Quantification method of minimum cut set of seismic PSA (Pressure Swing Adsorption) and related device thereof

Abstract

The invention discloses a quantification method of a minimum cut set of an earthquake PSA and a related device thereof, and the method comprises the steps: obtaining first data which comprises an earthquake risk curve, equipment object vulnerability information and equipment object random failure information; obtaining a target minimum cut set; a target binary decision diagram is generated based on the first data and the target minimum cut set, the data structure of the target binary decision diagram comprises a node probability, and the node probability is determined based on the first data; and based on the target binary decision diagram, determining a non-intersection cut set probability by using a recursive algorithm fused with a multiplication function, the multiplication function being related to a node probability. According to the scheme, the data structure of the binary decision diagram is optimized, and meanwhile, the cumulative multiplication algorithm is fused in the recursion process, so that the path probability can be obtained in real time in the recursion process, processing and calculation are not needed after a non-intersecting cut set is obtained, and the efficiency of the binary decision diagram algorithm and the accuracy of minimum cut set quantification are effectively improved.

Description

Technical Field

[0001] The present application relates to the technical field of nuclear power probabilistic safety analysis, and more specifically, to a method for quantifying a minimum cut set of an earthquake PSA and a related device thereof. Background Art

[0002] In the earthquake PSA analysis of nuclear power plants, quantifying the minimum cut set (MCS) of earthquake-induced core damage (SCDF) is one of the key technical elements. The accuracy of the quantification result directly affects the evaluation results of the earthquake PSA of nuclear power plants. Traditional PSA uses the minimum upper bound algorithm or the multi-order approximation algorithm to deal with the correlation between cut sets. When calculating the cut sets with a small probability of occurrence, these two algorithms can calculate approximate results that are closer to the real results in a shorter time. However, in the quantification process of earthquake PSA, the earthquake failure basic event will have a higher failure probability in the high ground motion interval. The use of the minimum upper bound algorithm or the multi-order approximation algorithm to quantify the SCDF cut set will produce a large error and cannot accurately reflect the quantitative analysis results of earthquake PSA. Summary of the invention

[0003] In view of this, this application provides the following technical solutions:

[0004] A method for quantifying the minimum cut set of seismic PSA, comprising:

[0005] Obtaining first data, the first data including earthquake hazard curves, equipment object vulnerability information, and equipment object random failure information;

[0006] Obtain the target minimum cut set;

[0007] Generate a target binary decision graph based on the first data and the target minimum cut set, wherein the data structure of the target binary decision graph includes node probabilities, and the node probabilities are determined based on the first data;

[0008] Based on the target binary decision graph, a recursive algorithm integrating a cumulative multiplication function is used to determine a disjoint cut set probability, wherein the disjoint cut set probability represents the sum of path probabilities of all recursive paths of the target binary decision graph, and the cumulative multiplication function is related to the node probability.

[0009] Optionally, generating a target binary decision diagram based on the first data and the target minimum cut set includes:

[0010] Generate equivalent Boolean algebra based on the target minimum cut set;

[0011] Determine an initial binary decision diagram based on the equivalent Boolean algebra;

[0012] The node probabilities of the nodes at each layer in the initial binary decision diagram are determined based on the first data, and the node probabilities are added to the data structure of the corresponding nodes to obtain a target binary decision diagram.

[0013] Optionally, the determining the disjoint cut set probability based on the target binary decision diagram using a recursive algorithm integrating a cumulative multiplication function includes:

[0014] Determining the path probability of each recursive path in the target binary decision graph;

[0015] The path probabilities of all paths in the target binary decision graph are summed to obtain the disjoint cut set probability.

[0016] Optionally, determining the path probability of each path in the target binary decision graph includes:

[0017] The path probability of the Nth layer node on the recursive path is multiplied by the edge probability corresponding to the Nth layer node as the path probability of the N+1th layer node, and the calculation is performed layer by layer until the path probability of the terminal node of the current recursive path is calculated, where N is a positive integer, and the edge probability is determined based on the node probability of the next layer node connected by the edge and the state of the edge;

[0018] The path probability of the termination node is determined as the final path probability of the current recursive path.

