Accelerated extrapolation evaluation method for service life of optical element

Abstract

The invention provides an accelerated extrapolation evaluation method for the service life of an optical element. The accelerated extrapolation evaluation method comprises the steps that the number of irradiation times when the transmittance of the optical element is degraded to a preset threshold value under different stresses is counted; obtaining the characteristic life of the optical element under each stress based on the two-parameter Weber distribution function; fitting a composite acceleration model; establishing a thermal-mechanical coupling simulation model of the optical element; forming a dynamic data set based on a preset stress step acceleration experiment; and determining the predicted service life of the optical element under the normal stress through a self-adaptive stress compression extrapolation algorithm. According to the method, the composite acceleration model is constructed on the basis of combination of actual data statistical modeling and physical simulation, so that the adaptability and prediction accuracy of the model under complex and changeable working conditions are greatly improved, and the problem of misalignment under the complex working conditions can be solved; the self-adaptive stress compression extrapolation algorithm is combined, key degradation data are obtained in an extremely short time, the experiment process is remarkably compressed, and accurate data support is provided for life prediction iteration while resource consumption is reduced.

Description

Technical Field

[0001] This disclosure relates to the field of optical element technology, and in particular to a method, apparatus, storage medium and electronic device for accelerating extrapolation evaluation of the service life of optical elements. Background Technology

[0002] Optical components play a crucial role in precision instruments, industrial lasers, and aerospace equipment; their performance degradation directly impacts system accuracy and reliability. Lifespan prediction can anticipate component failure points, preventing safety accidents and economic losses caused by sudden malfunctions. Especially in high-cost fields such as medical equipment and space exploration, accurate lifespan assessment can significantly reduce maintenance costs and extend equipment service life. Therefore, developing a scientific lifespan assessment system is of great significance for ensuring the stable operation of critical equipment.

[0003] Current optical component lifetime prediction technology faces three major bottlenecks: First, the failure mechanisms are complex. Optical components (such as lenses and mirrors) are often subjected to harsh environments involving the coupling of multiple physical fields, including heat, force, light, and chemistry, during actual service. Their damage mechanisms (such as thermal stress cracking, film ablation, and defect-induced damage) are intertwined, making it difficult to accurately describe and predict them using a single physical model. Second, the verification cycle is lengthy. Traditional lifetime assessments rely on long-term field testing or full-lifecycle simulation testing, often requiring thousands or even tens of thousands of hours, which is completely unsuitable for the market demands of rapid product iteration and development. Third, the lengthy testing cycle consumes a large amount of manpower, material resources, and expensive experimental resources, significantly increasing R&D costs and hindering the verification and application of new materials and processes. Existing methods mainly rely on accelerated aging experiments combined with statistical models for lifetime extrapolation, but this method has inherent flaws: the accelerated stress conditions (such as high temperature and high laser power) differ significantly from the actual service environment of the components, leading to activated failure mechanisms that do not match the actual situation, thus causing serious biases in the extrapolation model.

[0004] Insufficient mechanistic research results in predictive models lacking universality, requiring repeated experiments under different operating conditions, which significantly hinders R&D progress. The time-consuming and costly testing process further restricts component design optimization—engineers struggle to quickly verify the durability of new materials or structures, leading to extended product upgrade cycles. More importantly, the uncertainty of prediction results forces system designs to include excessive safety margins, resulting in wasted performance or compromised reliability, creating a vicious cycle in the "R&D-verification-optimization" chain. Summary of the Invention

[0005] The purpose of this disclosure is to provide an accelerated extrapolation evaluation method, apparatus, storage medium, and electronic device for the service life of optical components, in order to solve the problems of complex failure mechanisms, long verification cycles, and poor adaptability of conventional models to complex and variable operating conditions in the prior art.

