Industrial park odor pollution tracing system based on three-dimensional adaptive grid

Abstract

The present application relates to the technical field of pollution tracing, in particular to an industrial park malodor pollution tracing system based on three-dimensional adaptive grid. The system comprises: determining the emission intensity estimation value of each emission source based on the emission relationship between the gas pollutant monitoring value and the emission source, and determining whether the emission source meets the tracing analysis condition based on the emission intensity estimation value; performing initial grid construction and source analysis field initialization on the emission source meeting the tracing analysis condition, to generate the initial concentration field of the gas pollutant in the industrial park; updating the gas pollutant concentration field and the source analysis value of each emission source in the emission stage based on the emission intensity estimation value of the emission source; and calculating the source analysis value of the gas pollutant in the transmission stage, which can ensure that the tracing result can truly reflect the actual dynamic behavior of the pollutant in the environment, and improve the accuracy and reliability of the tracing result.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of pollution tracing, in particular to an industrial park malodor pollution tracing system based on a three-dimensional adaptive grid. BACKGROUND

[0002] Currently, two types of technologies are mainly used for malodor pollution tracing in industrial parks: observation-based source analysis methods and simulation tracing methods based on diffusion models. However, these existing technologies have significant limitations in practical applications.

[0003] Observation-based methods collect pollutant data at monitoring sites and perform source analysis combined with chemical characteristic spectrum matching. This type of method assumes that the characteristic components of pollutants produced by different emission sources are sufficiently different. However, the chemical characteristics of malodor substances (such as hydrogen sulfide, ammonia, volatile organic compounds, etc.) emitted by multiple sources in industrial parks are highly similar, making it difficult for this type of source analysis method to provide reliable results. In addition, the layout of fixed monitoring points cannot capture the spatiotemporal variation characteristics of malodor pollution, and increasing the monitoring density will significantly increase the cost.

[0004] Diffusion model methods can simulate the environmental behavior of pollutants, but there is a dilemma between computational accuracy and efficiency. Traditional models use fixed resolution computational grids (usually more than 1 kilometer), which cannot accurately depict the diffusion process of densely distributed pollution sources (usually less than 100 meters apart) in industrial parks. If the grid resolution is increased to the order of 50 meters, the computational load will increase exponentially, making it impossible to meet real-time analysis requirements.

[0005] Therefore, there is an urgent need for an industrial park malodor pollution tracing system based on a three-dimensional adaptive grid to solve the above problems. SUMMARY

[0006] The purpose of the present application is to provide an industrial park malodor pollution tracing system based on a three-dimensional adaptive grid: through three-dimensional dynamic grid optimization, multi-source data fusion, and phased tracing strategies, the accuracy, timeliness, and adaptability of industrial park malodor pollution tracing are significantly improved, providing reliable technical support for precise pollution control.

[0007] The industrial park malodor pollution tracing system based on a three-dimensional adaptive grid comprises:

[0008] A tracing condition analysis unit is configured to collect gas pollutant monitoring values at m monitoring points in the industrial park, determine emission intensity estimates for each emission source based on the emission relationship between the gas pollutant monitoring values and the emission sources, and determine whether the emission source meets the tracing analysis conditions based on the emission intensity estimates.

[0009] A traceability initialization unit is configured to perform initial grid construction and source analysis field initialization on the emission source meeting the traceability analysis condition, and generate an initial concentration field of the gas pollutant in the industrial park;

[0010] An emission traceability monitoring unit is configured to update the gas pollutant concentration field and the source analysis value of each emission source in the emission stage based on the emission intensity estimation value of the emission source;

[0011] A transmission traceability monitoring unit is configured to calculate the source analysis value of the gas pollutant in the transmission stage.

