Improved gas flow control method and system

Abstract

The invention discloses an improved gas flow control method, which relates to the technical field of gas flow detection, and comprises the following steps: a gas flow detection system based on a honeycomb phase array technology monitors the gas flow in a large pipeline in real time; analyzing airflow distribution in the large pipeline based on fluid dynamics simulation; establishing an associated mathematical model, and correcting a flow value; various errors are compensated; connecting measured data with a carbon emission monitoring system, and transmitting the measured data to a data center in real time; the invention further discloses an improved gas flow control system which comprises a gas flow detection module, a calculation processing module and a remote data transmission module. By combining the honeycomb phase array technology, the multi-sensor fusion technology and the fluid dynamics simulation analysis technology, the precise, reliable and intelligent gas flow control method and system are provided, the accuracy and stability of flow measurement can be improved, and industrial emission can be monitored and controlled in real time.

Description

Technical Field

[0001] The present invention relates to the technical field of gas flow detection, and in particular to an improved gas flow control method and system. Background Art

[0002] With the continuous growth of global energy demand and the improvement of environmental protection requirements, industrial emission management has become an important task in modern manufacturing. In many industrial applications, accurate measurement of gas flow is crucial for emission monitoring, energy management, and carbon emission control. However, due to the uneven distribution of airflow inside large pipeline systems, complex fluid dynamics characteristics and errors in the measurement equipment itself, traditional gas flow detection methods often find it difficult to achieve high precision and high reliability requirements.

[0003] Traditional gas flow meters have some limitations. For example, they are easily affected by factors such as pipeline shape, fluid state, temperature, pressure, etc., and the measurement errors are large. In addition, the distribution of gas flow in large industrial pipelines is often complex and uneven, so it is impossible to accurately reflect the flow conditions of the entire pipeline system through flow measurement at a single location. These problems make it difficult for traditional flow control methods to meet the needs of industrial emission monitoring in terms of accuracy and reliability. In order to improve the accuracy of flow measurement and effectively compensate for various errors, some new technologies have been developed in recent years, such as honeycomb phased array technology, multi-sensor fusion technology, fluid dynamics simulation analysis, etc. Through the combination of these advanced technologies, more accurate and stable gas flow monitoring can be achieved, thereby improving the industrial gas emission monitoring system, improving energy management level, helping industrial enterprises reduce carbon emissions, and achieving green development goals. Summary of the invention

[0004] In order to solve the above technical problems, an improved gas flow control method and system are provided. This technical solution solves the above problems.

[0005] In order to achieve the above purpose, the technical solution adopted by the present invention is:

[0006] An improved gas flow control method and system, comprising:

[0007] The gas flow detection system based on honeycomb phased array technology monitors the gas flow in large pipelines in real time;

[0008] Analyze the air flow distribution in large pipes based on fluid dynamics simulation, and optimize the measurement accuracy of the flow detection system according to the simulation results;

[0009] Based on the detection data, combined with the gas flow rate, temperature and pressure parameters of large pipelines, a related mathematical model is established to correct the flow value;

[0010] Based on the flow detection data, multi-sensor fusion technology is used to compensate for various errors;

[0011] The measurement data is connected to the carbon emission monitoring system and transmitted to the data center in real time to control the changes in gas emissions and carbon dioxide emission factors of industrial enterprises.

[0012] Preferably, the gas flow detection system based on honeycomb phased array technology performs real-time monitoring of the gas flow in a large pipeline, specifically comprising:

[0013] Design the layout and specifications of the flow rate detection unit according to the diameter, shape, airflow state, gas composition, temperature and pressure parameters of the pipeline to be tested;

[0014] Multi-point detection sensors, including differential pressure flowmeters, electromagnetic flowmeters and ultrasonic flowmeters, are embedded in each unit of the honeycomb structure to monitor gas flow rates in real time at different pipeline locations and collect data;

[0015] Through multi-point detection, the sensor collects airflow data at different locations in real time, including flow rate, pressure and temperature;

[0016] De-noising, smoothing and normalizing the collected raw data;

[0017] The data smoothing formula is:

[0018]

[0019] In the formula, S n is the smoothed signal, x k is a data point in the original signal, σ is the standard deviation, is the Gaussian weight function.

