Detection method and system of flue gas analyzer

Abstract

The invention discloses a detection method and system for a flue gas analyzer, and the method comprises the steps: carrying out the spectrum scanning of flue gas through a multispectral detector according to the spectral characteristics of the flue gas, and obtaining the reflection and transmission spectrum data of the flue gas in a plurality of wavelength ranges; establishing an abnormal value detection model by using an ensemble learning algorithm according to the spectral data so as to process the spectral data; analyzing the processed spectral data in real time, and identifying smoke components and concentration data thereof; a dynamic adaptability correction model is constructed according to real environment conditions, historical flue gas components and concentration data of the historical flue gas components so as to correct the concentration data of the identified flue gas components in real time, and the working state of the multispectral detector is adjusted through a feedback control unit; and monitoring data of a related environment where the flue gas is located, and integrating the corrected flue gas component and concentration data and the related environment data by using a data fusion algorithm to generate a flue gas analysis report. By utilizing the embodiment of the invention, the flue gas components can be monitored in real time, the defects of a traditional analysis instrument in the aspects of data processing and correction are overcome, and the accuracy and reliability of flue gas analysis are improved.

Description

Technical Field

[0001] The invention belongs to the technical field of flue gas analysis, and in particular to a detection method and system of a flue gas analyzer. Background Art

[0002] Flue gas analyzers are important instruments used to monitor and analyze gas components in industry and the environment. They are widely used in power plants, refineries, chemical companies, and other industries that may emit harmful gases. These instruments can detect various components in flue gas in real time, including but not limited to carbon dioxide (CO2), nitrogen oxides (NOx), sulfur dioxide (SO2), carbon monoxide (CO), etc., and have important functions such as monitoring pollutant emissions, evaluating combustion efficiency, and improving environmental quality.

[0003] Traditional flue gas analyzers usually rely on spectral analysis techniques, such as absorption spectroscopy and emission spectroscopy. These techniques use the absorption or scattering characteristics of gas molecules to light of a specific wavelength to identify their type and concentration. However, there are often some technical challenges in the flue gas analysis process. For example, changes in environmental conditions (such as temperature, humidity, air pressure, etc.) may cause changes in measurement data, and failure or instability of the instrument itself may also introduce noise and errors. Summary of the invention

[0004] The purpose of the present invention is to provide a detection method and system for a flue gas analyzer to solve the deficiencies in the prior art, to be able to monitor flue gas components in real time, to solve the deficiencies of traditional analytical instruments in data processing and correction, and to improve the accuracy and reliability of flue gas analysis.

[0005] An embodiment of the present application provides a detection method of a flue gas analyzer, the method comprising: According to the spectral characteristics of the flue gas, the multi-spectral detector integrated in the flue gas analyzer is used to perform spectral scanning on the flue gas to obtain the reflection and transmission spectral data of the flue gas in multiple wavelength ranges; Based on the acquired spectral data, an outlier detection model is established using an integrated learning algorithm to process the spectral data and eliminate abnormal data caused by environmental interference or instrument failure. Identify smoke components and their concentration data in smoke by analyzing the processed spectral data in real time; According to the real environmental conditions and historical smoke components and their concentration data, a dynamic adaptive correction model is constructed to correct the concentration data of the identified smoke components in real time, and the working state of the multi-spectral detector is adjusted through the feedback control unit to optimize the measurement accuracy; Monitor the relevant environmental data of the flue gas, apply data fusion algorithm, integrate the corrected flue gas components and their concentration data with relevant environmental data, and generate a flue gas analysis report.

[0006] Optionally, the method of establishing an outlier detection model based on the acquired spectral data using an integrated learning algorithm to process the spectral data and eliminate abnormal data caused by environmental interference or instrument failure includes: Use random forest or gradient boosting tree algorithm to train the outlier detection model, where the training sample set is divided into multiple subsets, and the bootstrap method is used to extract data from the subsets for model training respectively. The results of each model are combined through a voting mechanism; The trained anomaly detection model is used to predict the spectral data acquired in real time. By setting a threshold, the prediction result is compared with the threshold to identify abnormal spectral data points, and the spectral data points marked as abnormal are automatically removed.

[0007] Optionally, the real-time analysis of the processed spectral data to identify smoke components and their concentration data in the smoke includes: De-noising the processed spectral data, and extracting features by principal component analysis based on the denoised spectral data to obtain denoised spectral features; Run the pre-built SVM classification model to classify the denoised spectral features and identify the smoke components in the smoke; A partial least squares regression algorithm is applied to quantitatively estimate the concentration of the identified smoke components, wherein the partial least squares regression algorithm provides concentration prediction by establishing a linear regression relationship between the component concentration and the spectral characteristics.

[0008] Optionally, the dynamic adaptive correction model is constructed according to the real environmental conditions and historical smoke components and their concentration data to perform real-time correction on the concentration data of the identified smoke components, and the working state of the multi-spectral detector is adjusted through the feedback control unit to optimize the measurement accuracy, including: The multivariate linear regression analysis method is used to analyze the historical environmental parameters and historical smoke components and their concentration data under real environmental conditions, and an independent relationship model is established between the environmental parameters and the concentration of each smoke component; Collect environmental parameters in current environmental conditions, input the current environmental parameters into each relationship model respectively, and output the expected concentration value of the smoke component corresponding to the relationship model; For each identified smoke component, an adaptive filtering algorithm is applied to calculate the correction factor based on the corresponding concentration data and the expected concentration value output by the model; Applying the dynamic correction factor of each smoke component to the corresponding concentration data to obtain the corrected concentration data; The real-time corrected flue gas component concentration data is monitored through the PID control algorithm. If the corrected concentration data exceeds the set tolerance range, an alarm is automatically issued and the working state of the spectrum detector is adjusted to ensure the measurement accuracy.

