Physical process failure probability determination method and device, electronic equipment and medium

Abstract

The invention provides a physical process failure probability determination method and device, electronic equipment and a medium, and the physical process failure probability determination method applied to a passive system comprises the steps: obtaining N types of first parameters affecting the physical process failure of the passive system; inputting the N types of first parameters into a sensitivity analysis simulation model, and obtaining H second parameters corresponding to the N types of first parameters; the H second parameters are compiled, M first parameter combinations and M first probabilities corresponding to the M first parameter combinations are obtained, and the first probabilities are the probabilities that the second parameters in the first parameter combinations are combined with one another; based on a preset criterion, second probabilities corresponding to the M first parameter combinations are determined, and the second probabilities are probabilities of physical process failure under the first parameter combinations; and determining the physical process failure probability of the passive system under the target parameter combination based on the first probability and the second probability corresponding to the target parameter combination.

Description

Technical Field

[0001] The present invention relates to the field of probabilistic safety assessment of nuclear power plants, and in particular to a method, device, electronic equipment and medium for determining the failure probability of a physical process. Background Art

[0002] Passive systems usually rely on natural laws such as gravity, natural convection and heat transfer to operate without external power. Precisely because passive systems rely on natural laws to operate, their driving force is weak and they are more sensitive to environmental conditions and parameters than active systems. Physical process failure becomes an important factor in the failure of passive system operation. At present, the evaluation of physical process failure in the existing technology is only determined by analyzing the reliability of the operation of the passive system, and the probability of occurrence of the physical failure process has not yet been evaluated. Summary of the invention

[0003] The embodiments of the present application provide a method, device, electronic device and medium for determining the failure probability of a physical process, so as to solve the problem that the probability of occurrence of the physical failure process has not been evaluated and determined in the prior art.

[0004] To solve the above technical problems, this application is implemented as follows:

[0005] In a first aspect, the present application provides a method for determining a failure probability of a physical process, which is applied to a passive system, comprising:

[0006] Obtaining N types of first parameters of the physical process failure of the passive system, where N is an integer greater than or equal to 1;

[0007] Inputting the N types of first parameters into a sensitivity analysis simulation model to obtain H second parameters corresponding to each of the N types of first parameters;

[0008] The H second parameters are compiled to obtain M first parameter combinations and M first probabilities respectively corresponding to the M first parameter combinations, wherein the first parameter combination includes one second parameter corresponding to each type of the first parameters, the first probability is a probability of the second parameters in the first parameter combination occurring in combination with each other, and M is an integer greater than or equal to N;

[0009] Based on a preset criterion, determining a second probability corresponding to each of the M first parameter combinations, wherein the second probability is a probability of physical process failure occurring under the first parameter combination;

[0010] Based on the first probability and the second probability corresponding to a target parameter combination, a physical process failure probability of the passive system under the target parameter combination is determined, wherein the target parameter combination is one of the M first parameter combinations.

[0011] Optionally, the N types of first parameters are physical parameters that affect the operation of the passive system or cause the Internet of Things process to fail, and the N types of first parameters include temperature, pressure, and flow.

[0012] Optionally, inputting the N types of first parameters into a sensitivity analysis simulation model to obtain H second parameters corresponding to each of the N types of first parameters includes:

[0013] Inputting the N types of first parameters into a sensitivity analysis simulation model, and obtaining K first parameter intervals corresponding to each type of first parameter output by the sensitivity analysis simulation model, where K is an integer greater than or equal to 1;

[0014] Obtaining K interval ranges of the K first parameter intervals, and determining a middle value of each of the K interval ranges;

[0015] The intermediate value is determined as the second parameter corresponding to each of the first parameter intervals, to obtain H second parameters corresponding to N types of first parameters, wherein the value of H is the product of N and K.

