Method and device for tracing emission sources of various pollutants of airflow

Abstract

The invention provides a method and a device for tracing emission sources of various pollutants of an airflow, and belongs to the field of monitoring and tracking of emission sources of pollutants of the airflow. The method comprises the following steps: constructing a monitoring area pollutant concentration model based on a pollutant concentration function according to boundary pollutant concentration observation data of an airflow monitoring area; performing linear transformation on the pollutant concentration function and decoupling the pollutant concentration function into a linear combination of a test value function and an auxiliary function; based on the test value function and the auxiliary function, solving an inverse problem of the pollutant concentration model in the monitoring area, and obtaining the position of each predicted pollutant point source and the Fourier coefficient of the pollutant emission concentration of the point source; and screening false point sources from the predicted pollutant point sources to obtain a traceability result of the actual pollutant point sources. According to the method, the coupling effect between different pollutants can be described, pollution source identification in a high-dimensional scene is facilitated, and positioning and quantitative inversion of emission intensity of multiple pollution sources and multiple pollutants are realized.

Description

Technical Field

[0001] This invention belongs to the field of monitoring and tracing technology of multiple pollutant emission sources in gaseous and fluid systems, and specifically relates to a source tracing method and apparatus for multiple pollutant emission sources in gaseous and fluid systems. Background Technology

[0002] Monitoring, tracing, and managing pollutants (including typical inorganic pollutants such as carbon dioxide, ozone, and heavy metals, as well as water pollutants such as organic nitrogen) in atmospheric and aquatic environments is a key issue in ecological and environmental protection. It is noteworthy that multiple pollutants often coexist in the real environment, and pollution sources (such as factories) also emit multiple pollutants simultaneously. These pollutants may not only engage in complex behaviors such as coupling, reactions, or degradation, but their interactions may also alter diffusion pathways or pollution characteristics. Compared to tracing the source of a single pollutant, tracing the source of multiple pollutants is more complex and difficult due to its dynamic evolution and multi-physics field characteristics, especially requiring consideration of coupling mechanisms and multi-source heterogeneity.

[0003] Taking water pollution as an example, traditional source tracing methods rely on fixed-point monitoring, registration data, and manual investigation, which suffer from problems such as information lag, high cost, and weak anti-interference ability. In contrast, technical source tracing methods based on pollution diffusion models can utilize limited data to invert pollution sources, improving efficiency while reducing costs. In recent years, these methods have become a research hotspot thanks to the development of high-performance computing. Based on the general dynamic properties of pollutants, constructing inverse problems about pollutant source terms using reaction-convection-diffusion models is a relatively effective modeling approach. However, the adjoint operator method shows significant deviations in source tracing results when dealing with applications involving a large number of adjacent pollution sources or numerous point sources. Direct sampling methods have low computational accuracy and cannot accurately locate pollution sources.

[0004] Existing inverse problem modeling and numerical inversion methods for single pollutants (e.g., a source tracing method and device for gaseous and fluid pollutant emission sources, patent number: ZL202211480742X) still face significant challenges in extending to multi-pollutant scenarios. Firstly, single-pollutant models struggle to describe the reaction phenomena between multiple pollutants, necessitating the search for new mathematical models to characterize this complex model. Secondly, traditional adjoint operator methods are prone to source tracing bias when the number of pollution sources is large, while direct sampling methods have limited accuracy and cannot meet the requirements for refined localization. Furthermore, the single-pollutant source tracing algorithm cannot handle coupling terms in the model. Therefore, there is an urgent need to develop high-precision, robust pollutant source tracing models and algorithms suitable for multi-pollutant coupled systems. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of existing technologies and propose a method and apparatus for tracing the emission sources of multiple pollutants in gaseous and fluid streams. This invention can characterize the coupling effects between different pollutants, which is helpful for the identification of pollution sources in high-dimensional scenarios, and realizes the localization and quantitative inversion of the emission intensity of multiple pollution sources and multiple pollutants.

[0006] A first aspect of this invention provides a method for tracing the emission sources of multiple airborne and fluid pollutants, comprising:

[0007] Based on the boundary pollutant concentration observation data of the air-fluid monitoring area, a pollutant concentration model of the monitoring area based on the pollutant concentration function is constructed.

[0008] The pollutant concentration function is linearly transformed to obtain an updated pollutant concentration function, and then the updated pollutant concentration function is decoupled into a linear combination of a trial function and an auxiliary function.

[0009] Based on the trial function and the auxiliary function, the inverse problem of the pollutant concentration model in the monitoring area is solved to obtain the location of each predicted pollutant point source and the Fourier coefficient of the pollutant emission concentration at the predicted pollutant point source.

[0010] Based on the Fourier coefficients, false point sources are screened out from the predicted pollutant point sources, and the source tracing results of the actual pollutant point sources are finally obtained.

[0011] In a specific embodiment of the present invention, the step of constructing a monitoring area pollutant concentration model based on a pollutant concentration function includes:

[0012] Let the concentration of pollutant l at any coordinate x in the monitoring area at time t be denoted as a function U, where U = (u1(x,t),...,u...). l (x,t) is a vector-valued function, x∈Ω, u i (x,t) represents the concentration of the i-th pollutant at x at time t, i = 1, 2, ..., l, Ω represents the set of coordinates of all locations in the monitoring area, and t is any time in the time period [0,T], where time T represents the time when pollutant emission is observed at the boundary of the monitoring area, and time 0 represents the time when no pollutant emission occurs.