[0009] Another embodiment of the present application provides a detection system for a flue gas analyzer, the system comprising: An acquisition module is used to perform spectral scanning on the flue gas according to the spectral characteristics of the flue gas using a multi-spectral detector integrated in the flue gas analyzer to obtain reflection and transmission spectral data of the flue gas in multiple wavelength ranges; A processing module is used to establish an outlier detection model based on the acquired spectral data using an integrated learning algorithm to process the spectral data and eliminate abnormal data caused by environmental interference or instrument failure; An identification module is used to identify smoke components and their concentration data in the smoke by analyzing the processed spectral data in real time; A correction module is used to construct a dynamic adaptive correction model based on real environmental conditions and historical smoke components and their concentration data to perform real-time correction on the concentration data of identified smoke components, and to adjust the working state of the multi-spectral detector through a feedback control unit to optimize the measurement accuracy; The integration module is used to monitor the relevant environmental data of the flue gas, apply the data fusion algorithm, integrate the corrected flue gas components and their concentration data and related environmental data, and generate a flue gas analysis report.

[0010] Yet another embodiment of the present application provides a storage medium, wherein the storage medium stores a computer program, wherein the computer program is configured to execute any of the above methods when running.

[0011] Yet another embodiment of the present application provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute any of the methods described above.

[0012] Compared with the prior art, the detection method of a flue gas analyzer provided by the present invention uses a multi-spectral detector to scan the flue gas spectrum according to the spectral characteristics of the flue gas, and obtains the reflection and transmission spectral data of the flue gas in multiple wavelength ranges; according to the spectral data, an outlier detection model is established by using an integrated learning algorithm to process the spectral data; the processed spectral data is analyzed in real time to identify the flue gas components and their concentration data; according to the actual environmental conditions and historical flue gas components and their concentration data, a dynamic adaptive correction model is constructed to correct the concentration data of the identified flue gas components in real time, and the working state of the multi-spectral detector is adjusted through a feedback control unit; the relevant environmental data of the flue gas is monitored, and the corrected flue gas components and concentration data and the relevant environmental data are integrated by using a data fusion algorithm to generate a flue gas analysis report, so that the flue gas components can be monitored in real time, the deficiencies of traditional analytical instruments in data processing and correction are solved, and the accuracy and reliability of flue gas analysis are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 A hardware structure block diagram of a computer terminal for a detection method of a flue gas analyzer provided by an embodiment of the present invention; Figure 2 A schematic flow chart of a detection method of a flue gas analyzer provided in an embodiment of the present invention; Figure 3 A schematic diagram of the structure of a detection system of a flue gas analyzer provided in an embodiment of the present invention; Figure 4 A schematic diagram of a flue gas spectrum provided by an embodiment of the present invention; Figure 5 Another schematic diagram of a flue gas spectrum provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0014] The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, but should not be construed as limiting the present invention.

[0015] The embodiment of the present invention firstly provides a detection method of a flue gas analyzer, which can be applied to electronic devices such as computer terminals, specifically ordinary computers, etc.

[0016] The following describes it in detail by taking running on a computer terminal as an example. Figure 1 The hardware structure block diagram of a computer terminal of a detection method of a flue gas analyzer provided by an embodiment of the present invention. Figure 1 As shown, the computer terminal may include one or more ( Figure 1Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data. Optionally, the computer terminal may also include a transmission device 106 for communication functions and an input and output device 108. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above-mentioned computer terminal. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations are shown.

[0017] The memory 104 can be used to store software programs and modules of application software, such as program instructions / modules corresponding to the detection method of the flue gas analyzer in the embodiment of the present application. The processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, that is, implementing the above method. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories can be connected to the computer terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0018] The transmission device 106 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of a computer terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0019] See also Figure 2 , an embodiment of the present invention provides a detection method of a flue gas analyzer, which may include the following steps: S201, according to the spectral characteristics of the flue gas, using a multi-spectral detector integrated in the flue gas analyzer to perform spectral scanning on the flue gas to obtain reflection and transmission spectral data of the flue gas in multiple wavelength ranges; Specifically, before performing spectral scanning, the spectral characteristics of the target flue gas can be analyzed and selected first. According to existing literature and experimental results, the absorption peak position and characteristic spectrum of flue gas components (such as CO2, NOx, SO2, etc.) within a specific wavelength range can be determined.

[0020] For the configuration of the multi-spectral detector, necessary calibration and settings are carried out to ensure its stability and accuracy under different environmental conditions: the output intensity of the light source is measured at different wavelengths, and corresponding adjustments are made to ensure that the detector can work under the optimal light intensity; an environmental sensor is set in the flue gas analyzer to monitor environmental parameters such as temperature, humidity, and air pressure in real time during spectral scanning, and the spectral data is compensated for environmental factors.

[0021] According to the set scanning parameters, the multi-spectral detector is started to perform spectral scanning on the flue gas. During this process, the detector will continuously obtain the reflection and transmission spectrum data of the flue gas at multiple wavelengths. The spectrum data obtained by the detector is recorded in a timely manner through a high-frequency acquisition system to ensure the timeliness and integrity of the data. At this time, the reflection spectrum and transmission spectrum data corresponding to each wavelength should be stored in a structured format, for example, using a timestamp as an index to improve the efficiency of subsequent data processing. For example, a flue gas spectrum diagram such as Figure 4 , Figure 5 As shown, Figure 4 Including the absorption cross section processing of NO and NO2 gases (obtaining spectrum), Figure 5 Including absorption cross section processing of three gases: S02, C0, and C02 (obtaining spectrum).

