Source term inversion method and system with deposition information release rate prior

Abstract

The present application relates to a kind of source term inversion method and system with deposition information release rate prior, comprising: obtaining data parameters, and initialization correction coefficient matrix W and source term release rate σ;Obtain deposition data at selected observation site;Based on the deposition data at observation site, make release rate prior considering wet deposition;Based on the joint bias correction method with prior, solve correction coefficient matrix W and source term release rate σ until convergence, obtain transport matrix H new ;Using transport matrix H new Solve source term release rate σ until convergence, obtain the source term inversion result with deposition information release rate prior.The present application can obtain the source term inversion estimation result with deposition information release rate prior using deposition observation data and air concentration observation input.

Description

Technical Field

[0001] The present invention relates to a source term inversion method and system with a priori sedimentation information release, and relates to the field of nuclear application technology. Background Art

[0002] Radioactive aerosols are a hallmark of atmospheric radioactive releases from nuclear tests, nuclear accidents, nuclear power plants, and spent fuel reprocessing plants, and are a crucial indicator of atmospheric radioactive releases. In many recent atmospheric radioactive releases, radioactive aerosols have been the only substance detected, further highlighting their importance as a basis for determining releases. Due to the lack of clarity regarding the temporal evolution of release rates, traditional forward source modeling methods that rely on nuclear facility operating conditions and inverse source modeling methods based on air concentrations are unable to resolve leakage source terms, hindering nuclear emergency response and posing a new challenge to nuclear safety.

[0003] Under complex weather conditions such as clouds, rain, fog, and snow, air concentration data are highly sensitive to local meteorological factors such as wind and precipitation, and are prone to weather-driven fluctuations, which can overwhelm the leak location information. This results in a weak correlation between high air concentration measurement points and release locations, leading to the missing of key release peaks in the inverted release rate estimates, making it impossible to simulate and reproduce the observed high-concentration deposition areas. In sharp contrast, the high-concentration deposition areas during major release events all show a strong correlation with the release locations, covering the release locations, and more than 90% of the deposition at these locations is contributed by wet deposition.

[0004] However, surface cumulative deposition observations lack temporal variation information, and existing methods are unable to decompose release rate information from different release periods from the deposition. As a result, deposition data are often used to verify inversion results or simply included in the inversion as an element in the observation vector, unable to compensate for the missing key release peak information. Furthermore, there is a lack of effective methods for inverting source terms that consider release information derived from deposition information a priori. This makes it difficult to simultaneously address uncertainties arising from atmospheric diffusion patterns and information conflicts arising from different types of observation data, and to provide release rate estimates with continuous characteristics. Summary of the Invention

[0005] The present invention aims to address at least one of the technical problems existing in the prior art. To address this issue, the present invention provides a source term inversion method and system with a priori information on sedimentation release. This method is capable of analyzing sedimentation information under wet deposition, generating a priori information on source term release based on sedimentation observation data, and inverting the estimated time-series release rate of the source term.

[0006] In order to achieve the purpose of the invention, the technical solution provided by the present invention is:

[0007] The first aspect of the present application provides a source term inversion method with deposition information release rate prior, comprising:

[0008] Obtaining data parameters, and initializing a correction coefficient matrix W and a source term release rate σ;

[0009] Obtaining deposition data at selected observation sites;

[0010] Based on the deposition data at the observation sites, a release rate prior considering wet deposition is made;

[0011] Based on the joint bias correction method with the prior, the correction coefficient matrix W and the source term release rate σ are solved until convergence, and a transport matrix H new is obtained.

[0012] Using the transport matrix H new , the source term release rate σ is solved until convergence, and the source term inversion result with the deposition information release rate prior is obtained.

[0013] Further, the data parameters include a deposition observation site coordinate vector group (x o , y o ), an initial transport matrix, air concentration observation data, a regularization parameter λ, and a convergence condition, o represents the number of selected sites, wherein the convergence condition includes the maximum number of iterations and the allowed minimum error.

[0014] Further, the obtaining of the deposition data at the selected observation sites comprises: the rainfall of the selected observation sites, the deposition time series change rate dDep c n of the constant release σ o,t at the sites, the site deposition time series change rate aDep o,t obtained only by air concentration inversion, and the surface cumulative deposition observation value mDep o , wherein n represents the simulated time step, and t is the time.