[0006] The embodiments of this disclosure adopt the following technical solution: an accelerated extrapolation evaluation method for the service life of an optical element, comprising: collecting the damage morphology and microstructure changes of the optical element under high-energy laser irradiation, and determining the damage mechanism of the optical element; counting the number of irradiations when the transmittance of the optical element degrades to a preset threshold under different stresses, wherein the stress is formed based on different laser irradiation conditions, including power density, pulse width, and frequency; fitting the number of irradiations under different stresses based on a two-parameter Weiber distribution function to obtain the characteristic lifetime of the optical element under each stress; fitting a composite acceleration model based on the characteristic lifetime of the optical element under each stress, wherein the composite acceleration model is used to characterize the acceleration effect of temperature field and stress field on the characteristic lifetime; establishing a thermo-mechanical coupling simulation model of the optical element based on the composite acceleration model; obtaining the temperature field simulation results output by the thermo-mechanical coupling simulation model and the transmittance degradation data of the optical element based on a preset stress step acceleration experiment, forming a dynamic dataset; and determining the predicted lifetime of the optical element under constant stress using an adaptive stress compression extrapolation algorithm based on the dynamic dataset.

[0007] This disclosure also provides an accelerated extrapolation evaluation device for the service life of optical components, comprising: a damage mechanism determination module, used to collect the damage morphology and microstructure changes of the optical component under high-energy laser irradiation, and determine the damage mechanism of the optical component; a statistics module, used to count the number of irradiations when the transmittance degradation of the optical component under different stresses is limited to a preset threshold, wherein the stress is formed based on different laser irradiation conditions, including power density, pulse width, and frequency; a function fitting module, used to fit the number of irradiations under different stresses based on a two-parameter Weiber distribution function to obtain the characteristic lifetime of the optical component under each stress; and an accelerated model generation module. The system comprises the following modules: a module for fitting a composite acceleration model based on the characteristic lifetime of the optical element under each stress, wherein the composite acceleration model characterizes the acceleration effect of temperature and stress fields on the characteristic lifetime; a simulation model construction module for establishing a thermo-mechanical coupling simulation model of the optical element based on the composite acceleration model; a comparative experiment module for obtaining the temperature field simulation results output by the thermo-mechanical coupling simulation model and the transmittance degradation data of the optical element based on a preset stress step acceleration experiment, forming a dynamic dataset; and a prediction module for determining the predicted lifetime of the optical element under constant stress using an adaptive stress compression extrapolation algorithm based on the dynamic dataset.

[0008] This disclosure also provides a storage medium storing a computer program, which, when executed by a processor, implements the steps of the above-described accelerated extrapolation evaluation method for the service life of optical components.

[0009] This disclosure also provides an electronic device, including at least a memory and a processor. The memory stores a computer program, and the processor, when executing the computer program in the memory, implements the steps of the above-described accelerated extrapolation evaluation method for the service life of optical components.

[0010] The beneficial effects of the embodiments disclosed herein are as follows: Based on the combination of statistical modeling of actual data and physical simulation, a composite acceleration model is constructed, which greatly improves the adaptability and prediction accuracy of the model under complex and variable working conditions, can solve the inaccuracy problem under complex working conditions, and has wide applicability; at the same time, combined with the adaptive stress compression extrapolation algorithm, it can overcome the problem of long verification cycle of the original life acceleration experiment, obtain key degradation data in a very short time, significantly compress the experimental process, and provide accurate data support for life prediction iteration while reducing resource consumption. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in one or more embodiments of this specification or in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this specification. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 This is a flowchart of the accelerated extrapolation evaluation method for the service life of optical components in the first embodiment of this disclosure; Figure 2 This is a schematic diagram of real-time thermal effect monitoring during irradiation in the first embodiment of this disclosure; Figure 3 This is a schematic diagram of the global sensitivity results in the first embodiment of this disclosure; Figure 4 This is a schematic diagram of the structure of the accelerated extrapolation evaluation device for the service life of optical elements in the second embodiment of this disclosure. Detailed Implementation

[0013] To enable those skilled in the art to better understand the technical solutions in one or more embodiments of this specification, the technical solutions in one or more embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this specification, and not all of the embodiments. Based on one or more embodiments of this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of this document.

[0014] To address the challenges of complex failure mechanisms, long verification cycles, and poor adaptability of conventional models to complex and variable operating conditions in optical components, the first embodiment of this disclosure provides an accelerated extrapolation evaluation method for the service life of optical components, the flowchart of which is shown below. Figure 1 As shown, it mainly includes steps S100 to S700: S100 collects the damage morphology and microstructure changes of optical components under high-energy laser irradiation and determines the damage mechanism of the optical components.

