Probabilistic evaluation method for off-site radiation consequences of severe accidents in nuclear power plants

Abstract

The application provides a kind of nuclear power plant severe accident outside radiation consequence probability statistical evaluation method, including S1, probability is carried out to meteorological sequence;S2, consequence data collection of object of attention: on the basis of existing mainstream object of attention, it is formed to object of attention set by refinement and expansion, for each object of attention, obtain consequence result {D1, D2,..., DN} obtained by atmospheric diffusion calculation and analysis corresponding to all meteorological sequences;S3, on the basis of a series of consequence results of attention associated with different meteorological sequences obtained in the step S2, the probability of each consequence result of attention occurs is determined;S4, based on the consequence result of attention obtained in the step S2 and the corresponding probability determined in the step S3, generate dynamic matrix;S5, for the dynamic matrix generated in the step S4, carry out beyond probability calculation.The application covers wide consequence order of magnitude, adapts multi-scale risk expression, can ensure that in low value area is not too sparse, high value area is not blindly refined.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of nuclear power safety analysis, in particular to a method for probabilistic and statistical evaluation of off-site radiation consequences of a severe accident in a nuclear power plant. BACKGROUND

[0002] In the global energy structure, nuclear power as an important clean energy, its safe operation is crucial. With the increase of the number of nuclear power plants and the growth of the running time, the potential risk of nuclear accidents is of great concern. Accurate assessment of off-site radiation consequences of a severe accident in a nuclear power plant is the key to ensuring public safety, guiding emergency response and maintaining social stability.

[0003] However, the existing radiation consequence analysis technology has many shortcomings in dealing with complex and variable actual situations, and it is difficult to meet the current needs of accurate evaluation and efficient decision-making of nuclear safety.

[0004] Currently, the analysis of radiation consequences after a severe accident in a nuclear power plant mainly relies on deterministic methods and traditional probabilistic risk analysis, but there are the following limitations:

[0005] I. Deterministic method:

[0006] The deterministic method in the analysis of radiation consequences in a nuclear power plant usually builds a model based on extreme adverse assumptions. For example, when simulating the diffusion of radioactive substances, the worst weather conditions are often preset, ignoring the randomness and variability of actual weather sequences. This conservative analysis method can provide a safe bottom line evaluation, but in practical application it will lead to over-conservative emergency decision-making.

[0007] II. Traditional probabilistic risk analysis:

[0008] Traditional probabilistic risk analysis methods, such as Monte Carlo simulation, theoretically approximate the true probability distribution by generating a large number of random samples. However, in practical operation, this method requires the generation of a large number of samples, and the computational complexity grows geometrically, requiring high computational resources and long computation time.

[0009] In addition, when dealing with complex multi-index evaluation, such as considering radiation dose, health effects, and damage to the surrounding ecological environment, economic industry and other dimensions, the computational complexity further increases, and the results fluctuate greatly, making it difficult for the analysis results to intuitively and accurately reflect the exceeding probability of extreme events.

[0010] III. Consequence expression form:

[0011] The prior art is single in radiation consequence expression, and radiation dose, affected population and other basic information are usually presented in simple numerical values or conventional charts. There is no standardized consequence matrix structure, and the index system and expression method used by different analysis methods and research institutions are quite different, which is difficult to compare horizontally and is not conducive to the rapid development of emergency strategies.

[0012] Therefore, the present application provides a probability statistical evaluation method for off-site radiation consequences under severe accidents of nuclear power plants. SUMMARY

[0013] The present application provides a probability statistical evaluation method for off-site radiation consequences under severe accidents of nuclear power plants.

[0014] The present application provides a probability statistical evaluation method for off-site radiation consequences under severe accidents of nuclear power plants.

[0015] A probability statistical evaluation method for off-site radiation consequences under severe accidents of nuclear power plants, characterized in that the probability statistical evaluation method comprises:

[0016] S1, probabilizing meteorological sequences;

[0017] S2, collecting consequence data of the object of interest: based on existing mainstream objects of interest, the objects of interest are refined and expanded to form a set of objects of interest, and for each object of interest, the consequence results {D1, D2,..., DN} obtained by atmospheric diffusion calculation and analysis of all meteorological sequences are obtained;

[0018] wherein D1, D2,..., DN represent the consequence results of the object of interest under different meteorological sequences;

[0019] S3, based on the series of consequence results of the object of interest associated with different meteorological sequences obtained in step S2, the probabilities of the occurrence of each consequence result of the object of interest are determined;

[0020] S4, based on the consequence results of the object of interest obtained in step S2 and the corresponding probabilities determined in step S3, a dynamic matrix is generated;

[0021] S5, performing beyond-probability calculation for the dynamic matrix generated in step S4.

[0022] According to one embodiment of the present application, the step S1 comprises:

[0023] S 11 , collecting long-term ground meteorological monitoring station data and high-altitude meteorological detection data around the nuclear power plant, constructing a multi-source fusion meteorological data set, and preprocessing the meteorological data set;

[0024] S 12 , the sampling type used in combination with atmospheric diffusion calculation analysis, the number of sample layer types, and the number of meteorological data included in each sample layer, determine the probability pD of each of the extracted N meteorological sequences i , i = 1~N.

