CN114460232B - Multipoint source carbon-nitrogen ratio monitoring method and system based on vehicle-mounted measurement system - Google Patents
Multipoint source carbon-nitrogen ratio monitoring method and system based on vehicle-mounted measurement system Download PDFInfo
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- G—PHYSICS
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- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B11/00—Measuring arrangements characterised by the use of optical techniques
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- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C11/00—Photogrammetry or videogrammetry, e.g. stereogrammetry; Photographic surveying
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- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/0004—Gaseous mixtures, e.g. polluted air
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- G01N33/0027—General constructional details of gas analysers, e.g. portable test equipment concerning the detector
- G01N33/0036—Specially adapted to detect a particular component
- G01N33/0037—Specially adapted to detect a particular component for NOx
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- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/0004—Gaseous mixtures, e.g. polluted air
- G01N33/0009—General constructional details of gas analysers, e.g. portable test equipment
- G01N33/0027—General constructional details of gas analysers, e.g. portable test equipment concerning the detector
- G01N33/0036—Specially adapted to detect a particular component
- G01N33/004—Specially adapted to detect a particular component for CO, CO2
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- G—PHYSICS
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- G01W—METEOROLOGY
- G01W1/00—Meteorology
- G01W1/02—Instruments for indicating weather conditions by measuring two or more variables, e.g. humidity, pressure, temperature, cloud cover or wind speed
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- G06N3/12—Computing arrangements based on biological models using genetic models
- G06N3/126—Evolutionary algorithms, e.g. genetic algorithms or genetic programming
Abstract
The embodiment of the invention discloses a multipoint source carbon-nitrogen ratio monitoring method and a multipoint source carbon-nitrogen ratio monitoring system based on a vehicle-mounted measuring system, wherein the method comprises the following steps: acquiring measured data in a target area, wherein the measured data at least comprises position information and CO 2 Concentration data, NO 2 Concentration data and meteorological data; establishing an emission diffusion model; calculating the number of emission sources in the target area and the position of each emission source based on a genetic algorithm according to the measured data and the emission diffusion model; calculating accurate CO emitted by each emission source based on levenberg-marquardt algorithm 2 Strength and NO 2 Strength; according to the CO 2 Strength and NO 2 And calculating the carbon-nitrogen ratio of each emission source. The technical problem of poor timeliness of strong point source carbon emission and nitrogen emission monitoring in the prior art is solved.
Description
Technical Field
The invention relates to the technical field of environmental monitoring, in particular to a multipoint source carbon-nitrogen ratio monitoring method and system based on a vehicle-mounted measuring system.
Background
This section provides background information related to the present disclosure only and is not necessarily prior art.
CO 2 As the most important trace greenhouse gas, the heating effect on the earth has seriously threatened the living environment of human beings. Equal-strength CO of chemical plant and iron and steel plant 2 Point source is the main artificial CO 2 Emission sources, the prior art monitors strong point sources based on pre-stored emission lists. However, the low timeliness of the emissions inventory does not enable rapid, robust point-source, industrial park carbon emission real-time monitoring.
Disclosure of Invention
Therefore, the embodiment of the invention provides a multipoint source carbon-nitrogen ratio monitoring method and system based on a vehicle-mounted measuring system, and aims to at least partially solve the technical problems of poor timeliness and low precision of strong point source carbon emission monitoring in the prior art.
In order to achieve the above object, the embodiments of the present invention provide the following technical solutions:
a multipoint source carbon-nitrogen ratio monitoring method based on a vehicle-mounted measuring system comprises the following steps:
acquiring actual measurement data in a target area, wherein the actual measurement data at least comprises position information and CO 2 Concentration data, NO 2 Concentration data and meteorological data;
establishing an emission diffusion model;
calculating the number of emission sources in the target area and the position of each emission source based on a genetic algorithm according to the measured data and the emission diffusion model;
calculating CO emitted by each emission source based on levenberg-marquardt algorithm 2 Strength and NO 2 Strength;
according to the CO 2 Strength and NO 2 Strength ofAnd calculating the carbon-nitrogen ratio of each emission source.
Further, the acquiring measured data in the target area further includes:
a plan view of the target area is obtained based on the satellite remote sensing images, and the number and spatial relative positions of potential emission sources are determined through visual interpretation of the plan view.