[0020] Preferably, the flow distribution in the large pipeline is analyzed based on fluid dynamics simulation, and the measurement accuracy of the flow detection system is optimized according to the simulation results, specifically including:

[0021] Use CFD software to build a pipeline flow model, input pipeline geometry, fluid type, inlet velocity, and pressure boundary conditions, and set the turbulence model;

[0022] The fluid motion formula is:

[0023]

[0024] In the formula, is the convection term of the fluid, is the pressure gradient term, is the viscosity term, f is the external force term, u is the velocity field of the fluid, is the gradient operator, ρ is the density of the fluid, and v is the kinematic viscosity of the fluid;

[0025] Through CFD simulation, the velocity distribution, turbulence and pressure distribution of the airflow in the pipeline are obtained;

[0026] Obtain the change area of ​​air flow velocity distribution and analyze the air flow characteristics at different locations;

[0027] According to the simulation results, the flow meter is installed at a location where the airflow is stable and the flow velocity is evenly distributed. The flow field around the flow meter is analyzed by CFD to further optimize the measurement range and sensitivity of the flow meter.

[0028] Compare the actual flow measurement results with the CFD simulation results, evaluate the measurement accuracy of the flow detection system, and adjust the measurement position according to the differences.

[0029] Preferably, the flow distribution in the large pipeline is analyzed based on fluid dynamics simulation, and the measurement accuracy of the flow detection system is optimized according to the simulation results, specifically including:

[0030] According to the established mathematical model, the collected flow velocity data is corrected to compensate for the errors caused by the pipe shape and flow field non-uniformity factors, and obtain accurate flow value;

[0031] Based on multi-sensor fusion technology, data from different types of sensors are fused, and the Kalman filter algorithm is used to conduct comprehensive analysis and error correction on the data;

[0032] The measured gas flow data, temperature and pressure monitoring data are transmitted to the central control center in real time through the data transmission system;

[0033] The flow data is connected to the carbon emission monitoring system to achieve accurate calculation and monitoring of carbon emissions of industrial enterprises through real-time monitoring of gas flow and gas composition;

[0034] After the data is transmitted to the data center, it is analyzed in real time, and the control system adjusts the production process of the industrial enterprise based on the real-time monitoring results.

[0035] Preferably, the method of fusing data from different types of sensors based on multi-sensor fusion technology and performing comprehensive analysis and error correction on the data through a Kalman filter algorithm specifically includes:

[0036] Based on the current system state data, the current state is predicted and the error covariance is predicted. The prediction formula is:

[0037]

[0038] In the formula, is the state prediction value at the current moment, A is the state transfer matrix, is the state estimate of the previous moment, B is the control input matrix, uk is the control input, P k|k-1 is the predicted value of the error covariance matrix at the current moment, P k-1|k-1 is the estimated error covariance matrix of the previous moment, Q is the process noise covariance matrix, A T is the transpose of the state transfer matrix;

[0039] Correct the prediction results according to the sensor's observation data, calculate the Kalman gain, correct the prediction value through the Kalman gain and the measurement value, and update the error covariance matrix;

[0040] The correction formula is:

[0041]

[0042] In the formula, K k is the Kalman gain, H is the observation matrix, R is the observation noise covariance matrix, P k|k-1 is the forecast error covariance matrix, is the current state estimate, is the predicted state estimate, z k is the observation value from the sensor, is the residual between the observed and predicted values, P k|k is the corrected error covariance matrix, I is the identity matrix, K k H is the combination of Kalman gain and observation matrix, P k|k-1 Forecast error covariance matrix.

[0043] An improved gas flow control system, comprising:

[0044] Gas flow detection module: collects flow data based on flow velocity detection elements;

[0045] Calculation and processing module: The calculation and processing module is electrically connected to the gas flow detection module, and is used to receive the measurement data of the gas flow detection unit, and calculate the gas flow in real time and perform error correction in combination with the fluid dynamics analysis results;

[0046] Remote data transmission module: The remote data transmission module is electrically connected to the computing and processing module. The remote data transmission module is used to transmit measurement data and gas emission-related information to the control center in real time for emission monitoring, peak and frequency regulation, and energy management.

[0047] Preferably, the gas flow detection module specifically includes:

[0048] Flow rate sensor unit: used to directly measure the flow rate of gas, including thermal flow meter, ultrasonic flow meter and turbine flow meter;

[0049] Temperature sensor unit: used to monitor gas temperature. Temperature changes will affect gas density.