[0025] According to an embodiment of the present application, the meteorological monitoring station data and the upper air meteorological sounding data include hourly temperature, wind direction, wind speed, stability, precipitation intensity, and inversion layer height in the morning and afternoon in all four seasons.

[0026] According to an embodiment of the present application, the preprocessing includes: abnormal data detection, error data elimination, and missing data filling.

[0027] According to an embodiment of the present application, the set of objects of interest includes: the number of health effect cases in the region, the farthest region with an early death risk exceeding a set value, the number of people with a certain organ dose exceeding a set value, the average risk of individuals in the region, the collective dose of a certain organ in the region, the central line dose of a certain organ in the region, the central line risk of a certain organ in the region, and the population-weighted death risk in the region.

[0028] According to an embodiment of the present application, the step S3 includes: recording the probability corresponding to each result as a set {pD1, pD2, …, pDN}, where pDi is equivalent to the occurrence probability of the corresponding meteorological sequence, i = 1~N.

[0029] According to an embodiment of the present application, the step S4 includes:

[0030] S 41 , determining the starting value power level of the matrix;

[0031] S 42 , determining the terminal value power level of the matrix;

[0032] S 43 , standardizing and filling the values in the starting value power level m to the (n-1) power level;

[0033] S 44 , performing n power level matrix standardization filling;

[0034] S 45 , integrating all the values obtained by the step S 43 and the step S 44 in ascending order to construct a complete consequence matrix.

[0035] According to an embodiment of the present application, the step S 41include:

[0036] S 411 Obtain the concern consequence result D1 corresponding to the first meteorological sequence obtained in step S2;

[0037] S 412 Perform numerical magnitude analysis on D1 to determine the power series s;

[0038] S 413 The initial power value m of the consequence matrix is ​​obtained by calculating m=s-6.

[0039] According to an embodiment of the present invention, step S 42 include:

[0040] S 421 Obtain the results of the consequences of concern corresponding to all meteorological sequences in step S2;

[0041] S 422 Calculate the maximum value Dmax of the consequences of concern corresponding to all meteorological sequences, and use it as the termination value of the consequences matrix;

[0042] S 423 Perform numerical magnitude analysis on the maximum value Dmax to determine the power order n of the termination value.

[0043] According to an embodiment of the present invention, step S4 includes:

[0044] S 41 Perform frequency statistics on {D1, D2, ..., DN} to determine the optimal number of groups k;

[0045] S 42 Divide the intervals according to the principle of equal width or equal frequency, forming the interval set [b0, b1), [b1, b2), ..., [b k-1 , b k ];

[0046] S 43 Use the upper limit or median of each interval as the representative value of that node to construct a matrix {S1, S2, ..., S}. k}

[0047] According to an embodiment of the present invention, step S4 includes:

[0048] S 41 Sort {D1, D2, ..., DN} in ascending order;

[0049] S 42 Calculate the specified quantiles to obtain the corresponding consequence values ​​Q1, Q2, ..., Q. M ;

[0050] S 43 , taking each quantile value as a matrix node S j = Q j , an ordered matrix {S1, S2,..., S M} is constructed.

[0051] According to one embodiment of the present application, the step S4 comprises:

[0052] S 41 , determining the consequence range: D_min = min(Di), D_max = max(Di), i = 1, 2,..., N;

[0053] S 42 , setting the node number M or the step size ΔS, and calculating the node value:

[0054] S j = D_min + j × ΔS, j = 1, 2,..., M;

[0055] S 43 , constructing the matrix {S1, S2,..., S M}.

[0056] According to one embodiment of the present application, the formula used in the step S5 for calculating the exceeding probability is:

[0057]

[0058] wherein i represents the sequence number of the consequence result of interest; Num represents the total number of extracted meteorological sequences, taking N; D i represents the calculation result of the consequence of interest based on the i-th extracted meteorological sequence; pD i represents the probability of the occurrence of the consequence result D i of interest in the Num calculation results; j represents the sequence number of the value in the consequence matrix, taking 1-46; S j represents the j-th value in the consequence matrix; pS j represents the cumulative probability of the consequence result of interest exceeding the value S j .

[0059] The present application also provides an electronic device, characterized in that the electronic device comprises a processor and a memory, the memory stores programs or instructions executable on the processor, and the programs or instructions are executed by the processor to realize the probability statistical evaluation method of the off-site radiation consequence of a nuclear power plant under a severe accident as described above.

[0060] The application further provides a readable storage medium characterized in that a program or instruction is stored on the readable storage medium, and the program or instruction is executed by a processor to realize the probabilistic statistical evaluation method of consequences of off-site radiation under a severe accident of a nuclear power plant.

[0061] The positive progress effect of the application is that:

[0062] The probabilistic statistical evaluation method of consequences of off-site radiation under a severe accident of a nuclear power plant covers a wide range of consequence orders of magnitude and is suitable for multi-scale risk expression. The numerical interval range of eight application objects is fully considered, a matrix of 46 elements is used, the possible orders of magnitude of each result can be completely covered, the jump characteristics of low probability and high consequences can be captured, and each result of each object is ensured to be integrated into the matrix.

