Carbon emission intensity inversion and traceability method and system based on numerical simulation and reduced-order model

Abstract

The invention discloses a carbon emission intensity inversion and traceability method and system based on numerical simulation and a reduced-order model. The method comprises the following steps: constructing a full-size three-dimensional map; numerical simulation of pollutant diffusion is carried out according to map information and known environment conditions; reducing the order of the analog data through an intrinsic orthogonal decomposition method; establishing a carbon emission simulation database under the multi-working-condition variable environment based on the reduced-order model; the carbon dioxide concentration and meteorological data are measured in real time and on site, a simulation database is used as a supplement, and the intensity of a known emission source and the intensity and position of an unknown emission source are calculated by adopting a source item estimation method, a particle swarm algorithm and the like. According to the invention, full-size reduction of a limited area and high-precision small-scale carbon emission monitoring can be realized, and effective emission source tracing, intensity analysis, potential emission source prediction and the like can be carried out according to a small amount of field actual measurement data.

Description

Technical Field

[0001] The present invention relates to environmental monitoring, and in particular to a method and system for inverting carbon emission intensity and tracing the source based on numerical simulation and reduced-order models. Background Art

[0002] With the rapid development of LiDAR technology, it has become possible to quickly generate high-precision maps. Such maps play an important role in urban planning, land management, resource exploration, disaster response and other fields. Unmanned mobile devices equipped with LiDAR, such as drones, remote-controlled cars, and robot dogs, can quickly and accurately measure features such as surface topography, buildings, and vegetation. In addition, miniaturized and high-precision pollution detection sensors have made great progress and are widely used.

[0003] The development of the above technologies makes it possible to predict the carbon emission intensity and location tracing of strong point sources based on dynamic real-time carbon emission data sampled by fixed and mobile sensors. This method combines sensor technology, data analysis and spatial positioning systems, can quickly obtain a wide range of pollutant concentration data, and accurately identify and locate emission sources through mathematical models and algorithms. However, this "bottom-up" inversion method still faces many challenges. The diffusion process of atmospheric pollutants is often highly dynamic and random, and pollutant tracing requires a large amount of high-quality monitoring data. For this complex process, real-time data obtained only by fixed and mobile sensors has great uncertainty and insufficiency. The existing tracing theory and method are based on sufficient sensor distribution and ideal measurement environment. In actual process, sparse, insufficient, and variable data and complex atmospheric environment pose great challenges to the inversion and tracing of carbon emission sources. Summary of the invention

[0004] Purpose of the invention: The purpose of the present invention is to provide a method and system for inverting carbon emission intensity and tracing based on numerical simulation and reduced-order models with high accuracy and low dependence on a large amount of high-quality measured data. It can improve the quality and utilization efficiency of sampling data, perform a more accurate inversion process, and realize the intensity prediction and location tracing of carbon emission point sources.

[0005] Technical solution: The method for inverting carbon emission intensity and tracing the source based on numerical simulation and reduced-order model described in the present invention includes:

[0006] (1) Construct a virtual three-dimensional map of the detected limited area through the SLAM algorithm, and collect carbon emission concentration data and meteorological data of the detected limited area;

[0007] (2) Based on the virtual three-dimensional map, combined with the meteorological data of the limited area being detected and the intensity of known emission sources, computational fluid dynamics numerical simulation is performed to predict the velocity field, airflow field, carbon concentration value and distribution in space, and generate a spatial carbon emission simulation database; the data simulation results are used to guide the location, range and path of data collection;

[0008] (3) The most representative modes are extracted from the simulation database through the proper orthogonal decomposition method, and the main dynamic characteristics are retained to obtain a reduced-order model;

[0009] (4) Determine the sampling route for patrol monitoring based on the bionic algorithm and collect carbon dioxide concentration data and meteorological data in real time;

[0010] (5) Based on the measured real-time carbon dioxide concentration data and meteorological data, combined with the simulation database supplementary data under corresponding meteorological conditions, the intensity prediction and location tracing of carbon emission point sources are carried out for the traceable atmospheric pollution source problem.

