A source reconstruction method and system based on variable-speed release prior and Bayesian theory

Abstract

The present invention relates to a source reconstruction method and system based on variable-speed release prior and Bayesian theory, which includes: performing reverse atmospheric diffusion simulation based on a pre-made monitoring sample file to obtain reverse simulation results; constructing a source-receptor sensitivity (SRS) data file in a set format based on the reverse simulation results, and generating an SRS matrix H according to the SRS data file; constructing a prior distribution P(r) of the source position r, and using the SRS matrix H and monitoring data μ to construct a conditional prior P(q|μ,r) of the variable-speed release rate q; according to the conditional prior distribution, and using the Markov chain Monte Carlo method to sample the posterior distribution until convergence, so as to obtain the estimated result of the posterior probability distribution of the source position and the variable-speed release rate. The present invention does not rely on the assumption of constant release, and can quickly estimate the release source position and the time-series release rate in the case of unknown release behavior; it can be applied in the field of pollutant source tracing.

Description

Technical Field

[0001] The present invention relates to the technical field of pollutant source tracing, and particularly to a source reconstruction method and system based on variable-speed release prior and Bayesian theory. Background Art

[0002] For radioactive leakage events of unknown origin, the radionuclide monitoring station will detect an increase in the concentration of radionuclides. Even if such events have little impact on human health and the environment in the short term, countries or regions need to respond quickly to evaluate the consequences of the diffusion and transmission of radioactive substances in the atmosphere and take effective countermeasures in a timely manner. The common feature of such events is that the release source can be simplified to a single fixed point source. Since the release position is unknown, the source term inversion method cannot be used to estimate the source term. Therefore, it is necessary to use limited environmental monitoring data and meteorological information to infer the release source position and release rate, and this process is called source reconstruction. The source reconstruction problem is a typical inverse problem. Due to the non-linear relationship between monitoring and source position, and in practice, the number of monitoring data is small while the number of source parameters is large, this problem is often a non-linear underdetermined problem and is very difficult to solve, becoming a new challenge for nuclear emergency response in countries around the world. Summary of the Invention

[0003] Aiming at the above problems, the purpose of the present invention is to provide a source reconstruction method and system based on variable-speed release prior and Bayesian theory, which does not rely on the assumption of constant release and can quickly estimate the release source position and time-series release rate when the release behavior is unknown.

[0004] To achieve the above purpose, the present invention adopts the following technical solutions: A source reconstruction method based on variable-speed release prior and Bayesian theory, which includes: performing reverse atmospheric diffusion simulation based on a pre-made monitoring sample file to obtain a reverse simulation result; constructing a source-receptor sensitivity (SRS) data file in a set format based on the reverse simulation result, and generating an SRS matrix H according to the source-receptor sensitivity data file; constructing a prior distribution P(r) of the source position r, and using the SRS matrix H and monitoring data μ to construct a conditional prior P(q|μ, r) of the variable-speed release rate q; according to the conditional prior distribution, and using the Markov chain Monte Carlo method to sample the posterior distribution until convergence, to obtain the posterior probability distribution estimation results of the source position and variable-speed release rate.

[0005] Further, the header row information of the monitoring sample file includes:

[0006] Case name, monitoring start time, monitoring end time, monitoring data unit, data path. The data row information of the monitoring sample file includes sampling end time, sampling duration, latitude of the monitoring station, longitude of the monitoring station, monitoring concentration, name of the monitoring station, monitoring uncertainty, and monitoring threshold.

[0007] Furthermore, perform backward atmospheric diffusion simulation based on a pre-made monitoring sample file, including:

[0008] Based on the FLEXPART Lagrangian particle diffusion model, read the information of the monitoring sample file and perform atmospheric diffusion simulation according to the backward diffusion mode.