[0016] Optionally, compiling the H second parameters to obtain M first parameter combinations includes:

[0017] Combining one of the second parameters corresponding to each type of the first parameters to obtain the M first parameter combinations, where M is

[0018] Optionally, obtaining M first probabilities corresponding to each of the M first parameter combinations includes:

[0019] Acquire preset information, where the preset information includes the K second parameters corresponding to each category of the first parameters, and a third probability that each second parameter appears in all the second parameters of the corresponding first parameter category;

[0020] Based on the preset information, determine the third probability corresponding to each of the second parameters in each of the first parameter combinations;

[0021] Based on the product of each of the third probabilities corresponding to each of the second parameters in each of the first parameter combinations, the M first probabilities corresponding to each of the M first parameter combinations are obtained.

[0022] Optionally, determining the second probabilities corresponding to the M first parameter combinations respectively based on a preset criterion includes:

[0023] Input each of the first parameter combinations into the passive system physical model to obtain a third parameter corresponding to each of the first parameter combinations;

[0024] Based on the preset criteria, the second probability corresponding to each of the third parameters is determined to obtain the second probability corresponding to each combination of the first parameters, wherein the preset criteria includes multiple third parameters and the probabilities corresponding to the third parameters respectively.

[0025] Optionally, determining the physical process failure probability of the passive system under the target parameter combination based on the first probability and the second probability corresponding to the target parameter combination includes:

[0026] Based on the first probability and the second probability corresponding to the target parameter combination, obtaining a target product of a first probability corresponding to the target parameter combination and the corresponding second probability;

[0027] The target product is determined to be a physical process failure probability of the passive system under the target parameter combination.

[0028] In a second aspect, an embodiment of the present application further provides a physical process failure probability determination device, which is applied to a passive system, comprising:

[0029] A first acquisition module, used to acquire N types of first parameters of the physical process failure of the passive system, wherein N is an integer greater than or equal to 1;

[0030] A second acquisition module is used to input the N types of first parameters into a sensitivity analysis simulation model to obtain H second parameters corresponding to each of the N types of first parameters;

[0031] a compilation module, used to compile the H second parameters, obtain M first parameter combinations and M first probabilities respectively corresponding to the M first parameter combinations, wherein the first parameter combination includes one second parameter corresponding to each type of the first parameters, the first probability is a probability of the second parameters in the first parameter combination occurring in combination with each other, and M is an integer greater than or equal to N;

[0032] A first determination module, configured to determine, based on a preset criterion, a second probability corresponding to each of the M first parameter combinations, wherein the second probability is a probability of a physical process failure occurring under the first parameter combination;

[0033] The second determination module is used to determine the physical process failure probability of the passive system under the target parameter combination based on the first probability and the second probability corresponding to the target parameter combination, wherein the target parameter combination is one of the M first parameter combinations.

[0034] In a third aspect, an embodiment of the present application provides an electronic device, comprising: a processor, a memory, and a program stored in the memory and executable on the processor, wherein when the program is executed by the processor, the steps of the method for determining the failure probability of a physical process as described in any one of the first aspects are implemented.

[0035] In a fourth aspect, an embodiment of the present application further provides a readable storage medium having a program stored thereon, and when the program is executed by a processor, the steps of the method for determining the failure probability of a physical process as described in any one of the first aspects are implemented.

[0036] In an embodiment of the present application, multiple categories of first parameters that affect the physical failure process of the passive system are first determined, and then multiple second parameters corresponding to each category of first parameters are determined through a sensitivity analysis simulation model. Subsequently, a second parameter in one category of first parameters is combined with a second parameter in another category of first parameters, and the first probability corresponding to the obtained multiple first parameter combinations is determined. Finally, the second probability corresponding to each first parameter combination is determined by a preset criterion, and the physical process failure probability of the passive system under the first parameter combination can be determined by combining the first probability and the second probability. In this way, for the influencing parameters in the physical failure process of the passive system, there is no need to analyze the reliability of the passive system operation, and the probability of the occurrence of the physical failure process under different combinations of influencing factors can be conveniently determined. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the description of the embodiments of the present application will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0038] Figure 1 It is a flow chart of reliability analysis of passive system in the prior art;

[0039] Figure 2 is a flow chart of a method for determining failure probability of a physical process provided by an embodiment of the present application;

[0040] Figure 3 It is a structural schematic diagram of a physical process failure probability determination device provided in an embodiment of the present application;

[0041] Figure 4 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0042] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all of the embodiments. Therefore, it should be appreciated by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, the description of well-known functions and structures is omitted in the following description. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without making creative work are within the scope of protection of the present application.

