Method and device for converting expert information on structural reliability of liquid rocket engine

Abstract

The application provides a liquid rocket engine structure reliability expert information conversion method and device, relates to the technical field of data processing, and comprises the following steps: acquiring an influence factor set of a liquid rocket structure; acquiring the judgment results of each expert on the influence factors for any influence factor; obtaining the fuzzy membership degrees of each expert on the influence factors based on a pre-constructed different judgment result and fuzzy membership degree comparison table and the judgment results of each expert on the influence factors; taking the fuzzy membership degrees of any expert on each influence factor as the expert information of the expert; inputting the mean value and variance of the calculated expert information into a pre-constructed expert information equivalent success-failure model to obtain the equivalent success-failure times of the expert information. The application converts the unquantifiable expert information into a unified quantifiable index, and can be applied to liquid rocket engine structure reliability evaluation.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and more specifically, to a method and apparatus for converting expert information on the structural reliability of liquid rocket engines. Background Technology

[0002] Expert information serves as a crucial source of prior information on engine reliability and has significant practical application value. However, expert data is typically heterogeneous; therefore, it requires transformation and integration before application.

[0003] Existing methods for converting expert information using conversion tools, middleware data, or XML-based technology require manual pre-specification of conversion rules or mapping relationships between expert information and data. While semantic model-based methods can automatically generate mapping relationships between data elements based on semantic similarity, they fail to consider the influence of fuzzy factors and ultimately fail to convert expert information into an equivalent success or failure model. Therefore, they are not very applicable to the field of liquid rocket engine structural reliability. Summary of the Invention

[0004] The purpose of this application is to provide a method and apparatus for converting expert information on the structural reliability of liquid rocket engines, which solves the above-mentioned problems existing in the prior art and has strong applicability in the field of structural reliability of liquid rocket engines.

[0005] Firstly, a method for converting expert information on the structural reliability of a liquid rocket engine is provided, which may include:

[0006] Obtain a set of influencing factors for the structure of a liquid rocket; wherein the set of influencing factors includes multiple influencing factors;

[0007] For any influencing factor, obtain the judgment results of each expert on the influencing factor; wherein, the judgment result of each expert on the influencing factor is obtained by the expert scoring the reliability of the influencing factor according to fuzzy semantics;

[0008] Based on a pre-constructed table comparing different judgment results with fuzzy membership degrees and the judgment results of each expert on the influencing factors, the fuzzy membership degree of each expert on the influencing factors is obtained; wherein, the fuzzy membership degree is obtained by converting the judgment results of each expert on the influencing factors into interval numbers;

[0009] The fuzzy membership degree of any expert to each influencing factor is taken as the expert information of that expert;

[0010] The mean and variance of the calculated expert information are input into a pre-built equivalent success and failure model of expert information to obtain the equivalent success and failure count of the expert information.

[0011] In an optional implementation, the set of influencing factors may also include a comprehensive evaluation value for each influencing factor;

[0012] The method for constructing the comparison table of different judgment results and fuzzy membership degrees includes:

[0013] Obtain a set of alternative factors for liquid rocket structures;

[0014] For any influencing factor, determine the degree of membership between the influencing factor and any alternative factor;

[0015] Based on the membership degree between the influencing factor and any alternative factor, a comprehensive evaluation value for the influencing factor is obtained;

[0016] Based on the comprehensive evaluation values ​​of each influencing factor, a comparison table of different judgment results and fuzzy membership degrees for each influencing factor is constructed.

[0017] In an optional implementation, a comprehensive evaluation value for the influencing factor is obtained based on the membership degree between the influencing factor and any alternative factor, including:

[0018] Based on the membership degree between the influencing factors and each alternative factor, the evaluation matrix of the influencing factors is obtained;

[0019] The membership degrees of the influencing factors and each candidate factor are input into a pre-constructed triangular membership function to obtain the weights of the influencing factors.

[0020] Based on the evaluation matrix and the weights, a comprehensive evaluation value for the influencing factors is obtained.

[0021] In one optional implementation, the method for constructing the expert information equivalent success or failure model includes:

[0022] Based on the fuzzy membership degrees of each expert to the aforementioned influencing factors, a symmetric triangular membership degree function for fuzzy membership degrees is constructed.

[0023] The symmetric triangular membership function of the fuzzy membership is transformed into an equivalent success or failure model of the fuzzy membership.

