Hard light link reliability coupling method, system and device and storage medium

Abstract

The invention discloses a strong light link reliability coupling method, system and device and a storage medium, and relates to the technical field of reliability engineering.The method comprises the steps that reliability parameter mapping of all parts of a system is obtained based on a pre-constructed model-based system engineering MBSE digital model of a strong light link system; dynamically calculating a fault-free working time MTBF of the highlight link system according to the reliability parameter mapping; performing fault propagation simulation according to the MBSE digital model and a predefined fault mode library; and according to the MTBF and the fault propagation simulation result, generating reliability evaluation of the highlight link system. According to the method, based on parameter mapping and dynamic coupling calculation of the MBSE model, multi-physical field simulation can be integrated in real time, such as heat and electricity data or actually measured data, working condition parameters are dynamically corrected, and dynamic, precise and systematic analysis of reliability evaluation is realized.

Description

High-fidelity link reliability coupling methods, systems, devices and storage media Technical Field

[0001] This application relates to the field of reliability engineering technology, and in particular to a high-fidelity optical link reliability coupling method, system, device and storage medium. Background Technology

[0002] High-power laser link systems, as core components of high-power lasers, are widely used in critical fields such as national defense, communications, and lidar. The scientific nature of their design and evaluation directly determines system performance, reliability, and R&D efficiency. As systems evolve towards multi-domain coupling and increased complexity, extremely high demands are placed on the reliability of high-power laser link systems.

[0003] Currently, traditional systems engineering methods still dominate the design and evaluation of high-power optical link systems. These methods are characterized by "document-driven + experience-dependent + physical verification". They record system information through discrete CAD drawings, Excel parameter tables, and paper technical specifications, rely on static tools such as Failure Mode and Effects Analysis (FMECA) and Fault Tree Analysis (FTA) for reliability assessment, and conduct performance and reliability tests by building physical prototypes. Design changes need to be manually synchronized to all related documents and iterative optimization is carried out based on experience.

[0004] Therefore, in high-end complex high-power optical link systems, traditional methods are static and isolated in terms of reliability assessment, resulting in a lack of hierarchical logic and multi-physics data support for the calculation of mean time between failures (MTBF), and the assessment results deviate greatly from the actual operating state. In terms of risk identification, they are lagging and passive, relying on later physical prototype testing and expert experience, which means that high-risk links are often only discovered during prototype testing or actual operation, which seriously restricts the reliability design and R&D efficiency of high-power optical link systems.

[0005] The above content is only used to help understand the technical solution of this application and does not represent an admission that the above content is prior art. Summary of the Invention

[0006] The main objective of this application is to provide a high-power optical link reliability coupling method, system, device, and storage medium, which aims to solve the technical problem that it is impossible to perform accurate dynamic reliability assessment and early risk prediction for existing high-power optical link systems during the design phase.

[0007] To achieve the above objectives, this application proposes a high-fidelity optical link reliability coupling method, the method comprising:

[0008] Based on a pre-built Model-Based Systems Engineering (MBSE) digital model of the high-power optical link system, reliability parameter mappings for each component of the system are obtained. The mean time between failures (MTBF) of the high-power optical link system is dynamically calculated based on these reliability parameter mappings. Fault propagation simulations are performed using the MBSE digital model and a predefined fault mode library. A reliability assessment of the high-power optical link system is generated based on the MTBF and the fault propagation simulation results.

[0009] In one embodiment, the construction of the model-based systems engineering (MBSE) digital model of the high-power optical link system includes: obtaining the architecture definition of the high-power optical link system, wherein the architecture definition includes at least a power supply subsystem, a laser source subsystem, and a thermal management subsystem; based on the architecture definition, constructing a structural model of the high-power optical link system using Block Definition Graph (BBD) and Internal Block Graph (IBD) of a system modeling language; based on the structural model, constructing a behavioral model of the high-power optical link system using a state graph of a system modeling language; and based on the structural model and the behavioral model, constructing a parametric model of the high-power optical link system using a parametric graph of a system modeling language.

[0010] In one embodiment, the acquisition of reliability parameter mappings for each component of the system based on the pre-built model-based systems engineering (MBSE) digital model of the high-power optical link system includes: extracting the operating condition parameters of each component in the power supply subsystem, laser source subsystem, and thermal management subsystem based on the structural model and parameter model, wherein the operating condition parameters include at least the power supply voltage, laser power, and temperature distribution; and associating the operating condition parameters with the reliability parameters by constructing an MTBF dynamic calculation model, thereby mapping the operating condition parameters of each component to the corresponding component-level MTBF.

[0011] In one embodiment, the step of dynamically calculating the mean time between failures (MTBF) of the high-power optical link system based on the reliability parameter mapping includes: calculating the subsystem-level MTBF step by step using a preset recursive formula based on the component-level MTBF according to the cascaded system reliability model, and further calculating the system-level MTBF; acquiring updated data from multiphysics simulation or real-time monitoring, dynamically correcting the operating parameters, and updating the calculation results of the component-level MTBF and system-level MTBF in real time based on the corrected operating parameters.

[0012] In one embodiment, the step of performing fault propagation simulation based on the MBSE digital model and a predefined fault mode library includes: constructing a fault propagation dynamic simulation model based on the behavior model and the fault mode library, wherein the fault propagation dynamic simulation model defines the fault triggering conditions, the propagation path and impact range between the power supply subsystem, the laser source subsystem and the thermal management subsystem through block definition diagrams, internal block diagrams and state diagrams of the system modeling language; setting the initial state of system operation and fault triggering conditions in the fault propagation dynamic simulation model based on the parameter model and the updated operating condition parameters; running the fault propagation dynamic simulation model to simulate the hierarchical transmission process from component failure, through subsystem anomalies leading to system-level faults, and dynamically tracking the interaction and propagation path of the fault between subsystems; and outputting the fault propagation timing, affected components and final system state of the high-power link system based on the simulation results as the fault propagation simulation results.

[0013] In one embodiment, the step of constructing a dynamic simulation model for fault propagation based on the behavior model and the fault mode library includes: acquiring predefined fault modes, fault causes, and impact data based on the fault mode library; establishing a dynamic simulation framework reflecting the entire process of the system from startup and operation to fault handling using block definition diagrams, internal block diagrams, and state diagrams of a system modeling language based on the system dynamic process defined in the behavior model; and defining the triggering conditions, propagation paths, and impact ranges of each fault mode among the power supply subsystem, laser source subsystem, and thermal management subsystem within the dynamic simulation framework to construct the dynamic simulation model for fault propagation.