[0050] Pressure sensor unit: used to measure the gas pressure in the pipeline;

[0051] Flow sensor unit: calculates gas flow based on flow velocity, temperature and pressure;

[0052] Signal conditioning module unit: amplifies, filters and converts the original signal of the sensor;

[0053] Data acquisition interface unit: used to receive sensor data and transmit it to the computing and processing module.

[0054] Preferably, the calculation processing module specifically includes:

[0055] Data receiving unit: receiving flow measurement data from the gas flow detection module;

[0056] Signal processing and filtering unit: amplifies, denoises, filters and corrects the received raw data;

[0057] Flow calculation unit: Based on the flow rate, pressure and temperature input data, the gas flow is calculated in real time through fluid dynamics formulas;

[0058] Error correction unit: performs error correction on calculation results;

[0059] Real-time data processing unit: processes the collected measurement data in real time, performs calculations and analysis, and caches and stores the data.

[0060] Preferably, the real-time data processing unit specifically includes:

[0061] Data storage and backup: store the processed data;

[0062] Output interface: outputs the processed and corrected data to the remote data transmission module;

[0063] Algorithm optimization and adaptive adjustment unit: Automatically optimize the traffic calculation model based on machine learning.

[0064] Preferably, the remote data transmission module specifically includes:

[0065] Remote control and command receiving unit: The control center sends remote commands to the on-site equipment through this unit to adjust equipment parameters, perform remote fault diagnosis, and update configuration;

[0066] Remote monitoring and alarm unit: monitors the transmitted data in real time and issues alarms for emission and flow indicators based on set thresholds.

[0067] Compared with the prior art, the present invention has the following beneficial effects:

[0068] The present invention proposes to provide an accurate, reliable and intelligent gas flow control method and system by combining innovative methods such as honeycomb phased array technology, multi-sensor fusion technology, fluid dynamics simulation analysis and real-time data transmission. The method and system can not only improve the accuracy and stability of flow measurement, but also monitor and control industrial emissions in real time, effectively helping enterprises to achieve carbon emission reduction and energy optimization goals, and have significant environmental and economic benefits. Through analysis based on fluid dynamics simulation, the airflow distribution in the large pipeline is accurately modeled, and the layout of the flow detection system is optimized in combination with the simulation results. The appropriate flow meter installation position is selected to avoid measurement errors caused by flow field unevenness. Through multi-sensor fusion technology, data from different types of sensors are comprehensively analyzed, and various errors can be effectively corrected, thereby improving the reliability and accuracy of the system. Based on the improved gas flow detection system, key parameters such as airflow, temperature, and pressure in the large pipeline can be monitored in real time, and the measurement data can be transmitted to the data center to achieve precise control of gas emissions of industrial enterprises and avoid the risk of exceeding emissions. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 It is a step flow framework diagram of the present invention;

[0070] Figure 2 It is a system framework diagram of the present invention. DETAILED DESCRIPTION

[0071] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art may think of other obvious variations.

[0072] Reference Figure 1 As shown, an improved gas flow control method and system, comprising:

[0073] Step 1:

[0074] The gas flow detection system based on honeycomb phased array technology performs real-time monitoring of gas flow in a large pipeline, specifically including:

[0075] Design the layout and specifications of the flow rate detection unit according to the diameter, shape, airflow state, gas composition, temperature and pressure parameters of the pipeline to be tested;

[0076] Multi-point detection sensors, including differential pressure flowmeters, electromagnetic flowmeters and ultrasonic flowmeters, are embedded in each unit of the honeycomb structure to monitor gas flow rates in real time at different pipeline locations and collect data;

[0077] Through multi-point detection, the sensor collects airflow data at different locations in real time, including flow rate, pressure and temperature;

[0078] De-noising, smoothing and normalizing the collected raw data;

[0079] The data smoothing formula is:

[0080]

[0081] In the formula, S n is the smoothed signal, x k is a data point in the original signal, σ is the standard deviation, is the Gaussian weight function.