[0011] Furthermore, in step (1), a mobile device carrying a laser radar, an inertial measurement module, and a camera is used to obtain accurate distance measurement values ​​and environmental images from the surrounding environment, and a greenhouse gas monitoring module and a meteorological monitoring module are carried out to measure the carbon emission concentration data and meteorological data of the limited area being detected; the real-time position of the mobile device and the structural information of the surrounding environment are integrated through the SLAM algorithm to achieve modeling and positioning of the full-size scene.

[0012] Furthermore, the greenhouse gas monitoring module adopts a small non-dispersive infrared carbon dioxide monitoring module, and the meteorological monitoring module adopts a small meteorological station.

[0013] Furthermore, the mobile device includes a drone, a remote-controlled car, and a robot dog.

[0014] Furthermore, in step (1), the CFD numerical simulation adopts a fixed steady-state point carbon emission source diffusion model, which is expressed as follows:

[0015]

[0016] Where c is the atmospheric pollutant concentration measured by the sensor; (x, y, z) represents the position of the sensor in the three-dimensional coordinate system; q is the mass of pollutants generated by the emission source per unit time, that is, the emission rate; u is the wind speed; σ y , σ z They are the standard deviations of pollutant concentration along the y-axis and z-axis respectively;

[0017] The atmospheric diffusion model adopts the convection diffusion model:

[0018]

[0019] in, It means to find the divergence of the vector field; is the velocity vector; K c represents the diffusion coefficient matrix composed of the diffusion coefficients in the x, y, and z directions; S c Indicates the emission rate of atmospheric pollutants.

[0020] Further, step (3) comprises:

[0021] Taking the velocity distribution in the velocity field as an example, the data of all grid points in the velocity field of a certain section are extracted and arranged in a specific sequence to be represented as a matrix X:

[0022] X=[x1,x2,…,x n ]

[0023] Among them, x i is the state vector at the i-th time point, n is the number of time points;

[0024] Calculate the covariance matrix C of matrix X:

[0025] C=XX T / n

[0026] Among them, X T is the transposed matrix of X;

[0027] Perform eigenvalue decomposition on the covariance matrix C and obtain the eigenvalue λ i and the corresponding eigenvector φ i :

[0028] Cφ i =λ i φ i

[0029] Projecting the original data onto these eigenvectors, we get the modal coefficients a i (t):

[0030]

[0031] Among them, x(t) is the data vector at a certain moment;

[0032] Using a few dominant modes and their corresponding modal coefficients, the original data can be reconstructed:

[0033]

[0034] Where r is the number of selected modes;

[0035] After order reduction, the complex velocity field containing a large amount of data can be represented by a few main modes and corresponding modal coefficients. The same is true for the temperature field and pollutant concentration distribution field.

[0036] Further, step (4) includes: detecting the concentration change difference between the two positions to make a direction estimate;

[0037] Define C as the current concentration, C max is the maximum concentration from the initial to the nth position; V is the concentration change rate; V min To set the minimum change rate, V n is the concentration change rate of the nth step, defined as:

[0038] V n =(C n -C n-1 ) / C n-i

[0039] n is the nth sample at the start of the search; a is the angle with the current direction of the mobile device, d min is to set the minimum step size, d is the current step size, k is a , k d is the change factor related to V, which determines the change of a and d, according to the dynamic stimulus response:

[0040] a n =k a a n-1 ,d n =k d d n-1

[0041] When the repetition condition is not met, and C n Relative C max If the change is very small, it is the source of the smell, and ε is the minimum absolute value of the difference between the two. The search strategy is as follows: First, the sensor moves for random search, moving forward at a given step length and at any angle. If no gas is detected, the step length remains unchanged, and it randomly turns left or right by an angle a until gas is detected. At this time, the left turn or right turn is determined based on the gas concentration change rate of the mobile device in the last two steps, and its step length is changed; when the sensor collects data that meets the stop condition, the location is identified as the gas source;

[0042] The step length and rotation angle of the mobile device depend on the rate of change of gas concentration. The execution steps are as follows:

[0043] First, determine whether the angle needs to be converted. The current step size is less than the set minimum step size, that is, d n <d min ; or the current concentration change rate is less than the set minimum change rate, that is, V n <V min; or the absolute value of the current concentration difference is less than the minimum value, i.e., │C n -C max │ < ε; in the above three cases, it is determined that the angle does not need to be converted; the current step size is greater than or equal to the set minimum step size, the current concentration change rate is greater than or equal to the set minimum change rate, and the absolute value of the current concentration difference is greater than or equal to the minimum value, i.e., simultaneously satisfying d n ≥d min 、V n ≥V min and │C n -C max │≥ε, it is determined that the angle needs to be converted;

[0044] Then, judge the conversion direction. If the gas concentration shows a decreasing trend, i.e., V n <V n-1 , then it is determined to turn left at the n position, and the turning angle is a n and move forward d n ; if the gas concentration shows an increasing trend, i.e., V n ≥V n-1 , then it is determined to turn right at the n position, and the turning angle is a n and move forward d n .

[0045] Furthermore, in step (5), the method for determining the traceable air pollution source problem is as follows: Assume that the number of sensor measurement points is m, and the number of emission sources is n, m < n; the coefficient matrix A = m * n is a rectangular matrix, and solve the matrix equation:

[0046] A T c = A T Aq

[0047] where c is an array composed of m measurement values; q is an array composed of n emission source intensities;

[0048] When the above matrix equation has a unique solution, the air pollution source problem is traceable; if the above matrix equation does not have a unique solution, the number of sensor measurement points m should be appropriately increased until the source parameters can be estimated.

[0049] Furthermore, in step (5), the intensity prediction and location tracing of carbon emission point sources are carried out through source term estimation methods, non - linear tracing methods, particle swarm algorithms, Bayesian algorithms, optimization algorithms, or improved Gaussian diffusion inversion models.

[0050] The system for inverting carbon emission intensity and tracing based on numerical simulation and reduced - order model according to the present invention includes:

[0051] A data acquisition and storage module is used to record and store sampling data, wherein the sampling data includes sampling path information, sampling point spatial location information, distance measurement data, environmental images, CO2 concentration, and meteorological data;

[0052] The data calculation module is used to process the sampling data, establish a high-precision three-dimensional digital map, establish a simulation model, and verify the simulation results; reduce the POD model of the detected environmental concentration field, airflow field, and temperature field; establish a simulation database; make a traceability judgment; plan the sampling route for patrol monitoring; and analyze and trace the emission source intensity;

[0053] Data visualization module: used for data statistics and analysis, the data is presented in the form of cloud maps and statistical charts.

[0054] Beneficial effects: Compared with the prior art, the present invention has the following significant advantages:

[0055] This scheme examines the impact of meteorological factors on the diffusion of atmospheric pollutants through the mutual verification of numerical simulation and real-time field sampling data. By adjusting the simulation parameters, it predicts carbon emissions under different working conditions and different meteorological factors. It has high flexibility and wide coverage. The database based on actual measurements and simulations further guides the sampling methods and methods (such as the location of fixed sampling sensors, the path of mobile sampling sensors, etc.), which is conducive to improving sampling efficiency and data quality, thereby reducing the dependence of general inversion algorithms on large amounts of high-quality data. The simulated data is reduced in order through the intrinsic orthogonal decomposition method, and the relationship between the temperature field, velocity field, pollutant concentration distribution, meteorological factors, and the intensity and location of carbon emission sources is proposed, thereby constructing a carbon emission database covering different meteorological conditions and different carbon emission point source intensities and locations. A small amount of actual measured data can be compared with the database to predict the intensity and location of carbon emission point sources. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 It is a flowchart of a method for inverting carbon emission intensity and tracing the source based on numerical simulation and reduced-order model provided by an embodiment of the present invention;

[0057] Figure 2 is a three-dimensional digital map of the limited area to be detected in an embodiment of the present invention;

[0058] Figure 3 is the numerical simulation result in the embodiment of the present invention, wherein Figure 3 (a) is the carbon dioxide concentration distribution cloud map, Figure 3 (b) is the carbon dioxide flow field cloud map. DETAILED DESCRIPTION

[0059] The present invention will be further described below in conjunction with the accompanying drawings.