[0014] In one embodiment, the step of generating a reliability assessment of the high-power optical link system based on the MTBF and fault propagation simulation results includes: comparing the calculated system-level MTBF with a preset reliability threshold to obtain a first comparison result; locating early risk points that cause system failures or affect system reliability based on the affected components and fault propagation timing output by the fault propagation simulation results; generating a system reliability assessment conclusion that includes an analysis of the overall system reliability level and key weak links by combining the first comparison result and the early risk point location results; when the first comparison result does not meet the system reliability requirements, determining the design optimization direction based on the system reliability assessment conclusion and feeding it back to the MBSE digital model to initiate the optimization iteration process; otherwise, outputting the system reliability assessment conclusion.

[0015] Furthermore, to achieve the above objectives, this application also proposes a high-power optical link reliability coupling system, which includes: a parameter mapping unit for obtaining reliability parameter mappings for each component of the system based on a pre-constructed model-based system engineering (MBSE) digital model of the high-power optical link system; a dynamic calculation unit for dynamically calculating the mean time between failures (MTBF) of the high-power optical link system according to the reliability parameter mappings; a fault simulation unit for performing fault propagation simulations based on the MBSE digital model and a predefined fault mode library; and an evaluation generation unit for generating a reliability evaluation of the high-power optical link system based on the MTBF and the fault propagation simulation results.

[0016] In addition, to achieve the above objectives, this application also proposes a high-fidelity link reliability coupling device, the device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the high-fidelity link reliability coupling method described above.

[0017] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of the high-power optical link reliability coupling method described above.

[0018] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the high-power optical link reliability coupling method described above.

[0019] One or more technical solutions proposed in this application have at least the following technical effects: This application obtains reliability parameter mappings for each component of the system based on a pre-constructed model-based systems engineering (MBSE) digital model of the high-power optical link system; dynamically calculates the mean time between failures (MTBF) of the high-power optical link system according to the reliability parameter mappings; performs fault propagation simulation based on the MBSE digital model and a predefined fault mode library; and generates a reliability assessment of the high-power optical link system based on the MTBF and the fault propagation simulation results. This application realizes the transformation of reliability assessment from static to dynamic, from experience-based to model-driven, and from late-stage verification to early prediction, significantly improving the design reliability and R&D efficiency of the high-power optical link system. Attached Figure Description

[0020] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0021] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 is a flowchart illustrating the first embodiment of the high-fidelity link reliability coupling method of this application; Figure 2 is a flowchart illustrating the second embodiment of the high-fidelity link reliability coupling method of this application; Figure 3 is a flowchart illustrating the third embodiment of the high-fidelity link reliability coupling method of this application; Figure 4 is a data synchronization flowchart of the high-fidelity link system provided by the high-fidelity link reliability coupling method of this application; Figure 5 is a flowchart illustrating the fourth embodiment of the high-fidelity link reliability coupling method of this application; Figure 6 is a schematic diagram of the unit structure of the high-fidelity link reliability coupling system of this application; Figure 7 is a schematic diagram of the device structure of the hardware operating environment involved in the high-fidelity link reliability coupling method of this application.

[0023] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0024] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0025] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0026] Because existing technologies cannot accurately assess the dynamic reliability of existing high-power optical link systems and predict early risks during the design phase.

[0027] This application provides a solution that, based on a pre-built model-based systems engineering (MBSE) digital model of a high-power optical link system, obtains reliability parameter mappings for each component of the system; dynamically calculates the mean time between failures (MTBF) of the high-power optical link system based on the reliability parameter mappings; performs fault propagation simulation based on the MBSE digital model and a predefined fault mode library; and generates a reliability assessment of the high-power optical link system based on the MTBF and the fault propagation simulation results.

[0028] Based on this, the present application provides a high-fidelity link reliability coupling method. Referring to Figure 1, Figure 1 is a flowchart of the first embodiment of the high-fidelity link reliability coupling method of the present application.

[0029] In this embodiment, the high-power optical link reliability coupling method includes steps S10-S40: Step S10, based on a pre-constructed model-based systems engineering (MBSE) digital model of the high-power optical link system, obtains the reliability parameter mapping of each component of the system. It should be noted that in this embodiment, the high-power optical link system refers to a high-power laser core component composed of a power supply subsystem, a laser source subsystem, and a thermal management subsystem, used to realize the generation, transmission, and stable output of optical signals. The "model-based systems engineering (MBSE) digital model" refers to an integrated digital model that uses a model-based systems engineering approach, employing block definition diagrams, internal block diagrams, state diagrams, and parameter diagrams from a system modeling language (such as SysML), to uniformly describe the structure, behavior, and parameters of the high-power optical link system. Reliability parameter mapping refers to establishing a quantitative correlation between the performance parameters of each component of the system (such as power supply voltage, laser power, and temperature distribution) and reliability indicators (such as failure rate and mean time between failures), thereby transforming design parameters into reliability parameters.

[0030] This embodiment identifies the performance parameters of each component based on the structural and parameter views of the MBSE model; and constructs mathematical constraints from performance parameters to reliability parameters based on physical failure models, measured data, or empirical formulas, forming a parameter mapping network for the entire link.

[0031] In one possible implementation, reliability parameter mapping can be achieved through parametric graphs in a system modeling language, where constraint properties are defined to encapsulate the mapping equations and associate them with specific component blocks.

[0032] Step S20: Dynamically calculate the mean time between failures (MTBF) of the high-power optical link system based on the reliability parameter mapping. It should be noted that in this embodiment, the mean time between failures (MTBF) refers to the average time between failures, which represents the average uptime of the system before a failure occurs.

[0033] This embodiment is based on reliability parameter mapping to calculate the MTBF of the high-power optical link system in real time, so that the reliability assessment can be dynamically adjusted according to the system operating conditions, thereby improving the accuracy of the assessment.

[0034] In one possible implementation, the MBSE model provides structured input parameters (updated operating condition parameters). The system exports these parameters to external dedicated computing tools (such as Excel, MATLAB, or Python scripts), utilizing the powerful computing and iterative capabilities of these tools to perform specific reliability mathematical model solutions and MTBF aggregation calculations. After the calculations are completed, the latest component-level and system-level MTBF results are used as new "reliability parameters" and manually or through a semi-automatic interface are fed back and updated to the corresponding parameter attributes in the MBSE digital model.

[0035] In one specific implementation, the system corrects the MTBF value of the thermal management component in real time based on the temperature simulation results of the thermal management subsystem, and then recalculates the MTBF of the entire high-power optical link system. When the temperature rises and the failure rate of the components increases, the system-level MTBF decreases accordingly, thereby more accurately reflecting the system reliability under high-temperature conditions.

