A method for evaluating the lifespan of a pulsed laser equipment

Abstract

The embodiment of this specification provides a method for evaluating the lifespan of a pulsed laser device. The method includes establishing component degradation models for each core component based on a random Wiener process model; determining the degradation rate function of the components in each component degradation model according to environmental stress; calculating and obtaining the values of the parameters to be estimated in each component degradation model and each degradation rate function; according to the set failure thresholds of each core component and the values of the parameters to be estimated, substituting them into the corresponding component degradation models, calculating the time when each core component first crosses the set failure threshold, and determining the lifespan values of each core component. Based on the random process model, the present invention can evaluate the lifespan of pulsed laser devices with small batch sizes, pulsed operation, and complexity. Moreover, compared with the existing methods, the method of this embodiment has no requirements for the number of devices, does not require full-time real-time data monitoring of the devices, and is more effective and accurate in applying to the random failure detection of pulsed devices in harsh environments.

Description

Technical Field

[0001] This document relates to the technical field of equipment detection and maintenance, and particularly to a method for evaluating the life of a pulsed laser equipment. Background Art

[0002] Reliability is one of the important indicators for evaluating product quality, and the performance indicators of products are issues that people are very concerned about. Therefore, the research on the reliability of products is very important. With the development of science and technology, there are more and more products with high reliability and long life. Conducting life tests under normal conditions can no longer meet the requirements of reliability assessment. Accelerated life tests can be used to evaluate the reliability of products in a short time.

[0003] In engineering practice, it is difficult to conduct life tests on all products to measure or evaluate their reliability characteristic quantities. Especially for a certain type of laser ranging system with small batch size, high cost and complexity, only limited test data can be used to estimate its reliability through reliability assessment.

[0004] For example, for pulsed laser equipment, there are many differences in its life evaluation compared with other products. For example:

[0005] 1. The whole machine is expensive, which limits the quantity source of life data;

[0006] 2. For pulsed equipment, it is impossible to monitor data throughout the accelerated life test process;

[0007] 3. There is no practical method for evaluating the life of core components;

[0008] 4. The actual use environment is complex, not simply the change of extreme temperature, but also includes various situations such as rain, snow, and humidity.

[0009] Therefore, the life test and test methods applicable to continuous operation and based on a large amount of statistical data are not suitable for the life evaluation of pulsed laser equipment that works once under extreme conditions. In addition, the current research results show that the failure of laser equipment under various harsh environments such as high vibration and high temperature shock is random. Therefore, the life of laser equipment cannot be simply determined directly by the failure times of each component.

[0010] In view of this, there is an urgent need to provide a method for evaluating the life of a pulsed laser equipment that takes into account the random failure of the laser equipment. Summary of the Invention

[0011] One or more embodiments of this specification provide a method for evaluating the life of a pulsed laser equipment, including the steps of:

[0012] Based on the random Wiener process model, establish component degradation models for each core component of the pulsed laser equipment;

[0013] Based on the multi-stress generalized Arrhenius model according to the environmental stress, determine the degradation rate function of the components in each component degradation model;

[0014] Calculate and obtain the values of the parameters to be estimated in each component degradation model and each degradation rate function;

[0015] According to the set failure thresholds of each core component and the values of the parameters to be estimated, substitute them into the corresponding component degradation model, calculate the time when each core component first crosses the set failure threshold, and determine the life values of each core component;

[0016] Compare the life values of each core component, and determine that the minimum life value is the service life of the pulsed laser equipment.

[0017] The present invention also provides a life evaluation system for a pulsed laser equipment, and this system includes:

[0018] Component degradation model establishment unit: used to establish component degradation models for each core component of the pulsed laser equipment respectively based on the random Wiener process model;

[0019] Degradation rate function determination unit: According to the environmental stress, based on the multi-stress generalized Arrhenius model, determine the degradation rate function of the components in each component degradation model established by the component degradation model establishment unit;

[0020] Parameter to be estimated calculation unit: used to calculate and determine the parameters to be estimated in each component degradation model established by the component degradation model establishment unit and the degradation rate function determined by the parameter to be estimated calculation unit;

[0021] Component failure threshold setting unit: used to set the failure threshold of the corresponding component;

[0022] Component life value calculation unit: According to the failure thresholds of each core component set by the component failure threshold setting unit and the corresponding values of the parameters to be estimated calculated by the parameter to be estimated calculation unit, substitute them into the corresponding component degradation model, calculate the time value when each core component first crosses the set failure threshold, determine the life values of each core component, and output them to the comparison output unit;

[0023] Comparison unit: used to compare the time values when each core component first crosses the set failure threshold input by the component life value calculation unit, and determine the minimum time value, which is the overall service life of the pulsed laser equipment.