[0024] In an optional implementation, based on the comprehensive evaluation values ​​of each influencing factor, a comparison table of different judgment results and fuzzy membership degrees for each influencing factor is constructed, including:

[0025] Based on the comprehensive evaluation values ​​of each influencing factor, calculate the weighted average of each candidate factor;

[0026] Based on the weighted average of each candidate factor, a comparison table of different judgment results and fuzzy membership degrees for each influencing factor is constructed.

[0027] In an alternative implementation, the equivalent success or failure model for fuzzy membership is as follows:

[0028]

[0029]

[0030] Where n represents the total number of equivalent success-failure trials; s represents the number of successes in the equivalent success-failure trials; f represents the number of failures in the equivalent success-failure trials; μ represents the mean of the expert information; and σ 2 This represents the variance of expert information.

[0031] Secondly, a liquid rocket engine structural reliability expert information conversion device is provided, which may include:

[0032] An acquisition unit is used to acquire a set of influencing factors for the liquid rocket structure; wherein the set of influencing factors includes multiple influencing factors; for any influencing factor, the judgment results of each expert on the influencing factor are acquired; wherein the judgment result of any expert on the influencing factor is obtained by the expert scoring the reliability of the influencing factor according to fuzzy semantics;

[0033] A determining unit is used to obtain the fuzzy membership degree of each expert to the influencing factor based on a pre-constructed comparison table of different judgment results and fuzzy membership degrees, as well as the judgment results of each expert on the influencing factor; wherein the fuzzy membership degree is obtained by converting the judgment results of each expert on the influencing factor into interval numbers;

[0034] The conversion unit is used to take the fuzzy membership degree of any expert to each influencing factor as the expert information of the expert; and input the mean and variance of the calculated expert information into a pre-constructed expert information equivalent success and failure model to obtain the equivalent success and failure number of the expert information.

[0035] Thirdly, an electronic device is provided, which includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus;

[0036] Memory, used to store computer programs;

[0037] When a processor executes a program stored in memory, it implements any of the steps described in the first aspect above.

[0038] Fourthly, a computer-readable storage medium is provided, wherein a computer program is stored therein, and when executed by a processor, the computer program implements the steps of any of the methods described in the first aspect above.

[0039] This application utilizes a combination of expert scoring and fuzzy description to establish a factor set consisting of design, testing, and manufacturing. Through expert scoring and processing based on fuzzy average weights, a weight set for each factor is obtained. Then, based on expert scores for the reliability candidate set, fuzzy comprehensive evaluation is used to fuse and transform multi-source expert information to obtain a unified reliability evaluation index. Finally, based on the second-order moment method, it is transformed into an equivalent success-failure model. This transforms unquantifiable expert information into a unified, quantifiable index, which can then be applied to the structural reliability assessment of liquid rocket engines. Attached Figure Description

[0040] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0041] Figure 1 A flowchart of the liquid rocket engine structural reliability expert information conversion method provided in the embodiments of this application;

[0042] Figure 2 A schematic diagram of the structure of the liquid rocket engine structural reliability expert information conversion device provided in the embodiments of this application;

[0043] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0044] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0045] The liquid rocket engine structural reliability expert information conversion method provided in this application can be applied to servers or terminals with strong computing capabilities. The server can be a physical server, a server cluster composed of multiple physical servers, or a distributed system. It can also be a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms. The terminal can be a user equipment (UE) such as a mobile phone, smartphone, laptop, digital radio receiver, personal digital assistant (PDA), tablet computer (PAD), handheld device, in-vehicle device, wearable device, computing device, or other processing device connected to a wireless modem, mobile station (MS), mobile terminal, etc. The terminal and server can be directly or indirectly connected via wired or wireless communication methods, which is not limited herein.

[0046] The preferred embodiments of this application are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit this application. Furthermore, the embodiments and features in the embodiments of this application can be combined with each other without conflict.

[0047] Figure 1 This is a flowchart illustrating a method for converting expert information on the structural reliability of a liquid rocket engine, provided as an embodiment of this application. Figure 2 As shown, the method may include:

[0048] Step S110: Obtain the set of influencing factors for the liquid rocket structure; for any influencing factor, obtain the judgment results of each expert on the influencing factor.

[0049] In the embodiments of this application, the set of influencing factors includes multiple influencing factors and the comprehensive evaluation value of each influencing factor; the judgment result of any expert on an influencing factor is obtained by the expert scoring the reliability of the influencing factor according to fuzzy semantics (such as "maybe m", "maybe m", "very likely m", etc.).