[0060] The embodiment of the present invention provides a method for inverting carbon emission intensity and tracing the source based on numerical simulation and reduced-order model. Based on a reliable simulation database and an effective low-order model, it can achieve rapid, convenient and real-time monitoring and prediction of environmental carbon concentration, carbon distribution, carbon source intensity prediction and location, etc. on the basis of ensuring accuracy. This method is a method for full-scale restoration of the area to be tested, high-precision, small-scale actual measurement of carbon content and effective emission source tracing and evaluation, such as Figure 1 As shown, the specific steps include:

[0061] (1) In the limited area to be measured, a mobile device carrying sensors such as laser radar, inertial measurement module, and camera obtains accurate distance measurement values ​​and environmental images from the surrounding environment. The distance measurement data collected by the laser radar and the environmental images obtained by the camera are transmitted to the ground processing station. The mobile device can be a drone, remote control car, robot dog, etc. At the ground processing station, the acquired data is processed through the simultaneous positioning and mapping method (SLAM algorithm) to generate a three-dimensional high-precision map, such as Figure 2 As shown in the figure, the SLAM algorithm can integrate the real-time position of the mobile device and the structural information of the surrounding environment to achieve modeling and positioning of the full-scale scene.

[0062] While scanning the terrain, the greenhouse gas monitoring module and meteorological monitoring module carried by the mobile device also measure the carbon emission concentration data and meteorological data of the limited area being detected. The meteorological data include wind direction, wind speed, air pressure, temperature, humidity, etc. The measured data is not only used as a reference for setting the boundary conditions during the initial simulation in step (2), but also for verifying the simulation results. The monitoring module saves and transmits the acquired carbon emission concentration data and meteorological data to the ground processing station. Since the mobile device itself has a power source and a moving speed, such as the propeller of a drone, in order to reduce the impact of the above-mentioned interference factors on the meteorological data, the meteorological monitoring module can also use a fixed mode. In this embodiment, the greenhouse gas monitoring module uses a small non-dispersive infrared (NDIR) carbon dioxide sensor, and the meteorological monitoring module uses a small meteorological station.

[0063] (2) Based on Figure 2 The virtual model shown in the figure combines the meteorological data of the limited area under inspection and the intensity of the known emission sources to perform computational fluid dynamics (CFD) numerical simulation, predict the velocity field, airflow field, carbon concentration value and distribution in space, and generate a spatial carbon emission simulation database. The boundary conditions of the numerical simulation include the intensity of fixed emission sources, wind speed, temperature, etc. By comparing the wind speed and carbon dioxide concentration data at the same observation point, the reliability of the model can be verified if the relative error is <10%.

[0064] The results of data simulation are as follows Figure 3 As shown, Figure 3(a) shows the distribution cloud map of carbon dioxide concentration. Figure 3 (b) shows the airflow field distribution diagram. Figure 3 (a) The approximate distribution range and high concentration areas of pollutants can be known, which can further guide the deployment range and location distribution of carbon dioxide sensors. Figure 3 (b) The influence of meteorological factors on the diffusion of atmospheric pollutants in the observed area can be understood, which can further guide the mobile sampling path of carbon dioxide sensors and the location deployment of small meteorological stations.

[0065] Specifically, the CFD numerical simulation adopts a fixed steady-state point carbon emission source diffusion model (Gaussian plume model), which is expressed as follows:

[0066]

[0067] Where c is the atmospheric pollutant concentration measured by the sensor; (x, y, z) represents the position of the sensor in the three-dimensional coordinate system; q is the mass of pollutants generated by the emission source per unit time, that is, the emission rate; u is the wind speed; σ y , σ z They are the standard deviations of pollutant concentration along the y-axis and z-axis respectively;

[0068] The atmospheric diffusion model adopts the convection diffusion model:

[0069]

[0070] in, It means to find the divergence of the vector field; is the velocity vector; K c represents the diffusion coefficient matrix composed of the diffusion coefficients in the x, y, and z directions; S c Indicates the emission rate of atmospheric pollutants.

