Intelligent system coupling and virtual aging test method for harsh environment

Abstract

The application discloses a kind of intelligent system coupling and virtual aging test method for harsh environment, belong to intelligent system full stack test and reliability guarantee technical field.The application is by synchronous acquisition multi-physical field environmental stress data and chip-software stack-model full stack performance data, using PTP nanosecond level clock realizes timing accurate alignment, constructs environmental parameter-performance index nonlinear coupling mathematical model, quantifies temperature, vibration, radiation on inference accuracy, time delay, the influence law of reliability;Memory, clock crystal, power unit and other core devices aging model are established simultaneously, and virtual accelerated aging is realized by parameter gradual change injection, and the remaining service life of system is quickly predicted.The application breaks through the bottleneck of traditional environment test and calculation performance fragmentation, long aging test cycle, life cannot be quantified, has full stack observable, timing high-precision alignment, environment-computing joint quantization, virtual accelerated aging, defect reproducible and other advantages, suitable for end-side intelligent system constituted by domestic AI chip and inference framework, can be widely applied to industrial control, aerospace, deep-sea exploration, national defense equipment and other harsh scene full stack verification, reliability evaluation and life prediction.

Description

Technical Field

[0001] This invention belongs to the field of intelligent system full-stack testing and reliability assurance technology, specifically relating to a coupled testing method based on environment-computation joint quantitative analysis and virtual accelerated aging for edge intelligent systems composed of domestically produced AI chips, inference frameworks, and model applications in harsh environments. This invention achieves quantitative characterization of the effects of environmental factors such as temperature, vibration, and radiation on inference accuracy, latency, and reliability through simultaneous acquisition of multi-physics field environmental stress and computational performance, time-series alignment, coupled modeling, and virtual aging injection of core components. It also rapidly predicts the remaining service life of the system and is applicable to full-stack verification, reliability assessment, and lifespan prediction of intelligent systems in harsh scenarios such as industrial control, aerospace, deep-sea exploration, and defense equipment. Background Technology

[0002] Against the backdrop of the comprehensive rise of the domestic artificial intelligence ecosystem and the large-scale deployment of edge intelligent equipment, intelligent systems have deeply penetrated key fields such as industrial control, autonomous driving, aerospace, deep-sea exploration, and national defense equipment, becoming the core carrier for the high-quality development of the national digital economy and high-end manufacturing. Currently, intelligent systems exhibit distinct technical characteristics such as chip heterogeneity, framework diversification, model complexity, and extreme application scenarios. Their operational correctness, inference accuracy, real-time performance, environmental reliability, and long-term robustness directly determine the operational safety of high-end equipment, industrial economic benefits, and the success or failure of major national strategic tasks. However, intelligent systems are essentially composed of deeply coupled chip hardware, computing software stacks, model applications, data flow, and environmental conditions. Their hierarchical interactions are tight, fault propagation is hidden, and anomaly mechanisms are complex. Traditional testing and verification methods are no longer adequate for the full-stack quality assurance requirements, leading to increasingly prominent problems such as missed defect detection, ambiguous localization, difficulty in reproduction, and inaccurate lifespan assessment.

[0003] Currently, reliability testing of intelligent systems still largely follows the traditional verification model of discrete, offline, and single-environment loading, which presents a series of insurmountable technical bottlenecks. Environmental testing and computational performance are completely disconnected. Traditional high and low temperature, vibration, and radiation tests can only apply physical stress independently and cannot be linked with real-time inference accuracy, latency, power consumption, and operator execution status testing, making it impossible to quantify the coupled impact of the environment on system performance. Multi-source data lacks unified time synchronization, and environmental data, hardware status, software operation, and model output cannot be accurately aligned, making fault tracing and causal analysis difficult. Traditional aging tests rely on long-term real-world operation, which is time-consuming, costly, and uncontrollable, failing to meet the needs of rapid iteration and lifetime prediction for edge-side intelligent systems. At the same time, existing methods lack mathematical modeling and virtual injection methods for the aging of core components such as memory wear, clock frequency shift, and voltage drift, making it difficult to achieve rapid reproduction of long-lifecycle performance degradation and quantification of remaining lifetime.