[0022] Use timestamps to synchronize the reflection and transmission spectral data to ensure that the spectral data at the same time point is used for subsequent analysis. Spectral reconstruction technology is used to fuse reflection and transmission data and use specific algorithms (such as regularized least squares) to eliminate noise and redundant information, thereby improving the signal-to-noise ratio of the data. This method can effectively reduce the aliasing of spectral features caused by environmental interference.

[0023] Finally, the processed spectral data are integrated to form a preliminary spectral data set in preparation for subsequent outlier detection and component identification: the reflection and transmission data of each wavelength obtained by spectral scanning are organized into a systematic database, and the relevant metadata of the spectral features are attached to form a complete spectral data set; to provide a basis for subsequent algorithm model training and optimization, the preliminary data set is annotated into a data format that can be used for machine learning algorithm training, thereby improving the efficiency of subsequent analysis.

[0024] The above steps can ensure that the flue gas analyzer obtains high-quality spectral data during spectral scanning, thus laying a solid foundation for subsequent outlier detection, component identification and concentration estimation. This spectral scanning method not only improves the reliability and accuracy of the data, but also helps to maintain the consistency and repeatability of the analysis results in complex environments.

[0025] S202, based on the acquired spectral data, using an integrated learning algorithm to establish an outlier detection model to process the spectral data and eliminate abnormal data caused by environmental interference or instrument failure; This step aims to automatically identify and remove unreliable data points through intelligent algorithms to ensure the accuracy and reliability of subsequent analysis. During the flue gas analysis process, spectral data may be affected by many factors, such as temperature changes, humidity fluctuations, instrument failures, etc. These abnormal data will directly affect the identification of flue gas components and the estimation of concentration. Therefore, the use of an outlier detection model helps to improve the robustness and accuracy of the overall measurement system.

[0026] Specifically, the random forest or gradient boosting tree algorithm can be used to train the outlier detection model, wherein the training sample set is divided into multiple subsets, and the data are extracted from the subsets using the bootstrap method for model training respectively, and the results of each model are integrated through a voting mechanism; In this step, the random forest or gradient boosted tree (GBDT) algorithm is selected as the base learner to form a powerful ensemble model by building multiple decision trees. First, the training sample set is divided into multiple subsets, and samples are randomly selected from these subsets using the bootstrap method to train multiple base models separately. This method can not only enhance the generalization ability of the model, but also reduce the risk of overfitting by integrating the results of multiple models.

[0027] By integrating multiple models, we can make full use of the advantages of different models and improve the accuracy of outlier detection. Random forest enhances the robustness of the model by introducing diversity and randomness, while gradient boosting tree improves prediction ability by continuously optimizing residuals. This combination greatly improves the accuracy of anomaly detection.

[0028] The trained anomaly detection model is used to predict the spectral data acquired in real time. By setting a threshold, the prediction result is compared with the threshold to identify abnormal spectral data points, and the spectral data points marked as abnormal are automatically removed.

[0029] After the model training is completed, this step uses the trained outlier detection model to analyze and predict the spectral data acquired in real time. Set a reasonable threshold to determine whether a data point is abnormal. By predicting each data point and comparing the prediction result with the preset threshold, identify and mark those data points that are considered abnormal. Real-time detection and elimination of outliers are essential to ensure the accuracy and reliability of subsequent smoke composition analysis. Through automated anomaly detection, the need for manual intervention is reduced, thereby improving the system's work efficiency and data credibility.

[0030] In the model training phase, not only a fixed threshold is set, but the threshold is dynamically generated using statistical methods (such as standard deviation and mean) by analyzing the distribution of past spectral data and the characteristics of abnormal data. For example, the threshold can be set to the mean of normal data plus or minus 1.5 times the standard deviation, and adjusted according to real-time data distribution and historical records.

[0031] During the model application process, new spectral data is continuously collected and the model is updated. An incremental learning strategy is adopted to update the trained random forest or GBDT model in real time to adapt to new data features and environmental changes. This ensures that the threshold is consistent with the data characteristics and avoids outdated or inappropriate thresholds.

[0032] When abnormal data is identified, the data point and the model's prediction results are recorded and feedback is provided. These abnormal records are analyzed regularly to optimize the model parameters and threshold settings to form a closed-loop feedback loop. By analyzing the distribution characteristics of abnormal data, the model is continuously guided to learn potential abnormal patterns, further improving the model's anomaly detection capabilities.

[0033] For example, a flue gas analyzer is used to monitor the flue gas emissions of an industrial emission source. The multi-spectral detector of the analyzer can obtain spectral characteristic data of flue gas in multiple wavelength ranges. A random forest algorithm is used to establish an outlier detection model to ensure that the acquired data is accurate and reliable.