Further, a plan view of the target area is obtained based on the satellite remote sensing image, and the number and the spatial relative positions of the potential emission sources are determined through visual interpretation of the plan view, and the method specifically comprises the following steps:
determining a target area, acquiring a remote sensing image of the target area, and generating a plan view from the remote sensing image;
counting potential emission sources in the target area according to visual interpretation, wherein the potential emission sources are all point sources with emission characteristics in the target area;
and marking the longitude and latitude of each potential emission source in the plane map.
Further, establishing the emission diffusion model specifically includes:
establishing a diffusion coordinate system based on the spatial position of the vehicle-mounted measuring system;
based on the diffusion coordinate system, the following emission diffusion models are established:
wherein, (x, y, z) is the space position coordinate of the measuring point of the vehicle-mounted equipment, and C c (x, y, z) is CO at (x, y, z) coordinates 2 Concentration q (c，i) The carbon emission intensity of the ith strong point source is i =1,2,3 \8230, n is the number of strong point sources in the target area, u is the wind speed, H is H i Effective discharge height, sigma, for carbon emissions from power plants c,y And σ c,z Respectively, the horizontal diffusion parameter and the vertical diffusion parameter of the measurement position relative to the ith strong point source in the target region, B c Is a target area CO 2 Local background concentration of alpha c Is CO 2 Ground reflection coefficient of a c ，b c Is CO 2 Horizontal diffusion coefficient of (c) c ，d c Is CO 2 The vertical diffusion coefficient of (c). q. q of (N,i) Is NO at (x, y, z) coordinate 2 Concentration q (N, i) is NO of the ith strong point source 2 Discharge intensity, i =1,2,3 \8230; \8230n, σ N,y And σ N,z NO for the measurement location relative to the ith intense point source in the target region 2 Horizontal and vertical diffusion parameters, B N Is a target area NO 2 Local background concentration of alpha N Is NO 2 Ground reflection coefficient of (a) N ，b N Is NO 2 Horizontal diffusion coefficient of (c) N ，d N Is NO 2 The vertical diffusion coefficient of (c).
Further, calculating the number of emission sources in the target region and the position of each emission source based on a genetic algorithm specifically comprises:
setting the number of the emission sources in the target area as M, setting different permutation and combination modes for M, selecting different n, and bringing the positions corresponding to the emission sources into an emission diffusion model;
based on the collected CO 2 Data, selected n, CO collected by vehicle-mounted measuring system 2 The concentration, the spatial position information and the carbon emission diffusion model are used for q through a genetic algorithm c,i ,a c ,b c ,c c ,d c ,x i ,y i ,z i ,H i ,α c ,B c Performing primary solution;
the following formula is used to actually measure CO according to vehicle load at the same position 2 Concentration and simulated CO 2 The concentration correlation coefficient R judges the accuracy of the parameters to be solved:
wherein, C c ' is CO at different positions during vehicle-mounted measurement 2 Concentration analog value according to q of each iteration in genetic algorithm c,i ,a c ,b c ,c c ,d c ,x i ,y i ，z i ,H i ,α c ,B c Obtaining parameters by substituting the parameters into an emission diffusion model, C c Measured CO for on-board measurement system 2 And measuring point concentration values, and determining the number of emission sources and the positions of the emission sources based on the current emission source combination when the R reaches the maximum value.
Further, calculating CO emitted by each emission source based on a levenberg-marquardt algorithm 2 Strength and NO 2 The strength specifically comprises:
based on measured CO according to the number of emission sources and the position of each emission source 2 Concentration data and meteorological data and the levenberg-marquardt algorithm on the parameter q in the carbon emission diffusion model c,i ,a c ,b c ,c c ,d c ,H i ,α c ,B c Solving is carried out;
obtaining an optimal solution of an emission diffusion model based on a set initial value, a constraint limit and a preset evaluation coefficient, wherein the optimal solution is used as the emission source rowReleased CO 2 Strength and NO 2 Strength.
Further, according to the CO 2 Strength and NO 2 Calculating the carbon-nitrogen ratio of each emission source by using the intensity, and specifically comprises the following steps:
the carbon to nitrogen ratio was calculated using the following formula:
wherein the content of the first and second substances,
the carbon-nitrogen emission ratio of the ith emission source, q c,i CO as the ith emission source 2 Emission intensity, q N,i NO as the ith emission source 2 The discharge intensity.