[0036] Step S30: Perform fault propagation simulation based on the MBSE digital model and the predefined fault mode library. It should be noted that, in this embodiment, the predefined fault mode library refers to a set of fault modes built based on historical fault data, expert experience, or industry standards, containing information such as fault type, triggering conditions, causes, effects, and probability of occurrence. Fault propagation simulation refers to using the MBSE digital model to simulate the triggering, transmission, and impact of faults among various components of the system, in order to analyze the chain effects of faults and system behavior.

[0037] This embodiment associates fault modes in the fault mode library with components in the MBSE model; it constructs fault propagation logic based on behavioral models (such as state diagrams and activity diagrams; where state diagrams describe system state transitions and activity diagrams describe fault propagation processes), defines fault triggering conditions and propagation rules between subsystems; and it runs the model in a simulation environment to track the propagation timing and impact range of faults from the component level to the system level, which can identify potential fault propagation paths and risk points in the high-power optical link system and achieve early risk prediction.

[0038] Step S40: Based on the MTBF and fault propagation simulation results, generate a reliability assessment of the high-power optical link system.

[0039] It should be noted that, in this embodiment, reliability assessment refers to a comprehensive judgment of the reliability level of the high-power optical link system, including quantitative indicators (such as MTBF) and qualitative analysis (such as risk point location and weak link identification).

[0040] This embodiment integrates dynamic MTBF calculation results and fault propagation simulation results to generate comprehensive and accurate reliability assessment conclusions, enabling closed-loop feedback in reliability assessment, driving system design iteration, and improving R&D efficiency.

[0041] Further, referring to Figure 2, the second embodiment of the high-power optical link reliability coupling method of this application provides a flowchart. Based on the embodiment shown in Figure 2, the construction of the "model-based system engineering (MBSE) digital model of the high-power optical link system" in step S10 is further refined, including steps A201 to A204: Step A201, obtaining the architecture definition of the high-power optical link system, wherein the architecture definition includes at least a power supply subsystem, a laser source subsystem, and a thermal management subsystem; it should be noted that, in this embodiment, the high-power optical link system refers to a complex opto-electro-mechanical system composed of a high-power laser source, optical transmission components, and supporting energy and thermal control devices. The architecture definition refers to the top-level division of the system from the dimensions of function and physical implementation, clarifying the composition of the core functional units (subsystems) of the system and the basic interaction relationships between them. The power supply subsystem refers to the set of units that provide stable and compliant power to the entire high-power optical link, which usually includes a power module, power distribution lines, and control circuits. The laser source subsystem refers to the core unit that generates and outputs the required power and quality laser, which usually includes a laser pump source, gain medium, resonant cavity, and control module. The thermal management subsystem is responsible for controlling the operating temperature of the laser source and other heat-generating components in the high-intensity optical link to ensure their performance and reliability. It typically includes a heat sink, a coolant circulation device, and a temperature control module.

[0042] This embodiment is based on system requirements analysis and functional decomposition. The top-level system functional requirements are allocated to each subsystem layer by layer, and the interfaces for energy flow, signal flow or material flow between them are defined. The composition framework of the high-power optical link system is also clearly defined from the top level.

[0043] Step A202: Based on the aforementioned architecture definition, the structural model of the high-power optical link system is constructed using the Block Definition Graph (BBD) and Internal Block Graph (IBD) of the System Modeling Language (SysML). It should be noted that in this embodiment, the System Modeling Language (SysML) refers to a standardized graphical modeling language that supports the specification, analysis, design, verification, and validation of complex systems, and is the core carrier for implementing Model-Based Systems Engineering (MBSE). The Block Definition Graph (BDD) is used to define the types of the system and its components (i.e., "blocks"), as well as the hierarchical classification relationships between these types (such as whole-part relationships and generalization relationships). The Internal Block Graph (IBD) is used to describe the internal structure of a specific block (usually a system or subsystem), showing the connection relationships between its internal components (i.e., "component attributes"). The structural model refers to a set of models that graphically and formally describe the static composition architecture, components, and their interconnections of the system using a series of SysML diagrams such as BDD and IBD.

[0044] This embodiment uses BDD to construct the hierarchical block definition of the system to express the composition tree of "system-subsystem-subsystem-component"; and uses IBD to further refine the internal structure of each subsystem or subsystem to accurately depict the port connections and interface relationships between components, generating a "digital skeleton" of the high-power optical link system composition structure.

[0045] Step A203: Based on the structural model, construct the behavioral model of the high-power optical link system using state diagrams from a system modeling language. It should be noted that in this embodiment, a state diagram is a graphical representation in SysML used to describe how a block responds to external or internal events during its lifecycle, thereby transitioning between a finite set of states. A behavioral model refers to a model that describes the dynamic behavior, workflow, and logical sequence of a system or its components through a series of SysML diagrams such as state diagrams and activity diagrams.

[0046] This embodiment analyzes the lifecycle and typical working scenarios of key blocks in the structural model (such as blocks representing the entire high-power optical link system), and identifies all possible states. Then, it defines the transitions between states and assigns trigger events (such as "received power-on command"), monitoring conditions (such as "normal power supply voltage"), and possible actions to each transition, injecting dynamic logic into the static structural model.

[0047] Step A204: Based on the structural model and the behavioral model, construct the parameter model of the high-power optical link system using the parameter graph of the system modeling language.

[0048] It should be noted that in this embodiment, the parametric diagram is used to support engineering analysis. By associating "constraint attributes" (used to encapsulate equations, inequalities, and other constraints) with the system's "value attributes" (representing various engineering parameters, such as voltage, temperature, and MTBF), a quantitative relationship network between system parameters is established. A parametric model refers to a model that formally describes key engineering parameters such as system performance and reliability, as well as their mathematical and physical constraints, through parametric diagrams and other forms.

[0049] This embodiment defines relevant value attributes in the blocks related to the structural model, creates a parametric graph, instantiates constraint attributes representing physical or empirical formulas in the graph, and associates the parameters of the constraint attributes with the value attributes of the corresponding blocks.

[0050] In one possible implementation, the acquisition of reliability parameter mappings for each component of the system based on the pre-built model-based systems engineering (MBSE) digital model of the high-power optical link system includes: extracting the operating condition parameters of each component in the power supply subsystem, laser source subsystem, and thermal management subsystem based on the structural model and parameter model, wherein the operating condition parameters include at least the power supply voltage, laser power, and temperature distribution; and associating the operating condition parameters with the reliability parameters by constructing an MTBF dynamic calculation model, thereby mapping the operating condition parameters of each component to the corresponding component-level MTBF.

[0051] It should be noted that, in this embodiment, operating condition parameters refer to the key performance parameters exhibited by the system or its components during actual operation. These parameters dynamically change with varying operating conditions and serve as a core bridge connecting the system's dynamic behavior and reliability status. Reliability parameters are indicators used to quantitatively evaluate the reliability of components or systems. In this embodiment, they specifically refer to time-related reliability metrics, such as the failure rate (λ) and the mean time between failures (MTBF) derived from it. Component-level MTBF refers to the mean time between failures calculated for the smallest replaceable or analyzable unit (such as a laser pump source, power module, or cooling fan) in a high-power optical link system.

