Gas meter sealing long-term reliability evaluation method and system and storage medium

Abstract

The embodiment of the invention discloses a gas meter sealing long-term reliability evaluation method and system and a storage medium, and the reliability evaluation method comprises the steps: obtaining a reliability evaluation standard, a plurality of stress condition factors and a multiple regression model, and setting a group of standard test stress conditions and a plurality of groups of different acceleration test stress conditions; acquiring initial leakage rate information; a plurality of first test samples are extracted under each acceleration test stress condition, an acceleration stress test is performed on each first test sample, and a plurality of corresponding leakage rate values are measured under the standard test stress condition based on the duration of the respective acceleration stress test; solving the multiple regression model to obtain a leakage rate evolution model; and outputting a reliability evaluation result according to the reliability evaluation standard. The problem that the sealing long-term reliability evaluation precision is insufficient due to the fact that an existing model constructed through an accelerated stress test is limited by physical distortion of a single stress frame and representation deficiency of initial discreteness is solved.

Description

Technical Field

[0001] Multiple embodiments of this specification relate to the technical field of gas metering equipment seal evaluation, and more particularly, to the optimization of a time-varying leakage rate model. Background Art

[0002] For precision gas metering equipment like gas meters, the long-term airtightness of their housing and internal sealing systems is crucial for ensuring accurate measurement, safe operation, and compliance with regulations such as OIMLR 137 and JJG 577. During their service life, gas meters are subjected to multiple stresses, including temperature, pressure, humidity, gas composition, and mechanical vibration. These stresses can cause the metal and polymer materials that comprise the seals to gradually degrade (through corrosion, aging, creep, and fatigue), resulting in a nonlinear increase in the overall leakage rate over time, ultimately leading to failure.

[0003] Traditional accelerated life testing methods rely on observing a sufficient number of samples that ultimately fail, which is extremely time-consuming and costly for gas meters. Furthermore, due to the sparse failure data, the statistical confidence in the evaluation results is insufficient, and the reproducibility is also unsatisfactory. With the widespread adoption of smart gas meters, metering deviations caused by minor leaks have become a pain point in the industry. Accelerated stress testing is currently used to conduct long-term airtightness assessments on high-reliability, long-life products such as gas meters. By applying stresses higher than those under normal operating conditions, the product aging or failure process is accelerated, thereby predicting the product's lifespan and reliability in a relatively short period of time.

[0004] Due to the deep complexity of the stress coupling mechanism, the standards and specifications for accelerated test conditions usually limit them to a single stress type. Therefore, the existing accelerated stress test for gas meters has significant limitations in the model construction process, which directly leads to the distortion of the model's physical mapping of the actual service scenario, and the model does not consider the discreteness of the initial leakage rate caused by the manufacturing process. Summary of the Invention

[0005] The embodiments of this specification provide a method, system and storage medium for evaluating the long-term reliability of gas meter seals, which solves the problem that the model constructed by the existing accelerated stress test is subject to the physical distortion of the single stress framework and the lack of representation of the initial discreteness, resulting in insufficient accuracy in the long-term reliability evaluation of the seals.

[0006] The technical solution is as follows: In a first aspect, an embodiment of this specification provides a method for evaluating the long-term reliability of a gas meter seal, comprising the following steps: Obtaining a reliability evaluation standard for the product to be evaluated, a plurality of stress condition factors, and a multiple regression model, wherein the multiple regression model uses an initial value of the leakage rate, time, and each stress factor as independent variables and a leakage rate value as a dependent variable; A set of standard test stress conditions and multiple sets of different accelerated test stress conditions are set based on multiple stress condition factors; Obtaining initial leakage rate information, wherein the initial leakage rate information represents the initial sealing performance of the evaluated product under standard test stress conditions; For each accelerated test stress condition, a plurality of first test samples corresponding to the first test condition are extracted from the sample library of the product being evaluated, and accelerated stress tests are performed on each of the corresponding first test samples under each accelerated test stress condition; Under standard test stress conditions, measuring multiple leakage rate values ​​of any first test sample corresponding to different durations of the accelerated stress test to obtain a leakage rate value-test duration data set corresponding to the first test sample, repeating this step until a leakage rate value-test duration data set corresponding to all first test samples is obtained, wherein the leakage rate value-test duration data set includes an initial leakage rate value when the duration of the accelerated stress test is 0; Solving a multivariate regression model based on the accelerated test stress conditions and leakage rate value-test duration data set corresponding to all first test samples to obtain a leakage rate evolution model; The evolution trajectory of leakage rate over time is obtained based on the leakage rate evolution model, initial leakage rate information and standard test stress conditions; The reliability evaluation results of the evaluated product are output based on the evolution trajectory of the leakage rate over time and the reliability evaluation standard.

[0007] As a preferred solution, the obtaining of initial leakage rate information includes: extracting a plurality of second test samples of the product to be evaluated from a sample library of the product to be evaluated; For each second test sample, measure its corresponding leakage rate value under standard test stress conditions; Distribution fitting is performed based on the leakage rate value corresponding to each second test sample to obtain an initial leakage rate distribution model as the overall initial leakage rate information.

[0008] As a preferred solution, the leakage rate evolution model includes a parameter vector and a point estimate and a covariance matrix of the parameter vector; The leak rate evolution trajectory over time obtained based on the leak rate evolution model, the initial leak rate information and the standard test stress conditions includes: A plurality of random initial leakage rate values ​​are obtained by Monte Carlo simulation based on the initial leakage rate distribution model; Based on the point estimate of the parameter vector and the covariance matrix, multiple sets of random parameter values ​​are obtained by Monte Carlo simulation; Based on standard test stress conditions, multiple random initial leakage rate values, and multiple sets of random parameter values, the leakage rate evolution model obtains multiple leakage rate evolution trajectories over time; The reliability evaluation results of the evaluated product are output based on the evolution trajectory of the leakage rate over time and the reliability evaluation standard, including: The reliability evaluation results of the evaluated product are output based on multiple leakage rate evolution trajectories over time and reliability evaluation standards.

[0009] As a preferred solution, the reliability evaluation criteria of the evaluated product include a leakage rate failure value, a target service time, and a target failure rate corresponding to the target service time; The reliability evaluation results of the evaluated product are output based on the multiple leakage rate evolution trajectories over time and the reliability evaluation standards, including: Obtaining predicted failure times corresponding to the plurality of leakage rate evolution trajectories over time based on the plurality of leakage rate evolution trajectories over time and the leakage rate failure values; The predicted failure rate is obtained based on the predicted failure time and target service time corresponding to multiple leakage rate evolution trajectories over time; Output the reliability evaluation results of the evaluated product based on the predicted failure rate and target failure rate.