[0063] In addition, the probabilistic statistical evaluation method also adopts a structure of "layering by order of magnitude + fixed typical value in each layer", 4-5 representative nodes (such as 1, 2, 3, 5, 7) are set in each 10 times order of magnitude, the resolution of the low value area is ensured not to be too sparse, and the high value area is not blindly refined, so that the resolution deficiency of the low value area and the redundancy of the high value area of the traditional equal interval division are avoided. BRIEF DESCRIPTION OF DRAWINGS

[0064] The above and other features, properties, and advantages of the application will become more apparent by describing in detail the following embodiments with reference to the accompanying drawings, in which the same reference numerals represent the same features throughout the drawings, and wherein:

[0065] Fig. 1 The flowchart of the probabilistic statistical evaluation method of consequences of off-site radiation under a severe accident of a nuclear power plant is shown.

[0066] Fig. 2 The flowchart of the probabilistic statistical evaluation method of consequences of off-site radiation under a severe accident of a nuclear power plant is shown.

[0067] Fig. 3 The probabilistic statistical evaluation method of consequences of off-site radiation under a severe accident of a nuclear power plant is shown. DETAILED DESCRIPTION

[0068] In order to make the above-mentioned purposes, features and advantages of the application more obvious and easy to understand, the specific embodiments of the application will be described in detail below with reference to the accompanying drawings.

[0069] The embodiments of the application will now be described in detail with reference to the accompanying drawings. The preferred embodiments of the application will now be described in detail with reference to the drawings. In all the drawings, the same reference numerals will be used to represent the same or similar parts.

[0070] Furthermore, although the terms used in the present application are selected from publicly known and used terms, some of the terms mentioned in the description of the present application can be created by the applicant himself or herself in his or her own judgment. Therefore, the detailed meanings of these terms can be described in relevant parts of the description of the present application.

[0071] In addition, the present application should be understood not only by the actual terms used but also by the meanings implied by each term.

[0072] Embodiment 1:

[0073] As Figs. 1 to 3 shown, the present application discloses a method for probabilistic and statistical evaluation of off-site radiation consequences under a severe accident of a nuclear power plant, which comprises the following steps:

[0074] Step S1, probabilizing meteorological sequences.

[0075] Preferably, the step S1 comprises:

[0076] S 11 , collecting long-term ground meteorological monitoring station data and upper air meteorological sounding data around the nuclear power plant, constructing a multi-source fusion meteorological data set, and preprocessing the meteorological data set.

[0077] The meteorological monitoring station data and the upper air meteorological sounding data include hourly temperature, wind direction, wind speed, stability, precipitation intensity, and inversion layer height in the morning and afternoon of all four seasons. The preprocessing includes anomaly data detection, error data elimination, missing data filling, etc.

[0078] S 12 , determining the probability pD i of each meteorological sequence in all N extracted meteorological sequences by combining the sampling type used in atmospheric diffusion calculation and analysis, the number of sample layers, and the number of meteorological data included in each sample layer, i = 1~N.

[0079] Step S2, consequence data collection of the object of interest: based on the existing mainstream object of interest, the object of interest set is refined and expanded, and for each object of interest, the consequence results {D1, D2,..., DN} obtained by atmospheric diffusion calculation and analysis of all meteorological sequences are obtained.

[0080] D1, D2,..., DN represent the consequence results of the object of interest under different meteorological sequences.

[0081] The object of interest set preferably comprises the following eight object of interest sets:

[0082] The number of health effect cases in the region (e.g. including various early death numbers, early damage numbers, potential cancer numbers), the farthest region where the risk of early death exceeds a set value, the number of people whose organ dose exceeds a set value, the average risk of individuals in the region (e.g. including various early deaths, early damages, potential cancers), the collective dose of a certain organ in the region, the central line dose of a certain organ in the region, the central line risk of a certain organ in the region, the population-weighted death risk in the region (including early deaths, cancer deaths).

[0083] Step S3, on the basis of the series of concerned consequence results associated with different weather sequences obtained in the step S2, the probability of occurrence of each concerned consequence result is determined.

[0084] Preferably, the step S3 comprises: recording the probability corresponding to each result as a set {pD1, pD2, …, pDN}, wherein pDi is equivalent to the occurrence probability of the corresponding weather sequence, i = 1~N.

[0085] Specifically, on the basis of the series of concerned consequence results associated with different weather sequences obtained in the step S2, in order to achieve a reasonable assessment of the risk, the probability of occurrence of each concerned consequence result needs to be determined. The probability corresponding to each result is recorded as a set {pD1, pD2, …, pDN}, wherein pDi (i = 1~N) is equivalent to the occurrence probability of the corresponding weather sequence.

[0086] This probability determination process is based on the principles of probability theory and aims to quantify the possibility of the occurrence of the concerned consequence under different weather sequences, providing key parameters for the construction of the subsequent risk assessment model, and is an important link connecting the previous consequence acquisition and the subsequent comprehensive analysis.

[0087] Step S4, based on the concerned consequence results obtained in the step S2 and the corresponding probabilities determined in the step S3, a dynamic matrix is generated.

[0088] The consequence matrix generation is a key link in the risk assessment system, and a consequence matrix containing 46 values sorted from small to large is to be generated. The construction process is mainly based on the concerned consequence results obtained in the step S2 and the corresponding probabilities determined in the step S3.