[0052] Based on the MBSE digital model containing structural, behavioral, and parameter information, this application establishes a systematic and quantifiable mapping mechanism to convert "operating condition parameters" that reflect the dynamic operating state of the system into "reliability parameters" that evaluate its long-term operating capability. It can organically link and transform simulation data or measured data scattered in different disciplines (optics, mechanics, electronics, and thermodynamics) into a unified reliability language (MTBF) through a unified model carrier, realizing the connection from "multi-physics data" to "reliability indicators".

[0053] In one possible implementation, a multi-level mapping system is constructed, consisting of "multiphysics data → component-level intermediate parameters → component-level MTBF". First, the system locates specific components in each subsystem (power supply, laser source, thermal management) based on the structural model, and extracts or receives real-time / simulation operating condition parameters (such as voltage, power, and temperature distribution) bound to these components from the parameter model. For example, manual data or real-time data are used as initial values ​​for the operating condition parameters, which are then corrected using real-time / simulation operating condition parameters. Then, these operating condition parameters are used as input through an "MTBF dynamic calculation model" constructed within the parameter model. This model calculates the dynamic failure rate of each component based on physical laws or calibrated empirical functions, thereby obtaining the component-level MTBF, thus completing the direct mapping from the operating condition space to the reliability space.

[0054] In one possible implementation, the MTBF dynamic calculation model is implemented in the parameter graph as one or more constraint attribute blocks, each encapsulating a specific type of failure physics equation. For example, for the laser diode in the laser source subsystem, a constraint attribute can be defined whose internal constraint equation is a failure rate model based on temperature and current density. The input port of this constraint attribute is connected to the value attributes representing "laser diode junction temperature" and "operating current density" in the parameter graph, while the output port is connected to the value attribute representing "laser diode failure rate." The system completes the mapping calculation by parsing and executing this parameter graph. Furthermore, it should be noted that the extraction of operating parameters is not a one-time action but a continuous process. In addition, the system can be configured to monitor the output of multiphysics simulation software or the monitoring data stream of actual sensors. When new temperature distributions, voltage fluctuations, or other data are detected, a parameter extraction and MTBF mapping update process is automatically triggered, thereby achieving dynamic reliability assessment.

[0055] Further, referring to Figure 3, the third embodiment of the high-power optical link reliability coupling method of this application provides a flowchart. Based on the embodiment shown in Figure 3, the step of "dynamically calculating the mean time between failures (MTBF) of the high-power optical link system according to the reliability parameter mapping" in step S20 is further refined, including steps A301~A302: Step A301, based on the serial system reliability model and the component-level MTBF, the subsystem-level MTBF is calculated step by step using a preset recursive formula, and the system-level MTBF is further calculated. It should be noted that in this embodiment, the serial system reliability model refers to a model used in reliability engineering to evaluate a system composed of multiple independent components (or subsystems). For example, based on a serial system, a bottom-up reliability parameter transfer model is established from component-level MTBF to subsystem-level MTBF, and then from subsystem-level MTBF to system-level MTBF. Subsystem-level MTBF refers to the reliability index calculated for an intermediate functional assembly (such as a power supply subsystem, laser source subsystem, and thermal management subsystem) composed of multiple related components, reflecting the mean time between failures of the entire functional assembly. The system-level MTBF (Mean Time Between Failures) is a top-level reliability metric calculated for the entire high-power optical link system, and it is the core quantitative result for measuring the overall reliability of the system. The preset recursive formula refers to a mathematical expression based on the reliability model of a cascaded system, used to calculate the MTBF of the upper-level system from bottom to top based on the failure rate or MTBF of each component.

[0056] In one possible implementation, the preset recursive formula is: 1 / MTBF 系统 = 1 / MTBF 供电 + 1 / MTBF 激光源 + 1 / MTBF 热管理This enables dynamic updates of MTBF across all levels; the specific form of the recursive formula can be adjusted according to the exact reliability logic relationship between components (such as series, parallel, or mixed connections).

[0057] In one specific implementation, refer to Figure 4, which is a data synchronization flowchart of a high-power optical link system. Based on the hierarchical structure of the high-power optical link (component-subsystem-branch system-system), a three-level mapping system of "bottom-level data-intermediate parameters-top-level indicators" is established. The first-level mapping is multi-physics data → component-level intermediate parameters, specifically establishing a quantitative relationship between multi-physics data and key performance parameters of components based on physical laws, empirical formulas, and experimental data calibration. For example, for the "narrow-linewidth fiber laser" component under the laser source subsystem, the current operating parameters of the component are extracted from the parameter model integrating thermal-optical coupling simulation results: the component's ambient temperature T and input electrical power P from the temperature field data. 电 The electro-optical conversion efficiency η of the laser (η is a baseline value provided in the manufacturer's manual and corrected by actual measurement data) is expressed by the mapping formula P. 光 =P 电 ×η、I t =I0×(1+0.02×(T-20)) (I0 is the rated threshold current at 20℃), to obtain the component output laser power P. 光 Component threshold current I t For the "optical lens" component, extract the current operating parameters of the component: the surface temperature rise ΔT of the optical lens in the temperature field data. Assume that the surface absorptivity of the lens is A0 (A0 is the initial surface absorptivity of the lens before laser irradiation, provided by the manufacturer's manual). Simulate the surface temperature rise of the optical lens within the time interval Δt using finite element simulation software, and compare the simulated value of ΔT with the measured value. Correct the assumed value of the surface absorptivity of the lens in the simulation calculation based on the error between the simulation and the measured value to obtain the surface absorptivity A of the lens.

[0058] Furthermore, the second-level mapping is component-level intermediate parameters → subsystem MTBF. Based on the reliability model of a series system and combined with the failure rate corrected for the component intermediate parameters, the subsystem MTBF is recursively calculated. For example, the laser source subsystem includes a narrow-linewidth fiber laser, optical lenses, secondary power supplies, etc. Assuming that the components operate in series, the subsystem MTBF satisfies 1 / MTBF. 激光源 =Σ(1 / MTBF 部件 ), where the MTBF of each component 部件 =1 / λ 部件 , λ 部件 =λ0×f 中间参数 (λ0 is the component baseline failure rate, λ) 部件 f represents the component failure rate. 中间参数(This is a correction function based on intermediate parameters). For narrow-linewidth fiber lasers, λ of the narrow-linewidth fiber laser... 部件 =λ0×(I t / I0)×(P 光 / P 电 )^(-0.5), where I t For the corrected threshold current, P 光 The actual output power is represented by this formula, which indirectly maps the light field and temperature field data to the component failure rate, and then recursively calculates the subsystem MTBF.