[0010] As a preferred solution, the method of obtaining the predicted failure rate based on the predicted failure time and target service time corresponding to each of the multiple leakage rate evolution trajectories over time further includes: Based on the predicted failure time and the target service time corresponding to each of the multiple leakage rate evolution trajectories over time, a confidence interval corresponding to a preset confidence level of the predicted failure rate is obtained, wherein the confidence interval includes an upper limit value and a lower limit value of the interval; Outputting the reliability evaluation result of the evaluated product based on the predicted failure rate and the target failure rate includes: The reliability evaluation results of the evaluated product are output based on the predicted failure rate, target failure rate and the upper limit value of the confidence interval.

[0011] As a preferred solution, the outputting of the reliability evaluation result of the evaluated product based on the predicted failure rate, the target failure rate and the upper limit of the confidence interval includes: When the predicted failure rate is greater than the target failure rate, the reliability assessment result of the evaluated product is unreliable; When the predicted failure rate is less than or equal to the target failure rate and the upper limit of the confidence interval is greater than the target failure rate, the reliability assessment result of the evaluated product is not completely reliable; When the upper limit of the confidence interval is less than or equal to the target failure rate, the reliability assessment result of the evaluated product is completely reliable.

[0012] As a preferred solution, the multivariate regression model is solved based on the accelerated test stress conditions and leakage rate value-test duration data set corresponding to all first test samples to obtain a leakage rate evolution model, including: Performing a preliminary fitting of a multiple regression model based on the accelerated test stress conditions and leakage rate value-test duration data sets corresponding to all first test samples to obtain a preliminary fitting model; Determine a weight setting scheme based on the preliminary fitting model and a leakage rate value-test duration data set corresponding to any first test sample; Based on the accelerated test stress conditions and leakage rate value-test duration data sets corresponding to all first test samples and the weight setting scheme, a multivariate regression model is weightedly fitted to obtain a leakage rate evolution model.

[0013] As a preferred solution, under standard test stress conditions, measuring multiple leakage rate values ​​of any first test sample corresponding to different durations of the accelerated stress test to obtain a leakage rate value-test duration data set corresponding to the first test sample includes: Acquiring a preset time series including a plurality of measurement time nodes corresponding to an early stage, a middle stage, and a late stage of a degradation process during an accelerated stress test on a first test sample; Under standard test stress conditions, multiple leakage rate values ​​corresponding to the duration of the accelerated stress test of any first test sample are measured at all measurement time nodes to obtain a leakage rate value-test duration data set corresponding to the first test sample.

[0014] In a second aspect, an embodiment of this specification provides a gas meter seal long-term reliability assessment system, comprising an acquisition unit, an initial leak rate test unit, an accelerated leak rate test unit, a model fitting unit, a leak rate evolution unit, and a result assessment unit; The acquisition unit acquires a reliability evaluation standard of the product to be evaluated, a plurality of stress condition factors, and a multiple regression model, wherein the multiple regression model uses the initial value of the leakage rate, time, and each stress factor as independent variables and the leakage rate value as a dependent variable; and sets a set of standard test stress conditions and a plurality of different accelerated test stress conditions based on the plurality of stress condition factors; The initial leakage rate testing unit acquires initial leakage rate information, wherein the initial leakage rate information represents the initial sealing performance of the product being evaluated under standard test stress conditions; The accelerated leak rate testing unit extracts, for each accelerated test stress condition, a plurality of first test samples corresponding to the product being evaluated from a sample library, and performs an accelerated stress test on each of the plurality of first test samples under each accelerated test stress condition; under the standard test stress condition, measures a plurality of leakage rate values ​​corresponding to different durations of the accelerated stress test on any first test sample to obtain a leakage rate value-test duration data set corresponding to the first test sample, and repeats this step until a leakage rate value-test duration data set corresponding to all first test samples is obtained, wherein the leakage rate value-test duration data set includes an initial leakage rate value when the duration of the accelerated stress test is 0; The model fitting unit solves a multivariate regression model based on the accelerated test stress conditions and leakage rate value-test duration data sets corresponding to all first test samples to obtain a leakage rate evolution model; The leak rate evolution unit obtains the evolution trajectory of the leak rate over time based on the leak rate evolution model, the initial leak rate information and the standard test stress condition; The result evaluation unit outputs a reliability evaluation result of the evaluated product based on the evolution trajectory of the leakage rate over time and the reliability evaluation standard.

[0015] In a third aspect, an embodiment of this specification provides an electronic device comprising a processor and a memory; the processor is connected to the memory; the memory is used to store executable program code; the processor runs a program corresponding to the executable program code by reading the executable program code stored in the memory, so as to execute the steps described in the first aspect of the above embodiment.

[0016] In a fourth aspect, an embodiment of this specification provides a computer storage medium, wherein the computer storage medium stores a plurality of instructions, wherein the instructions are suitable for being loaded by a processor and executing the steps described in the first aspect of the above embodiment.

[0017] The beneficial effects of the technical solutions provided by some embodiments of this specification include at least: The present invention adopts statistical regression method for parameter estimation, constructs and applies a specific mathematical model that combines multi-stress accelerated test and the overall leakage rate evolution law, improves the scientificity and quantitative level of long-term air tightness reliability assessment of gas meters, and solves the problem that the current gas meter accelerated test model is subject to the physical distortion of the single stress framework and the lack of representation of initial discreteness, resulting in a large deviation between the evaluation results and the actual service performance.

[0018] This method improves test efficiency and reduces evaluation costs by focusing on leak rate degradation trajectory data rather than final failure data. Its evaluation framework enhances the reproducibility of results and the scalability of the method. It directly outputs reliability indicators with confidence intervals, providing a scientific basis for product conformity determination and supporting product design optimization and early quality risk assessment.

[0019] This method quantifies the uncertainty of the initial sealing performance and model parameters of the evaluated product, alleviating the uncertainty deviation of the model caused by limited test data, thereby obtaining a more accurate leakage rate evolution trajectory.

[0020] When performing regression fitting, in order to prevent the fitting results from being overly affected by data points with high leakage rate values ​​and to make the residual distribution more uniform, weights are introduced for weighted fitting to alleviate the impact of leakage rate measurement errors on model fitting and make the leakage rate evolution model close to the ideal state. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0022] Figure 1 This is a flow chart of a method for evaluating the long-term reliability of a gas meter seal provided in an embodiment of this specification; Figure 2 is an example graph of the statistical distribution of the initial leakage rate; Figure 3 is a plot of the standardized residuals from the model fit; Figure 4 This is a schematic diagram of the structure of a gas meter seal long-term reliability evaluation system provided in an embodiment of this specification; Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this specification. DETAILED DESCRIPTION

[0023] The technical solutions in the embodiments of this specification will be described clearly and completely below in conjunction with the drawings in the embodiments of this specification.