The invention also provides a multipoint source carbon-nitrogen ratio monitoring system based on the vehicle-mounted measuring system, which comprises:
a measured data acquisition unit for acquiring measured data in the target area, wherein the measured data at least comprises position information and CO 2 Concentration data, NO 2 Concentration data and meteorological data;
a diffusion model creation unit for creating an emission diffusion model;
the emission source parameter calculation unit is used for calculating the number of emission sources in the target area and the positions of the emission sources based on a genetic algorithm according to the measured data and the emission diffusion model;
an emission intensity result output unit for calculating CO emitted by each emission source based on a levenberg-marquardt algorithm 2 Strength and NO 2 Strength;
a carbon-nitrogen ratio calculating unit for calculating the CO ratio 2 Strength and NO 2 And calculating the carbon-nitrogen ratio of each emission source according to the intensity.
The invention also provides an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the method as described above when executing the program.
The invention also provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the method as described above.
The invention provides a multipoint source carbon-nitrogen ratio monitoring method and system based on a vehicle-mounted measuring system, which uses vehicle-mounted equipment to measure CO near a target area 2 Concentration, NO 2 Concentration, position information and meteorological data, and a multi-point source emission diffusion model is established, so that the intensity of carbon emission and nitrogen emission of each emission source is quantified, and the carbon-nitrogen ratio is further determined. The invention realizes the remote sensing data based on vehicle-mounted acquisition, carries out high-timeliness and high-precision monitoring on the carbon-nitrogen ratio discharged by each strong emission source in the industrial park, and solves the technical problem of poor timeliness of strong point source carbon emission monitoring in the prior art.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. It should be apparent that the drawings in the following description are merely exemplary, and that other embodiments can be derived from the drawings provided by those of ordinary skill in the art without inventive effort.
The structures, the proportions, the sizes, and the like shown in the specification are only used for matching with the contents disclosed in the specification, so that those skilled in the art can understand and read the present invention, and do not limit the conditions for implementing the present invention, so that the present invention has no technical essence, and any modifications of the structures, changes of the proportion relation, or adjustments of the sizes, should still fall within the scope of the technical contents disclosed in the present invention without affecting the efficacy and the achievable purpose of the present invention.
FIG. 1 is a flow chart of a method for monitoring a carbon-nitrogen ratio of a multi-point source based on a vehicle-mounted measurement system according to an embodiment of the present invention;
FIG. 2 is a graph of a scene;
FIG. 3 is a block diagram of a multi-point source carbon-nitrogen ratio monitoring system based on a vehicle-mounted measurement system according to an embodiment of the present invention;
fig. 4 is a block diagram of a computer device according to the present invention.
Detailed Description
The present invention is described in terms of specific embodiments, and other advantages and benefits of the present invention will become apparent to those skilled in the art from the following disclosure. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention provides a multipoint source carbon-nitrogen ratio monitoring method based on a vehicle-mounted measuring system, which is used for evaluating the carbon-nitrogen emission ratio in the production process of a power plant in real time, acquiring data with high precision and different spatial positions according to the vehicle-mounted system, and realizing quantitative calculation of the carbon emission intensity and the nitrogen emission intensity of the power plant based on an established multisource Gaussian diffusion model and an adaptive algorithm.
Referring to fig. 1, fig. 1 is a flowchart illustrating a method for monitoring a carbon-nitrogen ratio of a multi-point source according to an embodiment of the present invention.
In a specific embodiment, as shown in fig. 1, the multipoint source carbon-nitrogen ratio monitoring method based on the vehicle-mounted measurement system provided by the invention comprises the following steps:
s1: a plan view of the target area is obtained based on the satellite remote sensing images, and the number and spatial relative positions of potential emission sources are determined through visual interpretation of the plan view. Taking an industrial park as an example, the target area in this embodiment may be a part of or all of the industrial park.
In order to improve the accuracy of the information and the location, the step S1 includes the steps of:
s11: determining a target area, acquiring a remote sensing image of the target area, and generating a plan view from the remote sensing image;
s12: counting potential emission sources in the target area according to visual interpretation, wherein the potential emission sources are all point sources with emission characteristics in the target area;
s13: and marking the longitude and latitude of each potential emission source in the plane map.
In one usage scenario, after the target area to be measured is determined, when a high-resolution remote sensing image is acquired, the image acquisition is realized by using a high-resolution No. 2 satellite image and a high-resolution No. 6 satellite image, but not limited to. And (3) counting the emission sources in the industrial park according to visual interpretation, wherein all sources with emission characteristics are counted as potential emission sources, and recording the longitude and latitude of each potential emission source according to a remote sensing image plane graph.
S2: acquiring measured data in a target area, wherein the measured data at least comprises position information and CO 2 Concentration data, NO 2 Concentration data and meteorological data.