[0059] Furthermore, the three-level mapping is from subsystem MTBF to system-level MTBF. Using a hierarchical recursive formula, by integrating the MTBFs of the three subsystems—power supply, laser source, and thermal management—the system-level MTBF is obtained: 1 / MTBF. 系统 =1 / MTBF 供电 +1 / MTBF 激光源 +1 / MTBF 热管理 .

[0060] Step A302: Obtain updated data from multiphysics simulation or real-time monitoring, dynamically correct the operating parameters, and update the calculation results of the component-level MTBF and system-level MTBF in real time based on the corrected operating parameters.

[0061] It should be noted that in this embodiment, multiphysics simulation refers to the numerical simulation of physical phenomena in the real world using computer software. In the context of a high-intensity optical link system, it typically includes thermal simulation (calculating temperature distribution), structural mechanics simulation (calculating stress and strain), fluid simulation (calculating coolant flow), and electromagnetic simulation (calculating electromagnetic interference) to predict the internal state of the system under different operating conditions. "Real-time monitored updated data" refers to the system operating parameters collected and transmitted in real time by sensors (such as temperature sensors, voltage sensors, and optical power meters) installed on physical prototypes, test benches, or actual products. Dynamic correction refers to proactively and promptly adjusting the operating condition parameter values ​​used in the reliability assessment model based on the latest simulation results or monitoring data, ensuring that the model input is consistent with the current or predicted real state of the system. Real-time updating refers to immediately triggering or periodically re-executing the complete process from parameter mapping to MTBF aggregation calculation after the operating condition parameters are corrected, thereby refreshing the reliability assessment results.

[0062] Alternatively, the entire dynamic update process can be automated by setting up a scheduled task or event listener.

[0063] This embodiment, by introducing dynamism and timeliness, breaks through the limitations of fixed parameters and static evaluation results in traditional reliability assessment, and realizes real-time reliability status assessment and predictive maintenance based on actual operating data, which greatly improves the accuracy and practical value of reliability assessment.

[0064] In one possible implementation, the step of performing fault propagation simulation based on the MBSE digital model and a predefined fault mode library includes: constructing a fault propagation dynamic simulation model based on the behavior model and the fault mode library, wherein the fault propagation dynamic simulation model defines the fault triggering conditions, the propagation path and impact range between the power supply subsystem, the laser source subsystem and the thermal management subsystem through block definition diagrams, internal block diagrams and state diagrams of the system modeling language; setting the initial state of system operation and fault triggering conditions in the fault propagation dynamic simulation model based on the parameter model and the updated operating condition parameters; running the fault propagation dynamic simulation model to simulate the hierarchical transmission process from component failure, through subsystem anomalies to system-level failure, and dynamically tracking the interaction and propagation path of the fault between subsystems; and outputting the fault propagation timing, affected components and final system state of the high-power link system based on the simulation results as the fault propagation simulation results.

[0065] It should be noted that the "fault propagation dynamic simulation model" refers to an executable model that integrates system structure, behavioral logic, fault knowledge, and current operating parameters, used to simulate the occurrence, propagation, and ultimate impact of faults in a system. In this model, the "fault triggering conditions" define under what circumstances a specific fault mode will be activated (such as when a component parameter exceeds a threshold); the "propagation path" describes the logical route by which the fault effect spreads from the originating component through functional or physical connections to other related components and subsystems; and the "scope of impact" describes the functional areas and severity of the system affected by the fault.

[0066] In this embodiment, a behavioral model describing the normal working logic of the system is used as the framework. The abstract faults in the fault mode library are visualized as simulation logic elements (such as special states, events, or actions) that can be implanted into the model. Block definition diagrams and internal block diagrams are used to clarify the structural relationship between the fault source and the affected components. State diagrams are used to define the abnormal state migration path of the system after the introduction of a fault, thereby constructing a dynamic simulation model in which "health" and "fault" logic coexist. Then, parameter model data reflecting the current design or operating state (especially updated operating parameters) are used as the initial input for the simulation, and a specific, quantifiable starting point and fault triggering threshold are set for the model. Then, the simulation is started. The model will automatically simulate, according to the preset logic, how one or more faults are triggered, starting from the initial component, along the functional dependency and physical connection chain, causing functional abnormalities in related subsystems, and ultimately potentially leading to the failure of the entire system. During this process, the interaction and propagation trajectory of the fault are dynamically recorded. Finally, the simulation engine summarizes and structures the records of the entire simulation process to form a detailed simulation report including a timeline, a list of affected components, and the final system state.

[0067] In one specific implementation, a fault occurs in the power supply subsystem. Upon investigation, the voltage regulation circuit is found to be faulty, resulting in an excessively low output voltage. This leads to insufficient power input to the laser source subsystem, ultimately causing a decrease in the output laser power. This fault is a cross-domain cascading fault, specifically involving three steps: 1) Initial electrical domain fault: The power supply subsystem needs to stably output a DC voltage compatible with the laser source subsystem. This process relies on the closed-loop "sampling-feedback-adjustment" of the voltage regulation circuit. After prolonged operation, the voltage regulation circuit components experience performance degradation, causing the output voltage to fail to maintain its rated value, becoming the source of the entire cross-domain fault. 2) Electrical domain parameter transmission: The power supply subsystem and the laser source subsystem are interconnected via a "power transmission link + control signal link." The output voltage of the voltage regulation circuit is directly input to the laser source drive circuit through the power link. When the output voltage is too low, the voltage status signal of the power supply subsystem triggers a "low voltage alarm" feedback signal. 3) Electro-optic parameter mapping: The performance parameters of the laser source subsystem follow a quantitative mapping relationship of "electrical input → optical output". Insufficient input power causes the drive circuit to be unable to provide the rated threshold current to the laser gain medium. According to the current-power characteristic curve of the pump source, when the input current is less than the rated current, the photoelectric conversion efficiency decreases, the actual output power of the laser source subsystem decreases, triggering the "insufficient output power" fault signal of the laser source subsystem, which in turn leads to system failure and shutdown.