[0024] Throughout this specification, the claims, and the accompanying drawings, the terms "first," "second," "third," and the like are used to distinguish between different items, not to describe a particular order. Furthermore, the terms "including," "having," and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or elements is not limited to the listed steps or elements but may optionally include steps or elements not listed, or may include other steps or elements inherent to the process, method, product, or apparatus.

[0025] The following description provides examples and does not limit the scope, applicability, or examples set forth in the claims. Changes may be made to the functions and arrangements of the elements described without departing from the scope of this specification. Various examples may appropriately omit, replace, or add various processes or components. For example, the described methods may be performed in an order different from the order described, and various steps may be added, omitted, or combined. Furthermore, features described with respect to some examples may be combined in other examples.

[0026] During their service life, gas meters are subjected to multiple stresses, including temperature, pressure, humidity, gas composition, and mechanical vibration. These stresses cause the metal and polymer materials that make up the seal to gradually degrade (through corrosion, aging, creep, and fatigue), resulting in a nonlinear increase in the overall leakage rate over time, ultimately leading to failure. Accelerated stress testing is commonly used to assess the long-term airtightness of gas meters. By applying stresses higher than those experienced under normal operating conditions, the aging or failure process is accelerated, allowing for a quicker prediction of product lifespan and reliability.

[0027] However, due to the profound complexity of stress coupling mechanisms, standards and specifications for accelerated test conditions typically limit them to a single stress type. Currently, there is a lack of a systematic approach that balances accuracy and engineering practicality by effectively utilizing trajectory data from multiple stress acceleration conditions and scientifically extrapolating them to normal service conditions for quantitative reliability assessment. In particular, there are technical gaps in how to construct leakage rate evolution and acceleration models that reflect key degradation mechanisms and are easily applicable, as well as how to handle data variability and perform robust parameter estimation. Therefore, this application is filed.

[0028] Reference Figure 1 As shown, Figure 1 A flow chart of a method for evaluating the long-term reliability of a gas meter seal provided in an embodiment of this specification may include at least the following steps: Step 102: Obtain the reliability assessment standard, multiple stress condition factors, and a multiple regression model for the product being evaluated. The multiple regression models for all first test samples use the initial value of the leakage rate, time, and each stress factor as independent variables, and the leakage rate value as the dependent variable. Based on the multiple stress condition factors, set a set of standard test stress conditions and multiple sets of different accelerated test stress conditions.

[0029] To illustrate, we first need to define the evaluation object, for example, an ultrasonic gas meter of a specific model and specification produced by a specific manufacturer, or its specific key sealing subsystem (such as the base meter housing assembly) as the product to be evaluated. The reliability evaluation standard of the product to be evaluated is a pre-set failure criterion, which is usually determined by product design specifications, corporate standards, industry standards or user expectations. Then, the key accelerating stresses are identified. By analyzing the gas meter sealing structure, the characteristics of the materials used (such as steel, rubber, engineering plastics, etc.), and the environmental conditions that they may encounter in long-term service, and combining existing failure mode and effect analysis (FMEA) results or expert experience, multiple stress condition factors composed of key environments and working stresses that have a significant accelerating effect on the overall airtightness of the gas meter (i.e., increased leakage rate) are identified.

[0030] It is important to understand that in reliability engineering, accelerated stress testing is used to shorten testing time by applying stresses higher than those under normal use conditions to accelerate the aging or failure process of the product. This allows for the simulation of the product's performance degradation over years or even decades of normal use within a few days, weeks, or months, allowing for a rapid assessment of the product's long-term reliability. By applying accelerated stresses such as high temperature and high pressure, processes such as material aging and seal failure are "fast-forwarded," allowing for a quick prediction of long-term reliability. The selection of stress levels must meet two conditions: acceleration and mechanism consistency. Acceleration significantly shortens test time (e.g., compressing a 15-year life test to three months), while mechanism consistency ensures that abnormal failure modes are not introduced (e.g., high temperature causing material melting rather than actual aging).

[0031] For example, the ambient temperature and the equivalent constant pressure inside the gas meter are given priority as the main accelerated stress factors for explanation. The test stress condition S is set based on the temperature T and pressure P. The normal service stress state of the gas meter in a typical actual application scenario is determined based on various factors such as regional climate differences, installation location (indoor / outdoor), and gas pipeline pressure level. For simplicity, it is assumed that the normal service conditions are: the annual average equivalent temperature S Tn =20℃, internal equivalent constant pressure S Pn =5kPa. Set the normal service stress state as the standard test stress condition . When designing accelerated test stress conditions, in order to effectively reveal the quantitative relationship between the stress condition factor and the leakage rate evolution rate, and to ensure the reliability of the extrapolation, it is necessary to design a variety of stress condition factor combinations at different levels. The levels of these stress condition factor combinations should be significantly higher than the normal service levels, but it must be ensured that the applied stress does not introduce new, non-real failure modes or degradation mechanisms that do not usually occur or dominate under normal service conditions (for example, instantaneous yielding of materials, irreversible excessive compression deformation of seals, etc., which usually require preliminary exploratory tests or based on the material's extreme performance data to determine). In this embodiment, the following three accelerated test stress conditions are designed. : S a1 : Temperature S T1 =60°C, internal pressure S P1 =50kPa; S a2 : Temperature S T2 =90°C, internal pressure S P2 =50kPa; S a3 : Temperature S T3 =90°C, internal pressure S P3 =75kPa.

[0032] Such a design includes different gradients of temperature and pressure, and has combined changes, which helps in solving the subsequent model.

[0033] Step 104 : Acquire initial leakage rate information. The initial leakage rate information of all first test samples represents the initial sealing performance of the evaluated product under standard test stress conditions.

[0034] Illustratively, initial leak rate information is used to characterize the baseline sealing performance level of the evaluated product at the time of shipment and its inherent individual variations.

[0035] In one embodiment of the present specification, obtaining initial leakage rate information includes: extracting a plurality of second test samples of the product to be evaluated from a sample library of the product to be evaluated; For each second test sample, measure its corresponding leakage rate value under standard test stress conditions; Distribution fitting is performed based on the leakage rate value corresponding to each second test sample to obtain an initial leakage rate distribution model as the overall initial leakage rate information.

[0036] Explanatory, a distribution fitting is performed on the leakage rate values ​​of a plurality of second test samples, and the uncertainty of the initial leakage rate value is estimated according to the distribution model, which is used to generate the initial leakage rate value of the virtual sample in the subsequent Monte Carlo simulation.