[0050] In this embodiment of the application, the set of influencing factors includes multiple factors affecting the reliability of liquid rocket structures; each influencing factor can be classified into design-related influencing factors, experimental-related influencing factors, and manufacturing-related influencing factors.

[0051] Step S120: Based on the pre-constructed comparison table of different judgment results and fuzzy membership degrees, as well as the judgment results of each expert on the influencing factors, obtain the fuzzy membership degree of each expert on the influencing factors.

[0052] In the embodiments of this application, the fuzzy membership degree is obtained by converting the judgment results of each expert on the influencing factors into interval numbers.

[0053] In this application embodiment, the method for constructing the comparison table of different judgment results and fuzzy membership degrees includes:

[0054] Obtain a set of alternative factors for liquid rocket structure; for any influencing factor, determine the membership degree between the influencing factor and any alternative factor; based on the membership degree between the influencing factor and any alternative factor, obtain the comprehensive evaluation value of the influencing factor; based on the obtained comprehensive evaluation values ​​of each influencing factor, construct a comparison table of different judgment results and fuzzy membership degrees for each influencing factor.

[0055] In this embodiment of the application, a comprehensive evaluation value for the influencing factor is obtained based on the degree of membership between the influencing factor and any alternative factor, including:

[0056] Based on the membership degree between the influencing factors and each candidate factor, the evaluation matrix of this type of influencing factor is obtained; the membership degree between this type of influencing factor and each candidate factor is input into a pre-constructed triangular membership function to obtain the weight of this type of influencing factor; based on the evaluation matrix and the weight, the comprehensive evaluation value of this type of influencing factor is obtained.

[0057] In the embodiments of this application, the evaluation matrix of any type of influencing factor as follows:

[0058]

[0059] in, Indicates the i-th type of influencing factor u i v belongs to the kth alternative factor k The membership degree is denoted by m, where m represents the total number of candidate factors.

[0060] In this embodiment of the application, a comprehensive evaluation value for the influencing factors is obtained based on the evaluation matrix and weights, including:

[0061]

[0062] in, This represents the set of comprehensive evaluation values ​​for all influencing factors. This indicates the weight of this type of influencing factor; This represents the evaluation matrix for this type of influencing factor; This represents the comprehensive evaluation value of all alternative factors related to the influencing factors. The composition operator is represented by the "+" and "·" operators.

[0063] In one embodiment of this application, based on the evaluation matrix of any type of influencing factor and a pre-set triangular membership function threshold, the number of intervals corresponding to the triangular membership function threshold is obtained; based on the number of intervals and the weight of the type of influencing factor, the comprehensive evaluation value of the type of influencing factor is obtained.

[0064] In this embodiment of the application, based on the comprehensive evaluation values ​​of each influencing factor, a comparison table of different judgment results and fuzzy membership degrees for each influencing factor is constructed, including:

[0065] Based on the comprehensive evaluation values ​​of each influencing factor, the weighted average of each candidate factor is calculated; based on the weighted average of each candidate factor, a comparison table of different judgment results and fuzzy membership degrees of each influencing factor is constructed.

[0066] In this embodiment of the application, the formula for calculating the weighted average of each alternative factor is as follows:

[0067]

[0068] in, This represents the comprehensive evaluation value of the influencing factors under a given threshold λ; λ represents the threshold of the triangular membership function, which is preset.

[0069] Step S130: Take the fuzzy membership degree of any expert to each influencing factor as the expert information; input the mean and variance of the calculated expert information into the pre-constructed expert information equivalent success and failure model to obtain the equivalent success and failure times of the expert information.

[0070] In this embodiment of the application, the method for constructing an expert information equivalent success or failure model includes:

[0071] Based on the fuzzy membership degrees of various experts on influencing factors, a symmetric triangular membership function of fuzzy membership degrees is constructed; the symmetric triangular membership function of fuzzy membership degrees is then transformed into an equivalent success or failure model of fuzzy membership degrees.

[0072] In this embodiment, the second-order moment method is used to calculate the equivalent success and failure count based on the mean, variance, and corresponding equivalent success and failure model of the expert information; the formula for calculating the equivalent success and failure count is as follows:

[0073]

[0074]

[0075] Where n = s + f, n represents the total number of equivalent success-failure trials; s represents the number of successes in the equivalent success-failure trials; f represents the number of failures in the equivalent success-failure trials; μ represents the mean of the expert information, μ = E[u]; σ² represents the variance of the expert information; σ 2 =∫Uf(u)(uE[u] 2 du; U represents the domain, f(u) represents the triangular membership function expression, and E[u] represents the mathematical expectation of the triangular membership.