[0012] Further, the determination of the emission intensity estimation value of each emission source based on the emission relationship between the gas pollutant monitoring value and the emission source includes the following process:

[0013] Collecting the gas pollutant monitoring value of m monitoring points in the industrial park , , wherein the gas pollutant monitoring value is obtained from the gas pollutant data emitted by K mutually independent emission sources;

[0014] The emission relationship expression between the gas pollutant monitoring value and the emission source is: ; wherein,

[0015] , , ; wherein, represents the emission intensity of the emission source , ;

[0016] The emission relationship between the gas pollutant monitoring value and the emission source is described by a coefficient , and the coefficient is calculated as follows: taking the emission source as the coordinate axis origin and the coordinates of the monitoring point as ;

[0017] ; the gas pollutant concentration in the industrial park follows a normal distribution, wherein, is the mean value of the normal distribution, is the standard deviation of the normal distribution, represents the relative distance of the monitoring point i with respect to the emission source j along each y-axis direction, represents the relative distance of the monitoring point i with respect to the emission source j along each z-axis direction;

[0018] The calculation process of the emission intensity estimation value is converted into a linear least square solution problem of the emission relationship expression: ; represents the emission intensity estimation value, wherein the emission intensity is the mass of the gas pollutant generated at the emission source per unit time.

[0019] Further, determining whether the emission source meets the traceability analysis condition based on the emission intensity estimation value specifically includes the following process:

[0020] loading an emission intensity threshold value, wherein the emission intensity threshold value is stored in the system, determining whether the emission intensity estimation value exceeds the emission intensity threshold value, if yes, determining that the emission source meets the traceability analysis condition, if no, determining that the emission source does not meet the traceability analysis condition.

[0021] Further, the initial grid construction and source analysis field initialization are performed on the emission source meeting the traceability analysis condition to generate the initial concentration field of the gas pollutant in the industrial park, specifically including the following process:

[0022] taking the industrial park as a three-dimensional grid on a simulation domain , , representing the real number set, and counting the initial concentration field of the gas pollutant in the industrial park ;

[0023] constructing a source analysis field: taking to represent the initial source analysis value contributed by the i-th emission source on each grid node, the initial source analysis value satisfies the normalization constraint:

[0024] ;

[0025] splitting the corresponding gas pollution field on the simulation domain into K virtual source pollution subgraphs according to the location of the emission source , each subgraph representing the pollution distribution of the emission source k: ; wherein , and respectively represent the virtual source pollution subgraph, the source analysis field and the pollution distribution corresponding to the time step ;

[0026] for each virtual source pollution subgraph , a spatial interpolation algorithm is applied to map it from the grid corresponding to the time step to the new grid of the current time step n+1: ; wherein represents the spatial interpolation algorithm operation on , and represents the virtual source pollution subgraph corresponding to the new grid of the current time step n+1;

[0027] all subgraphs are fused through an aggregation process to initialize the source analysis field of the current new grid: ​

[0028] ; wherein, To prevent small constants from being zero, ensure numerical stability; Indicates the initial source analysis field corresponding to the new grid.

[0029] Further, based on the emission intensity estimate value of the emission source, the gas pollutant concentration field and the source analysis value of each emission source are updated during the emission phase, which includes the following processes:

[0030] The th emission source emits gas pollutants with an emission intensity estimate value during each time step , and the pollutant concentration field is updated as:

[0031] ; wherein, is the updated pollutant concentration field;

[0032] Update the source analysis value of each emission source: .

[0033] Further, the calculation of the source analysis value of the gas pollutant during the transmission phase includes the following processes:

[0034] Solve the dynamic equation independently for each virtual source pollutant subgraph :

[0035] ; wherein, is the wind speed vector, is the divergence operator, is the gradient operator, is the turbulent diffusion coefficient, and C is the gas pollutant concentration vector;

[0036] The virtual single-source pollution distribution calculated by the dynamic equation updates the corresponding source analysis value: , wherein, is the updated source analysis value during the transmission phase.

[0037] Further, the gas pollutant emission setting of the simulation domain includes the following process: the pollutant emission outlet pressure gradient in the simulation domain is set to 0.

[0038] Further, in the transmission process, the chemical reaction process between gas pollutants adopts a region-adaptive chemical mechanism, and the following reaction system is constructed:

[0039] The pollutant concentration is converted and mapped according to the principle of mass conservation:

[0040] ;

[0041] wherein, is the source resolved value after chemical reaction, is the source resolved value before chemical reaction, denotes the proportion of each reactant of the gaseous pollutant contributes to the mass of the product.

[0042] Further, the spatial interpolation algorithm includes an inverse distance weighting method.

[0043] Compared with the prior art, the present application has the beneficial effects of:

[0044] Accurate pollution source identification is achieved: the present application establishes a quantitative relationship model between monitoring data and emission sources, and uses linear least squares estimation to quickly calculate the emission intensity estimate of each potential emission source. Combined with the judgment of the emission intensity threshold, the system can quickly screen out the main emission sources that need to be traced.