[0019] Optionally, before determining the disjoint cut set probability by adopting a recursive algorithm integrating a cumulative multiplication function based on the target binary decision graph, the method further includes:

[0020] Initialize the path probability and the disjoint cut set probability, wherein the initial value of the path probability is 1, and the initial value of the disjoint cut set probability is 0.

[0021] Optionally, it also includes:

[0022] In the process of recursive traversal using a recursive algorithm, if the path probability of a non-terminal node in the recursive path is lower than a preset cutoff value, the path probability of the non-terminal node is determined as the final path probability of the recursive path related to the non-terminal node, and recursive calculation of the remaining nodes in the recursive path related to the non-terminal node is no longer performed.

[0023] Optionally, if the Nth layer node has a left side edge, the path characterization event formed by the left side edge occurs, and the edge probability of the left side edge is the node probability recorded in the data structure of the Nth layer node; if the Nth layer node has a right side edge, the path characterization event formed by the right side edge does not occur, and the edge probability of the right side edge is 1-[node probability of the Nth layer node].

[0024] The present application also discloses a device for quantifying the minimum cut set of seismic PSA, comprising:

[0025] A data acquisition module, used to acquire first data, wherein the first data includes an earthquake hazard curve, equipment object vulnerability information, and equipment object random failure information;

[0026] A cut set acquisition module is used to obtain the target minimum cut set;

[0027] A binary decision diagram generation module, configured to generate a target binary decision diagram based on the first data and the target minimum cut set, wherein the data structure of the target binary decision diagram includes node probabilities, and the node probabilities are determined based on the first data;

[0028] A probability determination module is used to determine the disjoint cut set probability based on the target binary decision graph by using a recursive algorithm integrating a cumulative multiplication function, wherein the disjoint cut set probability represents the sum of the path probabilities of all recursive paths of the target binary decision graph, and the cumulative multiplication function is related to the node probability.

[0029] The present application also discloses a computer program product, including computer-readable instructions. When the computer-readable instructions are executed on an electronic device, the electronic device implements any of the above-mentioned methods for quantifying the minimum cut set of seismic PSA.

[0030] The present application also discloses a computer storage medium, which carries one or more computer programs. When the one or more computer programs are executed by an electronic device, the electronic device can implement any of the above-mentioned methods for quantifying the minimum cut set of seismic PSA.

[0031] Through the above technical scheme, it can be known that the embodiment of the present application discloses a method for quantifying the minimum cut set of earthquake PSA and its related device, the method includes: obtaining first data, the first data includes earthquake hazard curve, equipment object vulnerability information and equipment object random failure information; obtaining a target minimum cut set; generating a target binary decision diagram based on the first data and the target minimum cut set, the data structure of the target binary decision diagram includes node probability, the node probability is determined based on the first data; based on the target binary decision diagram, a recursive algorithm integrating a cumulative function is used to determine the probability of non-intersecting cut sets, and the cumulative function is related to the node probability. The above scheme optimizes the data structure of the binary decision diagram, and at the same time integrates the cumulative algorithm into its recursive process, so that the path probability can be obtained in the recursive process without obtaining the non-intersecting cut set and then processing the calculation, so that the amount of calculation of the binary decision diagram algorithm can be greatly reduced, and the efficiency of the binary decision diagram algorithm and the accuracy of the minimum cut set quantification can be effectively improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0033] Figure 1 A flow chart of a method for quantifying the minimum cut set of a seismic PSA disclosed in an embodiment of the present application;

[0034] Figure 2 A flow chart of determining the probability of disjoint cut sets disclosed in an embodiment of the present application;

[0035] Figure 3 An example diagram of a binary decision diagram disclosed in an embodiment of the present application;

[0036] Figure 4 It is a schematic diagram of the overall implementation process of the method for quantifying the minimum cut set of PSA disclosed in the embodiment of the present application;

[0037] Figure 5 It is a schematic diagram of the structure of a device for quantifying the minimum cut set of seismic PSA disclosed in an embodiment of the present application. DETAILED DESCRIPTION

[0038] For the purpose of reference and clarity, the descriptions, abbreviations or acronyms of the technical terms used below are summarized as follows:

[0039] PSA: Probabilistic Safety Assessment, which means probabilistic safety analysis, is a risk quantification assessment technology based on probability theory. It evaluates the frequency of various accidents and conducts safety assessment by analyzing the failure probability of the system and combining the failure probability of each subsystem, component and external conditions. The PSA method was first applied on a large scale to the safety research of nuclear power plants and has developed into a standardized tool for safety assessment of nuclear power plants.