[0009] Furthermore, construct a source-receptor sensitivity data file in a set format based on the backward simulation results, including:

[0010] According to the nc file of the backward simulation results, convert the unit of the backward simulation results from s to 1 / m 3 , and construct an SRS file in txt format;

[0011] Read the txt file and construct a four-dimensional SRS matrix where M represents the number of monitoring samples, N x represents the number of grid points in the longitude direction, N y represents the number of grid points in the latitude direction, N t represents the number of release time steps considered.

[0012] Furthermore, construct a prior distribution P(r) of the source location r, and use the SRS matrix H and the monitoring data to construct a conditional prior distribution P(q|μ, r) of the variable-speed release rate q, including:

[0013] Construct a prior distribution P(r) of the source location r according to the prior distribution of longitude and the prior distribution of latitude;

[0014] For each sampling result of the source location in the prior distribution, perform inversion calculation of the release rate, and construct a conditional prior distribution of the variable-speed release rate according to the inversion calculation;

[0015] Construct a likelihood function to evaluate the monitoring-simulation error under different source parameters.

[0016] Furthermore, use the Markov chain Monte Carlo method to sample the posterior distribution until convergence, and obtain the posterior probability distribution estimation results of the source location and the variable-speed release rate, including:

[0017] Initialize the longitude and latitude according to the prior distribution, and use the Tikhonov regularization inversion method to calculate the release rate q 0 , and obtain the initial source parameter S 0 ;

[0018] Add perturbations sampled from a Gaussian distribution to the longitude and latitude, and use the Tikhonov regularization inversion method to calculate the release rate q′ to obtain a new source parameter S′;

[0019] Calculate the initial source parameter S0 The likelihood function values of the new source parameter S′ are obtained, and the ratio of the two is calculated. If the ratio is greater than or equal to the preset value, the new source parameter S′ is accepted; otherwise, it is rejected.

[0020] The perturbation is repeatedly added and the ratio is obtained until the convergence condition is reached, and the posterior probability distribution estimation results of the source position and the variable-speed release rate are obtained.

[0021] Furthermore, the convergence condition includes: the convergence value is less than the preset value or the maximum number of convergence times is reached.

[0022] The calculation of the convergence value is as follows:

[0023] The between-chain variance B between the sampling chains and the within-chain variance V within the sampling chains are calculated, and the variance of the source parameter is determined according to the between-chain variance B and the within-chain variance V.

[0024] The convergence value is determined according to the variance of the source parameter and the within-chain variance V.

[0025] A source reconstruction system based on variable-speed release prior and Bayesian theory, which includes: a reverse simulation module that performs reverse atmospheric diffusion simulation based on a pre-made monitoring sample file to obtain reverse simulation results; an SRS matrix generation module that constructs a source-receptor sensitivity SRS data file in a set format based on the reverse simulation results and generates an SRS matrix H according to the source-receptor sensitivity data file; a prior distribution module that constructs a prior distribution P(r) of the source position r and constructs a conditional prior P(q|μ, r) of the variable-speed release rate q by using the SRS matrix H and the monitoring data μ; a posterior distribution module that samples the posterior distribution according to the conditional prior distribution and uses the Markov chain Monte Carlo method until convergence to obtain the posterior probability distribution estimation results of the source position and the variable-speed release rate.

[0026] A computer-readable storage medium storing one or more programs, where the one or more programs include instructions that, when executed by a computing device, cause the computing device to execute any of the above methods.

[0027] A computing device, which includes: one or more processors, a memory, and one or more programs, where the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include instructions for executing any of the above methods.

[0028] Due to the above technical solutions adopted by the present invention, it has the following advantages:

[0029] 1. Since the present invention adopts a reverse diffusion model, the computational cost of atmospheric diffusion simulation can be greatly reduced in the case of sparse monitoring data, thereby accelerating the reconstruction calculation and being applicable to emergency scenarios.

[0030] 2. The present invention creates a monitoring sample file with a set format, facilitating the online update of monitoring information and perfectly adapting to the input file of the atmospheric diffusion model, thereby supporting the real-time optimization of the reconstruction result.

[0031] 3. The present invention adopts a variable-speed release prior to adapt to the real leakage scenario, makes full use of the monitoring information to reduce the parameter space of the source, and improves the convergence speed and optimizes the convergence direction of the Bayesian method.