Specifically, based on the vehicle-mounted measuring system, a large amount of measured data is acquired by back-and-forth navigation in the downwind direction to obtain the measured data, and the measured data comprises position information and CO 2 Concentration data, NO 2 Concentration data and meteorological data.
In a usage scenario, high frequency, high accuracy CO is performed in the lower tuyere region based on wind field information and actual road information at the monitoring time 2 And (4) concentration sampling, namely recording position information and meteorological parameters of the vehicle-mounted system in real time, wherein the meteorological parameters comprise wind speed, wind direction, atmospheric temperature, atmospheric humidity and atmospheric pressure.
Wherein CO is collected 2 Concentration data, NO 2 Monitoring instruments for concentration data include, but are not limited to, cavity ring-down instrument technology (CRDS), and off-axis integrated cavity output spectroscopy (OA-ICOS) mobile greenhouse gas analyzers. The position information is recorded by the GNSS instrument, and the meteorological parameters are collected by a mobile meteorological station equipped by the vehicle-mounted system.
S3: establishing an emission diffusion model, industryThe enterprise emission in the park is considered as strong point source emission, and the CO measured by the vehicle-mounted system 2 、NO 2 The concentration value is the superposition effect of the emission diffusion of a plurality of strong point sources, so a Gaussian diffusion model is selected to model the carbon and nitrogen emission of each emission source in the whole industrial park.
Specifically, step S3 includes the steps of:
s31: establishing a diffusion coordinate system based on the spatial position of the vehicle-mounted measuring system;
according to the concentration value of the acquired data, as shown in fig. 2, a measurement point before reaching the industrial park is arbitrarily selected as an initial point, the initial point is set as a coordinate origin, the wind direction is taken as an X axis, the direction perpendicular to the wind direction in the horizontal direction is taken as a Y axis, the direction perpendicular to the XOY plane is taken as a Z axis to establish a coordinate system, and the coordinate value of the vehicle-mounted equipment in the european three-dimensional coordinate system is determined by the corresponding longitude and latitude value and the set longitude and latitude value of the coordinate origin.
S32: based on the diffusion coordinate system, the following emission diffusion model is established:
wherein, (x, y, z) is the space position coordinate of the measuring point of the vehicle-mounted equipment, and C c (x, y, z) is CO at (x, y, z) coordinates 2 Concentration q (c，i) The carbon emission intensity of the ith strong point source is that i =1,2,3 \8230, where \8230, n is the number of strong point sources in the target area, u is the wind speed and H i Effective discharge height, sigma, for carbon emissions from power plants c,y And σ c,z Respectively a horizontal diffusion parameter and a vertical diffusion parameter of the measurement position relative to the ith strong point source in the target area, B c Is a target area CO 2 Local background concentration of alpha c Is CO 2 Ground reflection coefficient of (a) c ，b c Is CO 2 Horizontal diffusion coefficient of (c) c ，d c Is CO 2 The vertical diffusion coefficient of (c). q. q.s (N,i) Is NO at (x, y, z) coordinate 2 Concentration q (N, i) is NO of the ith strong point source 2 Discharge intensity, i =1,2,3 \8230; \8230n, σ N,y And σ N,z NO for the measurement location relative to the ith intense point source in the target region 2 Horizontal and vertical diffusion parameters, B N Is a target area NO 2 Local background concentration of alpha N Is NO 2 Ground reflection coefficient of a N ，b N Is NO 2 Horizontal diffusion coefficient of (c) N ，d N Is NO 2 The vertical diffusion coefficient of (c).
S4: and calculating the number of the emission sources in the target area and the positions of the emission sources based on a genetic algorithm according to the measured data and the emission diffusion model.
The method comprises the following steps of calculating the number of emission sources in the target region and the position of each emission source based on a genetic algorithm, and specifically comprises the following steps:
s41: setting the number of the emission sources in the target area as M, setting different permutation and combination modes for M, selecting different n, and bringing the positions corresponding to the emission sources into an emission diffusion model, more specifically, into the formula (1);
S42: based on the collected CO 2 Data, selected n, CO collected by vehicle-mounted measuring system 2 Formula (1) in the concentration, spatial location information, and carbon emission diffusion model versus q by genetic algorithm c,i ,a c ,b c ,c c ,d c ,x i ,y i ,z i ,H i ,α c ,B c Performing primary solution;
s43: the following formula is used to actually measure CO according to vehicle load at the same position 2 Concentration and simulated CO 2 And (3) judging the accuracy of the parameter to be solved by the concentration correlation coefficient R, as shown in a formula (7):
wherein, C c ' is CO at different positions during vehicle-mounted measurement 2 Concentration analog value according to q of each iteration in genetic algorithm c,i ,a c ,b c ,c c ,d c ,x i ,y i ，z i ,H i ,α c ,B c The parameters are brought into a carbon emission diffusion model to obtain C c Measured CO for on-board measurement system 2 And measuring a point concentration value, and when R reaches the maximum value, namely the current emission source combination mode is considered to be a real value, determining the number of the emission sources and the positions of the emission sources based on the current emission source combination.