[0045] Full-chain dynamic tracing of emission and transmission is achieved: the system clearly divides the tracing process into two stages of "emission" and "transmission", and models and updates them respectively. In the emission stage, the concentration and source resolution field changes caused by source strength are updated in real time; in the transmission stage, the dynamic equations of each virtual pollution subgraph are solved independently, and the transmission process of the pollutant under meteorological conditions such as wind speed and turbulent diffusion is accurately simulated. This full-chain dynamic simulation mechanism ensures that the tracing result can truly reflect the actual dynamic behavior of the pollutant in the environment, significantly improving the accuracy and reliability of the tracing result. BRIEF DESCRIPTION OF DRAWINGS

[0046] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments described in the present application, and other drawings can also be obtained by those skilled in the art based on these drawings.

[0047] Figure 1 is a system block diagram of an industrial park malodor pollution tracing system based on a three-dimensional adaptive grid according to an embodiment of the present application;

[0048] Figure 2 is a self-adaptive grid tracing framework process schematic diagram according to an embodiment of the present application;

[0049] Figure 3 is a structural block diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0050] With reference to the accompanying drawings, the technical solutions in the embodiments of the present application will be clearly and completely described below, obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the protection scope of the present application.

[0051] In addition, the described features, structures or characteristics can be combined in any suitable manner in one or more example embodiments. In the following description, many specific details are provided to give a full understanding of the example embodiments of the present disclosure. However, one skilled in the art will realize that the technical solutions of the present disclosure can be practiced without one or more of the specific details, or can employ other methods, components, steps, etc. In other cases, well-known structures, methods, implementations or operations are not shown or described in detail to avoid obscuring aspects of the present disclosure.

[0052] The embodiment provides an industrial park malodor pollution tracing system based on a three-dimensional adaptive grid, Figure 1 is a system block diagram of an industrial park malodor pollution tracing system based on a three-dimensional adaptive grid according to the embodiment of the present application, as Figure 1 shown, the system comprises:

[0053] The tracing condition analysis unit is configured to collect the gas pollutant monitoring values of m monitoring points in the industrial park, determine the emission intensity estimation value of each emission source based on the emission relationship between the gas pollutant monitoring values and the emission source, and determine whether the emission source meets the tracing analysis condition based on the emission intensity estimation value.

[0054] The tracing initialization unit is configured to perform initial grid construction and source analysis field initialization on the emission source meeting the tracing analysis condition, and generate an initial concentration field of the gas pollutant in the industrial park.

[0055] The emission tracing monitoring unit is configured to update the gas pollutant concentration field and the source analysis value of each emission source in the emission stage based on the emission intensity estimation value of the emission source.

[0056] The transmission tracing monitoring unit is configured to calculate the source analysis value of the gas pollutant in the transmission stage.

[0057] In summary, the emission intensity estimation value of each emission source is determined based on the emission relationship between the gas pollutant monitoring value and the emission source, and whether the emission source meets the traceability analysis condition is determined based on the emission intensity estimation value; the initial grid construction and source analysis field initialization are performed on the emission source meeting the traceability analysis condition, and the initial concentration field of the gas pollutant in the industrial park is generated; the concentration field of the gas pollutant and the source analysis value of each emission source are updated in the emission stage based on the emission intensity estimation value of the emission source; and the source analysis value of the gas pollutant in the transmission stage is calculated, which can ensure that the traceability result can truly reflect the actual dynamic behavior of the pollutant in the environment, and significantly improve the accuracy and reliability of the traceability result.

[0058] In some embodiments, determining the emission intensity estimation value of each emission source based on the emission relationship between the gas pollutant monitoring value and the emission source specifically includes the following process:

[0059] Collecting the gas pollutant monitoring value of m monitoring points in the industrial park , , wherein the gas pollutant monitoring value is obtained from the gas pollutant data jointly emitted by K independent emission sources;

[0060] The emission relationship expression between the gas pollutant monitoring value and the emission source is: ; wherein,

[0061] , , ; wherein, represents the emission intensity of the emission source ; ;

[0062] The emission relationship between the gas pollutant monitoring value and the emission source is described by a coefficient , and the coefficient is calculated as follows: taking the emission source as the coordinate axis origin and the coordinates of the monitoring point as ;