[0037] For example, a batch (e.g. N = 50) of new samples randomly drawn from the sample library of the product to be evaluated are used as the second test samples to measure their respective leakage rate values ​​L under standard test stress conditions. 0i (where i is the sample number, from 1 to N), in practice, the leakage rate value L of each second test sample is 0i Usually small and presents a certain degree of discreteness. For example, the measured data may be as follows: 9.39×10 -9 , 4.06×10 -9 , ..., 3.16×10 -9 Pa·m³ / s. For these L 0i Perform statistical distribution fitting analysis. Since leakage rates are usually positive and may span multiple orders of magnitude, the lognormal distribution is often used as a suitable model to describe such data. Normal distribution , combined with the attached Figure 2 , Figure 2 The horizontal axis is the initial value of the logarithmic leakage rate, and the vertical axis is the probability density. The curve in the figure is the fitted normal distribution PDF (probability density function), where μ and σ are The mean and standard deviation of these parameters can be obtained by 0i The data set is obtained by performing maximum likelihood estimation or other fitting methods. The initial leakage rate distribution model composed of these parameters will be used as the initial leakage rate information.

[0038] Step 106: For each accelerated test stress condition, extract a plurality of first test samples corresponding to the first test sample from the sample library of the product being evaluated, and perform an accelerated stress test on each of the plurality of first test samples under each accelerated test stress condition; under the standard test stress condition, measure a plurality of leakage rate values ​​corresponding to different durations of the accelerated stress test of any first test sample to obtain a leakage rate value-test duration data set corresponding to the first test sample. Repeat this step until a leakage rate value-test duration data set corresponding to all first test samples is obtained, wherein all leakage rate value-test duration data sets of the first test samples include an initial leakage rate value when the duration of the accelerated stress test is 0.

[0039] To illustrate, to ensure the robustness of the subsequent model solution and adequate capture of individual variability, the number of samples tested at each accelerated stress level is determined. For example, the number of first test samples under each accelerated stress condition is set to 15. Therefore, the total number of first test samples is 3 * 15 = 45. Both the first and second test samples are randomly selected from a sample library from the same production batch of the evaluated product, or from batches with similar manufacturing processes and material properties, to ensure consistency and representativeness of sample characteristics.

[0040] Explanatory note: Before the formal start of the accelerated stress test (denoted as t0 = 0 hours), all 45 selected first test samples are accurately measured and recorded under standard test stress conditions for their respective initial leakage rates. Afterwards, these 45 first test samples are placed in their corresponding accelerated test stress conditions (e.g., in a thermostat connected to a controllable pressure source) for accelerated stress testing. At each predetermined measurement time point, the first test sample is switched from the accelerated test stress condition to the standard test stress condition, and a calibrated helium mass spectrometer leak detector is used to accurately measure the initial leakage rate of each first test sample under its corresponding accelerated test stress condition S ak After the measurement is completed, the first test sample is switched from the standard test stress condition to the accelerated test stress condition as soon as possible to continue the test, thereby obtaining a leakage rate value-test duration data set.

[0041] When necessary, the total duration of the accelerated stress test should be planned in advance, with the goal of observing a statistically significant and recognizable increase in the leakage rate values ​​of most first test samples (especially first test samples at higher stress levels), or reaching the preset maximum allowable test duration (e.g. 3000 hours), so as to ensure that sufficient degradation data that can effectively support subsequent model solutions are obtained without excessively extending the test cycle.

[0042] In one embodiment of the present specification, under standard test stress conditions, a plurality of leakage rate values ​​corresponding to different durations of accelerated stress tests of any first test sample are measured to obtain a leakage rate value-test duration data set corresponding to the first test sample, including: Acquiring a preset time series including a plurality of measurement time nodes corresponding to an early stage, a middle stage, and a late stage of a degradation process during an accelerated stress test on a first test sample; Under standard test stress conditions, multiple leakage rate values ​​corresponding to the duration of the accelerated stress test of any first test sample are measured at all measurement time nodes to obtain a leakage rate value-test duration data set corresponding to the first test sample.

[0043] Explanatory, during the entire accelerated stress test, according to the pre-set time series t j Periodically measure the leakage rate values ​​of all first test samples. Preset time series t j The selection of each measurement time node should take into account the data collection in the early, middle and late stages of the test (relative to the degradation process) so as to capture the full picture of the evolution of the leakage rate. For example, the preset time series t j ={0, 250, 750, 1500, 3000} hours.

[0044] Step 108: Solve the multivariate regression model based on the accelerated test stress conditions and leakage rate value-test duration data set corresponding to all first test samples to obtain a leakage rate evolution model.

[0045] For example, during the accelerated stress test, the serial number of each first test sample and its corresponding accelerated test stress condition S should be recorded in detail. ak , leakage rate value-test duration data group constitutes the test data and serves as the input of the multiple regression model.

[0046] Specifically, the present invention proposes the following leakage rate evolution model to describe the leakage rate evolution of a single sample of the evaluated product under a constant accelerated stress S ak Under the following conditions, the evolution of leakage rate L with time t:

[0047] in, The initial value of the leakage rate of sample i is L0i, at a constant absolute temperature S TK (unit: K, Kelvin) and the internal apparent pressure S PK (Unit: Pa, Pascal) under the accelerated test stress conditions, the exposure time reaches t (unit: h, hour) The predicted leakage rate value (unit: Pa·m / s). L 0i is the initial value of the leakage rate measured for sample i at t=0 (Pa·m / s). This value is directly substituted into the multiple regression model as a known input, reflecting the differences in individual baseline performance. A is the basic degradation rate constant, and its physical meaning can be understood as the characteristic growth rate of the leakage rate value per unit time (if b=1) or time-related (if b≠1) under standard test stress conditions. Its unit needs to be adapted according to the units of other quantities in the model. For example, if t is measured in hours and b is dimensionless, the unit of A is Pa·m / s·h -b , is a parameter to be estimated. S PK is the internal apparent pressure applied in the accelerated stress test (Pa). PN is the reference pressure corresponding to the standard test stress condition, This item adopts the power-law relationship to describe the acceleration effect of pressure on the evolution rate of the leakage rate. m is the stress exponent corresponding to pressure (dimensionless), which reflects the sensitivity of the evolution rate of the leakage rate to pressure changes and is a parameter to be estimated. S TK is the absolute temperature (K) applied in the test. S TN is the reference temperature corresponding to the standard test stress condition. Ea is the apparent activation energy (unit: eV, electron volt), which represents the sensitivity of the evolution rate of the leakage rate to temperature changes and is related to the energy barrier of the physical and chemical processes dominating the degradation and is a parameter to be estimated. k B is the Boltzmann constant, and its value is 8.617333262×10 -5 eV / K. This item is the thermal acceleration factor based on the Arrhenius relationship, that is, the acceleration multiple of the evolution rate of the leakage rate by temperature relative to the reference temperature. is the cumulative exposure time (h) of the sample under the accelerated test stress condition. b is the time evolution exponent (dimensionless), which is the non-linear evolution form of the leakage rate increment with respect to time t. For example, if b = 1, the leakage rate increment increases linearly with time; if 0 < b < 1, the growth rate slows down with time; if b > 1, the growth rate accelerates with time. Usually b > 0 and it is a parameter to be estimated.