[0015] First, multi-scale observation equipment such as laser confocal microscopy and scanning electron microscopy (SEM) are used to collect the damage morphology of the optical component under accelerated stress, including high-energy laser irradiation. Then, combined with EDS energy dispersive spectroscopy analysis, the material element migration and subtle structural changes such as defects in failure characteristic regions such as cracks / ablation are located. Finally, based on deformation laws and microstructural evolution, combined with failure physics principles, the damage mechanism dominated by thermo-mechanical coupling (such as thermal stress fracture and coating peeling) is summarized. This embodiment ensures that the degradation mode induced by subsequent accelerated testing is consistent with the actual service failure mode of the original component by clarifying the damage mechanism, fundamentally guaranteeing the effectiveness of accelerated testing and the reliability of extrapolation, and preventing invalid life prediction conclusions due to inconsistent mechanisms.

[0016] S200 is the number of times an optical element is irradiated when its transmittance degrades to a preset threshold under different stresses.

[0017] In this embodiment, the transmittance of the optical element being predicted is selected as the core degradation index. A high-precision spectrometer and a high-speed data acquisition system are used to dynamically monitor the real-time transmittance data of the optical element under different stress conditions. The number of laser irradiations required for the transmittance of the optical element to degrade to a preset threshold is also counted. Given the laser pulse width and frequency, the corresponding relationship between the optical element and the number of irradiations (or time) can be obtained, thus characterizing the performance degradation of the optical element. In this embodiment, different stress conditions and corresponding laser irradiation conditions, including but not limited to power density, pulse width, and frequency, and different combinations of laser parameters constitute different stress levels, used to simulate and accelerate the different operating conditions that the optical element may experience in actual use.

[0018] In practical studies on the effects of specific laser irradiation conditions, the controlled variable method must be followed. When testing the same sample, other laser irradiation conditions should be kept constant. Finally, based on actual experimental requirements, sets of irradiation times under different stresses are obtained. After removing environmental noise interference, a degradation dataset is formed.

[0019] S300, based on the two-parameter Weiber distribution function, fits the number of irradiations under different stresses to obtain the characteristic lifetime of the optical element under each stress.

[0020] The number of irradiation cycles was grouped according to different stresses, and a nonlinear regression fitting was performed using a two-parameter Weibull distribution function to determine the distribution of failure rates corresponding to performance degradation (i.e., transmittance decreasing to a preset threshold) of optical elements under a certain fixed stress condition. The expression for the two-parameter Weibull distribution function is as follows:

[0021] in, This represents the cumulative probability of failure, which can be specifically understood as the probability of failure of an optical element after a certain number (or time) of irradiation, under constant stress conditions. The probability of a previous failure (i.e., transmittance degrading to a preset threshold). This indicates the time, which is actually positively correlated with the number of irradiations. Shape parameters characterize failure modes; typically, shape parameters... 1. This characterizes the failure as being caused by normal performance degradation rather than a quality issue, thus making the model predictions more meaningful. The scale parameter characterizes the characteristic lifetime. The adaptability of the distribution function under different stress levels is verified based on grouped data. The goodness of fit between the two-parameter Weibull distribution function and the experimental data points is considered. If the match is low, it indicates that the two are poorly matched and need to be refitted in formula (1). and For, until .

[0022] S400 fits a composite acceleration model based on the characteristic lifetime of optical elements under each stress.

[0023] Characteristic lifetime obtained by fitting the Weibull distribution function A mathematical mapping between the model and different stress conditions is established, and a composite acceleration model is fitted to characterize the acceleration effect of temperature and stress fields on the characteristic lifetime. This embodiment introduces the Arrhenius-inverse power-law composite model as the composite acceleration model, which is constructed based on the following formula:

[0024] in, Indicates characteristic lifetime, Indicates stress, Indicates temperature. Boltzmann's constant, As an acceleration factor, For activation energy, , All of these are fitting constants.

[0025] Based on the composite acceleration model, a thermal coupling simulation model of optical components is established for S500.

[0026] A thermo-mechanical coupling simulation model of an optical component under multiple stress fields was established using finite element analysis software to simulate its temperature and stress field distribution under high-speed laser irradiation. In the actual model construction, the model was built based on the optical component's structure and material properties, and the temperature and stress fields were mapped in the composite acceleration model to input the model's characteristics.