[0004] Furthermore, the existing testing system suffers from insufficient domestic adaptation, lacking low-level compatibility with domestic AI chips such as Ascend, Cambricon, and Moore's Threads, as well as inference frameworks like CANN and CNRT. This prevents the achievement of full-stack system-level joint verification under harsh environments. Defect localization and lifetime assessment methods are also crude, failing to generate complete quantitative conclusions from the perspectives of environmental coupling, device aging, and performance degradation. This makes it difficult to meet the engineering verification requirements of high-efficiency, high-reliability, quantifiable, and reproducible high-end intelligent equipment. Therefore, developing a testing method for intelligent systems that features joint environment-computation testing, precise timing alignment, quantifiable coupling patterns, and virtual acceleration of aging has become an urgent need and a key technological bottleneck for ensuring the security of the domestic artificial intelligence ecosystem and supporting the development of high-end equipment in harsh environments. Summary of the Invention

[0005] To address the technical bottlenecks in existing testing of intelligent systems in harsh environments, such as the disconnect between environmental testing and computational performance, the inability to synchronize multi-source data in a timely manner, the lengthy and inefficient traditional aging test cycle, the inability to quantify performance degradation and lifespan, and the difficulty in reproducing faults, this invention discloses a coupling and virtual aging test method for intelligent systems in harsh environments.

[0006] This invention utilizes synchronous acquisition by environmental sensors and full-stack test probes, along with precise alignment of PTP nanosecond-level timestamps, to construct a nonlinear coupled mathematical model of "environmental parameters-performance indicators." This model enables quantitative characterization of the effects of multi-physical field stresses such as temperature, vibration, and radiation on inference accuracy, latency, and reliability. Simultaneously, it establishes aging mathematical models for core components such as memory wear, clock crystal frequency shift, and computing unit attenuation. By employing parameter gradual injection, it achieves virtual acceleration of the aging process, rapidly predicting the remaining usable lifespan of the system. This completely breaks the traditional "environment first, test later" model, achieving a test technology upgrade from phenomenon detection to regular quantification and from real aging to virtual acceleration.

[0007] The technical solution of this invention is as follows: A method for coupling and virtual aging testing of intelligent systems in harsh environments, comprising the following main steps: Synchronous acquisition and timestamp alignment of environmental data and full-stack performance data through a unified precision clock; construction of an environment-performance nonlinear coupling model to quantify the impact of environmental stress; simultaneous establishment of an aging model for core components and completion of virtual accelerated aging with gradual parameter injection; ultimately achieving system performance degradation analysis and accurate prediction of remaining lifespan; the method first synchronously deploys environmental acquisition units and full-stack test probes to acquire environmental stress data such as temperature, vibration, and radiation in real time, as well as performance data from the chip hardware, computing software stack, and model application layers; and relies on a nanosecond-level clock to synchronously complete multi-source data timing calibration, forming a standardized environment-computing joint dataset; Using environmental parameters as input and performance indicators as output, a nonlinear coupling model is trained to quantify the changes in system inference accuracy, latency, and reliability under different harsh operating conditions. Aging mathematical models are established for key components such as memory, clock crystals, power modules, and computing units. Aging parameters are gradually injected via software to simulate the performance degradation process caused by long-term operation while simultaneously monitoring system status. Based on the performance degradation curve during the aging process, a lifetime prediction model is fitted to calculate the remaining lifetime and complete the assessment of reliability boundaries and safe operating ranges. Finally, an environment-computation coupling quantification report and a virtual accelerated aging assessment report are output, forming a complete set of reproducible, quantifiable, and traceable test conclusions. This provides a precise basis for the reliability design, lifetime assessment, and protection optimization of edge-side intelligent devices under harsh environments. Attached Figure Description

[0008] Figure 1 This is a schematic diagram of the overall architecture of the environment-computation joint quantization and virtual accelerated aging of the present invention;

[0009] Figure 2 This is a schematic diagram of the multi-source data synchronous acquisition and precise time-series alignment process of the present invention;

[0010] Figure 3 This is a schematic diagram illustrating the principle of nonlinear coupling modeling and quantitative analysis of environment and performance in this invention.

[0011] Figure 4 This is a schematic diagram of the process for constructing the aging model of the core device and gradually injecting parameters in this invention.

[0012] Figure 5 This is a schematic diagram of the remaining service life prediction and reliability assessment output of the present invention. Detailed Implementation

[0013] To better understand the technical content of the present invention, specific embodiments are described below in conjunction with the accompanying drawings.

[0014] Figure 1This is a schematic diagram of the overall architecture of the environment-computation joint quantization and virtual accelerated aging of the present invention; Figure 2 This is a schematic diagram of the multi-source data synchronous acquisition and precise time-series alignment process of the present invention; Figure 3 This is a schematic diagram illustrating the principle of nonlinear coupling modeling and quantitative analysis of environment and performance in this invention. Figure 4 This is a schematic diagram of the process for constructing the aging model of the core device and gradually injecting parameters in this invention. Figure 5 This is a schematic diagram illustrating the remaining useful life prediction and reliability assessment output of the present invention. In specific implementation, it includes the following steps:

[0015] ①Simultaneous acquisition of multi-physics environment and full-stack performance