Further, in order to improve the calculation accuracy, the step S4 further includes the steps of:
s44: determining the number of emission sources and corresponding locations based on the collected CO according to S43 2 Concentration data, and genetic algorithm on q in formula (1) c,i ,a c ,b c ,c c ,d c ,H i ,α c ,B c Solving the parameters, repeatedly calculating for 1000 times, and recording corresponding results;
s45: determining the number of emission sources and corresponding positions based on the collected NO according to S43 2 Concentration data, and genetic algorithm vs. formula (3)Middle q N,i ,a N ,b N ,c N ,d N ,α N ,B N And (5) solving, repeatedly calculating for 1000 times, and recording a corresponding result.
S5: calculating CO emitted by each emission source based on levenberg-marquardt algorithm 2 Strength and NO 2 Strength. That is, based on measured CO, based on the number of emissions sources and the location of each of the emissions sources 2 Concentration data and meteorological data and the levenberg-marquardt algorithm on the parameter q in the carbon emission diffusion model c,i ,a c ,b c ,c c ,d c ,H i ,α c ,B c Solving is carried out; obtaining an optimal solution of an emission diffusion model based on a set initial value, a constraint limit and a preset evaluation coefficient, wherein the optimal solution is used as CO emitted by the emission source 2 Strength and NO 2 Strength.
In one particular use scenario, the number and location of carbon emission sources are determined in accordance with S43 based on measured CO 2 Concentration data and meteorological data and levenberg-marquardt algorithm on q in equation (1) c,i ,a c ,b c ,c c ,d c ,H i ,α c ,B c The parameters are resolved. The average value of 1000 sets of parameter values calculated in S43 is used as an initial value, the upper and lower constraint limits of each parameter are set to 20% of the average value, and a judgment coefficient F is set, see formula (8). When the value of F reaches the minimum, the corresponding parameter combination is the optimal solution of the formula (1), namely the carbon emission intensity (q) of each emission source c,i B) and other diffusion-related parameters (a) c ,b c ,c c ,d c ,H i ,α c ,B c ) Thus obtaining the product.
Wherein, C c,k (x, y, z) is the measured CO at the vehicle-mounted measuring point 2 Concentration; c' c,k (x, y, z) is the parameter set root for each iteration using the levenberg-marquardt algorithmAccording to the formula (1) for the measurement point CO 2 Analog value of concentration, m1 is measured CO by the vehicle-mounted system 2 Total number of concentration data.
Based on the measured NO according to the number and location of carbon emission sources determined in S43 2 Concentration data and meteorological data and levenberg-marquardt algorithm on q in equation (3) N,i ,a N ,b N ,c N ,d N ,α N ,B N The parameters are resolved. The average value of 1000 sets of parameter values calculated in S44 was set as an initial value, and the upper and lower constraint limits for each parameter were set to 20% of the average value. The evaluation coefficient F is set, see equation 9. When the value of F reaches the minimum, the corresponding parameter combination is the optimal solution of equation 1, namely the nitrogen emission intensity (q) of each emission source N,i And other diffusion related parameters (q) N,i ,a N ,b N ,c N ,d N ,α N ,B N ) Thus obtaining the product.
C N,k (x, y, z) is the measured NO of the vehicle-mounted measuring point 2 Concentration; c' N,k (x, y, z) measurement points NO according to equation 1 for each iteration of the parameter set using the levenberg-marquardt algorithm 2 Analog value of concentration. m2 is NO measured by vehicle-mounted system 2 Total number of concentration data.
S6: according to the CO 2 Strength and NO 2 Calculating the carbon-nitrogen ratio of each emission source by using the intensity, and specifically comprises the following steps:
the carbon to nitrogen ratio was calculated using the following formula:
wherein the content of the first and second substances,
the carbon-nitrogen emission ratio of the ith emission source, q c,i Is the ithCO of emission source 2 Emission intensity, q N,i NO for the ith emission source 2 The discharge intensity.
