Identifying key performance integrated environmental worthiness test system

Abstract

The application relates to the technical field of environmental adaptability testing, and discloses a key performance comprehensive environmental adaptability testing system. The system comprises a multi-environment factor acquisition and processing module, which acquires data and carries out normalization preprocessing; an environmental scene dynamic modeling module, which constructs a multi-dimensional correlation graph and updates a topological structure; a performance index evaluation module, which evaluates a performance degradation index under a composite environment by using a space-time feature fusion algorithm; a failure mode prediction module, which generates potential failure modes under multi-environment coupling according to a performance degradation trend and a stress accumulation effect; an environmental tolerance analysis module, which integrates data to build a virtual model and outputs a specific environmental profile tolerance residual error; a test scheme optimization module, which generates an adaptive test sequence according to the residual error and the graph; and an environmental scene simulation control module, which combines multi-information to regulate test cabin parameters. The system can comprehensively and accurately evaluate the performance and tolerance capacity of equipment under a composite environment, and has a dynamic adjustment characteristic.

Description

Technical Field

[0001] This invention relates to the field of environmental adaptability testing technology, specifically to a comprehensive environmental adaptability testing system for identifying key performance characteristics. Background Technology

[0002] In modern industry, the operating environments of various equipment are becoming increasingly complex, ranging from hot and humid tropical regions to frigid and arid polar environments, and from dusty deserts to high-salt-fog marine settings. The diversity and coupling of environmental factors pose stringent challenges to equipment performance. Traditional environmental adaptability testing methods are often limited to simulating single environmental factors, such as testing only one of temperature, humidity, or vibration, which makes it difficult to truly reflect the multi-factor combined effects faced by equipment in actual applications.

[0003] In existing technologies, some testing systems attempt to incorporate combinations of multiple environmental factors, but they exhibit significant shortcomings in environmental data acquisition and processing. Most systems can only record a limited number of environmental parameters and lack the ability to normalize and preprocess the data, making it difficult to effectively correlate and analyze environmental data of different types and scales. Furthermore, in the modeling of environmental scenarios, static models dominate, failing to dynamically adjust the model's topology based on real-time environmental data. This results in a lag in the model's response to complex environmental changes and makes it difficult to accurately capture the dynamic relationships between environmental factors.

[0004] In equipment performance evaluation and failure prediction, traditional methods often rely on empirical judgment or simple statistical analysis, lacking in-depth exploration of the spatiotemporal correlation between environmental stress and equipment performance degradation. This results in insufficient accuracy in performance degradation index assessments and difficulty in predicting potential failure modes under complex conditions involving multiple coupled environments. Furthermore, existing testing systems are relatively weak in environmental tolerance analysis, unable to construct accurate virtual models to quantify the equipment's tolerance under specific environmental profiles, thus hindering the optimization of testing plans.

[0005] The standardization of test plans is another prominent issue. Most systems cannot dynamically generate adaptive test scenario sequences based on the real-time performance feedback of the equipment and the results of environmental tolerance analysis. This leads to redundancy or insufficiency in the test process, which wastes test resources and may also miss key environmental stress combinations, affecting the reliability and comprehensiveness of the test results. Summary of the Invention

[0006] The purpose of this invention is to provide a comprehensive environmental adaptability testing system for identifying key performance characteristics, in order to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides a comprehensive environmental adaptability testing system for identifying key performance characteristics, the system comprising:

[0008] The multi-environmental factor acquisition and processing module is used to collect environmental data monitored by various sensors deployed in the test environment chamber and perform normalization preprocessing.

[0009] The environmental scene dynamic modeling module constructs a multi-dimensional environmental association map based on preprocessed environmental data and dynamically updates the topology of the multi-dimensional environmental association map according to the environmental data stream.

[0010] The performance evaluation module receives the multidimensional environmental correlation map and preprocessed environmental data, and evaluates the performance degradation index of the device under test under composite environmental stress through a spatiotemporal feature fusion algorithm.

[0011] The failure mode prediction module generates a set of potential failure modes of the device under test in multi-environment coupling scenarios based on the evolution trend of the performance degradation index and the cumulative effect of environmental stress.

[0012] The environmental tolerance analysis module integrates the performance degradation index and potential failure mode set, constructs a virtual model of environmental stress tolerance, and outputs the tolerance residual of the tested equipment under a specific environmental profile.

[0013] The test scheme optimization module generates an adaptive environment test scenario sequence based on the tolerance residual and the multidimensional environment correlation map.

[0014] The environmental scenario simulation control module dynamically adjusts the parameter combination of the test environment chamber by combining the set of potential failure modes, the adaptive environmental test scenario sequence, and the tolerance residual.

[0015] Preferably, the specific process for performing the normalization preprocessing includes:

[0016] It receives raw environmental data collected by temperature sensors, vibration sensors, electromagnetic sensors and humidity sensors, groups the data by sensor type, and aligns the sampling timestamps of each group of data through a time sliding window.

[0017] Calculate the covariance matrix of each group of environmental data, determine the principal component direction of each group of data based on the covariance eigenvalue decomposition, and perform standardization scaling along the principal component direction to eliminate dimensional differences.

[0018] Traverse each set of standardized data, calculate its Mahalanobis distance distribution, and mark data points that exceed three times the standard deviation as environmental outliers.

[0019] Missing data points were filled by local weighted regression, and environmental outliers were handled by median replacement. Finally, range scaling was used to map each group of data to the zero-to-one interval.

[0020] Preferably, the specific process for the environmental scene dynamic modeling module to construct a multi-dimensional environmental association map includes:

[0021] Time series features of preprocessed temperature, vibration, electromagnetic interference, and humidity data are extracted to construct an environmental factor relationship matrix;

[0022] Historical environmental stress data, equipment performance degradation records, and environmental factor coupling relationships are used as initial nodes of the graph, and the environmental correlation strength between nodes is calculated through a graph attention mechanism.

[0023] The weight coefficients of the environmental factor relationship matrix are updated based on the environmental data stream, and the adjacency matrix of the environmental correlation strength is reconstructed through a graph convolutional network to generate a dynamic environmental stress propagation network.

[0024] Preferably, the specific process for the performance evaluation module to evaluate performance degradation indicators includes:

[0025] The dynamic environmental stress propagation network and the preprocessed environmental data are input into the spatiotemporal feature fusion model, and the spatial distribution features of environmental stress are extracted through the graph neural network layer.