[0063] ; the concentration of the gas pollutant in the industrial park follows a normal distribution, wherein, is the mean of the normal distribution, is the standard deviation of the normal distribution, represents the relative distance of the monitoring point i with respect to the emission source j along each y-axis direction, represents the relative distance of the monitoring point i with respect to the emission source j along each z-axis direction;

[0064] The calculation process of the emission intensity estimation value is converted into a linear least square solution problem of the emission relationship expression: ; represents the emission intensity estimation value, wherein the emission intensity is the mass of the gas pollutant generated at the emission source per unit time, and the linear least squares solution is solved by using a "normal equation method", and the specific solving process is not described in detail here.

[0065] In some embodiments, determining whether the emission source meets the traceability analysis condition based on the emission intensity estimation value specifically includes the following process:

[0066] loading an emission intensity threshold value, wherein the emission intensity threshold value is stored in the system, and it is determined whether the emission intensity estimation value exceeds the emission intensity threshold value, if yes, it is determined that the emission source meets the traceability analysis condition, and if no, it is determined that the emission source does not meet the traceability analysis condition. The selection of the emission intensity threshold value can include: assuming that a certain emission source j is the only contribution source (excluding multi-source superposition interference), combining the maximum allowable concentration Cmax (reference standard according to the "Odor Pollutant Emission Standard" GB14554-1993) of the monitoring point and the background concentration Cbg (the background concentration is the gas concentration of the industrial park when there is no emission of gas pollutants, and the background concentration Cbg can be statistically calculated from the historical data of the monitoring point), calculating the "maximum allowable concentration contribution" Cjmax=Cmax-Cbg that can be caused by the emission source, and setting the emission intensity threshold value corresponding to the emission source as , wherein, is the coefficient corresponding to the emission source.

[0067] In some embodiments, Figure 2 is a self-adaptive grid traceability framework process schematic diagram of an embodiment of the present application, as shown in Figure 2 In a three-dimensional dynamic self-adaptive grid system, the self-adaptive grid traceability framework is used to realize the separation, transmission calculation and result fusion of multi-source pollutants. The self-adaptive grid traceability framework mainly includes an "analysis process", a "function operation module" and an "aggregation process".

[0068] The analysis process is used for virtual source pollution subgraph construction. Assuming that there are K pollution emission sources in the region, the system first decomposes the total spatial distribution graph of the current pollutant according to the emission sources, constructs K virtual subgraphs, and represents the spatial distribution of the pollutant in the region caused by a single pollution source: each virtual subgraph k represents the spatial distribution of the pollutant in the region when only the emission source k exists in the entire simulation region; these subgraphs are generated by source term separation, pollution diffusion model, etc., supporting linear or nonlinear simulation, and facilitating the processing of the pollution diffusion characteristics of each source in the subsequent process.

[0069] The function operation module is used for interpolation and dynamic calculation of each virtual subgraph. The operation process of each virtual subgraph is independently executed and can be calculated in parallel.

[0070] Interpolation: Since the system adopts dynamic adaptive grid, the pollutant concentration field data on the old grid nodes needs to be interpolated and mapped to the new grid nodes during the grid updating process to ensure the continuity of the data field in the time evolution process. The interpolation methods can be Galerkin projection interpolation, optimal interpolation, weighted average, etc.

[0071] Dynamics equation solving: Based on the current grid structure, the advection transport behavior of the pollutant in this time step is modeled, and the advection diffusion equation of the pollutant transport is solved. The calculation result is the concentration change of the pollutant in three-dimensional space due to physical advection transport and diffusion process at the current time step.

[0072] The aggregation process is used to update the source analysis field by fusing the results of each virtual single-source pollution operation for feedback to the next time step of the model.

[0073] It is worth noting that, Figure 2 the virtual source distribution field in is equal to the virtual subgraph and the virtual source pollution subgraph.