[0071] (3) The most representative modes are extracted from the simulation database through the proper orthogonal decomposition method, and the main dynamic characteristics are retained to obtain a reduced-order model;

[0072] Specifically, the model reduction technology based on the Proper Orthogonal Decomposition (POD) method is combined with CFD simulation to model the detected scene and perform dimensionality reduction processing on the obtained simulation data, including velocity field, temperature field, pollutant concentration field, etc. The POD (Proper Orthogonal Decomposition) method is a commonly used data dimensionality reduction technology that extracts the main characteristic modes in the data and converts high-dimensional data into representation in a low-dimensional space, thereby simplifying calculation and analysis.

[0073] The result data of numerical simulation are reflected in the small grids that constitute its three-dimensional structure, such as the velocity distribution in the velocity field.

[0074] Extract the data of all grid points in the velocity field of a certain section and arrange them in a specific sequence to represent a matrix X:

[0075] X=[x1,x2,…,x n ]

[0076] Among them, x i is the state vector at the i-th time point, and n is the number of time points.

[0077] Calculate the covariance matrix C of matrix X:

[0078] C=XX T / n

[0079] Among them, X T is the transposed matrix of X.

[0080] Perform eigenvalue decomposition on the covariance matrix C and obtain the eigenvalue λ i and the corresponding eigenvector φ i :

[0081] Cφ i =λ i φ i

[0082] Eigenvector φ i It is called POD mode.

[0083] Projecting the original data onto these eigenvectors, we get the modal coefficients a i (t):

[0084]

[0085] Among them, x(t) is the data vector at a certain moment.

[0086] Using a few dominant modes and their corresponding modal coefficients, the original data can be reconstructed:

[0087]

[0088] Among them, r is the number of modes selected, usually the first few main modes are sufficient.

[0089] After the order reduction using the above method, the complex velocity field containing a large amount of data can be represented by a few main modes and corresponding modal coefficients, and the same is true for the temperature field and pollutant concentration distribution field. The modal coefficients depend on the boundary conditions in the numerical simulation, that is, the intensity of pollutant emission sources and meteorological data in reality. The low-order model constructed based on the above can quickly and conveniently obtain a carbon emission database covering carbon emission point sources of different intensities and locations and under different meteorological conditions. Since it is impossible for sensors to completely cover the detected area during the actual measurement process, the above carbon emission database can be used as a supplement to the actual measured data, thereby reducing the dependence of the carbon emission point source intensity prediction and source tracing inversion algorithm on a large amount of high-quality data.

[0090] (4) For the inversion of the intensity and location tracing of carbon emission point sources, it is only necessary to collect carbon dioxide concentration data and meteorological data from a few typical locations. For the location deployment of fixed carbon dioxide sensors and meteorological sensors, refer to step (2). The patrol monitoring sampling path of mobile carbon dioxide sensors and meteorological sensors mounted on drones or remote-controlled vehicles requires the use of bionic algorithms. The excitation mechanism is to generate an instantaneous estimate of a direction that is the same or opposite to the concentration gradient by comparing two or more sensors at the same time. According to this bionic principle, the concentration change difference at two locations is detected to make a direction estimate. This bionic source positioning algorithm does not rely on the absolute concentration of the sensor, can improve the search efficiency of mobile devices, and is independent of wind direction.

[0091] Define C as the current concentration, C max is the maximum concentration from the initial to the nth position; V is the concentration change rate; V min To set the minimum change rate, V n is the concentration change rate of the nth step, defined as:

[0092] V n =(C n -C n-1 ) / C n-i

[0093] n is the nth sample at the start of the search. a is the angle with the current direction of the mobile device, d min is to set the minimum step size, d is the current step size, k is a , k d is the change factor related to V, which determines the change of a and d, according to the dynamic stimulus response:

[0094] a n =k a a n-1 ,d n =k d d n-1

[0095] When the repetition condition is not met, and Cn Relative C max When the change is very small, it is the source of the smell, and ε is the minimum absolute value of the difference between the two. The search strategy is as follows: First, the sensor moves for random search, moving forward at a given step length and at any angle. If no gas is detected, the step length remains unchanged, and it randomly turns left or right by an angle a until gas is detected. At this time, the left turn or right turn is determined based on the gas concentration change rate of the mobile device in the last two steps, and its step length is changed; when the sensor collects data that meets the stop condition, the location is identified as a gas source.