[0082] Step 2:

[0083] Use CFD software to build a pipeline flow model, input pipeline geometry, fluid type, inlet velocity, and pressure boundary conditions, and set the turbulence model;

[0084] The fluid motion formula is:

[0085]

[0086] In the formula, is the convection term of the fluid, is the pressure gradient term, is the viscosity term, f is the external force term, u is the velocity field of the fluid, is the gradient operator, ρ is the density of the fluid, and v is the kinematic viscosity of the fluid;

[0087] Through CFD simulation, the velocity distribution, turbulence and pressure distribution of the airflow in the pipeline are obtained;

[0088] Obtain the change area of ​​air flow velocity distribution and analyze the air flow characteristics at different locations;

[0089] According to the simulation results, the flow meter is installed at a location where the airflow is stable and the flow velocity is evenly distributed. The flow field around the flow meter is analyzed by CFD to further optimize the measurement range and sensitivity of the flow meter.

[0090] Compare the actual flow measurement results with the CFD simulation results, evaluate the measurement accuracy of the flow detection system, and adjust the measurement position according to the differences.

[0091] Step 3:

[0092] According to the established mathematical model, the collected flow velocity data is corrected to compensate for the errors caused by the pipe shape and flow field non-uniformity factors, and obtain accurate flow value;

[0093] Based on multi-sensor fusion technology, data from different types of sensors are fused, and the Kalman filter algorithm is used to conduct comprehensive analysis and error correction on the data;

[0094] Based on the current system state data, the current state is predicted and the error covariance is predicted. The prediction formula is:

[0095]

[0096] In the formula, is the state prediction value at the current moment, A is the state transfer matrix, is the state estimate of the previous moment, B is the control input matrix, uk is the control input, P k|k-1 is the predicted value of the error covariance matrix at the current moment, P k-1|k-1 is the estimated error covariance matrix of the previous moment, Q is the process noise covariance matrix, A T is the transpose of the state transfer matrix;

[0097] Correct the prediction results according to the sensor's observation data, calculate the Kalman gain, correct the prediction value through the Kalman gain and the measurement value, and update the error covariance matrix;

[0098] The correction formula is:

[0099]

[0100] In the formula, K k is the Kalman gain, H is the observation matrix, R is the observation noise covariance matrix, P k|k-1 is the forecast error covariance matrix, is the current state estimate, is the predicted state estimate, z k is the observation value from the sensor, is the residual between the observed and predicted values, P k|k is the corrected error covariance matrix, I is the identity matrix, K k H is the combination of Kalman gain and observation matrix, P k|k-1 Forecast error covariance matrix;

[0101] The measured gas flow data, temperature and pressure monitoring data are transmitted to the central control center in real time through the data transmission system;

[0102] The flow data is connected to the carbon emission monitoring system to achieve accurate calculation and monitoring of carbon emissions of industrial enterprises through real-time monitoring of gas flow and gas composition;

[0103] After the data is transmitted to the data center, it is analyzed in real time, and the control system adjusts the production process of the industrial enterprise based on the real-time monitoring results.

[0104] Step 4:

[0105] Based on the flow detection data, multi-sensor fusion technology is used to compensate for various errors.

[0106] Step 5:

[0107] The measurement data is connected to the carbon emission monitoring system and transmitted to the data center in real time to control the changes in gas emissions and carbon dioxide emission factors of industrial enterprises.

[0108] Reference Figure 2 As shown, an improved gas flow control system comprises:

[0109] Gas flow detection module: collects flow data based on flow velocity detection elements;

[0110] The gas flow detection module specifically includes:

[0111] Flow rate sensor unit: used to directly measure the flow rate of gas, including thermal flow meter, ultrasonic flow meter and turbine flow meter;

[0112] Temperature sensor unit: used to monitor gas temperature. Temperature changes will affect gas density.

[0113] Pressure sensor unit: used to measure the gas pressure in the pipeline;

[0114] Flow sensor unit: calculates gas flow based on flow velocity, temperature and pressure;

[0115] Signal conditioning module unit: amplifies, filters and converts the original signal of the sensor;

[0116] Data acquisition interface unit: used to receive sensor data and transmit it to the computing and processing module.