[0034] 1. Spectral data example The spectral data obtained by the multi-spectral detector is shown in Table 1 below (the unit is relative reflectance):

[0035] 2. Model input data In order to train the outlier detection model, a set of training samples (including normal data and abnormal data) needs to be prepared. These samples will be used for training the random forest algorithm. Collect 50 samples, each of which has a reflectance value corresponding to the above wavelength, and a label (normal or abnormal). The sample model input data (training samples) are shown in Table 2:

[0036] 3. Model output data After the model training is completed, the trained random forest model is used to predict the spectral data obtained in real time. The following spectral data in Table 3 (such as the spectral data above) are obtained in real-time monitoring and input into the model:

[0037] 4. The outlier detection results use the set threshold (for example, the reflectivity deviation value greater than 0.05 is set as an abnormality), and the output results of the model are as follows:

[0038] In this example, the model predicts that the reflectance data at 750 nm is marked as an anomaly, so this data point is removed, ensuring that subsequent analysis is based only on normal data.

[0039] S203, identifying smoke components and concentration data in the smoke by real-time analysis of the processed spectral data; The purpose of this step is to use the spectral data after outlier detection and denoising to perform qualitative and quantitative analysis of the chemical components in the flue gas. By applying specific algorithms and models, this process can automatically identify the main components in the flue gas and their corresponding concentration values, which is of great significance for real-time monitoring and environmental management.

[0040] Specifically, the processed spectral data may be subjected to denoising, and feature extraction may be performed using principal component analysis based on the denoised spectral data to obtain denoised spectral features; First, the spectral data that has been detected by outliers is denoised to remove random noise and environmental interference in the data. Using methods such as wavelet transform or moving average can effectively retain the main features of the signal while reducing the impact of noise. After denoising, principal component analysis (PCA) is used to extract features from the spectral data, reducing the high-dimensional data into several main components, thereby retaining the most important information and obtaining the denoised spectral features.

[0041] The implementation of denoising and feature extraction helps improve the accuracy and effectiveness of subsequent analysis. By eliminating invalid information and extracting the most representative features for smoke component identification, the learning effect of the model can be significantly enhanced to ensure the reliability of the final result. This process provides a clearer and cleaner data foundation for subsequent smoke component identification.

[0042] Run the pre-built SVM classification model to classify the denoised spectral features and identify the smoke components in the smoke; The denoised spectral features are input into the pre-trained support vector machine (SVM) classification model. SVM is an effective supervised learning method that can classify different smoke components by constructing decision boundaries. The kernel function characteristics of SVM can handle complex nonlinear problems, thereby improving the accuracy of classification.

[0043] By applying the SVM classification model, efficient identification of complex smoke components can be achieved. This method not only improves the classification accuracy, but also can handle multi-category problems, so that the identification of different components can be carried out smoothly. This process is of great significance in real-time monitoring of smoke components, and is directly helpful for timely detection of potential environmental risks and protection of human health and ecological environment.

[0044] A partial least squares regression algorithm is applied to quantitatively estimate the concentration of the identified smoke components, wherein the partial least squares regression algorithm provides concentration prediction by establishing a linear regression relationship between the component concentration and the spectral characteristics.

[0045] Based on the identified smoke components, the partial least squares regression (PLSR) algorithm is used to quantify their concentrations. PLSR predicts the concentration of unknown samples by establishing a model of the linear relationship between spectral features and component concentrations. The advantage of this algorithm is that it can process high-dimensional data while avoiding multicollinearity problems, making it suitable for regression analysis of spectral data.

[0046] The partial least squares regression algorithm plays a vital role in the quantitative estimation of flue gas component concentrations. By accurately predicting the concentration of flue gas components, it can provide reliable data support for environmental monitoring and control. This has important practical significance for the compliance of industrial emissions, the improvement of environmental quality and the formulation of policies.

[0047] Among them, after the feature extraction is completed, we can consider using an integrated learning method to combine multiple regression models (such as partial least squares regression, ridge regression, elastic network, etc.) to form a more complex model by weighted averaging or stacking models, thereby improving the accuracy of concentration estimation.

[0048] On the basis of ensemble learning, a feature selection mechanism can be added. The tree-based feature selection method (such as the random forest mechanism based on feature importance) can be used to screen out the features with the highest correlation with concentration prediction, thereby reducing the complexity of input variables and improving the performance of the regression model. The feature selection process can effectively reduce the risk of overfitting and ensure the generalizability of the model.

[0049] The parameters of the integrated learning model can be updated regularly with newly acquired spectral data to adapt to changes in smoke composition under different environmental conditions. By adopting an online learning strategy, the model can continuously learn new knowledge during real-time analysis to maintain accuracy.

[0050] Through the above methods, not only the prediction accuracy of flue gas component concentration is improved, but it also helps to cope with changes in different environments, making the flue gas analysis instrument more flexible and adaptable in practical applications.

[0051] S204, constructing a dynamic adaptive correction model based on real environmental conditions and historical smoke components and their concentration data to perform real-time correction on the concentration data of the identified smoke components, and adjusting the working state of the multi-spectral detector through a feedback control unit to optimize the measurement accuracy; In flue gas analysis, the steps to build a dynamic adaptive correction model are based on real environmental conditions and historical flue gas components and their concentration data. By analyzing these data, a real-time correction feedback mechanism is obtained. The model can adjust the concentration data of identified flue gas components in real time to adapt to environmental changes, and optimize the working state of the multi-spectral detector through the feedback control unit, thereby improving the measurement accuracy.

[0052] Specifically, a multiple linear regression analysis method can be used to analyze historical environmental parameters and historical smoke components and their concentration data in real environmental conditions, and to establish an independent relationship model between environmental parameters and the concentration of each smoke component; Through multiple linear regression analysis, the relationship between historical environmental parameters (such as temperature, humidity, air pressure, etc.) and historical smoke component concentrations is studied. By establishing a regression model between the concentrations of different smoke components and environmental factors, the main environmental factors that affect the concentration of each smoke component can be identified. Establishing this relationship model can help understand the impact of environmental conditions on the concentration of smoke components, and then achieve effective prediction of concentration changes. This is crucial for optimizing smoke monitoring and achieving precise control, especially under complex and changing environmental conditions.