[0076] In one embodiment of this application, the method for converting expert information on the structural reliability of a liquid rocket engine may include:

[0077] (1) Calculation of comprehensive evaluation indicators based on expert information:

[0078] The transformation of expert information adopts the fuzzy comprehensive evaluation method, treating each influencing factor as an evaluation object. i u i Belongs to a certain candidate element v k The membership degree is (i = 1,,2,3, k = 1,2,3,…,m, where m is the number of candidate elements), thus the evaluation matrix is ​​obtained as follows:

[0079] i = 1, 2, 3, k = 1, 2, 3, ..., m

[0080] In the evaluation matrix Based on this, given the threshold λ for each membership function, the number of membership intervals is obtained, and the weights between each factor are considered. The comprehensive evaluation criteria were then obtained as follows:

[0081]

[0082] (2) Calculation of expert information comprehensive evaluation results:

[0083] To obtain a comprehensive evaluation result, (k = 1, 2, 3, ..., m) are used as weights for the candidate elements v k Taking the weighted average, we have:

[0084]

[0085] In the formula and All are interval numbers under a given threshold λ;

[0086] Experts judged and selected the reliability of various influencing factors according to fuzzy semantics. The reliability candidate domain was set as: V = {0.98, 0.985, 0.99, 0.995, 0.998}. The reliability evaluation results of each expert for each factor were obtained by using the semantic and fuzzy membership comparison table.

[0087] Using a threshold of λ = 0.98, the k-th candidate factor v k The membership degree adopts a symmetrical triangular membership function (WL = WR); a comparison table of fuzzy semantics and membership functions is established, with fuzzy semantics divided into "possibly m", "maybe m", and "very likely m". The membership functions corresponding to the three fuzzy semantics are (m, 0.15), (m, 0.08), and (m, 0.04), respectively. After establishing the comparison table, the reliability evaluation results of each influencing factor are given for each expert's information. This includes the reliability evaluation results of the "design" factor, the "experiment" factor, and the "processing" factor. The reliability evaluation results are further calculated according to the above formula. Under the threshold λ, the fuzzy numbers of the above evaluation matrix are transformed into interval numbers, and then fuzzy operations are performed to obtain the evaluation index.

[0088] Based on the above calculations, the comprehensive evaluation result of the experts on reliability is obtained under the threshold λ. When λ=λ0, the number of intervals of the comprehensive evaluation result is [a,b].

[0089] (3) Transformation of expert information into an equivalent success or failure model:

[0090] The mean of the reliability comprehensive evaluation intervals obtained above is λ0, and a symmetric triangular membership function is constructed based on this. Let the variance of the mean of the triangular membership degree of expert information be:

[0091] σ 2 =∫ U f(u)(uE[u]) 2 du

[0092] In the formula, U is the domain, f(u) is the expression of the triangular membership function, and E[u] is the mathematical expectation of the triangular membership. Thus, the variance of the above reliability interval can be calculated.

[0093] According to the second-order moment method, when the mean μ and variance σ of the reliability are known... 2 Let s be the number of successes in the equivalent success-failure trials, f be the number of failures in the equivalent success-failure trials, and n = s + f be the total number of equivalent success-failure trials. Solving the following equation will yield the number of equivalent success-failure trials:

[0094]

[0095]

[0096] Corresponding to the above method, this application also provides a liquid rocket engine structural reliability expert information conversion device, such as... Figure 2 As shown, the liquid rocket engine structural reliability expert information conversion device includes:

[0097] The acquisition unit 210 is used to acquire a set of influencing factors for the liquid rocket structure; wherein, the set of influencing factors contains multiple influencing factors; for any influencing factor, the judgment results of each expert on the influencing factor are acquired; wherein, the judgment result of any expert on the influencing factor is obtained by the expert scoring the reliability of the influencing factor according to fuzzy semantics;

[0098] Unit 220 is used to determine the fuzzy membership degree of each expert to the influencing factor based on a pre-constructed comparison table of different judgment results and fuzzy membership degrees, as well as the judgment results of each expert on the influencing factor; wherein, the fuzzy membership degree is obtained by converting the judgment results of each expert on the influencing factor into interval numbers;

[0099] The conversion unit 230 is used to take the fuzzy membership degree of any expert to each influencing factor as the expert information; and input the mean and variance of the calculated expert information into the pre-built expert information equivalent success and failure model to obtain the equivalent success and failure number of the expert information.

