Multi-objective optimization method for monitoring stationing after nuclear accident and corresponding device

Abstract

The invention discloses a multi-objective optimization method for monitoring point distribution after a nuclear accident and a corresponding device. The method comprises the following steps: constructing double objective functions: a logarithmic root-mean-square error objective function and a key region area function; estimating a release source item of the radionuclide, and determining an indication concentration field and a plurality of candidate concentration fields of the nuclear accident; randomly selecting a candidate concentration field from the plurality of candidate concentration fields, carrying out semi-variation function analysis on the basis of the candidate concentration field, and fitting to obtain a semi-variation function meeting an optimal fitting condition; performing interpolation processing on the plurality of candidate concentration fields based on a semi-variation function to obtain a plurality of corrected concentration fields, and averaging the corrected concentration fields to obtain a logarithmic root-mean-square error target average value and a key region area average value; and iterative optimization is carried out based on an NSGA-II algorithm, and the position of the optimal monitoring distribution point and a correction concentration field corresponding to the optimal monitoring distribution point are determined. Therefore, the monitoring distribution accuracy is improved, and the nuclear accident emergency decision safety is improved.

Description

Technical Field

[0001] The present application relates to the technical field of post-nuclear accident environmental monitoring and assessment, specifically to the technical fields of positioning of radioactive nuclide monitoring points after a nuclear accident and multi-objective optimization, and in particular to a multi-objective optimization method and corresponding device for post-nuclear accident monitoring points. Background Art

[0002] When an accident at a nuclear facility causes the release of radioactive materials into the external environment, it will pose a serious threat to the environment and personnel safety. In order to effectively mitigate the impact of the accident and reduce the harm to personnel and the environment, rapid and reasonable consequence assessment and emergency decision-making are essential. The basis of such decision-making is to be able to quickly and accurately assess the spatial distribution of radionuclides and calculate the possible exposure dose accordingly.

[0003] The existing aerosol monitoring point distribution methods mainly include grid distribution method, functional area distribution method, concentric circle distribution method, etc. When distributing points, the number of points is usually appropriately increased in the downwind direction and key positions. The conventional distribution method cannot take into account the impact on the correction of the predicted concentration field. The monitoring point distribution locations are not representative enough and cannot meet the needs of correcting the environmental concentration field outside a large range of scales.

[0004] Therefore, it is urgent to find an optimization solution to optimize the monitoring points after a nuclear accident. Summary of the invention

[0005] The present application provides a multi-objective optimization method and corresponding device for monitoring point layout after a nuclear accident, so as to solve the problem of inaccurate monitoring point layout after a nuclear accident.

[0006] The technical solution is as follows:

[0007] In the first aspect, a multi-objective optimization method for monitoring points after a nuclear accident is provided, including:

[0008] Construct the logarithmic root mean square error objective function of the monitoring points in the candidate concentration field and the revised concentration field, as well as the key area function where the concentration of released radionuclides is higher than the target concentration;

[0009] Estimate the release source term of radionuclides and determine the indicative concentration field and multiple candidate concentration fields of this nuclear accident;

[0010] Randomly selecting a candidate concentration field from the multiple candidate concentration fields, performing a semivariogram analysis based on the candidate concentration field, and fitting a semivariogram that satisfies the best fitting condition;

[0011] Based on the semivariogram, the multiple candidate concentration fields are interpolated to obtain multiple corrected concentration fields, each corrected concentration field and the corresponding candidate concentration field are respectively substituted into the logarithmic root mean square error objective function to calculate multiple logarithmic root mean square error target values, and each corrected concentration field and the corresponding candidate concentration field are respectively substituted into the key area function to calculate multiple key area values, and the logarithmic root mean square error target average value and the key area average value are respectively averaged;

[0012] Based on the NSGA-Ⅱ algorithm, the target average value of the logarithmic root mean square error and the average value of the key area are iteratively optimized to determine the location of the optimal monitoring points and the corrected concentration field corresponding to the optimal monitoring points.

[0013] In a possible implementation, a logarithmic root mean square error objective function of the monitoring points in the candidate concentration field and the corrected concentration field is constructed, specifically including:

[0014] The logarithmic error between the candidate concentration field and the corrected concentration field of the monitoring points is calculated by the following formula:

[0015]

[0016] Among them, y is the concentration value of the monitoring point in the candidate concentration field, To monitor the concentration value of the points in the corrected concentration field;

[0017] The logarithmic errors of all monitoring points are added together, and the logarithmic root mean square error objective function of the monitoring points in the candidate concentration field and the corrected concentration field is calculated by the following formula:

[0018]

[0019] Among them, y i is the concentration value of the ith monitoring point in the candidate concentration field, is the concentration value of the ith monitoring point in the corrected concentration field, and n is the number of monitoring points.

[0020] In a possible implementation, constructing a critical region area function where the concentration of released radionuclides is higher than the target concentration specifically includes:

[0021] Overlapping the corrected concentration field and the candidate concentration field to form four regions, including: a true positive region where the corrected concentration field and the candidate concentration field overlap, a false negative region in the candidate concentration field excluding the true positive region, a false positive region in the corrected concentration field excluding the true positive region, and a true negative region excluding the corrected concentration field and the candidate concentration field;

[0022] Based on the determined false negative area and false positive area, the critical area area function is determined by the following formula:

[0023] F2=k*A 假阳性 +(1-k)*A 假阴性

[0024] Among them, A 假阳性 and A 假阴性 They represent the areas of false positive and false negative regions in the simulation results respectively, and k is the weight factor.