[0013] The pollutant concentration model for the monitoring area is then constructed as shown in equation (1):

[0014]

[0015] in, Denotes the time derivative of U; β(x,t) is the propagation speed of the pollutant located at x at time t following the air fluid; A(x,t)U represents the chemical reaction of the generation or decomposition of the pollutant located at x at time t, and A(x,t) is an l×l matrix:

[0016]

[0017] where a ij (x,t) represents the reaction coefficient of the jth pollutant located at x at time t to the ith pollutant;

[0018] Let at time 0, the pollutant density distribution in the monitoring area is all zero, that is, U(x,0) = 0;

[0019] F(x) = (δ(s - s1),…,δ(s - s m )) represents m pollutant point sources in the real scenario, located at spatial positions s j , j = 1,2,…,m, and δ(s - s i ) represents the Dirac function at position s j ;

[0020] G(t) represents the pollutant emission intensity function at time t, and the expression is:

[0021]

[0022] where the intensity of the ith pollutant of the jth pollutant point source at time t is denoted as g ij (t); the pollutant completely disappears or stops emitting after time T * , that is, it satisfies T * < t < T, g ij (t) = 0;

[0023] κ(x,t) is the diffusion rate matrix of the pollutant in the air fluid at time t located at x, and the expression is:

[0024]

[0025] where κ i (x,t), i = 1,…,l represents the spatial diffusion rate of the ith pollutant located at x at time t.

[0026] In a specific embodiment of the present invention, the linear transformation of the pollutant concentration function to obtain the updated pollutant concentration function includes:

[0027] By performing a linear transformation on the pollutant concentration function U, the updated pollutant concentration function W(x,t) = P α U, where Pα is a linear transformation that transforms the coupling matrix into its Jordan canonical form J α and is defined as follows:

[0028]

[0029] where α represents the signal frequency corresponding to the Fourier coefficient of the desired intensity function;

[0030] The updated pollutant concentration function W(x, t) = (w1(x, t), …, w l (x, t)), where w i (x, t) is the updated concentration function of the i-th pollutant, i = 1, 2, …, l.

[0031] In a specific embodiment of the present invention, it further includes:

[0032] For any signal frequency α, a complex geometric optical solution is selected as a trial value function, denoted as v(x, t) = e αt e ξ·x , where α and ξ are complex numbers and complex vectors respectively, satisfying ξ·ξ = λ α , λ α is the eigenvalue of the Jordan canonical form J α of the coupling matrix corresponding to the signal frequency α;

[0033] Construct an auxiliary function v k (x, t), k = 0, 2, …, l - 1; where v 0 (x, t) = v(x, t); when 0 < k < l, v k (x, t) satisfies:

[0034] Δv k (x, t) - λv k (x, t) = v k-1 (x, t).

[0035] In a specific embodiment of the present invention, solving the inverse problem of the pollutant concentration model in the monitoring area includes:

[0036] Transform the pollutant concentration model shown in Equation (1) into the following optimization problem:

[0037]

[0038] where Equation (2) is the inverse problem of Equation (1); N represents the number of predicted pollutant point sources; represents the function set composed of trial value functions; represents the updated pollutant emission concentration of the i-th kind of the j-th pollutant point source at the corresponding frequency α Fourier coefficients, From the updated pollutant emission intensity function get:

[0039]

[0040] in,

[0041] This represents the functional corresponding to the updated concentration function of the i-th pollutant. The calculation process is as follows:

[0042]

[0043] Among them, the left-hand term of equation (3) is The first term on the right side of equation (3) is the integral over the boundary of Ω. For the boundary pollutant concentration observation data of the monitoring area; U(x,T) in the second term on the right side of equation (3) * ) is T * The matrix shows the pollutant concentration in the monitoring area at all times. The matrix M(v) consisting of the trial-valued function and the auxiliary function is:

[0044]

[0045] The third term I(v) on the right-hand side of equation (3) represents the integral value generated by the coupling term, and its expression is as follows:

[0046]

[0047] The auxiliary function matrix on the right-hand side of equation (4) Defined as:

[0048]

[0049] The process of solving equation (2) is as follows: First, let i = 1, and calculate based on the boundary pollutant concentration observation data. By solving the optimization problem of equation (2), we can obtain When 2≤i≤l, the optimization problem obtained by solving equation (2) in the (i-1)th step is obtained. calculate The i-th component is obtained The value of is then used to solve the optimization problem corresponding to the i-th iteration of equation (2) to obtain the value. When i = l, the iteration ends after the optimization problem of equation (2) is solved.

[0050] In one specific embodiment of the present invention, the filtering of false point sources includes:

[0051] 1) According to and the Fourier coefficients obtained from the inversion The Fourier coefficients of the predicted pollutant emission concentrations from point sources were obtained.

[0052] 2) Based on the results of step 1), according to the set of Fourier coefficients {F} of the pollutant emission concentration of the j-th predicted pollutant point source. ij (α)} α Calculate the total Fourier coefficient |F| of the pollutant emission concentration of the j-th predicted pollutant point source. j |:

[0053]

[0054] 3) Determine the result of step 2):

[0055] If |F j If the value is less than or equal to the set threshold, it indicates that the j-th predicted pollutant source is a false source. The j-th predicted pollutant source is discarded, and the total number of predicted pollutant sources is updated to N-1. Otherwise, the predicted pollutant source is retained.

[0056] After traversing all predicted pollutant point sources, the retained predicted pollutant point sources are taken as the final actual pollutant point sources. The number of retained predicted pollutant point sources is denoted as m. The locations and Fourier coefficients of the actual pollutant point sources are then obtained.

[0057] In one specific embodiment of the present invention, it further includes:

[0058] Fourier coefficient F for each actual pollutant point source ij Performing an inverse Fourier transform yields the pollutant intensity function g for each actual pollutant point source. ij (t), ultimately yielding the source tracing results of the pollutants from multiple points:

[0059] A second aspect of the present invention provides a source tracing device for multiple gaseous and fluid pollutant emission sources, comprising:

[0060] The pollutant concentration model building module is used to build a pollutant concentration model for the monitoring area based on the pollutant concentration observation data at the boundary of the air-fluid monitoring area.

