Method and system for evaluating life cycle quality and reliability of avionics equipment

Abstract

The invention relates to the technical field of electronic information, in particular to an avionics equipment whole life cycle quality and reliability evaluation method and system, and the method comprises the steps: building a quality and reliability evaluation database of avionics equipment, and carrying out the updating iteration; obtaining quality and reliability evaluation data of an evaluation object, performing data volume matching with the quality and reliability evaluation database, and determining sample attributes of the evaluation data; according to the sample attribute, calling a reliability evaluation mathematical model corresponding to the sample attribute to perform reliability evaluation, and obtaining a reliability evaluation result; and performing quality analysis and evaluation on the quality condition of the evaluation object based on the reliability evaluation result. And aiming at different sample attributes, a more suitable mathematical model is called, the model fitting effect is improved, the calculation process is simplified, and the evaluation efficiency and the accuracy of an evaluation result are improved.

Description

Technical Field

[0001] The present invention relates to the field of electronic information technology, and in particular to a method and system for evaluating the quality and reliability of avionics equipment throughout its life cycle. Background Art

[0002] Avionics equipment generates valuable reliability test, use and maintenance data during the development, production and use process. Avionics equipment is huge in number and models, and the data is complex; Avionics equipment generates reliability data at each stage of its life cycle, and the statistical specifications of reliability data at each stage are inconsistent, resulting in confusing data; The statistical data of field repairs or development tests of avionics equipment is missing because the focus is not on the reliability data of the equipment; The reliability data of avionics equipment lacks a unified statistical standard, and there is no separate storage, sorting and analysis system, so the equipment reliability data is difficult to use effectively.

[0003] At the same time, the reliability data of avionics equipment has multiple types, multiple sources, and multiple dimensions, such as functional performance test data, reliability test data, production inspection test data, and field use and maintenance data. How to manage the multi-source data of avionics equipment throughout its life cycle is an important part of quality and reliability evaluation. Due to factors such as the lack of effective use of reliability information at each stage, low utilization of similar product data, the lack of a dynamic link between data at each stage, and the relative independence of reliability assessment work between stages, the reliability test data accumulated in the early stages of avionics equipment cannot be effectively used in the current reliability assessment of avionics equipment, and both engineering development information and test data are not fully utilized.

[0004] In addition, due to the high development cost and high price of avionics equipment, the small number of test prototypes and the high cost of obtaining reliability test data, very little reliability test data can be collected for avionics equipment in the same time period. It is well known that the amount of data, that is, the richness of reliability information, seriously affects the accuracy and precision of reliability assessment results.

[0005] Therefore, how to improve the utilization rate of reliability data and reasonably judge whether the reliability test data belongs to the small sample state or the non-small sample state based on the effective utilization of reliability data, so as to realize the accurate assessment of the equipment reliability level based on the test data under different states, is an urgent problem to be solved in equipment reliability work. Summary of the invention

[0006] In view of this, the present invention provides a method and system for evaluating the quality and reliability of avionics equipment throughout its life cycle, aiming to solve all or part of the above-mentioned technical problems.

[0007] In order to solve the above technical problems, the technical solution of the present invention is to provide a method for evaluating the quality and reliability of avionics equipment throughout its life cycle, comprising:

[0008] Build a quality and reliability evaluation database for avionics equipment and update it;

[0009] Acquire quality and reliability evaluation data of the evaluation object, match the data volume with the quality and reliability evaluation database, and determine the sample attributes of the evaluation data;

[0010] According to the sample attributes, a corresponding reliability assessment mathematical model is called to perform reliability assessment to obtain a reliability assessment result;

[0011] A quality analysis and evaluation is performed on the quality of the evaluation object based on the reliability evaluation result.

[0012] As an implementation method, the construction of the quality and reliability evaluation database of avionics equipment and updating and iterating the database include:

[0013] Build a quality and reliability evaluation database for avionics equipment including product identification data, product time data, product space data, and product status data;

[0014] Periodically update the quality and reliability evaluation database, or,

[0015] The quality and reliability evaluation database is updated and iterated in real time.

[0016] As an implementation manner, the step of calling a corresponding reliability assessment mathematical model to perform reliability assessment according to the sample attributes includes:

[0017] If the sample attribute is small sample data, a multi-source information fusion reliability assessment model is called to perform reliability assessment;

[0018] If the sample attribute is non-small sample data, the classical probability distribution life model is called to perform reliability assessment.