[0089] Preferably, the step S4 comprises:

[0090] Step S 41 , the starting value power of the matrix is determined.

[0091] Preferably, the step S 41 comprises:

[0092] Step S 411Obtain the concern consequence result D1 corresponding to the first meteorological sequence obtained in step S2;

[0093] Step S 412 Perform numerical magnitude analysis on D1 to determine the power series s;

[0094] Step S 413 The initial power value m of the consequence matrix is ​​obtained by calculating m=s-6.

[0095] Specifically, based on the concern consequence result D1 corresponding to the first meteorological sequence obtained in step S2, numerical magnitude analysis is performed to determine the power order s. The specific method is as follows:

[0096] When D1 < 1, let D1 = a × 10 - k (1 ≤ a < 10, k is a positive integer), then s = -k;

[0097] When 1 ≤ D1 < 10, then s = 0;

[0098] When D1≥10, let D1=b×10k (1≤b<10, k is a non-negative integer), then s=k;

[0099] After obtaining the power order s, in order to cover the range of consequences and adapt the rules for constructing the consequences matrix, the initial power order m of the consequences matrix is ​​calculated by m=s-6, thereby achieving accurate positioning of the initial numerical order.

[0100] Step S 42 Determine the power order of the terminating value of the matrix.

[0101] Preferably, step S 42 include:

[0102] Step S 421 Obtain the results of the consequences of concern corresponding to all meteorological sequences in step S2;

[0103] Step S 422 Calculate the maximum value Dmax of the consequences of concern corresponding to all meteorological sequences, and use it as the termination value of the consequences matrix;

[0104] Step S 423 Perform numerical magnitude analysis on the maximum value Dmax to determine the power order n of the termination value.

[0105] Specifically, using the consequences of concern corresponding to all meteorological sequences obtained in step S2 as the sample space, the maximum value Dmax is identified and used as the termination value of the consequence matrix, i.e., Dmax = max(D1, D2, ..., DN). The method for determining its power order n is consistent with the logic for determining the power order of the initial value.

[0106] When Dmax<1, let Dmax=a*10 -k (1≤a<10, k is a positive integer), then n=-k;

[0107] When 1≤Dmax<10, then n=0;

[0108] When Dmax≥10, let Dmax=b*10 k (1≤b<10, k is a non-negative integer), then n=k.

[0109] Through this process, the upper limit of the magnitude of the matrix value is determined, and the boundary of the matrix construction is drawn.

[0110] Step S 43 , the values in the power level m to (n-1) power level are normalized and filled.

[0111] After determining the starting value power level m and the end value power level n, in order to realize the unified expression and effective analysis of values of different magnitudes, the values in the power level m to (n-1) power level need to be normalized and filled.

[0112] For any power level r (m≤r≤n-1), the number of values in the power level r is 5, and the standardization coefficient sequence c={1, 2, 3, 5, 7} is selected. Based on this, the calculation formula of the tth value in the power level r is:

[0113] V r,t =c t ×10 r .

[0114] Where, t=1, 2, 3, 4, 5, c t represents the tth coefficient in the sequence c.

[0115] For example, when r=m, t=1, V m,1 =1*10 m =1.00Em; when r=m+1, t=3,

[0116] V m+1,3 =3*10 m+1 =3.00E(m+1).

[0117] The standardization process follows the principle of numerical analysis, aiming to construct a consistent and comparable consequence matrix, and provides a standardized data structure for subsequent risk quantification and evaluation.

[0118] Step S 44 , the n power level matrix is normalized and filled.

[0119] After the termination value Dmax of the consequence matrix is determined, the internal values of the n power level need to follow certain rules. The maximum value in the n power level is explicitly taken as Dmax, which is based on the upper limit of the value in the entire value system of the consequence matrix.

[0120] Considering the magnitude expression of the value and the integrity of the matrix construction, for the values such as 1 × 10 n , 2 × 10 n , 3 × 10 n , 5 × 10 n , 7 × 10 n , these values in the n power level are compared with Dmax, and the values less than Dmax are included in the n power level.

[0121] This operation is to construct the consequence matrix comprehensively and orderly under the unified magnitude standard, to ensure that the values in the n power level can reasonably reflect the risk situation in this magnitude, so as to provide accurate data support for subsequent risk assessment. Therefore, the value set in the n power level is determined as 1 × 10 n , …, Dmax.

[0122] Step S 45 , all the values obtained by the step S 43 and the step S 44 are integrated according to the order from small to large, and a complete consequence matrix is constructed.

[0123] This process follows the basic logic of data sorting, aiming to construct an ordered value set for subsequent analysis and calculation.

[0124] In the integration process, if the number of values in the formed matrix is less than 46, considering the integrity and normative requirements of the structure of the consequence matrix, “N.D.” is used to supplement after the termination value. “N.D.” as a placeholder represents undefined or missing values, which ensures that the consequence matrix meets the preset dimension requirements in form, that is, composed of 46 elements.

[0125] Finally, the complete consequence matrix is denoted as { S1, S2, …, S46}, which becomes an important data basis for subsequent risk quantification analysis.