In the above embodiments, the multi-point source carbon-nitrogen ratio monitoring method based on the vehicle-mounted measuring system provided by the invention uses the vehicle-mounted equipment to measure CO near the target area 2 、NO 2 Concentration, position information and meteorological data, and a multi-point source emission diffusion model is established, so that the intensity of carbon emission and nitrogen emission of each emission source is quantified, and the carbon-nitrogen ratio is further determined. The invention realizes the high-timeliness and high-precision monitoring of the carbon-nitrogen ratio emitted by each strong emission source in the industrial park based on the remote sensing data acquired on the vehicle, and solves the technical problem of poor timeliness of the strong point source carbon emission monitoring in the prior art.
In addition to the above method, the present invention further provides a multipoint source carbon-nitrogen ratio monitoring system based on a vehicle-mounted measurement system, as shown in fig. 3, the system includes:
a measured data obtaining unit 100, configured to obtain measured data in a target area, where the measured data at least includes location information and CO 2 Concentration data, NO 2 Concentration data and meteorological data;
a diffusion model creation unit 200 for creating an emission diffusion model;
the emission source parameter calculating unit 300 is configured to calculate, according to the measured data and the emission diffusion model, the number of emission sources in the target region and the position of each emission source based on a genetic algorithm;
an emission intensity result output unit 400 for calculating CO emitted from each of the emission sources based on the levenberg-marquardt algorithm 2 Strength and NO 2 Strength;
a carbon-to-nitrogen ratio calculation unit 500 for calculating a carbon-to-nitrogen ratio based on the CO 2 Strength and NO 2 And calculating the carbon-nitrogen ratio of each emission source according to the intensity.
In a specific embodiment, the multipoint source carbon-nitrogen ratio monitoring system based on the vehicle-mounted measuring system provided by the invention uses vehicle-mounted equipment to measure CO near a target area 2 、NO 2 Concentration ofThe method comprises the steps of obtaining position information and meteorological data, and establishing a multipoint source emission diffusion model, so that the intensity of carbon emission and nitrogen emission of each emission source is quantified, and the carbon-nitrogen ratio is further determined. The invention realizes the high-timeliness and high-precision monitoring of the carbon-nitrogen ratio emitted by each strong emission source in the industrial park based on the remote sensing data acquired on the vehicle, and solves the technical problem of poor timeliness of the strong point source carbon emission monitoring in the prior art.
In one embodiment, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 4. The computer device includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a model prediction. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The model prediction of the computer device is used to store static information and dynamic information data. The network interface of the computer device is used for communicating with an external terminal through a network connection. Which computer program is executed by a processor to carry out the steps in the above-described method embodiments.
Those skilled in the art will appreciate that the configuration shown in fig. 4 is a block diagram of only a portion of the configuration associated with aspects of the present invention and is not intended to limit the computing devices to which aspects of the present invention may be applied, and that a particular computing device may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In correspondence with the above embodiments, embodiments of the present invention also provide a computer storage medium containing one or more program instructions therein. Wherein the one or more program instructions are for performing the method described above by a weight verification system.
In an embodiment of the present invention, the processor may be an integrated circuit chip having signal processing capability. The Processor may be a general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete Gate or transistor logic device, discrete hardware component.
The various methods, steps, and logic blocks disclosed in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present invention may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The processor reads the information in the storage medium and completes the steps of the method in combination with the hardware.
The storage medium may be a memory, for example, which may be volatile memory or nonvolatile memory, or may include both volatile and nonvolatile memory.
The nonvolatile Memory may be a Read-Only Memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an Electrically Erasable PROM (EEPROM), or a flash Memory.
The volatile Memory may be a Random Access Memory (RAM) which serves as an external cache. By way of example and not limitation, many forms of RAM are available, such as Static Random Access Memory (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), SLDRAM (SLDRAM), and Direct Rambus RAM (DRRAM).
The storage media described in connection with the embodiments of the invention are intended to comprise, without being limited to, these and any other suitable types of memory.
Those skilled in the art will appreciate that the functionality described in the present invention may be implemented in a combination of hardware and software in one or more of the examples described above. When software is applied, the corresponding functionality may be stored on or transmitted over as one or more instructions or code on a computer-readable medium. Computer-readable media includes both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A storage media may be any available media that can be accessed by a general purpose or special purpose computer.
The above embodiments are only for illustrating the embodiments of the present invention and are not to be construed as limiting the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made on the basis of the embodiments of the present invention shall be included in the scope of the present invention.