[0019] As an implementation mode, the method of invoking the classical probability distribution life model for reliability assessment includes:

[0020] Obtain reliability evaluation data of the evaluation object within a preset time period;

[0021] The classic probability distribution life model is called, and the maximum likelihood estimation method is used to solve its distribution parameters and goodness of fit test results;

[0022] Based on the distribution parameters and the goodness-of-fit test results, the classical probability distribution life model is updated;

[0023] The reliability of the evaluation object is evaluated using the updated classical probability distribution life model to obtain a first reliability evaluation result.

[0024] As an implementation mode, calling the classical probability distribution life model and using the maximum likelihood estimation method to solve its distribution parameters and goodness of fit test results include:

[0025] The probability distribution models of the classical probability distribution life model are respectively selected as exponential distribution and Wilbuh's distribution, and the distribution parameters of the exponential distribution and the distribution parameters of the Wilbuh's distribution are respectively solved by using the maximum likelihood estimation method;

[0026] Using the KS method, the distribution parameters of the exponential distribution and the distribution parameters of the Wilbull distribution are respectively tested for goodness of fit;

[0027] According to the result of the goodness of fit test, the probability distribution with a high goodness of fit test is determined as the probability distribution of the classical probability distribution life model.

[0028] As an implementation manner, the method of calling a multi-source information fusion reliability assessment model to perform reliability assessment includes:

[0029] Screening and cleaning the reliability and quality evaluation data of the evaluation object;

[0030] Call the multi-source information fusion reliability assessment model, and use the BAYES method to solve its distribution parameters and goodness of fit test results;

[0031] Based on the distribution parameters and the goodness-of-fit test results, updating the multi-source information fusion reliability assessment model;

[0032] The reliability evaluation of the evaluation object is performed using the updated multi-source information fusion reliability evaluation model to obtain a second reliability evaluation result.

[0033] As an implementation mode, calling the multi-source information fusion reliability assessment model and using the BAYES method to solve its distribution parameters and goodness of fit test results include:

[0034] Select similar products of the evaluation object and their weight scores through the BWM method;

[0035] Based on the similar products and their weight scores, the probability distribution models are selected as exponential distribution and Wilbull distribution, and the initial prior distribution of the evaluation object is obtained;

[0036] According to the prior information of the initial prior distribution, the posterior calculation formula of the evaluation object is obtained by using the BAYES method;

[0037] Using MCMC method to obtain the non-analytical solution of the BAYES method;

[0038] Performing a goodness of fit test using the posterior mean of the non-analytical solution as a posterior reliability characteristic parameter;

[0039] According to the result of the goodness of fit test, a probability distribution with a high goodness of fit test result is determined as the probability distribution of the multi-source information fusion reliability evaluation model.

[0040] As an implementation manner, the performing quality analysis and evaluation on the quality of the evaluation object based on the reliability evaluation result includes:

[0041] Calculate the batch failure rate of the evaluation object, the annual zeroing ratio, the failure ratio of each component unit of the evaluation object, the failure reason ratio of the evaluation object, the failure number ratio of each geographical environment of the evaluation object, fit the curve of the change of the failure number over time, fit the change of the failure number of each failure reason over time, and obtain statistical analysis results;

[0042] The reliability evaluation result is taken as the central main indicator, and the quality analysis and evaluation is carried out in combination with the statistical analysis result.

[0043] Accordingly, the present invention also provides an avionics equipment full life cycle quality and reliability evaluation system, which is applied to any one of the above-mentioned avionics equipment full life cycle quality and reliability evaluation methods, comprising:

[0044] Database building module, used to build the quality and reliability evaluation database of avionics equipment and update it;

[0045] A sample attribute determination module is used to obtain the quality and reliability evaluation data of the evaluation object, match the data volume with the quality and reliability evaluation database, and determine the sample attributes of the evaluation data;

[0046] A reliability evaluation module is used to call a corresponding reliability evaluation mathematical model to perform reliability evaluation according to the sample attributes to obtain a reliability evaluation result;

[0047] The quality evaluation module is used to perform quality analysis and evaluation on the quality of the evaluation object based on the reliability evaluation result.