[0126] Step S5, for the dynamic matrix generated by the step S4, the exceeding probability calculation is performed.

[0127] For the consequence matrix generated in step S4, in order to further evaluate the risk, the exceeding probability corresponding to each value (except "N.D.") needs to be determined. The exceeding probability is a key indicator in the field of risk assessment, which reflects the possibility of a certain value being exceeded, and is of great significance for understanding the potential impact of the risk.

[0128] Preferably, the formula used in the exceeding probability calculation in step S5 is:

[0129]

[0130] Wherein, i represents the serial number of the consequence result of interest; Num represents the total number of extracted meteorological sequences, taking N; D i represents the calculation result of the consequence of interest based on the i th extracted meteorological sequence; pD i represents the probability of a certain consequence result D i in the Num calculation results; j represents the serial number of the value in the consequence matrix, taking 1~46; S j represents the j th value in the consequence matrix; pS j represents the cumulative probability of exceeding the value S j in the consequence of interest result.

[0131] The above formula accurately quantifies the probability of each value in the consequence matrix being exceeded, and is the basis for risk assessment calculation. Its unique application scenario and significance in the field of nuclear safety are clear.

[0132] According to the above calculation formula, each valid value (non-"N.D.") in the consequence matrix { S1, S2, …, S46} is calculated one by one, and finally the exceeding probability matrix { pS1, pS2, …, pS46} is obtained. If S j is "N.D.", since it does not have actual numerical significance, there is no need to calculate its exceeding probability, and it is directly recorded as "N.D.".

[0133] Taking a core meltdown and containment integrity accident of a nuclear power plant as an example, Fig. 3 the exceeding probability distribution of the effective dose, red bone marrow dose, and thyroid dose generated at the plant site boundary is shown, which can intuitively present the personnel dose under different meteorological conditions, and provide strong data support and visual reference for risk decision-making and protection measures.

[0134] The probability statistical evaluation method of the present application for off-site radiation consequences of nuclear power plant severe accidents has the following characteristics:

[0135] I. The beyond probability calculation is applied in the statistical evaluation of off-site radiation consequences of severe accidents in nuclear power plants. The calculation formula accurately quantifies the probability of each value in the consequence matrix being exceeded, which is the basis for risk assessment. It also clearly defines its unique application scenarios and significance in the field of nuclear safety.

[0136] II. Dynamic matrix generation algorithm is adopted:

[0137] Power level division rule: The power level division rule of the consequence matrix is adopted, including the determination method of the starting value power level m (such as m = s-6, s is the power level determined according to the initial concerned consequence result) and the termination value power level n, to ensure that the matrix can reasonably cover the range of consequence changes.

[0138] Value filling strategy: The value filling rule is adopted, that is, the coefficient sequence {1, 2, 3, 5, 7} is used to fill the standardized values in different power levels, to ensure the consistency and comparability of matrix data.

[0139] Dynamic termination value determination: The determination method of dynamic termination value Dmax is adopted, which is determined according to the actual obtained sample space of concerned consequence data, so that the consequence matrix can adapt to the upper limit changes of consequence data under different accident scenarios.

[0140] III. Process design

[0141] Multi-step collaborative architecture: The multi-step collaborative process architecture of meteorological sequence probabilization, concerned object consequence data collection, consequence result probabilization, dynamic matrix generation, and beyond probability calculation is used. The data transmission logic and dependency relationship between each step are clearly defined to ensure the coherence and scientificity from raw data collection to final risk assessment result output.

[0142] Consequence matrix standardization structure: The consequence matrix structure with fixed 46 values and the value filling rule of the consequence matrix are adopted to ensure the uniformity of the consequence matrix structure under different accident analysis scenarios, facilitate horizontal comparison and subsequent risk quantification analysis, and maintain the comparability and standardization of the results.

[0143] IV. Application scenario expansion

[0144] Risk analysis of specific concerned consequences: The application mode of combining 8 types of concerned consequences (number of health effect cases in the region, early death risk area, number of organ dose exceeding the set value, etc.) with beyond probability analysis. It clearly defines the application method and value of this combination in the scenarios of nuclear power plant emergency strategy development (such as evacuation range delineation, medical resource allocation), safety target setting, and public risk communication, to prevent this application scenario from being arbitrarily imitated or bypassed.

[0145] The application further provides an electronic device, comprising a processor and a memory, wherein the memory stores programs or instructions which can be run on the processor, and the programs or instructions are executed by the processor to realize the method for statistically evaluating consequences of off-site radiation in a severe accident of a nuclear power plant.

[0146] The application further provides a readable storage medium, wherein the readable storage medium stores programs or instructions, and the programs or instructions are executed by a processor to realize the method for statistically evaluating consequences of off-site radiation in a severe accident of a nuclear power plant.

[0147] Embodiment two:

[0148] This embodiment is basically the same as embodiment one, and the difference is that the consequence matrix of this embodiment is generated based on a statistical histogram. After the data of the concerned consequences are obtained in step S2 and the probability corresponding to each consequence is obtained in step S3, a statistical histogram is drawn based on the data of the concerned consequences in step S4. According to the distribution range of the data, the interval division of the histogram is determined. For example, the data of the consequences are divided into several intervals according to a certain numerical interval, and the frequency of the data in each interval is counted. The boundary values of these intervals and the corresponding frequencies are used to construct the consequence matrix, and the matrix elements are no longer the standardized values generated according to the power rule, but the boundary values of the intervals and the frequency information of the data of the consequences in the intervals.