[0117] Calculation and processing module: The calculation and processing module is electrically connected to the gas flow detection module, and is used to receive the measurement data of the gas flow detection unit, and calculate the gas flow in real time and perform error correction in combination with the fluid dynamics analysis results;

[0118] The computing and processing modules specifically include:

[0119] Data receiving unit: receiving flow measurement data from the gas flow detection module;

[0120] Signal processing and filtering unit: amplifies, denoises, filters and corrects the received raw data;

[0121] Flow calculation unit: Based on the flow rate, pressure and temperature input data, the gas flow is calculated in real time through fluid dynamics formulas;

[0122] Error correction unit: performs error correction on calculation results;

[0123] Real-time data processing unit: processes the collected measurement data in real time, performs calculations and analysis, and caches and stores the data;

[0124] The real-time data processing unit specifically includes:

[0125] Data storage and backup: store the processed data;

[0126] Output interface: outputs the processed and corrected data to the remote data transmission module;

[0127] Algorithm optimization and adaptive adjustment unit: Automatically optimize the traffic calculation model based on machine learning.

[0128] Remote data transmission module: The remote data transmission module is electrically connected to the computing and processing module, and is used to transmit measurement data and gas emission related information to the control center in real time for emission monitoring, peak frequency regulation and energy management;

[0129] The remote data transmission module specifically includes:

[0130] Remote control and command receiving unit: The control center sends remote commands to the on-site equipment through this unit to adjust equipment parameters, perform remote fault diagnosis, and update configuration;

[0131] Remote monitoring and alarm unit: monitors the transmitted data in real time and issues alarms for emission and flow indicators based on set thresholds.

[0132] The use process of the present invention is:

[0133] Step 1: Design the layout and specifications of the appropriate flow rate detection unit according to the parameters such as the diameter, shape, airflow state, gas composition, temperature and pressure of the pipeline to be tested;

[0134] Step 2: Embed multi-point sensors such as differential pressure flowmeter, electromagnetic flowmeter and ultrasonic flowmeter at different locations in the pipeline to monitor the gas flow rate in real time;

[0135] Step 3: Collect airflow data at different locations in real time through sensors, including flow rate, pressure, temperature, etc.;

[0136] Step 4: De-noise, smooth and normalize the collected raw data to ensure the accuracy of the data;

[0137] Step 5: Apply the data smoothing formula to smooth the original signal to improve the stability and accuracy of the data;

[0138] Step 6: Use CFD software to build a flow model of the pipeline, input pipeline geometry, fluid type, inlet velocity and pressure boundary conditions;

[0139] Step 7: Set up an appropriate turbulence model according to the actual situation to simulate the movement of the fluid in the pipeline;

[0140] Step 8: Obtain the velocity distribution, turbulence and pressure distribution of the airflow in the pipeline through CFD simulation;

[0141] Step 9: Obtain the change area of ​​air flow velocity distribution, analyze the air flow characteristics at different positions, and determine the best position of the flow measurement point;

[0142] Step 10: According to the CFD simulation results, select a location with stable airflow and uniform velocity distribution to install the flow meter and optimize the measurement range and sensitivity;

[0143] Step 11: Compare the actual flow measurement results with the CFD simulation results to evaluate the measurement accuracy of the flow detection system;

[0144] Step 12: Correct the collected flow velocity data according to the mathematical model to compensate for the errors caused by the pipe shape and flow field non-uniformity;

[0145] Step 13: Use multi-sensor fusion technology to fuse data from different types of sensors to ensure the comprehensiveness and accuracy of the data;

[0146] Step 14: Perform comprehensive data analysis and error correction through Kalman filtering algorithm to improve system accuracy;

[0147] Step 15: According to the system state data, predict the current state and calculate the error covariance;

[0148] Step 16: Correct the prediction results according to the sensor's observation data, calculate the Kalman gain, and correct the error covariance matrix;

[0149] Step 17: Transmit the corrected gas flow data, temperature and pressure monitoring data to the central control center in real time;

[0150] Step 18: Connect the flow data to the carbon emission monitoring system to monitor the gas composition in real time and accurately calculate and monitor carbon emissions;

[0151] Step 19: The data is transmitted to the data center to analyze the gas flow and emission data in real time, and the control system adjusts the production process of the industrial enterprise according to the monitoring results;

[0152] Step 20: Transmit data to the control center in real time through the remote data transmission module for emission monitoring, peak and frequency regulation, and energy management.

[0153] In summary, the advantages of the present invention are:

[0154] Through analysis based on fluid dynamics simulation, the air flow distribution in the large pipeline is accurately modeled. The layout of the flow detection system is optimized based on the simulation results, and the appropriate flow meter installation location is selected to avoid measurement errors caused by flow field unevenness.