[0048] The primary improvement of the present invention is that: by building a quality and reliability evaluation database for avionics equipment and updating and iterating; obtaining the quality and reliability evaluation data of the evaluation object, matching the data volume with the quality and reliability evaluation database, and determining the sample attributes of the evaluation data; according to the sample attributes, calling the corresponding reliability evaluation mathematical model to perform reliability evaluation and obtain reliability evaluation results; based on the reliability evaluation results, performing quality analysis and evaluation on the quality of the evaluation object. The sample attributes of avionics equipment are effectively defined and divided, and at the same time, more suitable mathematical models are called for different sample attributes, the model fitting effect is improved, the calculation process is simplified, and the evaluation efficiency and the accuracy of the evaluation results are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0050] Figure 1 It is a schematic diagram of the steps of a method for evaluating the quality and reliability of avionics equipment throughout its life cycle provided by one embodiment of the present invention;

[0051] Figure 2 It is a schematic diagram of a device reliability assessment process provided by an embodiment of the present invention;

[0052] Figure 3 It is a schematic diagram of the structure of a system for evaluating the quality and reliability of avionics equipment throughout its life cycle provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0053] In order to enable those skilled in the art to better understand the embodiments of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0054] An embodiment of the present application provides Figure 1 The schematic diagram of the steps of the quality and reliability evaluation method for the entire life cycle of avionics equipment and Figure 2 The equipment reliability evaluation process diagram shown in the figure provides a detailed description of the evaluation method adopted in this embodiment.

[0055] S11. Build a quality and reliability evaluation database for avionics equipment and update it.

[0056] Since information is the basis of management and the source of decision-making, the reliability data of avionics equipment has multiple types, multiple sources, and multiple dimensions, such as functional performance test data, reliability test data, production inspection test data, and field use and maintenance data. Therefore, it is necessary to build a more comprehensive quality and reliability evaluation database.

[0057] The scope and content of avionics equipment quality and reliability data include, but are not limited to: product identification data (such as factory code, product number, system to which it belongs), product time data (such as failure time, cumulative working time, etc.), product space data (such as installation platform, mission profile, geographical environment, stress at failure, etc.), product status data (such as failure phenomenon, failure source, etc.). The scope and content information of the above avionics equipment quality and reliability data are added and updated to the reliability and quality evaluation database.

[0058] Through scientific and standardized management of the quality and reliability data of avionics equipment throughout its life cycle, dynamic evaluation of the quality and reliability of avionics equipment by product, level, stage, platform and environment can be achieved.

[0059] Furthermore, when building a database for the quality and reliability evaluation of avionics equipment, it is also necessary to manage the product structure tree and fault data information. The product structure tree management is used to provide maintenance and assurance of the product structure tree level (whole machine / subsystem / component), and at the same time provide basic node information including product type, product name, product number, factory code, manufacturer / department, affiliated system, MTBF index, development stage, installation platform, working geographical location, climate environment, working hours and other information addition and editing. Fault data information management is used to provide the import function of equipment node fault information (this embodiment is based on Excel template import); provide equipment node maintenance and management of product estimated fault information at each stage and record new fault information generated during the service life of the equipment. Fault information can be added, modified, and deleted; the basic information entered includes factory code, fault time, development stage, affiliated subsystem, fault occurrence time, fault phenomenon, stress at the time of fault, cumulative working time, fault source, fault location, cause analysis, fault category, fault responsible unit, corrective measures, zeroing situation, fault handling order number, completion time, implementation of learning from one example, tracking and processing situation, notes and other information.

[0060] S12. Acquire the quality and reliability evaluation data of the evaluation object, match the data volume with the quality and reliability evaluation database, and determine the sample attributes of the evaluation data.

[0061] S13. According to the sample attributes, the corresponding reliability assessment mathematical model is called to perform reliability assessment to obtain a reliability assessment result.

[0062] Based on the sample attributes of the obtained evaluation data, the most appropriate reliability assessment mathematical model is selectively called. Specifically, if the sample attribute is small sample data, the multi-source information fusion reliability assessment model is called to perform reliability assessment on the assessment object; if the sample attribute is not small sample data, the classical probability distribution life model is called to perform reliability assessment.