Claims (9)
1. A multipoint source carbon-nitrogen ratio monitoring method based on a vehicle-mounted measuring system is characterized by comprising the following steps:
acquiring actual measurement data in a target area, wherein the actual measurement data at least comprises position information and CO 2 Concentration data, NO 2 Concentration data and meteorological data;
establishing an emission diffusion model;
calculating the number of emission sources in the target area and the positions of the emission sources based on a genetic algorithm according to the measured data and the emission diffusion model; calculating each of the emission sources based on a levenberg-marquardt algorithmDischarged CO 2 Strength and NO 2 Strength;
according to the CO 2 Strength and NO 2 Calculating the carbon-nitrogen ratio of each emission source according to the intensity;
the method comprises the following steps of calculating the number of emission sources in the target region and the position of each emission source based on a genetic algorithm, and specifically comprises the following steps:
setting the number of emission sources in the target area as M, setting different permutation and combination modes for M, selecting different n, and bringing the corresponding positions of the emission sources into an emission diffusion model;
based on the collected CO 2 Data, selected n, CO collected by vehicle-mounted measuring system 2 The concentration, the spatial position information and the carbon emission diffusion model are paired q through a genetic algorithm (c，i) ,a c ,b c ,c c ,d c ,x i ,y i ,z i ,H i ,α c ,B c Performing primary solution;
actual measurement of CO on board at the same location using the following formula 2 Concentration and simulated CO 2 And (3) judging the accuracy of the parameters to be solved by the concentration correlation coefficient R:
wherein, C c ' is CO at different positions during vehicle-mounted measurement 2 Concentration analog value according to q of each iteration in genetic algorithm c,i ,a c ,b c ,c c ,d c ,x i ,y i ，z i ,H i ,α c ,B c Introduction of parameters into a carbon emission diffusion model, C c CO measured for on-board measurement systems 2 Measuring point concentration values, and when R reaches the maximum value, determining the number of the emission sources and the positions of the emission sources based on the current emission source combination;
(x i ,y i ,z i ) The spatial position coordinate of the ith strong point source is taken as the coordinate; q. q.s (c，i) Carbon as ith strong point sourceEmission intensity, i =1,2,3 \8230, \8230n, n is the number of strong point sources in the target region; h i Effective emission height for power plant carbon emission; b is c Is a target area CO 2 Local background concentration of alpha c Is CO 2 Ground reflection coefficient of (a) c ，b c Is CO 2 Horizontal diffusion coefficient of (c) c ，d c Is CO 2 The vertical diffusion coefficient of (c).
2. The method for monitoring the carbon-nitrogen ratio of the multipoint source according to claim 1, wherein the acquiring measured data in the target area further comprises:
a plan view of the target area is obtained based on the satellite remote sensing images, and the number and spatial relative positions of potential emission sources are determined through visual interpretation of the plan view.
3. The multipoint source carbon-nitrogen ratio monitoring method according to claim 2, wherein a plan view of a target area is obtained based on satellite remote sensing images, and the number and the spatial relative positions of potential emission sources are determined through visual interpretation of the plan view, and specifically comprises:
determining a target area, acquiring a remote sensing image of the target area, and generating a plan view from the remote sensing image;
counting potential emission sources in the target area according to visual interpretation, wherein the potential emission sources are all point sources with emission characteristics in the target area;
and marking the longitude and latitude of each potential emission source in the plane map.
4. The multipoint source carbon-nitrogen ratio monitoring method according to claim 3, wherein establishing the emission diffusion model specifically comprises:
establishing a diffusion coordinate system based on the spatial position of the vehicle-mounted measuring system;
based on the diffusion coordinate system, the following emission diffusion models are established:
wherein, (x, y, z) is the space position coordinate of the measuring point of the vehicle-mounted equipment, and C c (x, y, z) is CO at (x, y, z) coordinates 2 Concentration q (c，i) The carbon emission intensity of the ith strong point source is i =1,2,3 \8230, n is the number of strong point sources in the target area, u is the wind speed, H is H i Effective discharge height, sigma, for carbon emissions from power plants c,y And σ c,z Respectively a horizontal diffusion parameter and a vertical diffusion parameter of the measurement position relative to the ith strong point source in the target area, B c Is a target area CO 2 Local background concentration of alpha c Is CO 2 Ground reflection coefficient of a c ，b c Is CO 2 Horizontal diffusion coefficient of (c) c ，d c Is CO 2 The vertical diffusion coefficient of (d); q. q.s (N,i) Is NO at (x, y, z) coordinate 2 Concentration q (N, i) is NO of the ith strong point source 2 The intensity of the discharge is improved,i＝1,2,3……n，σ N,y and σ N,z NO for the measurement location relative to the ith intense point source in the target region 2 Horizontal and vertical diffusion parameters, B N Is a target area NO 2 Local background concentration of alpha N Is NO 2 Ground reflection coefficient of a N ，b N Is NO 2 Horizontal diffusion coefficient of (c) N ，d N Is NO 2 The vertical diffusion coefficient of (c).
