A multi-level product environmental adaptability design index collaborative allocation method, system, device and medium

Abstract

This application discloses a method, system, device, and medium for collaborative allocation of environmental adaptability design indicators for multi-level products, relating to the field of environmental adaptability design for complex products. The method includes: constructing a physical coupling model based on acquired life-cycle environmental profile data and performance parameters of the multi-level product; based on the physical coupling model, performing environmental adaptability margin allocation, environmental stress tolerance allocation, and environmental stress derating allocation at the system level, subsystem level, component level, and part level to obtain allocation results; and performing collaborative optimization of the allocation results using a multi-objective optimization algorithm to generate design indicator schemes under resource constraints. This application can comprehensively consider the heterogeneous environmental stress profiles, the coupling response relationships between multi-levels, and stringent resource constraints, thereby achieving collaborative optimization allocation of design indicators and ultimately maximizing the overall environmental adaptability performance of the multi-level product.

Description

Technical Field

[0001] This application relates to the field of environmental adaptability design for complex products, and in particular to a method, system, device and medium for the coordinated allocation of multi-level product environmental adaptability design indicators. Background Technology

[0002] With the rapid development of modern engineering technology, products in fields such as aerospace, high-end equipment, precision instruments, and automotive electronics are becoming increasingly complex. These complex products typically exhibit a multi-level architecture, including system-level (the highest level for completing a full function), subsystem-level (components of the system), component-level (functional units of the subsystem), and element-level (the most basic building blocks). Throughout their lifecycle, they will inevitably experience the combined effects of various harsh environments, including but not limited to climatic environments (such as high and low temperatures, humidity, and low air pressure), mechanical environments (such as vibration, shock, and acceleration), and biochemical environments (such as salt spray and mold). Environmental adaptability is defined as the ability of a product to achieve all its intended functions, performance, and / or be undamaged under the various environmental conditions it is expected to encounter during its lifespan. Environmental adaptability has become one of the core indicators for measuring the quality and reliability of complex products. To ensure the environmental adaptability of products, the engineering field widely employs three core technical methods: margin design, fault-tolerant design, and derating design. Among them, margin design refers to designing a product so that its performance threshold or strength is higher than the maximum environmental stress it is expected to encounter, usually reflected by a uniform safety factor; environmental stress fault-tolerant design refers to ensuring that the system as a whole can still maintain its critical functions when a local unit fails due to environmental stress exceeding its tolerance range through mechanisms such as functional redundancy and backup switching; derating design refers to making components or assemblies operate below their rated stress level, thereby improving their reliability and lifespan by reducing the electrical, thermal, and mechanical stresses they bear.

[0003] Although the above design methods are theoretically mature and have achieved significant results in their respective fields, their application in the engineering practice of complex products faces serious challenges, mainly in the following aspects: 1. Lack of system-level collaboration. In the current design process, margin design, fault-tolerant design, and derating design are often carried out separately by teams with different professional backgrounds. Each team uses independent design standards, analysis tools, and databases, lacking a unified design framework and an efficient data exchange mechanism. As a result, the various design measures not only fail to form a synergy, but may also have potential conflicts or resource overlaps.

[0004] 2. Traditional margin design methods are imprecise. Currently, many engineering projects still use the traditional uniform safety factor method for margin design, which assigns the same experience-based amplification factor to the entire product or all components. This ignores the differences brought about by the multi-level characteristics of complex products. The response mechanisms, sensitivities, and failure consequences of the system level, subsystem level, component level, and part level to the same environmental stress (such as temperature (operating temperature range, storage temperature range), vibration) are completely different. A uniform factor cannot accurately reflect these differences, inevitably leading to distortions in resource allocation.

[0005] 3. Environmental stress tolerance mechanisms lack systematic modeling and quantitative allocation. Existing environmental stress tolerance designs, especially the introduction of functional redundancy mechanisms, largely rely on engineers' experience and qualitative judgment, lacking a systematic and quantitative allocation method.

[0006] 4. There is a certain gap between derating design standards and overall design goals. Derating design currently relies heavily on industry-standard practices or internal company specifications. These standards are usually static and singular, failing to fully consider the specific usage environment profile of the product.

[0007] Based on the above description, how to break down the barriers between margin design, environmental stress tolerance design, and derating design in the multi-level architecture of complex products, and establish an integrated and quantitative collaborative allocation method, has become a technical problem that urgently needs to be solved in this field. Summary of the Invention

[0008] The purpose of this application is to provide a method, system, device and medium for the collaborative allocation of multi-level product environmental adaptability design indicators, which can comprehensively consider the heterogeneous environmental stress profile, the coupling response relationship between multiple levels and the stringent resource constraints, thereby achieving the collaborative optimization allocation of design indicators and ultimately maximizing the overall environmental adaptability performance of the product.