[0016] A multi-physics environment acquisition unit for temperature, vibration, radiation, and humidity is deployed around the intelligent system under test. Simultaneously, hardware status probes, runtime probes, and data flow probes are deployed within the intelligent system to construct a bypass, non-intrusive acquisition architecture. The environment acquisition module acquires environmental stress data in real time, including temperature change curves, vibration amplitude and frequency, and radiation dose rate. The hardware status probe acquires chip register, power consumption waveform, I / O timing, and power ripple data. The runtime probe acquires operator execution status, scheduling latency, memory access, and driver interaction information. The data flow probe acquires model inference accuracy, latency, output distortion, and quantization loss data. All acquisition channels are triggered by FPGA hardware and synchronized with a PTP precision clock, achieving a timestamp alignment accuracy of ≤100ns between environmental and performance data, ensuring complete consistency in timing of multi-source data.

[0017] ② Multi-source data time series calibration and construction of environment-computation joint dataset

[0018] The collected environmental, hardware, software, and model data are integrated into a unified processing unit. Asynchronous sampling and data from different frequencies undergo time-axis normalization, interpolation, resampling, data point addition, and outlier removal to achieve data denoising and standardization. Using a unified timestamp as an index, environmental parameters and system performance indicators are correlated time-by-time, forming a standardized environment-computation joint dataset that can be used for model training, defect reproduction, and coupling analysis, providing a high-quality data foundation for subsequent nonlinear coupling modeling.

[0019] ③ Environment-Performance Nonlinear Coupling Modeling and Quantitative Analysis

[0020] Using temperature, vibration, radiation, humidity, and air pressure as input features, and inference accuracy, latency, throughput, power consumption, failure rate, signal integrity, and robustness as output metrics, an environment-performance nonlinear coupling model is constructed based on machine learning algorithms such as random forest, BP neural network, and support vector machine. Through training and iterative optimization, the model's fitting accuracy reaches the preset index, enabling quantitative characterization and prediction of the performance degradation and reliability decline patterns of intelligent systems under different environmental stress combinations. The model outputs a complete quantitative relationship from environmental changes to performance degradation and then to the safe operating boundary, completing a test upgrade from phenomenon detection to systematic quantification.

[0021] ④ Construction of mathematical model for aging of core components

[0022] To address the long-lifecycle reliability verification requirements of edge-side intelligent systems, an aging model library for core components such as memory, clock crystals, computing units, and power modules is established. Wear and bit error accumulation models for NAND Flash and DDR memory are constructed, along with models for clock crystals. p PM-level frequency shift and period drift models, ADC / DAC reference voltage drift and accuracy degradation models, and AI chip computing unit aging and computational error increase models are all calibrated based on device datasheets and measured data to ensure that the aging trend is consistent with the actual physical degradation.

[0023] ⑤ Virtual accelerated aging and gradual parameter injection

[0024] Using a software-defined approach, aging parameters are gradually injected into the system under test, effectively simulating the long-term aging process without requiring prolonged real-world operation. The system progressively increases memory error rate, clock skew, voltage drift, computing power attenuation ratio, and noise intensity, while simultaneously monitoring inference accuracy, latency, output stability, failure rate, and operator execution status in real time. This fully reproduces the device's performance evolution throughout its entire lifecycle, from normal operation to degradation, deterioration, and near-failure.

[0025] ⑥ Remaining useful life estimation and reliability quantification assessment

[0026] Based on performance degradation data during virtual accelerated aging, a lifetime prediction model and a failure threshold determination model are fitted to calculate the remaining runtime of the system from the current state to the failure threshold, thus obtaining the Remaining Service Life (RUL). The system outputs reliability boundaries, safe operating ranges, lifetime limits, and degradation warning points under different environmental stresses, providing a quantitative basis for reliability design, lifetime assessment, and protection optimization of edge-side intelligent systems under harsh environments.

[0027] ⑦ Test report output and defect reproduction support

[0028] The system generates an environment-computation coupling quantitative analysis report and a virtual accelerated aging reliability assessment report. The reports include environment-performance coupling curves, quantification coefficients, impact weights, fitting accuracy, aging curves, failure thresholds, remaining lifetime, reliability indicators, and degradation rates. It also records complete fault scenarios, degradation reproduction conditions, test configurations, and time-series data, supporting one-click defect reproduction, full-process traceability, and multi-scenario comparative verification. This provides quantifiable and reproducible test conclusions for intelligent system optimization design.