[0026] Dilated convolutional layers are used to capture the long-term dependence features of environmental stress evolution over time, and the spatial distribution features and long-term dependence features are fused to form an environmental performance degradation feature vector.

[0027] The environmental performance degradation feature vector is mapped to a performance degradation index through a fully connected layer, and the key performance degradation curve of the device under test under composite environmental stress is output.

[0028] Preferably, the specific process for constructing the environmental stress tolerance virtual model by the environmental tolerance analysis module includes:

[0029] Collect the material physical properties and structural design parameters of the device under test, and establish a multi-physics coupled simulation model based on the finite element method;

[0030] The performance degradation index and potential failure mode set are input into the multiphysics coupling simulation model to solve the internal stress distribution equation of the equipment and output the environmental stress tolerance benchmark value.

[0031] The deviation between the tolerance benchmark value and the measured performance degradation index is compared. If the deviation exceeds the tolerance threshold, the model parameter correction mechanism is triggered.

[0032] The material aging coefficient of the multiphysics coupling simulation model is adjusted by the gradient backpropagation algorithm until the tolerance deviation converges to the preset tolerance range.

[0033] Preferably, the specific process by which the test scheme optimization module generates the adaptive environment test scenario sequence includes:

[0034] A test scenario decision space is constructed with environmental stress tolerance residual, test resource constraints, and failure risk level as optimization objectives.

[0035] Initialize the root node of the test scenario decision tree, and traverse the action space of environmental factors to generate multi-level child node branches;

[0036] The Monte Carlo tree search algorithm is used to select the branch node with the highest confidence upper limit starting from the root node, and to expand the unexplored combination of environmental factors and actions.

[0037] Simulate the equipment performance degradation path after executing a selected combination of environmental factors, calculate the test cost-benefit function, and backtrack to update the decision tree node weights.

[0038] When the rate of change of the test cost-benefit function is lower than the convergence threshold, the optimal environmental test scenario sequence and the corresponding load allocation strategy are output.

[0039] Preferably, the specific process for the environmental scenario simulation control module to dynamically adjust the parameter combination of the test environment chamber includes:

[0040] Analyze the temperature gradient, vibration spectrum, electromagnetic intensity, and humidity variation curves in the optimal environmental test scenario sequence;

[0041] Adjust the heater power, vibration table frequency, electromagnetic field generator intensity, and humidifier output of the test environment chamber according to the environmental factor coupling relationship matrix;

[0042] Real-time acquisition of performance response data of the device under test under environmental stress sequence, and feedback to the environmental scene dynamic modeling module to update the multi-dimensional environmental correlation map;

[0043] If the deviation between the performance response data and the expected degradation curve exceeds the control threshold, the test scheme optimization module will be re-triggered to generate an alternative environment test scenario sequence.

[0044] Preferably, the specific process by which the failure mode prediction module generates a set of potential failure modes includes:

[0045] Based on the node connection strength of the environmental stress propagation network, identify highly correlated environmental factor combination chains;

[0046] The stress accumulation trajectory of each environmental factor combination chain is predicted by a temporal convolutional network, and the failure probability density at the trajectory intersection is calculated.

[0047] By aggregating the environmental factor combination chains whose failure probability density exceeds a critical threshold, a potential failure mode coding table sorted by failure risk is generated.

[0048] Preferably, the system further includes an environment scene classification module for managing the input data of the environment scene simulation control module.

[0049] Receive real-time environment test scenario request data and extract its environmental factor combination feature vector;

[0050] Calculate the cosine similarity between the feature vector and the feature library of historical environment test scenarios. If the similarity is higher than the scenario matching threshold, it is classified as a compliant environment test scenario.

[0051] If the similarity is lower than the scene matching threshold, the environmental scene dynamic modeling module is invoked to construct a temporary environmental association map and the newly added environmental test scene features are output to the historical feature library.

[0052] Preferably, the specific process for the environmental tolerance analysis module to output tolerance residuals includes:

[0053] The baseline value of tolerance predicted by the virtual model of environmental stress tolerance is compared with the measured performance degradation index by point-by-point difference calculation.

[0054] The difference results are smoothed by Gaussian filtering to generate tolerance residual curves and transmit them to the test scheme optimization module.

[0055] When the slope of the tolerance residual curve changes beyond the warning threshold, the environmental scenario simulation control module is triggered to pause the current test sequence and initiate the safety protocol.

[0056] Compared with the prior art, the beneficial effects of the present invention are:

[0057] The multi-environmental factor acquisition and processing module effectively collected monitoring data from various sensors within the test environment chamber. Normalization preprocessing eliminated differences in magnitude and format barriers between different data types, providing a unified and high-quality data foundation for subsequent environmental modeling and performance analysis. This data processing approach allows previously scattered and isolated environmental parameters to form an organic whole, facilitating in-depth exploration of the intrinsic relationships between various factors.

[0058] The dynamic environmental scene modeling module constructs a multi-dimensional environmental correlation map based on preprocessed environmental data and dynamically updates the topology according to the data flow, breaking through the limitations of traditional static models. The dynamically updated map can reflect the interaction relationships and intensity changes between environmental factors in real time, accurately capturing the dynamic evolution process of the environmental scene. It provides intuitive and accurate model support for understanding the complex environmental background of the equipment, and helps to more realistically simulate the environmental encounters of the equipment in actual applications.

[0059] The performance evaluation module incorporates a spatiotemporal feature fusion algorithm, combining multidimensional environmental correlation maps with preprocessed environmental data to comprehensively assess the distribution characteristics of environmental stress in both time and space dimensions and its overall impact on equipment performance. This evaluation method transcends the limitations of traditional methods that focus on a single time or space dimension, delving into the spatiotemporal coupling patterns between environmental stress and equipment performance degradation. This makes the evaluation of performance degradation indicators more closely reflect the actual performance of equipment in complex environments.

[0060] The failure mode prediction module combines the evolution trend of performance degradation indicators with the cumulative effect of environmental stress to identify potential failure modes that may occur in equipment in multi-environment coupling scenarios from multiple dimensions and perspectives. This module not only considers the long-term effects of single environmental factors, but also focuses on the cumulative effects of multiple factors working together, thus covering a wider range of failure possibilities and providing a comprehensive reference for taking preventive measures in advance.