[0024] Based on the random process model, the present invention can evaluate the life of pulsed laser equipment with small batch, pulsed operation and complexity. Moreover, compared with the existing methods, the method of this embodiment has no requirement for the number of equipment, does not need to monitor the whole-process real-time data of the equipment, and is more effective and accurate in applying to the random failure detection of pulsed equipment in harsh environments. Brief Description of the Drawings

[0025] In order to more clearly illustrate the technical solutions in one or more embodiments of this specification or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments recorded in this specification. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0026] Figure 1 It is a flowchart of a method for evaluating the life of a pulsed laser device provided for one or more embodiments of this specification;

[0027] Figure 2 It is a schematic structural diagram of a system for evaluating the life of a pulsed laser device provided for one or more embodiments of this specification;

[0028] Figure 3 It is a schematic structural diagram of a computer device provided for one or more embodiments of this specification. Detailed Embodiments

[0029] In order to enable those skilled in the art to better understand the technical solutions in one or more embodiments of this specification, the following will clearly and completely describe the technical solutions in one or more embodiments of this specification in conjunction with the drawings in one or more embodiments of this specification. Obviously, the described embodiments are only a part of the embodiments of this specification, rather than all the embodiments. Based on one or more embodiments of this specification, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of this document. The following will make a detailed description of the present invention in conjunction with the specific embodiments and the drawings of the specification.

[0030] Method Embodiment

[0031] According to an embodiment of the present invention, a method for evaluating the life of a pulsed laser device is provided. As Figure 1 shown, according to the method for evaluating the life of a pulsed laser device in the embodiment of the present invention, the method includes:

[0032] S1. Based on the random Wiener process model, establish component degradation models for each core component of the pulsed laser device;

[0033] S2. According to the environmental stress, based on the multi-stress generalized Arrhenius model, determine the degradation rate function of the components in each component degradation model;

[0034] S3. Calculate and obtain the values of the parameters to be estimated in each component degradation model and each degradation rate function;

[0035] S4. According to the set failure thresholds of each core component and the corresponding parameter values to be estimated obtained in step S3, substitute them into the corresponding component degradation model, calculate the time when each core component first crosses the set failure threshold, and determine the life values of each core component; in this embodiment, since other parameters in the model are known numbers, substituting the value at component failure - the failure threshold into the model can inversely deduce the integration time, that is, the failure time or life value.

[0036] S5. Compare the life values of each core component, determine the minimum life value, which is the overall service life of the pulsed laser equipment.

[0037] The method of this embodiment is based on a stochastic process model, which can evaluate the life of pulsed laser equipment with small batch, pulsed operation, and complexity. Moreover, compared with the existing methods, the method of this embodiment has no requirement for the number of equipment, does not need to monitor the whole - process real - time data of the equipment, and is more effective and accurate in detecting the random failure of pulsed equipment in harsh environments.

[0038] In this embodiment method, there are many core components that affect the service life of pulsed laser equipment, including, for example, lasers, photodetectors, circuit components, laser crystals, dye sheets, total - reflection mirrors, condenser cavities, etc. By using the method of this embodiment to establish a degradation model for any component based on the stochastic Wiener process model, the model is specifically

[0039]

[0040] where X(t) represents the degradation amount of the component at time t, σ is the diffusion coefficient; B(t) represents Brownian motion, which follows the normal distribution N(0, t), reflecting the random dynamics of the component performance degradation process. The mean value of the component performance degradation amount is a function of time t, that is, E[X(t)] = λt. The drift coefficient λ is not only related to the performance degradation amount but also closely related to the degradation trend.

[0041] In practical applications, the performance degradation of each component in pulsed laser equipment may be affected by environmental stresses such as temperature, humidity, atmospheric pressure, and salt - fog concentration during operation or storage. Therefore, the performance degradation amount of the core components of pulsed laser equipment is a function of the above - mentioned parameters. In this embodiment, only the effects of temperature, humidity, and atmospheric pressure on component performance are considered. According to the form of the multi - stress generalized Arrhenius model, the degradation rate function of the corresponding component is established:

[0042] E[X(t)] = E[w 1 (t), w 2 (t), w 3 (t)] = a 0 + a 1 * w 1 (t)+ a1 / w 2 (t) + a 3 *w 3 (t)(2)

[0043] Among them, w 1 (t) represents the temperature at time t, w 2 (t) represents the humidity at time t, w 3 (t) represents the atmospheric pressure at time t.