[0074] Based on the above adaptive grid tracing framework, the initial grid construction and source analysis field initialization are performed on the emission sources that meet the tracing analysis conditions, and the initial concentration field of the gas pollutant in the industrial park is generated, including the following processes:

[0075] The industrial park is taken as a three-dimensional grid on the simulation domain , represents the real set, and the initial concentration field of the gas pollutant in the industrial park is counted ;

[0076] Constructing source analysis field: let represent the initial source analysis value contributed by the th emission source on each grid node, and the initial source analysis value satisfies the normalization constraint:

[0077] ;

[0078] The corresponding gas pollution field on the simulation domain is split into K virtual source pollution subgraphs according to the location of the emission source, each subgraph represents the pollution distribution of the emission source k: ; wherein, , and represent the virtual subgraph, source analysis field and pollution distribution corresponding to the time step , respectively;

[0079] For each virtual source pollution subgraph , a spatial interpolation algorithm is applied to map it from the time step The corresponding grid is mapped to the new grid of the current time step n+1: ; wherein, represents the spatial interpolation algorithm operation on , represents the corresponding virtual source pollution subgraph of the new grid of the current time step n+1; wherein, the spatial interpolation algorithm includes the inverse distance weighting method, and the interpolation process is not described in detail here.

[0080] All subgraphs are fused through the aggregation process to initialize the source analysis field of the current new grid:

[0081] ; wherein, is a small constant to prevent division by zero and ensure numerical stability; represents the initial source analysis field corresponding to the new grid.

[0082] Further, the estimation value of the emission intensity of the emission source is used to update the gas pollutant concentration field and the source analysis value of each emission source during the emission phase, which specifically includes the following processes:

[0083] The th emission source emits gas pollutants with an emission intensity estimation value in each time step , and the pollutant concentration field is updated as:

[0084] ; wherein, is the updated pollutant concentration field;

[0085] The source analysis value of each emission source is updated as: .

[0086] Further, the source analysis value of the gas pollutant during the transmission phase is calculated, which specifically includes the following processes:

[0087] Each virtual source pollution subgraph is independently solved for the dynamic equation:

[0088] ; wherein, is the wind speed vector, which is obtained from the meteorological station data in the industrial park, is the divergence operator, is the gradient operator, is the turbulent diffusion coefficient, which is obtained from the field measurement data, and C is the gas pollutant concentration vector.

[0089] The virtual single-source pollution distribution calculated by the dynamic equation updates the corresponding source analysis value: , wherein, This is the updated source resolution value during the transmission phase.

[0090] It is worth noting that after calculating the source apportionment value, the system can monitor the concentration of gaseous pollutants in real time based on the source apportionment value, and can draw a histogram of pollution source concentration based on the concentration change, which more intuitively shows the gas concentration change of malodorous pollution sources.

[0091] During transport, the chemical reactions between gaseous pollutants employ a region-adaptive chemical mechanism, constructing the following reaction system:

[0092] Pollutant concentrations change, and this can be mapped according to the principle of mass conservation:

[0093] ;

[0094] in, The source apportionment value is the result of the chemical reaction. This is the source apportionment value before the chemical reaction. Indicates the reactants of gaseous pollutants The proportion of the product's mass contribution. It is worth noting that the source apportionment value before the chemical reaction was obtained based on the method described above for calculating source apportionment values, for each reactant. The mass contribution ratio of the product is set by the system or specifically set according to the chemical formula of the reaction, with each reactant corresponding to a mass contribution ratio.

[0095] In some embodiments, the setting of gaseous pollutant emissions in the simulation domain includes the following process: the pressure gradient at the pollutant emission outlet in the simulation domain is set to 0. As an optional embodiment, taking an industrial park as an example, a building distribution simulation map is drawn based on a high-precision satellite map, combined with the geometry and measured height of buildings and production equipment. To meet the accuracy requirements for microscale meteorological and pollution diffusion calculations, the simulation domain is 6200 meters east-west, 5700 meters north-south, and 250 meters vertically. The model's time resolution reaches 5 seconds, the maximum number of grid cells in the simulation setting is 200,000, and the minimum adaptive grid resolution is 50 meters, with a maximum of 1000 meters. The grid is adaptively adjusted every 10 time steps. The pressure gradient at the simulation domain outlet is set to 0 to ensure air outflow. Slip boundary conditions are set on the top, sides, and building surfaces (where only the normal wind speed is 0 m / s, meaning air can flow along the surface), and no-slip boundary conditions are set on the bottom surface (where the wind speed is 0 m / s). The test first ran the wind field under pollution-free conditions, and then introduced emission sources after the flow field stabilized. For high-precision source tracing of odor and low-concentration gaseous pollution, the test added 36 pollution emission points and 94 building emission sources, simulating the emission of 28 kinds of gaseous pollutants including acetic acid and ethanol.