[0068] Furthermore, the MBSE digital model in this application also possesses a closed-loop feedback mechanism of "evaluation results - quantitative indicators - design optimization - model update," specifically including: 1) Based on the dynamic calculation results of the system's MTBF and the dynamic simulation results of fault propagation, determining whether the initial parameters of the system meet the reliability requirements, and locating early risk points of the system. If they meet the requirements, the reliability assessment results can be directly output; if not, the current system failure rate λ is calculated. 当前值 The difference between the failure rate (calculated as λ = 1 / MTBF) and the target value is Δλ = λ. 目标值 -λ 当前值 1) Decompose the contribution of each subsystem to Δλ and clarify the optimization weight of each subsystem. 2) Based on the optimization weight of each subsystem, the Δλ subsystem can be further decomposed from top to bottom layer by layer to determine the system optimization direction, generate targeted optimization schemes, and update the MBSE model design parameters. 3) Synchronize the optimized design parameters to the MBSE model and return to the multi-level mapping system of "multiphysics data → component-level intermediate parameters → component-level MTBF" to re-execute the simulation and calculation to verify the optimization effect. If the optimized system MTBF meets the requirements, the reliability assessment result of the final model is output; if it does not meet the requirements, the above feedback process is repeated until the requirements are met, realizing the closed-loop reliability assessment of the strong light link model. Specifically, in the thermal management subsystem, if the calculated system MTBF does not meet the system MTBF requirements, the heat exchanger design is optimized according to the optimization weight of each subsystem to reduce the system failure rate. The MBSE is improved and iterated multiple times to obtain a new calculated system MTBF result so that the system MTBF meets the requirements.

[0069] In one specific implementation, the system identifies key components from the MBSE model, including the "narrow linewidth fiber laser" and "collimating lens" in the laser source subsystem, and the "liquid cooling plate" in the thermal management subsystem. The system extracts and processes multiphysics coupling data: 1) Optical-thermal coupling: Optical path simulation shows that due to trace contamination on the surface of the "collimating lens" (intermediate parameter: surface absorptivity A increases from 0.1% to 0.5%), additional heat deposition occurs when the laser passes through. The thermal simulation results are updated accordingly, showing that the local temperature of the lens increases by ΔT=15℃, and the downstream spot position shifts by micrometers. 2) Thermal-mechanical-optical coupling: The lens temperature rise ΔT is mapped to a small change in the lens curvature radius (intermediate parameter) through the thermal expansion constraint property in the parametric model, and further mapped to "outgoing beam deflection angle θ shift of 0.1mrad" through the optical model. 3) Optical-thermal-electrical coupling: The beam shift parameter θ and the "fiber end face position" parameter of the "narrow linewidth fiber laser" have spatial alignment constraints in the parametric model. Beam deflection causes a decrease in the efficiency η of laser coupling back to the fiber (e.g., from 95% to 92%). This efficiency decrease serves as an intermediate parameter, on the one hand, through the optical power mapping formula P.光 =P 电 ×η results in effective output power P 光 On the one hand, the uncoupled stray light power is reduced; on the other hand, it is converted into heat within the laser assembly, causing its junction temperature T_j to increase (mapped via the thermal model). 4) Final reliability mapping: The system calls the dynamic calculation model of MTBF for each component. For the "laser", the increased junction temperature T_j and the reduced effective power P are used to calculate the MTBF. 光 Substituting into the second-level mapping formula, the failure rate was calculated to increase, with the MTBF decreasing from 150,000 hours to 110,000 hours. For the "collimating lens," its contamination itself may not directly correspond to an MTBF, but the system traces back through the parameter network, associating the system reliability degradation caused by its chain reaction back to the component and marking it as a critical risk point. Through this series of tightly coupled mapping calculations, the cross-domain propagation path of a surface contamination failure originating in the optical domain is fully quantified and ultimately reflected in the changes in the reliability indicators of key electronic components (lasers), fully demonstrating the uniqueness and advantages of multi-physics coupling reliability assessment of high-intensity optical links.

[0070] Further, referring to Figure 5, the fourth embodiment of the high-power optical link reliability coupling method of this application provides a flowchart. Based on the embodiment shown in Figure 5, the step of "constructing a dynamic simulation model of fault propagation based on the behavior model and fault mode library" in step S30 is further refined, including steps A401 to A403: Step A401, based on the fault mode library, obtain predefined fault modes, fault causes, and impact data; it should be noted that in this embodiment, the fault mode library is a structured data set, which is constructed based on failure mode and effect analysis, historical fault data, expert experience, and industry standards, and is used to systematically store and manage known or potential failure states of the high-power optical link system. Fault causes refer to the fundamental physical, chemical, or human factors that lead to the occurrence of the fault mode. Impact data refers to the qualitative or quantitative description of the consequences after the occurrence of the fault mode, which usually includes the direct impact on the function of the component itself, the local impact on the function of the subsystem to which it belongs, and the final impact on the function of the entire high-power optical link system.

[0071] Step A402: Based on the system dynamic flow defined in the behavioral model, a dynamic simulation framework reflecting the entire process of the system from startup, operation to fault handling is established using block definition diagrams, internal block diagrams, and state diagrams from a system modeling language. It should be noted that in this embodiment, the system dynamic flow specifically refers to the orderly sequence of state transitions and activities of the high-power optical link system in the time dimension, described by state diagrams and activity diagrams of a system modeling language (such as SysML), from power-on startup, initialization of various subsystems, entering stable operation, responding to external commands, to normal shutdown or handling abnormal events. The dynamic simulation framework refers to the skeleton of a simulation model with executable semantics built on the behavioral model, defining the boundaries, core state variables, event types, and basic operational logic of the system simulation.

[0072] In this embodiment, the normal dynamic process of the system described in the behavioral model is analyzed and understood, especially the state transition diagram and activity sequence diagram. Then, in the simulation modeling environment, the block definition diagram and internal block diagram of the system modeling language are used to define the instances of each "block" (i.e., system, subsystem, key component) participating in the simulation and the connection relationships between them, thus forming the static structural framework of the simulation. Finally, using the state diagram, the normal process described in the behavioral model is transformed into an executable, event-driven state machine model, which serves as the core logic engine for simulation operation, thereby establishing a dynamic simulation framework that can simulate the entire process of the system from startup, operation to (normal) processing.

[0073] Step A403: In the dynamic simulation framework, define the triggering conditions for each fault mode, the propagation path and the range of influence between the power supply subsystem, the laser source subsystem and the thermal management subsystem, in order to construct the fault propagation dynamic simulation model.

[0074] In this embodiment, each fault mode record obtained from the fault mode library is traversed; within the dynamic simulation framework, the simulation object corresponding to the component or subsystem associated with the fault mode is found; then, in the simulation logic of this object, judgment logic for monitoring "triggering conditions" is added and bound to relevant operating parameters (such as real-time calculation of parameter models); once the conditions are met, a predefined "fault event" is triggered or the behavior mode of the object is changed (such as setting its output signal to an abnormal value or jumping its internal state to a "failure" state); next, based on the "impact data" of the fault and the connection structure of the system, how the fault event or abnormal state affects other objects in the simulation framework through signal connections, function calls, or parameter coupling is defined, thereby forming a clear "propagation path"; finally, the actual "scope of influence" is automatically verified and defined through simulation operation. This embodiment realizes the deep integration of discrete fault knowledge, system behavior logic, and multi-domain parameter states into an executable and observable digital simulation environment.