[0100] The functions of each functional unit of the liquid rocket engine structural reliability expert information conversion device provided in the above embodiments of this application can be realized through the above methods and steps. Therefore, the specific working process and beneficial effects of each unit in the liquid rocket engine structural reliability expert information conversion device provided in the embodiments of this application will not be repeated here.

[0101] This application also provides an electronic device, such as... Figure 3 As shown, it includes a processor 310, a communication interface 320, a memory 330, and a communication bus 340, wherein the processor 310, the communication interface 320, and the memory 330 communicate with each other through the communication bus 340.

[0102] Memory 330 is used to store computer programs;

[0103] When the processor 310 executes the program stored in the memory 330, it performs the following steps:

[0104] Obtain a set of influencing factors for liquid rocket structure; wherein the set of influencing factors contains multiple influencing factors; for any influencing factor, obtain the judgment results of each expert on the influencing factor; wherein the judgment result of each expert on the influencing factor is obtained by the expert scoring the reliability of the influencing factor according to fuzzy semantics;

[0105] Based on a pre-constructed comparison table of different judgment results and fuzzy membership degrees, as well as the judgment results of each expert on the influencing factors, the fuzzy membership degree of each expert on the influencing factors is obtained; where the fuzzy membership degree is obtained by converting the judgment results of each expert on the influencing factors into interval numbers.

[0106] The fuzzy membership degree of any expert to each influencing factor is taken as the expert information; the mean and variance of the calculated expert information are input into the pre-constructed expert information equivalent success and failure model to obtain the equivalent success and failure number of expert information.

[0107] The communication bus mentioned above can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus can be divided into address bus, data bus, control bus, etc. For ease of illustration, only one thick line is used to represent it in the diagram, but this does not mean that there is only one bus or one type of bus.

[0108] The communication interface is used for communication between the aforementioned electronic devices and other devices.

[0109] The memory may include random access memory (RAM) or non-volatile memory (NVM), such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.

[0110] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0111] The implementation methods and beneficial effects of the various components of the electronic device in the above embodiments for solving the problem can be found in [reference needed]. Figure 1The steps in the illustrated embodiments are used to implement the electronic device. Therefore, the specific working process and beneficial effects of the electronic device provided in this application will not be repeated here.

[0112] In another embodiment provided in this application, a computer-readable storage medium is also provided, which stores instructions that, when executed on a computer, cause the computer to perform any of the liquid rocket engine structural reliability expert information conversion methods described in the above embodiments.

[0113] In another embodiment provided in this application, a computer program product containing instructions is also provided, which, when run on a computer, causes the computer to execute any of the liquid rocket engine structural reliability expert information conversion methods in the above embodiments.

[0114] Those skilled in the art will understand that the embodiments in this application can be provided as methods, systems, or computer program products. Therefore, the embodiments in this application can take the form of entirely hardware embodiments, entirely software embodiments, or embodiments combining software and hardware aspects. Furthermore, the embodiments in this application can take the form of computer program products implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0115] This application describes embodiments of methods, apparatus (systems), and computer program products according to embodiments of this application with reference to flowchart illustrations and / or block diagrams. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0116] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0117] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable apparatus for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0118] Although preferred embodiments have been described in this application, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of this application.

[0119] Obviously, those skilled in the art can make various modifications and variations to the embodiments of this application without departing from the spirit and scope of the embodiments of this application. Therefore, if these modifications and variations to the embodiments of this application fall within the scope of the claims in this application and their equivalents, then this application also intends to include these modifications and variations.