[0009] To achieve the above objectives, this application provides the following solution: Firstly, this application provides a multi-level product environmental adaptability design index collaborative allocation method, including: Obtain environmental profile data of multi-level products throughout their life cycle and performance parameters under environmental stress. Based on the environmental profile data over the lifetime and the performance parameters, a physical coupling model is constructed; the physical coupling model is used to quantify the interaction between environmental stress, performance parameters and time-cumulative effects. Based on the physical coupling model, environmental adaptability margin allocation, environmental stress tolerance allocation, and environmental stress derating allocation are performed at the system level, subsystem level, component level, and device level to obtain the allocation results. The allocation results are collaboratively optimized using a multi-objective optimization algorithm to generate a design index scheme under resource constraints.

[0010] Secondly, this application provides a multi-level product environmental adaptability design index collaborative allocation system, including: The data acquisition module is used to acquire environmental profile data of multi-level products throughout their life cycle and performance parameters under environmental stress. The physical coupling model construction module is used to construct a physical coupling model based on the lifetime environmental profile data and the performance parameters; the physical coupling model is used to quantify the interaction relationship between environmental stress, performance parameters and time cumulative effects. The environmental adaptability allocation module is used to perform environmental adaptability margin allocation, environmental stress tolerance allocation, and environmental stress derating allocation at the system level, subsystem level, component level, and device level based on the physical coupling model, and obtain the allocation results. The design index scheme generation module is used to collaboratively optimize the allocation results through a multi-objective optimization algorithm to generate design index schemes under resource constraints.

[0011] Thirdly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the multi-level product environmental adaptability design index collaborative allocation method provided above.

[0012] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the multi-level product environmental adaptability design index collaborative allocation method provided above.

[0013] According to the specific embodiments provided in this application, this application has the following technical effects: This application provides a method, system, device, and medium for the collaborative allocation of environmental adaptability design indicators for multi-level products. It utilizes a physical coupling model constructed based on life-cycle environmental profile data and performance parameters to allocate environmental adaptability margins, environmental stress tolerance, and environmental stress derating at the system, subsystem, component, and part levels. This ensures that the indicators allocated to each level are based on a solid physical foundation, effectively avoiding over-design or under-design caused by inaccurate models, and improving the accuracy and reliability of the design from the source. Furthermore, a multi-objective optimization algorithm is used to collaboratively optimize the allocation results, generating design indicator schemes under resource constraints. This comprehensively considers the heterogeneity of environmental stress profiles (i.e., life-cycle environmental profile data), the coupling response relationships between multiple levels, and stringent resource constraints, achieving collaborative optimization of the allocation of design indicators and ultimately maximizing the overall environmental adaptability performance of the multi-level product. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 A flowchart illustrating a multi-level product environmental adaptability design index collaborative allocation method provided in an embodiment of this application; Figure 2 A schematic diagram of the functional modules of a multi-level product environmental adaptability design index collaborative allocation system provided in an embodiment of this application; Figure 3 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0016] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0017] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0018] In this application, the environmental adaptability index system includes: (1) Environmental dimension indicators: Temperature: Operating temperature range and storage temperature range.

[0019] Humidity and heat: humidity range and alternation cycle.

[0020] Vibration: the magnitude and frequency range of vibration.

[0021] Sensitivity to environmental stress, environmental stress, etc.

[0022] (2) Performance Dimension Indicators: Functional integrity: The ability to maintain function under environmental stress.

[0023] Performance stability: parameter drift range.

[0024] Precision retention: the range of variation in measurement or control precision.

[0025] Functional importance, failure mode weights, etc.

[0026] (3) Time dimension indicators: Task profile: Environmental requirements and duration of each task phase of the product.

[0027] Environmental profile: Time series of environmental stresses throughout the entire life cycle.

[0028] In one exemplary embodiment, this application provides a multi-level product environmental adaptability design index collaborative allocation method. This method is executed by a computer device, specifically by a terminal or server alone, or by both a terminal and a server. In this embodiment, the method is described using a server as an example. Figure 1 As shown, the method includes: Step 100: Obtain environmental profile data of the multi-level product throughout its life cycle and its performance parameters under environmental stress.