[0075] In one possible implementation, the step of generating a reliability assessment of the high-power optical link system based on the MTBF and fault propagation simulation results includes: comparing the calculated system-level MTBF with a preset reliability threshold to obtain a first comparison result; locating early risk points that cause system failures or affect system reliability based on the affected components and fault propagation timing output by the fault propagation simulation results; generating a system reliability assessment conclusion that includes an analysis of the overall system reliability level and key weaknesses by combining the first comparison result with the early risk point location results; when the first comparison result does not meet the system reliability requirements, determining the design optimization direction based on the system reliability assessment conclusion and feeding it back to the MBSE digital model to initiate the optimization iteration process; otherwise, outputting the system reliability assessment conclusion.

[0076] It should be noted that in this embodiment, the preset reliability threshold is a minimum target value that the system-level MTBF must reach or exceed, pre-set according to the product specification, industry standards, or customer requirements. It serves as an objective benchmark for determining whether the design meets reliability requirements. Fault propagation simulation results refer to the structured data packets output by running the fault propagation dynamic simulation model, containing information such as the timing of fault events, a list of affected components, and the final state of the system. These data reveal the system's behavior and weaknesses under specific fault scenarios. Early risk points specifically refer to potential defects or vulnerabilities identified during the system design phase through model-based simulation analysis. These vulnerabilities may be overlooked before physical prototype verification, but once they occur, they will significantly affect the system's reliability or functionality. They can manifest as a specific component, a sensitive combination of parameters, or a specific fault propagation path. System reliability assessment conclusions specifically refer to a summary report that integrates quantitative indicator compliance judgments and qualitative risk analysis. This embodiment transforms reliability assessment from a traditional post-hoc "compliance inspection" to a "guiding optimization" process throughout the design phase, greatly improving design maturity and R&D efficiency.

[0077] In one feasible implementation, the reliability assessment and decision-making system evaluates the high-intensity light transmission link of a certain type of airborne lidar. The system first reads the system-level MTBF value output by the dynamic calculation module as 65 hours and compares it with the preset reliability threshold of 70 hours, obtaining a first comparison result of "not meeting the standard." Simultaneously, the system analyzes the report output by the fault simulation module and finds that when simulating a fault of "increased output voltage ripple in the power supply subsystem," it propagates through the path of "abnormal laser source drive circuit" → "laser output power jitter" → "thermal load fluctuation" → "thermal management response delay," ultimately leading to a protective shutdown of the system within a short time. The report identifies "power supply ripple suppression capability" and "thermal management dynamic response speed" as two key early risk points. Combining the first comparison result and the early risk points, the system generates an assessment conclusion: the system-level MTBF (65 hours) is lower than the requirement (70 hours), and the main weaknesses are insufficient power supply ripple suppression and slow dynamic response of the thermal management system. The coupling of these two factors exacerbates the risk of system instability under power grid disturbances. Since the first comparison result does not meet the requirements, the system determines the design optimization direction based on the conclusion as follows: 1) Optimize the filter circuit parameters of the power supply subsystem to reduce output ripple; 2) Improve the response bandwidth of the control algorithm of the thermal management subsystem.

[0078] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the high-light link reliability coupling method of this application. Any simple modifications based on this technical concept are within the protection scope of this application.

[0079] This application also provides a high-power optical link reliability coupling system. Referring to Figure 6, the high-power optical link reliability coupling system includes: a parameter mapping unit 10, used to obtain reliability parameter mappings for each component of the system based on a pre-built model-based system engineering (MBSE) digital model of the high-power optical link system; a dynamic calculation unit 20, used to dynamically calculate the fault-free operating time (MTBF) of the high-power optical link system according to the reliability parameter mappings; a fault simulation unit 30, used to perform fault propagation simulation based on the MBSE digital model and a predefined fault mode library; and an evaluation generation unit 40, used to generate a reliability evaluation of the high-power optical link system based on the MTBF and the fault propagation simulation results.

[0080] The high-power optical link reliability coupling system provided in this application, employing the high-power optical link reliability coupling method described in the above embodiments, can solve the technical problem of being unable to perform accurate dynamic reliability assessment and early risk prediction for existing high-power optical link systems during the design phase. Compared with the prior art, the beneficial effects of the high-power optical link reliability coupling system provided in this application are the same as those of the high-power optical link reliability coupling method provided in the above embodiments, and other technical features of the high-power optical link reliability coupling system are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0081] This application provides a high-intensity optical link reliability coupling device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the high-intensity optical link reliability coupling method in the above embodiment 1.

[0082] Referring to Figure 7 below, a schematic diagram of a structure suitable for implementing a high-intensity optical link reliability coupling device according to embodiments of this application is shown. The high-intensity optical link reliability coupling device in embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. The high-intensity optical link reliability coupling device shown in Figure 7 is merely an example and should not impose any limitations on the functionality and scope of use of embodiments of this application.

[0083] As shown in Figure 7, the strong optical link reliability coupling device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory 1002 or a program loaded from a storage device 1003 into a random access memory 1004. The random access memory 1004 also stores various programs and data required for the operation of the strong optical link reliability coupling device. The processing unit 1001, the read-only memory 1002, and the random access memory 1004 are interconnected via a bus 1005. An input / output interface 1006 is also connected to the bus. Typically, the following systems can be connected to the input / output interface 1006: input devices 1007 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1009. Communication device 1009 allows the strong optical link reliability coupling device to communicate wirelessly or wiredly with other devices to exchange data. While the figure shows strong optical link reliability coupling devices with various systems, it should be understood that implementation or possession of all the systems shown is not required. More or fewer systems may be implemented alternatively.

[0084] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from read-only memory 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0085] The high-voltage optical link reliability coupling device provided in this application, employing the high-voltage optical link reliability coupling method described in the above embodiments, can solve the technical problem of being unable to perform accurate dynamic reliability assessment and early risk prediction for existing high-voltage optical link systems during the design phase. Compared with the prior art, the beneficial effects of the high-voltage optical link reliability coupling device provided in this application are the same as those of the high-voltage optical link reliability coupling method provided in the above embodiments, and other technical features in this high-voltage optical link reliability coupling device are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0086] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0087] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0088] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the high-intensity optical link reliability coupling method in the above embodiments.

[0089] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0090] The aforementioned computer-readable storage medium may be included in the high-intensity optical link reliability coupling device; or it may exist independently and not be assembled into the high-intensity optical link reliability coupling device.