[0015] Further, the making of the release rate prior considering wet deposition based on the deposition data at the observation sites comprises:

[0016] Obtaining a wet deposition dominant period tR, and obtaining the deposition time series change rate dDep o,tR of the constant release sites in the corresponding dominant period, and the site deposition time series change rate aDep o,tR obtained only by air concentration inversion in the dominant period;

[0017] Converting the release rate σ s o,tR of each site in the dominant period tR considering wet deposition, and calculating the release rate prior σ i of the release point in the dominant period tR considering wet deposition.o,tR , making the release of wet deposition into consideration first test σ dep n .

[0018] Furthermore, the release rate σ considering wet deposition during the dominant period tR of each station is converted s o,tR , and calculate the release priority σ considering wet deposition in the dominant period tR at the release point i o,tR , making the release of wet deposition into consideration first test σ dep n ,include:

[0019] Release rate σ considering wet deposition during the dominant period tR at each station s o,tR The calculation process is:

[0020]

[0021]

[0022] Where, σ c tR is the constant release rate during the wet deposition dominant period tR;

[0023] Release point (x r ,y r ) in the dominant period, considering the release of wet deposition, the first test σ i o,tR :

[0024]

[0025]

[0026] σ i tR =σ s o,tR ·weight o (5)

[0027] Considering the release of wet deposition, first verify σ dep n The calculation formula is:

[0028]

[0029] Where, σ i t is the release rate at t = i, σ c t The release rate is constant.

[0030] Furthermore, the joint deviation correction method based on prior solves the correction coefficient matrix W and the source term release rate σ until convergence, and obtains the transport matrix H new ,include:

[0031] Using the alternating minimization method, by alternately solving equations (7) and (8) until the convergence condition is met, the final correction coefficient matrix is ​​obtained. The final correction coefficient matrix is ​​multiplied with the original transport matrix by equation (9) to obtain the deviation-corrected transport matrix H new ,in:

[0032] σ-step:

[0033]

[0034] -step:

[0035]

[0036] When the convergence condition is reached, we can get:

[0037] H new =W·H (9)

[0038] In the above formula, is the correction coefficient vector, W is the correction coefficient matrix, and the relationship between the two is H is the transport matrix, μ is the observation vector, represents the central moment of the correction coefficient vector, Const is the imposed constant constraint, δ is the source term leakage rate to be estimated, and δ dep A preliminary test is performed to consider the release of wet deposition.

[0039] Furthermore, the transport matrix H new Solve the source term release rate δ until convergence, and obtain the source term inversion result with a priori sedimentation information release, including: using the alternating direction multiplier method to solve Equation (10) until the convergence condition is met, and iteratively solve the target source term release rate δ:

[0040]

[0041] In a second aspect, the present invention further provides a source term inversion system with a priori sedimentation information release, the system comprising:

[0042] The parameter input and initialization unit is configured to obtain data parameters and initialize the correction coefficient matrix W and the source term release rate σ;

[0043] a sedimentation data acquisition unit configured to acquire sedimentation data at a selected observation site;

[0044] A release priori prediction unit considering wet deposition is configured to calculate and generate a release priori prediction considering wet deposition based on deposition data at an observation site;

[0045] The transport matrix obtaining unit is configured to solve the correction coefficient matrix W and the source term release rate σ based on the joint deviation correction method with prior until convergence, and obtain the transport matrix H new ;

[0046] The inversion solver with priors is configured to use the transport matrix H new The source term release rate δ is solved until convergence, and the source term inversion result with a priori sedimentation information release is obtained.

[0047] In a third aspect, the present invention further provides a processing device, comprising at least a processor and a memory, wherein a computer program is stored on the memory, and wherein the processor executes the computer program to implement the source term inversion method with a priori deposition information release.

[0048] In a fourth aspect, the present invention further provides a computer storage medium having computer-readable instructions stored thereon, wherein the computer-readable instructions can be executed by a processor to implement the source term inversion method with a priori release of deposition information.

[0049] The present invention adopts the above technical solution, which has the following characteristics:

[0050] 1. The present invention considers the deposition observation data and converts the release prior based on the deposition information at the release point by comparing with the constant release deposition and the air concentration-only inversion simulation deposition, so that the source term release rate information implicit in the highest deposition area can be taken into account.