[0096] The above formulas are all de-dimensioned to calculate the numerical values, and the preset parameters in the formulas are set by a person skilled in the art according to actual conditions.

[0097] The embodiment also provides an electronic device, Figure 3 is a structural block diagram of an electronic device according to an embodiment of the present application, as shown in the figure, the electronic device comprises a memory 301 and a processor 302, the memory 301 stores a computer program; the computer program is executed by the processor 302, so that the processor 302 executes the three-dimensional adaptive grid-based industrial park odor pollution tracing system of any one of the above embodiments. Figure 3

[0098] The memory 301 can be an electronic memory such as a flash memory, an EEPROM (electrically erasable programmable read-only memory), an EPROM, a hard disk or a ROM. The memory 301 has a storage space 303 for program codes 313 for executing any of the method steps in the above method. For example, the storage space 303 for program codes can include respective program codes 313 for implementing respective steps in the above method. These program codes can be read from or written to one or more computer program products. These computer program products include program code carriers such as hard disks, compact disks (CDs), memory cards or floppy disks. The program codes can be compressed in a suitable form, for example. These codes, when executed by a computing processing device, cause the computing processing device to perform respective steps in the above-described method. These program codes can be read from or written to one or more computer program products. These computer program products include program code carriers such as hard disks, compact disks (CDs), memory cards or floppy disks. The program codes can be compressed in a suitable form, for example. These codes, when executed by a computing processing device, cause the computing processing device to perform respective steps in the above-described three-dimensional adaptive grid-based industrial park odor pollution tracing system.

[0099] ​The above-described embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented by software, the above-described embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are wholly or partially generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center through a wired (for example, infrared, wireless, microwave, etc.) manner. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. containing one or more available medium collections. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a magnetic tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state disk.

[0100] Those skilled in the art can realize that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be realized in electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0101] Those skilled in the art can clearly understand that, for the convenience and brevity of the description, the specific working processes of the above-described system, device and unit can refer to the corresponding processes in the foregoing method embodiments, which will not be described here.

[0102] In several embodiments provided in the present application, it should be understood that the disclosed system, device and method can be implemented in other ways. For example, the above-described device embodiments are only schematic, for example, the division of the units is only a logical function division, and actual implementation can have another division manner, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some interface, device or unit, and can be electrical, mechanical or other forms.

[0103] The units described as separate components may or may not be physically separate, and the components displayed as units may or may not be physical units, that is, may be located in one place, or may also be distributed to multiple network units. Part or all of the units can be selected to achieve the purpose of the embodiment of the present application according to actual needs.

[0104] The above is merely specific implementation of the present application, but the protection scope of the present application is not limited to this. Any person skilled in the art can easily think of changes or replacements within the technical range disclosed by the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A system for odor pollution source tracing in industrial parks based on three-dimensional adaptive mesh, characterized in that the system Comprising: a traceability condition analysis unit for collecting gas pollutant monitoring values of m monitoring points in the industrial park, determining emission intensity estimation values of each emission source based on the emission relationship between the gas pollutant monitoring values and the emission source, and determining whether the emission source meets the traceability analysis condition based on the emission intensity estimation values; a traceability initialization unit for performing initial grid construction and source analysis field initialization on the emission source meeting the traceability analysis condition, and generating an initial concentration field of the gas pollutant in the industrial park; wherein the initial grid construction and source analysis field initialization on the emission source meeting the traceability analysis condition to generate the initial concentration field of the gas pollutant in the industrial park specifically includes the following processes: Industrial park as a three-dimensional grid on a simulation domain , , denotes the set of real numbers and statistical initial concentration field of gaseous pollutants in the industrial park ; Constructing source-resolved fields: Let denote the initial source-resolved value at each grid node contributed by the th emission source, which satisfies the normalization constraint: ； The simulation domain is divided into a plurality of virtual source pollution subgraphs according to the locations of the emission sources The corresponding gas pollution field is divided into a plurality of virtual source pollution subgraphs according to the locations of the emission sources Each subgraph represents an emission source The pollution distribution of each subgraph is obtained by analyzing the corresponding virtual source pollution subgraph The pollution distribution of each subgraph is obtained by analyzing the corresponding virtual source pollution subgraph ; wherein, , and respectively represent a time step The corresponding virtual source pollution subgraph, source analysis field, and pollution distribution; for each virtual source pollution subgraph , apply a spatial interpolation algorithm to map it from the time step to the new grid corresponding to the current time step n+1: ; wherein, denotes the operation of a spatial interpolation algorithm on , and denotes the virtual source pollution subgraph corresponding to the new grid of the current time step n+1. fuse all subgraphs through the aggregation process to initialize the source analysis field of the current new grid: ; wherein, To prevent small constants from being zero, numerical stability is ensured; represents the initial source analytical field corresponding to the new grid; an emission traceability monitoring unit for updating the gas pollutant concentration field and the source analysis value of each emission source based on the emission intensity estimation value of the emission source during the emission stage; wherein the updating of the gas pollutant concentration field and the source analysis value of each emission source based on the emission intensity estimation value of the emission source during the emission stage specifically includes the following processes: The first emission source estimates an emission intensity estimate for each time step The concentration field of the pollutant is updated as follows: ; wherein is the updated pollutant concentration field; updating the source apportionment values for each emission source: ; a transmission traceability monitoring unit for calculating the source analysis value of the gas pollutant during the transmission stage; wherein the calculation of the source analysis value of the gas pollutant during the transmission stage specifically includes the following processes: for each virtual source pollution subgraph solving the kinetic equations independently: ; wherein, is the wind velocity vector, is the divergence operator, is the gradient operator, is the turbulent diffusion coefficient, and C is the gas pollutant concentration vector; Virtual single source pollution distribution calculated using the kinetic equation updating the corresponding source resolved value: wherein is the source resolved value updated in the transmission phase.