[0043] It is understandable that in the prior art, reliability analysis is usually performed on the physical failure process of the passive system to evaluate the reliability of the passive system under specific accident scenarios. Figure 1 As shown in the figure, the key factors affecting the operation of the specific passive system will be determined first. This process can use variance analysis or hierarchical analysis to determine the influencing factors. Subsequently, the criticality of the influencing factors obtained above is evaluated. Since the effects and importance of each influencing parameter are different under different circumstances, the influencing parameters need to be screened. If it is identified as a non-critical parameter, its value uses the design value. Only when the influencing factor belongs to the critical factor, the actual parameter value of the factor is used to evaluate the reliability of the passive system. Monte Carlo simulation, dynamic event tree and other methods can be used to evaluate the reliability of the passive system. Finally, after the reliability analysis of the passive system, specific indicators will be obtained, through which the reliability results of the passive system can be obtained.

[0044] Furthermore, when performing reliability analysis on passive systems in the prior art, the reliability index usually obtained cannot directly reflect the probability of occurrence of the passive system physical failure process, and needs to be determined in combination with the reliability analysis results. In different situations, passive systems have different reliabilities, and need to be analyzed one by one. The determination process is complicated and difficult to be widely used. Therefore, this application proposes a method for determining the probability of physical process failure, which can easily obtain the probability of physical failure of passive systems under various combinations of influencing parameters.

[0045] Please refer to Figure 2 , the embodiment of the present application provides a method for determining the failure probability of a physical process, and the specific steps include:

[0046] Step 101: Obtain N types of first parameters of physical process failure of the passive system, where N is an integer greater than or equal to 1.

[0047] It should be noted that the embodiments of the present application are proposed for passive systems, which are systems that operate according to natural laws and do not require external power. For example, the fluid in the pipeline can use its own gravity, temperature difference, pressure difference, etc. as a power source. When the system is put into operation, the valve is opened. Due to the lack of power source provided by the above factors, even if the valve and other equipment are successfully put into operation, the fluid in the pipeline cannot flow. Therefore, to determine the probability of physical failure of the passive system under different circumstances, it is necessary to determine the various factors that may cause it to fail physically. Since different passive systems have different factors that affect the failure of their physical processes, it is necessary to analyze according to the actual scenario and obtain the corresponding first parameters of multiple types.

[0048] Step 102: input the N types of first parameters into a sensitivity analysis simulation model to obtain H second parameters corresponding to each of the N types of first parameters.

[0049] It is worth mentioning that the above sensitivity analysis simulation model can be a simulation model that has been determined in the prior art, and the data range corresponding to each type of first parameter is determined by historical first parameter data. By inputting multiple types of first parameters into the sensitivity analysis simulation model, multiple sensitive intervals corresponding to each type of first parameter can be obtained.

[0050] Specifically, a sensitive interval may correspond to a second parameter. For example, Table 1 shows that after determining that the first parameters include temperature, pressure, and flow, multiple sensitive intervals corresponding to each type of first parameter can be determined through a sensitivity analysis simulation model. Table 2 shows that a corresponding second parameter can be determined through multiple sensitive intervals.

[0051] Table 1

[0052]

[0053] Among them, T0, T1, T2, T3, and T4 are temperature values; P0, P1, P2, P3, and P4 are pressure values; and F0, F1, F2, F3, and F4 are flow values.

[0054] Table 2

[0055]

[0056] Among them, T (1) , T (2) , T (3) , T (4) is the second parameter corresponding to temperature; P (1) , P (2) , P (3) , P (4) is the second parameter corresponding to pressure; F (1) 、F(2) 、F (3) 、F (4) is the second parameter corresponding to the flow rate.

[0057] Furthermore, after the multiple sensitivity intervals corresponding to each type of first parameter are determined by the sensitivity analysis simulation model, a second parameter can be determined for each sensitive interval. Specifically, the median value of the sensitive interval can be taken as the second parameter, or the end value of the sensitive interval can be taken as the second parameter. This application does not make any restrictions here and can be set according to actual conditions.