[0096] The step length and rotation angle of the mobile device depend on the rate of change of gas concentration. The execution steps are as follows:

[0097] First, determine whether the angle needs to be converted. The current step size is less than the set minimum step size, that is, d n <d min ; or the current concentration change rate is less than the set minimum change rate, that is, V n <V min ; or the absolute value of the current concentration difference is less than the minimum value, that is, │C n -C max │<ε. In the above three cases, it is determined that the angle does not need to be changed. The current step length is greater than or equal to the set minimum step length, the current concentration change rate is greater than or equal to the set minimum change rate, and the current concentration difference absolute value is greater than or equal to the minimum value, that is, d n ≥d min 、V n ≥V min and│C n -C max │≥ε, determine whether the angle needs to be converted.

[0098] Then determine the conversion direction. If the gas concentration is decreasing, that is, V n <V n-1 , then it is determined to turn left at position n, and the turning angle is a n and move forward n ; If the gas concentration is increasing, that is, V n ≥V n-1 , then it is determined to turn right at position n, and the turning angle is a n and move forward n .

[0099] (5) Furthermore, based on the measured real-time carbon dioxide concentration data and meteorological data, combined with the simulation database supplementary data under corresponding meteorological conditions, the intensity inversion and location tracing of carbon emission point sources are carried out.

[0100] First, we make a traceability judgment: we discuss the traceability of the source of air pollution, that is, whether the source parameters can be estimated:

[0101] Assume that the number of data points of the sensor measurement points is m, and the number of emission sources is n, where m < n; the coefficient matrix A = m * n is a rectangular matrix, and solve the matrix equation:

[0102] A T c = A T Aq

[0103] where c is an array composed of m measurement values; q is an array composed of n emission source intensities;

[0104] When the above matrix equation has a unique solution, the problem of atmospheric pollution source tracing can be traced. If the above matrix equation does not have a unique solution, the number of sensor measurement points data m should be appropriately increased until the source parameters can be estimated.

[0105] Furthermore, for the traceable problem, based on the above measured data and supplementary data from the database, methods including but not limited to source term estimation methods, non - linear source tracing methods such as particle swarm algorithm, Bayesian algorithm, optimization algorithm, improved Gaussian diffusion inversion model, etc. are used for the intensity prediction and location tracing of carbon emission point sources.

[0106] The embodiment of the present invention also provides a system for inverting carbon emission intensity and tracing based on numerical simulation and reduced - order model, including:

[0107] A data acquisition and storage module, used to record and store sampling data, where the sampling data includes sampling path information, sampling point spatial position information, distance measurement data, environmental images, CO2 concentration, meteorological data, etc.;

[0108] A data calculation module, used to process the sampling data, establish a high - precision three - dimensional digital map, establish a simulation model, verify the simulation results; perform POD model reduction of the concentration field, airflow field, and temperature field of the detected environment; establish a simulation database; perform traceability judgment; plan a circuit monitoring sampling path; analyze and trace the emission source intensity, etc.;

[0109] A data visualization module: used for data statistics and analysis, and the data is presented in the form of cloud maps, statistical charts, etc.

[0110] In summary, the method for inverting carbon emission intensity and tracing based on numerical simulation and reduced - order model provided by the present invention has the advantages of convenience, real - time performance, and high efficiency. This method combines sensor technology, model reduction of proper orthogonal decomposition (POD), data analysis, and spatial positioning system, and can portably obtain pollutant data of different point source intensities and different meteorological conditions, including air velocity distribution, temperature distribution, concentration distribution, etc., and perform real - time and accurate intensity inversion identification and location tracing of emission sources through existing mathematical models and algorithms. This method proposes using simulation data as a supplement to measured data, and can use a small amount of measured data to推算 the overall carbon content, fixed carbon emission source intensity, potential emission source tracing, etc.