2. The three-dimensional adaptive mesh based industrial park malodor pollution source tracing system according to claim 1, characterized in that, determining the emission intensity estimation values of each emission source based on the emission relationship between the gas pollutant monitoring values and the emission source specifically includes the following processes: Collecting gas pollutant monitoring values of m monitoring points in an industrial park , , wherein the gas pollutant monitoring values are obtained from gas pollutant data of K mutually independent emission sources The expression for the emission relationship between gaseous pollutant monitoring values ​​and emission sources is as follows: ;in, , , ;in, Indicates emission source Emission intensity, ; The emission relationship between the gas pollutant monitoring value and the emission source is represented by a coefficient The coefficient is described The calculation process is as follows: taking the emission source as the coordinate axis origin, the coordinate of the monitoring point is ; ; the concentration of the gas pollutant in the industrial park follows a normal distribution, wherein, is a mean of the normal distribution, is a standard deviation of the normal distribution, represents a relative distance of the monitoring point i with respect to the emission source j along each y-axis direction, represents a relative distance of the monitoring point i with respect to the emission source j along each z-axis direction; The calculation process of the emission intensity estimation value is converted into a linear least square solution problem for the emission relationship expression: ; represents the emission intensity estimation value, wherein the emission intensity is the mass of the gas pollutant generated at the emission source per unit time.

3. The three-dimensional adaptive mesh based industrial park malodor pollution tracing system according to claim 1, characterized in that, determining whether the emission source meets the traceability analysis condition based on the emission intensity estimation value specifically includes the following processes: loading the emission intensity threshold, wherein the emission intensity threshold is stored in the system, determining whether the emission intensity estimation value exceeds the emission intensity threshold, if yes, determining that the emission source meets the traceability analysis condition, if not, determining that the emission source does not meet the traceability analysis condition. Gas pollutant emission setting of the simulation domain 4. The three-dimensional adaptive mesh based industrial park malodor pollution tracing system according to claim 1, characterized in that, including the following process: setting the pollutant emission outlet pressure gradient in the simulation domain to 0. In the transmission process, the chemical reaction process between the gas pollutants adopts a region-adaptive chemical mechanism to construct the following reaction system:

5. The three-dimensional adaptive mesh based industrial park malodor pollution source tracing system according to claim 1, wherein, The pollutant concentration is converted and mapped according to the principle of mass conservation: The spatial interpolation algorithm includes the inverse distance weighting method. ； wherein, is the source resolved value after the chemical reaction, is the source resolved value before the chemical reaction, represents the proportion of the mass contribution of each reactant of the gaseous pollutant to the product.

6. The three-dimensional adaptive mesh based industrial park malodor pollution source tracing system according to claim 1, wherein, ​

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