[0058] Step 103: compile the H second parameters to obtain M first parameter combinations and M first probabilities corresponding to the M first parameter combinations, wherein the first parameter combination includes one second parameter corresponding to each type of the first parameter, the first probability is the probability of the second parameters in the first parameter combination occurring in combination with each other, and M is an integer greater than or equal to N.

[0059] Specifically, each second parameter in each type of first parameter is combined with a second parameter in another type of first parameter to determine a plurality of first parameter combinations. At the same time, since each second parameter corresponds to a probability of appearing in all second parameters of the same type of first parameter, when completing the first parameter combination, the probability corresponding to each second parameter in the combination is multiplied to determine the probability of the combination appearing, that is, the first probability. As shown in Table 3, each type of first parameter in the first parameter combination in Table 3 corresponds to two second parameters.

[0060] Table 3

[0061]

[0062]

[0063] in, The probabilities corresponding to the multiple second parameters corresponding to the temperature; is the probability corresponding to each of the multiple second parameters of pressure; are the probabilities corresponding to multiple second parameters of the traffic.

[0064] Step 104: Determine a second probability corresponding to each of the M first parameter combinations based on a preset criterion, wherein the second probability is a probability of physical process failure occurring under the first parameter combination.

[0065] It is worth mentioning that the above-mentioned preset criteria can be an acceptance criterion determined by historical experimental data, or it can be an acceptance criterion determined based on the simulation of the existing preset model. For example, the second probability corresponding to each of the multiple first parameter combinations is determined according to the preset criteria. When the preset criteria is determined by the historical experimental results, when the temperature difference and pressure difference are (10, 20), the physical process can achieve 100% success and no physical failure process will occur. When the temperature difference and pressure difference are (10, 10), the physical process can only achieve 50% success, and there is also a 50% probability of a physical failure process. In this way, the second probability value corresponding to each first parameter combination can be determined, and the second probability is used to represent the probability of physical process failure under this first parameter combination.

[0066] Step 105: Determine the physical process failure probability of the passive system under the target parameter combination based on the first probability and the second probability corresponding to the target parameter combination, wherein the target parameter combination is one of the M first parameter combinations.

[0067] In a specific implementation, what is determined is the probability of failure of the physical process of the passive system under a specific first parameter combination. Therefore, it can be determined by combining the first probability and the second probability corresponding to the first parameter combination. For example, the first probability and the second probability can be multiplied, and the product is used as the probability of failure of the physical process corresponding to the first parameter combination. The above-mentioned target parameter combination is any first parameter combination, and there is no limitation here. The embodiment of the present application can determine the probability of failure of the physical process corresponding to each first parameter combination, without analyzing the reliability of the operation of the passive system, and conveniently determine the probability of occurrence of the physical failure process under different combinations of influencing factors.

[0068] Optionally, the N types of first parameters are physical parameters that affect the operation of the passive system or cause the Internet of Things process to fail, and the N types of first parameters include temperature, pressure, and flow.

[0069] It can be understood that the above-mentioned first parameter is an important factor that can affect the failure of the physical process of the passive system. Since the passive system is greatly affected by the natural environment, temperature, pressure, flow, wind speed, pressure, etc. can be used as the first parameter. For example, the first parameter that affects the flow of fluid in the pipeline can be temperature, pressure, flow, etc. Due to the wide application of passive systems, the embodiment of the present application does not limit the specific influencing factors corresponding to the first parameter, and it can be specifically set according to different passive systems.

[0070] Optionally, inputting the N types of first parameters into a sensitivity analysis simulation model to obtain H second parameters corresponding to each of the N types of first parameters includes:

[0071] Inputting the N types of first parameters into a sensitivity analysis simulation model, and obtaining K first parameter intervals corresponding to each type of first parameter output by the sensitivity analysis simulation model, where K is an integer greater than or equal to 1;

[0072] Obtaining K interval ranges of the K first parameter intervals, and determining a middle value of each of the K interval ranges;

[0073] The intermediate value is determined as the second parameter corresponding to each of the first parameter intervals, to obtain H second parameters corresponding to N types of first parameters, wherein the value of H is the product of N and K.