[0032] 4. The present invention adopts the Markov chain Monte Carlo method, which allows sampling from a complex posterior distribution to obtain the distribution information of the source parameter estimation. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 It is a flowchart of the source reconstruction method based on the variable-speed release prior and Bayesian theory in an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0034] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are some but not all of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the described embodiments of the present invention fall within the scope of protection of the present invention.

[0035] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0036] Many existing methods assume a constant release and reconstruct the release start time, release end time, release total amount, and release location based on optimization methods or Bayesian theory. Such methods ignore the details of the release over time, resulting in large deviations in the estimation of the release location and release time and being unable to accurately evaluate the accident consequences of radioactive leakage. If the release is assumed to be variable-speed, due to the high parameter space, overfitting is likely to occur based on optimization methods, and multimodality is likely to occur in Bayesian theory, leading to high uncertainty in the estimation results. In addition, due to the need for a large number of iterations and a forward diffusion simulation in each iteration, the computational cost of the reconstruction is very high, and its rapidity is difficult to guarantee. Therefore, the present invention proposes a source reconstruction method and system based on variable-speed release prior and Bayesian theory, which can quickly and be applicable to the source reconstruction in variable-speed release scenarios.

[0037] Due to the lack of an effective method for source reconstruction considering variable-speed release scenarios, it is difficult to provide real continuous release characteristic information, increasing the uncertainty of source localization results. The source reconstruction method and system based on variable-speed release prior and Bayesian theory provided by the present invention include: making a monitoring sample file and performing backward atmospheric diffusion simulation based on this sample file; constructing a source-receptor sensitivity (SRS) data file in a specific format based on the backward simulation results, and reading the file to make an SRS matrix H; constructing a prior distribution P(r) of the source position, and using the SRS matrix H and monitoring data μ to construct a conditional prior P(q|μ, r) of the variable-speed release rate q; using the Markov chain Monte Carlo method to sample the posterior distribution until convergence to obtain the posterior probability distribution estimation results of the source position and variable-speed release rate. Since the present invention only considers sampling of the source position in Bayesian sampling, and the release rate is directly determined by the source position and monitoring data, the convergence of Bayesian reconstruction is improved, and the uncertainty brought by the huge source parameter space is reduced. The present invention can use monitoring sample information and variable-speed release prior to obtain source parameter reconstruction results with confidence intervals.

[0038] In an embodiment of the present invention, a source reconstruction method based on variable-speed release prior and Bayesian theory is provided, which is mainly used in the field of radioactive nuclide tracing. In this embodiment, as Figure 1 shown, the method includes the following steps:

[0039] 1) Perform backward atmospheric diffusion simulation based on a pre-made monitoring sample file to obtain backward simulation results;

[0040] Specifically, the made monitoring sample file contains the following information: the header row information of the monitoring sample file includes the case name, monitoring start time, monitoring end time, monitoring data unit, data path, and the data row information of the monitoring sample file includes the sampling end time, sampling duration, monitoring station latitude, monitoring station longitude, monitoring concentration, monitoring station name, monitoring uncertainty, and monitoring threshold.

[0041] An example of monitoring data is as follows:

[0042]

[0043] In this embodiment, the FLEXPART Lagrangian particle diffusion model is adopted to read the information of the monitoring sample file and perform atmospheric diffusion simulation according to the backward diffusion mode.

[0044] Specifically, the case name is ETEX, the monitoring start time is October 23, 1994, the monitoring end time is October 27, 1994, and the concentration unit is ng / m 3, the data path is \expdir\etex.txt. The first line of the data row indicates that the end time of the monitoring data sampling is 09:00 UTC on October 25, 1994, the sampling duration is 3 hours, the latitude of the monitoring station is 54.41°N, the longitude is 13.26°E, and the concentration is 0.01 ng / m 3 , the name of the monitoring station is D02, and the uncertainty of the monitoring data is 0.001 ng / m 3 , the detection limit is 0.01 ng / m 3 .