[0048] It should be noted that in the above leakage rate evolution model, there are a total of four unknown parameters in the multiple regression model solved through test data, that is, the parameter vector .

[0049] In an embodiment of this specification, based on the accelerated test stress conditions and leakage rate value - test duration data sets corresponding to all the first test samples respectively, the multiple regression model is solved to obtain the leakage rate evolution model, including: Based on the accelerated test stress conditions and leakage rate value - test duration data sets corresponding to all the first test samples respectively, the multiple regression model is preliminarily fitted to obtain a preliminary fitting model; Based on the preliminary fitting model and the leakage rate value - test duration data set corresponding to any one of the first test samples, a weight setting scheme is determined; Based on the accelerated test stress conditions and leakage rate value - test duration data sets corresponding to all the first test samples respectively and the weight setting scheme, the multiple regression model is weighted-fitted to obtain the leakage rate evolution model.

[0050] For illustrative purposes, since leak rate measurement errors are often not constant (i.e., they exhibit heteroscedasticity), large leak rate values ​​generally result in larger absolute or relative measurement error variances. To minimize the impact of high leak rate data points on the multivariate regression model's fit and to ensure a more uniform distribution of residuals, a weighting scheme is introduced. Common weighting methods are based on a functional relationship between the measurement error variance and the leak rate value.

[0051] For example, if it is assumed that the standard deviation of the measurement error is proportional to the leak rate value (ie, the relative error is approximately constant), the weight If we assume that the variance of the measurement error is proportional to the leak rate value, the weight can be set as If we assume that the measurement error is approximately constant standard deviation , then ordinary nonlinear least squares can be used (i.e., all weights are 1). The specific form of the weights is best determined by analyzing the residuals from a preliminary fit (e.g., first using ordinary least squares), and choosing a weighting scheme that makes the weighted residual distribution closest to the ideal state (e.g., independent and identically distributed, homogeneous variances). See the attached Figure 4 The standardized residual plot is shown in . ijk is the weight of the observation data of the i-th sample at the j-th time point under the k-th stress condition, L ijk is the leakage rate value of the observation data of the i-th sample at the j-th time point under the k-th stress condition.

[0052] Explanatory, incremental leak rate values , then the objective function becomes:

[0053] The weighted nonlinear least squares method is used to calculate the test data of all samples at all non-initial time points under all accelerated test stress conditions. Perform joint weighted fitting to obtain the leakage rate evolution model. The minimization objective when solving the multivariate regression model is the weighted residual sum of squares:

[0054] Step 110: Obtain a trajectory of the leakage rate evolution over time based on the leakage rate evolution model, the initial leakage rate information, and the standard test stress conditions.

[0055] As an illustration, the established and validated leak rate evolution model is extrapolated to the normal service stress conditions of a gas meter in actual use. By substituting the obtained parameter vector, the initial leak rate value determined in the initial leak rate information, and the standard test stress conditions as known quantities into the leak rate evolution model, a complete trajectory of the leak rate evolution over time is obtained.

[0056] In one embodiment of the present specification, the leakage rate evolution model includes a parameter vector and a point estimate and a covariance matrix of the parameter vector; Based on the leakage rate evolution model, initial leakage rate information and standard test stress conditions, the leakage rate evolution trajectory over time is obtained, including: A plurality of random initial leakage rate values ​​are obtained by Monte Carlo simulation based on the initial leakage rate distribution model; Based on the point estimate of the parameter vector and the covariance matrix, multiple sets of random parameter values ​​are obtained by Monte Carlo simulation; Based on standard test stress conditions, multiple random initial leakage rate values, and multiple sets of random parameter values, the leakage rate evolution model obtains multiple leakage rate evolution trajectories over time; Based on the evolution of the leakage rate over time and the reliability assessment criteria, the reliability assessment results of the evaluated product are output, including: The reliability evaluation results of the evaluated product are output based on multiple leakage rate evolution trajectories over time and reliability evaluation standards.

[0057] Explanatory, nonlinear optimization problems in solving multivariate regression models usually require the use of iterative algorithms (such as Gauss-Newton algorithm, etc.), which can be achieved through professional statistical analysis software. By fitting, the parameters Point estimate of and the (approximate) covariance matrix of these parameter estimates The covariance matrix is ​​used to account for uncertainty in parameter estimates in subsequent Monte Carlo simulations.

[0058] Illustratively, due to the uncertainty inherent in the estimation of model parameters caused by limited test data, coupled with the individual differences in the initial leakage rate values ​​of the evaluated products when they leave the factory, various sources of uncertainty are comprehensively considered through the Monte Carlo simulation technology, and the evolution trajectories of the leakage rates of a large number of virtual samples under standard test stress conditions over time are generated.

[0059] Specifically, to fully reflect the uncertainty of the prediction results, a large-scale Monte Carlo simulation is required (at least X = 10^6 simulation iterations). In each simulation iteration x (x from 1 to X), the following operations are performed: 1. According to the initial leakage rate distribution model, randomly generate an initial leakage rate value of a virtual sample .

[0060] 2. Based on the obtained point estimates of the leakage rate evolution model parameters and its (approximate) covariance matrix , assuming that these parameters (or their appropriate transformations, such as logarithmic transformations to ensure that the parameters are positive) follow a multivariate normal distribution , from which a set of parameter values ​​are randomly drawn for the virtual sample .

[0061] 3. According to and As well as the standard test stress conditions, the predicted leakage rate values ​​of the virtual sample at a series of time points under the standard test stress conditions are calculated according to the leakage rate evolution model, thereby obtaining a complete leakage rate evolution trajectory over time. .

[0062] After multiple simulation iterations, multiple leakage rate evolution trajectories over time can be obtained.

[0063] Step 112: Output the reliability evaluation result of the evaluated product based on the evolution trajectory of the leakage rate over time and the reliability evaluation standard.

[0064] In one embodiment of the present specification, the reliability evaluation criteria of the evaluated product include a leakage rate failure value, a target service time, and a target failure rate corresponding to the target service time.

[0065] Based on multiple leakage rate evolution trajectories over time and reliability assessment standards, the reliability assessment results of the evaluated product are output, including: Obtaining predicted failure times corresponding to the plurality of leakage rate evolution trajectories over time based on the plurality of leakage rate evolution trajectories over time and the leakage rate failure values; The predicted failure rate is obtained based on the predicted failure time and target service time corresponding to multiple leakage rate evolution trajectories over time; Output the reliability evaluation results of the evaluated product based on the predicted failure rate and target failure rate.

[0066] Descriptive, clearly specifying the target service life T of the product being evaluated G For example, for household gas meters, the common T G 10 or 15 years. According to the relevant enterprise standards, determine the leakage rate failure value L of airtight failure C When the leakage rate of the gas meter reaches or exceeds this threshold, it is determined that the gas meter is airtight. C May be set to 3.0×10 -7 Pa·m / s (under a test pressure of 45kPa). At the same time, set the target failure rate for this reliability assessment, for example, requiring the target service time to be = At the end of 15 years, the proportion of failed gas meters in the evaluated products to the total number of gas meters should not be greater than the target failure rate, for example, 1.0×10-4 .