[0025] In a possible implementation, a candidate concentration field is randomly selected from the multiple candidate concentration fields, and a semivariogram analysis is performed based on the candidate concentration field to fit a semivariogram that satisfies the best fitting condition, specifically including:

[0026] Randomly select a candidate concentration field from the plurality of candidate concentration fields, and use the candidate concentration field to perform a difference with the corresponding concentration data in the indicated concentration field to obtain a residual concentration field;

[0027] Conduct semivariogram analysis on each monitoring point in the residual concentration field, calculate the semivariance between pairs of monitoring points, and obtain the experimental variogram curve;

[0028] The least square method is used to fit the range value, sill value and nugget value in multiple models respectively, and multiple semivariogram curves after fitting are obtained;

[0029] The multiple semivariogram curves are compared with the experimental variogram curve respectively, and the semivariogram curve closest to the experimental variogram curve is selected as the semivariogram that meets the best fitting condition.

[0030] In a possible implementation, interpolating the multiple candidate concentration fields based on the semivariogram to obtain multiple modified concentration fields specifically includes:

[0031] For each candidate concentration field, perform the following operations:

[0032] Subtract the nuclide concentration of the candidate concentration field from the nuclide concentration at the corresponding position of the indicated concentration field to obtain the concentration residual values ​​of all monitoring points in the candidate concentration field;

[0033] Using the semivariogram, the concentration residual values ​​of the candidate concentration field are interpolated using the Kriging interpolation method to obtain a concentration residual interpolation field;

[0034] The concentration residual interpolation field is added to the indicated concentration field to obtain a modified concentration field corresponding to the candidate concentration field.

[0035] In a possible implementation, the setting of multiple models includes: an exponential model, a Gaussian model, and a spherical model.

[0036] In the second aspect, a multi-objective optimization device for monitoring points after a nuclear accident is provided, comprising:

[0037] A construction module is used to construct a logarithmic root mean square error objective function of the monitoring points in the candidate concentration field and the corrected concentration field, and a critical area function where the concentration of the released radionuclides is higher than the target concentration;

[0038] A determination module is used to estimate the release source term of radionuclides and determine the indicative concentration field and multiple candidate concentration fields of this nuclear accident;

[0039] A fitting module, used for randomly selecting a candidate concentration field from the plurality of candidate concentration fields, performing a semivariogram analysis based on the candidate concentration field, and fitting a semivariogram satisfying an optimal fitting condition;

[0040] An interpolation module, for interpolating the plurality of candidate concentration fields based on the semivariogram to obtain a plurality of modified concentration fields, substituting each modified concentration field and the corresponding candidate concentration field into the logarithmic root mean square error objective function to obtain a plurality of logarithmic root mean square error target values, and substituting each modified concentration field and the corresponding candidate concentration field into the key area function to obtain a plurality of key area values, and averaging them to obtain a logarithmic root mean square error target average value and a key area average value;

[0041] The optimization module is used to iteratively optimize the target average value of the logarithmic root mean square error and the average value of the area of ​​the key area based on the NSGA-Ⅱ algorithm to determine the location of the optimal monitoring points and the corrected concentration field corresponding to the optimal monitoring points.

[0042] In a third aspect, an electronic device is provided, including:

[0043] at least one processor; and

[0044] a memory communicatively connected to the at least one processor; wherein,

[0045] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method of any possible implementation manner and the aspects described above.

[0046] In a fourth aspect, a computer-readable storage medium is provided, wherein at least one instruction is stored in the storage medium, and the at least one instruction is loaded and executed by a processor to implement the method of the above-mentioned aspect and any possible implementation manner.

[0047] According to a fifth aspect, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the above-mentioned aspects and any possible implementation method.

[0048] The beneficial effects of the technical solution provided by this application include at least:

[0049] It can be seen from the above technical scheme that the embodiment of the present application can construct a dual objective function: a logarithmic root mean square error objective function and a critical area area function; estimate the release source term of radioactive nuclides to determine the indicative concentration field and multiple candidate concentration fields of this nuclear accident; randomly select a candidate concentration field from multiple candidate concentration fields, and perform a semivariogram analysis based on the candidate concentration field to fit a semivariogram that meets the best fitting conditions; interpolate multiple candidate concentration fields based on the semivariogram to obtain multiple corrected concentration fields, and average them to obtain the logarithmic root mean square error target average value and the critical area average value; and perform iterative optimization based on the NSGA-Ⅱ algorithm to determine the location of the optimal monitoring points and the corrected concentration field corresponding to the optimal monitoring points. Thereby, the accuracy of the monitoring points is improved, and the safety of nuclear accident emergency decision-making is improved.

[0050] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0052] Figure 1 It is a schematic diagram of the steps of a multi-objective optimization method for post-nuclear accident monitoring point layout provided in an embodiment of the present application.

[0053] Figure 2 It is a schematic diagram of a real concentration field provided by another embodiment of the present application.

[0054] Figure 3 It is a schematic diagram of four areas formed within the research scope by key areas of the candidate concentration field and the modified concentration field provided in one embodiment of the present application.

[0055] Figure 4 It is a schematic diagram indicating a concentration field provided by an embodiment of the present application.

[0056] Figure 5It is a schematic diagram of a randomly selected candidate concentration field provided by an embodiment of the present application.

[0057] Figure 6 It is a schematic diagram of a residual concentration field obtained by subtracting an indication concentration field from a randomly selected candidate concentration field provided by an embodiment of the present application.

[0058] Figure 7 It is a schematic diagram of a semivariogram function curve provided by an embodiment of the present application.

[0059] Figure 8a It is a schematic diagram of randomly arranged monitoring points provided in an embodiment of the present application.

[0060] Figure 8b It is a schematic diagram of the monitoring points after dual-objective optimization provided in an embodiment of the present application.

[0061] Fig. 9 It is a structural block diagram of a multi-objective optimization device for post-nuclear accident monitoring point layout provided in yet another embodiment of the present application.

[0062] Fig.10 It is a block diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0063] The following is a description of exemplary embodiments of the present application in conjunction with the accompanying drawings, including various details of the embodiments of the present application to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present application. Similarly, for the sake of clarity and conciseness, the description of well-known functions and structures is omitted in the following description.

[0064] Obviously, the described embodiments are only part of the embodiments of the present application, but not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0065] It should be noted that the terminal devices involved in the embodiments of the present application may include but are not limited to mobile phones, personal digital assistants (PDAs), wireless handheld devices, tablet computers and other smart devices; display devices may include but are not limited to personal computers, televisions and other devices with display functions.