[0061] The environmental tolerance analysis module integrates performance degradation indicators and a set of potential failure modes. The constructed environmental stress tolerance virtual model can quantify the tolerance residuals of equipment under specific environmental profiles. This quantitative analysis method transforms the environmental tolerance capability of equipment from a vague qualitative description into specific calculable parameters, clearly presenting the tolerance boundaries of equipment under different environmental conditions, and providing a precise basis for test scenario design and equipment improvement.

[0062] The test scheme optimization module generates an adaptive environmental test scenario sequence based on tolerance residuals and multi-dimensional environmental correlation maps, enabling the testing process to be dynamically adjusted according to the actual tolerance capabilities of the equipment and environmental characteristics. This adaptive adjustment avoids the blindness of traditional fixed test schemes, focusing on environmental stress combinations that significantly affect equipment performance. While ensuring comprehensive testing, it improves testing efficiency and reduces unnecessary resource consumption.

[0063] The environmental scenario simulation control module, combining a set of potential failure modes, an adaptive test scenario sequence, and tolerance residuals, dynamically adjusts the parameter combinations of the test environment chamber, achieving real-time closed-loop control between the test environment and equipment performance feedback. This dynamic adjustment mechanism enables the test environment to accurately reproduce the key environmental stress combinations that the equipment may encounter, ensuring that the testing process fully exposes the equipment's performance shortcomings and potential failure risks under complex environments, thus improving the authenticity and effectiveness of the test results. Attached Figure Description

[0064] Figure 1 This is a schematic diagram of the working principle of the comprehensive environmental adaptability testing system for identifying key performance characteristics as described in this invention.

[0065] Figure 2 Flowchart for normalization preprocessing;

[0066] Figure 3 A flowchart for evaluating performance degradation metrics;

[0067] Figure 4 A flowchart for generating a sequence of adaptive environment test scenarios;

[0068] Figure 5 A flowchart generated for a set of potential failure modes. Detailed Implementation

[0069] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0070] Please see Figure 1 The present invention provides a comprehensive environmental adaptability testing system for identifying key performance characteristics. The system includes a multi-environmental factor acquisition and processing module that connects to temperature sensors, vibration sensors, electromagnetic sensors and humidity sensors deployed in the test environment chamber to acquire raw environmental data streams in real time.

[0071] This module groups data by sensor type and aligns the sampling timestamps of each group of data using a time sliding window; it calculates the covariance matrix of each group of environmental data, determines the principal component direction based on covariance eigenvalue decomposition, and performs standardization scaling along this direction to eliminate dimensional differences; it traverses the standardized data to calculate the Mahalanobis distance distribution, and marks data points exceeding three times the standard deviation as environmental outliers; it uses local weighted regression to fill missing data points, and the median replacement method to handle outliers; finally, it maps the data to the [0,1] interval through range scaling.

[0072] The environmental scene dynamic modeling module extracts the time series features of preprocessed temperature, vibration, electromagnetic, and humidity data to construct an environmental factor relationship matrix. Using historical environmental stress data, equipment performance degradation records, and environmental factor coupling relationships as initial nodes, it calculates the environmental correlation strength between nodes through a graph attention mechanism. Based on the real-time environmental data stream, it updates the weight coefficients of the relationship matrix and reconstructs the adjacency matrix using a graph convolutional network to form a dynamic environmental stress propagation network.

[0073] The performance evaluation module inputs the dynamic environmental stress propagation network and preprocessed environmental data into the spatiotemporal feature fusion model: the graph neural network layer extracts the spatial distribution features of environmental stress, the dilated convolutional layer captures the long-term dependence features of environmental stress, and the fusion generates an environmental performance degradation feature vector; the fully connected layer maps this vector into a performance degradation index and outputs the key performance decay curve under composite environmental stress.

[0074] The failure mode prediction module identifies highly correlated environmental factor combination chains based on the node connection strength of the environmental stress propagation network; predicts the stress accumulation trajectory of each combination chain through a temporal convolutional network, calculates the failure probability density at the intersection of the trajectories; and aggregates the failure probability densities exceeding the critical threshold to generate a potential failure mode coding table sorted by risk.

[0075] The environmental tolerance analysis module integrates the material physical properties and structural design parameters of the equipment under test, and establishes a multi-physics coupled simulation model based on the finite element method. The performance degradation index and potential failure mode set are input into the model, and the internal stress distribution equation of the equipment is solved to output the tolerance benchmark value. The measured performance degradation index is compared with the actual performance degradation index. If the deviation exceeds the threshold, the material aging coefficient is adjusted through gradient backpropagation until the deviation converges.

[0076] The test scheme optimization module constructs a decision space with environmental stress tolerance residuals, test resource constraints, and failure risk levels as objectives. After initializing the root node of the decision tree, it traverses the environmental factor combination action space to generate multi-level child node branches. The Monte Carlo tree search algorithm is used to select the branch node with the highest confidence upper limit and expand the unexplored environmental factor combination actions. The module simulates the equipment performance degradation path after the actions are executed, calculates the test cost-benefit function, and backtracks to update the node weights. When the rate of change of the benefit function is lower than the convergence threshold, the optimal environmental test scenario sequence and load allocation strategy are output.

[0077] The environmental scenario simulation control module analyzes the temperature gradient, vibration spectrum, electromagnetic intensity, and humidity curves in the optimal sequence; it controls the heater power, vibration table frequency, electromagnetic field intensity, and humidifier output of the test chamber based on the environmental factor coupling relationship matrix; it collects equipment performance response data in real time and feeds it back to the environmental scenario dynamic modeling module to update the correlation map; if the performance response deviates from the expected curve by more than the control threshold, the test scheme optimization module is re-triggered to generate an alternative sequence.

[0078] Example 1: See Figure 2The multi-environmental factor acquisition and processing module obtains raw environmental parameters from the sensor array within the test environment chamber. Temperature sensors record temperature changes at multiple points within the chamber at a sampling frequency of twice per second; vibration sensors acquire three-dimensional acceleration data at a rate of two thousand times per second; electromagnetic sensors capture electromagnetic field fluctuations at a frequency of ten thousand times per second; and humidity sensors record relative humidity values ​​at a frequency of once per second. The raw data is divided into independent data channels based on the physical characteristics of the sensors and processed separately. All channels require a unified time reference, and synchronization is achieved through a sliding time window. Specifically, a millisecond-level timestamp is used as the reference. Cubic spline interpolation is performed on the humidity data with lower sampling frequencies to match its timestamp with the high-frequency vibration data; downsampling and averaging are performed on the high-frequency electromagnetic data to ensure that each channel has comparable data frames at the same time point.