[0061] The pollutant concentration function linear transformation module is used to perform a linear transformation on the pollutant concentration function to obtain an updated pollutant concentration function, and then decouple the updated pollutant concentration function into a linear combination of a trial value function and an auxiliary function;

[0062] The pollutant point source prediction module is used to solve the inverse problem of the pollutant concentration model in the monitoring area based on the trial function and the auxiliary function, so as to obtain the location of each predicted pollutant point source and the Fourier coefficient of the pollutant emission concentration at the predicted pollutant point source.

[0063] The actual pollutant point source tracing module is used to filter out false point sources from the predicted pollutant point sources based on the Fourier coefficients, and finally obtain the tracing results of the actual pollutant point sources.

[0064] A third aspect of the present invention provides an electronic device comprising:

[0065] At least one processor; and a memory communicatively connected to said at least one processor;

[0066] The memory stores instructions that can be executed by the at least one processor, and the instructions are configured to perform the above-described method for tracing the emission sources of multiple pollutants from gaseous and fluid sources.

[0067] A fourth aspect of the present invention provides a computer-readable storage medium storing computer instructions for causing the computer to execute the above-described method for tracing multiple sources of gaseous and fluid pollutants.

[0068] Features and beneficial effects of the present invention:

[0069] 1) Based on the convection-diffusion-reaction mechanism that is ubiquitous among pollutants, this invention constructs an inverse problem model of a vector-type reaction-convection-diffusion system with the pollution source term as the unknown. The reaction matrix term characterizes the reaction effect between different pollutants, thereby improving the applicability of the model.

[0070] 2) This invention proposes a decoupling method for complex models. By using a linear transformation dependent on frequency, the matrix corresponding to the reaction term can be decoupled, and the original pollutants can be linearly combined to form new pollutants, so that the corresponding system has low coupling, which reduces the difficulty of subsequent treatment while retaining all the information of the pollution source.

[0071] 3) This invention effectively handles the coupling terms of the mutual reaction and diffusion of multiple pollutants by solving the inverse problem step by step through iterative solution, solving the difficulty of tracing the source of multiple pollutants at the same time. It has a wide range of applications and fast calculation speed.

[0072] 4) The present invention can be directly applied to the scenario of locating and quantitatively retrieving multiple fixed pollutant point sources by using local boundary monitoring data of multiple pollutants in the gas flow observation area within a time period, and can accurately retrieve the dynamic emission process of multiple pollutants over time. Attached Figure Description

[0073] Figure 1 This is an overall flowchart of a method for tracing the emission sources of multiple pollutants from gaseous and fluid sources according to an embodiment of the present invention. Detailed Implementation

[0074] This invention proposes a method and apparatus for tracing the emission sources of multiple pollutants from gaseous and fluid sources. The following detailed description is provided in conjunction with the accompanying drawings and specific embodiments.

[0075] The first aspect of this invention proposes a method for tracing the emission sources of multiple pollutants from air and fluid. This method uses multi-pollutant concentration data from water body boundary monitoring stations (such as riverbanks) or air quality monitoring stations to track and trace the location and emission concentration of emission sources in water or the atmosphere, including:

[0076] Based on the boundary pollutant concentration observation data of the air-fluid monitoring area, a pollutant concentration model of the monitoring area based on the pollutant concentration function is constructed.

[0077] The pollutant concentration function is linearly transformed to obtain an updated pollutant concentration function, and then the updated pollutant concentration function is decoupled into a linear combination of a trial function and an auxiliary function.

[0078] Based on the trial function and the auxiliary function, the inverse problem of the pollutant concentration model in the monitoring area is solved to obtain the location of each predicted pollutant point source and the Fourier coefficient of the pollutant emission concentration at the predicted pollutant point source.

[0079] Based on the Fourier coefficients, false point sources are screened out from the predicted pollutant point sources, and the source tracing results of the actual pollutant point sources are finally obtained.

[0080] In a specific embodiment of the present invention, the overall process of the method for tracing the emission sources of multiple pollutants in gaseous and fluid streams is as follows: Figure 1 As shown, it includes the following steps:

[0081] 1) Obtain observation data on the concentration of pollutants at the boundary of the monitoring area.

[0082] In one specific embodiment of the present invention, various pollutant concentration observation data are obtained from monitoring stations or sampling points at the boundary of the monitoring area. Multiple monitoring stations or sampling points are distributed along the boundary of the monitoring area, which can be approximated as obtaining the pollutant concentration at any point on the boundary. In this embodiment, the monitoring stations or sampling points will continuously monitor changes in water quality indicators. When an indicator becomes abnormal, i.e., the pollutant concentration exceeds a set threshold (in this embodiment, the threshold is 2% of the allowable emission limit calculated based on water environmental quality standards such as GB3838 and environmental benchmarks), it indicates the presence of a pollutant emission source. Then, the time is traced back to an earlier period when the indicator was normal, i.e., when there was no pollutant emission, designated as time zero t=0. The entire time period up to the current time is denoted as T, and designated as the termination time. The pollutant concentration records of all monitoring stations or sampling points within the time interval [0, T] constitute the observation data. In this embodiment, the coordinate set of all locations in the monitoring area is denoted as Ω, and the coordinate set of the area boundary is denoted as... By using monitoring stations or sampling points along the regional boundary, pollutant concentration observation data from time 0 to time T is obtained. This data is used to obtain pollutant concentration observation data at any coordinate x along both riverbanks and upstream and downstream, i.e., along the regional boundary, and is denoted as...