[0066] In addition, the term "and / or" in this article is only a description of the association relationship between the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0067] When additional monitoring points are urgently needed during an accident, it is crucial to ensure that the deployed monitoring points can represent the real situation of the entire affected area, so as to more accurately adjust and reflect the concentration distribution of radionuclides. The existing conventional point layout method cannot meet this requirement. In order to optimize the layout of monitoring points, it is crucial to set a reasonable objective function, that is, it is necessary to ensure the accuracy of the global prediction of aerosol concentration, and also to pay attention to the identification of key areas, which involves multi-objective optimization problems.

[0068] In view of this, the present application proposes a multi-objective optimization scheme for monitoring points after a nuclear accident. The inventive concept is to: construct a dual objective function: a logarithmic root mean square error objective function and a critical area area function; estimate the release source term of radioactive nuclides, determine the indicated concentration field and multiple candidate concentration fields of this nuclear accident; randomly select a candidate concentration field from multiple candidate concentration fields, and perform a semivariogram analysis based on the candidate concentration field to fit a semivariogram that meets the best fitting conditions; interpolate multiple candidate concentration fields based on the semivariogram to obtain multiple corrected concentration fields, and average them to obtain the logarithmic root mean square error target average value and the critical area average value; and perform iterative optimization based on the NSGA-Ⅱ algorithm to determine the location of the optimal monitoring points and the corrected concentration field corresponding to the optimal monitoring points. Thus, the accuracy of monitoring points is improved, and the safety of nuclear accident emergency decision-making is improved.

[0069] In order to accurately describe the algorithm flow in this application, the concentration fields involved in this application scheme are first defined:

[0070] Real concentration field: The real distribution of radionuclides in the ambient air after a nuclear facility accident. In reality, the concentration information at the monitoring location can only be obtained through sampling at some monitoring points.

[0071] Indicative concentration field: The simulation parameters are set according to the estimated source terms and climate conditions after the accident. The concentration field generated by the atmospheric diffusion simulation model has a certain deviation from the actual concentration field due to the inherent uncertainty in the diffusion model and the data uncertainty in the diffusion parameters. However, the values ​​of key parameters in the indicative concentration field should be confirmed by experts to ensure that the simulation results are as close to the actual situation as possible, and the results are of reference value.

[0072] Candidate concentration field: Since the true concentration field is actually unknown in reality, in order to reflect the uncertainty in reality, random sampling is performed in the possible value range of the diffusion parameters of the diffusion model to generate a candidate concentration field data set, that is, to simulate the possible real situation. In this method, a total of 200 groups of diffusion simulation results are randomly generated to form the candidate concentration field. In the process of the monitoring point optimization algorithm, each case in the candidate concentration field is treated as a real concentration field that may actually occur.

[0073] Corrected concentration field: After determining the monitoring points, obtain the radionuclide concentration information at the sampling location through environmental aerosol sampling, and correct the indicated concentration field through interpolation method to make it more consistent with the actual distribution of radionuclides, thus forming a corrected concentration field.

[0074] Reference Figure 1 As shown, it is a schematic diagram of the steps of a multi-objective optimization method for monitoring points after a nuclear accident provided by an embodiment of the present application. The execution subject of the method is a multi-objective optimization device for monitoring points after a nuclear accident, which can be a hardware device or a software module, for example, a terminal device such as a computer, a tablet computer, a smart phone, a smart wearable device, or a functional module or component integrated in the above hardware device.

[0075] like Figure 1 As shown, the multi-objective optimization method for monitoring points after a nuclear accident may include the following steps:

[0076] Step 102: construct a logarithmic root mean square error objective function of the monitoring points in the candidate concentration field and the corrected concentration field, and a key area function where the concentration of released radionuclides is higher than the target concentration.

[0077] Optionally, in the present application, when constructing the logarithmic root mean square error objective function of the monitoring points in the candidate concentration field and the corrected concentration field, the logarithmic error of the monitoring points in the candidate concentration field and the corrected concentration field can be calculated by the following formula:

[0078]

[0079] Among them, y is the concentration value of the monitoring point in the candidate concentration field, To monitor the concentration value of the points in the corrected concentration field;

[0080] Afterwards, based on the logarithmic errors between the candidate concentration field and the corrected concentration field, the logarithmic errors of all monitoring points are added together, and the logarithmic root mean square error objective function of the monitoring points in the candidate concentration field and the corrected concentration field is calculated by the following formula:

[0081]

[0082] Among them, y i is the concentration value of the ith monitoring point in the candidate concentration field, is the concentration value of the ith monitoring point in the corrected concentration field, and n is the number of monitoring points.

[0083] Optionally, when constructing a critical area area function where the concentration of released radionuclides is higher than the target concentration, the corrected concentration field and the candidate concentration field may be overlapped to form four areas, including: a true positive area where the corrected concentration field and the candidate concentration field overlap, a false negative area in the candidate concentration field except the true positive area, a false positive area in the corrected concentration field except the true positive area, and a true negative area except the corrected concentration field and the candidate concentration field; based on the determined false negative area and false positive area, the critical area area function is determined by the following formula:

[0084] F2=k*A 假阳性 +(1-k)*A 假阴性

[0085] Among them, A 假阳性 and A 假阴性 They represent the areas of false positive and false negative regions in the simulation results respectively, and k is the weight factor.

[0086] Optionally, since the consequences caused by the false negative area are more serious, in this method case, the value of k is 0.1, thereby increasing the weight of the false negative area in the entire key area.

[0087] It should be understood that the multi-objective function constructed in the present application scheme can be the above two objective functions, or the above two objective functions together with other objective functions. That is, the above two objective functions are only used as examples, and can also be a combination of other objective functions, for example, the logarithmic root mean square error objective function F1 of the candidate concentration field and the corrected concentration field and the third type objective function F3, or the critical area function F2 and the third type objective function F3. The third type objective function should be a function that can affect the concentration field of radionuclides.