[0074] In an embodiment of the present application, each type of first parameter is input into a sensitivity analysis simulation model, and a process of obtaining multiple second parameters corresponding to each type of first parameter can be that the sensitivity analysis simulation model outputs multiple first parameter intervals corresponding to each type of first parameter after inputting each type of first parameter, as shown in Table 1. Since each first parameter interval has a corresponding interval range, the middle value of the interval range can be determined as the value of the second parameter, so that multiple second parameters corresponding to each type of first parameter can be determined, wherein one type of first parameter corresponds to K first parameter intervals, and one first parameter interval can determine one second parameter, therefore, one type of first parameter can also correspond to K second parameters. In this way, if there are N types of first parameters, since each type of first parameter corresponds to K second parameters, there are a total of H second parameters, and H is the product of N and K. In this way, the multiple second parameters corresponding to each type of first parameter are more accurately determined.

[0075] Optionally, compiling the H second parameters to obtain M first parameter combinations includes:

[0076] Combining one of the second parameters corresponding to each type of the first parameters to obtain the M first parameter combinations, where M is

[0077] In a specific implementation, referring to the contents in Table 1 and Table 2 above, the second parameters corresponding to each type of first parameters can be arranged and combined to determine multiple first parameter combinations. (1) , T (2) , the two second parameters P in pressure (1) , P (2) , and the two second parameters F in the flow (1) 、F (2) By combining, we can determine the first parameter combination as (T (1) ,P (1) ,F (1) )、(T(2) ,P (1) ,F (1) )、(T (1) ,P (2) ,F (1) )、(T (1) ,P (1) ,F (2) )、(T (1) ,P (2) ,F (2) )、(T (2) ,P (2) ,F (2) )、(T (2) ,P (1) ,F (2) )、(T (2) ,P (2) ,F (1) ).

[0078] Optionally, obtaining M first probabilities corresponding to each of the M first parameter combinations includes:

[0079] Acquire preset information, where the preset information includes the K second parameters corresponding to each category of the first parameters, and a third probability that each second parameter appears in all the second parameters of the corresponding first parameter category;

[0080] Based on the preset information, determine the third probability corresponding to each of the second parameters in each of the first parameter combinations;

[0081] Based on the product of each of the third probabilities corresponding to each of the second parameters in each of the first parameter combinations, the M first probabilities corresponding to each of the M first parameter combinations are obtained.

[0082] In another embodiment of the present application, to determine the first probability corresponding to each first parameter combination, it is first necessary to determine the specific second parameters contained in the first parameter combination, and determine the third probability corresponding to each second parameter. Among them, the determined third probability can be specifically determined by preset information, and the preset information can be determined in advance based on the analysis of the environment in which the passive system is located, related random fluctuations, failures of the interface system, etc. The possibility of occurrence in reality under a first parameter such as temperature conditions can be determined, and the possibility can be converted into a probability, which is presented as the probability of occurrence when the temperature is at a specific second parameter value, that is, the third probability. Finally, as shown in Table 3, the third probabilities corresponding to the second parameters in a first parameter combination can be multiplied to obtain the first probability corresponding to the first parameter combination.

[0083] Optionally, determining the second probabilities corresponding to the M first parameter combinations respectively based on a preset criterion includes:

[0084] Input each of the first parameter combinations into the passive system physical model to obtain a third parameter corresponding to each of the first parameter combinations;

[0085] Based on the preset criteria, the second probability corresponding to each of the third parameters is determined to obtain the second probability corresponding to each combination of the first parameters, wherein the preset criteria includes multiple third parameters and the probabilities corresponding to the third parameters respectively.

[0086] In another specific embodiment of the present application, the second probabilities corresponding to each of the plurality of first parameter combinations are determined by a preset criterion, and the second probabilities corresponding to each of the first parameter combinations can be evaluated based on a standard determined based on existing historical experimental data information.

