Reliability prediction method and system for autonomous driving electronic systems based on deep learning

Abstract

The present invention proposes a reliability prediction method and system for autonomous driving electronic systems based on deep learning. The method includes: establishing an electromagnetic environment and component interaction model and using time-frequency analysis to track electromagnetic interference sources; using a dynamic LSTM model to predict the failure mode evolution path of system components and introducing a time-varying perturbation model for dynamic electromagnetic interference regularization; combining the failure mode prediction results with an adaptive time window and time series regression model to predict the combined impact of electromagnetic interference on system failure; adjusting system control signals and resource configurations through an adaptive fault-tolerant control algorithm and a dynamic resource allocation strategy based on failure mode prediction and electromagnetic interference prediction values; dynamically triggering a failure prevention mechanism based on a health assessment function and optimizing preventive measures through a feedback correction mechanism. The present invention effectively addresses the shortcomings of existing technologies in terms of prediction accuracy, interference identification, and fault-tolerant mechanisms, providing a new technical path for improving the reliability of autonomous driving systems.

Description

Technical Field

[0001] The present invention belongs to the field of deep learning, and in particular to a method and system for predicting the reliability of an autonomous driving electronic system based on deep learning. Background Art

[0002] With the rapid development of autonomous driving technology, the electronic systems of autonomous vehicles are becoming increasingly complex, encompassing a large number of hardware components, including sensors, control units, power management modules, actuators, and more. To ensure safe and efficient vehicle operation under various road and environmental conditions, the electronic components of autonomous driving systems must exhibit extremely high reliability and stability. This requires the system to continuously monitor the operating status of each component and take timely preventive and remedial measures in the event of anomalies such as failures or electromagnetic interference. However, existing autonomous driving electronic systems still face many challenges in terms of reliability prediction and fault tolerance.

[0003] Currently, reliability prediction technologies for autonomous driving systems primarily rely on traditional static methods and simple historical data analysis. These traditional static methods typically make predictions based on established failure modes and empirical models. These methods lack the ability to dynamically adapt to changing environments and often overlook the impact of external factors such as electromagnetic interference (EMI). Electromagnetic interference (EMI) is a common and serious problem in autonomous driving systems, especially in complex traffic environments. Autonomous vehicles require real-time communication with other vehicles, traffic infrastructure, and wireless networks, and their numerous internal electronic components make them susceptible to external electromagnetic signals. EMI not only affects sensor signal quality but can also cause control unit failure or data transmission errors. Currently, EMI identification and mitigation strategies often rely on empirical rules or simple filtering techniques. However, these methods often fail to effectively address complex and dynamic electromagnetic environments, potentially leading to insufficient system fault tolerance.

[0004] Furthermore, existing fault-tolerance mechanisms are typically based on redundant designs and pre-set fault-tolerance strategies. While these methods can provide protection against some common fault conditions, they often fail to perform adequately when the system experiences complex failure modes or is subject to strong electromagnetic interference. Especially with the continuous advancement of deep learning and artificial intelligence technologies, traditional fault-tolerance strategies appear to be lagging behind and are unable to meet the high reliability and fault-tolerance requirements of autonomous driving systems. Therefore, predicting system failure modes and optimizing the system's fault-tolerance mechanisms in real time under complex and dynamic operating environments has become a critical issue in improving the reliability of autonomous driving electronic systems.

[0005] The existing technology also faces the following major problems:

[0006] Insufficient dynamics in failure prediction: Existing methods lack the ability to monitor and dynamically predict changes in system operating status in real time and cannot adapt to changes in different working environments.

[0007] Limited ability to identify and respond to electromagnetic interference: Traditional electromagnetic interference identification methods fail to effectively combine internal system data analysis with the actual impact of the electromagnetic environment, and lack accurate prediction and fault tolerance mechanisms for system failure modes caused by electromagnetic interference.

[0008] Limited adaptability of fault-tolerant mechanisms: Traditional fault-tolerant strategies are based on static models and redundant designs, but these methods are difficult to respond promptly and effectively when the system encounters unknown faults or complex electromagnetic interference. Summary of the Invention

[0009] The purpose of this invention is to propose a reliability prediction method and system for autonomous driving electronic systems based on deep learning. By introducing a hybrid intelligent model, electromagnetic interference identification and adaptive fault tolerance mechanism, and dynamic optimization strategy, a new reliability prediction and fault tolerance optimization method for autonomous driving electronic systems is provided.

[0010] To achieve the above objectives, a first aspect of the present invention provides a method for predicting the reliability of an autonomous driving electronic system based on deep learning, comprising the following steps:

[0011] S1: Build an electromagnetic environment and component interaction model, generate system health status input through multi-dimensional sensor data fusion, and use time-frequency analysis to track electromagnetic interference sources;

[0012] S2: Identify system failure modes based on high-order statistical features of health status; use a dynamic LSTM model to predict the evolution path of failure modes of system components, and introduce a time-varying perturbation model for dynamic electromagnetic interference regularization;

[0013] S3: Combined with the failure mode prediction results, adaptive time window and time series regression model are used to predict the joint impact of electromagnetic interference on system failure;

[0014] S4: Based on the failure mode prediction and electromagnetic interference prediction value, the system control signal and resource configuration are adjusted through adaptive fault-tolerant control algorithm and dynamic resource allocation strategy;

[0015] S5: Design a health assessment function based on real-time feedback, dynamically trigger the failure prevention mechanism based on the health assessment function, and optimize the preventive measures through the feedback correction mechanism.

[0016] Furthermore, the electromagnetic environment and component interaction model quantifies the impact of each interference source on the system module through the electromagnetic interference factor; the electromagnetic interference factor is expressed as:

[0017]

[0018] Among them, I EM (t) is the electromagnetic interference factor, α k is the intensity coefficient of the kth electromagnetic source, reflecting the electromagnetic field intensity of the electromagnetic source; r k (t) is the distance between the kth electromagnetic source and the target component at time t. The distance changes dynamically with the movement of the system; λ k is the propagation attenuation factor of electromagnetic waves; N is the number of electromagnetic sources in the system;

[0019] The system health status is obtained by weighting the fusion data of multi-dimensional sensor data and the electromagnetic interference factor.