[0029] Step 101: Construct a physical coupling model based on lifetime environmental profile data and performance parameters. The physical coupling model is used to quantify the interaction between environmental stress, performance parameters, and time-cumulative effects.

[0030] Step 102: Based on the physical coupling model, perform environmental adaptability margin allocation, environmental stress tolerance allocation, and environmental stress derating allocation at the system level, subsystem level, component level, and device level to obtain the allocation results.

[0031] Step 103: The allocation results are collaboratively optimized using a multi-objective optimization algorithm to generate a design index scheme under resource constraints. The optimization objectives are to minimize total cost, minimize total quantity, and maximize environmental adaptability. Resource constraints include weight constraints, cost constraints, maintenance cycle constraints, combat readiness rate constraints, damage consistency constraints, and variable boundary constraints.

[0032] In one exemplary embodiment of this application, to improve the real-time performance and accuracy of acquiring life-cycle environmental profile data and performance parameters under environmental stress, this embodiment utilizes a sensor network (such as temperature and humidity sensors, vibration accelerometers, salt spray collectors, etc.) deployed across multiple levels of the product's expected service environment to collect life-cycle environmental profile data, including climatic, mechanical, and biochemical environmental stress data. Simultaneously, performance parameters under environmental stress (such as operating temperature range, vibration tolerance level, communication error rate, etc.) are extracted from product design specifications. This provides a data foundation for constructing a physical coupling model.

[0033] In one exemplary embodiment of this application, in order to achieve precise and efficient configuration of environmental stress tolerance capability, the blindness of existing functional redundancy design is overcome. In this embodiment, the implementation process of step 101 above may include: Step 101-1: Based on lifetime environmental profile data and performance parameters, determine the performance degradation function through accelerated testing or historical data regression analysis. The performance degradation function is expressed as: .

[0034] In the formula, In order to withstand environmental stress Under the influence of time The current performance values ​​(such as strength, accuracy, output power, etc.) after that. These are the initial performance values ​​of the product. Environmental sensitivity coefficient, For reference environmental stress (normal temperature 25℃, standard atmospheric pressure, etc.). This represents the squared deviation of environmental stress from the reference value. The time degradation coefficient, This represents an exponential function with the natural constant e as its base. Environmental stress. This can be the actual environmental stress value that the product experiences, such as temperature (°C), humidity (%RH), vibration level (g), etc. Environmental sensitivity coefficient. This represents the sensitivity of material properties to environmental stress, obtained through environmental sensitivity tests on materials or products (such as temperature-strength tests, humidity-insulation resistance tests). Time degradation coefficient. It represents the inherent aging rate of performance over time and can be obtained through accelerated life testing or regression analysis of historical degradation data.

[0035] Step 101-2: Construct a cumulative damage model based on the physical principle of Miner's linear cumulative damage law.

[0036] Step 101-3: Use the performance degradation function and the cumulative damage model as a physical coupling model. The cumulative damage model is expressed as: .

[0037] In the formula, The total degree of damage, Lifespan. The environmental stress damage coefficient is obtained through fatigue test curves of products or their materials, and reflects the amount of damage caused by specific environmental stress per unit time. This refers to time-varying environmental stress, which is the actual environmental stress value (such as temperature, vibration level, salt spray concentration, etc.) that the product experiences at time t. It is a performance state impact function, that is, in time Time-varying performance of the product The modulation factor for the damage accumulation rate is obtained by fitting through step stress test or damage-performance correlation test. For time-varying performance, that is, at time 10:00... The current performance values ​​of a product (such as strength, accuracy, insulation resistance, etc.) are obtained by recursive calculation using a performance degradation function, reflecting the dynamic process of performance degradation over time under environmental stress.

[0038] The fatigue test curve used in this application can be the SN curve, which can be carried out according to standard methods in mechanical experiments, such as GB / T 3075 and ASTM E466 for metallic materials.

[0039] The time-varying effects of environmental stress on the physical state of a product are quantitatively characterized based on physical mechanisms or experimental data, providing accurate input for subsequent index allocation.

[0040] In an exemplary embodiment of this application, to achieve dynamic and refined adjustment of derating design, transforming it from rigid standard compliance to an optimization process that actively adapts to the real-world usage environment, a dynamic adjustment mechanism can be used to tightly couple derating design with the actual environmental profile of the product, thereby improving the scientific rigor and relevance of derating design. Furthermore, to further enhance the accuracy and physical consistency of environmental adaptability index allocation, when allocating margin, tolerance, and derating, the allocation basis is no longer vague empirical values, but rather these precise model outputs. This ensures that the indicators allocated to each level are based on a solid physical foundation, effectively avoiding over-design or under-design caused by inaccurate models, thus improving the accuracy and reliability of the design from the source. Therefore, the constructed physical coupling model is not independent of the allocation process, but provides it with triple support: physical parameter input, calibration basis, and constraint boundaries. Based on this, the implementation process of step 102 includes: Step 102-1: Environmental adaptability margin allocation (i.e., margin allocation).