[0149] In the embodiment, the step S4 comprises:

[0150] Step S 41 , frequency statistics are performed on {D1, D2,..., DN} to determine the optimal grouping number k;

[0151] Step S 42 , intervals are divided according to the equal-width or equal-frequency principle to form an interval set [b0, b1), [b1, b2),..., [b k-1 , b k ].

[0152] Step S 43 , the upper limit value or the median value of each interval is taken as the representative value of the node to construct a matrix {S1, S2,..., S k}.

[0153] If a consequence value falls into a certain interval, the corresponding node is marked as “activated” or the cumulative frequency is accumulated.

[0154] Then, in step S5, the exceeding probability calculation method is redefined according to the new consequence matrix structure. The exceeding probability corresponding to each interval boundary value in the consequence matrix is determined by calculating the proportion of the sum of the frequency of all intervals greater than a certain interval boundary value in the total frequency, so that the analysis process focuses more on the actual distribution frequency characteristics of the data rather than strict numerical standardization calculation.

[0155] Embodiment Three

[0156] This embodiment is basically the same as Embodiment One, except that it is based on a quantile-based consequence matrix construction. After obtaining the concerned consequence data in step S2 and obtaining the probability corresponding to each consequence in step S3, the data is processed using a quantile method in step S4.

[0157] A plurality of quantiles of the data, such as quartiles, deciles, etc., are calculated. The consequence data is divided into different intervals according to these quantiles. The quantiles are used as the key nodes of the consequence matrix, and the consequence matrix is constructed. The elements in the matrix can be quantiles and information such as the data range description between adjacent quantiles, rather than standardized numerical values based on power level rules.

[0158] In the specific implementation, the step S4 includes:

[0159] Step S 41 , arranging {D1, D2,..., DN} in ascending order;

[0160] Step S 42 , calculating the specified quantiles to obtain the corresponding consequence values Q1, Q2,..., Q M .

[0161] Step S 43 , using the quantile values as the matrix nodes S j = Q j , and constructing an ordered matrix {S1, S2,..., S M}.

[0162] If a consequence value falls into a certain interval, the corresponding node is marked as "activated" or the cumulative frequency.

[0163] Then, in step S5, the calculation of the exceeding probability is adjusted according to the new consequence matrix structure. The exceeding probability corresponding to a quantile is determined by calculating the proportion of the data greater than the quantile in the total data, which can evaluate the risk from the perspective of the distribution position of the data and provide a different perspective for risk analysis, realizing the core function of "structured expression + probability mapping".

[0164] Embodiment Four

[0165] The embodiment is basically the same as embodiment one, and the difference is that the embodiment is based on the consequence matrix generated by equal interval division. After obtaining the attention consequence data in step S2, the maximum value and the minimum value of the data are determined, and then equal interval division is performed in the value range. For example, the range between the maximum value and the minimum value is evenly divided into several intervals, and the length of each interval is equal.

[0166] The number of consequence data in each interval or the average value of data in the interval is counted, and the boundary values of the intervals and the corresponding statistical quantities are used to construct the consequence matrix. The equal interval division method in the embodiment is simple and direct to organize the elements of the consequence matrix.

[0167] In the specific implementation, the step S4 includes:

[0168] Step S 41 , determine the consequence range: D_min = min(D_i), D_max = max(D_i), i = 1, 2,..., N;

[0169] Step S 42 , set the number of nodes M or the step size ΔS, and calculate the node value:

[0170] S j = D_min + j × ΔS, j = 1, 2,..., M;

[0171] Step S 43 , construct the matrix {S1, S2,..., S M}.

[0172] Then, in step S5, the exceedance probability is recalculated for the new consequence matrix structure. By counting the proportion of the number of data greater than the boundary value of a certain interval to the total number of data, the exceedance probability corresponding to the boundary value of the interval is determined, which simplifies the calculation process and reflects the risk situation from the simple interval distribution of data.

[0173] Although embodiments two to four differ from embodiment one in the node division strategy, they have high consistency with the main scheme in terms of technical purpose, technical means and technical effect:

[0174] The technical purpose is the same: both are to convert discrete consequence calculation results into a structured matrix that can be used for exceedance probability analysis.

[0175] The technical means are similar: both establish a mapping relationship between the consequence level and the occurrence probability by setting a set of ordered threshold nodes.

[0176] The technical effects are equivalent: both can support subsequent exceedance probability curve generation, risk level evaluation and safety margin analysis.

[0177] According to the above description, the probability statistical evaluation method of the present application for off-site radiation consequences of nuclear power plant severe accidents can provide solid and reliable data support for the scientific formulation of nuclear power plant emergency strategies by performing systematic and in-depth statistical analysis on the radiation consequences of interest and reasonably calculating the exceeding probability of radiation consequences.