[0027] After the simulation model is built, under the same irradiation conditions, the relative deviation between the simulated transient temperature and simulated stress field distribution output by the thermo-coupling simulation model and the measured transient temperature and measured stress field distribution of the optical element can be determined to verify and correct the composite acceleration model. If the relative deviation is less than or equal to the first threshold (e.g., 10%), it means that the composite acceleration model has been verified and can correctly reflect the coupling acceleration effect of the optical element. If the relative deviation is greater than the first threshold, it is necessary to refit the parameters of the composite acceleration model or optimize the material constitutive relationship until the relative deviation meets the requirements.

[0028] The S600, based on a pre-set stress step acceleration experiment, obtains the temperature field simulation results output by the thermo-coupling simulation model, as well as the transmittance degradation data of optical elements, forming a dynamic dataset.

[0029] Based on the D-optimal design principle, a pre-set stress-step acceleration experiment is designed, for example, using a stepwise increase in laser power density as the experimental condition, starting from... Beginning, with The step size was increased incrementally, with each step lasting 5 hours. Under the above experimental conditions, irradiation experiments were conducted on the optical element, and its transmittance degradation data were collected simultaneously. The same experimental conditions were simulated using a thermo-mechanical coupling simulation model established by S500, and the data on the changes of theoretical temperature field and stress field over time were calculated to construct a dynamic dataset for extrapolation.

[0030] S700, based on a dynamic dataset, uses an adaptive stress compression extrapolation algorithm to determine the predicted lifetime of optical components under constant stress.

[0031] Based on the dynamic dataset, an adaptive stress compression extrapolation algorithm is constructed. The short-cycle high-stress data collected in step S600 is used as input. The lifespan under normal stress is inferred by damage capacity. At the same time, a temperature correction aging factor is introduced to compensate for the nonlinear degradation caused by the temperature field, so as to obtain the predicted lifespan of the optical element under normal working conditions.

[0032] Specifically, the composite acceleration model quantifies the accelerating effect of stress level on lifetime. Based on the transmittance degradation data obtained from the high-stress stepped accelerated test, the composite acceleration model can be used to determine the calculated lifetime of the optical element under constant stress conditions when it reaches the same degree of transmittance degradation as the stepped test data. Meanwhile, the temperature correction aging factor is designed as follows:

[0033] in, This represents the temperature-corrected aging factor, used to quantify the aging process at a specific temperature. and irradiation time Below, the degree of performance degradation caused by thermal effects, and This represents the fitting parameters related to the optical component material. This represents the recovery activation energy. In this embodiment, the simulation results of the temperature field under high stress are combined with the simulation model. The temperature correction aging factor is used to compensate for the accelerated effect of the temperature field generated under high stress on the lifetime, and the compensated lifetime is obtained. Finally, based on the calculated lifetime obtained based on the composite acceleration model and the compensated lifetime obtained based on the temperature correction aging factor, the predicted lifetime of the optical element under constant stress is determined.

[0034] It is important to note that the extrapolated predicted lifespan can be verified by comparing the predicted lifespan distribution with the actual measured lifespan using the Kolmogorov-Smirnov (KS) test to obtain a standard statistic D. If the statistic is less than the second threshold (usually D < 0.05 at a 95% confidence level), the verification is successful; otherwise, the fitting parameters of the composite acceleration model and / or the fitting parameters of the temperature-corrected aging factor are iteratively corrected based on the bias characteristics of the statistic. Specifically, if the confidence interval of the extrapolation result is too wide, the fitting parameters of the temperature-corrected aging factor are corrected first. If the KS test still fails after correction, it indicates that the root cause of the bias is not in the temperature-corrected aging factor, and the composite acceleration model, and even the distribution function in step S300, are then corrected accordingly until the KS test passes.

[0035] This embodiment combines statistical modeling based on actual data with physical simulation to construct a composite acceleration model, which greatly improves the model's adaptability and prediction accuracy under complex and variable working conditions. It can solve the inaccuracy problem under complex working conditions and has wide applicability. At the same time, combined with the adaptive stress compression extrapolation algorithm, it can overcome the problem of long verification cycle in the original life acceleration experiment, obtain key degradation data in a very short time, significantly compress the experimental process, and provide accurate data support for life prediction iteration while reducing resource consumption.