[0041] (11) Determine the sensitivity of each level to environmental stress based on the performance degradation function. The sensitivity to environmental stress can be directly calculated from the partial derivative of the performance degradation function with respect to the environment, reflecting the true sensitivity of each level's performance to stress changes, rather than an empirically assigned value. The sensitivity is expressed as: .

[0042] In the formula, In order to withstand environmental stress Under the action, the first Each level has a time The current performance value after that.

[0043] (12) Allocate the total system margin (such as temperature margin, lifetime margin, etc.) to the system level, subsystem level, component level, and part level according to the sensitivity and functional importance of each level to environmental stress, thus obtaining the margin for each level and completing the environmental adaptability margin allocation. The margin for each level is expressed as follows: .

[0044] In the formula, For the first Each level of margin For the total system margin, For the first Sensitivity of each level to environmental stress For the first The sensitivity of each level to environmental stress. For functional importance, ,or , Let i be the risk priority number for the i-th level. Let j be the risk priority number for the j-th level. For the first The severity of the consequences of failure at each level (1-10 points). For the first The probability level of each level of failure (1~10 points). For the first Difficulty of detecting failures at each level (1-10 points). For the first The severity of the consequences of failure at each level (1-10 points). For the first The probability level of each level of failure (1~10 points). For the first Difficulty of detecting failures at each level (1-10 points).

[0045] Based on this step, specific design margin values ​​at each level can be output. Among them, the margin design process is defined as the allowance added on the basis of the benchmark requirements. Its types include: (1) Temperature margin: the allowance beyond the operating temperature range. (2) Vibration margin: the allowance beyond the specified vibration level. (3) Time margin: the allowance beyond the specified life. (4) Performance margin: the allowance beyond the rated performance.

[0046] Step 102-2: Environmental stress tolerance allocation (i.e., fault tolerance allocation).

[0047] (21) Determine the failure probability based on the cumulative damage model, and determine the functional failure mode weights based on the cumulative damage model and impact analysis. Failure refers to loss of function or performance exceeding acceptable limits, rather than component damage.

[0048] Performance degradation mode and impact analysis (PDMA) is a dynamic and performance-oriented extension of traditional failure mode and impact analysis (FMEA). Its core innovation lies in integrating a constructed physical coupling model into the analysis process, enabling the identification and assessment of failure modes to be based on the quantitative calculation of performance degradation trajectories and cumulative damage. Based on this, the implementation process of PDMA includes: Step 1: Define the analysis object and boundary.

[0049] Determine the system hierarchy for analysis (system level, subsystem level, component level, part level). Define the functional definitions and performance parameters for each level.

[0050] Define quantitative criteria for failure (such as performance degradation exceeding 20%, accuracy exceeding the allowable range, etc.), and output a list of analysis objects and a failure criterion table.

[0051] Step 2: Identify performance degradation patterns.

[0052] Based on the performance degradation function, the performance degradation trajectory of each level under environmental stress is identified, and the degradation mode type is determined: linear degradation, exponential degradation, stepped degradation, sudden failure, etc. Key environmental stress types (temperature, vibration, humidity, salt spray, etc.) are identified, and a list of performance degradation modes is output.

[0053] Step 3: Analyze the impact of degradation on function.

[0054] Establish a mapping relationship between performance parameters and functional implementation, and analyze the degree of functional loss when performance degrades to different thresholds.

[0055] The identification of functional degradation phase adopts a process of intact function → partial loss of function → loss of critical function → complete failure, and outputs a functional impact matrix.

[0056] Step 4: Calculate the failure mode weights .

[0057] Based on the functional impact analysis results, weights are assigned to each failure mode. The weight reflects the degree of impact of the failure mode on the success of system tasks. The analytic hierarchy process (AHP) or expert scoring method is used to calculate the failure mode weights. The formula is: .

[0058] In the formula, The severity of the impact (1-10 points) was determined based on the functional impact matrix. The failure frequency level (1~10 points) is calculated by the cumulative damage model. Failure detectability (1-10 points) is determined based on monitoring methods. The severity of the impact of the m-th failure mode. Let m be the frequency level of the m-th failure mode. For the detectability of the m-th failure mode, For all failure modes.