[0088] Step 104: Estimate the release source term of radionuclides to determine the indicative concentration field and multiple candidate concentration fields of this nuclear accident.

[0089] In the present application, the release source term can be estimated, and the simulation parameters can be set according to the meteorological parameter conditions. The radionuclide diffusion simulation model can be used to obtain the indicative concentration field. The radionuclide diffusion simulation model can use the current more mature Gaussian diffusion model and Lagrangian diffusion model, and the specific diffusion model is not limited. According to experts or established multiple calculation parameters and the values ​​of each calculation parameter, the indicative concentration field of this nuclear accident is determined. At the same time, according to the simulation results, multiple candidate concentration fields of this nuclear accident can be determined from multiple calculation parameters and the corresponding value ranges of each calculation parameter.

[0090] Step 106: randomly selecting a candidate concentration field from the multiple candidate concentration fields, and performing a semivariogram analysis based on the candidate concentration field to obtain a semivariogram that satisfies the best fitting condition.

[0091] Optionally, when the present application randomly selects a candidate concentration field from the multiple candidate concentration fields, performs a semivariogram analysis based on the candidate concentration field, and fits a semivariogram that meets the best fitting conditions, a candidate concentration field can be randomly selected from the multiple candidate concentration fields, and the candidate concentration field is used to make a difference with the corresponding concentration data in the indicator concentration field to obtain a residual concentration field; a semivariogram analysis is performed on each monitoring point in the residual concentration field, and the semivariance between the monitoring point pairs is calculated to obtain an experimental variance function curve; the range value, base value and nugget value in the set multiple models are fitted using the least squares method to obtain multiple semivariogram curves after fitting; the multiple semivariogram curves are respectively compared with the experimental variance function curve, and the semivariogram curve closest to the experimental variance function curve is selected as the semivariogram that meets the best fitting conditions. The set multiple models include: exponential model, Gaussian model, spherical model, etc.

[0092] A case is randomly selected from the candidate concentration field as the possible true concentration field. The radionuclide concentration at each simulated position is subtracted from the radionuclide concentration at the corresponding position in the indicated concentration field to obtain the concentration residual field. Based on the Kriging interpolation method, semivariogram analysis and model fitting are carried out on the obtained concentration residual field to obtain the semivariogram of the residual field.

[0093] Step 108: Based on the semivariogram function, the multiple candidate concentration fields are interpolated to obtain multiple corrected concentration fields, each corrected concentration field and the corresponding candidate concentration field are respectively substituted into the logarithmic root mean square error objective function to calculate multiple logarithmic root mean square error target values, and each corrected concentration field and the corresponding candidate concentration field are respectively substituted into the key area function to calculate multiple key area values, and the values ​​are averaged to obtain the logarithmic root mean square error target average value and the key area average value.

[0094] Optionally, when the multiple candidate concentration fields are interpolated based on the semivariogram to obtain multiple corrected concentration fields, the following operations may be performed for each candidate concentration field: subtract the nuclide concentration of the candidate concentration field from the nuclide concentration of the corresponding position of the indicator concentration field to obtain the concentration residual values ​​of all monitoring points in the candidate concentration field; use the semivariogram to interpolate the concentration residual values ​​of the candidate concentration field using the Kriging interpolation method to obtain a concentration residual interpolation field; add the concentration residual interpolation field to the indicator concentration field to obtain the corrected concentration field corresponding to the candidate concentration field. Thus, multiple corrected concentration fields may be obtained.

[0095] Afterwards, each corrected concentration field and the corresponding candidate concentration field are respectively substituted into the logarithmic root mean square error objective function and the key area function of step 102 to obtain a plurality of logarithmic root mean square error objective function values ​​and a plurality of key area function values. Then, the plurality of logarithmic root mean square error objective function values ​​are averaged to obtain a logarithmic root mean square error target average value, and the plurality of key area function values ​​are averaged to obtain a key area average value.

[0096] Step 110: Based on the NSGA-Ⅱ algorithm, the target average value of the logarithmic root mean square error and the average value of the key area are iteratively optimized to determine the location of the optimal monitoring points and the corrected concentration field corresponding to the optimal monitoring points.

[0097] In this step, the NSGA-Ⅱ algorithm can be applied to generate and select new monitoring locations through mutation, crossover, selection and other processes. The fifth step is used to calculate the corresponding final objective function value for each generated monitoring location until the preset number of iterations is reached, and the monitoring location corresponding to the minimum final objective function value is recorded.

[0098] Through the above technical solution, in response to the need for optimization of monitoring points after a nuclear accident, the logarithmic root mean square error between the concentration field and the true concentration field and the optimization objectives of the key area are integrated, and a multi-objective optimization algorithm is used to effectively improve the accuracy of monitoring point optimization and obtaining the radioactive aerosol concentration field.

[0099] The following is a detailed description of the multi-objective optimization scheme of this application through a constructed nuclear accident simulation case. In this case, it is assumed that a nuclear accident occurs in a nuclear facility, causing a 5% enriched uranium leak of 5×1011Bq. The main exposure pathway for the public is considered to be inhalation internal exposure. The dose conversion factors of U-234, U-235, and U-238 are 9.40×10-6Sv / Bq, 8.50×10-6Sv / Bq, and 8.00×10-6Sv / Bq, respectively. The adult respiratory rate is 8×103m3 / a. The dose control value of the public dose caused by the accident 1 hour after the accident is 5mSv. The corresponding total U concentration control value is calculated to be 11.8Bq / m3. In this method case, the area with a total U concentration greater than 10Bq / m3 is conservatively considered as a key area to calculate the objective function value.

[0100] In order to conveniently display the final optimization results, a set of real concentration fields is set. Only the radionuclide concentration data at the monitoring points after the selected monitoring positions are taken as the assumed actual measured values ​​for concentration field correction. As a display of the optimization results, the 20km range east of the radionuclide release position is selected as the study area. The main calculation parameters of the generated real concentration field are shown in Table 1. The schematic diagram of the real concentration field is shown in Figure 2 shown.