[0020] Furthermore, the high-order statistical features include the skewness of the health status data, the kurtosis of the health status data, the standard deviation of the health status, and the mean of the health status;

[0021] The high-order statistical feature identification system based on the health state uses a multidimensional high-order statistical feature classifier to identify the failure mode of the system; the multidimensional high-order statistical feature classifier identifies the failure mode by constructing a classifier based on a support vector machine.

[0022] Furthermore, the dynamic LSTM model is a dynamic LSTM structure based on an adaptive convolution gate. The dynamic LSTM structure based on the adaptive convolution gate can introduce a convolution layer into the LSTM model to dynamically weight the input features, and dynamically adjust the convolution kernel through a gating mechanism to cope with different system loads and electromagnetic interference conditions. The dynamic LSTM structure is expressed as:

[0023] h t =LSTM(X t ,W1)+ACG(X t ,W2)

[0024] Among them, h t is the dynamic LSTM model output at time t; X t is the input feature, which combines health status data and electromagnetic interference factors; W1 is the weight parameter of the dynamic LSTM model; W2 is the weight parameter of the convolution layer; ACG is the adaptive convolution gate, which is used to adjust the size and weight of the convolution kernel.

[0025] Furthermore, the time-varying disturbance model is introduced to perform dynamic electromagnetic interference regularization, wherein the time-varying disturbance model weights the electromagnetic interference by a dynamic weighting factor, and the weighting factor changes with time, thereby simulating the nonlinear time-varying characteristics of the electromagnetic interference effect.

[0026] Furthermore, the adaptive time window automatically adjusts the size of the time window according to the historical data of electromagnetic interference to capture the changing pattern of electromagnetic interference; the time series regression model takes the failure mode prediction and the historical data of electromagnetic interference as input to jointly predict the linkage effect of electromagnetic interference and failure mode; the time series regression model is expressed as:

[0027]

[0028] in, is the electromagnetic interference intensity predicted at time t+1; F predicted (t) is the failure mode prediction value, which represents the failure risk at the current moment; is the mean value of electromagnetic interference at time t-τ; Var[I EM (t)] is the electromagnetic interference variance at time t; β1, β2, and β3 are the coefficients of the regression model, which are used to weight each input feature.

[0029] Furthermore, the electromagnetic interference prediction results are combined with the failure mode prediction results to construct a joint prediction model, and the final system failure prediction is obtained, which is expressed as:

[0030]

[0031] Among them, F final (t) is the final system failure prediction result; F predicted (t) is the failure mode prediction; is the electromagnetic interference prediction; λ3 and λ4 are weight coefficients used to balance the contribution of failure mode prediction and electromagnetic interference prediction.

[0032] Furthermore, the adaptive fault-tolerant control algorithm is expressed as:

[0033]

[0034] Among them, u(t) is the real-time control signal used to adjust the system behavior; γ1, γ2, γ3 are new adjustment coefficients, representing the weight coefficients of failure mode prediction, interference prediction and early control signal adjustment respectively; F predicted (t) is the failure mode prediction value at time t, reflecting the failure risk of the system in the current state; is the electromagnetic interference prediction value at time t, indicating the possible impact of the electromagnetic environment on the system; Δu(t-1) is the change in the control signal at the previous moment, which is used to smooth the adjustment of the system and avoid system instability caused by excessive control;

[0035] The resource allocation problem of the dynamic resource allocation strategy is:

[0036]

[0037] Where R(t) represents the resource allocation vector of the system at time t; represents the system's effectiveness function, which measures the performance of the current resource allocation under the conditions of failure risk and electromagnetic interference; F predicted (t) is the failure mode prediction value at the current moment, which is used to determine the additional resources required by the system when a failure is about to occur; is the electromagnetic interference prediction value at the current moment, which is used to adjust the system resource configuration to adapt to the electromagnetic environment; Δu(t) represents the change of the control signal, which affects the system resource allocation decision.

[0038] Furthermore, the health assessment function is expressed as:

[0039]

[0040] in, is the health assessment value of the system. The larger the value, the higher the failure risk of the system. predicted (t) is the failure mode prediction value, which indicates the possibility of potential failure of the system; is the electromagnetic interference prediction value, reflecting the impact of external interference on the system; Δu(t) is the real-time control signal change, indicating the system's adaptability to external disturbances; w(t) is the dynamic adjustment factor, which is adjusted based on the system's current operating status and changes in the external environment; the value of w(t) automatically adjusts as the system operates and the external environment changes. The highest w(t) indicates that the system's health is most affected by the external environment.

[0041] Furthermore, the failure prevention mechanism is triggered based on the real-time value of the health assessment. If the threshold is exceeded, the redundant system startup or load adjustment measures are triggered; the feedback correction mechanism optimizes the execution of preventive measures by dynamically adjusting the system health assessment value.

[0042] In a second aspect of the present invention, a deep learning-based autonomous driving electronic system reliability prediction system is provided, the system comprising:

[0043] The signal acquisition module is used to establish an interaction model between the electromagnetic environment and components, generate system health status input through multi-dimensional sensor data fusion, and track electromagnetic interference sources using time-frequency analysis;

[0044] The failure prediction module is used to identify the failure mode of the system based on the high-order statistical characteristics of the health state. It uses a dynamic LSTM model to predict the evolution path of the failure mode of the system components and introduces a time-varying perturbation model for dynamic electromagnetic interference regularization.

[0045] Joint analysis module, which combines failure mode prediction results and uses adaptive time windows and time series regression models to predict the combined impact of electromagnetic interference on system failures;

[0046] A signal configuration module is used to adjust system control signals and resource configurations through adaptive fault-tolerant control algorithms and dynamic resource allocation strategies based on failure mode prediction and electromagnetic interference prediction values;

[0047] The feedback optimization module is used to design a health assessment function based on real-time feedback, dynamically trigger the failure prevention mechanism based on the health assessment function, and optimize the preventive measures through the feedback correction mechanism.