5. The multipoint-source carbon-to-nitrogen ratio monitoring method of claim 4, wherein calculating CO emitted by each of the emission sources is based on a levenberg-marquardt algorithm 2 Strength and NO 2 The strength specifically comprises:
based on measured CO according to the number of emission sources and the position of each emission source 2 Concentration data and meteorological data and the levenberg-marquardt algorithm to parameter q in the carbon emission diffusion model c,i ,a c ,b c ,c c ,d c ,H i ,α c ,B c Solving is carried out;
obtaining an optimal solution of an emission diffusion model based on a set initial value, a set constraint limit and a preset evaluation coefficient, wherein the optimal solution is used as CO emitted by the emission source 2 Strength and NO 2 And (4) strength.
6. The multipoint source carbon to nitrogen ratio monitoring method of claim 5, wherein according to said CO 2 Strength and NO 2 Calculating the carbon-nitrogen ratio of each emission source by using the intensity, and specifically comprises the following steps:
the carbon to nitrogen ratio was calculated using the following formula:
wherein the content of the first and second substances,
carbon to nitrogen emission ratio of the ith emission source, q c,i CO as the ith emission source 2 Emission intensity, q N,i NO as the ith emission source 2 The discharge intensity.
7. A multipoint source carbon-nitrogen ratio monitoring system based on an on-board measurement system is characterized by comprising:
a measured data acquisition unit for acquiring measured data in the target area, wherein the measured data at least comprises position information and CO 2 Concentration data, NO 2 Concentration data and meteorological data;
a diffusion model creation unit for creating an emission diffusion model;
the emission source parameter calculation unit is used for calculating the number of emission sources in the target area and the positions of the emission sources based on a genetic algorithm according to the measured data and the emission diffusion model;
an emission intensity result output unit for calculating CO emitted by each emission source based on levenberg-marquardt algorithm 2 Strength and NO 2 Strength;
a carbon-nitrogen ratio calculation unit for calculating the CO ratio based on the CO 2 Strength and NO 2 Calculating the carbon-nitrogen ratio of each emission source according to the intensity;
the method comprises the following steps of calculating the number of emission sources in the target region and the position of each emission source based on a genetic algorithm, and specifically comprises the following steps:
setting the number of emission sources in the target area as M, setting different permutation and combination modes for M, selecting different n, and bringing the corresponding positions of the emission sources into an emission diffusion model;
based on the CO collected 2 Data, selected n, CO collected by vehicle-mounted measuring system 2 The concentration, the spatial position information and the carbon emission diffusion model are paired q through a genetic algorithm c,i ,a c ,b c ,c c ,d c ,x i ,y i ,z i ,H i ,α c ,B c Performing primary solution;
using the followingFormula, actual measurement of CO on board at the same location 2 Concentration and simulated CO 2 And (3) judging the accuracy of the parameters to be solved by the concentration correlation coefficient R:
wherein, C c ' is CO at different positions during vehicle-mounted measurement 2 Concentration analog value according to q of each iteration in genetic algorithm (c，i) ,a c ,b c ,c c ,d c ,x i ,y i ，z i ,H i ,α c ,B c The parameters are brought into a carbon emission diffusion model to obtain C c Measured CO for on-board measurement system 2 Measuring point concentration values, and determining the number of emission sources and the positions of the emission sources based on the current emission source combination when R reaches the maximum value;
(x i ,y i ,z i ) The spatial position coordinate of the ith strong point source is taken as the coordinate; q. q of (c，i) The carbon emission intensity of the ith strong point source is i =1,2,3 \8230 \ 8230:n, n is the number of strong point sources in the target area; h i Effective emission height for power plant carbon emission; b is c Is a target area CO 2 Local background concentration of alpha c Is CO 2 Ground reflection coefficient of a c ，b c Is CO 2 Horizontal diffusion coefficient of (c) c ，d c Is CO 2 The vertical diffusion coefficient of (c).
8. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the method according to any of claims 1 to 6 when executing the program.
9. A non-transitory computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 6.
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