[0045] 2) Construct a source-receptor sensitivity SRS data file in a set format based on the inverse simulation results, and generate an SRS matrix H according to the source-receptor sensitivity data file;

[0046] Specifically, it includes the following steps:

[0047] 2.1) According to the nc file of the inverse simulation results, convert the unit of the inverse simulation results from s to 1 / m 3 , and construct an SRS file in txt format;

[0048] 2.2) Read the txt file and construct a four-dimensional SRS matrix where M represents the number of monitoring samples, N x represents the number of grid points in the longitude direction, N y represents the number of grid points in the latitude direction, N t represents the number of release time steps considered.

[0049] In this embodiment, the meanings of the first line (header) of the SRS file are as follows:

[0050] Longitude of the station / Latitude of the station / Start time of sampling (year / month / day) / Start time of sampling (hour) / End time of sampling (year / month / day) / End time of sampling (hour) / Total release in the inverse mode / Maximum number of hours in the inverse / Output frequency (hours) / Average time (hours) / Grid resolution in the longitude direction (degrees) / Grid resolution in the latitude direction (degrees) / Name of the station.

[0051] The meanings of the data rows of the SRS file are as follows:

[0052] Longitude of the grid point / Latitude of the grid point / Inverse time step number / Plume concentration.

[0053] An example of the SRS file is as follows:

[0054] 12.57 47.03 19941026 09 19941026 12 1.000000E+15 153 3 3 0.25 0.25 “CH01”

[0055] 46.375 11.875 0 8.4881E-01

[0056] 46.375 12.125 0 5.3890E-01

[0057] In this embodiment, assuming the grid height is 150 m, the unit of the reverse simulation result M(s) is converted to srs(1 / m 3 ) by the formula:

[0058] srs = M / (3 * 3600 * 0.25 * 100000 * 0.25 * 100000 * 150)

[0059] In this embodiment, the monitoring data can be calculated by the following formula:

[0060] μ = H(lon, lat)q + ε

[0061] In the formula, represents the release rate vector composed of the release rates at N t time steps, and represents the error between the monitoring and the simulation.

[0062] 3) Construct the prior distribution P(r) of the source location r, and use the SRS matrix H and the monitoring data to construct the conditional prior distribution P(q|μ, r) of the variable-speed release rate q;

[0063] Specifically, it includes the following steps:

[0064] 3.1) Construct the prior distribution P(r) of the source location r according to the prior distribution of the source location longitude and the prior distribution of the latitude;

[0065] In this embodiment, the Bayesian theory is:

[0066] P(S|μ) ∝ P(μ|S)P(S)

[0067] Among them, P(S) is the prior distribution, representing the knowledge of the source parameter information before reconstruction, P(μ|S) is the likelihood function, measuring the difference between the simulation and the observation. P(S|μ) represents the posterior distribution, which is proportional to the product of the prior and the likelihood.

[0068] The prior distribution of the source location is:

[0069] P(r) = P(lon)P(lat)

[0070] In the formula, P(lon) represents the prior distribution of the longitude, P(lat) represents the prior distribution of the latitude, both are set as uniform distributions, that is, P(lon) ~ U(L lon , Hlon ),P(lat) ∼ U(L lat ,H lat ), where L lon and H lon represent the upper and lower limits of the longitude considered, and L lat and H lat represent the upper and lower limits of the latitude considered.

[0071] 3.2) For each set of sampling results of the source location of the prior distribution, perform the inversion calculation of the release rate, and construct the conditional prior distribution of the variable-speed release rate according to the inversion calculation;

[0072] In this embodiment, for each set of sampling results of the source location of the prior distribution, perform the inversion calculation of the release rate, and adopt the Tikhonov regularization inversion method. The formula is as follows:

[0073]

[0074] Then, based on this inversion method, the conditional prior distribution of the variable-speed release rate can be constructed: P(q|μ, lon, lat). Therefore, the prior distribution P(S) of the source parameters S = (lon, lat, q) is in the following form:

[0075] P(S) ∝ P(lon)P(lat)P(q|μ, lon, lat)

[0076] 3.3) Construct the likelihood function to evaluate the monitoring-simulation error under different source parameters.