Claims

1. A method for inverting carbon emission intensity and tracing sources based on numerical simulation and reduced-order model, characterized in that: Including: (1) Construct a virtual three-dimensional map of the detected limited area through the SLAM algorithm, and collect carbon emission concentration data and meteorological data of the detected limited area; (2) Based on the virtual three-dimensional map, combine the meteorological data of the detected limited area and the intensity of known emission sources, conduct computational fluid dynamics numerical simulation, predict the velocity field, airflow field, carbon concentration value and distribution in space, and generate a spatial carbon emission simulation database; use the data simulation results to guide the location, scope and path of data collection; (3) Extract the most representative modes from the simulation database through the proper orthogonal decomposition method, and retain the main dynamic characteristics to obtain a reduced-order model; (4) Determine the tour monitoring sampling path based on the bionic algorithm and collect carbon dioxide concentration data and meteorological data in real time on-site; (5) Based on the measured real-time carbon dioxide concentration data and meteorological data, combined with the supplementary data of the simulation database under the corresponding meteorological conditions, conduct intensity prediction and location tracing of carbon emission point sources for the traceable air pollution tracing problem.

2. The method for inverting carbon emission intensity and tracing the source based on numerical simulation and reduced-order model according to claim 1 is characterized in that: In step (1), use a mobile device to carry a lidar, an inertial measurement module, and a camera to obtain accurate distance measurement values and environmental images from the surrounding environment, and carry a greenhouse gas monitoring module and a meteorological monitoring module to actually measure the carbon emission concentration data and meteorological data of the detected limited area; through the SLAM algorithm, fuse the real-time position of the mobile device and the structural information of the surrounding environment to realize the modeling and positioning of the full-scale scene.

3. The method for inverting carbon emission intensity and tracing the source based on numerical simulation and reduced-order model according to claim 2 is characterized in that: The greenhouse gas monitoring module adopts a small non-dispersive infrared carbon dioxide monitoring module, and the meteorological monitoring module adopts a small weather station.

4. The method for inverting carbon emission intensity and tracing the source based on numerical simulation and reduced-order model according to claim 2 is characterized in that: The mobile device includes an unmanned aerial vehicle, a remote control vehicle, and a robotic dog.

5. The method for inverting carbon emission intensity and tracing the source based on numerical simulation and reduced-order model according to claim 1 is characterized in that: In step (1), the CFD numerical simulation adopts a fixed steady-state point carbon emission source diffusion model, which is expressed as follows: Where c is the atmospheric pollutant concentration measured by the sensor; (x, y, z) represents the position of the sensor in the three-dimensional coordinate system; q is the mass of pollutants generated by the emission source per unit time, that is, the emission rate; u is the wind speed; σ y , σ z They are the standard deviations of pollutant concentration along the y-axis and z-axis respectively; The atmospheric diffusion model adopts a convective diffusion model: in, It means to find the divergence of the vector field; is the velocity vector; K c represents the diffusion coefficient matrix composed of the diffusion coefficients in the x, y, and z directions; S c Indicates the emission rate of atmospheric pollutants.

6. The method for inverting carbon emission intensity and tracing the source based on numerical simulation and reduced-order model according to claim 1 is characterized in that: Step (3) includes: Taking the velocity distribution in the velocity field as an example, extract the data within all grid points of the velocity field of a certain cross-section and arrange them in a specific sequence to be represented as a matrix X: X=[x1,x2,...,x n ] Among them, x i is the state vector at the i-th time point, n is the number of time points; Calculate the covariance matrix C of matrix X: C=XX T / n Among them, X T is the transposed matrix of X; Perform eigenvalue decomposition on the covariance matrix C and obtain the eigenvalue λ i and the corresponding eigenvector φ i : Cφ i =λ i f i Projecting the original data onto these eigenvectors, we get the modal coefficients a i (t): where x(t) is the data vector at a certain moment; Using a few main modes and the corresponding modal coefficients, the original data can be reconstructed: where r is the number of modes selected; After reduction, the complex velocity field containing a large amount of data can be represented by a few main modes and the corresponding modal coefficients, and the same applies to the temperature field and the pollutant concentration distribution field.