[0044] Actually, both the independent terms and interaction terms of environmental stress will affect the degradation rate of the pulsed laser equipment. However, usually, the influence of the independent terms is greater, and the influence of the interaction terms is relatively smaller. In this embodiment, to reduce the number of parameters to be estimated, the interaction terms of environmental stress are not considered in Equation (2), and only the independent terms of environmental stress are included. Therefore, in Equation (2), there are a 0 、a 1 、a 2 、a 3 four parameters to be estimated. In addition to these four parameters to be estimated in Equation (1), there is also a parameter to be estimated, that is, the diffusion parameter σ. The specific steps for estimating the diffusion parameter σ in this embodiment are as follows:

[0045] Step A1: Eliminate the cumulative effect term generated by the degradation rate in the data:

[0046]

[0047] Among them, the data here refers to the performance degradation data and environmental profile data obtained by observing the above-mentioned temperature, humidity, and atmospheric pressure, etc.; in the formula, m is the cumulative number of observations of the degradation variable from time 0 to time T, and i refers to the i-th observation.

[0048] Step A2: According to the characteristics of the normal distribution σB(t): N(0, σ 2 t) that Brownian motion follows, process the degradation data: ΔH(t i ): N(0, σ 2 Δt i ), and the likelihood function of this data is:

[0049]

[0050] The log-likelihood function is:

[0051]

[0052] Step A3: Calculate the first-order partial derivative of the log-likelihood function:

[0053]

[0054] Let Equation (6) be equal to 0, and the estimated value of the diffusion parameter σ can be obtained as follows:

[0055]

[0056] In this embodiment, for the parameters a 0 、a 1 、a 2 、a 3 The estimation steps are as follows:

[0057] Step B1. Based on the independent increment property of the Brownian motion process, approximate the integration of the component degradation model by cumulative summation. The component degradation model in Equation (1) is written as:

[0058]

[0059] where m is the cumulative number of observations of the degradation variable from time 0 to time T; Δt i is the observation time interval, Δt i =t i -t i-1 , w k (t i ) is the environmental stress within the time range [t i-1 -t i , and k = 1, 2, 3.

[0060] Step B2. Use the independent increment property of the Brownian motion to process the degradation data measured in the experiment:

[0061] ΔX(t i ) = X(t i ) - X(t i-1 ) (8)

[0062] ΔX(t i ): N(E[w 1 (t i ), w 2 (t i ), w 3 (t i )]Δt i , σ 2 Δt i ) (9)

[0063]

[0064] That is, substitute the performance degradation data of the component into formulas (8)-(10) to obtain the degradation data ΔX(t i ) / Δt i .

[0065] Step B3. For each degradation data ΔX(ti ) / Δt i Substitute it into formula (2), and then use the multiple regression method to determine the parameter a to be estimated 0 , a 1 , a 2 , a 3 .

[0066] Substitute the calculated parameters a 0 , a 1 , a 2 , a 3 and the diffusion parameter σ into formula (1), and set the failure threshold of the corresponding component, and calculate and determine the time when the component first crosses the failure threshold, that is, the service life. For example, the failure threshold of the laser is that "the power X(t) at time t is 80% of the power X 0 at the initial time", then the time when the laser in the pulsed laser equipment first crosses the failure threshold, that is, the service life, can be calculated using formula (1).

[0067] Similarly, based on the above calculation process, calculate the service life of the core components such as photodetectors and each circuit component. On this basis, compare the service life of each core component. Since the overall machine life is limited by the component with the shortest life, the overall machine service life of the pulsed laser equipment is determined by the life of the component with the minimum life.

[0068] System embodiment

[0069] According to an embodiment of the present invention, there is provided a life evaluation system for a pulsed laser equipment based on the above life evaluation method of a pulsed laser equipment, as Figure 2 shown. The life evaluation system for a pulsed laser equipment according to an embodiment of the present invention includes:

[0070] Component degradation model establishment unit: used to establish component degradation models for each core component of the pulsed laser equipment based on the random Wiener process model respectively.

[0071] Degradation rate function determination unit: According to the environmental stress, based on the multi-stress generalized Arrhenius model, determine the degradation rate function of the components in each component degradation model established by the component degradation model establishment unit.