[0083] 2) Based on the observation data obtained in step 1), construct a pollutant concentration model for the monitoring area based on the pollutant concentration function.

[0084] In this embodiment, the concentration of pollutant l at any coordinate x in the monitoring area at time t is denoted as a function U, U = (u1(x,t),...,u l (x,t) is a vector-valued function, x∈Ω, u i (x,t) represents the concentration of the i-th pollutant at location x at time t, where i = 1, 2, ..., 1. Within the entire monitoring area (in one specific embodiment of this invention, the monitoring area is a rectangular water surface), t is any time within the time period [0, T]. This represents the time derivative of U.

[0085] The pollutant concentration model for the monitoring area is then constructed as shown in equation (1):

[0086]

[0087] in, U represents the time derivative of U; β(x,t) is the propagation speed of the pollutant at location x at time t along with the airflow (water in this example); A(x,t)U represents the chemical reaction such as the formation or decomposition of the pollutant at location x at time t, where A(x,t) is an l×l matrix.

[0088]

[0089] Among them, a ij (x, t) represents the reaction coefficient of the j-th pollutant at position x at time t to the i-th pollutant, which is assumed to be a constant independent of the position coordinates and time in this embodiment.

[0090] In this embodiment, at time 0, that is, when starting to monitor this area, the pollutant density distribution in the monitoring area is all zero, that is, U(x, 0) = 0.

[0091] F(x) = (δ(s - s1), …, δ(s - s m )) represents m pollutant point sources in the real scenario, which are respectively at spatial positions s j , j = 1, 2, …, m, and δ(s - s i ) represents the Dirac function at position s j . G(t) represents the pollutant emission intensity function at time t, and the specific expression is:

[0092]

[0093] Among them, the intensity of the i-th pollutant of the j-th pollutant point source at time t is denoted as g ij (t), and g ij (t) is a function of time, and the pollutant will completely disappear or stop emitting at a certain time T * After that, that is, it satisfies T * < t < T, g ij (t) = 0. κ(x, t) is the diffusion rate matrix of the pollutant at position x in the gas fluid at time t, and the specific expression is:

[0094]

[0099] Among them, κ i (x, t), i = 1, …, l represents the spatial diffusion rate of the i-th pollutant at position x at time t.

[0096] 3) Perform a linear transformation on the pollutant concentration function in step 2) to obtain an updated pollutant concentration function, and then decouple the updated pollutant concentration function into a linear combination of a trial value function and an auxiliary function; the specific steps are as follows:

[0097] 3-1) Perform a linear transformation on the pollutant concentration function in step 2) to obtain an updated pollutant concentration function.

[0098] In this embodiment, by performing a linear transformation on the pollutant concentration function U, an updated pollutant concentration function W(x, t) = P α U is obtained, where P αThis allows the coupling matrix to be transformed similarly to its Jordan canonical form J. α The linear transformation of is defined as follows:

[0099]

[0100] Where α represents the signal frequency corresponding to the Fourier coefficients of the intensity function that we want to obtain.

[0101] The updated pollutant concentration function W(x,t)=(w1(x,t),…,w l (x,t)) has low coupling, i.e., w i (x,t) is at most the same as w i-1 (x,t) coupling, where w i (x,t) is the updated concentration function of the i-th pollutant, i = 1, 2, ..., l.

[0102] It should be noted that in this embodiment, the original concentration function U of l pollutants is linearly combined to obtain a new l pollutants and their corresponding concentration function W(x,t). In this process, the location of the pollution source remains unchanged, while the emission intensity of the pollutants is also transformed in the same linear combination manner.

[0103] 3-2) Decouple the updated pollutant concentration function obtained in step 3-1) into a linear combination of trial value function and auxiliary function.

[0104] In this embodiment, a trial-valued function v(x,t) and a corresponding auxiliary function v are constructed. k The updated pollutant concentration function W(x,t) can be expressed as a linear combination of a series of trial-valued functions and auxiliary functions.

[0105] In one specific embodiment of the present invention, for any selected signal frequency α, a complex geometrical optical solution is selected as the trial function, generally expressed as v(x,t)=e αt e ξ·x Let α and ξ be a complex number and a complex vector, respectively, satisfying ξ·ξ=λ α , λ α J is the coupling matrix corresponding to the signal frequency α in Jordan canonical form. α eigenvalues.

[0106] Since the updated pollutant concentration function still has coupling terms, this embodiment requires the use of an auxiliary function v. k (x,t) handles the coupling term in the concentration function of the (k+1)th pollutant, where k = 0, 2, ..., l-1; where v 0(x, t) = v(x, t); When 0 < k < l, it is defined as follows:

[0107] Δv k (x, k) - λv k (x, t) = v k-1 (x, t)

[0108] In common cases, the more different values of α and ξ, the more trial functions, and the more accurate the inversion result, but the specific implementation time is also longer. It can be adjusted according to the requirements of efficiency and accuracy for the scenario. In a specific embodiment of the present invention, For the eigenvalue λ of its corresponding coupling matrix α , the expression of the complex vector ξ is as follows:

[0109]

[0110] In the formula, θ k and γ k are the angles corresponding to the k-th real and imaginary part vectors in the complex vector, r1 and r2 are the moduli of the k-th real and imaginary part vectors in the complex vector respectively, represents the imaginary unit, that is, the square root of -1. The definitions of r1 and r2 and w α are as follows:

[0111]

[0112] where Re() and Im() represent taking the real part and the imaginary part.

[0113] 4) Based on the results of step 3), solve the inverse problem of the pollutant concentration model by stepwise iteration to obtain the position of each predicted pollutant point source and the Fourier coefficients of the pollutant emission concentration at this point source.