[0178] I. Comprehensive and systematic

[0179] A complete analysis process of the exceeding probability of radiation consequences of nuclear power plant severe accidents is constructed, from the probability calculation of meteorological sequences to the generation of consequence matrices, and finally the exceeding probability matrix is output to form a closed-loop analysis system.

[0180] II. Dynamic adaptability

[0181] The consequence matrix can be dynamically adjusted according to actual data (such as automatic determination of the starting value series m and the termination value Dmax), which is suitable for different accident scenarios, result objects and meteorological conditions.

[0182] III. High efficiency and scalability

[0183] Through the fixed matrix structure of 46 numerical values and the standardized power level division (1 / 2 / 3 / 5 / 7 coefficients), the calculation complexity is simplified, and multiple types of consequence analysis (such as health effects, dose risks, etc.) are supported.

[0184] IV. Precise decision support

[0185] The exceeding probability matrix directly quantifies the extreme risk of radiation consequences, providing data-driven basis for emergency strategies (such as evacuation range, medical resource allocation).

[0186] For those skilled in the art, the above disclosure of the present application is only as an example, and does not constitute a limitation on the present application. Although it is not explicitly stated here, those skilled in the art can make various modifications, improvements and corrections to the present application. Such modifications, improvements and corrections are suggested in the present application, so such modifications, improvements and corrections still belong to the spirit and scope of the exemplary embodiments of the present application.

[0187] At the same time, specific words are used in the present application to describe the embodiments of the present application. For example, "one embodiment", "an embodiment", and / or "some embodiments" means a certain feature, structure or characteristic related to at least one embodiment of the present application. Therefore, it should be emphasized and noted that the "one embodiment" or "one embodiment" or "one alternative embodiment" mentioned in different places in the specification does not necessarily refer to the same embodiment. In addition, some features, structures or characteristics in one or more embodiments of the present application can be properly combined.

[0188] Aspects of the application can be implemented in, completely, in hardware, completely in software (including firmware, resident software, micro-code, etc.), or combinations thereof. The foregoing hardware or software can be referred to as a "data block", "module", "engine", "unit", "component", or "system". A processor can be one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DAPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), processors, controllers, micro-controllers, microprocessors, or combinations thereof. Furthermore, aspects of the application can be presented in a computer program product, which can include a computer-readable medium having stored computer-readable program codes that can be executed by one or more computer processors. For example, the computer-readable medium can include, but is not limited to, magnetic storage devices (e.g., hard disk; floppy disk; magnetic strips...), optical disks (e.g., compact disk (CD); digital versatile disk (DVD)...), smart cards, and flash memory devices (e.g., card; stick; key drive...).

[0189] The computer readable medium can include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. The computer readable program code can be transmitted on a variety of mediums including, but not limited to, wireless, wireline, optical fiber cable, RF, etc., or any suitable combination thereof. A computer readable medium can be any medium that can be read by a computer processor, including, but not limited to, memory devices, optical storage devices, and any combination thereof. The computer readable program code can be downloaded over a network from a remote computer program product or from a server, or can be downloaded from another computer readable medium, such as a hard drive, floppy disk, or CD-ROM. The computer program product can comprise all the respective features enabling the implementation of the method described herein and which are, taken together, instrumental in the performance of the method.

[0190] It should also be noted that, while the foregoing description of implementations has been presented in terms of particular embodiments found in the description of the application, certain features of the application can be combined, substituted, or deleted, and other embodiments of the application can be used, without departing from the scope of the application as set forth in the claims. Also, it will be appreciated that some embodiments have been described as primarily operating in one mode of operation (e.g., in a mobile device or in a server). However, it will be appreciated that some embodiments can operate in multiple modes of operation, and that the description of embodiments as operating in a single mode of operation is for ease of description only.

[0191] Although the specific embodiments of the present application have been described above, it is understood by those skilled in the art that these are merely illustrative, and the scope of protection of the present application is defined by the appended claims. Those skilled in the art can make various changes or modifications to the embodiments without departing from the principles and the essence of the present application, and such changes and modifications fall within the scope of protection of the present application.

Claims

1. A probabilistic statistical evaluation method for the off-site radiation consequences of a severe nuclear power plant accident, characterized in that, The probability and statistical evaluation method includes: S1. Probabilize the meteorological sequence; S2. Consequence data collection for objects of concern: Form a set of objects of concern, and for each object of concern, obtain the consequences {D1, D2, ..., DN} obtained from atmospheric diffusion calculation and analysis of all meteorological sequences; The set of objects of interest includes: the number of health effect cases in the region, the farthest region where the risk of early death exceeds a set value, the number of people whose dose to a certain organ exceeds a set value, the average risk of individuals in the region, the collective dose of a certain organ irradiated in the region, the centerline dose of a certain organ irradiated in the region, the centerline risk of a certain organ irradiated in the region, and the population-weighted mortality risk in the region. Where D1, D2, ..., DN represent the consequences of the object of interest under different meteorological sequences; S3. Based on the series of consequences of concern associated with different meteorological sequences obtained in step S2, determine the probability of each consequence of concern occurring. S4. Based on the consequences of concern obtained in step S2 and the corresponding probabilities determined in step S3, generate a dynamic matrix; Step S4 includes: S 41 1. Determine the initial power order of the matrix; Step S 41 include: S 411 Obtain the concern consequence result D1 corresponding to the first meteorological sequence obtained in step S2; S 412 Perform numerical magnitude analysis on D1 to determine the power series s; S 413 The initial power value m of the consequence matrix is ​​obtained by calculating m=s-6; S 42 Determine the power order of the terminating value of the matrix; Step S 42 include: S 421 Obtain the results of the consequences of concern corresponding to all meteorological sequences in step S2; S 422 Calculate the maximum value Dmax of the consequences of concern corresponding to all meteorological sequences, Dmax=max(D1, D2, ..., DN), and use it as the termination value of the consequences matrix; S 423 Perform numerical magnitude analysis on the maximum value Dmax to determine the power order n of the termination value; S 43 Standardize and fill the values ​​from the initial power m to the power of (n-1); S 44 Perform standardized filling of n-power matrices; S 45 Step S 43 and the step S 44 All the obtained values ​​are integrated in ascending order to construct a complete consequence matrix; S5. Calculate the exceedance probability for the dynamic matrix generated in step S4.