[0072] Parameter to be estimated calculation unit: used to calculate and determine the component degradation models established by the component degradation model establishment unit and the parameters to be estimated in the degradation rate function determined by the parameter to be estimated calculation unit.

[0073] Component failure threshold setting unit: used to set the failure threshold of the corresponding component.

[0074] Component life value calculation unit: According to the failure thresholds of each core component set by the component failure threshold setting unit and the corresponding estimated parameter values calculated by the estimated parameter calculation unit, substitute them into the corresponding component degradation model, calculate the time values when each core component first crosses the set failure threshold, determine the life values of each core component, and output them to the comparison output unit.

[0075] Comparison unit: Used to compare the time values when each core component first crosses the set failure threshold input by the component life value calculation unit, determine the minimum time value, which is the overall service life of the pulsed laser equipment.

[0076] In this embodiment, preferably, the component degradation model established by the component degradation model establishment unit is specifically as follows:

[0077]

[0078] Among them, X(t) represents the degradation amount of the component at time t, σ is the diffusion coefficient; B(t) represents Brownian motion, which follows a normal distribution N(0, t), reflecting the random dynamics of the component performance degradation process. The mean value of the component performance degradation amount is a function of time t, that is, E[X(t)] = λt. The drift coefficient λ is not only related to the component performance degradation amount but also closely related to the degradation trend.

[0079] In this embodiment, only considering the influence of temperature, humidity, and atmospheric pressure on the component performance, based on the multi-stress generalized Arrhenius model form, the degradation rate function determination unit establishes the degradation rate function of the corresponding component as:

[0080] E[X(t)] = E[w 1 (t), w 2 (t), w 3 (t)] = a 0 + a 1 * w 1 (t) + a 1 / w 2 (t) + a 3 * w 3 (t) (12)

[0081] Among them, w 1 (t) represents the temperature at time t, w 2 (t) represents the humidity at time t, w 3 (t) represents the atmospheric pressure at time t.

[0082] In this embodiment, the estimated parameter calculation unit includes a degradation model estimated parameter calculation module and a degradation rate function estimated parameter calculation module; specifically:

[0083] Degradation model estimated parameter calculation module: Used to calculate the estimated parameters in each component degradation model;

[0084] In this embodiment, to reduce the number of parameters to be estimated, the interaction terms of environmental stress are not considered in Equation (12), and only the independent terms of environmental stress are included. Therefore, in Equation (12), it contains a 0 , a 1 , a 2 , a 3 Four parameters to be estimated, and in Equation (11), in addition to these four parameters to be estimated, there is also a parameter to be estimated, namely the diffusion parameter σ. The specific steps for estimating the diffusion parameter σ in this embodiment are as follows:

[0085] Eliminate the cumulative effect term generated by the degradation rate in the data:

[0086]

[0087] According to the characteristics of the normal distribution σB(t): N(0, σ 2 t) that Brownian motion follows, process the degradation data: ΔH(t i ): N(0, σ 2 Δt i ). The likelihood function of this data is:

[0088]

[0089] The log-likelihood function is:

[0090]

[0091] Find the first-order partial derivative of the log-likelihood function:

[0092]

[0093] And let Equation (16) be equal to 0, then the estimated value of the diffusion parameter σ can be obtained as:

[0094]

[0095] Parameter calculation module for the degradation rate function: used to calculate the parameters to be estimated in the degradation rate function, and the calculation process is as follows:

[0096] Based on the independent increment characteristic of the Brownian motion process, approximate the integration of the component degradation model by cumulative summation, and the component degradation model (Equation (11)) can be written as:

[0097]

[0098] Among them, m is the cumulative number of observations of the degradation variable from time 0 to time T; Δt i is the observation time interval, Δt i = t i - ti-1 , w k (t i ) is each environmental stress within the time range [t i-1 -t i , where k = 1, 2, 3.

[0099] Process the degradation data measured in the experiment using the independent increment property of Brownian motion:

[0100] ΔX(t i ) = X(t i ) - X(t i-1 ) (18)

[0101] ΔX(t i ): N(E[w 1 (t i ), w 2 (t i ), w 3 (t i )]Δt i , σ 2 Δt i ) (19)

[0102]

[0103] That is, substitute the performance degradation data of the component into formulas (19)-(20) to obtain the degradation data ΔX(t i ) / Δt i .