[0040] MCS: The abbreviation of minimal cut set, which means minimum cut set, is one of the basic concepts of reliability statistics.

[0041] SCDF: Seismic Core Damage Frequency, an important parameter in the safety assessment of nuclear power plants, is the expected frequency of core damage caused by earthquakes in nuclear power plants.

[0042] SSC: As the abbreviation of nuclear power plant systems (SYSTEMS), structures (STRUCTURES), and components (COMPONENTS), it includes the main equipment objects for power plant management in nuclear power plants.

[0043] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0044] Figure 1 This is a flow chart of a method for quantifying the minimum cut set of a seismic PSA disclosed in an embodiment of the present application. Figure 1 As shown, the method may include:

[0045] Step 101: Obtain first data, wherein the first data includes an earthquake hazard curve, equipment object vulnerability information, and equipment object random failure information.

[0046] The quantitative method of the minimum cut set of earthquake PSA described in this embodiment can be applied to the field of nuclear power probabilistic safety analysis technology, and can also be applied to other fields that require PSA, and the present application does not limit this. Among them, the earthquake hazard curve can predict the intensity and probability of future disasters by analyzing the geological conditions of the plant site and historical disaster records. The equipment object can be the equipment managed by the nuclear power plant, and the equipment object vulnerability information and equipment object random failure information can be obtained based on traditional acquisition methods. For example, in the PSA modeling process, the possible failure mode of SSC will be analyzed, and the failure probability of the equipment will be selected according to the failure mode. The corresponding statistical data can be queried and obtained.

[0047] Step 102: Obtain a target minimum cut set MCS.

[0048] The target minimum cut set MCS, that is, the cut set that needs to be quantified, may be a SCDF cut set, for example.

[0049] Step 103: Generate a target binary decision graph based on the first data and the target minimum cut set, wherein the data structure of the target binary decision graph includes node probabilities, and the node probabilities are determined based on the first data.

[0050] As a non-approximate quantitative method, the binary decision diagram (BDD) algorithm can theoretically provide an accurate solution to the MCS. However, due to the inherent characteristics of the binary decision diagram algorithm, its computational complexity will expand exponentially with the increase of the model scale. The computational burden of the binary decision diagram has become the main obstacle to its application in practical engineering. Therefore, the present application generates an optimized binary decision diagram data structure based on the first data and the target minimum cut set. The optimized binary decision diagram data structure includes not only four variables: traditional node ID, variable name corresponding to the node, variable order, and downstream node ID, but also node probability.

[0051] Specifically, the binary decision diagram data structure stores data in units of nodes. The conventional binary decision diagram data structure only contains four basic variables: node ID, variable name corresponding to the node, variable order, and downstream node ID. The optimized binary decision diagram data structure of this application adds "node probability" on the basis of the conventional data structure. That is, the data structure of a node contains five variables: node ID, variable name corresponding to the node, variable order, downstream node ID, and node probability. The data type and function of each variable are shown in Table 1 below.

[0052] Table 1 Binary decision diagram data structure

[0053]

[0054] The implementation of determining the node probability based on the first data is a known content in the field and will not be explained in detail here.

[0055] Step 104: Based on the target binary decision graph, a recursive algorithm integrating a cumulative multiplication function is used to determine a disjoint cut set probability, wherein the disjoint cut set probability represents the sum of path probabilities of all recursive paths of the target binary decision graph, and the cumulative multiplication function is related to the node probability.

[0056] The disjoint cut sets refer to mutually exclusive cut sets. If you want to calculate the probability of occurrence of several disjoint cut sets, that is, the probability of disjoint cut sets, you can directly add the occurrence probabilities of the disjoint cut sets. Based on the binary decision graph, multiple recursive paths can be determined by a recursive algorithm. Each recursive path corresponds to a disjoint cut set, and the path probability of the recursive path is the occurrence probability of the corresponding disjoint cut set.

[0057] Since conventional binary decision diagram data lacks probability data of variables, calculating the probability of disjoint cut sets requires first obtaining the disjoint cut sets through recursion, and then searching the memory for the probabilities corresponding to the variables in the disjoint cut sets for cumulative multiplication.