[0101] Table 1 Main calculation parameters of the true concentration field

[0102]

[0103]

[0104] The first step is to construct the objective function.

[0105] (1) Logarithmic root mean square error

[0106] Since the concentration field formed by the migration of radionuclides in the atmosphere after release has a large data range and distribution skew, the concentration usually decreases rapidly in an exponential form. In order to evaluate the accuracy of the concentration field after correction by the algorithm constructed by this method, the conventional root mean square error or relative error is used directly as the evaluation index, which may lead to the optimization process focusing only on the data in the area near the maximum concentration value, while ignoring the remaining areas. Therefore, the logarithmic root mean square error is used as the objective function for optimization, which can reduce the impact of large numerical target variables, make the evaluation process pay more attention to relative errors, and amplify small errors, so that the model pays more attention to errors in data with smaller orders of magnitude, and comprehensively measure the gap between the predicted value and the true value of each sample on the logarithmic scale.

[0107] For each sample point in the concentration field, the logarithmic error is:

[0108]

[0109] Where y is the concentration value of the sample point in the candidate concentration field, is the concentration value of the sample point in the corrected concentration field.

[0110] For the entire concentration field, the logarithmic root mean square error objective function is:

[0111]

[0112] where y i is the concentration value of the i-th sample point in the candidate concentration field, is the concentration value of the i-th sample point in the corrected concentration field, and n is the number of sample points in the concentration field.

[0113] (2) Area of ​​key areas

[0114] After radioactive nuclides are released into the atmosphere, areas with high concentrations of radionuclides will appear in the ambient atmosphere. During the emergency response process, overall resources may be limited, and it is necessary to focus on areas with high concentrations of radionuclides, where the human dose caused may exceed the management target value. Emergency response measures such as evacuation and withdrawal should be given priority in these key areas. Therefore, in the emergency decision-making process, the identification of these key areas is crucial.

[0115] Figure 3 The figure shows the four areas formed by the key areas of the candidate concentration field and the corrected concentration field generated by the simulation within the research scope. The false positive area indicates the part that belongs to the key area in the simulation but does not actually belong to the key area. The consequence of such misjudgment may be that the emergency response is carried out in the area with lower risk, resulting in a waste of some resources; and the false negative area indicates the part that actually belongs to the key area but is not identified in the simulation. The consequence of such misjudgment is that the emergency response is not carried out at the location where the emergency response should be carried out, which may affect the health of personnel. The consequences of the false negative area are far more serious than those of the false positive area. Therefore, the goal of this method to optimize the monitoring points is to reduce the area of ​​the false negative area as much as possible. The key area objective function set is:

[0116] F2=k·A 假阳性 +(1-k)·A 假阴性

[0117] Where F2 is the target function value of the key area (km2), A 假阳性 and A 假阴性 They represent the areas of false positive and false negative areas in the simulation results (km2) respectively, and k is the weight factor. Since the consequences of false negative areas are more serious, the value of k in this method is 0.1.

[0118] The second step is to estimate the release source term and obtain the indicated concentration field. The calculation parameters of the indicated concentration field assumed in this method case are shown in Table 2, and the schematic diagram of the indicated concentration field is shown in Figure 4 The indicated concentration field is determined based on the calculation parameters in Table 2 evaluated by experts.

[0119] Table 2 indicates the main calculation parameters of the concentration field

[0120]

[0121] The third step is to obtain the candidate concentration field. In order to simulate the possible real situation and reflect the uncertainty in the prediction, random sampling is performed within the possible value range of the calculation parameters. In this method case, 200 sets of candidate concentration fields are generated. The value range of each calculation parameter when calculating the candidate concentration field is shown in Table 3.

[0122] Table 3 Value ranges of various calculation parameters when calculating candidate concentration fields

[0123]

[0124] The fourth step is to randomly select a case from the candidate concentration fields as the possible real concentration field for kriging interpolation semivariogram analysis and model fitting. The candidate concentration field selected in this method case is as follows: Figure 5 As shown, each concentration data in the candidate concentration field is subtracted from the corresponding data of the indicated concentration field to obtain the residual concentration field, as shown in Figure 6 shown.

[0125] The semivariogram of each sample point in the residual concentration field is analyzed, the semivariance between the sample point pairs is calculated, and the experimental variogram is made. The least squares method is used to fit the range value, sill value and nugget value in the exponential model, Gaussian model and spherical model respectively to obtain the fitted variogram curve. The obtained variogram curve is shown in the figure. Figure 7 As shown in the figure, it can be seen that the spherical variogram has the best fitting effect, and its range value, sill value and nugget value are 8994, 1.34 and 0 respectively. This semivariogram will be used as prior information for kriging interpolation in the future.

[0126] In the fifth step, assume that for each of the 200 sets of candidate concentration fields in this method case, the radionuclide concentration at the corresponding position of the indicator concentration field is subtracted from the radionuclide concentration of the candidate concentration field to obtain the concentration residual values ​​at all monitoring positions. Apply the residual field semivariogram obtained in the fourth step, and apply the Kriging interpolation method to interpolate the concentration residual values ​​at the 200 sets of monitoring positions to obtain the concentration residual interpolation field, and add the concentration residual interpolation field to the indicator concentration field to obtain 200 sets of corrected concentration fields. For each corrected concentration field, compare the corrected concentration field with the corresponding candidate concentration field, calculate the objective function value set in this method, and average all objective function values ​​as the final objective function value.

[0127] In the sixth step, the NSGA-Ⅱ algorithm is used to optimize the final objective function value calculated in the fifth step to obtain a better monitoring point layout. The main calculation process can be as follows:

[0128] (1) Decoding and generating the initial population

[0129] Let N be the number of individuals in the population, and the program randomly generates the initial population P0. In this method case, the initialization population is to randomly generate the layout locations of environmental monitoring points.