[0053] Adaptive weighted regression methods can be used to dynamically adjust the weight of each historical data point. According to the changes in current environmental parameters, historical data points with higher relevance are given greater weights, while points with lower relevance have lower weights. This approach can more accurately capture the impact of different environmental conditions on the concentration of smoke components, thereby improving the prediction accuracy of the model.

[0054] Collect environmental parameters in current environmental conditions, input the current environmental parameters into each relationship model respectively, and output the expected concentration value of the smoke component corresponding to the relationship model; In this step, the current environmental parameters are collected in real time and input into each pre-established relationship model to output the expected concentration value of the corresponding flue gas component. This step ensures that the model can make timely concentration predictions based on the latest environmental data. Through the real-time input of current environmental data, the monitoring system can obtain real-time expected concentration values, providing a basis for subsequent concentration correction. This mechanism enhances the timeliness and accuracy of the monitoring system and helps to respond to possible environmental changes in a timely manner. Exemplary: 1. Model input data The following are the input data, including current environmental parameters, historical flue gas component concentration data, and the corresponding relationship model.

[0055] a. The current environmental parameters are: Temperature (°C): 25; Humidity (%): 60; Atmospheric pressure (kPa): 100; Wind speed (m / s): 2.5.

[0056] b. Historical smoke component concentration data, as shown in Table 5:

[0057] c. Relationship model example (multiple linear regression model) According to the historical environmental parameters and the corresponding concentration data, a relationship model was established through multiple linear regression analysis (example model coefficients): - CO2 concentration = 0.9*temperature + 1.5*humidity - 0.005*atmospheric pressure + 0.3*wind speed + C_1; - CO concentration = 0.2*temperature + 0.8*humidity - 0.002*atmospheric pressure + 0.1*wind speed + C_2; - NO concentration = 0.5*temperature + 0.3*humidity - 0.001*atmospheric pressure - 0.05*wind speed + C_3; - SO2 concentration = 0.4*temperature + 0.1*humidity - 0.003*atmospheric pressure + 0.2*wind speed + C_4.

[0058] Among them, C_1, C_2, C_3, and C_4 are constant terms that reflect the baseline impact of environmental factors on concentration.

[0059] 2. Model output data Using the parameters of the current environmental conditions, the various relationship models are input to calculate the expected concentration value of each smoke component: CO2 concentration prediction: Expected CO2 concentration = 0.9*25 + 1.5*60 - 0.005*100 + 0.3*2.5 + C_1, in this application C_1 = 100, then: 22.5 + 90 - 0.5 + 0.75 + 100 = 213.75ppm.

[0060] CO concentration prediction: Expected CO concentration = 0.2*25 + 0.8*60 - 0.002*100 + 0.1* 2.5 + C_2, in this application C_2 = 5, then: 5 + 48 - 0.2 + 0.25 + 5 = 58.05ppm.

[0061] NO concentration prediction: NO expected concentration = 0.5 * 25 + 0.3 * 60 - 0.001 * 100 - 0.05 * 2.5 + C_3, in this application C_3 = 0.5, then: 12.5 + 18 - 0.1 - 0.125 + 0.5 = 30.775ppm.

[0062] SO2 concentration prediction: Expected SO2 concentration = 0.4*25 + 0.1*60 - 0.003*100 + 0.2* 2.5 + C_4, in this application C_4 = 1, then: 10 + 6 - 0.3 + 0.5 + 1 = 17.2ppm.

[0063] The final output results are shown in Table 6:

[0064] For each identified smoke component, an adaptive filtering algorithm is applied to calculate the correction factor based on the corresponding concentration data and the expected concentration value output by the model; In this step, the system uses an adaptive filtering algorithm to calculate a correction factor for each identified smoke component, which reflects the difference between the actual concentration and the expected concentration. The calculation of this correction factor is the key to achieving dynamic correction of concentration data. By accurately calculating the correction factor, the system can effectively adjust the concentration data of the actual smoke components to ensure the accuracy of the measurement data. This process is crucial to reducing measurement errors and improving system reliability.

[0065] Adaptive filtering algorithms (such as Kalman filtering) can be applied to calculate dynamic correction factors by combining the real-time measured smoke concentration with the expected concentration value generated by the model. The specific steps are as follows: the real-time smoke concentration and the expected concentration output by the model are input into the Kalman filter as state quantities, and the initial state and covariance matrix are set; through the Kalman filtering process, the state estimate is continuously updated to generate the correction factor under the current environmental conditions. This correction factor reflects the deviation between the actual measured value and the expected value of the model, and can be dynamically adjusted to optimize the correction result.

[0066] Applying the dynamic correction factor of each smoke component to the corresponding concentration data to obtain the corrected concentration data; The previously calculated correction factor is applied to the concentration data of each smoke component to generate the corrected concentration data. This process ensures the real-time adjustment of the concentration data and helps to improve the accuracy of the monitoring results. By dynamically applying the correction factor, the monitoring system can reflect the true concentration of the smoke components in real time, effectively reduce the errors caused by environmental factors, enhance the reliability and accuracy of monitoring, and provide accurate data support for the formulation of environmental protection measures.