[0104] Substitute each degradation data ΔX(t i ) / Δt i into formula (12), and then use the multiple regression method to determine the parameters to be estimated a 0 , a 1 , a 2 , a 3 .

[0105] In this embodiment, the component life value calculation unit substitutes the parameters a 0 , a 1 , a 2 , a 3 calculated by the degradation rate function parameter to be estimated calculation module and the diffusion parameter σ calculated by the degradation model parameter to be estimated calculation module into the corresponding component degradation model, and calculates the time value when each core component first crosses the set failure threshold according to the set failure threshold of the component, thereby determining the life value of each core component.

[0106] The embodiments of the present invention are method embodiments corresponding to the above system embodiments. The specific operations of each processing step can be understood with reference to the description of the method embodiments, and will not be elaborated here.

[0107] As Figure 3 shown, the present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the life evaluation method of the pulsed laser equipment in the above embodiments, or when the computer program is executed by a processor, it implements the life evaluation method of the pulsed laser equipment in the above embodiments.

[0108] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the embodiments provided in the present application can include non-volatile and / or volatile memories. Non-volatile memories can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memories can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0109] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other. Each embodiment focuses on the differences from other embodiments. In particular, for the apparatus or system embodiments, since they are basically similar to the method embodiments, they are described relatively simply. For the relevant parts, reference can be made to the description of the method embodiments. The apparatus and system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated. The components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. A person of ordinary skill in the art can understand and implement it without creative efforts.

[0110] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some or all of the technical features. These modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for evaluating the life of a pulsed laser equipment, characterized in that, it includes the steps of: Based on the random Wiener process model, establish component degradation models for each core component of the pulsed laser equipment; According to the environmental stress, based on the multi-stress generalized Arrhenius model, determine the degradation rate function of the components in each component degradation model; Calculate and obtain the values of the parameters to be estimated in each component degradation model and each degradation rate function; According to the set failure thresholds of each core component and the values of the parameters to be estimated, substitute them into the corresponding component degradation models, calculate the time when each core component first crosses the set failure threshold, and determine the life values of each core component; Compare the life values of each core component, and determine the minimum life value as the service life of the pulsed laser equipment; The specific method for establishing the component degradation models for each core component is as follows: Among them, X(t) represents the degradation amount of the component at time t, σ is the diffusion coefficient; B(t) represents Brownian motion, which follows the normal distribution N(0, t), reflecting the random dynamics of the component performance degradation process. The mean value of the component performance degradation amount is a function of time t, that is, E[X(t)] = λt, and λ is the drift coefficient; The specific method for establishing the degradation rate function of the corresponding component according to the form of the multi-stress generalized Arrhenius model is as follows: E[X(t)] = E[w 1 (t), w 2 (t), w 3 (t)] = a 0 + a 1 * w 1 (t) + a 1 / w 2 (t) + a 3 * w 3 (t) (2); Among them, w 1 (t) represents the temperature at time t, w 2 (t) represents the humidity at time t, w 3 (t) represents the atmospheric pressure at time t; The specific estimation steps of the diffusion parameter σ are as follows: Eliminate the cumulative effect term generated by the degradation rate in the data: According to the characteristics of the normal distribution that Brownian motion follows, σB(t) ~ N(0, σ 2 t), the degraded data is processed as follows: ΔH(t i ) ~ N(0, σ 2 Δt i ). The likelihood function of this data is as follows: The log-likelihood function is: Find the first-order partial derivative of the log-likelihood function: Let Equation (6) be equal to 0, and the estimated value of the diffusion parameter σ can be obtained as: In the formula, m is the cumulative number of observations of the degradation variable from time 0 to time T,, and i is the i-th observation.

2. The method for evaluating the life of a pulsed laser equipment according to claim 1, characterized in that, The estimation steps for parameter a 0 and a 1 and a 2 and a 3 are as follows: Based on the independent increment characteristic of the Brownian motion process, approximate the integration of the component degradation model by cumulative summation, and the component degradation model in Equation (1) is written as: where m is the cumulative number of observations of the degradation variable from time 0 to time T; Δt i is the observation time interval, Δt i = t i - t i-1 , w k (t i ) is each environmental stress within the time range [t i-1 - t i , where k = 1, 2, 3; Use the independent increment characteristic of Brownian motion to process the measured degradation data: ΔX(t i ) = X(t i ) - X(t i-1 ) (8); ΔX(t i )~N(E[w 1 (t i ),w 2 (t i ),w 3 (t i )]Δt i ,σ 2 Δt i ) (9); Bring the performance degradation data of the component into Formulas (8)-(10) to obtain the degradation data ΔX(t i ) / Δt i ; Substitute each piece of degradation data ΔX(t i ) / Δt i into Equation (2), and then use the multiple regression method to determine the values of the parameters to be estimated a 0 、a 1 、a 2 、a 3 .