[0067] Explanatory, by substituting the leakage rate failure value into the multiple leakage rate evolution trajectories obtained by Monte Carlo simulation over time, we can obtain the corresponding multiple predicted failure times. Among them, the gas meter with a service life shorter than the target is a premature failure. Then, the predicted failure rate can be calculated and compared with the target failure rate to output the reliability evaluation result of the evaluated product.

[0068] In one embodiment of the present specification, the predicted failure rate is obtained based on the predicted failure time and the target service time corresponding to each of the multiple leakage rate evolution trajectories over time, and then further includes: Based on the predicted failure time and target service time corresponding to multiple leakage rate evolution trajectories over time, a confidence interval of the predicted failure rate corresponding to a preset confidence level is obtained, and the confidence interval includes an upper limit value and a lower limit value of the interval; Output the reliability assessment results of the evaluated product based on the predicted failure rate and target failure rate, including: The reliability evaluation results of the evaluated product are output based on the predicted failure rate, target failure rate and the upper limit value of the confidence interval.

[0069] For explanatory purposes, the number of prematurely failed gas meters is recorded as , the predicted failure rate is recorded as The number of prematurely failed gas meters can be considered as obeying the parameter The observed value of a binomial random variable, so statistical methods can be used to calculate Confidence interval corresponding to a preset confidence level, such as a 95% two-sided confidence interval , is the lower limit of the interval, is the upper limit of the interval. Thus, the relationship between the confidence interval and the target failure rate is further considered to output the reliability assessment result.

[0070] In one embodiment of the present specification, outputting a reliability evaluation result of the evaluated product based on the predicted failure rate, the target failure rate, and the upper limit of the confidence interval includes: When the predicted failure rate is greater than the target failure rate, the reliability assessment result of the evaluated product is unreliable; When the predicted failure rate is less than or equal to the target failure rate and the upper limit of the confidence interval is greater than the target failure rate, the reliability assessment result of the evaluated product is not completely reliable; When the upper limit of the confidence interval is less than or equal to the target failure rate, the reliability assessment result of the evaluated product is completely reliable.

[0071] For example, the predicted failure rate calculated based on multiple leakage rate evolution trajectories over time and the upper limit of its 95% confidence interval are compared with the set target failure rate (e.g., ≤1.0×10 -4 ) for comparison.

[0072] If the predicted failure rate is ≤1.0×10 -4 , it can be determined (with a one-sided confidence level of 97.5%) that the evaluated product meets the long-term airtightness reliability requirements within the target service life.

[0073] If the predicted failure rate is ≤1.0×10 -4 But the upper limit of the interval is >1.0×10 -4 If the predictions meet the requirements, there is still a risk that the product will not meet the target due to uncertainty. Further analysis may be required, such as increasing the number of Monte Carlo simulations, improving the model, or collecting more experimental data to reduce the uncertainty of parameter estimates.

[0074] If the predicted failure rate is >1.0×10 -4 , it indicates that the evaluated product is likely to fail to meet the reliability requirements.

[0075] In one embodiment of the present specification, the final reliability assessment results may record and present in detail all key information of the entire assessment process, including but not limited to: the exact description of the assessment object, the set assessment objectives and failure criteria, the detailed accelerated test plan (including stress conditions, sample quantity, measurement plan, etc.), the collected original leakage rate data (or its summary statistics), the specific mathematical form of the adopted leakage rate evolution model, the estimation results of the model parameters (point estimates, standard errors or confidence intervals, covariance matrix, etc.), the detailed process and results of the model validation (such as charts, statistical test values, etc.), the predicted probability distribution diagram of the leakage rate evolving over time under normal service conditions, the predicted life (failure time) distribution diagram, the point estimate of the cumulative failure probability or reliable probability during the target service period and its confidence interval, as well as the final reliability determination conclusion and any necessary recommendations based on these results.

[0076] How the reliability assessment method works: Experimental preparation and data collection phase.

[0077] 1.1, clarify the evaluation standards and judgment criteria; 1.2, Design a multi-stress accelerated test plan; 1.3. Perform accelerated tests and periodically collect leakage rate degradation data.

[0078] Clarify the evaluation objectives, identify key accelerated stress condition factors, design a test plan that includes at least two different combinations of accelerated test stress conditions, and test representative samples under each accelerated stress condition. Periodically and accurately measure the initial leakage rate value L0 of each sample and the leakage rate value L at subsequent time points to obtain a degradation data set showing the evolution of leakage rate values ​​over time.

[0079] 2. Leakage rate evolution and acceleration integrated modeling, parameter estimation and verification stage.

[0080] 2.1, build leakage rate evolution model; 2.2, model parameter estimation; 2.3, Model validation.

[0081] Construct a model that can describe the leakage rate value L with time t, accelerated test stress conditions S (mainly temperature S T and pressure S P ) and a mathematical model for the evolution of the initial value L0 of the leakage rate. This model directly integrates the nonlinear time evolution characteristics of the leakage rate with the accelerating effect of the stress conditioning factor and takes into account the individual differences in the initial leakage rate value.

[0082] 3. Reliability prediction and evaluation stage.

[0083] 3.1, predict the probability distribution of the evolution trajectory of leakage rate over time under standard test stress conditions; 3.2, predict the probability distribution of airtightness failure time (i.e. product life); 3.3. Evaluate the reliability indicators within the target service time and make a final judgment.

[0084] The leakage rate evolution model is extrapolated to the standard test stress condition S n , and determine whether the product meets the long-term airtightness reliability requirements based on the reliability evaluation standards.

[0085] This solves the problem that the current gas meter accelerated test model is subject to the physical distortion of the single stress framework and the lack of representation of initial discreteness, which leads to a large deviation between the evaluation results and the actual service performance.

[0086] The foregoing description of this specification describes specific embodiments. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in an order different from that described in the embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order shown or the sequential order to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0087] See next Figure 4 , Figure 4 A schematic structural diagram of a gas meter seal long-term reliability evaluation system provided in an embodiment of this specification is shown.