[0067] The original concentration value can be adjusted by multiplying the original concentration data with the correction factor or other weighted processing to obtain a corrected concentration value to ensure that it is closer to the actual concentration level.

[0068] The real-time corrected flue gas component concentration data is monitored through the PID control algorithm. If the corrected concentration data exceeds the set tolerance range, an alarm is automatically issued and the working state of the spectral detector is adjusted (for example, increasing the sampling frequency, adjusting the sensitivity of the detector, etc.) to ensure the measurement accuracy.

[0069] The PID (proportional-integral-differential) control algorithm is used to monitor the real-time corrected concentration data. When the corrected concentration data is found to exceed the preset tolerance range, the system will automatically issue an alarm and adjust the working state of the multi-spectral detector to ensure the measurement accuracy. This process can achieve rapid feedback and adjustment of the concentration of flue gas components, ensuring that effective measures are taken in abnormal situations to avoid potential environmental risks. Through real-time monitoring and control, the safety and reliability of the entire detection system are improved.

[0070] S205, monitor the relevant environmental data of the flue gas, apply the data fusion algorithm, integrate the corrected flue gas components and their concentration data and the relevant environmental data, and generate a flue gas analysis report.

[0071] By monitoring the environmental data of the flue gas in real time (such as temperature, humidity, atmospheric pressure, etc.), and combining these data with the corrected flue gas composition and its concentration data, the data fusion algorithm is used for integration. Finally, a detailed flue gas analysis report is generated to provide a basis for decision-making. The data fusion algorithm can effectively integrate data from different sources, eliminate redundancy and noise, and thus provide more accurate information.

[0072] Specifically, the smoke composition data and environmental data can be standardized to ensure that different data sources have the same dimensions and ranges for subsequent fusion analysis. Convolutional neural networks (CNNs) are used to extract features from smoke composition data, while recurrent neural networks (RNNs) are used to process time series environmental data. This combination can handle the complex relationship between temporal information and spatial features. In the deep learning model, a multi-level fusion architecture is constructed to integrate the extracted features from different sources. The features of smoke components are fused with environmental features by weighted summation or splicing to form a comprehensive feature vector. The fused feature vector is processed using a fully connected layer to generate an output report for smoke analysis. This report covers the concentration of smoke components and their environmental impact, and assesses the potential risks of smoke to the environment.

[0073] This multi-level deep learning fusion algorithm can effectively meet the high requirements for data accuracy and intelligence in flue gas monitoring, thereby providing more accurate information support for environmental management and decision-making.

[0074] It can be seen that according to the spectral characteristics of flue gas, a multi-spectral detector is used to scan the flue gas spectrum to obtain the reflection and transmission spectral data of flue gas in multiple wavelength ranges; according to the spectral data, an integrated learning algorithm is used to establish an outlier detection model to process the spectral data; the processed spectral data is analyzed in real time to identify the flue gas components and their concentration data; according to the actual environmental conditions and historical flue gas components and their concentration data, a dynamic adaptive correction model is constructed to correct the concentration data of the identified flue gas components in real time, and the working state of the multi-spectral detector is adjusted through the feedback control unit; the relevant environmental data of the flue gas is monitored, and the data fusion algorithm is applied to integrate the corrected flue gas components and concentration data and the relevant environmental data to generate a flue gas analysis report, so that the flue gas components can be monitored in real time, the deficiencies of traditional analytical instruments in data processing and correction can be solved, and the accuracy and reliability of flue gas analysis can be improved.

[0075] Another embodiment of the present invention provides a detection system for a flue gas analyzer, see Figure 3 , the system may include: The acquisition module 301 is used to perform spectral scanning on the flue gas according to the spectral characteristics of the flue gas using a multi-spectral detector integrated in the flue gas analyzer to obtain reflection and transmission spectral data of the flue gas in multiple wavelength ranges; The processing module 302 is used to establish an outlier detection model based on the acquired spectral data using an integrated learning algorithm to process the spectral data and eliminate the abnormal data caused by environmental interference or instrument failure; The identification module 303 is used to identify the smoke components and their concentration data in the smoke by analyzing the processed spectral data in real time; The correction module 304 is used to construct a dynamic adaptive correction model according to the real environmental conditions and historical smoke components and their concentration data, so as to perform real-time correction on the concentration data of the identified smoke components, and adjust the working state of the multi-spectral detector through the feedback control unit to optimize the measurement accuracy; The integration module 305 is used to monitor the relevant environmental data of the smoke, and to integrate the corrected smoke components and concentration data and the relevant environmental data using a data fusion algorithm to generate a smoke analysis report.

[0076] It can be seen that according to the spectral characteristics of flue gas, a multi-spectral detector is used to scan the flue gas spectrum to obtain the reflection and transmission spectral data of flue gas in multiple wavelength ranges; according to the spectral data, an integrated learning algorithm is used to establish an outlier detection model to process the spectral data; the processed spectral data is analyzed in real time to identify the flue gas components and their concentration data; according to the actual environmental conditions and historical flue gas components and their concentration data, a dynamic adaptive correction model is constructed to correct the concentration data of the identified flue gas components in real time, and the working state of the multi-spectral detector is adjusted through the feedback control unit; the relevant environmental data of the flue gas is monitored, and the data fusion algorithm is applied to integrate the corrected flue gas components and concentration data and the relevant environmental data to generate a flue gas analysis report, so that the flue gas components can be monitored in real time, the deficiencies of traditional analytical instruments in data processing and correction can be solved, and the accuracy and reliability of flue gas analysis can be improved.