[0048] The beneficial technical effects of the present invention are at least as follows:

[0049] (1) This invention proposes a new failure mode prediction method by combining deep learning with physical modeling. Deep learning models (such as LSTM and CNN) can dynamically predict the failure modes of various system components by analyzing historical failure data and real-time monitoring data, while physical modeling ensures the accuracy and reliability of model predictions. This model can make more accurate failure predictions based on real-time monitored environmental changes and system status, overcoming the shortcomings of static failure mode prediction methods in the prior art.

[0050] (2) The present invention innovatively combines electromagnetic interference identification with an adaptive fault-tolerance mechanism. Through deep learning algorithms (such as CNN and GNN), it can identify electromagnetic interference characteristics in real time, analyze the impact of interference sources on the system, and predict the possible failure modes caused by them. Based on this prediction, the system can automatically adjust operating parameters (such as frequency, voltage, etc.) to reduce the impact of electromagnetic interference on system stability. This adaptive fault-tolerance mechanism based on interference prediction can effectively improve the reliability of the system in complex electromagnetic environments, solving the problem that traditional electromagnetic interference response strategies cannot be adjusted in real time.

[0051] (3) By combining real-time data acquisition with failure prediction results, the present invention proposes a dynamic optimization and system-level failure prevention strategy. Based on real-time monitored state changes and fault predictions, the system automatically adjusts the operating status of each module, performs fault-tolerant switching, or enables redundancy, thereby effectively ensuring the continued operation of the system in the event of a failure. This dynamic optimization mechanism overcomes the static and pre-set nature of traditional fault-tolerant strategies and improves the system's adaptability and stability in complex environments.

[0052] (4) By introducing a hybrid intelligent model, electromagnetic interference identification and adaptive fault-tolerance mechanism, and dynamic optimization strategy, a new method for reliability prediction and fault-tolerance optimization of autonomous driving electronic systems is provided. This method can not only accurately predict system failure modes, but also respond to the impact of external electromagnetic interference in real time, improving the system's dynamic adaptability and fault-tolerance performance. Through these innovations, the present invention effectively solves the shortcomings of existing technologies in terms of prediction accuracy, interference identification, and fault-tolerance mechanism, and provides a new technical path for improving the reliability of autonomous driving systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] The present invention is further described with reference to the accompanying drawings. However, the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative effort.

[0054] Figure 1 This is a schematic diagram of the steps of the deep learning-based autonomous driving electronic system reliability prediction method of the present invention. DETAILED DESCRIPTION

[0055] The following describes embodiments of the present invention in detail. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended only to explain the present invention and are not to be construed as limiting the present invention.

[0056] In one or more embodiments, Figure 1 As shown, a method for predicting reliability of an autonomous driving electronic system based on deep learning is disclosed, and the method includes the following steps:

[0057] S1: Establish an electromagnetic environment and component interaction model, generate system health status input through multi-dimensional sensor data fusion, and use time-frequency analysis to track electromagnetic interference sources.

[0058] This step first requires building a foundational model that reflects the real-time state of the autonomous driving electronic system through precise modeling and data collection. This model's primary task is to capture the electromagnetic environment's impact during the system's dynamic operation, as well as the operating status of various electronic modules.

[0059] Specifically, the present invention constructs a system modeling - an electromagnetic environment and component interaction model, including:

[0060] Electromagnetic interference (EMI) is a significant factor in autonomous driving systems, potentially leading to unexpected system behavior. Therefore, we first need to develop an EMI impact model to describe the interaction between electromagnetic sources and electronic components and quantify the impact of each interference source on system modules.

[0061] The model uses the following equations to describe the impact of each electromagnetic source on each module of the autonomous driving system:

[0062]

[0063] Among them, α k is the intensity coefficient of the kth electromagnetic source, reflecting the electromagnetic field intensity of the electromagnetic source. k (t) is the distance between the kth electromagnetic source and the target component at time t. The distance will change dynamically as the system moves. k is the propagation attenuation factor of electromagnetic waves, which depends on factors such as the frequency of the electromagnetic source and the propagation medium. N is the number of electromagnetic sources in the system.

[0064] Through this model, the system can calculate the electromagnetic interference factor IF in real time at each moment t , which will be used for subsequent failure mode prediction and electromagnetic interference identification.

[0065] Optimally, the autonomous driving system consists of multiple sensors that continuously collect environmental and system status data. To fully utilize this data, we use multi-dimensional sensor data fusion technology to integrate data from different sensors into a unified health status representation:

[0066] Assume that the system consists of M modules, each with K m The data collected by the sensor at each time t is X m (t) = [x m1 (t),x m2 (t),…,x mKm (t)]. By denoising, normalizing and preprocessing the sensor data of each module, we obtain the normalized data vector of each module Then, the data of all modules are fused into a comprehensive system state vector D f (t):

[0067]

[0068] Among them, ω mk is the weighting coefficient of the kth sensor in the mth module, reflecting the importance of the sensor in the comprehensive system health assessment; x mk (t) is the original data collected by the kth sensor of the mth module at time t; Df (t) is the fused global system state representation, which serves as the input for subsequent steps.

[0069] Through this weighted fusion method, we ensure that the data from different sensors are reasonably integrated, avoiding the influence of noise and errors from sensors on the system evaluation results.

[0070] In autonomous driving systems with complex electromagnetic environments, the location and identification of electromagnetic interference sources are crucial. In order to accurately identify electromagnetic sources and track their changes, we propose an electromagnetic interference source tracking method based on time-frequency analysis.

[0071] First, the sensor data X m (t) Perform time-frequency analysis and use short-time Fourier transform (STFT) to extract the frequency features in the signal:

[0072]

[0073] Among them, X f (t,ω) is the frequency domain representation of the signal X(t) after short-time Fourier transform; ω is the frequency variable, reflecting the frequency components in the signal. Through this method, we can identify specific frequency bands in the signal, which usually correspond to the frequency characteristics of the electromagnetic interference source. Combined with the electromagnetic interference model (Section 1.1), we can dynamically adjust the electromagnetic interference factor (I EM (t)) and perform precise tracking.

[0074] Optimally, based on the previous EMI analysis and data fusion, we will generate preliminary input for the system's health status in this step. This input data will support subsequent failure prediction and fault tolerance mechanisms. Since we are not performing a specific health assessment at this point, but are instead focusing on providing raw health data and EMI factors, the core of this step is to generate input data for subsequent steps.