[0077] In this embodiment, the likelihood function adopts the logarithmic Cauchy distribution, and the specific form is as follows:

[0078]

[0079] In the formula, r is the diagonal parameter of the covariance matrix R = rI, which measures the mean of the observation-simulation error variance.

[0080] 4) According to the conditional prior distribution, and use the Markov chain Monte Carlo method to sample the posterior distribution until convergence, to obtain the estimated result of the posterior probability distribution of the source location and the variable-speed release rate.

[0081] Specifically, it includes the following steps:

[0082] 4.1) Initialize the longitude and latitude according to the prior distribution, and calculate the release rate q using the Tikhonov regularization inversion method 0 , to obtain the initial source parameters S 0 ;

[0083] In this embodiment, initialize the longitude and latitude according to the prior distribution: lon = lon 0and lat = lat 0 ;

[0084] Use the Tikhonov regularization inversion method to calculate the release rate q 0 as:

[0085]

[0086] Initial source parameter S 0 =(lon 0 , lat 0 , q 0 ).

[0087] 4.2) Add perturbations sampled from a Gaussian distribution to the longitude and latitude, and use the Tikhonov regularization inversion method to calculate the release rate q', obtaining a new source parameter S';

[0088] In this embodiment, add perturbations to the longitude and latitude: lon' = lon 0 + dlon, lat' = lat 0 + dlat;

[0089] where dlon and dlat are sampled from a Gaussian distribution;

[0090] Similarly, use the Tikhonov regularization inversion method to calculate the release rate q' as:

[0091]

[0092] Then the new source parameter S' = (lon', lat', q').

[0093] 4.3) Calculate the likelihood function values of the initial source parameter S 0 and the new source parameter S', obtain the ratio of the two, and if the ratio is greater than or equal to the preset value, the new source parameter S' is accepted, otherwise it is rejected;

[0094] In this embodiment, calculate two likelihood function values: P(S 0 |μ) and P(S'|μ), then the ratio between them is:

[0095] δP = P(S'|μ) / P(S 0 |μ);

[0096] where the preset value is a random number between 0 and 1 If then S' is accepted, otherwise S' is rejected.

[0097] 4.4) Repeat adding perturbations and obtaining the ratio until the convergence condition is reached, obtaining the posterior probability distribution estimation results of the source location and the variable-speed release rate.

[0098] In this embodiment, the convergence condition includes: the convergence value is less than a preset value or the maximum number of convergence times is reached.

[0099] Among them, the calculation of the convergence value includes the following steps:

[0100] 4.4.1) Calculate the between-chain variance B between the sampling chains and the within-chain variance V within the sampling chains, and determine the variance of the source parameter according to the between-chain variance B and the within-chain variance V;

[0101] The convergence situation of reaching the posterior distribution is estimated by calculating the variance between the sampling chains and the variance within the sampling chains. If there are m Markov chains of length n used to estimate the source parameter S, and its value is represented by s, then the between-chain variance B can be calculated as:

[0102]

[0103] Among them,

[0104]

[0105] And:

[0106]

[0107] The within-chain variance V is calculated as:

[0108]

[0109] Among them,

[0110]

[0111] 4.4.2) Determine the convergence value according to the variance of the source parameter and the within-chain variance V;

[0112] In this embodiment, the convergence value is:

[0113]

[0114] Among them, the variance of the source parameter S is defined as:

[0115]

[0116] In this embodiment, preferably, the convergence condition is defined as the convergence value being less than 1.2 or reaching the maximum number of convergence times 20000.

[0117] The final source reconstruction estimation results calculated in this embodiment are the posterior probability distribution of the source location and the temporal curve change of the release rate, which fully demonstrate the characteristics of continuous release, reduce the uncertainty of source location estimation, and provide more detailed source term information for accident consequence assessment.