[0079] After channel data synchronization, principal component standardization is performed. A five-minute data segment is selected, and the covariance matrix of multiple data dimensions within each sensor channel is calculated. By analyzing the eigenvectors of the covariance matrix, the main directions affecting the data distribution are determined. The original data is then projected along the vector direction corresponding to the largest eigenvalue to eliminate systematic biases caused by differences in sensor range or installation position offsets. The data distribution after coordinate transformation exhibits approximately normal characteristics, and standard deviation scaling is performed based on this. The standardized data space is traversed, and the deviation of each data point from the standard distribution is calculated. This deviation is measured using multidimensional Mahalanobis distance, with the statistical control limit set to the mean plus or minus three standard deviations. Data points whose Mahalanobis distance exceeds the upper limit are identified as environmental outliers, and their spatiotemporal coordinates are recorded.

[0080] Anomaly data processing employs two mechanisms: First, for data gaps at non-anomaly locations, local weighted regression prediction is used to fill the gaps based on the changing trends of data at adjacent time points. The weighting function uses the data time distance as the independent variable and defines the weight coefficients in an exponential decay form. Second, for identified anomaly data points, the median value of the same type of sensor at the same time is used for replacement. After a complete cleaning process, all sensor channel data enters the normalization stage. The minimum and maximum values ​​of the cleaned data for each channel are calculated and transformed into dimensionless values ​​in the zero-to-one range using a linear mapping function. These values ​​retain the relative variation characteristics of the original data while eliminating dimensional differences in parameters such as temperature, vibration, electromagnetic fields, and humidity, forming an environmental state matrix that can be directly analyzed across dimensions.

[0081] The environmental scene dynamic modeling module receives the above-mentioned normalized environmental state matrix and extracts dynamic change features from it. Temperature series analysis focuses on two features: calculating the mean within the sliding window to reflect the overall temperature level, and the standard deviation to reflect the intensity of temperature fluctuations. Vibration data undergoes Fast Fourier Transform to extract the main frequency domain components, and the energy integral value within a specified frequency band is calculated as feature input. Electromagnetic interference data processing focuses on transient peak statistics, recording the number of peaks exceeding three times the background noise and their duration. Humidity data is fitted with the humidity change rate through linear regression. These features form the basic dimensions of the multidimensional environmental factor dynamic relationship matrix.

[0082] The historical database provides the initial structural parameters for graph construction. Preloaded environmental extreme event data recorded from past tests, including high-temperature and high-humidity coupling events, electromagnetic resonance events, and their corresponding equipment performance degradation records, are used. These records are transformed into graph starting nodes and associated with each other through connecting edges. A graph attention mechanism dynamically quantifies the connection strength between nodes: the dot product similarity of environmental feature vectors between nodes is calculated as the initial weight; a positional encoding parameter is introduced to correct for the influence of time intervals; and a device response weight coefficient is added to strengthen key associations. This mechanism ultimately outputs a time-varying association strength factor matrix.

[0083] Real-time data streams drive the dynamic evolution of the graph. When a new environmental state matrix is ​​input, the weights of each dimension in the environmental factor relationship matrix are first updated. This update process introduces a forgetting factor mechanism, giving higher weight coefficients to recent data. The updated relationship matrix is ​​then input into a graph convolutional network for adjacency matrix reconstruction. The network consists of three layers of convolutional kernels: the first layer extracts feature combinations from adjacent nodes, the second layer aggregates second-order adjacent node features, and the third layer integrates global feature relationships. The output of each layer undergoes a non-linear transformation through an activation function. The reconstructed adjacency matrix contains the connection relationships updated across all nodes, ultimately forming a dynamically changing environmental stress propagation graph. In this graph, nodes represent environmental state features, the direction of connecting edges represents stress propagation paths, and the edge weights represent the coupling strength between environmental factors. After each update, a new graph version is generated and transmitted to downstream modules.

[0084] During continuous testing, the multi-environmental factor acquisition and processing module continuously performs dynamic data cleaning and alignment. The environmental scenario dynamic modeling module receives new data frames five times per second and performs a complete map reconstruction once per second. The historical data warehouse updates its storage content in a scrolling window manner, retaining all high-resolution environmental data from the past 72 hours as the basis for map structure evolution. The real-time generated multi-dimensional environmental correlation map fully records the coupling modes of various factors in the complex environmental stress, providing an analytical basis for accurately quantifying the synergistic effects between environmental factors. This dynamic modeling mechanism adapts to the continuous variation characteristics of complex environmental parameters, and is particularly suitable for simulating extreme working conditions where temperature abrupt changes and vibration shocks occur simultaneously.

[0085] Example 2: See Figure 3 The performance evaluation module receives the multidimensional environmental correlation graph data structure and preprocessed environmental data stream generated by the environmental scene dynamic modeling module. The multidimensional environmental correlation graph represents the topological relationships between current environmental factors in the form of an adjacency matrix. Node attributes include temperature distribution feature values, vibration spectrum feature values, electromagnetic interference feature values, and humidity change feature values. The spatiotemporal feature fusion model first processes spatial dimension information: the graph neural network layer loads the adjacency matrix and node attribute matrix, and performs adjacent node feature aggregation operations. This aggregation operation includes the summation of the product of the node feature vector and the association weight, and the aggregation range covers the first-order and second-order adjacent nodes of the target node. The feature transfer process uses a nonlinear activation function to transform the node state, generating a spatial feature tensor to record the distribution pattern of environmental stress within the test chamber. This tensor contains spatial information such as the thermal stress intensity and vibration stress propagation direction of different surface areas of the equipment.

[0086] The temporal processing of environmental data streams relies on dilated convolutional structures. Preprocessed sensor data is organized along the time axis into a sliding window sequence, with the window length set to include data from the most recent acquisition period. The convolutional kernels slide along the time axis, expanding the temporal receptive field by setting a dilation factor greater than one. Each convolutional layer contains multiple sets of kernels of different sizes operating in parallel, capturing both short-term fluctuations and long-term evolution trends in device performance response. The kernel outputs are gated to control the feature propagation intensity, suppressing interference from non-critical time nodes. The convolutional results are transformed into a feature matrix describing the cumulative effect of environmental stress over time, with the matrix rows and columns corresponding to the time window position and feature channel dimensions, respectively.