[0130] (2) Fast Non-Dominated Sorting

[0131] The fast non-dominated sorting of individuals in the population is achieved through the non-dominated sorting algorithm O(gN2). The algorithm records the number of individuals in the population as N and the target number as g.

[0132] (3) Crowding distance calculation

[0133] Set as the crowding distance of the boundary individual, and calculate the crowding degree of other individuals by the following formula:

[0134]

[0135] In the formula, the number of objective functions is recorded as g, the level of the lth individual is recorded as d, the number of individuals with level d is recorded as n, the sth objective function value is recorded as ES, and the crowding distance of the lth individual is recorded as δ d (l), the maximum value of the sth objective function is recorded as The minimum value of the sth objective function is recorded as In this case, the number of objective functions g is 2.

[0136] (4)Select

[0137] According to the calculation results of crowding degree and sorting, all individuals in the population will be assigned two attributes: non-dominated order and crowding distance. When the non-dominated order of individuals is equal, the individual with a larger crowding distance is considered to be superior.

[0138] (5) Elite Strategy

[0139] The objective functions F1 and F2 constructed by the method are used to compare the differences between the parent and child populations, and then the optimal N individuals are extracted and set as the next parent population.

[0140] It should be understood that the specific implementations of the above steps (1)-(5) are merely examples listed for ease of understanding and do not limit the implementation of the optimization algorithm.

[0141] After optimizing the location of environmental monitoring points using the NSGA-Ⅱ algorithm, actual monitoring data is obtained at the corresponding location in the real concentration field, and the indicated concentration field is corrected. The corrected concentration field is as follows: Figure 8a and Figure 8b As shown. Figure 8a Among them, 30 monitoring points (indicated by stars in the figure) are relatively scattered, and most of them are not in key areas; Figure 8b As shown, the monitoring points are relatively dense, and many of them fall into the key area. The comparison of the optimized index values ​​of the uncorrected indicated concentration field and each corrected concentration field is shown in Table 4. In order to reflect the optimization effect of this method, the case of randomly arranged sampling positions is set at the same time, and the logarithmic error and key area difference are calculated as a comparison of the optimization results of the monitoring points.

[0142] Table 4 Optimization index values ​​of various concentration fields

[0143]

[0144]

[0145] The case optimization results constructed by this method show that the logarithmic root mean square error objective function F1 value decreased from 2.41 to 2.29, and the critical area objective function F2 value decreased from 10.43km2 to 3.50km2. The optimization effect was achieved in both evaluation indicators, and the overall prediction accuracy and the recognition ability of key areas were improved to a certain extent. Specifically, a better monitoring location layout plan can be obtained, which can ultimately help obtain a more accurate distribution of radionuclide concentration fields, and provide technical support for subsequent nuclear accident emergency decision-making.

[0146] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0147] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0148] Fig. 9 FIG. 1 shows a structural block diagram of a multi-objective optimization device for monitoring points after a nuclear accident provided by an embodiment of the present application. Fig. 9 As shown. The multi-objective optimization device 900 for monitoring points after a nuclear accident in this embodiment may include a construction module 901, a determination module 902, a fitting module 903, an interpolation module 904 and an optimization module 905. Among them, the construction module 901 is used to construct a logarithmic root mean square error objective function of the monitoring points in the candidate concentration field and the corrected concentration field, and a key area function where the concentration of the released radioactive nuclides is higher than the target concentration. The determination module 902 is used to estimate the release source term of the radioactive nuclides, determine the indicative concentration field of this nuclear accident and multiple candidate concentration fields. The fitting module 903 is used to randomly select a candidate concentration field from the multiple candidate concentration fields, and perform a semivariogram analysis based on the candidate concentration field, and fit a semivariogram function that meets the best fitting condition. The interpolation module 904 is used to interpolate the multiple candidate concentration fields based on the semivariogram to obtain multiple modified concentration fields, substitute each modified concentration field and the corresponding candidate concentration field into the logarithmic root mean square error objective function to calculate multiple logarithmic root mean square error target values, and substitute each modified concentration field and the corresponding candidate concentration field into the key area function to calculate multiple key area values, and average them to obtain the logarithmic root mean square error target average value and the key area average value. The optimization module 905 is used to iteratively optimize the logarithmic root mean square error target average value and the key area average value based on the NSGA-Ⅱ algorithm to determine the location of the optimal monitoring points and the modified concentration field corresponding to the optimal monitoring points.

[0149] It should be noted that part or all of the multi-objective optimization device for post-nuclear accident monitoring deployment in this embodiment may be an application located in a local terminal, or may also be a functional unit such as a plug-in or software development kit (SDK) provided in an application located in a local terminal, or may also be a processing engine located in a network-side server, or may also be a distributed system located on the network side, and this embodiment does not specifically limit this.

[0150] It is understandable that the application may be a local program (nativeApp) installed on the local terminal, or may be a webpage program (webApp) of a browser on the local terminal, which is not limited in this embodiment.

[0151] Optionally, in a possible implementation of this embodiment, when constructing the logarithmic root mean square error objective function of the monitoring points in the candidate concentration field and the corrected concentration field, the construction module 901 is specifically used to calculate the logarithmic error of the monitoring points in the candidate concentration field and the corrected concentration field by the following formula:

[0152]

[0153] Among them, y is the concentration value of the monitoring point in the candidate concentration field, To monitor the concentration value of the points in the corrected concentration field;

[0154] The logarithmic errors of all monitoring points are added together, and the logarithmic root mean square error objective function of the monitoring points in the candidate concentration field and the corrected concentration field is calculated by the following formula:

[0155]

[0156] Among them, y i is the concentration value of the ith monitoring point in the candidate concentration field, is the concentration value of the ith monitoring point in the corrected concentration field, and n is the number of monitoring points.