3. A system for evaluating the life of a pulsed laser equipment, characterized in that, This system includes: Component degradation model establishment unit: used to establish component degradation models for each core component of the pulsed laser equipment based on the random Wiener process model; Degradation rate function determination unit: According to the environmental stress, based on the multi-stress generalized Arrhenius model, determine the degradation rate function of the components in each component degradation model established by the component degradation model establishment unit; Parameter to be estimated calculation unit: used to calculate and determine the parameters to be estimated in each component degradation model established by the component degradation model establishment unit and the degradation rate function determined by the parameter to be estimated calculation unit; Component failure threshold setting unit: used to set the failure threshold of the corresponding component; Component life value calculation unit: According to the failure thresholds of each core component set by the component failure threshold setting unit and the corresponding parameter values to be estimated calculated by the parameter to be estimated calculation unit, substitute them into the corresponding component degradation models, calculate the time value when each core component first crosses the set failure threshold, determine the life values of each core component, and output them to the comparison and output unit; Comparison unit: It is used to compare the time values when each core component input by the component life value calculation unit first crosses the set failure threshold, and determine the minimum time value, which is the overall service life of the pulsed laser equipment; The component degradation model established by the component degradation model establishment unit is specifically as follows: Wherein, X(t) represents the degradation amount of the component at time t, and σ is the diffusion coefficient; B(t) represents Brownian motion, which follows the normal distribution N(0, t), reflecting the random dynamics of the component performance degradation process. The mean value of the component performance degradation amount is a function of time t, that is, E[X(t)] = λt, and λ is the drift coefficient; The degradation rate function determination unit establishes the degradation rate function corresponding to the component according to the multi-stress generalized Arrhenius model form as: E[X(t)] = E[w 1 (t), w 2 (t), w 3 (t)] = a 0 + a 1 * w 1 (t)+ a 1 / w 2 (t)+ a 3 * w 3 (t) (12); Among them, w 1 (t) represents the temperature at time t, w 2 (t) represents the humidity at time t, w 3 (t) represents the atmospheric pressure at time t; The parameter to be estimated calculation unit includes a degradation model parameter to be estimated calculation module: It is used to calculate the parameters to be estimated in each component degradation model. The estimation steps of the diffusion parameter σ are specifically as follows: Eliminate the cumulative effect term generated by the degradation rate in the data: According to the characteristics of the normal distribution that Brownian motion follows, σB(t) ~ N(0, σ 2 t), the degraded data is processed as follows: ΔH(t i ) ~ N(0, σ 2 Δt i ). The likelihood function of this data is as follows: The log-likelihood function is: Find the first-order partial derivative of the log-likelihood function: And let Equation (16) be equal to 0, then the estimated value of the diffusion parameter σ can be obtained as: In the formula, m is the cumulative number of observations of the degradation variable from time 0 to time T,, and i refers to the i-th observation.

4. The life evaluation system of the pulsed laser equipment according to claim 3, Characterized in that, The parameter to be estimated calculation unit includes a degradation rate function parameter to be estimated calculation module, which is used to calculate the parameters to be estimated in the degradation rate function. The calculation process is as follows: Based on the independent increment characteristic of the Brownian motion process, the integral of the component degradation model is approximated by cumulative summation, and the component degradation model Equation (11) is written as: where m is the cumulative number of observations of the degradation variable from time 0 to time T; Δt i is the observation time interval, Δt i = t i - t i-1 , w k (t i ) are the respective environmental stresses within the time range [t i-1 - t i , where k = 1, 2, 3; Use the independent increment characteristic of Brownian motion to process the measured degradation data: ΔX(t i ) = X(t i ) - X(t i-1 ) (18); ΔX(t i )~N(E[w 1 (t i ),w 2 (t i ),w 3 (t i )]Δt i σ 2 Δt i ) (19); That is, the performance degradation data of the component is brought into Formulas (19)-(20) to obtain the degradation data ΔX(t i ) / Δt i ; Substitute each degradation data ΔX(t i ) / Δt i into Equation (12), and then use the multiple regression method to determine the values of the parameters a 0 、a 1 、a 2 、a 3 to be estimated.

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