7. The method for inverting carbon emission intensity and tracing the source based on numerical simulation and reduced-order model according to claim 2 is characterized in that: Step (4) includes: detecting the concentration change difference between two positions to make a direction estimate; Define C as the current concentration, C max is the maximum concentration from the initial to the nth position; V is the concentration change rate; V min To set the minimum change rate, V n is the concentration change rate of the nth step, defined as: V n =(C n -C n-1 ) / C n-i n is the nth sample at the start of the search; a is the angle with the current direction of the mobile device, d min is to set the minimum step size, d is the current step size, k is a , k d is the change factor related to V, which determines the change of a and d, according to the dynamic stimulus response: a n =k a a n-1 ,d n =k d d n-1 When the repetition condition is not met, and C n Relative C max If the change is very small, it is the source of the smell, and ε is the minimum absolute value of the difference between the two. The search strategy is as follows: First, the sensor moves for random search, moving forward at a given step length and at any angle. If no gas is detected, the step length remains unchanged, and it randomly turns left or right by an angle a until gas is detected. At this time, the left turn or right turn is determined based on the gas concentration change rate of the mobile device in the last two steps, and its step length is changed; when the sensor collects data that meets the stop condition, the location is identified as the gas source; The step size and rotation angle of the mobile device depend on the gas concentration change rate, and the execution steps are as follows: First, determine whether the angle needs to be converted. The current step size is less than the set minimum step size, that is, d n <d min ; or the current concentration change rate is less than the set minimum change rate, that is, V n <V min ; or the absolute value of the current concentration difference is less than the minimum value, that is, │C n -C max │<ε; In the above three cases, it is determined that the angle does not need to be changed; the current step length is greater than or equal to the set minimum step length, the current concentration change rate is greater than or equal to the set minimum change rate, and the current concentration difference absolute value is greater than or equal to the minimum value, that is, d n ≥d min 、V n ≥V min and│C n -C max │≥ε, determine the need to convert the angle; Then determine the conversion direction. If the gas concentration is decreasing, that is, V n <V n-1 , then it is determined to turn left at position n, and the turning angle is a n and move forward n ; If the gas concentration is increasing, that is, V n ≥V n-1 , then it is determined to turn right at position n, and the turning angle is a n and move forward n .

8. The method for inverting carbon emission intensity and tracing the source based on numerical simulation and reduced-order model according to claim 1 is characterized in that: In step (5), the method for determining the traceable air pollution tracing problem is: assume that the number of sensor measurement points is m, and the number of emission sources is n, m < n; the coefficient matrix A = m * n is a rectangular matrix, and solve the matrix equation: A T c=A T Aq where c is an array composed of m measurement values; q is an array composed of the intensities of n emission sources; When the above matrix equation has a unique solution, the air pollution tracing problem is traceable; if the above matrix equation does not have a unique solution, the number of sensor measurement points m should be appropriately increased until the source parameters can be estimated.

9. The method for inverting carbon emission intensity and tracing the source based on numerical simulation and reduced-order model according to claim 1, characterized in that: In step (5), the intensity prediction and location tracing of carbon emission point sources are performed by using source term estimation method, nonlinear tracing method particle swarm algorithm, Bayesian algorithm, optimization algorithm or improved Gaussian diffusion inversion model.

10. A system for inverting carbon emission intensity and tracing sources based on numerical simulation and reduced-order model, characterized in that: include: A data acquisition and storage module is used to record and store sampling data, wherein the sampling data includes sampling path information, sampling point spatial location information, distance measurement data, environmental images, CO2 concentration, and meteorological data; The data calculation module is used to process the sampling data, establish a high-precision three-dimensional digital map, establish a simulation model, and verify the simulation results; reduce the POD model of the detected environmental concentration field, airflow field, and temperature field; establish a simulation database; make a traceability judgment; and plan the sampling route for patrol monitoring; Emission source intensity analysis and tracing; Data visualization module: used for data statistics and analysis, the data is presented in the form of cloud maps and statistical charts.

Images