Claims

1. A method for coupling and virtual aging testing of intelligent systems in harsh environments, characterized in that, To address the full-stack reliability verification requirements of edge-side intelligent systems in harsh environments, this method achieves synchronous acquisition and nanosecond-level timestamp alignment of environmental stress data and system performance data through environmental sensors and full-stack test probes. A nonlinear coupled mathematical model of "environmental parameters-performance indicators" is constructed to quantify the impact of environmental stresses such as temperature, vibration, and radiation on inference accuracy, latency, power consumption, and reliability. Simultaneously, aging mathematical models for core components such as memory, clock crystals, and computing units are established. The aging process is virtually accelerated through software-defined parameter gradual injection, enabling rapid prediction of the remaining lifespan of the intelligent system. This method comprises the following steps: 1) Simultaneously deploy environmental acquisition units and full-stack test probes in the intelligent system under test to build a non-intrusive, multi-dimensional, and time-unified acquisition architecture, and collect real-time environmental stress data of temperature, vibration, radiation, and other physical fields, as well as full-dimensional performance data of the chip hardware layer, computing software stack layer, and model application layer. 2) The environmental stress data and the full-stack performance data are precisely aligned with nanosecond-level timestamps based on a unified PTP precision clock. Data normalization, interpolation, resampling and noise reduction are completed to construct a standardized environmental-computation joint dataset. 3) Using environmental parameters as input and performance indicators as output, a machine learning algorithm is used to train an environment-performance nonlinear coupling model to quantify the magnitude of system performance degradation and reliability decline under different stress combinations. 4) Construct aging mathematical models for memory wear, clock frequency shift, voltage drift, and computing power decay, forming an aging model library covering core components; 5) Gradually inject aging parameters into the system under test through software to simulate the device degradation process caused by long-term operation, monitor system performance changes simultaneously, and complete virtual accelerated aging test; 6) Based on the performance degradation curve during the aging process, fit the lifetime prediction model, calculate the remaining useful life (RUL), and complete the quantitative assessment of the reliability boundary and safe operating range. 7) Output environment - computational coupling quantitative analysis report and virtual accelerated aging reliability assessment report, including coupling relationship, aging curve, remaining lifetime, defect reproduction conditions and optimization suggestions.

2. The synchronous deployment environment acquisition unit and full-stack test probe according to claim 1, characterized in that: 1) The environmental acquisition unit integrates temperature, vibration, radiation, and humidity sensors to achieve real-time synchronous acquisition of environmental parameters from multiple physical fields; 2) The full-stack test probes are divided into hardware status probes, runtime probes, and data flow probes, which respectively collect data on chip registers, instruction stream, power consumption, I / O timing, operator execution, scheduling latency, memory access, inference accuracy, latency, and robustness. 3) Environmental and performance data acquisition uses a unified PTP precision clock and FPGA hardware triggering synchronization, with timestamp alignment accuracy ≤100ns; 4) The data acquisition architecture adopts a bypass non-intrusive design, which does not occupy the resources of the system under test and does not affect the normal inference operation.

3. The method for precisely aligning environmental data and performance data with nanosecond-level timestamps as described in claim 1, characterized in that: 1) Perform unified timeline normalization and time series calibration on environmental data, hardware data, software data, and model data; 2) Perform interpolation, data point supplementation, resampling, and outlier removal on asynchronously sampled data collected at different frequencies; 3) Establish a unified time-series index structure to achieve precise time-by-time correspondence between environmental stress and system performance; 4) Generate a standardized environment for model training, fault reproduction, and quantitative analysis – a joint computation dataset.

4. The method for training a nonlinear coupled model and quantifying the influence of environmental stress according to claim 1, characterized in that: 1) Use temperature, vibration, radiation, humidity, and air pressure as input features for the model; 2) Use inference accuracy, latency, throughput, power consumption, failure rate, signal integrity, and robustness as output metrics; 3) A nonlinear coupling model of environment and performance is constructed using random forest, BP neural network, and support vector machine; 4) Output quantitative mapping relationship: Environmental change amount → Performance degradation amount → Reliability decrease amount → Safe working boundary.

5. The mathematical model for establishing the aging of core components according to claim 1, characterized in that: 1) Establish NAND Flash and DDR memory wear models, read / write error rate increase models, and bit error accumulation models; 2) Establish a ppm-level frequency shift model, a periodic drift model, and a long-term phase jitter model for the clock crystal; 3) Establish the ADC / DAC reference voltage drift model, gain error model, and accuracy attenuation model; 4) Establish an aging model for AI chip computing power units, a model for increasing computing errors, and a model for degrading operator execution efficiency.

6. The virtual accelerated aging process performed by parameter gradient injection according to claim 1, characterized in that: 1) Gradual aging parameters are injected in a software-defined manner, which can effectively simulate long-term aging without long-term real-world operation; 2) Gradually improve memory error rate, clock skew, voltage drift, computing power attenuation ratio, and noise intensity; 3) Synchronously monitor inference accuracy, latency, output stability, failure rate, and operator execution status trends in real time; 4) Fully reproduce the performance evolution process of the device throughout its entire life cycle, from normal operation, degradation, deterioration to near failure.

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