[0036] In some embodiments, a global sensitivity analysis algorithm can also be used to determine the contribution of each design parameter of the optical element to the predicted lifetime. The design parameters include, but are not limited to, coating thickness, substrate curvature, material purity, surface roughness, ambient temperature, etc. For design parameters whose contribution is greater than a third threshold, they are identified as key parameters affecting the predicted lifetime of the optical element. The third threshold is determined based on the actual sensitivity analysis results, for example, it can be set to 0.8. When optimizing the design of the optical element in the future, this key parameter is used as the main direction of design optimization to drive targeted improvements to the element structure and start the next round of accelerated verification, forming a closed loop of "prediction-optimization-re-verification".

[0037] The following example uses fused silica samples, combined with Figure 2 and Figure 3 The component lifetime prediction process is described in detail below: S1. The fused silica sample to be tested was placed on an accelerated experimental platform and subjected to high power density laser irradiation. The surface microcrack defects were observed to be significantly evolved by SEM, AFM and other methods. Combined with the 4% increase in EDS oxygen content, it was determined that the sample under accelerated conditions was thermal damage induced by air pollutants.

[0038] S2, selects transmittance as the core degradation index, and monitors different laser power densities in real time. The transmittance variation of the sample was analyzed, and the data was smoothed and filtered to create a degraded dataset. A typical data example is shown below: Under stress, irradiation approximately After that, the sample transmittance decreased from 99.5% to 95%, while... Under stress, approximately [amount] irradiation is required to achieve the same degree of degradation. Second-rate.

[0039] S3, group the degradation data at different stress levels from step 2, and fit them using a two-parameter Weibull distribution. Taking the data set as an example, the shape parameters are obtained through fitting. Scale parameters Next, goodness of fit It meets the requirement (>0.9).

[0040] S4, based on the characteristic lifetime obtained in step 3 A mapping relationship between the laser power density and the laser power density was established. Using the Arrhenius-inverse power-law composite model and substituting experimental data, the model parameters were determined through multivariate nonlinear regression. This model quantifies the accelerating effect of stress level on lifespan and predicts lifespan under constant stress. The lifespan is approximately Secondary irradiation.

[0041] S5. A thermo-mechanical coupling model of the optical component was established using COMSOL Multiphysics. Laser parameters and material properties were input, and the transient temperature and stress field distributions under laser irradiation were simulated. The simulation results showed a maximum surface temperature rise of approximately 550 K and a peak thermal stress of approximately 110 MPa. Compared with measured data from an infrared thermal imager and strain gauges, the relative deviation was <9%, which is within an acceptable range (<10%). The model was validated. Figure 2 As shown.

[0042] S6, based on the D-optimal design principle, implements a stepped acceleration experiment: laser power density from Beginning, with The step size was increased incrementally, with each step lasting 5 hours. During this process, transmittance data was acquired at high speed (sampling rate 1kHz) and fused with simulation data to construct a dynamic dataset for extrapolation.

[0043] S7, based on the short-cycle (approximately 50 hours in total) high-stress data obtained in step 6, applies the constructed adaptive stress compression extrapolation algorithm to inversely deduce the constant stress ( The lifespan under the given conditions is calculated. A temperature-corrected aging factor is introduced to compensate for nonlinear effects, and the predicted lifespan is... Sub-irradiation, 95% confidence interval is .

[0044] S8. Perform a KS test on the extrapolation results from step 7. The calculated statistic D = 0.032, which is less than the critical value of 0.05 (95% confidence level), indicating that the predicted lifespan distribution is not significantly different from the model's expectations, thus passing the validation.

[0045] S9. A global sensitivity analysis was performed using the Sobol method to evaluate the contribution of component design parameters (such as coating thickness and substrate curvature radius) to the lifetime prediction results. The results are as follows: Figure 3 As shown in the figure. Analysis indicates that the sensitivity index of coating thickness is 0.88 (>0.8), which is identified as the most critical parameter and the primary direction for subsequent structural optimization.

[0046] S10, based on the output of step 9, optimize the coating thickness from the initial 50nm to 80nm. After fabricating a new sample, perform accelerated verification testing under the same conditions. ,Grand total (After several iterations), its transmittance only dropped to 98.2%, which is better than the 95% before optimization, proving that the optimization is effective, thus forming a closed-loop design process of "prediction-optimization-re-verification".