[0087] The spatial feature tensor and temporal feature matrix enter the fusion stage. The spatial tensor, after transpose and recombination, is concatenated with the temporal feature matrix along the channel dimension. The resulting fused feature matrix is ​​input into a two-layer fully connected network. The first layer's node count matches the fusion matrix dimension, and redundant features are compressed through linear transformation. The second layer outputs an environmental performance degradation feature vector of a preset dimension. This feature vector is input to a mapping layer containing a non-linear activation function, transforming it into a performance degradation index value ranging from zero to one. The mapping layer weight parameters are initialized using historical test data and iteratively updated based on real-time data feedback during system operation. The performance degradation index is continuously output as a time-series curve, with the horizontal axis representing the test time and the vertical axis reflecting the degradation rate of key equipment performance relative to its initial state. The curve data is transmitted to downstream modules to provide a quantitative characterization of the equipment's state under combined environmental stress.

[0088] The environmental tolerance analysis module initiates the equipment modeling process. The material physical properties of the tested equipment include preset constants such as the range of thermal expansion coefficients, reference values ​​for elastic modulus, and conductivity thresholds; structural design parameters include measured geometric dimensions, assembly tolerance data, and component connection methods defined in technical documents. Based on these parameters, a three-dimensional solid mesh model is constructed, with the mesh element type using either tetrahedral or hexahedral topology depending on the analysis requirements. The model boundary conditions include fixed constraint surfaces, load application points, and heat conduction interfaces. The finite element solver is configured with a multi-field coupled solver that includes the heat conduction control differential equation, structural stress equilibrium equation, and electromagnetic field control equation. The heat conduction equation considers the anisotropic characteristics of the material's thermal conductivity; the structural equation introduces a nonlinear elastoplastic model of the material; and the electromagnetic equation loads the polarization response parameters of the medium.

[0089] The performance degradation index curve data is used as the external excitation condition input to the multiphysics coupling model. At each time step, the model applies temperature sensor readings as thermal loads, vibration sensor data is converted into the direction of mechanical load application, and electromagnetic monitoring values ​​are mapped to field strength boundary conditions. The solver performs implicit time integration, iteratively calculating the stress tensor distribution field inside the equipment at each time increment step, outputting physical quantities such as the thermal strain value induced by the temperature gradient, parameters of the vibration stress concentration region, and electromagnetic induced eddy current density value. Upon completion of the calculation, it outputs an environmental stress tolerance benchmark curve, whose time nodes are synchronized with the performance degradation index curve. Each benchmark value characterizes the theoretical tolerance limit strength of the equipment in its current state.

[0090] The system continuously compares the deviations of measured performance degradation indicators with tolerance benchmark values. Deviation calculation uses point-to-point differencing of values ​​at corresponding time points. When the deviation values ​​at several consecutive time points exceed the preset threshold range, the model parameter self-correction process is initiated. The correction process focuses on the material aging coefficient as the adjustment target, with its initial value derived from the recommended range in the equipment technical manual. A computational graph structure is constructed with tolerance deviation as output and aging coefficient as input, and the gradient direction is calculated using the backpropagation algorithm. The aging coefficient value is adjusted along the negative gradient direction using a preset learning rate, and the updated value is input into the finite element model to recalculate the deviation. The iterative process continues until the average deviation within the current batch time window enters the tolerance range. The corrected material aging coefficient is retained for use in subsequent calculation cycles, achieving online incremental optimization of model parameters. A tolerance residual variable is added to the model output data, serving as a quantitative indicator describing the dynamic gap between the actual state of the equipment and the theoretical tolerance boundary. The residual sequence is transmitted to the test scheme optimization module to guide subsequent environmental scenario configuration adjustments.

[0091] Example 3: See Figure 4The test scheme optimization module receives the tolerance residual sequence output by the environmental tolerance analysis module and constructs a three-dimensional optimization target space by combining test resource constraints and failure risk level parameters. After preprocessing, the tolerance residual sequence forms a time-discrete vector, with vector elements reflecting the dynamic deviation between the actual performance of the equipment and the theoretical tolerance boundary. Test resource constraints are quantitatively expressed as hard boundary indicators such as total available test time, energy consumption limits, and equipment usage frequency restrictions. Failure risk level parameters are derived from the potential failure mode coding table generated by the failure mode prediction module, and a comprehensive risk assessment value is obtained by weighted summarization of the risk probability values ​​corresponding to each code. These three dimensions constitute the basic coordinate system of the decision space, with each candidate test scheme corresponding to a specific coordinate point in this space.

[0092] The environmental factor combination action space defines the set of executable operations for the test environment chamber. The temperature control dimension is divided into twenty evenly spaced setpoints, covering the upper and lower limits of the operating temperature range defined in the equipment's technical specifications. The vibration control dimension includes three basic types: swept frequency mode, fixed frequency mode, and random vibration mode, each with ten intensity level parameters. The electromagnetic field control dimension sets eight typical interference waveforms, each corresponding to five field strength levels. The humidity control dimension employs two modes: linear gradual change and step change, each with seven humidity setpoints. The Cartesian product of these control dimensions constitutes a vast environmental factor combination action space, and test scheme optimization requires intelligent search of this space within a reasonable timeframe.

[0093] The decision tree construction process begins by expanding from the current environmental state node. The root node records the real-time monitored values ​​of temperature, vibration, electromagnetic, and humidity parameters within the test chamber. First-level child nodes are generated by enumerating feasible directions of change for the temperature setpoint. Each temperature child node carries a vibration mode selection branch, which further extends to electromagnetic field configuration nodes. Finally, the humidity control mode selection is completed at the fourth-level node. This hierarchical tree structure transforms the combined action space of environmental factors into a traversable set of decision paths. Each decision path corresponds to a sequence of environmental test scenarios, and each step in the sequence defines the transition mode and duration of environmental parameters.

[0094] The Monte Carlo tree search algorithm performs selective expansion on a decision tree. The search process starts from the root node and calculates the upper confidence limit for each child node at each decision point.

[0095] ;

[0096] in, Indicates the upper confidence limit. This indicates the child node currently being evaluated. This indicates its parent node. This is the cumulative test revenue value of this node. Parent node visit count, Record the number of times the node is accessed. The exploration coefficient is used to balance the weights of development and exploration. The algorithm prioritizes extending downwards from the branch with the highest confidence upper bound, creating new child nodes when encountering insufficiently explored nodes. The initial access count for new nodes is set to one, and the test reward value is estimated by interpolation based on the historical data of the parent node.