[0059] Connection with physical models: Failure frequency level The total damage level is determined by the cumulative damage model. According to Miner's linear cumulative damage rule, when the current total damage level... Failure is considered to have occurred at a certain time. Therefore, a failure frequency level is defined. have: .

[0060] In the formula, =0.9 is the warning threshold. This indicates upward verification. The calculation result is limited to the range of 1-10.

[0061] Step 5: Generate fault-tolerant design inputs.

[0062] Comprehensive Failure Mode Weights and failure frequency level Determine the priority and type of fault-tolerant design measures, and output the fault-tolerant performance function. Required input parameters.

[0063] (22) Based on the failure frequency level and functional failure mode weights, a fault tolerance effectiveness function is adopted to select fault tolerance measures under cost constraints and complete the environmental stress fault tolerance allocation. Specifically, under cost constraints, specific fault tolerance measures (such as adding a backup flight control computer, using dual power supplies, etc.) can be selected to output a fault tolerance architecture design scheme. The fault tolerance effectiveness function is used to quantitatively evaluate the environmental adaptability effectiveness of different fault tolerance measures, and its expression is as follows: .

[0064] In the formula, For fault tolerance performance function, Increase the weight of the probability of task success. This represents the increase in the probability of mission success (calculated based on the cumulative damage model). To maintain the weight of performance enhancement capabilities, The improvement in performance retention capability (calculated based on the performance degradation function). As the weight of the cost increment, and These are cost and weight increments, respectively. The weight of the weight increment.

[0065] Based on the above description, the establishment process of step (22) can be divided into five stages: ① Generate candidate fault-tolerant measures based on FMEA (Failure Mode and Effects Analysis). ② Calculate the effectiveness components of each measure. ③ Determine the weight coefficients. ④ Calculate and rank the effectiveness values. ⑤ Make a combined optimization decision and select the optimal fault-tolerant architecture scheme.

[0066] At the constraint boundary level, the failure probability in fault tolerance allocation is calculated by the total damage level using the cumulative damage model. The decision is made to trigger a redundancy mechanism when damage approaches the failure threshold, ensuring that the system mission success probability (Rmission) meets design requirements.

[0067] In this application, fault tolerance is defined as the ability to maintain basic functionality in the event of partial failure. Its forms include: (1) functional redundancy: parallel functional channels; (2) performance redundancy: performance reserve capacity; (3) time redundancy: fault recovery capability; and (4) information redundancy: error detection and correction.

[0068] Step 102-3: Environmental stress derating allocation (i.e., derating allocation).

[0069] (31) Determine the environmental correction factor based on the performance degradation function, and obtain the current environmental stress and standard derating value.

[0070] (32) Based on the environmental stress damage coefficient in the cumulative damage model, the derating of the current environmental stress is dynamically adjusted to obtain the adjusted environmental stress.

[0071] (33) Based on the environmental correction factor, current environmental stress, standard derating value, and adjusted environmental stress, determine the specific derating parameters (such as operating voltage, current, power, etc.) at the system level, subsystem level, component level, and part level, and complete the environmental stress derating allocation (which can output a fault-tolerant architecture design scheme). The formula for determining the derating parameters is: D i =Dstd×[1+Kenv×(Eactual-Estd)].

[0072] In the formula, D i For the first The derating parameters are used at each level, where Dstd is the standard derating value, Kenv is the environmental correction factor, Eactual is the actual environmental stress, and Estd is the standard reference environmental stress.

[0073] Among them, the environmental correction coefficient Kenv and the environmental sensitivity coefficient in the performance degradation function The actual environmental stress is positively correlated. Eactual dynamically adjusts the derating range of multi-level products through the damage coefficient in the cumulative damage model, so that the derating design is closely coupled with the real environmental profile.

[0074] Derating is defined as improving reliability by reducing the load, and its categories include: (1) Thermal derating: reducing power density. (2) Electrical derating: reducing voltage / current stress. (3) Mechanical derating: reducing mechanical load. (4) Time derating: reducing usage frequency.

[0075] Therefore, the allocation of each item is essentially a quantitative output under the digital twin framework constructed by the physical coupling model, which enables the three design methods of margin, fault tolerance, and derating to shift from experience-driven to model-driven, and achieves true collaborative optimization.