[0077] An embodiment of the present invention further provides a storage medium, in which a computer program is stored, wherein the computer program is configured to execute the steps of any of the above method embodiments when running.

[0078] Specifically, in this embodiment, the above storage medium may be configured to store a computer program for performing the following steps: S201, according to the spectral characteristics of the flue gas, using a multi-spectral detector integrated in the flue gas analyzer to perform spectral scanning on the flue gas to obtain reflection and transmission spectral data of the flue gas in multiple wavelength ranges; S202, based on the acquired spectral data, using an integrated learning algorithm to establish an outlier detection model to process the spectral data and eliminate abnormal data caused by environmental interference or instrument failure; S203, identifying smoke components and concentration data in the smoke by real-time analysis of the processed spectral data; S204, constructing a dynamic adaptive correction model based on real environmental conditions and historical smoke components and their concentration data to perform real-time correction on the concentration data of the identified smoke components, and adjusting the working state of the multi-spectral detector through a feedback control unit to optimize the measurement accuracy; S205, monitor the relevant environmental data of the flue gas, apply the data fusion algorithm, integrate the corrected flue gas components and their concentration data and the relevant environmental data, and generate a flue gas analysis report.

[0079] It can be seen that according to the spectral characteristics of flue gas, a multi-spectral detector is used to scan the flue gas spectrum to obtain the reflection and transmission spectral data of flue gas in multiple wavelength ranges; according to the spectral data, an integrated learning algorithm is used to establish an outlier detection model to process the spectral data; the processed spectral data is analyzed in real time to identify the flue gas components and their concentration data; according to the actual environmental conditions and historical flue gas components and their concentration data, a dynamic adaptive correction model is constructed to correct the concentration data of the identified flue gas components in real time, and the working state of the multi-spectral detector is adjusted through the feedback control unit; the relevant environmental data of the flue gas is monitored, and the data fusion algorithm is applied to integrate the corrected flue gas components and concentration data and the relevant environmental data to generate a flue gas analysis report, so that the flue gas components can be monitored in real time, the deficiencies of traditional analytical instruments in data processing and correction can be solved, and the accuracy and reliability of flue gas analysis can be improved.

[0080] An embodiment of the present invention further provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0081] Specifically, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0082] Specifically, in this embodiment, the processor may be configured to perform the following steps through a computer program: S201, according to the spectral characteristics of the flue gas, using a multi-spectral detector integrated in the flue gas analyzer to perform spectral scanning on the flue gas to obtain reflection and transmission spectral data of the flue gas in multiple wavelength ranges; S202, based on the acquired spectral data, using an integrated learning algorithm to establish an outlier detection model to process the spectral data and eliminate abnormal data caused by environmental interference or instrument failure; S203, identifying smoke components and concentration data in the smoke by real-time analysis of the processed spectral data; S204, constructing a dynamic adaptive correction model based on real environmental conditions and historical smoke components and their concentration data to perform real-time correction on the concentration data of the identified smoke components, and adjusting the working state of the multi-spectral detector through a feedback control unit to optimize the measurement accuracy; S205, monitor the relevant environmental data of the flue gas, apply the data fusion algorithm, integrate the corrected flue gas components and their concentration data and the relevant environmental data, and generate a flue gas analysis report.

[0083] It can be seen that according to the spectral characteristics of flue gas, a multi-spectral detector is used to scan the flue gas spectrum to obtain the reflection and transmission spectral data of flue gas in multiple wavelength ranges; according to the spectral data, an integrated learning algorithm is used to establish an outlier detection model to process the spectral data; the processed spectral data is analyzed in real time to identify the flue gas components and their concentration data; according to the actual environmental conditions and historical flue gas components and their concentration data, a dynamic adaptive correction model is constructed to correct the concentration data of the identified flue gas components in real time, and the working state of the multi-spectral detector is adjusted through the feedback control unit; the relevant environmental data of the flue gas is monitored, and the data fusion algorithm is applied to integrate the corrected flue gas components and concentration data and the relevant environmental data to generate a flue gas analysis report, so that the flue gas components can be monitored in real time, the deficiencies of traditional analytical instruments in data processing and correction can be solved, and the accuracy and reliability of flue gas analysis can be improved.

[0084] The above describes in detail the structure, features and effects of the present invention based on the embodiments shown in the drawings. The above is only a preferred embodiment of the present invention, but the present invention is not limited to the scope of implementation shown in the drawings. Any changes made according to the concept of the present invention, or modifications to equivalent embodiments with equivalent changes, which still do not exceed the spirit covered by the description and drawings, should be within the protection scope of the present invention.

Claims

1. A detection method of a flue gas analyzer, characterized in that: The method comprises: According to the spectral characteristics of the flue gas, the multi-spectral detector integrated in the flue gas analyzer is used to perform spectral scanning on the flue gas to obtain the reflection and transmission spectral data of the flue gas in multiple wavelength ranges; Based on the acquired spectral data, an outlier detection model is established using an integrated learning algorithm to process the spectral data and eliminate abnormal data caused by environmental interference or instrument failure. Identify smoke components and their concentration data in smoke by analyzing the processed spectral data in real time; According to the real environmental conditions and historical smoke components and their concentration data, a dynamic adaptive correction model is constructed to correct the concentration data of the identified smoke components in real time, and the working state of the multi-spectral detector is adjusted through the feedback control unit to optimize the measurement accuracy; Monitor the relevant environmental data of the flue gas, apply data fusion algorithm, integrate the corrected flue gas components and their concentration data and related environmental data, and generate a flue gas analysis report.