[0118] In one embodiment of the present invention, a source reconstruction system based on variable-speed release prior and Bayesian theory is provided, which includes:

[0119] A reverse simulation module that performs reverse atmospheric diffusion simulation based on a pre-made monitoring sample file to obtain reverse simulation results;

[0120] An SRS matrix generation module that constructs a source-receptor sensitivity SRS data file in a set format based on the reverse simulation results, and generates an SRS matrix H according to the source-receptor sensitivity data file;

[0121] A prior distribution module that constructs a prior distribution P(r) of the source location r, and constructs a conditional prior P(q|μ, r) of the variable-speed release rate q using the SRS matrix H and the monitoring data μ;

[0122] A posterior distribution module that samples the posterior distribution according to the conditional prior distribution and uses the Markov chain Monte Carlo method until convergence to obtain the posterior probability distribution estimation results of the source location and the variable-speed release rate.

[0123] In the above embodiment, the header row information of the monitoring sample file includes:

[0124] Case name, monitoring start time, monitoring end time, monitoring data unit, data path. The data row information of the monitoring sample file includes sampling end time, sampling duration, monitoring site latitude, monitoring site longitude, monitoring concentration, monitoring site name, monitoring uncertainty, and monitoring threshold.

[0125] In the above embodiment, performing reverse atmospheric diffusion simulation based on a pre-made monitoring sample file includes:

[0126] Based on the FLEXPART Lagrangian particle diffusion model, read the information of the monitoring sample file and perform atmospheric diffusion simulation according to the reverse diffusion mode.

[0127] In the above embodiment, constructing a source-receptor sensitivity data file in a set format based on the reverse simulation results includes:

[0128] According to the nc file of the reverse simulation results, convert the unit of the reverse simulation results from s to 1 / m 3 , and construct an SRS file in txt format;

[0129] Read the txt file and construct a four-dimensional SRS matrix where \(M\) represents the number of monitoring samples, \(N\) x表 represents the number of longitude - direction grids, \(N_y\) represents the number of latitude - direction grids, \(N\) t represents the number of release time steps considered.

[0130] In the above - mentioned embodiment, constructing the prior distribution \(P(r)\) of the source location \(r\), and using the SRS matrix \(H\) and the monitoring data \(\mu\) to construct the conditional prior distribution \(P(q|\mu,r)\) of the variable - speed release rate \(q\) includes:

[0131] Constructing the prior distribution \(P(r)\) of the source location \(r\) according to the prior distribution of longitude and the prior distribution of latitude;

[0132] For each set of sampling results of the source location of the prior distribution, performing the inversion calculation of the release rate, and constructing the conditional prior distribution of the variable - speed release rate according to the inversion calculation;

[0133] Constructing a likelihood function to evaluate the monitoring - simulation error under different source parameters.

[0134] In the above - mentioned embodiment, using the Markov chain Monte Carlo method to sample the posterior distribution until convergence, and obtaining the estimated results of the posterior probability distribution of the source location and the variable - speed release rate, including:

[0135] Initializing longitude and latitude according to the prior distribution, and using the Tikhonov regularization inversion method to calculate the release rate \(q\) 0 , obtaining the initial source parameter \(S\) 0 ;

[0136] Adding perturbations sampled from a Gaussian distribution to the longitude and latitude, and using the Tikhonov regularization inversion method to calculate the release rate \(q'\), obtaining the new source parameter \(S'\);

[0137] Calculating the likelihood function values of the initial source parameter \(S\) 0 and the new source parameter \(S'\), obtaining the ratio of the two. If the ratio is greater than or equal to the preset value, the new source parameter \(S'\) is accepted, otherwise it is rejected;

[0138] Repeating adding perturbations and obtaining the ratio until the convergence condition is reached, and obtaining the estimated results of the posterior probability distribution of the source location and the variable - speed release rate.

[0139] In this embodiment, the convergence condition includes: the convergence value is less than the preset value or the maximum number of convergence times is reached.