[0157] Optionally, in a possible implementation of this embodiment, when constructing the critical area area function where the concentration of released radionuclides is higher than the target concentration, the construction module 901 is specifically used to overlap the corrected concentration field and the candidate concentration field to form four areas, including: a true positive area where the corrected concentration field and the candidate concentration field overlap, a false negative area in the candidate concentration field except the true positive area, a false positive area in the corrected concentration field except the true positive area, and a true negative area except the corrected concentration field and the candidate concentration field; based on the determined false negative area and false positive area, the critical area area function is determined by the following formula:

[0158] F2=k*A假阳性 +(1 - k)*A 假阴性

[0159] where A 假阳性 and A 假阴性 represent the areas of the false positive region and the false negative region that appear in the simulation results respectively, and k is the weight factor.

[0160] Optionally, in a possible implementation manner of this embodiment, when the fitting module 903 randomly selects a candidate concentration field from the multiple candidate concentration fields and performs semi - variogram analysis based on this candidate concentration field to obtain a semi - variogram that meets the best fitting condition, it is specifically used to randomly select a candidate concentration field from the multiple candidate concentration fields, subtract the concentration data corresponding to the candidate concentration field from the concentration data in the indicated concentration field to obtain a residual concentration field; perform semi - variogram analysis on each monitoring point in the residual concentration field, calculate the semi - variance between the monitoring points to obtain an experimental variogram curve; use the least - squares method to fit the range value, sill value, and nugget value in a set of multiple models respectively to obtain multiple fitted semi - variogram curves; compare the multiple semi - variogram curves with the experimental variogram curve respectively, and select the semi - variogram curve that is closest to the experimental variogram curve as the semi - variogram that meets the best fitting condition.

[0161] Optionally, in a possible implementation manner of this embodiment, when the interpolation module 904 performs interpolation processing on the multiple candidate concentration fields respectively based on the semi - variogram to obtain multiple corrected concentration fields, it is specifically used to perform the following operations for each candidate concentration field respectively: subtract the nuclide concentration at the corresponding position in the indicated concentration field from the nuclide concentration of this candidate concentration field to obtain the concentration residual value of all monitoring points in this candidate concentration field; use the semi - variogram and perform interpolation processing on the concentration residual value of this candidate concentration field by Kriging interpolation method to obtain a concentration residual interpolation field; add the concentration residual interpolation field to the indicated concentration field to obtain the corrected concentration field corresponding to this candidate concentration field.

[0162] Optionally, in a possible implementation manner of this embodiment, the set of multiple models includes: exponential model, Gaussian model, spherical model.

[0163] In this embodiment, a dual objective function can be constructed: a logarithmic root mean square error objective function and a critical area area function; the release source term of radionuclides is estimated to determine the indicated concentration field and multiple candidate concentration fields of this nuclear accident; a candidate concentration field is randomly selected from multiple candidate concentration fields, and a semivariogram function analysis is performed based on the candidate concentration field to fit a semivariogram function that meets the best fitting conditions; multiple candidate concentration fields are interpolated based on the semivariogram function to obtain multiple corrected concentration fields, and the logarithmic root mean square error target average value and the critical area average value are obtained by averaging; and iterative optimization is performed based on the NSGA-Ⅱ algorithm to determine the location of the optimal monitoring points and the corrected concentration field corresponding to the optimal monitoring points. Thus, the accuracy of the monitoring points is improved, and the safety of nuclear accident emergency decision-making is improved.

[0164] An embodiment of the present application provides a computer-readable storage medium, in which at least one instruction is stored. The at least one instruction is loaded and executed by a processor to implement the multi-objective optimization method for post-nuclear accident monitoring point layout as described above.

[0165] An embodiment of the present application provides an electronic device, which includes a processor and a memory, wherein the memory stores at least one instruction, and the instruction is loaded and executed by the processor to implement the multi-objective optimization method for post-nuclear accident monitoring point layout as described above.

[0166] In the technical solution of this application, the collection, storage, use, processing, transmission, provision and disclosure of user personal information involved are in compliance with the relevant laws and regulations and do not violate public order and good morals.

[0167] Fig.10 A schematic block diagram of an example electronic device 1000 that can be used to implement an embodiment of the present application is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present application described and / or required herein.

[0168] like Fig.10As shown, the electronic device 1000 includes a computing unit 1001, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 1002 or a computer program loaded from a storage unit 1008 into a random access memory (RAM) 1003. In the RAM 1003, various programs and data required for the operation of the electronic device 1000 can also be stored. The computing unit 1001, the ROM 1002, and the RAM 1003 are connected to each other through a bus 1004. An input / output (I / O) interface 1005 is also connected to the bus 1004.

[0169] Multiple components in the electronic device 1000 are connected to the I / O interface 1005, including: an input unit 1006, such as a keyboard, a mouse, etc.; an output unit 1007, such as various types of displays, speakers, etc.; a storage unit 1008, such as a magnetic disk, an optical disc, etc.; and a communication unit 1009, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 1009 allows the electronic device 1000 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0170] The computing unit 1001 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 1001 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The computing unit 1001 executes the various methods and processes described above, such as the multi-objective optimization method for monitoring point layout after a nuclear accident. For example, in some embodiments, the multi-objective optimization method for monitoring point layout after a nuclear accident can be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as the storage unit 1008. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 1000 via the ROM 1002 and / or the communication unit 1009. When the computer program is loaded into the RAM 1003 and executed by the computing unit 1001, one or more steps of the multi-objective optimization method for monitoring point layout after a nuclear accident described above can be executed. Alternatively, in other embodiments, the computing unit 1001 can be configured to execute the multi-objective optimization method for monitoring point layout after a nuclear accident in any other appropriate manner (e.g., by means of firmware).

[0171] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0172] The program code for implementing the method of the present application can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, implements the functions / operations specified in the flow chart and / or block diagram. The program code can be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0173] In the context of the present application, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0174] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0175] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0176] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0177] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps disclosed in this application can be performed in parallel, sequentially or in different orders, as long as the desired results of the technical solution disclosed in this application can be achieved, and this document does not limit this.

[0178] The above specific implementations do not constitute a limitation on the protection scope of this application. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of this application should be included in the protection scope of this application.