[0058] The present application solution merges the occurrence probability data of the variables corresponding to the nodes into the binary decision diagram data structure, and adds a cumulative multiplication function in the recursive algorithm. When the recursive algorithm recurses from one node of the binary decision diagram to the next node, it can directly multiply the probability data of the node. By cumulatively multiplying the occurrence probabilities of all experienced nodes on the path, the occurrence probability of the disjoint cut set can be obtained, and the probability of the disjoint cut set can be calculated in the recursive process. Finally, the path probabilities of all recursive paths (that is, the occurrence probabilities of the disjoint cut sets corresponding to each recursive path) are added together to obtain the disjoint cut set probability.

[0059] In the process of determining the probability of disjoint cut sets, the above scheme does not need to store the disjoint cut sets generated by the binary decision diagram, nor does it need to search the variable data in the disjoint cut sets in the memory to calculate their probabilities. This solves the problem that the previous binary decision diagram algorithm has large amount of calculation, long calculation time and cannot be applied in engineering.

[0060] The quantification method of the minimum cut set of PSA described in this embodiment optimizes the data structure of the binary decision diagram, and integrates the multiplication algorithm into its recursive process. Therefore, the path probability can be obtained in the recursive process without obtaining the disjoint cut sets and then processing the calculations. Therefore, the amount of calculation of the binary decision diagram algorithm can be greatly reduced, effectively improving the efficiency of the binary decision diagram algorithm and the accuracy of the minimum cut set quantification.

[0061] In the above embodiment, generating a target binary decision diagram based on the first data and the target minimum cut set may include: generating an equivalent Boolean algebra based on the target minimum cut set; determining an initial binary decision diagram based on the equivalent Boolean algebra; determining the node probabilities of nodes at each layer in the initial binary decision diagram based on the first data, and adding the node probabilities to the data structure of the corresponding nodes to obtain a target binary decision diagram.

[0062] Figure 2 The flowchart of determining the probability of disjoint cut sets disclosed in the embodiment of the present application is shown in FIG. Figure 2 As shown, the method of determining the probability of disjoint cut sets based on the target binary decision diagram by using a recursive algorithm integrating a cumulative multiplication function may include:

[0063] Step 201: Determine the path probability of each recursive path in the target binary decision graph.

[0064] Figure 3 This is an example of a binary decision diagram shown in an embodiment of the present application, where a solid arrow is the left edge, representing an event that has occurred, and the edge probability of the left edge is the node probability of the node from which the arrow originates; a dotted arrow is the right edge, representing an event that has not occurred, and the edge probability of the right edge is 1-the node probability of the node from which the arrow originates. In the framework of the entire binary decision diagram, the node probabilities of two nodes representing the same event are the same, such as Figure 3 The B1 and B2 nodes in the graph, the B1 node corresponds to the solid line edge from A to B1, representing the recursive path where the first event occurs, and the B2 node corresponds to the dotted line edge from A to B2, representing the recursive path where the first event does not occur. The probabilities of the B1 and B2 nodes are the same. Figure 3 The binary decision diagram shown contains three recursive paths, namely AB1D, AB1CD and AB2CD. Through recursive calculation, each recursive path will get a path probability.

[0065] Step 202: summing the path probabilities of all paths in the target binary decision graph to obtain the disjoint cut set probability.

[0066] The sum of the path probabilities of all recursive paths is the final disjoint cut set probability. For example, Figure 3 The path probability of the recursive path AB1D is 0.1, the path probability of the recursive path AB1CD is 0.2, and the path probability of the recursive path AB2CD is 0.2. Then the probability of disjoint cut sets is 0.1+0.2+0.2=0.5.

[0067] In one implementation, determining the path probability of each path in the target binary decision graph includes: multiplying the path probability of the Nth layer node on the recursive path with the edge probability corresponding to the Nth layer node as the path probability of the N+1th layer node, calculating layer by layer until the path probability of the terminal node of the current recursive path is calculated, N is a positive integer, and the edge probability is determined based on the node probability of the node emitted by the edge and the state of the edge; determining the path probability of the terminal node as the final path probability of the current recursive path.