2. The probabilistic statistical evaluation method for off-site radiation consequences under a severe nuclear power plant accident as described in claim 1, characterized in that, Step S1 includes: S 11 The system comprehensively collects long-term ground meteorological monitoring station data and upper-air meteorological sounding data in the area surrounding the nuclear power plant, constructs a multi-source fusion meteorological dataset, and preprocesses the meteorological dataset. S 12 By combining the sampling type, the number of sample layers, and the number of meteorological data points contained in each sample layer used in atmospheric diffusion calculations and analysis, the probability pD of each of the N extracted meteorological sequences is determined. i , i = 1~N.

3. The probabilistic statistical evaluation method for off-site radiation consequences under a severe nuclear power plant accident as described in claim 2, characterized in that, The meteorological monitoring station data and the upper-air meteorological detection data include: hourly temperature, wind direction, wind speed, stability, precipitation intensity, and the height of the inversion layer in the morning and afternoon throughout the year.

4. The probabilistic statistical evaluation method for off-site radiation consequences under a severe nuclear power plant accident as described in claim 2, characterized in that, The preprocessing includes: abnormal data detection, erroneous data removal, and missing data filling.

5. The probabilistic statistical evaluation method for off-site radiation consequences under a severe nuclear power plant accident as described in claim 1, characterized in that, Step S3 includes: recording the probability corresponding to each result as a set {pD1, pD2, …, pDN}, where pDi is equivalent to the occurrence probability of the corresponding meteorological sequence, i = 1~N.

6. The probabilistic statistical evaluation method for off-site radiation consequences under a severe nuclear power plant accident as described in claim 1, characterized in that, Step S4 includes: S 41 Perform frequency statistics on {D1, D2, ..., DN} to determine the optimal number of groups k; S 42 Divide the intervals according to the principle of equal width or equal frequency, forming the interval set [b0, b1), [b1, b2), ..., [b k-1 ,b k ]; S 43 Use the upper limit or median of each interval as the node representative value to construct a matrix {S1, S2, ..., S}. k } 7. The probabilistic statistical evaluation method for off-site radiation consequences under a severe nuclear power plant accident as described in claim 1, characterized in that, Step S4 includes: S 41 Sort {D1, D2, ..., DN} in ascending order; S 42 Calculate the specified quantiles to obtain the corresponding consequence values ​​Q1, Q2, ..., Q. M ; S 43 Using each quantile value as a matrix node S j = Q j Construct an ordered matrix {S1, S2, ..., S} M } 8. The probabilistic statistical evaluation method for off-site radiation consequences under a severe nuclear power plant accident as described in claim 1, characterized in that, Step S4 includes: S 41 1. Determine the range of consequences: D_min = min(Di), D_max = max(Di), i=1, 2, ..., N; S 42 Set the number of nodes M or the step size ΔS, and calculate the node values: S j = D_min + j × ΔS, j = 1, 2, ..., M; S 43 Construct the matrix {S1, S2, ..., S} M } 9. The probabilistic statistical evaluation method for off-site radiation consequences under a severe nuclear power plant accident as described in claim 1, characterized in that, The formula used for calculating the exceedance probability in step S5 is as follows: Where i represents the sequence number of the consequences of concern; Num represents the total number of meteorological sequences extracted, which is N; D i pD represents the result of the concern consequence calculation based on the i-th extracted meteorological sequence. i This indicates that among Num computational results, the result D of a certain consequence of interest... i The probability of occurrence; j represents the index of the value in the consequence matrix, ranging from 1 to 46; S j pS represents the j-th value in the consequence matrix. j This indicates that the concern is about consequences exceeding the numerical value S. j The cumulative probability.

10. An electronic device, characterized in that, The electronic device includes a processor and a memory, the memory storing programs or instructions that can run on the processor, the programs or instructions being executed by the processor to implement the probabilistic statistical evaluation method for off-site radiation consequences under a severe nuclear power plant accident as described in any one of claims 1-9.

11. A readable storage medium, characterized in that, The readable storage medium stores a program or instructions, which, when executed by a processor, implement the probabilistic statistical evaluation method for off-site radiation consequences under a severe nuclear power plant accident as described in any one of claims 1-9.

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