[0091] The aforementioned computer-readable storage medium carries one or more programs. When these programs are executed by the high-fidelity link reliability coupling device, the high-fidelity link reliability coupling device: obtains reliability parameter mappings for each component of the system based on a pre-built model-based systems engineering (MBSE) digital model of the high-fidelity link system; dynamically calculates the mean time between failures (MTBF) of the high-fidelity link system based on the reliability parameter mappings; performs fault propagation simulation based on the MBSE digital model and a predefined fault mode library; and generates a reliability assessment of the high-fidelity link system based on the MTBF and the fault propagation simulation results.

[0092] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0093] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0094] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0095] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described high-power optical link reliability coupling method. This solves the technical problem of being unable to perform accurate dynamic reliability assessment and early risk prediction for existing high-power optical link systems during the design phase. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the high-power optical link reliability coupling method provided in the above embodiments, and will not be repeated here.

[0096] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the high-intensity optical link reliability coupling method described above.

[0097] The computer program product provided in this application can solve the technical problem of being unable to perform accurate dynamic reliability assessment and early risk prediction for existing high-power optical link systems during the design phase. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the high-power optical link reliability coupling method provided in the above embodiments, and will not be repeated here.

[0098] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A high-fidelity optical link reliability coupling method, characterized in that, The high-power optical link reliability coupling method includes: obtaining reliability parameter mappings for each component of the system based on a pre-constructed model-based system engineering (MBSE) digital model of the high-power optical link system; dynamically calculating the mean time between failures (MTBF) of the high-power optical link system based on the reliability parameter mappings; performing fault propagation simulation based on the MBSE digital model and a predefined fault mode library; and generating a reliability assessment of the high-power optical link system based on the MTBF and the fault propagation simulation results.

2. The high-strength optical link reliability coupling method as described in claim 1, characterized in that, The construction of the model-based systems engineering (MBSE) digital model of the high-power optical link system includes: obtaining the architecture definition of the high-power optical link system, wherein the architecture definition includes at least a power supply subsystem, a laser source subsystem, and a thermal management subsystem; based on the architecture definition, constructing the structural model of the high-power optical link system using Block Definition Graph (BBD) and Internal Block Graph (IBD) of the system modeling language; based on the structural model, constructing the behavioral model of the high-power optical link system using a state graph of the system modeling language; and based on the structural model and the behavioral model, constructing the parametric model of the high-power optical link system using a parametric graph of the system modeling language.

3. The high-strength optical link reliability coupling method as described in claim 2, characterized in that, The model-based system engineering (MBSE) digital model of the pre-built high-power optical link system is used to obtain the reliability parameter mapping of each component of the system. This includes: extracting the operating condition parameters of each component in the power supply subsystem, laser source subsystem, and thermal management subsystem based on the structural model and parameter model. The operating condition parameters include at least the power supply voltage, laser power, and temperature distribution. The operating condition parameters are associated with the reliability parameters by constructing an MTBF dynamic calculation model, and the operating condition parameters of each component are mapped to the corresponding component-level MTBF.

4. The high-fidelity link reliability coupling method as described in claim 3, characterized in that, The step of dynamically calculating the mean time between failures (MTBF) of the high-power optical link system based on the reliability parameter mapping includes: calculating the subsystem-level MTBF step by step using a preset recursive formula based on the component-level MTBF according to the cascaded system reliability model, and further calculating the system-level MTBF; acquiring updated data from multiphysics simulation or real-time monitoring, dynamically correcting the operating parameters, and updating the calculation results of the component-level MTBF and system-level MTBF in real time based on the corrected operating parameters.

5. The high-fidelity link reliability coupling method as described in claim 4, characterized in that, The steps for performing fault propagation simulation based on the MBSE digital model and the predefined fault mode library include: constructing a fault propagation dynamic simulation model based on the behavior model and the fault mode library, wherein the fault propagation dynamic simulation model defines the fault triggering conditions, the propagation path and impact range between the power supply subsystem, the laser source subsystem and the thermal management subsystem through block definition diagrams, internal block diagrams and state diagrams of the system modeling language; setting the initial state of system operation and fault triggering conditions in the fault propagation dynamic simulation model based on the parameter model and the updated operating condition parameters; running the fault propagation dynamic simulation model to simulate the hierarchical transmission process from component failure, through subsystem anomalies to system-level failure, and dynamically tracking the interaction and propagation path of the fault between subsystems; and outputting the fault propagation timing, affected components and final system state of the high-power link system based on the simulation results as the fault propagation simulation results.

6. The high-fidelity link reliability coupling method as described in claim 5, characterized in that, The steps for constructing a dynamic simulation model of fault propagation based on the behavior model and fault mode library include: acquiring predefined fault modes, fault causes, and impact data based on the fault mode library; establishing a dynamic simulation framework reflecting the entire process of the system from startup and operation to fault handling using block definition diagrams, internal block diagrams, and state diagrams of a system modeling language, based on the system dynamic process defined in the behavior model; and defining the triggering conditions, propagation paths, and impact ranges of each fault mode among the power supply subsystem, laser source subsystem, and thermal management subsystem within the dynamic simulation framework to construct the dynamic simulation model of fault propagation.

7. The high-fidelity link reliability coupling method as described in claim 6, characterized in that, The steps for generating a reliability assessment of the high-power optical link system based on the MTBF and fault propagation simulation results include: comparing the calculated system-level MTBF with a preset reliability threshold to obtain a first comparison result; locating early risk points that cause system failures or affect system reliability based on the affected components and fault propagation timing output by the fault propagation simulation results; generating a system reliability assessment conclusion that includes an analysis of the overall system reliability level and key weak links by combining the first comparison result and the early risk point location results; when the first comparison result does not meet the system reliability requirements, determining the design optimization direction based on the system reliability assessment conclusion and feeding it back to the MBSE digital model to initiate the optimization iteration process; otherwise, outputting the system reliability assessment conclusion.

8. A high-performance optical link reliability coupling system, characterized in that, The high-power optical link reliability coupling system includes: a parameter mapping unit, used to obtain reliability parameter mappings for each component of the system based on a pre-built model-based system engineering (MBSE) digital model of the high-power optical link system; a dynamic calculation unit, used to dynamically calculate the mean time between failures (MTBF) of the high-power optical link system according to the reliability parameter mappings; a fault simulation unit, used to perform fault propagation simulations based on the MBSE digital model and a predefined fault mode library; and an evaluation generation unit, used to generate a reliability evaluation of the high-power optical link system based on the MTBF and the fault propagation simulation results.

9. A high-fidelity optical link reliability coupling device, characterized in that, The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the high-intensity optical link reliability coupling method as described in any one of claims 1 to 7.

10. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, it implements the steps of the high-power optical link reliability coupling method as described in any one of claims 1 to 7.

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