[0075] We will pass D f (t) and I EM The comprehensive analysis of (t) generates the original health assessment input of the system status. Specifically, we weight the status data of each module of the system and the electromagnetic interference factor according to a certain weight to obtain a comprehensive health status index H t :

[0076]

[0077] Among them, H t is the original assessment result of the system health status, which serves as the input for subsequent failure mode prediction; m is the weight coefficient of the mth module; is the standardized data of the mth module; α is the weighted coefficient of the electromagnetic interference factor. This comprehensive health status indicator H t It will serve as input to provide necessary data support for subsequent failure mode prediction, electromagnetic interference identification and implementation of fault tolerance mechanisms.

[0078] Through this step, we successfully established a real-time modeling framework based on electromagnetic interference and system status, and through real-time data collection and fusion, provided a solid foundation for subsequent failure prediction, electromagnetic interference identification, and fault tolerance optimization.

[0079] S2: Identify system failure modes based on high-order statistical features of health status; use a dynamic LSTM model to predict the evolution path of failure modes of system components, and introduce a time-varying perturbation model for dynamic electromagnetic interference regularization.

[0080] In this step, our goal is to use the health status input (H t ), and electromagnetic interference (I EM ) and build a model that can accurately predict system failure modes. We need not only to identify possible failure modes but also to predict how these failure modes will evolve over time. This goal requires modeling the dynamic evolution of failure modes and being able to account for the complex nonlinear behavior of the system and the influence of the electromagnetic environment.

[0081] Specifically, first perform dynamic failure mode identification (based on high-order statistical features):

[0082] The key to failure mode identification lies in accurately extracting high-level statistical information representing system failure characteristics from multi-dimensional health status data and identifying failure modes based on these characteristics. Traditional methods typically use simple feature extraction and classification models. However, for complex autonomous driving systems, simple features often fail to capture the full picture of system failures. Therefore, we propose an innovative "dynamic high-order statistical feature extraction" method.

[0083] We start from the health status data H t By extracting high-order statistics (such as skewness and kurtosis) from the data and combining them with time series analysis methods, we can obtain more complex nonlinear features. Through high-order statistics, we can capture more subtle fluctuations in the data, which often reflect potential failures of the system.

[0084] We define “dynamic high-order statistical features” as Where t represents the time step, feature It is defined by the following formula:

[0085]

[0086] Among them, skew(H t ) is the skewness of health status data, reflecting the asymmetry of data distribution; kurt(H t ) is the kurtosis of health status data, reflecting the sharpness of data distribution; std(H t ) is the standard deviation, describing the fluctuation range of health status;

[0087] mean(H t ) is the mean, which indicates the average level of health status.

[0088] After extracting these high-order statistical features, we can use them as input features to identify the failure mode of the system through the Multidimensional High-Order Statistical Feature Classifier (MHSFC). The MHSFC model identifies failure modes by building a classifier based on a support vector machine (SVM):

[0089]

[0090] Among them, F predicted is the predicted failure mode; It is a dynamic high-order statistical feature extracted from health status data.

[0091] Ideally, based on failure mode identification, the next step is to perform time series prediction of failure modes. Traditional LSTM models can capture long-term dependencies in time series, but in complex autonomous driving electronic systems, failure modes are often affected by multiple nonlinear factors (such as temperature and electromagnetic interference). Therefore, a dynamic LSTM model is needed to handle this complexity.

[0092] We propose a dynamic LSTM structure based on adaptive convolution gate (ACG). This structure can dynamically weight input features by introducing convolution layers in LSTM and dynamically adjust the convolution kernel through a gating mechanism to cope with different system loads and electromagnetic interference conditions. The formula is defined as:

[0093] h t =LSTM(X t ,W1)+ACG(X t ,W2) (7)

[0094] Among them, h t is the LSTM output at time t; X tThe input features combine health status data and electromagnetic interference factors; W1 is the LSTM weight parameter; W2 is the weight parameter of the convolution layer; and ACG is the adaptive convolution gate, which adjusts the size and weight of the convolution kernel. The adaptive convolution gate dynamically selects the appropriate convolution kernel size and weight to enhance the prediction of failure modes in different electromagnetic interference environments.

[0095] Through this innovative dynamic LSTM structure, we are able to accurately predict the evolution path of failure modes in future time steps while taking into account the influence of electromagnetic interference.

[0096] Preferably, a dynamic electromagnetic interference regularization is constructed (based on a time-varying disturbance model):

[0097] In order to further enhance the stability of the model, we introduce an innovative dynamic electromagnetic interference regularization mechanism. EM ) is an important factor affecting the reliability of autonomous driving systems, but its effect is often time-varying, so we cannot regard it as a static factor.

[0098] To this end, we define a time-varying disturbance model (TVDM) to describe the dynamic impact of electromagnetic interference on failure mode prediction. The core of the TVDM model is to weight the electromagnetic interference using a dynamic weighting factor. This weighting factor changes over time, thereby simulating the nonlinear time-varying characteristics of the electromagnetic interference effect. The loss function of the TVDM is defined as:

[0099]

[0100] Among them, α t I is the time-varying weighting factor of electromagnetic interference, which dynamically adjusts the sensitivity to electromagnetic interference; EM (t) is the electromagnetic interference factor at time t; is the electromagnetic interference factor predicted by the model. This regularization term adjusts the failure mode prediction results according to the real-time electromagnetic environment, so that the model can maintain high accuracy in a changing electromagnetic environment.

[0101] Preferably, after completing the failure mode recognition and time series prediction model, we use a composite loss function to optimize the model. This composite loss function combines failure mode recognition, failure mode prediction, and electromagnetic interference regularization to ensure the comprehensive performance of the model. The final training loss function is:

[0102] L final =L SVM +λ1L LSTM +λ2L EMI (9)

[0103] Among them, L SVMis the SVM loss for failure mode recognition; L LSTM LSTM loss for failure mode prediction; L EMI is the electromagnetic interference regularization loss; λ1, λ2 are the weight coefficients of each task.

[0104] S3: Combined with the failure mode prediction results, the adaptive time window and timing regression model are used to predict the joint impact of electromagnetic interference on system failure.