[0051] 2. The present invention uses a joint bias correction method with a priori sedimentation information release, using a correction coefficient matrix to correct the estimation result bias caused by the uncertainty of the atmospheric diffusion model itself, and provides a bias-corrected transport matrix for subsequent inversion calculations;

[0052] 3. The present invention uses a total variation method with a priori information release of deposition information. Taking the new transport matrix as one of the inputs, the method obtains a release rate estimate with continuity characteristics through iterative solution. This release rate estimate can adaptively consider the prior input and compensate for the peak loss phenomenon that exists only in the air concentration observation input.

[0053] In summary, the present invention can use deposition observation data and air concentration observation inputs to obtain a priori source term inversion estimation results with deposition information release, and verify it using Fukushima nuclear accident data. The obtained release rate estimate can obtain the key release peak and the high concentration area that was missing in previous estimation simulations. BRIEF DESCRIPTION OF DRAWINGS

[0054] Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The detailed description is made with reference to the accompanying drawings.

[0055] Figure 1 Flow chart of the source inversion method with prior information of deposition information release rate for the embodiments of the present application.

[0056] Figure 2 Structure diagram of the electronic device for the embodiments of the present application. DETAILED DESCRIPTION

[0057] It is to be understood that the terminology used herein is for the purpose of describing particular example embodiments only and is not intended to be limiting. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. The terms "comprises", "comprising", "includes", "including" and "has" are inclusive and therefore specify the presence of stated features, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, steps, operations, elements, components, and / or groups thereof. The method steps, processes, and operations described herein are not to be construed as necessarily requiring their performance in the particular order in which they are described, unless specifically identified as an order dependent step. It is also to be understood that additional or alternative steps can be employed.

[0058] Although the terms first, second, third, and the like can be used herein to describe various elements, components, regions, layers and / or sections, these elements, components, regions, layers and / or sections should not be limited by these terms. These terms can be only used to distinguish one element, component, region, layer or section from another region, layer or section. Terms such as "first", "second", and other numerical terms when used herein do not imply a sequence or order unless clearly indicated by the context. Thus, a first element, component, region, layer or section discussed below could be termed a second element, component, region, layer or section without departing from the teachings of the example embodiments.

[0059] Spatially relative terms, such as "inner", "outer", "beneath", "below", "lower", "above", "upper", and the like, can be used herein for ease of description to describe one element or feature's relationship to another element(s) or feature(s) as illustrated in the figures. Spatially relative terms can be intended to encompass different orientations of the device in use or operation in addition to the orientation depicted in the figures.

[0060] Due to the lack of an effective method for inverting the source term using a release prior derived from deposition information, it is difficult to simultaneously address the uncertainty derived from the atmospheric diffusion model and the information conflicts caused by different types of observation data, thereby providing a release rate estimate with continuous characteristics. The source term inversion method with a release prior derived from deposition information provided by the present invention first obtains the wet deposition dominant period tR. Then, using deposition observation data, constant release deposition simulation, and air concentration-only release rate deposition simulation data, a release prior derived from deposition information at the release point is obtained. This allows cumulative surface deposition without temporal variation characteristics to be converted into a release prior with temporal variation. In addition, during the inversion calculation process, the present invention first uses a joint bias correction method with a release prior derived from deposition information. This prior information is absorbed during the iterative process, and the transport matrix bias caused by the uncertainty of the atmospheric diffusion model is corrected. Ultimately, a new transport matrix containing the prior information and corrected for the model bias is provided. Using this new transport matrix, a total variation method with a release prior derived from deposition information is used to shape the continuity characteristics of the estimated release rate during the iterative process, and the temporal variation derived from the prior is considered, thereby ensuring that the source term estimate has deposition observation information. Therefore, the present invention makes up for the missing key release peak that existed in the past when relying solely on air concentration inversion, and can simulate the high concentration area corresponding to the actual surface cumulative deposition.

[0061] Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments described herein. Rather, these embodiments are provided to enable a more thorough understanding of the present invention and to fully convey the scope of the present invention to those skilled in the art.

[0062] Example 1: Figure 1 As shown, the source term inversion method with a priori sedimentation information release provided in this embodiment includes:

[0063] S1. Input data parameters and initialize the correction coefficient matrix W∈R m×m and source term release rate σ∈R n , R represents the data domain.

[0064] Specifically, the input data parameters include: sedimentation observation site coordinate vector group (x o ,y o ), transport matrix H∈R m×n , air concentration observation data b∈R m , regularization parameter λ, and convergence conditions.