[0155] Through multi-sensor fusion technology, comprehensive analysis of data from different types of sensors can effectively correct various errors, thereby improving the reliability and accuracy of the system;

[0156] Based on the improved gas flow detection system, it is possible to monitor key parameters such as airflow, temperature, and pressure in large pipelines in real time, and transmit the measurement data to the data center, so as to achieve precise control of gas emissions of industrial enterprises and avoid the risk of exceeding emission standards;

[0157] By linking with the carbon emission monitoring system, it can obtain the changes in gas flow and gas composition in real time, accurately calculate the carbon dioxide emission factor, optimize energy use, and help industrial enterprises achieve low-carbon emissions, energy conservation and emission reduction goals;

[0158] The remote data transmission module is used to realize real-time data upload and remote monitoring. Data analysis and regulation are carried out through the control center, which can optimize the production process, make dynamic adjustments, effectively improve production efficiency and reduce energy consumption;

[0159] The remote control and command receiving unit allows the control center to send commands to the on-site equipment for remote diagnosis, parameter adjustment and configuration update, which improves the operation and maintenance efficiency of the equipment;

[0160] With the help of real-time data processing and algorithm optimization modules, the system can automatically optimize the flow calculation model and control strategy, improve the intelligence level of the overall system, provide data support for industrial enterprises, and assist decision makers in adjusting production processes and optimizing energy efficiency;

[0161] Through precise flow measurement, energy management and carbon emission control, the present invention helps enterprises reduce energy consumption and emissions while ensuring compliance, thereby saving energy costs and improving the overall benefits of the enterprise.

[0162] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions only describe the principles of the present invention. The present invention may be subject to various changes and improvements without departing from the spirit and scope of the present invention. These changes and improvements fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the attached claims and their equivalents.

Claims

1. An improved gas flow control method, characterized in that: include: The gas flow detection system based on honeycomb phased array technology monitors the gas flow in large pipelines in real time; Analyze the air flow distribution in large pipes based on fluid dynamics simulation, and optimize the measurement accuracy of the flow detection system according to the simulation results; Based on the detection data, combined with the gas flow rate, temperature and pressure parameters of large pipelines, a related mathematical model is established to correct the flow value; Based on the flow detection data, multi-sensor fusion technology is used to compensate for various errors; The measurement data is connected to the carbon emission monitoring system and transmitted to the data center in real time to control the changes in gas emissions and carbon dioxide emission factors of industrial enterprises.

2. An improved gas flow control method and system according to claim 1, characterized in that: The gas flow detection system based on honeycomb phased array technology performs real-time monitoring of gas flow in a large pipeline, specifically including: Multi-point detection sensors are embedded in each unit of the honeycomb structure to monitor gas flow rates in real time at different pipeline locations and collect data; Through multi-point detection, the sensor collects airflow data at different locations in real time, including flow rate, pressure and temperature; De-noising, smoothing and normalizing the collected raw data; The data smoothing formula is: In the formula, S n is the smoothed signal, x k is a data point in the original signal, σ is the standard deviation, is the Gaussian weight function.

3. An improved gas flow control method according to claim 2, characterized in that: The flow distribution in the large pipeline is analyzed based on fluid dynamics simulation, and the measurement accuracy of the flow detection system is optimized according to the simulation results, specifically including: Use CFD software to build a pipeline flow model, input pipeline geometry, fluid type, inlet velocity, and pressure boundary conditions, and set the turbulence model; The fluid motion formula is: In the formula, is the convection term of the fluid, is the pressure gradient term, is the viscosity term, f is the external force term, u is the velocity field of the fluid, is the gradient operator, ρ is the density of the fluid, and v is the kinematic viscosity of the fluid; Through CFD simulation, the velocity distribution, turbulence and pressure distribution of the airflow in the pipeline are obtained; Obtain the change area of ​​air flow velocity distribution and analyze the air flow characteristics at different locations; According to the simulation results, the flow meter is installed at a location with stable airflow and uniform flow velocity distribution. The flow field around the flow meter is analyzed through CFD to further optimize the measurement range and sensitivity of the flow meter.