[0114] In this embodiment, the pollutant concentration model shown in formula (1) is transformed into the following optimization problem:

[0115]

[0116] where formula (2) is the inverse problem of formula (1). N represents the number of predicted pollutant point sources, and the initial value of N depends on the maximum number of possible pollution sources in the actual situation, such as the total number of factories and sewage pipes in the region. In a specific embodiment of the present invention, the initial value of N is taken as 10. represents the function set composed of trial functions. represents the Fourier coefficient of the i-th updated pollutant emission concentration of the j-th pollutant point source at the corresponding frequency α , by the updated pollutant emission intensity function get:

[0117]

[0118] in,

[0119] This represents the functional corresponding to the updated concentration function of the i-th pollutant, and its computational expression requires the use of step 3) to construct...

[0120]

[0121] The trial-valued function v(x,t) and auxiliary function are constructed. Specifically:

[0122] Among them, the left-hand term of equation (3) is The first term on the right side of equation (3) is the integral over the boundary of the two-dimensional water body Ω. This refers to the observation data of pollutant concentrations at the boundary of the monitoring area. In the second term on the right-hand side of equation (3), U(x,T) * ) is T * The pollutant concentration in the monitoring area at all times, matrix Q α Defined as The matrix M(v) consisting of the trial-valued function and the auxiliary function is defined as follows:

[0123]

[0124] The integral of the second term on the right-hand side of the corresponding equation (3) can be calculated using algorithms for boundary control problems, such as the conjugate gradient algorithm of regularization operators. It should be noted that, depending on the specific monitoring equipment and conditions of the scenario, when the observation data includes time T... * Pollutant concentration U(x,T) across the entire region * When ), the second term of equation (3) is integral

[0125] ∫ Ω M(v)Q α U(x,T * )dx

[0126] It does not require an algorithm for boundary control problems; it can be obtained directly through integration.

[0127] The third term I(v) on the right-hand side of equation (3) represents the integral value generated by the coupling term, defined as follows:

[0128]

[0129] The auxiliary function matrix on the right-hand side of equation (4) Defined as:

[0130]

[0131] In this embodiment, during the solution process, due to Since it is a strictly lower triangular matrix, the first component of I(v) is always 0, and when 2≤i≤l, the i-th component of I(v) is only related to... This is relevant. Based on this, the solution can be obtained iteratively using the following steps: First, when i=1, the solution can be directly calculated based solely on the observed concentration data of boundary pollutants. By solving the optimization problem of equation (2), we can obtain When 2≤i≤l, the optimization problem obtained by solving equation (2) in the (i-1)th step is obtained. Calculate The i-th component and based on this, the i-th component is obtained The value of is then used to solve the optimization problem corresponding to the i-th iteration of equation (2) to obtain the value. In each iteration, this embodiment uses the L-BFGS (Limited-memory Broyden–Fletcher–Goldfarb–Shanno) algorithm to solve equation (2) to obtain the location of each predicted pollutant point source and its corresponding updated Fourier coefficients of pollutant emission concentration, denoted as . When i = l, the iteration ends after the corresponding optimization problem is solved.

[0132] 5) Based on the results of step 4), perform an inverse linear transform on the Fourier coefficients of the predicted pollutant emission concentration function of the pollutant point source to obtain the predicted pollutant emission intensity function of the pollutant point source, thereby filtering out false point sources and obtaining the prediction results of the actual pollutant point sources; the steps are as follows: (Details omitted)

[0133] 5-1) According to and the Fourier coefficients obtained from the inversion use The Fourier coefficients of the predicted pollutant emission concentrations from point sources were obtained.

[0134] 5-2) Based on the results of step 5-1), according to the set of Fourier coefficients {F} of the pollutant emission concentration of the j-th predicted pollutant point source. ij (α)} α All Fourier coefficients F ij (α) (generally taken as α = 0, 1, 2), calculate the total Fourier coefficient |F_j| of the pollutant emission concentration of the j-th predicted pollutant point source. j |, that is:

[0135]

[0136] 5-3) Determine the result of step 5-2):

[0137] If |F j If the pollutant intensity of the j-th predicted pollutant source is less than or equal to the set threshold, such as 2% of the allowable emission limit calculated according to water environmental quality standards and environmental benchmarks like GB3838, then the pollutant intensity of the j-th predicted pollutant source is zero. This predicted pollutant source is a false source, so the j-th predicted pollutant source is discarded, and the total number of predicted pollutant sources is updated to N-1; otherwise, the predicted pollutant source is retained.

[0138] After traversing all predicted pollutant point sources, the retained predicted pollutant point sources are taken as the final actual pollutant point sources. The number of retained predicted pollutant point sources is denoted as m. The locations and Fourier coefficients of the actual pollutant point sources are then obtained.

[0139] 6) Based on the results obtained in step 5), calculate the Fourier coefficient F for each actual pollutant point source. ij Performing an inverse Fourier transform yields the pollutant intensity function g for each actual pollutant point source. ij (t), ultimately obtaining the location and quantitative information of multiple pollutant sources:

[0140] To achieve the above embodiments, a second aspect of the present invention provides a source tracing device for multiple gaseous and fluid pollutant emission sources, comprising:

[0141] The pollutant concentration model building module is used to build a pollutant concentration model for the monitoring area based on the pollutant concentration observation data at the boundary of the air-fluid monitoring area.

[0142] The pollutant concentration function linear transformation module is used to perform a linear transformation on the pollutant concentration function to obtain an updated pollutant concentration function, and then decouple the updated pollutant concentration function into a linear combination of a trial value function and an auxiliary function;

[0143] The pollutant point source prediction module is used to solve the inverse problem of the pollutant concentration model in the monitoring area based on the trial function and the auxiliary function, so as to obtain the location of each predicted pollutant point source and the Fourier coefficient of the pollutant emission concentration at the predicted pollutant point source.