Claims

1. A method for converting expert information on the structural reliability of a liquid rocket engine, characterized in that, The method includes: Obtain a set of influencing factors for the structure of a liquid rocket; wherein the set of influencing factors includes multiple influencing factors and a comprehensive evaluation value for each influencing factor; For any influencing factor, obtain the judgment results of each expert on the influencing factor; wherein, the judgment result of each expert on the influencing factor is obtained by the expert scoring the reliability of the influencing factor according to fuzzy semantics; Based on a pre-constructed comparison table of different judgment results and fuzzy membership degrees, and the judgment results of each expert on the influencing factors, the fuzzy membership degree of each expert on the influencing factors is obtained; wherein, the fuzzy membership degree is obtained by converting the judgment results of each expert on the influencing factors into interval numbers; the method for constructing the comparison table of different judgment results and fuzzy membership degrees includes: obtaining a set of candidate factors for liquid rocket structure; for any influencing factor, determining the membership degree between the influencing factor and any candidate factor; based on the membership degree between the influencing factor and any candidate factor, obtaining a comprehensive evaluation value of the influencing factor, including: obtaining an evaluation matrix of the influencing factor based on the membership degree between the influencing factor and each candidate factor; inputting the membership degree between the influencing factor and each candidate factor into a pre-constructed triangular membership degree function to obtain the weight of the influencing factor; based on the evaluation matrix and the weight, obtaining a comprehensive evaluation value of the influencing factor; based on the obtained comprehensive evaluation values ​​of each influencing factor, constructing a comparison table of different judgment results and fuzzy membership degrees for each influencing factor; The fuzzy membership degree of any expert to each influencing factor is taken as the expert information of that expert; The calculated mean and variance of the expert information are input into a pre-constructed equivalent success / failure model of expert information to obtain the equivalent success / failure frequency of the expert information. The method for constructing the equivalent success / failure model of expert information includes: constructing a symmetric triangular membership function of fuzzy membership based on the fuzzy membership degree of each expert to the influencing factors; converting the symmetric triangular membership function of fuzzy membership into an equivalent success / failure model of fuzzy membership; the equivalent success / failure model of fuzzy membership is as follows: ; Where n represents the total number of equivalent success and failure trials; s represents the number of successes in the equivalent success and failure trials; and f represents the number of failures in the equivalent success and failure trials. This represents the mean of the expert information. This represents the variance of expert information.

2. The method as described in claim 1, characterized in that, Based on the comprehensive evaluation values ​​of each influencing factor, a comparison table of different judgment results and fuzzy membership degrees for each influencing factor is constructed, including: Based on the comprehensive evaluation values ​​of each influencing factor, calculate the weighted average of each candidate factor; Based on the weighted average of each candidate factor, a comparison table of different judgment results and fuzzy membership degrees for each influencing factor is constructed.

3. A liquid rocket engine structural reliability expert information conversion device, characterized in that, The device includes: An acquisition unit is used to acquire a set of influencing factors for the liquid rocket structure; wherein the set of influencing factors includes multiple influencing factors and a comprehensive evaluation value for each influencing factor; for any influencing factor, the unit acquires the judgment results of each expert on the influencing factor; wherein the judgment result of any expert on the influencing factor is obtained by the expert scoring the reliability of the influencing factor according to fuzzy semantics; A determining unit is used to obtain the fuzzy membership degree of each expert for the influencing factor based on a pre-constructed comparison table of different judgment results and fuzzy membership degrees, and the judgment results of each expert on the influencing factor; wherein, the fuzzy membership degree is obtained by converting the judgment results of each expert on the influencing factor into interval numbers; the method for constructing the comparison table of different judgment results and fuzzy membership degrees includes: obtaining a set of candidate factors for liquid rocket structure; determining the membership degree of the influencing factor with any candidate factor for any influencing factor; obtaining a comprehensive evaluation value of the influencing factor based on the membership degree of the influencing factor with any candidate factor, including: obtaining an evaluation matrix of the influencing factor based on the membership degree of the influencing factor with each candidate factor; inputting the membership degree of the influencing factor with each candidate factor into a pre-constructed triangular membership degree function to obtain the weight of the influencing factor; obtaining a comprehensive evaluation value of the influencing factor based on the evaluation matrix and the weight; and constructing a comparison table of different judgment results and fuzzy membership degrees for each influencing factor based on the obtained comprehensive evaluation values ​​of each influencing factor. A conversion unit is used to take the fuzzy membership degree of any expert to each influencing factor as the expert information of the expert; input the mean and variance of the calculated expert information into a pre-constructed equivalent success and failure model of expert information to obtain the equivalent success and failure number of the expert information; the method for constructing the equivalent success and failure model of expert information includes: constructing a symmetric triangular membership degree function of fuzzy membership degree based on the fuzzy membership degree of each expert to the influencing factor; converting the symmetric triangular membership degree function of fuzzy membership degree into an equivalent success and failure model of fuzzy membership degree; the equivalent success and failure model of fuzzy membership degree is as follows: ; Where n represents the total number of equivalent success and failure trials; s represents the number of successes in the equivalent success and failure trials; and f represents the number of failures in the equivalent success and failure trials. This represents the mean of the expert information. This represents the variance of expert information.

4. An electronic device, characterized in that, The electronic device includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; A processor, when executing a program stored in memory, implements the method of any one of claims 1-2.

5. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method described in any one of claims 1-2.

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