[0063] Furthermore, when faced with sample attributes that are not small sample data, first obtain the reliability evaluation data of the evaluation object within a preset time period, then call the classical probability distribution life model, and use the maximum likelihood estimation method to solve its distribution parameters and goodness-of-fit test results, and then update the classical probability distribution life model based on the obtained distribution parameters and goodness-of-fit test results, and finally use the updated classical probability distribution life model to perform reliability evaluation on the evaluation object to obtain the first reliability evaluation result.

[0064] Among them, the basic idea of ​​maximum likelihood estimation (MLE) is that since the sample comes from the population, it can reflect the characteristics of the population to a certain extent. If the sample observation values ​​t1, t2, ...t n , then we can say that since this event occurred in one experiment, the probability of this event occurring is very high. Therefore, if the estimated parameter of the population is It can take many values, because we don't know its true value in everything. Among the possible values ​​of , select a value that maximizes the probability of the sample observation result as The estimated value of This is the maximum likelihood estimate.

[0065] Furthermore, in the process of calling the classical probability distribution life model and using the maximum likelihood estimation method to solve its distribution parameters and goodness-of-fit test results, it is necessary to select the probability distribution model of the classical probability distribution life model as exponential distribution f(t) = λexp(-λt) and Wilbu distribution respectively. Usually, the location parameter γ is taken as 0, and the maximum likelihood estimation method is used to solve the distribution parameter λ of the exponential distribution and the distribution parameters m and η of the Wilbu distribution. Then, the KS method is used to perform a goodness of fit test on the distribution parameters of the exponential distribution and the distribution parameters of the Wilbu distribution. According to the results of the goodness of fit test, the probability distribution with a high goodness of fit test is determined as the probability distribution of the classical probability distribution life model.

[0066] Further, based on the above content, assuming that the data comes from exponential distribution and Wilbull distribution, the maximum likelihood estimation functions corresponding to the quantitative model under random truncation are:

[0067] Exponential distribution likelihood function: ln(L exp (t|λ))=rln(λ)-λT, and we get the point estimate of λ: λ=r / T, MTBF=1 / λ.

[0068] Wilbur Distribution: Among them, r is the number of faulty samples at the end of statistics, s is the number of fault-free samples at the time of statistics, and n=r+s is the number of all statistical samples at the end of statistics. It is the accumulation of statistical time of all samples within the randomly truncated statistical interval.

[0069] Furthermore, the KS method is used to perform goodness of fit tests on the distribution parameters of the exponential distribution and the distribution parameters of the Wilbull distribution, respectively, including:

[0070] For exponential distribution test: (1) F test: n samples are drawn from the population for a truncation test. In the test, r fault samples appear, and the fault times are t1, t2, t3, ..., t r ,remember Then the F test statistic is It can be expressed as: (2) χ 2 Test: n samples are drawn from the population for truncation test. In the test, r fault samples appear, and the fault times are t1, t2, t3, …, t r ,remember Then 2 The test statistic can be expressed as:

[0071] Test for Wilb distribution: (1) F test: n samples are drawn from the population for a truncation test. There are r fault samples in the test, and the fault times are t1, t2, t3, …, t r , remember X i = lnt i , Z i =(X i -μ) / δ, μ=ln, δ=1 / m, and Then the F test statistic is It can be expressed as: (2) χ 2 Test: n samples are drawn from the population for truncation test. In the test, r fault samples appear, and the fault time is t1, t2, t3…, t r , remember X i= lnt i , V i =(ri)(X r-i+1 -X r-i ), then χ 2 The test statistic can be expressed as

[0072] Among them, the exponential distribution describes the situation where the failure rate is constant, indicating that the failure of the equipment during its life is random. The exponential distribution is generally applicable to complex systems composed of multiple components, electronic systems or electronic components, systems with constant failure rates, etc. It is also applicable to calculating the life of components whose randomness is independent of time. The Weibull distribution was discovered and proposed by Swedish physicist Weibull. When studying the strength of a chain, Weibull found that when the chain was subjected to tension, the weakest link of the chain broke first, that is, the weakest link model, and derived the Weibull distribution from this. In a broad sense, a model in which the failure of any part will lead to overall failure is the weakest link model. The Weibull distribution can describe various distribution phenomena by changing its shape parameter, location parameter, and scale parameter, and is easy to process with mathematical methods. It is a "universal distribution".