[0087] In addition, the above preset criteria can be expressed as a second parameter combination corresponding to a second probability, and the second parameter combination also corresponds one-to-one to the above first parameter combination. For example, a first parameter combination with a probability of failure of a physical process of 0 can be used as a standard parameter combination, and the difference between other first parameter combinations and the standard parameter combination can exist as a second parameter combination. Since the preset standard has determined the second probability corresponding to the second parameter combination (as shown in Table 4), the second probability corresponding to each first parameter combination can be directly determined.

[0088] Table 4

[0089] The first parameter combination The second parameter combination Second probability <![CDATA[(T (1) ,P (1) ,F (1) )]]> Temperature difference 1, pressure difference 1, flow difference 1 <![CDATA[P1]]> <![CDATA[(T (2) ,P (1) ,F (1) )]]> Temperature difference 2, pressure difference 2, flow difference 2 <![CDATA[P2]]> <![CDATA[(T (1) ,P (2) ,F (1) )]]> Temperature difference 2, pressure difference 2, flow difference 2 <![CDATA[P3]]>

[0090] Among them, P1, P2 and P3 are second probabilities corresponding to the second parameter combination.

[0091] Optionally, determining the physical process failure probability of the passive system under the target parameter combination based on the first probability and the second probability corresponding to the target parameter combination includes:

[0092] Based on the first probability and the second probability corresponding to the target parameter combination, obtaining a target product of a first probability corresponding to the target parameter combination and the corresponding second probability;

[0093] The target product is determined to be a physical process failure probability of the passive system under the target parameter combination.

[0094] In a specific embodiment of the present application, the probability of occurrence of physical process failure corresponding to the non-energized system under the target parameter combination can be determined by the first probability and the second probability corresponding to the target parameter combination, as shown in the example in Table 5 below.

[0095] Table 5

[0096]

[0097] See also Figure 3 The embodiment of the present application further provides a physical process failure probability determination device 200, which is applied to a passive system and includes:

[0098] A first acquisition module 201 is used to acquire N types of first parameters of the passive system physical process failure, where N is an integer greater than or equal to 1;

[0099] A second acquisition module 202 is used to input the N types of first parameters into a sensitivity analysis simulation model to obtain H second parameters corresponding to each of the N types of first parameters;

[0100] a compilation module 203, configured to compile the H second parameters, obtain M first parameter combinations and M first probabilities respectively corresponding to the M first parameter combinations, wherein the first parameter combination includes one second parameter corresponding to each type of the first parameters, the first probability is a probability of the second parameters in the first parameter combination occurring in combination with each other, and M is an integer greater than or equal to N;

[0101] A first determination module 204 is used to determine a second probability corresponding to each of the M first parameter combinations based on a preset criterion, wherein the second probability is a probability of a physical process failure occurring under the first parameter combination;

[0102] The second determination module 205 is used to determine the physical process failure probability of the passive system under the target parameter combination based on the first probability and the second probability corresponding to the target parameter combination, wherein the target parameter combination is one of the M first parameter combinations.

[0103] Optionally, the N types of first parameters are physical parameters that affect the operation of the passive system or cause the Internet of Things process to fail, and the N types of first parameters include temperature, pressure, and flow.

[0104] Optionally, the second acquisition module 202 includes:

[0105] A first acquisition submodule, used for inputting the N types of first parameters into a sensitivity analysis simulation model, and obtaining K first parameter intervals corresponding to each type of first parameter output by the sensitivity analysis simulation model, wherein K is an integer greater than or equal to 1;

[0106] A second acquisition submodule, used to acquire K interval ranges of the K first parameter intervals, and determine a middle value of each of the K interval ranges;

[0107] The third acquisition submodule is used to determine the intermediate value as the second parameter corresponding to each of the first parameter intervals, and obtain H second parameters corresponding to N types of first parameters, wherein the value of H is the product of N and K.

[0108] Optionally, the compilation module 203 includes:

[0109] A combination submodule is used to combine one of the second parameters corresponding to each type of the first parameters to obtain the M first parameter combinations, where M is

[0110] Optionally, the compilation module 203 includes:

[0111] a fourth acquisition submodule, configured to acquire preset information, wherein the preset information includes the K second parameters corresponding to each category of the first parameters, and a third probability of each second parameter appearing in all the second parameters of the corresponding first parameter category;

[0112] A first determination submodule, configured to determine, based on the preset information, the third probability corresponding to each of the second parameters in each of the first parameter combinations;

[0113] The fifth acquisition submodule is used to acquire the M first probabilities corresponding to each of the M first parameter combinations based on the product of each of the third probabilities corresponding to each of the second parameters in each of the first parameter combinations.