[0144] The actual pollutant point source tracing module is used to filter out false point sources from the predicted pollutant point sources based on the Fourier coefficients, and finally obtain the tracing results of the actual pollutant point sources.

[0145] It should be noted that the aforementioned explanation of the method for tracing multiple pollutant emission sources in a gaseous fluid also applies to the device for tracing multiple pollutant emission sources in a gaseous fluid according to this embodiment, and will not be repeated here. According to the embodiment of the present invention, a device for tracing multiple pollutant emission sources in a gaseous fluid constructs a pollutant concentration model of the monitoring area based on the boundary pollutant concentration observation data of the gaseous fluid monitoring area; the pollutant concentration function is linearly transformed to obtain an updated pollutant concentration function, and then the updated pollutant concentration function is decoupled into a linear combination of a trial function and an auxiliary function; based on the trial function and the auxiliary function, the inverse problem of the pollutant concentration model of the monitoring area is solved to obtain the location of each predicted pollutant point source and the Fourier coefficient of the pollutant emission concentration at the predicted pollutant point source; based on the Fourier coefficient, false point sources are screened out from the predicted pollutant point sources, and finally the tracing result of the actual pollutant point source is obtained. This allows for the location and quantitative inversion of multiple fixed pollutant point sources using local boundary monitoring data of multiple pollutants in a gaseous fluid monitoring area over a time period, and can accurately invert the dynamic emission process of multiple pollutants over time.

[0146] In a specific embodiment of the present invention, the step of constructing a monitoring area pollutant concentration model based on a pollutant concentration function includes:

[0147] Let the concentration of pollutant l at any coordinate x in the monitoring area at time t be denoted as a function U, where U = (u1(x,t),...,u...). l (x,t) is a vector-valued function, x∈Ω, u i (x,t) represents the concentration of the i-th pollutant at x at time t, i = 1, 2, ..., l, Ω represents the set of coordinates of all locations in the monitoring area, and t is any time in the time period [0,T], where time T represents the time when pollutant emission is observed at the boundary of the monitoring area, and time 0 represents the time when no pollutant emission occurs.

[0148] The pollutant concentration model for the monitoring area is then constructed as shown in equation (1):

[0149]

[0150] in, Let U represent the time derivative of U; β(x,t) be the propagation velocity of the pollutant at location x at time t following the airflow; A(x,t)U represents the chemical reaction of the formation or decomposition of the pollutant at location x at time t, and A(x,t) is an l×l matrix.

[0151]

[0152] Among them, aij (x, t) represents the reaction coefficient of the j-th pollutant at position x at time t to the i-th pollutant;

[0153] Let at time 0, the pollutant density distribution in the monitoring area be all zero, that is, U(x, 0) = 0;

[0154] F(x) = (δ(s - s1), …, δ(s - s m )) represents m pollutant point sources in the real scenario, located at spatial positions s j , j = 1, 2, …, m, δ(s - s i ) represents the Dirac function at position s j ;

[0155] G(t) represents the pollutant emission intensity function at time t, and the expression is:

[0156]

[0157] Among them, the intensity of the i-th pollutant of the j-th pollutant point source at time t is denoted as g ij (t); the pollutant completely disappears or stops emitting after time T * , that is, it satisfies T * < t < T, g ij (t) = 0;

[0158] κ(x, t) is the diffusion rate matrix of the pollutant at position x at time t in the air fluid, and the expression is: [[ID=​​​​​​​​​​​​​​​​​​​​​​​​​

[0165] The updated pollutant concentration function \(W(x,t)=(w_1(x,t),\ldots,w l (x,t))\), where \(w i (x,t)\) is the updated concentration function of the \(i\)-th pollutant, \(i = 1,2,\ldots,l\).

[0166] In a specific embodiment of the present invention, it further includes:

[0167] For any signal frequency \(\alpha\), a complex geometric optical solution is selected as the trial value function, denoted as \(v(x,t)=e αt e ξ·x \), where \(\alpha\) and \(\xi\) are complex numbers and complex vectors respectively, satisfying \(\xi\cdot\xi=\lambda α \), and \(\lambda α is the eigenvalue of the Jordan canonical form \(J α of the coupling matrix corresponding to the signal frequency \(\alpha\);

[0168] Construct auxiliary functions \(v k (x,t)\), \(k = 0,2,\ldots,l - 1\); where \(v 0 (x,t)=v(x,t)\); when \(0\lt k\lt l\), \(v k (x,t)\) satisfies:

[0169] \(\Delta v k (x,t)-\lambda v k (x,t)=v k-1 (x,t)\).

[0170] In a specific embodiment of the present invention, solving the inverse problem of the pollutant concentration model in the monitoring area includes:

[0171] Converting the pollutant concentration model shown in Equation (1) into the following optimization problem:

[0172]

[0173] where Equation (2) is the inverse problem of Equation (1); \(N\) represents the number of predicted pollutant point sources; represents the function set composed of trial value functions; represents the Fourier coefficient of the \(i\)-th updated pollutant emission concentration of the \(j\)-th pollutant point source corresponding to the frequency \(\alpha ; is obtained from the updated pollutant emission intensity function :

[0174]

[0175] where,

[0176] This represents the functional corresponding to the updated concentration function of the i-th pollutant. The calculation process is as follows:

[0177]

[0178] Among them, the left-hand term of equation (3) is The first term on the right side of equation (3) is the integral over the boundary of Ω. For the boundary pollutant concentration observation data of the monitoring area; U(x,T) in the second term on the right side of equation (3) * ) is T * The matrix shows the pollutant concentration in the monitoring area at all times. The matrix M(v) consisting of the trial-valued function and the auxiliary function is:

[0179]

[0180] The third term I(v) on the right-hand side of equation (3) represents the integral value generated by the coupling term, and its expression is as follows:

[0181]

[0182] The auxiliary function matrix on the right-hand side of equation (4) Defined as:

[0183]

[0184] The process of solving equation (2) is as follows: First, let i = 1, and calculate based on the boundary pollutant concentration observation data. By solving the optimization problem of equation (2), we can obtain When 2≤i≤l, the optimization problem obtained by solving equation (2) in the (i-1)th step is obtained. calculate The i-th component is obtained The value of is then used to solve the optimization problem corresponding to the i-th iteration of equation (2) to obtain the value. When i = l, the iteration ends after the optimization problem of equation (2) is solved.

[0185] In one specific embodiment of the present invention, the filtering of false point sources includes:

[0186] 1) According to and the Fourier coefficients obtained from the inversion The Fourier coefficients of the predicted pollutant emission concentrations from point sources were obtained.

[0187] 2) Based on the results of step 1), according to the set of Fourier coefficients {F} of the pollutant emission concentration of the j-th predicted pollutant point source.ij (α)} α Calculate the total Fourier coefficient |F| of the pollutant emission concentration of the j-th predicted pollutant point source. j |:

[0188]

[0189] 3) Determine the result of step 2):

[0190] If |F j If the value is less than or equal to the set threshold, it indicates that the j-th predicted pollutant source is a false source. The j-th predicted pollutant source is discarded, and the total number of predicted pollutant sources is updated to N-1. Otherwise, the predicted pollutant source is retained.

[0191] After traversing all predicted pollutant point sources, the retained predicted pollutant point sources are taken as the final actual pollutant point sources. The number of retained predicted pollutant point sources is denoted as m. The locations and Fourier coefficients of the actual pollutant point sources are then obtained.

[0192] In one specific embodiment of the present invention, it further includes:

[0193] Fourier coefficient F for each actual pollutant point source ij Performing an inverse Fourier transform yields the pollutant intensity function g for each actual pollutant point source. ij (t), ultimately yielding the source tracing results of the pollutants from multiple points:

[0194] To implement the above embodiments, a third aspect of the present invention provides an electronic device, comprising:

[0195] At least one processor; and a memory communicatively connected to said at least one processor;

[0196] The memory stores instructions that can be executed by the at least one processor, and the instructions are configured to perform the above-described method for tracing the emission sources of multiple pollutants from gaseous and fluid sources.

[0197] To implement the above embodiments, a fourth aspect of the present invention provides a computer-readable storage medium storing computer instructions for causing the computer to execute the above-described method for tracing multiple sources of gaseous and fluid pollutants.

[0198] It should be noted that the computer-readable medium described in this disclosure can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this disclosure, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0199] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device. The aforementioned computer-readable medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to perform a source tracing method for multiple gaseous and fluid pollutant emission sources according to the above embodiments.

[0200] Computer program code for performing the operations of this disclosure can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0201] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0202] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0203] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the function involved, as will be understood by those skilled in the art to which embodiments of this application pertain.

[0204] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which programs can be printed, because programs can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0205] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0206] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0207] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0208] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A method of tracing a source of multiple pollutants emitted from an air flow, characterized by, The method comprises the following steps: According to the boundary pollutant concentration observation data of the airflow monitoring area, a pollutant concentration model of the monitoring area based on a pollutant concentration function is constructed; The pollutant concentration function is linearly transformed to obtain an updated pollutant concentration function, and then the updated pollutant concentration function is decoupled into a linear combination of a trial function and an auxiliary function; Based on the trial function and the auxiliary function, the inverse problem of the pollutant concentration model of the monitoring area is solved to obtain the position of each predicted pollutant point source and the Fourier coefficient of the pollutant emission concentration at the predicted pollutant point source; Based on the Fourier coefficient, false point sources are filtered out from the predicted pollutant point sources to finally obtain the tracing result of the actual pollutant point source.

2. The method of claim 1, wherein, The construction of the pollutant concentration model of the monitoring area based on the pollutant concentration function comprises: Let the concentration of pollutant l at any coordinate x in the monitoring area at time t be denoted as a function U, where U = (u1(x,t),...,u...). l (x,t) is a vector-valued function, x∈Ω, u i (x,t) represents the concentration of the i-th pollutant at x at time t, i = 1, 2, ..., l, Ω represents the set of coordinates of all locations in the monitoring area, and t is any time in the time period [0,T], where time T represents the time when pollutant emission is observed at the boundary of the monitoring area, and time 0 represents the time when no pollutant emission occurs. The construction of the pollutant concentration model of the monitoring area is shown in formula (1): wherein, denotes the time derivative of U; β(x, t) is the propagation speed of the pollutant following the air flow at time t and at location x; A(x, t)U represents the chemical reactions of generation or decomposition of the pollutant at time t and at location x, A(x, t) being a lxl matrix: wherein a ij (x, t) represents the reaction coefficient of the jth pollutant to the ith pollutant at time t and position x. Let the pollutant density distribution in the monitoring area at time 0 be all zero, that is, U(x, 0) = 0; F(x) = (δ(s - s1),..., δ(s - sm))T m represents m point sources of pollutants in the real scene, respectively at spatial locations s j j = 1, 2,..., m, δ(s - s i ) represents the Dirac function at location s j j G(t) represents the emission intensity function of the pollutant at time t, and the expression is: Wherein, the jth pollutant point source at time t of the ith pollutant intensity is denoted as g ij (t); the pollutant at time T * After completely disappearing or stopping discharging, that is, satisfying T * <t < T, g ij (t) = 0; κ(x, t) is the diffusion rate matrix of the pollutant at time t and position x in the airflow, and the expression is: where k i (x, t), i = 1,..., / denotes the spatial dispersion rate of the i-th pollutant at time t and location x.