[0076] In one exemplary embodiment of this application, a threshold boosting method based on a performance degradation function can be used to determine the total system margin. The calculation process includes: Step 1: Determine the failure threshold. Obtain the baseline failure threshold P from the performance parameters under environmental stress. failure .

[0077] Step 2: Establish a performance degradation trajectory. Based on the performance degradation function, calculate the performance degradation trajectory over the lifetime. T The minimum performance value without margin is expressed as: : .

[0078] In the formula, Let the environmental stress at time t be... Instantaneous performance value at time.

[0079] Step 3: Calculate the required performance improvement. To ensure that performance always exceeds the failure threshold, the minimum required performance improvement is: ,have: .

[0080] Step 4: Convert to total system margin. Based on the conversion relationship between performance parameters and margin physical quantities (such as the relationship between material strength and temperature, and the relationship between stress and life), convert... Convert to the corresponding margin value : .

[0081] In the formula, The conversion factor is determined by the material constitutive relation or empirical formula.

[0082] In an exemplary embodiment of this application, in order to further achieve synergistic optimization allocation of design metrics and maximize the overall environmental adaptability of the product, the implementation process of step 103 in this embodiment includes: Step 103-1: Using the allocation results (i.e., the allocated margin, fault tolerance, derating schemes, etc.) as input, construct a multi-objective optimization model with total cost (such as material cost, weight, etc.), environmental adaptability performance, and system task success probability as objectives. Environmental adaptability performance is a comprehensive evaluation index, which can be quantified through weighted comprehensive functional integrity rate, performance retention, etc. System task success probability replaces the previously vague system reliability, specifically referring to the probability that the product can successfully complete the predetermined task under the expected environmental profile, thus more directly reflecting the ultimate goal of environmental adaptability.

[0083] Step 103-2: Using optimization algorithms such as NSGA-II (Non-dominated Sorting Genetic Algorithm II), the multi-objective optimization model is iteratively solved to obtain the Pareto optimal solution set. The objective function vector of the multi-objective optimization model is... Represented as: .

[0084] In the formula, For total cost, Total weight For environmental adaptability.

[0085] The multi-objective optimization model satisfies the following resource constraints: In the formula, To maximize cost, For maximum weight, For maintenance cycle, This represents the probability of task success. This is the lowest acceptable threshold for the probability of task success (i.e., the lower limit of integrity requirements). For the current input The corresponding total degree of damage.

[0086] Furthermore, during the iterative solution process, each individual (i.e., the design scheme) is coded by three types of decision variables: ① margin (Real numbers, such as structural corrosion allowance). ② Selection of fault tolerance measures (binary, 0 / 1). ③ Derating parameters (i.e., derating coefficient). D k (Real number). The initial population can be set to 200 individuals, with 80% randomly generated and 20% generated based on engineering experience.

[0087] Iterative process: ① Non-dominated sorting to define Pareto front levels. ② Calculating crowding distance to maintain diversity. ③ Tournament selection of parents. ④ SBX crossover (probability 0.9) and polynomial mutation (probability 0.1) to generate offspring. ⑤ Merging parents and offspring, reordering, and selecting the top 200 for the next generation. ⑥ Repeating until 1000 generations or convergence, outputting the Pareto optimal solution set for TOPSIS decision selection. In practical engineering applications, multiple termination conditions are typically used; iteration stops when any one condition is met.

[0088] Step 103-3: Select the best solution from the Pareto optimal solution set through the TOPSIS decision program, and generate a design specification scheme that includes system-level, subsystem-level, component-level and component-level final design specifications (such as 1.5mm corrosion margin for wing structure, triple redundancy for flight control system, and derating of XX chip operating voltage to 5V, etc.).

[0089] Based on the description in step 103-2 above, all solutions in the obtained Pareto optimal solution set are non-dominated solutions, meaning that improvement in any objective inevitably leads to deterioration in other objectives. Therefore, objective weights are set according to design preferences (e.g., efficiency weight 0.5, cost 0.3, weight 0.2), and the Euclidean distance between each solution and the ideal solution (minimum cost, minimum weight, maximum efficiency) and the negative ideal solution is calculated to obtain the relative proximity. The solution with the highest proximity is the optimal solution, representing the closest proximity to the ideal solution and the furthest distance from the negative ideal solution under the weight preferences, thus achieving the optimal trade-off among multiple objectives.

[0090] In this application, constraints such as weight, cost, power consumption, and space of multi-level products are adopted. These constraints are related to the actual product and will not be elaborated here.

[0091] In one exemplary embodiment of this application, based on the above description, this embodiment provides a specific process for implementing dynamic fine-tuning of derating design, including: Step 1: Real-world stress acquisition.