[0114] Optionally, the first determining module 204 includes:

[0115] A sixth acquisition submodule, used for inputting each of the first parameter combinations into the passive system physical model to acquire a third parameter corresponding to each of the first parameter combinations;

[0116] The second determination submodule is used to determine the second probability corresponding to each of the third parameters based on the preset criteria, and obtain the second probability corresponding to each combination of the first parameters, wherein the preset criteria include multiple third parameters and the probabilities corresponding to the third parameters respectively.

[0117] Optionally, the second determining module 205 includes:

[0118] a seventh acquisition submodule, configured to acquire a target product of a first probability corresponding to a target parameter combination and a corresponding second probability based on the first probability corresponding to the target parameter combination and the second probability;

[0119] The third determination submodule is used to determine that the target product is the physical process failure probability of the passive system under the target parameter combination.

[0120] The tag identification device 200 provided in the embodiment of the present application can achieve Figure 2 The various processes implemented by the method embodiment described in the embodiment can achieve the same beneficial effects. To avoid repetition, they will not be described here.

[0121] like Figure 4 As shown, the embodiment of the present application also provides an electronic device 300, including a processor 301, a memory 302, a program or instruction stored in the memory 302 and executable on the processor 301, and when the program or instruction is executed by the processor 301, the following is achieved: Figure 1 The various processes of the method embodiment shown can achieve the same technical effect, and will not be described again here to avoid repetition.

[0122] It should be noted that the electronic devices in the embodiments of the present application include the above-mentioned mobile electronic devices and non-mobile electronic devices.

[0123] The embodiment of the present application also provides a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, the following is implemented: Figure 1 The various processes of the method embodiment shown can achieve the same technical effect, and will not be described again here to avoid repetition.

[0124] Wherein, the processor is the processor in the electronic device in the above-mentioned embodiment. The readable storage medium includes a computer-readable storage medium, such as a computer read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc. It should be noted that, in this article, the terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements includes not only those elements, but also includes other elements that are not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of more restrictions, the elements defined by the sentence "including one..." do not exclude the presence of other identical elements in the process, method, article or device including the element.

[0125] It should be understood that "one embodiment" or "an embodiment" mentioned throughout the specification means that specific features, structures or characteristics related to the embodiment are included in at least one embodiment of the present invention. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. In addition, these specific features, structures or characteristics can be combined in one or more embodiments in any suitable manner. It should be understood that in various embodiments of the present invention, the size of the serial number of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention. The serial numbers of the above-mentioned embodiments of the present invention are only for description and do not represent the advantages and disadvantages of the embodiments.

[0126] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, a magnetic disk, or an optical disk), and includes a number of instructions for a terminal (which can be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to execute the methods described in each embodiment of the present application.

[0127] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present application, ordinary technicians in this field can also make many forms without departing from the purpose of the present application and the scope of protection of the claims, all of which are within the protection of the present application.

Claims

1. A method for determining the failure probability of a physical process, applied to a passive system, characterized in that: include: Obtaining N types of first parameters of the physical process failure of the passive system, where N is an integer greater than or equal to 1; Inputting the N types of first parameters into a sensitivity analysis simulation model to obtain H second parameters corresponding to each of the N types of first parameters; The H second parameters are compiled to obtain M first parameter combinations and M first probabilities respectively corresponding to the M first parameter combinations, wherein the first parameter combination includes one second parameter corresponding to each type of the first parameters, the first probability is a probability of the second parameters in the first parameter combination occurring in combination with each other, and M is an integer greater than or equal to N; Based on a preset criterion, determining a second probability corresponding to each of the M first parameter combinations, wherein the second probability is a probability of physical process failure occurring under the first parameter combination; Based on the first probability and the second probability corresponding to a target parameter combination, a physical process failure probability of the passive system under the target parameter combination is determined, wherein the target parameter combination is one of the M first parameter combinations.