[0097] After selecting a decision path, a virtual test simulation is executed. The multiphysics coupling model of the environmental tolerance analysis module is invoked, and the sequence of environmental parameter changes corresponding to the input path is used for rapid simulation. The simulation process employs a simplified calculation mode, with the time step set to ten times the speed of the actual test, and the calculation accuracy of key physical quantities controlled within the engineering allowable range. The simulation output predicts the equipment performance degradation trajectory, and calculates three optimization objective values ​​under this trajectory: the integral area of ​​the tolerance residual reflects the overall deviation, the cumulative value of resource consumption quantifies the test cost, and the peak value of failure risk indicates the maximum potential hazard. These three values ​​are combined to form the test cost-benefit function value for the current decision path, with the function weight coefficients dynamically adjusted according to the testing phase.

[0098] The path evaluation results are used to backtrack and update the decision tree node data. Starting from the newly added node, the search path is traced upwards, updating the visit count and cumulative reward value of each ancestor node. The visit count is simply accumulated by the number of additions, and the cumulative reward value is updated using a moving average method, retaining higher weights for more recent evaluation results. When the node update ratio of the entire tree exceeds a preset threshold, a global weight rebalancing operation is triggered to eliminate the differences in evaluation scales between different search paths.

[0099] Convergence determination is based on the stability monitoring of changes in the test cost-benefit function. The sequence of benefit function values ​​generated by the ten most recent complete search iterations is recorded, and the coefficient of variation of the sequence is calculated as the convergence metric. When the coefficient of variation is less than one-thousandth for three consecutive calculation cycles, the search process is considered to have reached a stable state. At this point, the decision path with the highest cumulative benefit value is selected as the optimal environmental test scenario sequence output. This sequence defines in detail the environmental parameter configurations, transition times, hold times, and monitoring requirements for each stage, and also includes resource load allocation suggestions for each stage, such as limiting the vibration table power to below 80% of its rated value.

[0100] The environmental scenario simulation control module analyzes the execution details of the optimal test scenario sequence. Temperature control commands include parameters such as the heating rate setpoint, target temperature holding time, and cooling gradient. Vibration control parameters specify the sweep start and end frequencies, sweep rate, amplitude, and direction of action. Electromagnetic field configuration specifies technical details such as the interference waveform type, field strength variation curve, and pulse repetition frequency. Humidity control defines execution elements such as the target humidity value, rate of change, and number of cycles. These parameters are converted into executable commands by the test environment chamber's control interface, while dynamically compensating for the coupling effects between environmental factors.

[0101] A closed-loop feedback mechanism is established for real-time control. During the execution of the test scenario sequence, the monitoring system collects equipment response data five times per second, including direct performance indicators such as power current fluctuations, communication signal quality, and mechanical vibration response. After preprocessing, the data stream is converted into standard performance degradation index values ​​and compared in real-time with the expected degradation curve. Deviation calculation uses the root mean square error within a sliding window; when the error value exceeds the control threshold, the current test step is immediately interrupted. The interruption signal triggers the emergency response process of the test scheme optimization module, generating an alternative environment test scenario sequence based on the latest equipment status data. The alternative sequence prioritizes environmental combinations with lower risk levels while maintaining consistency with the test objectives of the original sequence.

[0102] The anomaly handling process includes safety protocol activation. When performance deviates beyond a safety threshold, the control system automatically switches to preset baseline environmental conditions. The temperature parameter drops to room temperature within thirty seconds, the vibration table immediately stops working, the electromagnetic field generator cuts off its output, and the humidity is adjusted to a constant 50%. Simultaneously, the equipment's protective self-test program is initiated, recording a complete snapshot of environmental parameters and the equipment status log at the time of the anomaly. This data serves as key input for subsequent test scheme optimization, used to correct parameter deviations in the environmental factor coupling relationship model. The safe state continues until the operator confirms the equipment status, at which point, based on the optimization module's recommendation, either to continue executing the alternative sequence or terminate the test process.

[0103] A dynamic resource allocation mechanism continuously operates throughout the test sequence execution. It monitors energy consumption rates and remaining test time budgets in real time. When resource usage trends predict exceeding limits, a resource warning signal is sent to the test plan optimization module. Based on this, the optimization module adjusts the resource allocation strategy for subsequent scenario sequences, potentially employing a balance between extending single-step test times and reducing environmental stress intensity. This dynamic adjustment ensures that the entire testing process achieves its predetermined test objectives within resource constraints, preventing test interruptions due to resource depletion.

[0104] Example 4: Reference Figure 5The failure mode prediction module extracts highly correlated node combinations from the environmental stress propagation network. The topology data of the environmental stress propagation network records the dynamic coupling relationships between various environmental factors. Nodes represent the states of environmental parameters such as temperature, vibration, electromagnetic fields, and humidity, and edge weights reflect the strength of mutual influence between parameters. The system scans the network adjacency matrix and filters node pairs with connection strength exceeding a preset threshold; for example, the edge weight between a temperature node and a vibration node reaches 0.82, and the edge weight between an electromagnetic node and a humidity node is 0.76. These high-weighted edges constitute the basic elements of environmental factor combination chains, and each combination chain contains two or more environmental factors with strong coupling relationships.

[0105] A temporal convolutional network processes the stress accumulation process of combined chains. Historical data for each environmental factor combined chain is input into the network for training. The network structure contains four layers of causal convolutional modules, and the output of each layer is controlled by a gating mechanism to control feature propagation. In the temperature-vibration combined chain, the network learns that when the temperature continuously exceeds 75°C and the vibration frequency is in the 100-150Hz range, the thermal fatigue accumulation rate of the equipment's metal components exhibits a non-linear growth characteristic. For the electromagnetic-humidity combined chain, the network identifies that the synergistic effect of pulsed electromagnetic interference with a field strength exceeding 25V / m and relative humidity greater than 90% accelerates the aging process of the circuit board insulation material. The network output predicts the stress accumulation trajectory of each combined chain in future test cycles. The trajectory data includes three dimensions of information: timestamp, stress intensity, and accumulation rate.

[0106] Failure probability density calculation focuses on trajectory intersection analysis. The system projects the stress accumulation trajectories of different environmental factor combinations onto a unified spatiotemporal coordinate system and detects the intersection points between the trajectories. For example, if the predicted stress values ​​of the temperature-vibration combination and the electromagnetic-humidity combination simultaneously reach the critical threshold at the 83rd minute after the start of the test, the coordinates of this intersection point are recorded as a potential failure event point. A three-dimensional Gaussian distribution model is established with the intersection point as the center to calculate the failure event probability density value within this region. The probability density calculation considers the frequency of failures under similar environmental conditions in the equipment's historical failure data, as well as the impact of the degree of material performance degradation in the current testing phase.