[0065] In this embodiment, the sedimentation observation site coordinate vector group (x o ,y o ) is composed of any number of coordinates of the area with sediment observation data, where o represents the number of selected stations. For example, when o is 5, (x 5 ,y 5 ) represents the five coordinates (x1,y1), (x2,y2),…, (x5,y5).

[0066] In this embodiment, the transport matrix H∈R m×n It can be made by the atmospheric diffusion model, which will not be described in detail here. Among them, m represents the amount of data provided by the measuring point in the complete simulation period, and n represents the time step of the simulation. If the number of measuring points is num, and the number of measurements at any measuring point in the complete simulation period is f, then m = num*f.

[0067] In this embodiment, the observation data b∈R m It consists of real ambient air concentration observation data, where the order of the observation data should be consistent with the order of the data in any column of the transport matrix.

[0068] In this embodiment, the regularization parameter λ is determined by a generalized cross-validation method and is a parameter required for subsequent inversion iterations.

[0069] In this embodiment, the convergence conditions include a maximum number of iterations and a minimum allowable error. Specifically, the number of iterations can be set as needed, and can be set to 2000. Taking this as an example, but not limited to this, the relative error threshold of the release rate estimation value between two adjacent steps is set to 1e-3, and the relative error threshold of the correction coefficient vector between two adjacent steps is set to 1e-3. The determination formulas for the two relative errors are:

[0070] Correction coefficient vectors for the kth and k-1th steps The relative error When it is less than 1e-3, the iteration is terminated.

[0071] The relative error of the release rate estimates σ between the kth step and the k-1th step When it is less than 1e-3, the iteration is terminated.

[0072] The correction coefficient matrix W is a parameter in the joint method and is calculated as in The k-1 step correction coefficient matrix W will be used to update the source term release rate σ of the k-1 step n , and the source term release rate σ in the kth step n Can be used to update the k-th step

[0073] In this embodiment, the calibration coefficient vector is initialized That is, all its elements are set to 1.

[0074] In this embodiment, the initialization source leakage rate vector σ=(σ1,σ2,…,σ m ), which sets all elements in the vector to 0.

[0075] S2, that is, obtaining sedimentation data at the selected observation site

[0076] Specifically, we first use an arbitrary atmospheric diffusion model to simulate the gridded time series data using constant release and air concentration inversion source terms. According to the selected observation station coordinates, we obtain the rainfall r corresponding to the selected observation station in the selected data from the observation data and the existing simulation data. o,t , constant release σ c n Sedimentation time series change rate dDep at the lower site o,t , the site deposition time series change rate aDep obtained by inverting only the air concentration o,t and surface cumulative deposition observation value mDep o .

[0077] In this embodiment, the rainfall r o,t It is generated by the atmospheric diffusion model using meteorological data. When the station coordinates are given, the rainfall at the corresponding location can be obtained by indexing.

[0078] In this embodiment, the constant release σ c n Sedimentation time series change rate dDep at the lower site o,t : Constant release σ c n The release at each moment is 8470644763.04348 Bq / h. The atmospheric diffusion model can be used to obtain the corresponding sedimentation time series change rate in the simulation area at each moment. When the station coordinates are given, the sedimentation time series change rate dDep at the corresponding position can be obtained by indexing. o,t .

[0079] In this embodiment, the inversion of air concentration only refers to the inversion of pollutant air concentration data only, and the atmospheric diffusion model is used to obtain the corresponding deposition time series change rate in the simulation area at each moment. When the station coordinates are given, the deposition time series change rate aDep at the corresponding position can be obtained by indexing. o,t .

[0080] In this embodiment, the surface cumulative deposition observation value mDep o It consists of actual measured data and provides important information based on sedimentation data priors.

[0081] S3. Based on the sedimentation data at the observation site, make the release of wet deposition into consideration.dep n ,include:

[0082] S31. Obtain the dominant period of wet deposition tR and obtain the deposition time series change rate dDep of the constant release site under the dominant period tR o,tR The site deposition time series change rate aDep obtained by inverting only the air concentration during the dominant period o,tR .

[0083] In this embodiment, a rainfall threshold threr is set when wet deposition dominates. When the rainfall at the selected station exceeds the rainfall threshold during a certain period, it is the period tR when wet deposition dominates and deposition occurs.