[0088] The reliability evaluation system 200 includes: an acquisition unit 201, an initial leak rate testing unit 202, an accelerated leak rate testing unit 203, a model fitting unit 204, a leak rate evolution unit 205 and a result evaluation unit 206; An acquisition unit 201 acquires a reliability evaluation standard for the product being evaluated, multiple stress condition factors, and a multiple regression model. The multiple regression models for all first test samples use the initial leakage rate value, time, and each stress factor as independent variables, and the leakage rate value as a dependent variable. A set of standard test stress conditions and multiple sets of different accelerated test stress conditions are set based on the multiple stress condition factors. An initial leakage rate testing unit 202 is configured to obtain initial leakage rate information. The initial leakage rate information of all first test samples represents the initial sealing performance of the product being evaluated under standard test stress conditions. The accelerated leak rate testing unit 203 extracts, for each accelerated test stress condition, a plurality of first test samples corresponding to the first test sample from the sample library of the product being evaluated, and performs an accelerated stress test on each of the plurality of first test samples under each accelerated test stress condition. Under the standard test stress condition, the unit measures a plurality of leakage rate values ​​corresponding to each of the first test samples for different durations of the accelerated stress test to obtain a leakage rate value-test duration data set corresponding to the first test sample. This step is repeated until a leakage rate value-test duration data set corresponding to all first test samples is obtained, wherein all leakage rate value-test duration data sets for the first test samples include an initial leakage rate value when the duration of the accelerated stress test is 0. The model fitting unit 204 solves a multivariate regression model based on the accelerated test stress conditions and the leakage rate value-test duration data set corresponding to all first test samples to obtain a leakage rate evolution model; The leakage rate evolution unit 205 obtains the evolution trajectory of the leakage rate over time based on the leakage rate evolution model, the initial leakage rate information and the standard test stress condition; The result evaluation unit 206 outputs a reliability evaluation result of the evaluated product based on the evolution trajectory of the leakage rate over time and the reliability evaluation standard.

[0089] The various embodiments in this specification are described in a progressive manner. Similar portions between the various embodiments can be referenced to each other. Each embodiment focuses on the differences from the other embodiments. In particular, the reliability assessment system embodiment is generally similar to the reliability assessment method embodiment, so its description is relatively simple. For relevant portions, refer to the description of the reliability assessment method embodiment.

[0090] See also Figure 5 A schematic structural diagram of an electronic device provided in an embodiment of this specification is shown.

[0091] like Figure 5 As shown, the electronic device 300 may include: at least one processor 301 , at least one network interface 303 , a user interface 304 , a memory 305 and at least one communication bus 302 .

[0092] The communication bus 302 may be used to implement the connection and communication between the above components.

[0093] The user interface 304 may include buttons, and the optional user interface may also include a standard wired interface or a wireless interface.

[0094] The network interface 303 may include, but is not limited to, a Bluetooth module, an NFC module, a Wi-Fi module, and the like.

[0095] Among them, the processor 301 may include one or more processing cores. The processor 301 uses various interfaces and lines to connect the various parts of the entire electronic device 300, and executes various functions of the electronic device 300 and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 305, and calling data stored in the memory 305. Optionally, the processor 301 can be implemented in at least one hardware form of DSP, FPGA, and PLC. The processor 301 can integrate one or a combination of CPU, GPU, and modem. Among them, the CPU mainly processes the operating system, user interface, and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; and the modem is used to handle wireless communications. It is understandable that the above-mentioned modem may not be integrated into the processor 301, but may be implemented separately through a chip.

[0096] Memory 305 may include either RAM or ROM. Optionally, memory 305 may include non-transitory computer-readable media. Memory 305 may be used to store instructions, programs, codes, code sets, or instruction sets. Memory 305 may include a program storage area and a data storage area. The program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, sound playback function, image playback function, etc.), instructions for implementing the aforementioned method embodiments, etc.; the data storage area may store data related to the aforementioned method embodiments, etc. Memory 305 may also optionally be at least one storage device located remotely from the processor 301. Memory 305, as a computer storage medium, may include an operating system, a network communication module, a user interface module, and a reliability assessment application. Processor 301 may be configured to invoke the reliability assessment application stored in memory 305 and execute the steps of the reliability assessment method described in the aforementioned embodiments.

[0097] The embodiments of this specification also provide a computer-readable storage medium containing instructions that, when executed on a computer or processor, cause the computer or processor to perform one or more steps of the reliability assessment method embodiments described above. If the components of the electronic device described above are implemented as software functional units and sold or used as independent products, they may be stored in a computer-readable storage medium.

[0098] In the above embodiments, all or part of the embodiments may be implemented using software, hardware, firmware, or any combination thereof. When implemented using software, all or part of the embodiments may be implemented in the form of a computer program product. A computer program product comprises one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions according to the embodiments of this specification are generated. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted via a computer-readable storage medium. The computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, optical fiber, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server or data center that includes one or more available media. Available media may be magnetic media (eg, floppy disks, hard disks, magnetic tapes), optical media (eg, digital versatile discs (DVDs)), or semiconductor media (eg, solid state disks (SSDs)).

[0099] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing the relevant hardware through a computer program. The program can be stored in a computer-readable storage medium. When executed, the program can include the processes of the above-described method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks. The technical features of this embodiment and the implementation scheme can be combined in any manner unless they conflict.

[0100] The above embodiments are merely preferred embodiments of this specification and are not intended to limit the scope of this specification. Without departing from the design spirit of this specification, various modifications and improvements made to the technical solutions of this specification by ordinary technicians in this field should fall within the scope of protection determined by the claims of this specification.

Claims

1. A method for evaluating the long-term reliability of gas meter seals, characterized in that: The following steps are involved: Obtaining a reliability evaluation standard for the product to be evaluated, a plurality of stress condition factors, and a multiple regression model, wherein the multiple regression model uses an initial value of the leakage rate, time, and each stress factor as independent variables and a leakage rate value as a dependent variable; A set of standard test stress conditions and multiple sets of different accelerated test stress conditions are set based on multiple stress condition factors; Obtaining initial leakage rate information, wherein the initial leakage rate information represents the initial sealing performance of the evaluated product under standard test stress conditions; For each accelerated test stress condition, a plurality of first test samples corresponding to the first test condition are extracted from the sample library of the product being evaluated, and accelerated stress tests are performed on each of the corresponding first test samples under each accelerated test stress condition; Under standard test stress conditions, measuring multiple leakage rate values ​​of any first test sample corresponding to different durations of the accelerated stress test to obtain a leakage rate value-test duration data set corresponding to the first test sample, repeating this step until a leakage rate value-test duration data set corresponding to all first test samples is obtained, wherein the leakage rate value-test duration data set includes an initial leakage rate value when the duration of the accelerated stress test is 0; Solving a multivariate regression model based on the accelerated test stress conditions and leakage rate value-test duration data set corresponding to all first test samples to obtain a leakage rate evolution model; The evolution trajectory of leakage rate over time is obtained based on the leakage rate evolution model, initial leakage rate information and standard test stress conditions; The reliability evaluation results of the evaluated product are output based on the evolution trajectory of the leakage rate over time and the reliability evaluation standard.