[0068] That is, the recursive path starts from the topmost node and accumulates the edge probabilities of the edges corresponding to the nodes on the path until the path reaches the terminal node. When the left edge of a node points to the next node, the edge probability of the left edge is the node probability of the node; when the right edge of a node points to the next node, the edge probability of the right edge is (1-the node probability of the node).

[0069] Specifically, if the N-th layer node has a left side edge, the path characterization event formed by the left side edge occurs, and the edge probability of the left side edge is the node probability recorded in the data structure of the N-th layer node. If the N-th layer node has a right side edge, the path characterization event formed by the right side edge does not occur, and the edge probability of the right side edge is 1-[node probability of the N-th layer node].

[0070] In one implementation, before determining the disjoint cut set probability based on the target binary decision diagram using a recursive algorithm integrating a cumulative multiplication function, it also includes: initializing the path probability and the disjoint cut set probability, wherein the initial value of the path probability is 1, and the initial value of the disjoint cut set probability is 0.

[0071] The above content discloses the specific implementation of determining the probability of disjoint cut sets, which is convenient for technicians in the field to better understand and implement the technical solution of the present application.

[0072] Based on the contents disclosed in the aforementioned embodiments, the quantification method of the minimum cut set of PSA may further include: in the process of recursive traversal using a recursive algorithm, if the path probability of a non-terminal node in the recursive path is lower than a preset cutoff value, the path probability of the non-terminal node is determined as the final path probability of the recursive path related to the non-terminal node, and recursive calculation of the remaining nodes in the recursive path related to the non-terminal node is no longer performed.

[0073] The above implementation is also called recursive truncation. In order to eliminate the occupation of computing resources by the disjoint cut sets with too small probability, this method adds a truncation function to the recursive algorithm, and truncates the disjoint cut sets with too small probability during the recursive traversal. The specific process is that in the recursive process, the recursive algorithm compares the probability accumulation result with the truncation value each time it recurses. If the accumulation result is greater than the truncation value, it continues to recurse downward until it recurses to the termination node to output the result; if the current result is less than the truncation value, it stops recursively outputting the result.

[0074] The implementation of recursive truncation can further reduce the computational complexity of the binary decision diagram algorithm. In applications, the truncation value can be set according to actual conditions to adjust the balance between truncation efficiency and truncation error.

[0075] Figure 4 This is a schematic diagram of the overall implementation process of the quantitative method of the minimum cut set of PSA disclosed in the embodiment of this application. In the recursive traversal, the left side represents the "1" in the node binary decision, that is, the event occurs; the right side represents the "0" in the node binary decision, that is, the event does not occur. Figure 4 Understand the contents of each part in the aforementioned embodiments.

[0076] The technical solution of the present application can significantly reduce the amount of computation of the binary decision diagram algorithm while introducing only a very small truncation error by optimizing the binary decision diagram algorithm, thereby effectively improving the efficiency of the binary decision diagram algorithm and the accuracy of the PSA minimum cut set quantification, which is of great significance for improving the safety level of nuclear power plants and other enterprises that pay great attention to safety issues and optimizing disaster resistance designs.

[0077] For the aforementioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the order of the actions described, because according to the present application, some 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.

[0078] The method is described in detail in the embodiments disclosed in the above-mentioned application. The method of the application can be implemented by various forms of devices. Therefore, the application also discloses a device, and a specific embodiment is given below for detailed description.

[0079] Figure 5 This is a schematic diagram of a structure of a device for quantifying the minimum cut set of PSA disclosed in an embodiment of the present application, see Figure 5 As shown, the quantification device 50 of the minimum cut set of PSA may include:

[0080] The data acquisition module 501 is used to obtain first data, wherein the first data includes earthquake hazard curves, equipment object vulnerability information, and equipment object random failure information.

[0081] The cut set obtaining module 502 is used to obtain the target minimum cut set.

[0082] The binary decision diagram generation module 503 is used to generate a target binary decision diagram based on the first data and the target minimum cut set, wherein the data structure of the target binary decision diagram includes node probabilities, and the node probabilities are determined based on the first data.

[0083] The probability determination module 504 is used to determine the disjoint cut set probability based on the target binary decision graph by using a recursive algorithm integrating a cumulative multiplication function, wherein the disjoint cut set probability represents the sum of the path probabilities of all recursive paths of the target binary decision graph, and the cumulative multiplication function is related to the node probability.