3. The method of claim 1, wherein, The linear transformation of the pollutant concentration function to obtain the updated pollutant concentration function comprises: By performing a linear transformation on the pollutant concentration function U, an updated pollutant concentration function W(x, t) = P α U is obtained, where P α is a linear transformation that couples the matrix to its Jordan canonical form J α defined as follows: Wherein, α represents the signal frequency corresponding to the Fourier coefficient of the intensity function to be obtained; the updated pollutant concentration function W(x, t) = (w1(x, t),..., wl(x, t)), where w l (x, t) is the updated i-th pollutant concentration function, i = 1, 2,..., l. i (x, t) is the updated i-th pollutant concentration function, i = 1, 2,..., l.

4. The method of claim 3, wherein, Further comprising: For any signal frequency a, the complex geometrical-optics solution is chosen as the trial function, denoted by v(x, t) = e αt e ξ·x , a and ξ are complex number and complex vector, respectively, satisfying ξ · ξ = λ α , λ α is the eigenvalue of the Jordan normal form J α of the coupling matrix corresponding to the signal frequency a; Constructing the auxiliary function v k (x,t), k = 0, 2,..., l-1; where v 0 (x,t) = v(x,t); when 0 < k < l, v k (x,t) satisfies: Δv k (x,t) - λv k (x,t) = v k-1 (x,t).

5. The method of claim 4, wherein, The solving of the inverse problem of the pollutant concentration model of the monitoring area comprises: The pollutant concentration model shown in formula (1) is converted into the following optimization problem: wherein, formula (2) is the inverse problem of formula (1);N represents the number of predicted point sources of pollutants; represent a function set composed of trial value functions; represent the Fourier coefficient of the i-th updated pollutant emission concentration of the j-th point source of pollutants corresponding to the frequency α, obtained from the updated pollutant emission intensity function ​​ wherein, denotes the updated functional corresponding to the i-th pollutant concentration function, The calculation process is as follows: where the left-hand term of equation (3) is The first term on the right-hand side of equation (3) is the integral over the boundary of Ω, is the observed data of the boundary pollutant concentration of the monitoring region; the second term on the right-hand side of equation (3) is * is the pollutant concentration at time T * over the monitoring region, and the matrix The matrix M(v) composed of the trial function and the auxiliary function is: The third term I(v) on the right side of the formula (3) represents the integral value of the coupling term, and the expression is as follows: The auxiliary function matrix on the right side of the equation of formula (4) is defined as: The process of solving equation (2) is as follows: in the first step, let i = 1, and calculate by solving the optimization problem of equation (2) in the first step j , when 2≤i≤l, the value of {s} j , 1≤k≤i-1, the i-th component of {s} is calculated to obtain the value of {s} , and then the optimization problem of equation (2) corresponding to the i-th step iteration is solved to obtain {s} j , when i = l, the optimization problem of equation (2) is solved, and the iteration ends.

6. The method of claim 5, wherein, The filtering of false point sources comprises: 1) According to and the Fourier coefficients obtained by inversion obtaining the Fourier coefficients of the predicted pollutant emission concentration of the point source of pollution 2) Based on the results of step 1), according to the set of Fourier coefficients {F} of the pollutant emission concentration of the j-th predicted pollutant point source. ij (α)} α Calculate the total Fourier coefficient |F| of the pollutant emission concentration of the j-th predicted pollutant point source. j |: 3) The result of step 2) is judged: If |F j If |Fj| is less than or equal to a set threshold, it indicates that the jth predicted pollutant point source is a false point source, the jth predicted pollutant point source is discarded, and the total number of predicted pollutant point sources is updated to N-1; otherwise, the predicted pollutant point source is retained. After the iteration of all the predicted pollutant point sources, the remaining predicted pollutant point sources are taken as the final obtained actual pollutant point sources, the number of the remaining predicted pollutant point sources is recorded as m, and the position and Fourier coefficient of the actual pollutant point sources are obtained:

7. The method of claim 6, wherein, Further comprising: Fourier coefficients F of each actual pollutant point source ij performing inverse Fourier transform to obtain the pollutant intensity function g of each actual pollutant point source ij (t), ultimately obtaining the tracing result of the pollutant multi-point source:

8. An apparatus for tracing a source of multiple pollutants in an air stream, comprising: Comprising: A pollutant concentration model construction module is configured to construct a pollutant concentration model of a monitoring area based on a pollutant concentration function according to boundary pollutant concentration observation data of an airflow monitoring area; A pollutant concentration function linear transformation module is configured to linearly transform the pollutant concentration function to obtain an updated pollutant concentration function, and then decouple the updated pollutant concentration function into a linear combination of a trial function and an auxiliary function; A predicted pollutant point source solving module is configured to solve the inverse problem of the pollutant concentration model of the monitoring area based on the trial function and the auxiliary function to obtain the position of each predicted pollutant point source and the Fourier coefficient of the pollutant emission concentration at the predicted pollutant point source; An actual pollutant point source tracing module is configured to filter out false point sources from the predicted pollutant point sources based on the Fourier coefficient to finally obtain the tracing result of the actual pollutant point source.

9. An electronic device, comprising: Comprising: At least one processor; And a memory in communication connection with the at least one processor; Wherein, the memory stores instructions executable by the at least one processor, and the instructions are configured to execute the method of any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer instructions for causing the computer to perform the method of any one of claims 1-7.

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