[0084] Use time period tR to filter dDep in S2 o,t and aDep o,t The time series change rate of station sedimentation during the period dominated by wet deposition dDep o,tR with aDep o,tR .

[0085] S32. Calculate the release rate σ considering wet deposition during the dominant period tR of each station s o,tR , calculate the release of wet deposition at the release point for the dominant period tR first test σ i o,tR , making the release of wet deposition into consideration first test σ dep n .

[0086] In this embodiment, the release rate σ of wet deposition considered in the dominant period tR of each station is s o,tR It is mDep in S2 o dDep in S3 o,tR with aDep o,tR The calculation process is shown in formula (1) and formula (2).

[0087]

[0088]

[0089] Where, σ c tR is the constant release rate during the wet deposition-dominated period tR.

[0090] The release of the dominant period at the release point taking into account the wet deposition is first tested σ i o,tR , is in σ s o,tR Based on the inverse distance weighted method, the release point (x r,y r ) is converted into the prior information of the release point. The calculation process is shown in Equations (3) to (5).

[0091]

[0092]

[0093] σ i tR =σ s o,tR ·weight o (5)

[0094] Considering the release of wet deposition, first verify σ dep n , is to use σ i o,tR Replace the constant release σ c n The release rate during the period dominated by wet deposition is obtained, and the calculation process is shown in formula (6).

[0095]

[0096] Where, σ i t is the source term release rate at t = i, σ c t The release rate is constant.

[0097] S4. Use the joint bias correction method with priors to alternately solve the correction coefficient matrix W and the source term release rate σ until convergence, and obtain the new transport matrix H with corrected bias new .

[0098] In this embodiment, the alternating minimization method is used to alternately solve equations (7) and (8) until convergence, that is, the correction coefficient vectors of the kth step and the k-1th step are The relative error Less than 1e-3, or the relative error between the release rate estimates σ at the kth and k-1th steps Less than 1e-3, the final correction coefficient matrix is ​​obtained. The final correction coefficient matrix is ​​multiplied with the original transport matrix by formula (9) to obtain the new transport matrix H with the corrected deviation. new ,in:

[0099] σ-step:

[0100]

[0101] Where σ is the source leakage rate to be estimated, W is the correction coefficient matrix, H is the transport matrix, μ is the observation vector, λ is the regularization parameter, and σ is dep A preliminary test is performed to consider the release of wet deposition.

[0102] -step:

[0103]

[0104] Where, is the correction coefficient vector, W is the correction coefficient matrix, and the relationship between the two is H is the transport matrix, μ is the observation vector, represents the central moment of the correction coefficient vector, and Const is the imposed constant constraint.

[0105] When the convergence condition is reached, we can get:

[0106] H new =W·H (9)

[0107] S5. Use the total variation method with priors and the new transport matrix H with corrected deviations new Perform release rate calculation to solve the source term release rate σ until convergence, and obtain the final estimated vector σ final n .

[0108] In this example, the alternating direction multiplier method in the total variation method with priors is used to solve Equation (10) until the convergence condition is met, that is, the relative error of the release rate estimate σ between the kth step and the k-1th step Less than 1e-3, the solution process splits the original problem into three sub-processes, iteratively solving the target source term release rate σ. This process is an existing technology and will not be described in detail here.

[0109]

[0110] The final estimated vector σ calculated in this embodiment final n This is the inversion result of the source term with wet deposition data prior. The deposition simulation obtained using this source term can improve the phenomenon of insufficient deposition in high-concentration areas and obtain more accurate source terms for accident consequence assessment.

[0111] Example 2: The aforementioned Example 1 provides a source term inversion method with a priori information release for sedimentation information. Correspondingly, this example provides a source term inversion system with a priori information release for sedimentation information. The system provided in this example can implement the source term inversion method with a priori information release for sedimentation information release of Example 1. This system can be implemented through software, hardware, or a combination of software and hardware. For ease of description, this example is described separately by functionally categorized units. Of course, during implementation, the functions of each unit can be implemented in the same or multiple software and / or hardware components. For example, the system can include integrated or separate functional modules or units to perform the corresponding steps of each method in Example 1. Because the system in this example is substantially similar to the method example, the description of this example is relatively simple. For relevant details, please refer to the partial description of Example 1. The example of the source term inversion system with a priori information release for sedimentation information provided by the present invention is merely illustrative.