2. The method according to claim 1, characterized in that The N types of first parameters are physical parameters that affect the operation of the passive system or cause the Internet of Things process to fail, and the N types of first parameters include temperature, pressure, and flow.

3. The method according to claim 1, characterized in that The step of inputting the N types of first parameters into the sensitivity analysis simulation model to obtain H second parameters corresponding to the N types of first parameters respectively includes: Inputting the N types of first parameters into a sensitivity analysis simulation model, and obtaining K first parameter intervals corresponding to each type of first parameter output by the sensitivity analysis simulation model, where K is an integer greater than or equal to 1; Obtaining K interval ranges of the K first parameter intervals, and determining a middle value of each of the K interval ranges; The intermediate value is determined as the second parameter corresponding to each of the first parameter intervals, to obtain H second parameters corresponding to N types of first parameters, wherein the value of H is the product of N and K.

4. The method according to claim 3, characterized in that The step of compiling the H second parameters to obtain M first parameter combinations includes: Combining one of the second parameters corresponding to each type of the first parameters to obtain the M first parameter combinations, where M is 5. The method according to claim 4, characterized in that The obtaining of M first probabilities corresponding to the M first parameter combinations respectively includes: Acquire preset information, where the preset information includes the K second parameters corresponding to each category of the first parameters, and a third probability that each second parameter appears in all the second parameters of the corresponding first parameter category; Based on the preset information, determine the third probability corresponding to each of the second parameters in each of the first parameter combinations; Based on the product of each of the third probabilities corresponding to each of the second parameters in each of the first parameter combinations, the M first probabilities corresponding to each of the M first parameter combinations are obtained.

6. The method according to claim 5, characterized in that The determining, based on a preset criterion, second probabilities corresponding to each of the M first parameter combinations includes: Input each of the first parameter combinations into the passive system physical model to obtain a third parameter corresponding to each of the first parameter combinations; Based on the preset criteria, the second probability corresponding to each of the third parameters is determined to obtain the second probability corresponding to each combination of the first parameters, wherein the preset criteria includes multiple third parameters and the probabilities corresponding to the third parameters respectively.

7. The method according to claim 1, characterized in that The determining, based on the first probability and the second probability corresponding to the target parameter combination, a physical process failure probability of the passive system under the target parameter combination, comprises: Based on the first probability and the second probability corresponding to the target parameter combination, obtaining a target product of a first probability corresponding to the target parameter combination and the corresponding second probability; The target product is determined to be a physical process failure probability of the passive system under the target parameter combination.

8. A physical process failure probability determination device, applied to a passive system, characterized in that: include: A first acquisition module, used to acquire N types of first parameters of the failure of the physical process of the passive system, where N is an integer greater than or equal to 1; A second acquisition module is used to input the N types of first parameters into a sensitivity analysis simulation model to obtain H second parameters corresponding to each of the N types of first parameters; a compilation module, used to compile the H second parameters, obtain M first parameter combinations and M first probabilities respectively corresponding to the M first parameter combinations, wherein the first parameter combination includes one second parameter corresponding to each type of the first parameters, the first probability is a probability of the second parameters in the first parameter combination occurring in combination with each other, and M is an integer greater than or equal to N; A first determination module, configured to determine, based on a preset criterion, a second probability corresponding to each of the M first parameter combinations, wherein the second probability is a probability of a physical process failure occurring under the first parameter combination; The second determination module is used to determine the physical process failure probability of the passive system under the target parameter combination based on the first probability and the second probability corresponding to the target parameter combination, wherein the target parameter combination is one of the M first parameter combinations.

9. An electronic device, characterized in that: include: A processor, a memory, and a program stored in the memory and executable on the processor, wherein when the program is executed by the processor, the steps of the method for determining the failure probability of a physical process as claimed in any one of claims 1 to 7 are implemented.

10. A readable storage medium, characterized in that: The readable storage medium stores a program, and when the program is executed by a processor, the steps of the method for determining the failure probability of a physical process according to any one of claims 1 to 7 are implemented.

Images