches the influence type in the experimental record;

[0104] If there is a perfect match, the correction coefficient is generated directly based on the experimental records.

[0105] If the match is not perfect, then construct the affected simulation model based on the experimental records;

[0106] Input the current environmental operating condition parameters into the simulation model to obtain the simulation output results;

[0107] The simulation results were validated, and model biases were corrected.

[0108] The final correction coefficients are determined based on the verified simulation results;

[0109] The detection parameters of each gas component are compensated and corrected using correction coefficients, and real-time monitoring data is output.

[0110] It accurately identifies various environmental influencing factors such as temperature, humidity, air pressure, and gas cross-interference, and adaptively selects correction methods based on the degree of matching between on-site conditions and experimental databases. For perfectly matched conditions, it can quickly generate correction coefficients, while for non-standard conditions, it obtains accurate compensation parameters through modeling simulation and deviation verification. It can dynamically offset detection errors caused by environmental drift and component crosstalk, enabling multi-component gases to maintain high-precision, low-drift, and stable output monitoring data even under complex mixing conditions, greatly improving the system's detection accuracy.

[0111] In industrial parks where methane, carbon monoxide, and VOCs are simultaneously monitored, the system automatically identifies changes in ambient temperature and humidity and the types of gas interference, and retrieves the corresponding gas interference experimental database. If the current environmental conditions are consistent with the experimental standard conditions, the system directly uses existing correction coefficients to correct the detection results. If the on-site conditions are complex and the parameters are mismatched, the system constructs an interference simulation model based on historical experimental data, substitutes real-time environmental parameters to complete simulation and error verification, generates a unique correction coefficient adapted to the current scenario, accurately compensates for the detection concentration of each gas, and finally outputs high-precision real-time monitoring data after interference elimination.

[0112] Example 2

[0113] Figure 2 This invention provides a high-precision real-time monitoring method for multi-component mixed gases, used to implement the monitoring system described in any of the above claims, comprising the following steps:

[0114] Step S1: The field continuously collects spectral signals of the mixed gas, environmental parameters and equipment operating status information, completes data collection, classification and time series processing, and combines historical data to deduce the concentration change pattern and calculate the concentration distribution characteristic parameters.

[0115] Step S2 involves performing multi-dimensional feature identification on the collected gas detection signals, completing signal compression, feature fusion, and generating a comprehensive feature map, and separating the specific effective feature parameters for each gas component.

[0116] Step S3 involves calculating concentrations and assessing risks based on component characteristics, on-site conditions, and interference models. It distinguishes between automatic analysis and manual verification modes to complete the status assessment. Then, it combines the matching correction coefficients of influencing factors to compensate and optimize the detection parameters and output monitoring data.

[0117] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0118] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A high-precision real-time monitoring system for multi-component mixed gases, characterized in that, include: Data statistics module, feature extraction module, evaluation and analysis module, and adjustment and correction module; The data statistics module is used to collect and summarize raw monitoring data, environmental parameter data and equipment operating status data of multi-component mixed gas in real time, and to complete the integration, classification and time-series statistics of multi-source data; The feature extraction module is used to obtain independent and effective gas characteristic parameters for each component; The assessment and analysis module is used to combine multi-component gas characteristic parameters, preset threshold standards and gas coupling interference models to complete accurate calculation of the concentration of each gas component, identification of operating conditions, assessment of the degree of interference of mixed gases and analysis of safety risk levels. The adjustment and correction module is used to dynamically correct the detection calculation parameters of each gas component based on the evaluation and analysis results, compensate for the cross-interference error between different gases and the environmental drift error, and output the corrected real-time monitoring data.

2. The high-precision real-time monitoring system for multi-component mixed gas according to claim 1, characterized in that: The data statistics module operation specifically includes the following: Collect spectral detection data of multi-component mixed gas at the target monitoring point and transmit the spectral detection data to the edge computing unit in real time; Based on historical monitoring data, the distribution of concentration fluctuation trends of each gas component is predicted, and the predicted distribution probability of each gas component in different concentration ranges is obtained. The predicted distribution entropy of the gas concentration at the target monitoring point is calculated by using the predicted distribution probability of each different concentration range.