[0047] Compared to long-cycle research, this embodiment significantly reduces resource consumption through extrapolation, facilitates automated data analysis, and lowers the barrier to entry for product lifecycle research. Furthermore, compared to traditional methods, it abandons reliance on empirical formulas or single models, instead employing a combination of statistical modeling based on actual data and physical simulation to construct a composite accelerated model. This greatly improves the model's adaptability and predictive accuracy under complex and variable operating conditions, addressing inaccuracies in complex environments and demonstrating broad applicability. Predictions achieved through short-term testing also provide an early warning system for optical component lifecycles, supporting component screening. A simplified accelerated testing process can quickly identify batches of components with potential defects or insufficient durability, enabling early intervention and quality control.

[0048] Based on the same inventive concept, the second embodiment of this disclosure provides an accelerated extrapolation evaluation device for the service life of optical components, the structural schematic of which is shown below. Figure 4 As shown, it mainly includes: The damage mechanism determination module 10 is used to collect the damage morphology and microstructure changes of optical components under high-energy laser irradiation and determine the damage mechanism of the optical components; the statistics module 20 is used to count the number of irradiations when the transmittance degradation of the optical components under different stresses is limited to a preset threshold, where the stress is formed based on different laser irradiation conditions, including power density, pulse width, and frequency; the function fitting module 30 is used to fit the number of irradiations under different stresses based on a two-parameter Weiber distribution function to obtain the characteristic lifetime of the optical components under each stress; the accelerated model generation module 40 is used to generate models based on the optical components. For each stress-induced characteristic lifetime, a composite acceleration model is fitted. The composite acceleration model is used to characterize the acceleration effect of temperature and stress fields on the characteristic lifetime. The simulation model construction module 50 is used to establish a thermo-mechanical coupling simulation model of the optical element based on the composite acceleration model. The comparative experiment module 60 is used to obtain the temperature field simulation results output by the thermo-mechanical coupling simulation model and the transmittance degradation data of the optical element based on the preset stress step acceleration experiment, forming a dynamic dataset. The prediction module 70 is used to determine the predicted lifetime of the optical element under constant stress based on the dynamic dataset through an adaptive stress compression extrapolation algorithm.

[0049] The specific functions implemented by each module in this embodiment and the principle of achieving component lifetime prediction have been described in detail in the first embodiment of this disclosure, and will not be repeated here. When actually setting up the device of this embodiment, it can be placed in the host computer of the optical laboratory to realize communication connection with optical experimental systems such as SEM, AFM, and EDS, so as to facilitate data acquisition and analysis.

[0050] The third embodiment of this disclosure provides a storage medium storing a computer program, which, when executed by a processor, implements the steps of the accelerated extrapolation evaluation method for the service life of optical components described in the first embodiment of this disclosure.

[0051] The fourth embodiment of this disclosure provides an electronic device, which includes at least a memory and a processor. The memory stores a computer program, and when the processor executes the computer program in the memory, it implements the steps of the accelerated extrapolation evaluation method for the service life of optical elements described in the first embodiment of this disclosure.

[0052] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this disclosure, and are not intended to limit them. Although this disclosure has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this disclosure.

Claims

1. A method for accelerated extrapolation evaluation of the service life of optical components, characterized in that, include: The damage morphology and microstructure changes of optical components under high-energy laser irradiation were collected, and the damage mechanism of the optical components was determined. The number of irradiations required when the transmittance of the optical element degrades to a preset threshold under different stresses is counted. The stresses are formed based on different laser irradiation conditions, including power density, pulse width, and frequency. The characteristic lifetime of the optical element under each stress is obtained by fitting the number of irradiations under different stresses based on the two-parameter Weiber distribution function. Based on the characteristic lifetime of the optical element under each stress, a composite acceleration model is fitted, which is used to characterize the acceleration effect of the temperature field and the stress field on the characteristic lifetime. Based on the composite acceleration model, a thermal coupling simulation model of the optical element is established; Based on the preset stress step acceleration experiment, the temperature field simulation results output by the thermo-coupling simulation model and the transmittance degradation data of the optical element are obtained to form a dynamic dataset. Based on the dynamic dataset, the predicted lifetime of the optical element under constant stress is determined using an adaptive stress compression extrapolation algorithm.