[0073] Furthermore, when faced with small sample data with sample attributes, the reliability and quality evaluation data of the evaluation object are first screened and cleaned, and then the multi-source information fusion reliability evaluation model is called, and the BAYES method is used to solve its distribution parameters and goodness-of-fit test results. Then, based on the distribution parameters and goodness-of-fit test results, the multi-source information fusion reliability evaluation model is updated, and finally, the updated multi-source information fusion reliability evaluation model is used to perform reliability evaluation on the evaluation object to obtain a second reliability evaluation result.

[0074] Among them, the BAYES method, namely the Bayesian estimation method, regards the unknown parameters of the distribution model as random variables and uses the probability density function to describe the uncertainty of the parameters. The Bayesian theorem is: Among them, p(θ|y) is called the posterior density function, p(θ) is called the prior density function, and f(θ|y) is the sampling density function of the data. When the experiment ends, the y value is certain. At this time, the sampling distribution can be regarded as a function of the unknown parameter θ. At this time, the sampling distribution function is also called the likelihood function. In the above formula, ∫f(y|θ)p(θ)dθ is a constant term, so for the prior distribution, the posterior distribution and the likelihood function have the following relationship: posterior distribution ∝ prior distribution × likelihood function. Bayesian estimation uses a combination of sample information and prior information, and can update the prior information through posterior information and observed data, so that the estimated result is closer to the actual situation.

[0075] Furthermore, when calling the multi-source information fusion reliability assessment model and using the BAYES method to solve its distribution parameters and goodness-of-fit test results, the BWM method can be used to select similar products of the assessment object and their weight scores; based on the similar products and their weight scores, the probability distribution models are selected as exponential distribution and Wilbull distribution, respectively, and the initial prior distribution of the assessment object is obtained; according to the prior information of the initial prior distribution, the BAYES method is used to obtain the posterior calculation formula of the assessment object; the MCMC method is used to obtain the non-analytical solution of the BAYES method; the posterior mean of the non-analytical solution is used as the posterior reliability characteristic parameter for a goodness-of-fit test; according to the results of the goodness-of-fit test, the probability distribution with a high goodness-of-fit test is determined as the probability distribution of the multi-source information fusion reliability assessment model.

[0076] It should be noted that selecting similar products of the evaluation object and their weight scores through the BWM method means using the rank sum test method to perform compatibility test on the selected reliability data of the similar products, screen out similar product models that meet the requirements, and then determine the weight score of each screened similar product model.

[0077] Furthermore, a conjugate prior distribution is adopted as the prior distribution of the exponential distribution; a normal distribution or a uniform distribution is selected as the prior distribution of the Wilbuh distribution; and a weighted fusion is performed on the prior distribution of the exponential distribution and the prior distribution of the Wilbuh distribution based on the score weights of similar products to obtain the initial prior distribution of the evaluation object.

[0078] For the exponential distribution, the commonly used prior distribution for λ is its conjugate prior distribution: Gamma distribution, which has the following form: Where α and β are the distribution parameters of λ.

[0079] For Wilb distribution Select normal distribution or uniform distribution as the distribution function of the distribution parameters η and m of the Weibull distribution, and the distributions of the two are independent of each other, and take their joint distribution as the prior distribution:

[0080] When the normal distribution is: have

[0081] π(η)=(η U -η L ) -1 ,η∈(η L , η U ) π(m)=(m U -m L ) -1 ,m∈(m L ,m U ) When distributed uniformly: π (m, η) = π (η) π (m) = (η U -η L ) -1 (m U -m L ) -1 .

[0082] Furthermore, in the Bayesian estimation model, it is very difficult to obtain the marginal posterior distribution of the parameter to be estimated by removing redundant parameters through high-dimensional integration. If the model is more complex, it is even impossible to obtain the marginal posterior distribution of the parameter to be estimated. Therefore, this embodiment adopts the MCMC method, which can solve the numerical solution of the Bayes posterior distribution when the life distribution obeys the Weibull distribution, thereby obtaining the corresponding reliability evaluation result through the posterior distribution.

[0083] S14. Conduct quality analysis and evaluation on the quality of the assessment object based on the reliability assessment results.