2. The method according to claim 1, characterized in that The method of establishing an outlier detection model based on the acquired spectral data by using an integrated learning algorithm to process the spectral data and eliminate abnormal data caused by environmental interference or instrument failure includes: Use random forest or gradient boosting tree algorithm to train the outlier detection model, where the training sample set is divided into multiple subsets, and the bootstrap method is used to extract data from the subsets for model training respectively. The results of each model are combined through a voting mechanism; The trained anomaly detection model is used to predict the spectral data acquired in real time. By setting a threshold, the prediction result is compared with the threshold to identify abnormal spectral data points, and the spectral data points marked as abnormal are automatically removed.

3. The method according to claim 2, characterized in that The real-time analysis of the processed spectral data to identify the smoke components and their concentration data in the smoke includes: De-noising the processed spectral data, and extracting features by principal component analysis based on the denoised spectral data to obtain denoised spectral features; Run the pre-built SVM classification model to classify the denoised spectral features and identify the smoke components in the smoke; A partial least squares regression algorithm is applied to quantitatively estimate the concentration of the identified smoke components, wherein the partial least squares regression algorithm provides concentration prediction by establishing a linear regression relationship between the component concentration and the spectral characteristics.

4. The method according to claim 3, characterized in that The method constructs a dynamic adaptive correction model based on the real environmental conditions and historical smoke components and their concentration data to correct the concentration data of the identified smoke components in real time, and adjusts the working state of the multi-spectral detector through the feedback control unit to optimize the measurement accuracy, including: The multivariate linear regression analysis method is used to analyze the historical environmental parameters and historical smoke components and their concentration data under real environmental conditions, and an independent relationship model is established between the environmental parameters and the concentration of each smoke component; Collect environmental parameters in current environmental conditions, input the current environmental parameters into each relationship model respectively, and output the expected concentration value of the smoke component corresponding to the relationship model; For each identified smoke component, an adaptive filtering algorithm is applied to calculate the correction factor based on the corresponding concentration data and the expected concentration value output by the model; Applying the dynamic correction factor of each smoke component to the corresponding concentration data to obtain the corrected concentration data; The real-time corrected flue gas component concentration data is monitored through the PID control algorithm. If the corrected concentration data exceeds the set tolerance range, an alarm is automatically issued and the working state of the spectrum detector is adjusted to ensure the measurement accuracy.

5. A detection system for a flue gas analyzer, characterized in that: The system comprises: An acquisition module is used to perform spectral scanning on the flue gas according to the spectral characteristics of the flue gas using a multi-spectral detector integrated in the flue gas analyzer to obtain reflection and transmission spectral data of the flue gas in multiple wavelength ranges; A processing module is used to establish an outlier detection model based on the acquired spectral data using an integrated learning algorithm to process the spectral data and eliminate abnormal data caused by environmental interference or instrument failure; An identification module is used to identify smoke components and their concentration data in the smoke by analyzing the processed spectral data in real time; A correction module is used to construct a dynamic adaptive correction model based on real environmental conditions and historical smoke components and their concentration data to perform real-time correction on the concentration data of identified smoke components, and to adjust the working state of the multi-spectral detector through a feedback control unit to optimize the measurement accuracy; The integration module is used to monitor the relevant environmental data of the flue gas, apply the data fusion algorithm, integrate the corrected flue gas components and their concentration data and related environmental data, and generate a flue gas analysis report.

6. The system according to claim 5, characterized in that The processing module is specifically used for: Use random forest or gradient boosting tree algorithm to train the outlier detection model, where the training sample set is divided into multiple subsets, and the bootstrap method is used to extract data from the subsets for model training respectively. The results of each model are combined through a voting mechanism; The trained anomaly detection model is used to predict the spectral data acquired in real time. By setting a threshold, the prediction result is compared with the threshold to identify abnormal spectral data points, and the spectral data points marked as abnormal are automatically removed.

7. The system according to claim 6, characterized in that The identification module is specifically used for: De-noising the processed spectral data, and extracting features by principal component analysis based on the denoised spectral data to obtain denoised spectral features; Run the pre-built SVM classification model to classify the denoised spectral features and identify the smoke components in the smoke; A partial least squares regression algorithm is applied to quantitatively estimate the concentration of the identified smoke components, wherein the partial least squares regression algorithm provides concentration prediction by establishing a linear regression relationship between the component concentration and the spectral characteristics.

8. The system according to claim 7, characterized in that The correction module is specifically used for: The multivariate linear regression analysis method is used to analyze the historical environmental parameters and historical smoke components and their concentration data under real environmental conditions, and an independent relationship model is established between the environmental parameters and the concentration of each smoke component; Collect environmental parameters in current environmental conditions, input the current environmental parameters into each relationship model respectively, and output the expected concentration value of the smoke component corresponding to the relationship model; For each identified smoke component, an adaptive filtering algorithm is applied to calculate the correction factor based on the corresponding concentration data and the expected concentration value output by the model; Applying the dynamic correction factor of each smoke component to the corresponding concentration data to obtain the corrected concentration data; The real-time corrected flue gas component concentration data is monitored through the PID control algorithm. If the corrected concentration data exceeds the set tolerance range, an alarm is automatically issued and the working state of the spectrum detector is adjusted to ensure the measurement accuracy.

9. A storage medium, characterized in that: The storage medium stores a computer program, wherein the computer program is configured to execute the method according to any one of claims 1 to 4 when executed.

10. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to run the computer program to perform the method according to any one of claims 1 to 4.

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