[0107] Table 1 presents the failure probability analysis results of typical environmental factor combination chains. The data in the table comes from a continuous eight-hour composite environmental stress test, including monitoring data and calculation results for four highly correlated combination chains.

[0108] Table 1: Example of failure probability analysis of environmental factor combination chain.

[0109] ;

[0110] The process of generating the potential failure mode set involves risk ranking. The system aggregates all failure probability densities, exceeding a critical threshold. The environmental factor combination chains are sorted in descending order of density value to generate a coding table. Each record in the coding table contains four pieces of information: failure mode number, description of the environmental factor combination, predicted occurrence time window, and risk level label. Failure mode number 01 corresponds to a high-risk state of the temperature-electromagnetic combination chain around 65 minutes; it is recommended to strengthen the synchronous monitoring of temperature and electromagnetic parameters during this time window. Failure mode number 02 targets the stress peak of the temperature-vibration combination chain around 107 minutes; it is recommended to adjust the vibration spectrum in advance to avoid the resonance frequency band.

[0111] The environmental scenario classification module processes real-time test request data. Newly arrived test requests contain target environmental parameter configurations, such as "temperature cycling from 85°C to -40°C, superimposed with random vibrations of 10-500Hz". The module extracts the feature vector of this configuration, including twelve dimensions of features such as temperature change gradient, vibration energy distribution, and electromagnetic background value. It calculates the cosine similarity between this vector and 327 executed scenarios in the historical feature database. The similarity calculation uses a weighted algorithm, assigning higher weights to key parameters such as extreme temperature values. When the highest similarity reaches 0.93, the request is classified as a recorded compliant test scenario and executed directly using historically optimized parameters.

[0112] A new scene feature learning mechanism handles low-similarity requests. In one test request, the feature vector for "salt spray overlaid with temperature shock" had a similarity of only 0.61 with the best match in the historical database, triggering a new scene processing flow. The environmental scene dynamic modeling module constructs a temporary association graph, with the initial connection strength between the salt spray node and the temperature node set to a default value of 0.5. By monitoring the correlation coefficient between the salt spray deposition rate and the temperature change rate in real time, the connection strength is dynamically adjusted to 0.68. The feature codes generated by the new graph are added to the historical feature database, and the recommended monitoring scheme for this scene is recorded, including a suggestion to increase the corrosion rate sampling frequency to twice per minute.

[0113] The dynamic feedback during test execution updates the spectral structure. Actual test data shows that salt spray deposition accelerates at high temperatures, causing the connection strength between temperature nodes and salt spray nodes to be corrected to 0.71. The environmental scene classification module performs an incremental update of the feature library every hour. New data is normalized and then merged into the historical feature vector set. The update operation adopts a rolling window mechanism, retaining all scene feature data from the most recent three months, while early, less frequently used scene features are automatically archived to a secondary repository. This mechanism ensures that the system continuously adapts to the testing requirements of new environmental combinations while maintaining the query efficiency of the core feature library.

[0114] The failure mode prediction module and the environmental scenario classification module work together to form a closed loop. When a new environmental scenario triggers a potential failure mode, the prediction module immediately generates an early warning signal and sends it to the test execution system. The early warning signal includes suggestions for adjusting the affected environmental parameters. For example, if a salt spray-temperature combination is detected as potentially causing seal failure, it is recommended to limit the temperature change rate to below 5°C / minute. These suggestions are converted into fine-tuning instructions for the control parameters of the test environment chamber, achieving risk avoidance through the closed-loop feedback mechanism of the execution system. Simultaneously, any actual performance anomalies are recorded as failure mode samples, used to enhance the training dataset of the temporal convolutional network and improve subsequent prediction accuracy.

[0115] Example 5: After the tolerance residual calculation process is started, the system establishes a dual-channel synchronous data transmission system. The first channel continuously receives the baseline data stream output by the environmental stress tolerance virtual model, with a sampling frequency of ten data points per second. Each data point includes a timestamp and the corresponding theoretical tolerance strength value. The second channel connects to the equipment performance sensor network to collect real-time measurements of actual performance degradation indicators. After preprocessing, the measured values ​​are strictly aligned with the time axis of the baseline values. The dual data streams complete millisecond-level timestamp matching at the residual calculation node to ensure the consistency of the underlying data time for subsequent differential calculations.

[0116] The difference operation module performs absolute difference calculations on each successfully matched pair of data points. The calculation process is pipelined on a dedicated hardware accelerator, completing the subtraction operation and absolute value extraction for each pair of data points per clock cycle. The original residual sequence is cached in a circular data queue in time-series form, with a queue depth set to a capacity of 3.6 million data points, equivalent to storing ten hours of high-density residual historical data. This queue also provides a fast access interface for both real-time residual data and short-term historical data windows.

[0117] Gaussian filtering is applied to suppress noise in the original residual sequence. The filter kernel size is fixed at 61, corresponding to a data window length of approximately six seconds. The standard deviation parameter of the Gaussian function is set to half the sampling interval to ensure a balance between filtering effect and system response speed. The filtering process employs a symmetrical boundary processing mode to avoid data distortion at both ends of the sequence. The filtered residual data is transmitted to the decision buffer of the test scheme optimization module via the data bus. The optimization module reads the latest data from the buffer every second for dynamic adjustment of the environmental test scenario sequence.

[0118] The residual change trend monitoring mechanism operates independently. The system sets a dynamic analysis window for the tolerance residual curve, with the window length automatically adjusted according to the testing phase: 60 seconds for the regular testing phase and 20 seconds for the accelerated stress testing phase. Linear regression fitting is performed on the residual data within the analysis window to calculate the average rate of change over that time period. A robustness handling mechanism is added to the regression calculation process, automatically eliminating outliers to ensure the stability of the slope calculation. The slope calculation results for three consecutive analysis windows are stored in a trend judgment register, which is initially set to zero.

[0119] The warning threshold comparator monitors the slope register value in real time. When three consecutive slope changes recorded in the register exceed the preset threshold, the system generates a warning event marker. The warning event includes a detailed residual evolution analysis report: the trigger time point, the changes in the initial and final residual values, and the cumulative slope change over the duration, among other key parameters. The warning data packet is transmitted to the priority message queue of the environmental scenario simulation control module via a network protocol.