4. An improved gas flow control method according to claim 3, characterized in that: The flow distribution in the large pipeline is analyzed based on fluid dynamics simulation, and the measurement accuracy of the flow detection system is optimized according to the simulation results, specifically including: According to the established mathematical model, the collected flow velocity data is corrected to compensate for the errors caused by the pipe shape and flow field non-uniformity factors, and obtain accurate flow value; Based on multi-sensor fusion technology, data from different types of sensors are fused, and the Kalman filter algorithm is used to conduct comprehensive analysis and error correction on the data; The measured gas flow data, temperature and pressure monitoring data are transmitted to the central control center in real time through the data transmission system; The flow data is connected to the carbon emission monitoring system to achieve accurate calculation and monitoring of carbon emissions of industrial enterprises through real-time monitoring of gas flow and gas composition; After the data is transmitted to the data center, it is analyzed in real time, and the control system adjusts the production process of the industrial enterprise based on the real-time monitoring results.

5. An improved gas flow control method according to claim 4, characterized in that: The multi-sensor fusion technology is based on fusing data from different types of sensors, and the Kalman filter algorithm is used to perform comprehensive analysis and error correction on the data, specifically including: Based on the current system state data, the current state is predicted and the error covariance is predicted. The prediction formula is: In the formula, is the state prediction value at the current moment, A is the state transfer matrix, is the state estimate of the previous moment, B is the control input matrix, uk is the control input, P k|k-1 is the predicted value of the error covariance matrix at the current moment, P k-1|k-1 is the estimated error covariance matrix of the previous moment, Q is the process noise covariance matrix, A T is the transpose of the state transfer matrix; Correct the prediction results according to the sensor's observation data, calculate the Kalman gain, correct the prediction value through the Kalman gain and the measurement value, and update the error covariance matrix; The correction formula is: In the formula, K k is the Kalman gain, H is the observation matrix, R is the observation noise covariance matrix, P k|k-1 is the forecast error covariance matrix, is the current state estimate, is the predicted state estimate, z k is the observation value from the sensor, is the residual between the observed and predicted values, P k|k is the corrected error covariance matrix, I is the identity matrix, K k H is the combination of Kalman gain and observation matrix, P k|k-1 Forecast error covariance matrix.

6. An improved gas flow control system, characterized in that: include: Gas flow detection module: collects flow data based on flow velocity detection elements; Calculation and processing module: The calculation and processing module is electrically connected to the gas flow detection module, and is used to receive the measurement data of the gas flow detection unit, and calculate the gas flow in real time and perform error correction in combination with the fluid dynamics analysis results; Remote data transmission module: The remote data transmission module is electrically connected to the computing and processing module. The remote data transmission module is used to transmit measurement data and gas emission-related information to the control center in real time for emission monitoring, peak and frequency regulation, and energy management.

7. An improved gas flow control system according to claim 6, characterized in that: The gas flow detection module specifically includes: Flow rate sensor unit: used to directly measure the flow rate of gas, including thermal flow meter, ultrasonic flow meter and turbine flow meter; Temperature sensor unit: used to monitor gas temperature. Temperature changes will affect gas density. Pressure sensor unit: used to measure the gas pressure in the pipeline; Flow sensor unit: calculates gas flow based on flow velocity, temperature and pressure; Signal conditioning module unit: amplifies, filters and converts the original signal of the sensor; Data acquisition interface unit: used to receive sensor data and transmit it to the computing and processing module.

8. An improved gas flow control system according to claim 7, characterized in that: The calculation processing module specifically includes: Data receiving unit: receiving flow measurement data from the gas flow detection module; Signal processing and filtering unit: amplifies, denoises, filters and corrects the received raw data; Flow calculation unit: Based on the flow rate, pressure and temperature input data, the gas flow is calculated in real time through fluid dynamics formulas; Error correction unit: performs error correction on calculation results; Real-time data processing unit: processes the collected measurement data in real time, performs calculations and analysis, and caches and stores the data.

9. An improved gas flow control system according to claim 8, characterized in that: The real-time data processing unit specifically includes: Data storage and backup: store the processed data; Output interface: outputs the processed and corrected data to the remote data transmission module; Algorithm optimization and adaptive adjustment unit: Automatically optimize the traffic calculation model based on machine learning.

10. An improved gas flow control system according to claim 9, characterized in that: The remote data transmission module specifically includes: Remote control and command receiving unit: The control center sends remote commands to the on-site equipment through this unit to adjust equipment parameters, perform remote fault diagnosis, and update configuration; Remote monitoring and alarm unit: monitors the transmitted data in real time and issues alarms for emission and flow indicators based on set thresholds.

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