[0140] Among them, the calculation of the convergence value is:

[0141] Calculating the between - chain variance \(B\) between sampling chains and the within - chain variance \(V\) within sampling chains, and determining the variance of the source parameter according to the between - chain variance \(B\) and the within - chain variance \(V\);

[0142] Determine the convergence value according to the variance of the source parameters and the within-chain variance V.

[0143] The system provided in this embodiment is used to execute the above method embodiments. For the specific process and detailed content, please refer to the above embodiments and will not be elaborated here.

[0144] In summary, the present invention can use the input of monitoring sample information to obtain the source reconstruction estimation result with a variable-speed release prior, and it is verified by ETEX experimental data. The obtained source position is very close to the true position, with an error of about 100 km, and the estimation of the total release amount and release timing also fits well with the true values.

[0145] In a computing device provided in an embodiment of the present invention, the computing device may be a terminal, which may include: a processor, a communications interface, a memory, a display screen, and an input device. Among them, the processor, the communications interface, and the memory complete mutual communication through a communication bus. The processor is used to provide computing and control capabilities. The memory includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. When the computer program is executed by the processor, it implements the methods in the above embodiments; the internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communications interface is used to communicate with an external terminal in a wired or wireless manner. The wireless manner can be achieved through WIFI, a carrier network, NFC (Near Field Communication), or other technologies. The display screen may be a liquid crystal display screen or an electronic ink display screen, and the input device may be a touch layer covering the display screen, or a button, a trackball, or a touchpad provided on the housing of the computing device, or an external keyboard, a touchpad, or a mouse, etc. The processor can call the logical instructions in the memory.

[0146] In addition, when the logical instructions in the above memory are implemented in the form of a software functional unit and sold or used as an independent product, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions to enable a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. And the foregoing storage medium includes: USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs, etc., which can store program codes.

[0147] In one embodiment of the present invention, a computer program product is provided. The computer program product includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions that, when executed by a computer, enable the computer to execute the methods provided in the above-described method embodiments.

[0148] In one embodiment of the present invention, a non-transitory computer-readable storage medium is provided. The non-transitory computer-readable storage medium stores server instructions that cause a computer to execute the methods provided in the above-described embodiments.

[0149] For a computer-readable storage medium provided in the above embodiment, its implementation principle and technical effects are similar to those of the above method embodiment, and will not be elaborated here.

[0150] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the specified functions in one Figure 1 flow or multiple flows and / or blocks Figure 1 or multiple blocks.

[0151] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means that implement the specified functions in one Figure 1 flow or multiple flows and / or blocks Figure 1 or multiple blocks.

[0152] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the specified functions in one Figure 1 flow or multiple flows and / or blocks Figure 1 or multiple blocks.

[0153] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A source reconstruction method based on variable-speed release prior and Bayesian theory, characterized in that Including: Performing inverse atmospheric diffusion simulation based on a pre-made monitoring sample file to obtain inverse simulation results; Constructing a source-receptor sensitivity SRS data file in a set format based on the inverse simulation results, and generating an SRS matrix H according to the source-receptor sensitivity data file; Constructing a prior distribution P(r) of the source location r, and constructing a conditional prior P(q|μ,r) of the variable release rate q by using the SRS matrix H and the monitoring data μ; According to the conditional prior distribution, using the Markov chain Monte Carlo method to sample the posterior distribution until convergence, and obtaining the estimated results of the posterior probability distribution of the source location and the variable release rate.

2. The source reconstruction method based on variable-speed release prior and Bayesian theory according to claim 1, wherein The header line information of the monitoring sample file, including: Case name, monitoring start time, monitoring end time, monitoring data unit, data path. The data line information of the monitoring sample file includes sampling end time, sampling duration, monitoring station latitude, monitoring station longitude, monitoring concentration, monitoring station name, monitoring uncertainty, and monitoring threshold.