3. The high-precision real-time monitoring system for multi-component mixed gas according to claim 2, characterized in that: The feature extraction module operation specifically includes the following: Feature detection operators in different directions are determined when extracting features from gas detection signals, and multi-directional component feature identification is performed on the gas detection signals based on the feature detection operators in each direction; Obtain the feature calculation blocks corresponding to each sampling point in the gas detection signal of the target monitoring point; Select a sampling point as the selected sampling point, calculate the feature calculation block of the selected sampling point and the feature detection operators in each direction, and then obtain the feature parameters of the selected sampling point; Continue to determine the characteristic parameters of the remaining sampling points; Based on all the characteristic parameters, a component feature map corresponding to the gas detection signal is constructed, from which the independent characteristic parameters of each gas component at the target monitoring point under the current monitoring state are identified.

4. The high-precision real-time monitoring system for multi-component mixed gas according to claim 3, characterized in that: Determine the signal compression coefficient of the gas detection signal in the intensity channel; Based on the signal compression coefficient and the signal statistical parameters of the intensity channel, the gas detection signal is projected and compressed to obtain the intensity projection characteristics of each gas component at the target monitoring point under the current illumination conditions. Heterogeneous feature fusion is performed on the gas detection signal based on the component characteristic parameters and the intensity projection characteristics to obtain a comprehensive feature spectrum of the multi-component mixed gas.

5. The high-precision real-time monitoring system for multi-component mixed gas according to claim 4, characterized in that: The evaluation and analysis module includes the following components: Different evaluation and analysis tasks are triggered based on the gas composition information and on-site operating conditions at the target monitoring points. Determine whether the real-time concentration values ​​of each gas component can be obtained directly through online sensing devices; If so, trigger an automated evaluation and analysis task; If not, retrieve the most recent historical monitoring record for that monitoring item from the current moment; Obtain the initial concentration value corresponding to this historical monitoring record; By combining the environmental condition change data after this historical record, the operating condition influencing factors are extracted; Based on the initial concentration value and the operating condition influence factor, the predicted concentration value under the current condition is calculated; If the predicted concentration value falls within the preset warning range, the manual review process will be initiated, and the standard review action flow will be loaded.

6. The high-precision real-time monitoring system for multi-component mixed gas according to claim 5, characterized in that: During manual review, local analysis steps are executed sequentially to obtain the first analysis value of the current step; If the first analysis value does not reach the preset trigger threshold, then continue to execute the subsequent steps; If the first analysis value reaches the trigger threshold, the analysis data features of the current step and the previous step are extracted to generate the first feature set; The first feature set is compared and matched with the second feature set in the preset standard verification feature library; If a match is successful, the execution order and verification rules of subsequent steps will be adjusted according to the standard feature library. If the match fails, an error message will be generated based on the first analysis value to remind the reviewers to conduct a thorough check.

7. The high-precision real-time monitoring system for multi-component mixed gas according to claim 6, characterized in that: The adjustment and correction module operation specifically includes the following: Identify the target impact types of each gas component; Retrieve the affected experimental data records for the corresponding components; Determine whether the target type of the operating condition influencing factor completely matches the influence type in the experimental record; If there is a perfect match, the correction coefficient is generated directly based on the experimental records. If the match is not perfect, then construct the affected simulation model based on the experimental records; Input the current environmental operating condition parameters into the simulation model to obtain the simulation output results; The simulation results were validated, and model biases were corrected. The final correction coefficients are determined based on the verified simulation results; The detection parameters of each gas component are compensated and corrected using correction coefficients, and real-time monitoring data is output.

8. A method for high-precision real-time monitoring of multi-component mixed gases, used to implement the monitoring system according to any one of claims 1-7, characterized in that, Includes the following steps: Step S1: The field continuously collects spectral signals of the mixed gas, environmental parameters and equipment operating status information, completes data collection, classification and time series processing, and combines historical data to deduce the concentration change pattern and calculate the concentration distribution characteristic parameters. Step S2 involves performing multi-dimensional feature identification on the collected gas detection signals, completing signal compression, feature fusion, and generating a comprehensive feature map, and separating the specific effective feature parameters for each gas component. Step S3 involves calculating concentrations and assessing risks based on component characteristics, on-site conditions, and interference models. It distinguishes between automatic analysis and manual verification modes to complete the status assessment. Then, it combines the matching correction coefficients of influencing factors to compensate and optimize the detection parameters and output monitoring data.

Images