[0084] Furthermore, the batch failure rate of the evaluation object, the annual zeroing ratio, the failure proportion of each component unit of the evaluation object, the failure cause proportion of the evaluation object, the failure number proportion of the evaluation object in various geographical environments, the curve of fitting the change of the number of failures over time, the change of the number of failures for various failure causes over time are fitted to obtain the statistical analysis results, based on the calculation results of the statistical analysis and the time trend graph, with the reliability assessment results as the central main indicator, combined with the statistical analysis results, quality analysis and evaluation are carried out, specifically including: analyzing the quality characteristics of the evaluation object in combination with the statistical analysis results of different dimensions; analyzing the quality control situation according to the changes in failure trends; analyzing the key points of product design improvement according to the distribution of failure types; analyzing the field use guarantee capability according to the maintenance efficiency.

[0085] The method for evaluating the quality and reliability of avionics equipment throughout its life cycle provided by the present invention builds a quality and reliability evaluation database for avionics equipment and updates and iterates; obtains the quality and reliability evaluation data of the evaluation object, matches the data volume with the quality and reliability evaluation database, and determines the sample attributes of the evaluation data; calls the corresponding reliability evaluation mathematical model to perform reliability evaluation according to the sample attributes, and obtains the reliability evaluation result; and performs quality analysis and evaluation on the quality of the evaluation object based on the reliability evaluation result. The sample attributes of avionics equipment are effectively defined and divided, and more suitable mathematical models are called for different sample attributes to improve the model fitting effect, simplify the calculation process, and improve the evaluation efficiency and the accuracy of the evaluation results.

[0086] Accordingly, an embodiment of the present invention also provides a system for evaluating the quality and reliability of avionics equipment throughout its life cycle, such as Figure 3As shown, the method for evaluating the quality and reliability of avionics equipment throughout its life cycle as described in the above embodiment specifically includes:

[0087] Database building module, used to build the quality and reliability evaluation database of avionics equipment and update it;

[0088] A sample attribute determination module is used to obtain the quality and reliability evaluation data of the evaluation object, match the data volume with the quality and reliability evaluation database, and determine the sample attributes of the evaluation data;

[0089] A reliability evaluation module is used to call a corresponding reliability evaluation mathematical model to perform reliability evaluation according to the sample attributes to obtain a reliability evaluation result;

[0090] The quality evaluation module is used to conduct quality analysis and evaluation on the quality of the evaluation object based on the reliability evaluation results.

[0091] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system 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 system. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or system including the element.

[0092] The above are only preferred embodiments of the present invention. It should be noted that the above preferred embodiments should not be regarded as limiting the present invention, and the protection scope of the present invention should be based on the scope defined by the claims. For ordinary technicians in this technical field, several improvements and modifications can be made without departing from the spirit and scope of the present invention, and these improvements and modifications should also be regarded as the protection scope of the present invention.

Claims

1. A method for evaluating the quality and reliability of avionics equipment throughout its life cycle, characterized in that: include: Build a quality and reliability evaluation database for avionics equipment and update it; Acquire quality and reliability evaluation data of the evaluation object, match the data volume with the quality and reliability evaluation database, and determine the sample attributes of the evaluation data; According to the sample attributes, a corresponding reliability assessment mathematical model is called to perform reliability assessment to obtain a reliability assessment result; A quality analysis and evaluation is performed on the quality of the evaluation object based on the reliability evaluation result.

2. The method for evaluating the quality and reliability of avionics equipment throughout its life cycle according to claim 1, characterized in that: The said construction of the quality and reliability evaluation database of avionics equipment and its updating and iteration include: Build a quality and reliability evaluation database for avionics equipment including product identification data, product time data, product space data, and product status data; Periodically update the quality and reliability evaluation database, or, The quality and reliability evaluation database is updated and iterated in real time.

3. The method for evaluating the quality and reliability of avionics equipment throughout its life cycle according to claim 1, characterized in that: The step of calling a corresponding reliability assessment mathematical model to perform reliability assessment according to the sample attributes includes: If the sample attribute is small sample data, a multi-source information fusion reliability assessment model is called to perform reliability assessment; If the sample attribute is non-small sample data, the classical probability distribution life model is called to perform reliability assessment.