[0092] 1) Collect actual environmental stress data of the product's service environment through a sensor network (temperature and humidity sensors, vibration accelerometers, salt spray collectors).

[0093] 2) Obtain time-varying environmental stresses, such as temperature spectrum, vibration spectrum, and salt spray concentration spectrum.

[0094] 3) Identify the types of critical environmental stresses.

[0095] Step 2: Physical coupling model parameter calibration.

[0096] Based on the performance degradation function, an environmental sensitivity coefficient is extracted.

[0097] Based on the cumulative damage model, the damage coefficient and its rate of change with environmental stress are calculated.

[0098] Calibration environmental correction factor : , is the conversion factor.

[0099] Step 3: Calculate the dynamic reduction value.

[0100] 1) Obtain standard derating values ​​from industry standards, such as the 80% voltage derating specified in GJB / Z 35.

[0101] 2) Determine the standard reference environment, such as a room temperature of 25°C.

[0102] 3) Substitute the values ​​into the dynamic adjustment formula to calculate the actual reduction.

[0103] Step 4: Handling the boundary of the reduction value.

[0104] 1) Check whether the calculated values ​​exceed the feasible range of the project.

[0105] 2) Ensure that the reduction value meets the minimum requirements of industry standards (e.g., not less than 50% of the rated value).

[0106] Step 5: Output and Design Application.

[0107] 1) Write the dynamically adjusted reduction value into the design specification document.

[0108] 2) Provide guidance on component selection, such as selecting components with higher ratings to meet derating requirements.

[0109] 3) Provide guidance on circuit design, such as setting the upper limit of the operating voltage and the junction temperature monitoring threshold.

[0110] Based on the same inventive concept, this application also provides a multi-level product environmental adaptability design index collaborative allocation system for implementing the multi-level product environmental adaptability design index collaborative allocation method described above. The solution provided by this system is similar to the implementation scheme described in the above method. Therefore, the specific limitations of one or more multi-level product environmental adaptability design index collaborative allocation system embodiments provided below can be found in the limitations of the multi-level product environmental adaptability design index collaborative allocation method described above, and will not be repeated here.

[0111] In one exemplary embodiment, such as Figure 2 As shown, a multi-level product environmental adaptability design index collaborative allocation system is provided, including: a data acquisition module 200, a physical coupling model construction module 201, an environmental adaptability allocation module 202, and a design index scheme generation module 203.

[0112] The data acquisition module 200 is used to acquire environmental profile data of multi-level products throughout their life cycle and performance parameters under environmental stress.

[0113] The physical coupling model construction module 201 is used to construct a physical coupling model based on lifetime environmental profile data and performance parameters. The physical coupling model is used to quantify the interaction between environmental stress, performance parameters, and time-cumulative effects.

[0114] The environmental adaptability allocation module 202 is used to perform environmental adaptability margin allocation, environmental stress tolerance allocation, and environmental stress derating allocation at the system level, subsystem level, component level, and device level based on the physical coupling model, and obtain the allocation results.

[0115] The design index scheme generation module 203 is used to perform collaborative optimization of the allocation results through a multi-objective optimization algorithm to generate design index schemes under resource constraints.

[0116] Based on the above description, this application is applicable to the environmental adaptability design of high-reliability products.

[0117] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 3As shown, the computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs in the non-volatile storage media. The database stores multi-level product environment adaptability design index collaborative allocation data. The I / O interfaces are used for information exchange between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a multi-level product environment adaptability design index collaborative allocation method.

[0118] Those skilled in the art will understand that Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer equipment to which the present application is applied. Specific computer equipment may include, for example, [the following is a list of possible additional structures]. Figure 3 The diagram shows more or fewer components, or combinations of certain components, or different component arrangements.

[0119] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0120] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0121] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0122] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0123] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (RRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0124] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0125] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0126] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for collaborative allocation of multi-level product environmental adaptability design indicators, characterized in that, include: Obtain environmental profile data of multi-level products throughout their life cycle and performance parameters under environmental stress. Based on the environmental profile data over the lifetime and the performance parameters, a physical coupling model is constructed; the physical coupling model is used to quantify the interaction between environmental stress, performance parameters and time-cumulative effects. Based on the physical coupling model, environmental adaptability margin allocation, environmental stress tolerance allocation, and environmental stress derating allocation are performed at the system level, subsystem level, component level, and device level to obtain the allocation results. The allocation results are collaboratively optimized using a multi-objective optimization algorithm to generate a design index scheme under resource constraints.