[0112] Specifically, the source term inversion system with a priori sedimentation information release provided in this embodiment includes:

[0113] The present invention also provides a source term inversion system with a priori sedimentation information release, the system comprising:

[0114] The parameter input and initialization unit is configured to obtain data parameters and initialize the correction coefficient matrix W and the source term release rate σ;

[0115] a sedimentation data acquisition unit configured to acquire sedimentation data at a selected observation site;

[0116] A release priori prediction unit considering wet deposition is configured to calculate and generate a release priori prediction considering wet deposition based on deposition data at an observation site;

[0117] The transport matrix obtaining unit is configured to solve the correction coefficient matrix W and the source term release rate σ based on the joint deviation correction method with prior until convergence, and obtain the transport matrix H new ;

[0118] The inversion solver with priors is configured to use the transport matrix H new The source term release rate σ is solved until convergence, and the source term inversion result with a priori sedimentation information release is obtained.

[0119] Embodiment 3: This embodiment provides an electronic device corresponding to the source term inversion method with a priori deposition information release provided in this embodiment 1. The electronic device can be an electronic device for a client, such as a mobile phone, a laptop computer, a tablet computer, a desktop computer, etc., to execute the method of embodiment 1.

[0120] like Figure 2As shown, the electronic device includes a processor, a memory, a communication interface and a bus, and the processor, the memory and the communication interface are connected through the bus to complete the communication between each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Component (EISA) bus, etc. The memory stores a computer program that can be run on the processor, and the processor executes the computer program when it runs it to execute the above method. Its implementation principle and technical effect are similar to those of Example 1 and will not be repeated here. Those skilled in the art will understand that Figure 2 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computing device to which the solution of the present application is applied. The specific computing device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0121] In a preferred embodiment, the logic instructions in the above-mentioned memory can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), optical disk and other media that can store program code.

[0122] In a preferred embodiment, the processor may be a central processing unit (CPU), a digital signal processor (DSP), or other general-purpose processors of various types, which are not limited herein.

[0123] Embodiment 4: This embodiment provides a computer program product, which may include a computer program stored on a computer-readable storage medium. The computer program includes program instructions. When the program instructions are executed by a computer, the computer can execute the method provided in the above-mentioned embodiment 1. Its implementation principle and technical effects are similar to those of embodiment 1 and will not be repeated here.

[0124] In one preferred embodiment, the computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium can be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any combination thereof. The computer readable storage medium stores a computer program implementing the method provided by the first embodiment.

[0125] The various embodiments in the specification are described in progressive manner, and the same or similar parts between the various embodiments can be referred to each other, and each embodiment focuses on the difference from other embodiments. In the description of the specification, the description referring to the terms "one preferred embodiment", "another preferred embodiment", etc. means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the embodiments in the specification. In the specification, the illustrative description of the above terms does not necessarily refer to the same embodiment or example. Also, the specific features, structures, materials or characteristics described can be combined in any appropriate manner in any one or more embodiments or examples. In addition, the person skilled in the art can combine and combine the different embodiments or examples described in the specification and the features of the different embodiments or examples without contradiction.

[0126] The present application is described with reference to flowcharts and / or block diagrams that illustrate the method, device (system), and computer program product according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing apparatus to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing apparatus produce a device that implements the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in the flowcharts and / or block diagrams Figure 1 The functions specified in the flowcharts and / or block diagrams

[0127] These computer program instructions can also be stored in a computer readable storage medium that can direct the computer or other programmable data processing apparatus to work in a specific manner, so that the instructions stored in the computer readable storage medium produce a product including instruction means, which implements the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in the flowcharts and / or block diagrams Figure 1 The functions specified in the flowcharts and / or block diagrams

[0128] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0129] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A source term inversion method with a priori sedimentation information release, characterized in that include: Obtain data parameters and initialize the correction coefficient matrix W and source term release rate σ; Obtain sedimentation data at selected observation sites; Based on the sedimentation data at the observation site, a release priori considering wet deposition is generated; Based on the joint bias correction method with priors, the correction coefficient matrix W and the source term release rate σ are solved until convergence, and the bias-corrected transport matrix H is obtained. new ,include: Using the alternating minimization method, by alternately solving equations (7) and (8) until the convergence condition is met, the final correction coefficient matrix is ​​obtained. The final correction coefficient matrix is ​​multiplied with the original transport matrix by equation (9) to obtain the deviation-corrected transport matrix H new ,in: σ-step: -step: When the convergence condition is reached, we can get: H new =W·H (9) In the above formula, is the correction coefficient vector, W is the correction coefficient matrix, and the relationship between the two is H is the transport matrix, μ is the observation vector, represents the central moment of the correction coefficient vector, Const is the imposed constant constraint, σ is the source leakage rate to be estimated, σ dep To consider the release prior of wet deposition, λ is the regularization parameter; Using the bias-corrected transport matrix H new The source term release rate σ is solved until convergence, and the source term inversion result with a priori sedimentation information release is obtained.