3. The source reconstruction method based on variable-speed release prior and Bayesian theory as described in claim 1, wherein Performing inverse atmospheric diffusion simulation based on a pre-made monitoring sample file, including: Based on the FLEXPART Lagrangian particle diffusion model, reading the information of the monitoring sample file, and performing atmospheric diffusion simulation according to the inverse diffusion mode.

4. The source reconstruction method based on variable-speed release prior and Bayesian theory according to claim 1, characterized in that Constructing a source-receptor sensitivity data file in a set format based on the inverse simulation results, including: According to the nc file of the reverse simulation results, convert the unit of the reverse simulation results from s to 1 / m 3 , construct an SRS file in txt format; Read the txt file and construct a four-dimensional SRS matrix where M represents the number of monitored samples, N x represents the number of grid points in the longitude direction, N y represents the number of grid points in the latitude direction, N t represents the number of release time steps considered.

5. The source reconstruction method based on variable-speed release prior and Bayesian theory according to claim 1, wherein Construct the prior distribution \(P(r)\) of the source location \(r\), and utilize the SRS matrix \(H\) and the monitoring data Construct the conditional prior distribution \(P(q|\mu,r)\) of the variable release rate \(q\), including: Constructing a prior distribution P(r) of the source location r according to the prior distribution of longitude and the prior distribution of latitude; For each sampling result of the source location in the prior distribution, performing inversion calculation of the release rate, and constructing a conditional prior distribution of the variable release rate according to the inversion calculation; Constructing a likelihood function to evaluate the monitoring-simulation error under different source parameters.

6. The source reconstruction method based on variable-speed release prior and Bayesian theory according to claim 1, characterized in that Using the Markov chain Monte Carlo method to sample the posterior distribution until convergence, and obtaining the estimated results of the posterior probability distribution of the source location and the variable release rate, including: Initialize the longitude and latitude according to the prior distribution, and calculate the release rate q using the Tikhonov regularization inversion method 0 to obtain the initial source parameter S 0 ; Adding perturbations sampled from a Gaussian distribution to the longitude and latitude, using the Tikhonov regularization inversion method to calculate the release rate q′, and obtaining a new source parameter S′; Calculate the initial source parameter S 0 Obtain the likelihood function values of the new source parameter S' and the initial source parameter S, and calculate the ratio between them. If the ratio is greater than or equal to a preset value, the new source parameter S' is accepted; otherwise, it is rejected. Repeating adding perturbations and obtaining ratios until the convergence condition is reached, and obtaining the estimated results of the posterior probability distribution of the source location and the variable release rate.

7. The source reconstruction method based on variable-speed release prior and Bayesian theory according to claim 6, wherein The convergence condition, including: the convergence value is less than a preset value or the maximum number of convergence times is reached; The calculation of the convergence value is: Calculating the between-chain variance B between sampling chains and the within-chain variance V within the sampling chain, and determining the variance of the source parameter according to the between-chain variance B and the within-chain variance V; Determining the convergence value according to the variance of the source parameter and the within-chain variance V.

8. A source reconstruction system based on variable-speed release prior and Bayesian theory, characterized in that, Including: An inverse simulation module that performs inverse atmospheric diffusion simulation based on a pre-made monitoring sample file to obtain inverse simulation results; An SRS matrix generation module that constructs a source-receptor sensitivity SRS data file in a set format based on the inverse simulation results, and generates an SRS matrix H according to the source-receptor sensitivity data file; A prior distribution module that constructs a prior distribution P(r) of the source location r, and constructs a conditional prior P(q|μ,r) of the variable release rate q by using the SRS matrix H and the monitoring data μ; The posterior distribution module, based on the conditional prior distribution, uses the Markov chain Monte Carlo method to sample the posterior distribution until convergence, and obtains the estimated results of the posterior probability distribution of the source location and the variable speed release rate.

9. A computer-readable storage medium storing one or more programs, characterized in that, The one or more programs include instructions that, when executed by a computing device, cause the computing device to perform any of the methods described in claims 1 to 7.

10. A computing device, characterized in that, Comprising: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include instructions for performing any of the methods described in claims 1 to 7.

Images