2. The method for collaborative allocation of multi-level product environmental adaptability design indicators according to claim 1, characterized in that, Based on the environmental profile data over the lifetime and the performance parameters, a physical coupling model is constructed, including: Based on the environmental profile data over the lifespan and the performance parameters, the performance degradation function is determined through accelerated testing or regression analysis of historical data. Based on the physical principles of Miner's linear cumulative damage law, a cumulative damage model is constructed. The performance degradation function and the cumulative damage model are used as the physical coupling model.

3. The method for collaborative allocation of multi-level product environmental adaptability design indicators according to claim 2, characterized in that, The performance degradation function is expressed as: ； In the formula, In order to withstand environmental stress Under the influence of time The current performance value after that, These are the initial performance values ​​of the product. Environmental sensitivity coefficient, For reference environmental stress, The time degradation coefficient, It represents an exponential function with the natural constant e as its base.

4. The method for collaborative allocation of multi-level product environmental adaptability design indicators according to claim 2, characterized in that, The cumulative damage model is expressed as follows: ； In the formula, The total degree of damage, For lifespan, Environmental stress damage coefficient, For time-varying environmental stress, This is a function that affects performance status. For time-varying performance, For a moment.

5. The method for collaborative allocation of multi-level product environmental adaptability design indicators according to claim 2, characterized in that, Based on the aforementioned physical coupling model, environmental adaptability margin allocation, environmental stress tolerance allocation, and environmental stress derating allocation are performed at the system level, subsystem level, component level, and device level, yielding allocation results including: The sensitivity of each level to environmental stress is determined based on the performance degradation function. The total system margin is allocated to the system level, subsystem level, component level and element level according to the sensitivity and functional importance of each level to environmental stress, so as to obtain the margin of each level and complete the allocation of environmental adaptability margin. The failure probability is determined based on the cumulative damage model, and the weights of functional failure modes are determined based on the cumulative damage model and impact analysis. Based on the failure probability and the functional failure mode weights, a fault tolerance efficiency function is adopted, and fault tolerance measures are selected under cost constraints to complete the environmental stress fault tolerance allocation. The environmental correction factor is determined based on the performance degradation function, and the current environmental stress and standard derating value are obtained. The derating rate of the current environmental stress is dynamically adjusted based on the environmental stress damage coefficient in the cumulative damage model to obtain the adjusted environmental stress. Based on the environmental correction factor, the current environmental stress, the standard derating value, and the adjusted environmental stress, the derating parameters at the system level, subsystem level, component level, and part level are determined to complete the environmental stress derating allocation.

6. The method for collaborative allocation of multi-level product environmental adaptability design indicators according to claim 5, characterized in that, The margin at each level is represented as follows: ； In the formula, For the first Each level of margin For the total system margin, For the first Sensitivity of each level to environmental stress For the first Sensitivity of each level to environmental stress Functional importance.

7. The method for collaborative allocation of multi-level product environmental adaptability design indicators according to claim 1, characterized in that, The allocation results are collaboratively optimized using a multi-objective optimization algorithm to generate a design index scheme under resource constraints, including: Using the allocation results as input, a multi-objective optimization model is constructed with total cost, environmental adaptability, and system task success probability as objectives; The NSGA-II optimization algorithm is used to iteratively solve the multi-objective optimization model to obtain the Pareto optimal solution set; The TOPSIS decision-making process selects the best solution from the Pareto optimal solution set and generates a design index scheme that includes final design indices at the system level, subsystem level, component level, and part level.

8. A multi-level product environmental adaptability design index collaborative allocation system, characterized in that, include: The data acquisition module is used to acquire environmental profile data of multi-level products throughout their life cycle and performance parameters under environmental stress. The physical coupling model construction module is used to construct a physical coupling model based on the lifetime environmental profile data and the performance parameters; the physical coupling model is used to quantify the interaction relationship between environmental stress, performance parameters and time cumulative effects. The environmental adaptability allocation module is used to perform environmental adaptability margin allocation, environmental stress tolerance allocation, and environmental stress derating allocation at the system level, subsystem level, component level, and device level based on the physical coupling model, and obtain the allocation results. The design index scheme generation module is used to collaboratively optimize the allocation results through a multi-objective optimization algorithm to generate design index schemes under resource constraints.

9. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that the processor executes the computer program to implement the multi-level product environmental adaptability design index collaborative allocation method according to any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the multi-level product environmental adaptability design index collaborative allocation method as described in any one of claims 1-7.

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