[0105] In step 2, we have obtained the predicted value F of the failure mode predicted and electromagnetic interference raw data I EM In this step, our goal is to combine these two to identify the strength of EMI and accurately predict its impact on system failure. To achieve this goal, we propose an innovative EMI Timing Analysis and Impact Prediction Method (EMI-TAPM), which combines timing analysis techniques with regression models to effectively capture the time-dependent and nonlinear characteristics of EMI.

[0106] Specifically, electromagnetic interference is time-varying, and its impact fluctuates significantly over time. Therefore, we need to use time series modeling to capture the dynamic characteristics of electromagnetic interference. To this end, we introduced the Adaptive Time Window (ATW). Its core concept is to automatically adjust the time window size based on historical electromagnetic interference data to capture the changing patterns of electromagnetic interference. The time window adjustment formula is as follows:

[0107]

[0108] Where Δt EMI (t) represents the dynamic time window at time t, including the mean and variance of electromagnetic interference; is the mean value of electromagnetic interference at time τ in the past, reflecting the impact of historical electromagnetic interference; Var[I EM (t)] is the variance of electromagnetic interference at the current moment, describing the fluctuation of electromagnetic interference at the current moment. By adaptively adjusting the time window, the model can better capture the time-varying characteristics of electromagnetic interference and provide dynamic window data for subsequent prediction processes.

[0109] Preferably, in order to effectively predict the future trend of electromagnetic interference, combined with the failure mode prediction (F predicted ) to conduct the final system failure assessment, we designed an innovative Time-Series Regression Model (TSRM) that performs joint prediction based on past EMI data and failure mode prediction.

[0110] Specifically, we use a multiple regression model to predict the failure mode (F predicted ) and historical data of electromagnetic interference (I EM ) as input to build a model to predict the impact of future electromagnetic interference. The formula of the regression model is as follows:

[0111]

[0112] in, is the electromagnetic interference intensity predicted at time t+1; F predicted (t) is the failure mode prediction value obtained in step 2, which represents the failure risk at the current moment; is the mean value of electromagnetic interference at time t-τ;

[0113] Var[I EM (t)] is the electromagnetic interference variance at time t; β1, β2, β3 are the coefficients of the regression model, which are used to weight each input feature.

[0114] Through joint regression analysis, we can combine electromagnetic interference and failure mode prediction to obtain the intensity of future electromagnetic interference and provide a reliable basis for subsequent failure assessment.

[0115] Preferably, in order to achieve the best effect of the electromagnetic interference prediction model, we use a composite loss function to simultaneously optimize the accuracy and stability of the time series regression model. The composite loss function takes into account the error of electromagnetic interference prediction and the influence of failure mode, and the form is:

[0116] L final =L EMI +λ1L regression +λ2L stability (12)

[0117] Among them, L EMI is the prediction error of electromagnetic interference; L regression is the regression error of the time series regression model; L stability is the stability loss of the model during training, which is used to prevent overfitting; λ1 and λ2 are the weight coefficients in the loss function, which are used to balance various tasks.

[0118] By optimizing the loss function, we can better balance electromagnetic interference prediction and failure mode prediction, thereby improving the robustness and stability of the system.

[0119] Preferably, at the end of this step, we combine the electromagnetic interference prediction results with the failure mode prediction results to obtain the final system failure prediction. We introduce a joint prediction model (F final (t)):

[0120]

[0121] Among them, F final (t) is the final system failure prediction result; F predicted (t) is the failure mode prediction obtained in step 2; is the electromagnetic interference prediction obtained in step 3; λ3, λ4 are weight coefficients used to balance the contribution of failure mode prediction and electromagnetic interference prediction.

[0122] Through this joint model, the system can comprehensively consider the impact of electromagnetic interference and the risk of failure modes, provide more accurate failure predictions, and provide a reliable basis for subsequent fault-tolerant decisions.

[0123] Through this method, the system can predict the intensity of electromagnetic interference in real time and analyze its impact on the system, thereby providing accurate decision-making basis for the fault-tolerant mechanism in future electromagnetic interference environments, and improving the reliability and safety of autonomous driving systems in complex environments.

[0124] S4: Based on the failure mode prediction and electromagnetic interference prediction value, the system control signal and resource configuration are adjusted through adaptive fault-tolerant control algorithm and dynamic resource allocation strategy.

[0125] One of the core tasks of this step is to feed real-time electromagnetic interference and failure mode prediction data into the system control decision-making process, dynamically adjusting control parameters to prevent premature system failure. To achieve this goal, we proposed an adaptive fault-tolerant control algorithm based on prediction data. This algorithm incorporates the predicted values ​​of failure modes and electromagnetic interference into the decision-making process through a weighted approach.

[0126] Specifically, based on the prediction information obtained in the previous step, we designed the following control algorithm to adjust the control signal in real time:

[0127]

[0128] Where u(t) is a real-time control signal used to adjust system behavior (e.g., adjusting the power, load, and operating mode of the device). γ1, γ2, and γ3 are new adjustment coefficients, representing the weight coefficients of failure mode prediction, interference prediction, and previous control signal adjustment, respectively. predicted (t) is the failure mode prediction value at time t, reflecting the failure risk of the system in the current state; is the predicted electromagnetic interference value at time t, indicating the possible impact of the electromagnetic environment on the system. Δu(t-1) is the change in the control signal at the previous moment, which is used to smooth system adjustments and avoid system instability caused by over-control.

[0129] The key to this control algorithm is to dynamically update the system control signal using the predicted value and historical adjustment information. For example, under high failure mode risk (F predicted (t) is large), the system will reduce the probability of failure by reducing power or adjusting load; in a high electromagnetic interference environment ( If the signal processing capability is large, the system may need to strengthen its signal processing capability or adopt redundant design to ensure stable operation.

[0130] In addition to fault-tolerant control, another core task of this step is to optimize the allocation of system resources in real time, ensuring optimal utilization of remaining resources when a fault occurs. To achieve this goal, we introduce a real-time optimization algorithm based on system performance. This algorithm uses electromagnetic interference and failure mode prediction to dynamically adjust resource allocation, ensuring the long-term stability and short-term performance of the system in an optimal manner.