2. The source term inversion method with a priori sedimentation information release according to claim 1, characterized in that: The data parameters include the sedimentation observation site coordinate vector group (x o ,y o ), the initial transport matrix, the air concentration observation data, the regularization parameter λ and the convergence condition, o represents the number of selected stations, where the convergence condition includes the maximum number of iterations and the minimum allowed error.

3. The source term inversion method with a priori sedimentation information release according to claim 2, characterized in that: The acquisition of the deposition data at the selected observation site includes: the rainfall corresponding to the selected observation site, the constant release σ c n Sedimentation time series change rate dDep at the lower site o,t , the site deposition time series change rate aDep obtained by inverting only the air concentration o,t and surface cumulative deposition observation value mDep o , where n represents the simulation time step and t is the time.

4. The source term inversion method with a priori sedimentation information release according to claim 3, characterized in that: The method of making a release priori considering wet deposition based on the sedimentation data at the observation site includes: Obtain the dominant period of wet deposition tR and obtain the deposition time series change rate dDep of the constant release site under the corresponding dominant period o,tR The site deposition time series change rate aDep obtained by inverting only the air concentration during the dominant period o,tR ; Convert the release rate σ considering wet deposition during the dominant period tR of each station s o,tR , and calculate the release priority σ considering wet deposition in the dominant period tR at the release point i o,tR , making the release of wet deposition into consideration first test σ dep n .

5. The source term inversion method with a priori sedimentation information release according to claim 4 is characterized in that: The release rate σ considering wet deposition during the dominant period tR of each station is converted s o,tR , and calculate the release priority σ considering wet deposition in the dominant period tR at the release point i o,tR , making the release of wet deposition into consideration first test σ dep n ,include: Release rate σ considering wet deposition during the dominant period tR at each station s o,tR The calculation process is: Where, σ c tR is the constant release rate during the wet deposition dominant period tR; Release point (x r ,y r ) in the dominant period, considering the release of wet deposition, the first test σ i o,tR : s i tR =s s o,tR ·weight o (5) Considering the release of wet deposition, first verify σ dep n The calculation formula is: Where, σ i t is the release rate at t = i, σ c t The release rate is constant.

6. The source term inversion method with a priori sedimentation information release according to claim 2, characterized in that: The use of the bias-corrected transport matrix H new Solve the source term release rate σ until convergence, and obtain the source term inversion result with a priori sedimentation information release, including: using the alternating direction multiplier method to solve Equation (10) until the convergence condition is met, and iteratively solve the target source term release rate σ:

7. A source term inversion system for implementing the source term inversion method with a priori sedimentation information release according to any one of claims 1 to 6, characterized in that: The system includes: The parameter input and initialization unit is configured to obtain data parameters and initialize the correction coefficient matrix W and the source term release rate σ; a sedimentation data acquisition unit configured to acquire sedimentation data at a selected observation site; A release priori prediction unit considering wet deposition is configured to calculate and generate a release priori prediction considering wet deposition based on deposition data at an observation site; The transport matrix obtaining unit is configured to solve the correction coefficient matrix W and the source term release rate σ based on the joint deviation correction method with prior until convergence, and obtain the deviation-corrected transport matrix H new ; The inversion solver with priors is configured to use the bias-corrected transport matrix H new The source term release rate σ is solved until convergence, and the source term inversion result with a priori sedimentation information release is obtained.

8. A processing device, comprising at least a processor and a memory, wherein a computer program is stored in the memory, wherein: When the processor runs the computer program, the computer program is executed to implement the source term inversion method with a priori deposition information release according to any one of claims 1 to 6.

9. A computer storage medium, characterized in that Computer-readable instructions are stored thereon, and the computer-readable instructions can be executed by a processor to implement the source term inversion method with a priori release of deposition information as claimed in any one of claims 1 to 6.

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