Claims

1. A multi-objective optimization method for monitoring points after a nuclear accident, characterized in that: include: Construct the logarithmic root mean square error objective function of the monitoring points in the candidate concentration field and the revised concentration field, as well as the key area function where the concentration of released radionuclides is higher than the target concentration; Estimate the release source term of radionuclides and determine the indicative concentration field and multiple candidate concentration fields of this nuclear accident; Randomly selecting a candidate concentration field from the multiple candidate concentration fields, performing a semivariogram analysis based on the candidate concentration field, and fitting a semivariogram that satisfies the best fitting condition; Based on the semivariogram, the multiple candidate concentration fields are interpolated to obtain multiple corrected concentration fields, each corrected concentration field and the corresponding candidate concentration field are respectively substituted into the logarithmic root mean square error objective function to calculate multiple logarithmic root mean square error target values, and each corrected concentration field and the corresponding candidate concentration field are respectively substituted into the key area function to calculate multiple key area values, and the logarithmic root mean square error target average value and the key area average value are respectively averaged; Based on the NSGA-Ⅱ algorithm, the target average value of the logarithmic root mean square error and the average value of the key area are iteratively optimized to determine the location of the optimal monitoring points and the corrected concentration field corresponding to the optimal monitoring points.

2. The method according to claim 1, characterized in that The logarithmic root mean square error objective function of the monitoring points in the candidate concentration field and the corrected concentration field is constructed, including: The logarithmic error between the candidate concentration field and the corrected concentration field of the monitoring points is calculated by the following formula: Among them, y is the concentration value of the monitoring point in the candidate concentration field, To monitor the concentration value of the points in the corrected concentration field; The logarithmic errors of all monitoring points are added together, and the logarithmic root mean square error objective function of the monitoring points in the candidate concentration field and the corrected concentration field is calculated by the following formula: Among them, y i is the concentration value of the ith monitoring point in the candidate concentration field, is the concentration value of the ith monitoring point in the corrected concentration field, and n is the number of monitoring points.

3. The method according to claim 1, characterized in that Construct a critical area function where the concentration of released radionuclides is higher than the target concentration, including: Overlapping the corrected concentration field and the candidate concentration field to form four regions, including: a true positive region where the corrected concentration field and the candidate concentration field overlap, a false negative region in the candidate concentration field excluding the true positive region, a false positive region in the corrected concentration field excluding the true positive region, and a true negative region excluding the corrected concentration field and the candidate concentration field; Based on the determined false negative area and false positive area, the critical area area function is determined by the following formula: F2=k*A 假阳性 +(1-k)*A 假阴性 Among them, A 假阳性 and A 假阴性 They represent the areas of false positive and false negative regions in the simulation results respectively, and k is the weight factor.

4. The method according to claim 1, characterized in that A candidate concentration field is randomly selected from the multiple candidate concentration fields, and a semivariogram analysis is performed based on the candidate concentration field to obtain a semivariogram that satisfies the best fitting condition by fitting, specifically including: Randomly select a candidate concentration field from the plurality of candidate concentration fields, and use the candidate concentration field to perform a difference with the corresponding concentration data in the indicated concentration field to obtain a residual concentration field; Conduct semivariogram analysis on each monitoring point in the residual concentration field, calculate the semivariance between pairs of monitoring points, and obtain the experimental variogram curve; The least square method is used to fit the range value, sill value and nugget value in the set multiple models respectively, and multiple semivariogram curves after fitting are obtained; The multiple semivariogram curves are compared with the experimental variogram curve respectively, and the semivariogram curve closest to the experimental variogram curve is selected as the semivariogram that meets the best fitting condition.

5. The method according to claim 4, characterized in that Based on the semivariogram function, the plurality of candidate concentration fields are interpolated to obtain a plurality of modified concentration fields, specifically comprising: For each candidate concentration field, perform the following operations: Subtract the nuclide concentration of the candidate concentration field from the nuclide concentration at the corresponding position of the indicated concentration field to obtain the concentration residual values ​​of all monitoring points in the candidate concentration field; Using the semivariogram, the concentration residual values ​​of the candidate concentration field are interpolated using the Kriging interpolation method to obtain a concentration residual interpolation field; The concentration residual interpolation field is added to the indicated concentration field to obtain a modified concentration field corresponding to the candidate concentration field.

6. The method according to claim 4 or 5, characterized in that The setting of multiple models includes: an exponential model, a Gaussian model, and a spherical model.

7. A multi-objective optimization device for monitoring points after a nuclear accident, characterized in that: include: A construction module is used to construct a logarithmic root mean square error objective function of the monitoring points in the candidate concentration field and the corrected concentration field, and a critical area function where the concentration of the released radionuclides is higher than the target concentration; A determination module is used to estimate the release source term of radionuclides and determine the indicative concentration field and multiple candidate concentration fields of this nuclear accident; A fitting module, used for randomly selecting a candidate concentration field from the plurality of candidate concentration fields, performing a semivariogram analysis based on the candidate concentration field, and fitting a semivariogram satisfying an optimal fitting condition; An interpolation module, for interpolating the plurality of candidate concentration fields based on the semivariogram to obtain a plurality of modified concentration fields, substituting each modified concentration field and the corresponding candidate concentration field into the logarithmic root mean square error objective function to obtain a plurality of logarithmic root mean square error target values, and substituting each modified concentration field and the corresponding candidate concentration field into the key area function to obtain a plurality of key area values, and averaging them to obtain a logarithmic root mean square error target average value and a key area average value; The optimization module is used to iteratively optimize the target average value of the logarithmic root mean square error and the average value of the area of ​​the key area based on the NSGA-Ⅱ algorithm to determine the location of the optimal monitoring points and the corrected concentration field corresponding to the optimal monitoring points.

8. An electronic device comprising: at least one processor; and a memory communicatively coupled to the at least one processor; The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method according to any one of claims 1-6.

9. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-6.

10. A computer program product, comprising a computer program, which, when executed by a processor, implements the method according to any one of claims 1 to 6.

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