4. The method for evaluating the quality and reliability of avionics equipment throughout its life cycle according to claim 3, characterized in that: The method for using the classical probability distribution life model to perform reliability assessment includes: Obtain reliability evaluation data of the evaluation object within a preset time period; The classic probability distribution life model is called, and the maximum likelihood estimation method is used to solve its distribution parameters and goodness of fit test results; Based on the distribution parameters and the goodness-of-fit test results, the classical probability distribution life model is updated; The reliability of the evaluation object is evaluated using the updated classical probability distribution life model to obtain a first reliability evaluation result.

5. The method for evaluating the quality and reliability of avionics equipment throughout its life cycle according to claim 4, characterized in that: The calling of the classical probability distribution life model and the use of the maximum likelihood estimation method to solve its distribution parameters and goodness of fit test results include: The probability distribution models of the classical probability distribution life model are respectively selected as exponential distribution and Wilbuh's distribution, and the distribution parameters of the exponential distribution and the distribution parameters of the Wilbuh's distribution are respectively solved by using the maximum likelihood estimation method; Using the KS method, the distribution parameters of the exponential distribution and the distribution parameters of the Wilbull distribution are respectively tested for goodness of fit; According to the result of the goodness of fit test, the probability distribution with a high goodness of fit test is determined as the probability distribution of the classical probability distribution life model.

6. The method for evaluating the quality and reliability of avionics equipment throughout its life cycle according to claim 3, characterized in that: The method of calling the multi-source information fusion reliability assessment model to perform reliability assessment includes: Screening and cleaning the reliability and quality evaluation data of the evaluation object; Call the multi-source information fusion reliability assessment model, and use the BAYES method to solve its distribution parameters and goodness of fit test results; Based on the distribution parameters and the goodness-of-fit test results, updating the multi-source information fusion reliability assessment model; The reliability of the evaluation object is evaluated by using the updated multi-source information fusion reliability evaluation model to obtain a second reliability evaluation result.

7. The method for evaluating the quality and reliability of avionics equipment throughout its life cycle according to claim 6, characterized in that: The multi-source information fusion reliability evaluation model is called, and the distribution parameters and goodness-of-fit test results are solved by the BAYES method, including: Select similar products of the evaluation object and their weight scores through the BWM method; Based on the similar products and their weight scores, the probability distribution models are selected as exponential distribution and Wilbull distribution, and the initial prior distribution of the evaluation object is obtained; According to the prior information of the initial prior distribution, the posterior calculation formula of the evaluation object is obtained by using the BAYES method; Using MCMC method to obtain the non-analytical solution of the BAYES method; Performing a goodness of fit test using the posterior mean of the non-analytical solution as a posterior reliability characteristic parameter; According to the result of the goodness of fit test, a probability distribution with a high goodness of fit test result is determined as the probability distribution of the multi-source information fusion reliability evaluation model.

8. The method for evaluating the quality and reliability of avionics equipment throughout its life cycle according to claim 1, characterized in that: The performing quality analysis and evaluation on the quality of the evaluation object based on the reliability evaluation result includes: Calculate the batch failure rate of the evaluation object, the annual zeroing ratio, the failure ratio of each component unit of the evaluation object, the failure reason ratio of the evaluation object, the failure number ratio of each geographical environment of the evaluation object, fit the curve of the change of the failure number over time, fit the change of the failure number of each failure reason over time, and obtain statistical analysis results; The reliability evaluation result is taken as the central main indicator, and the quality analysis and evaluation is carried out in combination with the statistical analysis result.

9. A system for evaluating the quality and reliability of avionics equipment throughout its life cycle, applied to the method for evaluating the quality and reliability of avionics equipment throughout its life cycle as claimed in any one of claims 1 to 8, characterized in that: include: Database building module, used to build the quality and reliability evaluation database of avionics equipment and update it; A sample attribute determination module is used to obtain the quality and reliability evaluation data of the evaluation object, match the data volume with the quality and reliability evaluation database, and determine the sample attributes of the evaluation data; A reliability evaluation module is used to call a corresponding reliability evaluation mathematical model to perform reliability evaluation according to the sample attributes to obtain a reliability evaluation result; The quality evaluation module is used to perform quality analysis and evaluation on the quality of the evaluation object based on the reliability evaluation result.

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