[0131] We define the system resource allocation problem as:

[0132]

[0133] Among them, R(t) is the resource allocation vector representing the system at time t, which usually includes energy, computing power and redundant resources. To represent the system's effectiveness function, the performance of the current resource allocation is measured under the conditions of failure risk and electromagnetic interference. It can be a multi-objective function that comprehensively considers multiple dimensions such as system stability, response speed, energy efficiency, etc. predicted (t) is the predicted value of the failure mode at the current moment, which is used to determine the additional resources required by the system when a failure is about to occur. is the electromagnetic interference prediction value at the current moment, which is used to adjust the system resource configuration to adapt to the electromagnetic environment. Δu(t) represents the change in the control signal in the previous step, which affects the system resource allocation decision.

[0134] In practical applications, the goal of this optimization problem is to maximize the overall performance of the system in the face of failures and interference by adjusting R(t) (for example, increasing computing resources, adding redundant systems, etc.). For example, the system can be based on F predicted Adjust redundancy, or according to Enhance signal processing capabilities and optimize resource allocation to maintain efficient system operation.

[0135] Preferably, as the system state changes, the fault tolerance mechanism needs to be dynamically adjusted to respond to different operating environments and failure modes. To achieve this, we designed a dynamic fault tolerance correction mechanism that uses real-time feedback to correct the previous fault tolerance strategy and fine-tune the system.

[0136] The fault tolerance adjustment formula is as follows:

[0137]

[0138] Where ΔF adjusted (t) is the failure mode prediction value after dynamic adjustment, which represents the revised fault-tolerant decision. δ1, δ2, δ3, and δ4 are new adjustment coefficients used to adjust the influence of failure prediction, interference prediction, control signal changes, and feedback information. predicted (t) is the failure mode prediction value at the current moment, which is used to adjust the fault tolerance strategy. is the electromagnetic interference prediction value at the current moment, which affects the fault tolerance decision of the system. Δu(t) is the change of the system control signal, which represents the adjustment range of the adaptive control. feedback (t) is the system feedback signal, which represents the correction value of the system failure mode during actual operation.

[0139] Through this dynamic adjustment formula, the system can adjust its fault tolerance mechanism in real time according to the prediction of electromagnetic interference and failure mode, avoiding excessive correction or slow response of the system. feedback (t) is an indispensable part of the dynamic system. It corrects the fault-tolerant decision by the difference from the predicted value, so that the system always maintains the best performance.

[0140] To further enhance the system's stability and adaptability, we designed a multi-objective optimization algorithm. This algorithm not only predicts failure modes and electromagnetic interference but also incorporates multiple optimization objectives: long-term stability, short-term performance, and energy efficiency. By introducing a multi-objective feedback loop, the system can automatically select the optimal strategy in different operating scenarios.

[0141] Multi-objective optimization can be expressed in the following form:

[0142]

[0143] Among them, R(t) is the system resource allocation vector. It is a multi-objective optimization function, which means that multiple objectives such as system performance, stability and energy efficiency are optimized simultaneously.

[0144] Through this optimization model, the system can adjust resource allocation and control strategies according to environmental changes (including electromagnetic interference and failure prediction) at every moment, ultimately achieving long-term stability and short-term efficient operation of the system.

[0145] S5: Design a health assessment function based on real-time feedback, dynamically trigger the failure prevention mechanism based on the health assessment function, and optimize the preventive measures through the feedback correction mechanism.

[0146] Specifically, the core goal of this step is to conduct system-level health assessment and failure prevention based on the failure modes and electromagnetic interference information predicted in the previous steps, combined with real-time feedback. We will identify potential failure risks by assessing the overall health of the system and take necessary preventative measures based on the assessment results.

[0147] The input for this step is:

[0148] Failure mode prediction value F predicted (t);

[0149] Electromagnetic interference prediction value

[0150] Real-time control signal change Δu(t).

[0151] These inputs will provide the necessary parameters for the health assessment model to evaluate the health status of the system in real time and take timely intervention measures when there is a risk.

[0152] Preferably, the health assessment function is designed as follows:

[0153] System health assessment function It not only reflects the influence of failure modes and electromagnetic interference, but also considers the impact of control signal changes on system health. The design takes into account the following key factors:

[0154] Failure mode prediction value F of the system predicted (t);

[0155] Electromagnetic interference impact of the system

[0156] The change of the real-time control signal Δu(t(, which reflects the system's ability to respond to external interference.

[0157] Innovatively, we introduced a dynamic weight adjustment factor (w*t), which automatically adjusts the weights of various factors based on the current operating status of the system and changes in the external environment, thereby more accurately assessing the health status of the system. The core formula of the health assessment function is:

[0158]

[0159] in, is the health assessment value of the system. The larger the value, the higher the risk of failure of the system. predicted (t) is the failure mode prediction value, which indicates the possibility of potential failure of the system. is the electromagnetic interference prediction value, reflecting the impact of external interference on the system. Δu*t( is the real-time control signal change, indicating the system's adaptability to external disturbances. w(t) is a dynamic adjustment factor, adjusted based on the system's current operating status and external environmental changes. The value of w(t) automatically adjusts as the system operates and the external environment changes. A higher w(t) indicates that the system's health is more susceptible to external influences, while a lower w(t) indicates a relatively stable system.

[0160] This innovative design enables the health assessment model to respond more flexibly to different external environmental changes and fluctuations in system status, thereby more accurately assessing health risks.

[0161] Preferably, once the system health assessment value Exceeds a preset threshold The system then needs to trigger a failure prevention mechanism. In this step, we propose a triggering mechanism based on dynamic health assessment and introduce a special prevention adjustment factor k(t), which dynamically adjusts the intensity of the preventive measures based on the rate of change of the system's current health assessment value.

[0162] The formula for the failure prevention trigger mechanism is as follows:

[0163]

[0164] Among them, T(t) is the failure prevention trigger signal. Trigger the preventive action; otherwise, do not trigger it. The preset threshold of the health assessment value determines whether to trigger the prevention mechanism. The triggering of failure prevention is based on the real-time value of the health assessment. If the system health status is poor (i.e. If the load exceeds the threshold, preventive measures are triggered, such as reducing the load and starting the redundant system.