2. The accelerated extrapolation evaluation method according to claim 1, characterized in that, The composite acceleration model is constructed based on the following formula: in, Indicates characteristic lifetime, Indicates stress, Indicates temperature. As an acceleration factor, For activation energy, Boltzmann's constant, , All of these are fitting constants.

3. The accelerated extrapolation evaluation method according to claim 2, characterized in that, The step of establishing a thermal coupling simulation model of the optical element based on the composite acceleration model includes: Based on the mapping of temperature field and stress field in the composite acceleration model, a thermo-mechanical coupling simulation model of the optical element is established, and the material properties of the optical element are input. Under the same irradiation conditions, determine the relative deviations between the simulated transient temperature and simulated stress field distribution output by the thermo-coupling simulation model and the measured transient temperature and measured stress field distribution of the optical element; If the relative deviation is greater than a first threshold, the composite acceleration model is refitted until the relative deviation is less than or equal to the first threshold.

4. The accelerated extrapolation evaluation method according to claim 3, characterized in that, The step of determining the predicted lifetime of the optical element under constant stress using an adaptive stress compression extrapolation algorithm based on the dynamic dataset includes: The calculated lifetime of the optical element under constant stress conditions is determined based on the composite acceleration model when it reaches the same degree of degradation as the transmittance degradation data. The compensation life is determined based on the temperature correction aging factor and the temperature field simulation results. Based on the calculated lifetime and the compensated lifetime, the predicted lifetime of the optical element under constant stress is determined.

5. The accelerated extrapolation evaluation method according to claim 4, characterized in that, The temperature-corrected aging factor is constructed based on the following formula: in, Indicates compensatory lifespan. and This represents the fitting parameters related to the material of the optical element. This indicates the restoration of activation energy.

6. The accelerated extrapolation evaluation method according to claim 5, characterized in that, After determining the predicted lifetime of the optical element under constant stress using an adaptive stress compression extrapolation algorithm based on the dynamic dataset, the method further includes: The predicted lifespan was subjected to a Komogorov-Smirnov test to determine the statistic; The verification is passed when the statistic is less than the second threshold; otherwise, the fitting parameters of the composite acceleration model and / or the fitting parameters of the temperature-corrected aging factor are iteratively corrected based on the statistic.

7. The accelerated extrapolation evaluation method according to any one of claims 1 to 6, characterized in that, After determining the predicted lifetime of the optical element under constant stress using an adaptive stress compression extrapolation algorithm based on the dynamic dataset, the method further includes: Based on the global sensitivity analysis algorithm, the contribution of each design parameter of the optical element to the predicted lifetime is determined; Design parameters whose contribution level is greater than the third threshold are identified as key parameters, and these key parameters are used as optimization references for the design of the optical element.

8. An accelerated extrapolation evaluation device for the service life of optical components, characterized in that, include: The damage mechanism determination module is used to collect the damage morphology and microstructure changes of optical components under high-energy laser irradiation, and to determine the damage mechanism of the optical components. The statistics module is used to count the number of irradiations when the transmittance of the optical element degrades to a preset threshold under different stresses. The stresses are formed based on different laser irradiation conditions, including power density, pulse width, and frequency. The function fitting module is used to fit the number of irradiations under different stresses based on the two-parameter Weiber distribution function to obtain the characteristic lifetime of the optical element under each stress. An acceleration model generation module is used to fit a composite acceleration model based on the characteristic lifetime of the optical element under each stress. The composite acceleration model is used to characterize the acceleration effect of the temperature field and the stress field on the characteristic lifetime. The simulation model building module is used to establish a thermal coupling simulation model of the optical element based on the composite acceleration model. The comparative experiment module is used to obtain the temperature field simulation results output by the thermo-coupling simulation model and the transmittance degradation data of the optical element based on the preset stress step acceleration experiment, forming a dynamic dataset. The prediction module is used to determine the predicted lifetime of the optical element under constant stress based on the dynamic dataset using an adaptive stress compression extrapolation algorithm.

9. A storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the accelerated extrapolation evaluation method for the service life of optical elements as described in any one of claims 1 to 7.

10. An electronic device, comprising at least a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program on the memory, it implements the steps of the accelerated extrapolation evaluation method for the service life of optical elements as described in any one of claims 1 to 7.

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