[0084] The quantification device for the minimum cut set of PSA described in this embodiment optimizes the data structure of the binary decision diagram, and integrates the multiplication algorithm into its recursive process. Therefore, the path probability can be obtained in the recursive process without obtaining the disjoint cut sets and then processing and calculating them separately. Therefore, the amount of calculation of the binary decision diagram algorithm can be greatly reduced, effectively improving the efficiency of the binary decision diagram algorithm and the accuracy of the minimum cut set quantification.

[0085] In one implementation, the decision diagram generation module can be specifically used to: generate an equivalent Boolean algebra based on a target minimum cut set; determine an initial binary decision diagram based on the equivalent Boolean algebra; determine the node probabilities of nodes at each layer in the initial binary decision diagram based on the first data, and add the node probabilities to the data structure of the corresponding nodes to obtain a target binary decision diagram.

[0086] In one implementation, the probability determination module may include: a first determination module, used to determine the path probability of each recursive path in the target binary decision graph; a second determination module, used to sum the path probabilities of all recursive paths in the target binary decision graph to obtain the probability of a disjoint cut set.

[0087] In one implementation, the first determination module can be specifically used to: multiply the path probability of the Nth layer node on the recursive path by the occurrence probability of the Nth layer node as the path probability of the N+1th layer node, and calculate layer by layer until the path probability of the terminal node of the current recursive path is calculated, where N is a positive integer, and the occurrence probability of the node is determined based on the node probability of the node; determine the path probability of the terminal node as the final path probability of the current recursive path.

[0088] In one implementation, the device may also include: an initialization module, used to initialize the path probability and the disjoint cut set probability before determining the disjoint cut set probability based on the target binary decision diagram using a recursive algorithm integrating a multiplication function, wherein the initial value of the path probability is 1, and the initial value of the disjoint cut set probability is 0.

[0089] In one implementation, the device may also include: a recursive truncation module, which is used to determine the path probability of a non-terminal node as the final path probability of the recursive path related to the non-terminal node, without performing recursive calculations on the remaining nodes in the recursive path related to the non-terminal node, if the path probability of the non-terminal node in the recursive path is lower than a preset truncation value during the recursive traversal using a recursive algorithm.

[0090] In one implementation, if the N-th layer node has a left side edge, the path characterization event formed by the left side edge occurs, and the edge probability of the left side edge is the node probability recorded in the data structure of the N-th layer node. If the N-th layer node has a right side edge, the path characterization event formed by the right side edge does not occur, and the edge probability of the right side edge is 1-[node probability of the N-th layer node].

[0091] The specific implementation of the minimum cut set quantification device of PSA and the modules it contains as well as other possible implementations can be found in the corresponding parts of the method embodiment, which will not be repeated here.

[0092] The quantification device for the minimum cut set of any one of the PSAs described in the above embodiments includes a processor and a memory. The data acquisition module, cut set acquisition module, decision diagram generation module, probability determination module, recursive truncation module, initialization module, first determination module, second determination module, etc. in the above embodiments are all stored in the memory as program modules, and the processor executes the above program modules stored in the memory to realize corresponding functions.

[0093] The processor includes a kernel, which retrieves the corresponding program module from the memory. One or more kernels can be set, and the processing of the access data can be realized by adjusting the kernel parameters.

[0094] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0095] In an exemplary embodiment, a computer-readable storage medium is also provided, which can be directly loaded into the internal memory of a computer and contains software code. After being loaded and executed by a computer, the computer program can implement the steps shown in any embodiment of the above-mentioned method for quantifying the minimum cut set of PSA.

[0096] In an exemplary embodiment, a computer program product is also provided, which can be directly loaded into the internal memory of a computer and contains software code. After being loaded and executed by a computer, the computer program can implement the steps shown in any embodiment of the method for quantifying the minimum cut set of PSA described above.

[0097] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0098] It should also be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0099] The steps of the method or algorithm described in conjunction with the embodiments disclosed herein may be implemented directly using hardware, a software module executed by a processor, or a combination of the two. The software module may be placed in a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0100] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for quantifying the minimum cut set of earthquake PSA, characterized in that: include: Obtaining first data, the first data including earthquake hazard curves, equipment object vulnerability information, and equipment object random failure information; Obtain the target minimum cut set; Generate a target binary decision graph based on the first data and the target minimum cut set, wherein the data structure of the target binary decision graph includes node probabilities, and the node probabilities are determined based on the first data; Based on the target binary decision graph, a recursive algorithm integrating a cumulative multiplication function is used to determine a disjoint cut set probability, wherein the disjoint cut set probability represents the sum of path probabilities of all recursive paths of the target binary decision graph, and the cumulative multiplication function is related to the node probability.