[0165] Preferably, to ensure the stability and long-term healthy operation of the system, we introduce a feedback correction mechanism to optimize the execution of preventive measures by dynamically adjusting the system health assessment value. In this mechanism, we compare the current health assessment value with the past assessment value and adapt to the changes in the system health status through the correction factor δ(t). The feedback correction formula is:

[0166]

[0167] in, is the corrected health assessment value, reflecting the current health status of the system after considering past health change trends. δ(t) is the feedback correction factor, which indicates the correction strength of the health assessment change rate. is the time derivative of the health assessment value, which indicates the rate of change of the health assessment value.

[0168] By introducing a feedback correction mechanism, the system can dynamically adjust the intensity of preventive measures based on the changing trends of health assessment values. This approach avoids triggering preventive measures based solely on the current health assessment value and instead considers the changing trends of the system state, achieving more precise failure prevention.

[0169] In one or more embodiments, another embodiment of the present invention provides a deep learning-based autonomous driving electronic system reliability prediction system, the system comprising:

[0170] The signal acquisition module is used to establish an interaction model between the electromagnetic environment and components, generate system health status input through multi-dimensional sensor data fusion, and track electromagnetic interference sources using time-frequency analysis;

[0171] The failure prediction module is used to identify the failure mode of the system based on the high-order statistical characteristics of the health state. It uses a dynamic LSTM model to predict the evolution path of the failure mode of the system components and introduces a time-varying perturbation model for dynamic electromagnetic interference regularization.

[0172] Joint analysis module, which combines failure mode prediction results and uses adaptive time windows and time series regression models to predict the combined impact of electromagnetic interference on system failures;

[0173] A signal configuration module is used to adjust system control signals and resource configurations through adaptive fault-tolerant control algorithms and dynamic resource allocation strategies based on failure mode prediction and electromagnetic interference prediction values;

[0174] The feedback optimization module is used to design a health assessment function based on real-time feedback, dynamically trigger the failure prevention mechanism based on the health assessment function, and optimize the preventive measures through the feedback correction mechanism.

[0175] In summary, this invention provides a novel method for reliability prediction and fault-tolerance optimization of autonomous driving electronic systems by introducing a hybrid intelligent model, electromagnetic interference identification and adaptive fault-tolerance mechanisms, and dynamic optimization strategies. This method not only accurately predicts system failure modes but also responds to the effects of external electromagnetic interference in real time, improving the system's dynamic adaptability and fault-tolerance performance. Through these innovations, the invention effectively addresses the shortcomings of existing technologies in terms of prediction accuracy, interference identification, and fault-tolerance mechanisms, providing a new technical path for improving the reliability of autonomous driving systems.

[0176] The present invention may be a method, an apparatus, a system and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for executing various aspects of the present invention.

[0177] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0178] Although the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the claims and their equivalents.

[0179] The various illustrative logic blocks, modules, and circuits described in conjunction with the embodiments disclosed herein may be implemented or performed using a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general-purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration.

[0180] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. The software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor so that the processor can read and write information from / to the storage medium. In an alternative, the storage medium may be integrated into the processor. The processor and storage medium may reside in an ASIC. The ASIC may reside in a user terminal. In an alternative, the processor and storage medium may reside in a user terminal as discrete components.

[0181] In one or more exemplary embodiments, the functions described may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software as a computer program product, the functions may be stored on or transmitted via a computer-readable medium as one or more instructions or codes. Computer-readable media include both computer storage media and communication media, including any media that facilitates the transfer of a computer program from one location to another. A storage medium may be any available medium that can be accessed by a computer. By way of example and not limitation, such computer-readable media may include RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer. Any connection is also properly referred to as a computer-readable medium. For example, if the software is transmitted from a website, server, or other remote source using a coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwaves, then the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwaves are included in the definition of medium. As used herein, disk and disc include compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), floppy disk and Blu-ray disc, where disks typically reproduce data magnetically, while discs reproduce data optically with lasers. Combinations of the above should also be included within the scope of computer-readable media.

[0182] The previous description of the disclosure is provided to enable any person skilled in the art to make or use the disclosure. Various modifications to the disclosure will be apparent to those skilled in the art, and the general principles defined herein may be applied to other variations without departing from the spirit or scope of the disclosure. Thus, the disclosure is not intended to be limited to the examples and designs described herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A deep learning-based reliability prediction method for autonomous driving electronic systems, characterized by: The following steps are involved: S1: Build an electromagnetic environment and component interaction model, generate system health status input through multi-dimensional sensor data fusion, and use time-frequency analysis to track electromagnetic interference sources; S2: Identify system failure modes based on high-order statistical features of health status; use a dynamic LSTM model to predict the evolution path of failure modes of system components, and introduce a time-varying perturbation model for dynamic electromagnetic interference regularization; S3: Combined with the failure mode prediction results, adaptive time window and time series regression model are used to predict the joint impact of electromagnetic interference on system failure; S4: Based on the failure mode prediction and electromagnetic interference prediction value, the system control signal and resource configuration are adjusted through adaptive fault-tolerant control algorithm and dynamic resource allocation strategy; S5: Design a health assessment function based on real-time feedback, dynamically trigger the failure prevention mechanism based on the health assessment function, and optimize the preventive measures through the feedback correction mechanism.

2. The deep learning-based reliability prediction method for autonomous driving electronic systems according to claim 1, characterized in that: The electromagnetic environment and component interaction model quantifies the impact of each interference source on the system module through the electromagnetic interference factor; the electromagnetic interference factor is expressed as: Among them, I EM (t) is the electromagnetic interference factor, α k is the intensity coefficient of the kth electromagnetic source, reflecting the electromagnetic field intensity of the electromagnetic source; r k (t) is the distance between the kth electromagnetic source and the target component at time t. The distance changes dynamically with the movement of the system; λ k is the propagation attenuation factor of electromagnetic waves; N is the number of electromagnetic sources in the system; The system health status is obtained by weighted calculation based on fusion data of multi-dimensional sensor data and electromagnetic interference factors.