2. A method for evaluating the long-term reliability of a gas meter seal according to claim 1, characterized in that: The obtaining of initial leakage rate information includes: extracting a plurality of second test samples of the product to be evaluated from a sample library of the product to be evaluated; For each second test sample, measure its corresponding leakage rate value under standard test stress conditions; Distribution fitting is performed based on the leakage rate value corresponding to each second test sample to obtain an initial leakage rate distribution model as the overall initial leakage rate information.

3. A method for evaluating the long-term reliability of a gas meter seal according to claim 2, characterized in that: The leakage rate evolution model includes a parameter vector and a point estimate and a covariance matrix of the parameter vector; The leak rate evolution trajectory over time obtained based on the leak rate evolution model, the initial leak rate information and the standard test stress conditions includes: A plurality of random initial leakage rate values ​​are obtained by Monte Carlo simulation based on the initial leakage rate distribution model; Based on the point estimate of the parameter vector and the covariance matrix, multiple sets of random parameter values ​​are obtained by Monte Carlo simulation; Based on standard test stress conditions, multiple random initial leakage rate values, and multiple sets of random parameter values, the leakage rate evolution model obtains multiple leakage rate evolution trajectories over time; The reliability evaluation results of the evaluated product are output based on the evolution trajectory of the leakage rate over time and the reliability evaluation standard, including: The reliability evaluation results of the evaluated product are output based on multiple leakage rate evolution trajectories over time and reliability evaluation standards.

4. A method for evaluating the long-term reliability of a gas meter seal according to claim 3, characterized in that: The reliability evaluation criteria of the evaluated product include leakage rate failure value, target service time and target failure rate corresponding to the target service time; The reliability evaluation results of the evaluated product are output based on the multiple leakage rate evolution trajectories over time and the reliability evaluation standards, including: Obtaining predicted failure times corresponding to the plurality of leakage rate evolution trajectories over time based on the plurality of leakage rate evolution trajectories over time and the leakage rate failure values; The predicted failure rate is obtained based on the predicted failure time and target service time corresponding to multiple leakage rate evolution trajectories over time; Output the reliability evaluation results of the evaluated product based on the predicted failure rate and target failure rate.

5. A method for evaluating the long-term reliability of a gas meter seal according to claim 4, characterized in that: The method further includes obtaining the predicted failure rate based on the predicted failure time and target service time corresponding to each of the multiple leakage rate evolution trajectories over time: Based on the predicted failure time and the target service time corresponding to each of the multiple leakage rate evolution trajectories over time, a confidence interval corresponding to a preset confidence level of the predicted failure rate is obtained, wherein the confidence interval includes an upper limit value and a lower limit value of the interval; Outputting the reliability evaluation result of the evaluated product based on the predicted failure rate and the target failure rate includes: The reliability evaluation results of the evaluated product are output based on the predicted failure rate, target failure rate and the upper limit value of the confidence interval.

6. A method for evaluating the long-term reliability of gas meter seal according to claim 5, characterized in that: The outputting of the reliability evaluation result of the evaluated product based on the predicted failure rate, the target failure rate and the upper limit value of the confidence interval includes: When the predicted failure rate is greater than the target failure rate, the reliability assessment result of the evaluated product is unreliable; When the predicted failure rate is less than or equal to the target failure rate and the upper limit of the confidence interval is greater than the target failure rate, the reliability assessment result of the evaluated product is not completely reliable; When the upper limit of the confidence interval is less than or equal to the target failure rate, the reliability assessment result of the evaluated product is completely reliable.

7. A method for evaluating the long-term reliability of a gas meter seal according to claim 1, characterized in that: Solving the multivariate regression model based on the accelerated test stress conditions and leakage rate value-test duration data set corresponding to all first test samples to obtain a leakage rate evolution model includes: Performing a preliminary fitting of a multiple regression model based on the accelerated test stress conditions and leakage rate value-test duration data sets corresponding to all first test samples to obtain a preliminary fitting model; Determine a weight setting scheme based on the preliminary fitting model and a leakage rate value-test duration data set corresponding to any first test sample; Based on the accelerated test stress conditions and leakage rate value-test duration data sets corresponding to all first test samples and the weight setting scheme, a multivariate regression model is weightedly fitted to obtain a leakage rate evolution model.

8. A method for evaluating the long-term reliability of a gas meter seal according to claim 1, characterized in that: The method of measuring a plurality of leakage rate values ​​of different durations of the accelerated stress test for any first test sample under standard test stress conditions to obtain a leakage rate value-test duration data set corresponding to the first test sample includes: Acquiring a preset time series including a plurality of measurement time nodes corresponding to an early stage, a middle stage, and a late stage of a degradation process during an accelerated stress test on a first test sample; Under standard test stress conditions, multiple leakage rate values ​​corresponding to the duration of the accelerated stress test of any first test sample are measured at all measurement time nodes to obtain a leakage rate value-test duration data set corresponding to the first test sample.

9. A gas meter seal long-term reliability assessment system, characterized by: It includes an acquisition unit, an initial leak rate test unit, an accelerated leak rate test unit, a model fitting unit, a leak rate evolution unit and a result evaluation unit; The acquisition unit acquires a reliability evaluation standard of the product to be evaluated, a plurality of stress condition factors, and a multiple regression model, wherein the multiple regression model uses the initial value of the leakage rate, time, and each stress factor as independent variables and the leakage rate value as a dependent variable; and sets a set of standard test stress conditions and a plurality of different accelerated test stress conditions based on the plurality of stress condition factors; The initial leakage rate testing unit acquires initial leakage rate information, wherein the initial leakage rate information represents the initial sealing performance of the product being evaluated under standard test stress conditions; The accelerated leak rate testing unit extracts, for each accelerated test stress condition, a plurality of first test samples corresponding to the product being evaluated from a sample library, and performs an accelerated stress test on each of the plurality of first test samples under each accelerated test stress condition; under the standard test stress condition, measures a plurality of leakage rate values ​​corresponding to different durations of the accelerated stress test on any first test sample to obtain a leakage rate value-test duration data set corresponding to the first test sample, and repeats this step until a leakage rate value-test duration data set corresponding to all first test samples is obtained, wherein the leakage rate value-test duration data set includes an initial leakage rate value when the duration of the accelerated stress test is 0; The model fitting unit solves a multivariate regression model based on the accelerated test stress conditions and leakage rate value-test duration data sets corresponding to all first test samples to obtain a leakage rate evolution model; The leak rate evolution unit obtains the evolution trajectory of the leak rate over time based on the leak rate evolution model, the initial leak rate information and the standard test stress condition; The result evaluation unit outputs a reliability evaluation result of the evaluated product based on the evolution trajectory of the leakage rate over time and the reliability evaluation standard.

10. A computer-readable storage medium having a computer program stored thereon, wherein the computer-readable storage medium stores instructions, which, when the instructions are executed on a computer or a processor, cause the computer or processor to execute the steps of the method according to any one of claims 1 to 8.

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