2. The method for quantifying the minimum cut set of earthquake PSA according to claim 1, characterized in that: The generating a target binary decision diagram based on the first data and the target minimum cut set includes: Generate equivalent Boolean algebra based on the target minimum cut set; Determine an initial binary decision diagram based on the equivalent Boolean algebra; The node probabilities of the nodes at each layer in the initial binary decision diagram are determined based on the first data, and the node probabilities are added to the data structure of the corresponding nodes to obtain a target binary decision diagram.

3. The method for quantifying the minimum cut set of earthquake PSA according to claim 1, characterized in that: The method of determining the probability of disjoint cut sets based on the target binary decision diagram by using a recursive algorithm integrating a cumulative multiplication function includes: Determining the path probability of each recursive path in the target binary decision graph; The path probabilities of all paths in the target binary decision graph are summed to obtain the disjoint cut set probability.

4. The method for quantifying the minimum cut set of earthquake PSA according to claim 3, characterized in that: Determining the path probability of each path in the target binary decision graph includes: The path probability of the Nth layer node on the recursive path is multiplied by the edge probability corresponding to the Nth layer node as the path probability of the N+1th layer node, and the calculation is performed layer by layer until the path probability of the terminal node of the current recursive path is calculated, where N is a positive integer, and the edge probability is determined based on the node probability of the next layer node connected by the edge and the state of the edge; The path probability of the termination node is determined as the final path probability of the current recursive path.

5. The method for quantifying the minimum cut set of earthquake PSA according to claim 3, characterized in that: Before determining the probability of disjoint cut sets by adopting a recursive algorithm integrating a cumulative multiplication function based on the target binary decision diagram, the method further includes: Initialize the path probability and the disjoint cut set probability, wherein the initial value of the path probability is 1, and the initial value of the disjoint cut set probability is 0.

6. The method for quantifying the minimum cut set of earthquake PSA according to claim 4, characterized in that: Also includes: In the process of recursive traversal using a recursive algorithm, if the path probability of a non-terminal node in the recursive path is lower than a preset cutoff value, the path probability of the non-terminal node is determined as the final path probability of the recursive path related to the non-terminal node, and recursive calculation of the remaining nodes in the recursive path related to the non-terminal node is no longer performed.

7. The method for quantifying the minimum cut set of earthquake PSA according to claim 4, characterized in that: If the N-th layer node has a left side edge, the path representation event formed by the left side edge occurs, and the edge probability of the left side edge is the node probability recorded in the data structure of the N-th layer node. If the N-th layer node has a right side edge, the path representation event formed by the right side edge does not occur, and the edge probability of the right side edge is 1-[node probability of the N-th layer node].

8. A device for quantifying the minimum cut set of earthquake PSA, characterized in that: include: A data acquisition module, used to acquire first data, wherein the first data includes an earthquake hazard curve, equipment object vulnerability information, and equipment object random failure information; A cut set acquisition module is used to obtain the target minimum cut set; A binary decision diagram generation module, configured to generate a target binary decision diagram based on the first data and the target minimum cut set, wherein the data structure of the target binary decision diagram includes node probabilities, and the node probabilities are determined based on the first data; A probability determination module is used to determine the disjoint cut set probability based on the target binary decision graph by using a recursive algorithm integrating a cumulative multiplication function, wherein the disjoint cut set probability represents the sum of the path probabilities of all recursive paths of the target binary decision graph, and the cumulative multiplication function is related to the node probability.

9. A computer program product, characterized in that The method comprises computer-readable instructions, and when the computer-readable instructions are executed on an electronic device, the electronic device implements the method for quantifying the minimum cut set of PSA according to any one of claims 1 to 7.

10. A computer storage medium, characterized in that: The storage medium carries one or more computer programs, which, when executed by an electronic device, enable the electronic device to implement the method for quantifying the minimum cut set of seismic PSA as described in any one of claims 1 to 7.

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