3. The deep learning-based reliability prediction method for autonomous driving electronic systems according to claim 1, characterized in that: The high-order statistical features include the skewness of the health status data, the kurtosis of the health status data, the standard deviation of the health status and the mean of the health status; Then the high-order statistical feature identification of the health state-based failure mode of the system adopts a multi-dimensional high-order statistical feature classifier to identify the failure mode of the system; The multi-dimensional high-order statistical feature classifier identifies the failure mode by constructing a classifier based on a support vector machine.

4. The deep learning-based reliability prediction method for autonomous driving electronic systems according to claim 3, characterized in that: The dynamic LSTM model is a dynamic LSTM structure based on an adaptive convolution gate. The dynamic LSTM structure based on the adaptive convolution gate can introduce a convolution layer into the LSTM model to dynamically weight the input features and dynamically adjust the convolution kernel through a gating mechanism to cope with different system loads and electromagnetic interference conditions. The dynamic LSTM structure is expressed as: h t =LSTM(X t ,W1)+ACG(X t ,W2) Among them, h t is the dynamic LSTM model output at time t; X t is the input feature, which combines health status data and electromagnetic interference factors; W1 is the weight parameter of the dynamic LSTM model; W2 is the weight parameter of the convolution layer; ACG is the adaptive convolution gate, which is used to adjust the size and weight of the convolution kernel.

5. The deep learning-based reliability prediction method for autonomous driving electronic systems according to claim 4, characterized in that: The time-varying disturbance model is introduced to perform dynamic electromagnetic interference regularization, wherein the time-varying disturbance model weights the electromagnetic interference through a dynamic weighting factor, and the weighting factor changes with time, thereby simulating the nonlinear time-varying characteristics of the electromagnetic interference effect.

6. The deep learning-based reliability prediction method for autonomous driving electronic systems according to claim 1, characterized in that: The adaptive time window automatically adjusts the size of the time window according to the historical data of electromagnetic interference to capture the changing pattern of electromagnetic interference. The time series regression model takes the failure mode prediction and the historical data of electromagnetic interference as input to jointly predict the linkage effect of electromagnetic interference and failure mode. The time series regression model is expressed as: in, is the electromagnetic interference intensity predicted at time t+1; F predicted (t) is the failure mode prediction value, which represents the failure risk at the current moment; is the mean value of electromagnetic interference at time t-τ; Var[I EM (t)] is the electromagnetic interference variance at time t; β1, β2, and β3 are the coefficients of the regression model, which are used to weight each input feature.

7. The deep learning-based reliability prediction method for autonomous driving electronic systems according to claim 6, characterized in that: The prediction results of electromagnetic interference are combined with the failure mode prediction results to build a joint prediction model, and the final system failure prediction is obtained, which is expressed as: Among them, F final (t) is the final system failure prediction result; F predicted (t) is the failure mode prediction; is the electromagnetic interference prediction; λ3 and λ4 are weight coefficients used to balance the contribution of failure mode prediction and electromagnetic interference prediction.

8. The deep learning-based reliability prediction method for autonomous driving electronic systems according to claim 7, characterized in that: The adaptive fault-tolerant control algorithm is expressed as: Among them, u(t) is the real-time control signal used to adjust the system behavior; γ1, γ2, γ3 are new adjustment coefficients, representing the weight coefficients of failure mode prediction, interference prediction and early control signal adjustment respectively; F predicted (t) is the failure mode prediction value at time t, reflecting the failure risk of the system in the current state; is the electromagnetic interference prediction value at time t, indicating the possible impact of the electromagnetic environment on the system; Δu(t-1) is the change in the control signal at the previous moment, which is used to smooth the adjustment of the system and avoid system instability caused by excessive control; The resource allocation problem of the dynamic resource allocation strategy is: Where R(t) represents the resource allocation vector of the system at time t; represents the system's effectiveness function, which measures the performance of the current resource allocation under the conditions of failure risk and electromagnetic interference; F predicted (t) is the failure mode prediction value at the current moment, which is used to determine the additional resources required by the system when a failure is about to occur; is the electromagnetic interference prediction value at the current moment, which is used to adjust the system resource configuration to adapt to the electromagnetic environment; Δu(t) represents the change of the control signal, which affects the system resource allocation decision.

9. The deep learning-based reliability prediction method for autonomous driving electronic systems according to claim 8, characterized in that: The health assessment function is expressed as: in, is the health assessment value of the system. The larger the value, the higher the failure risk of the system. predicted (t) is the failure mode prediction value, which indicates the possibility of potential failure of the system; is the electromagnetic interference prediction value, reflecting the impact of external interference on the system; Δu(t) is the real-time control signal change, indicating the system's adaptability to external disturbances; w(t) is a dynamic adjustment factor, which is adjusted based on the system's current operating status and changes in the external environment. The value of w(t) automatically adjusts as the system operates and the external environment changes. The highest w(t) indicates that the system's health is most affected by external factors. The failure prevention mechanism is triggered based on the real-time value of the health assessment. If the threshold is exceeded, the redundant system startup or load adjustment measures are triggered; the feedback correction mechanism optimizes the execution of preventive measures by dynamically adjusting the system health assessment value.

10. The reliability prediction system of autonomous driving electronic system based on deep learning is characterized by: The system comprises: The signal acquisition module is used to establish an interaction model between the electromagnetic environment and components, generate system health status input through multi-dimensional sensor data fusion, and track electromagnetic interference sources using time-frequency analysis; The failure prediction module is used to identify the failure mode of the system based on the high-order statistical characteristics of the health state. It uses a dynamic LSTM model to predict the evolution path of the failure mode of the system components and introduces a time-varying perturbation model for dynamic electromagnetic interference regularization. Joint analysis module, which combines failure mode prediction results and uses adaptive time windows and time series regression models to predict the combined impact of electromagnetic interference on system failures; A signal configuration module is used to adjust system control signals and resource configurations through adaptive fault-tolerant control algorithms and dynamic resource allocation strategies based on failure mode prediction and electromagnetic interference prediction values; The feedback optimization module is used to design a health assessment function based on real-time feedback, dynamically trigger the failure prevention mechanism based on the health assessment function, and optimize the preventive measures through the feedback correction mechanism.

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