[0120] After the safety protocol execution process is initiated, the environmental scenario simulation control module immediately enters the interrupt response mode. The interrupt command contains three levels of control commands: The first-level command is sent to the actuator unit of the test environment chamber, requiring all environmental stress application devices to be shut down within a specified time. Upon receiving the zero-power signal, the temperature control unit stops heating and cooling operations, the vibration table actuator executes the motor emergency stop procedure, the electromagnetic field generator disconnects its power supply, and the humidity regulator switches to natural ventilation mode. The second-level command activates the equipment's protective self-test program, covering key safety parameters such as power input stability, communication link integrity, and mechanical structure displacement. The third-level command initiates the environmental parameter recovery process, gradually guiding the chamber conditions to a preset safety baseline environment.

[0121] The manual intervention interface is automatically activated upon triggering of the safety protocol. The console displays a real-time interruption report, including dynamic change curves of environmental parameters and equipment status monitoring charts. After authorization, operators can view detailed diagnostic logs: tolerance residual curve expansion analysis before and after the interruption point, synchronous fluctuation records of relevant environmental factors, and historical data such as abnormal fluctuation patterns of equipment performance indicators. The interface dialog box provides subsequent operation options: the "Continue Testing" option includes a confirmation item for loading backup test sequences, and the "Terminate Testing" option triggers a data archiving and cleanup process.

[0122] The backup test sequence loading requires dual verification. The first verification checks whether the environmental parameters of the backup sequence are within safe thresholds, the extreme temperature does not exceed equipment specification limits, and the vibration spectrum avoids known resonance point regions. The second verification tests resource adaptability, predicting whether the total time and energy consumption required to execute the backup sequence are lower than the current available resource margin. After successful verification, the environmental scenario simulation control module reconfigures the execution parameters: setting the sequence loading point to five basic time units offset from the interrupt position, and setting a buffer transition period of approximately two minutes before the sequence starts.

[0123] The recovery process employs a phased parameter gradual change strategy. The first phase restores the basic environmental conditions to their pre-interruption state, with temperature control adjusted gradually at one-fifth of its maximum rate to avoid abrupt thermal shock. The second phase restarts individual environmental factor application devices, activating them sequentially in the priority order of vibration-electromagnetic-humidity, maintaining a three-minute stabilization period after each activation. The third phase restores the combined stress effect by reducing the rate of change of environmental factors to enhance process monitoring density. Throughout the recovery process, the tolerance residual slope is continuously monitored. Once the slope returns to the safe range, the system clears the warning markers and resumes normal monitoring mode.

[0124] The data archiving module records operation logs throughout the execution of the security protocol. Log files record the precise time points of interruption events, the complete dataset of tolerance residual curves, the sequence of control commands for environmental parameters, operator response actions, and timestamps. Log data is stored in an encrypted and compressed format, tagged with test batch numbers and device identification information. Each interruption event generates an independent archive unit, supporting rapid retrieval and querying by time range or event type. Archived data also serves as input material for the system's self-learning database, used to optimize the boundary condition settings of the tolerance virtual model.

[0125] The system performance monitoring unit periodically generates residual analysis reports. These reports, organized by test phase, statistically analyze the distribution characteristics of tolerance residuals: the mean residual represents the overall deviation level, the variance reflects the fluctuation range, and extreme values ​​mark the maximum instantaneous deviation. When a persistently high mean residual is detected for a particular test phase, the system automatically generates model parameter calibration suggestions for engineers to review and input into the environmental tolerance analysis module for model updates. This closed-loop feedback mechanism enables the system to adapt to changes in material properties during long-term testing, maintaining consistency between the theoretical model and the actual conditions.

[0126] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0127] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A comprehensive environmental adaptability testing system for identifying key performance characteristics, characterized in that: include: The multi-environmental factor acquisition and processing module is used to collect environmental data monitored by various sensors deployed in the test environment chamber and perform normalization preprocessing. The environmental scene dynamic modeling module constructs a multi-dimensional environmental association map based on preprocessed environmental data and dynamically updates the topology of the multi-dimensional environmental association map according to the environmental data stream. The performance evaluation module receives the multidimensional environmental correlation map and preprocessed environmental data, and evaluates the performance degradation index of the device under test under composite environmental stress through a spatiotemporal feature fusion algorithm. The failure mode prediction module generates a set of potential failure modes of the device under test in multi-environment coupling scenarios based on the evolution trend of the performance degradation index and the cumulative effect of environmental stress. The environmental tolerance analysis module integrates the performance degradation index and potential failure mode set, constructs a virtual model of environmental stress tolerance, and outputs the tolerance residual of the tested equipment under a specific environmental profile. The test scheme optimization module generates an adaptive environment test scenario sequence based on the tolerance residual and the multidimensional environment correlation map. The environmental scenario simulation control module dynamically adjusts the parameter combination of the test environment chamber by combining the set of potential failure modes, the adaptive environmental test scenario sequence and the tolerance residual. The specific process for evaluating performance degradation indicators by the performance indicator evaluation module includes: The dynamic environmental stress propagation network and preprocessed environmental data are input into the spatiotemporal feature fusion model, and the spatial distribution features of environmental stress are extracted through the graph neural network layer. Dilated convolutional layers are used to capture the long-term dependence features of environmental stress evolution over time, and the spatial distribution features and long-term dependence features are fused to form an environmental performance degradation feature vector. The environmental performance degradation feature vector is mapped to a performance degradation index through a fully connected layer, and the key performance degradation curve of the device under test under composite environmental stress is output. The specific process for constructing a virtual model of environmental stress tolerance by the environmental tolerance analysis module includes: Collect the material physical properties and structural design parameters of the device under test, and establish a multi-physics coupled simulation model based on the finite element method; The performance degradation index and potential failure mode set are input into the multiphysics coupling simulation model to solve the internal stress distribution equation of the equipment and output the environmental stress tolerance benchmark value. The deviation between the tolerance benchmark value and the measured performance degradation index is compared. If the deviation exceeds the tolerance threshold, the model parameter correction mechanism is triggered. The material aging coefficient of the multiphysics coupling simulation model is adjusted by the gradient backpropagation algorithm until the tolerance deviation converges to the preset tolerance range. The specific process by which the test scheme optimization module generates an adaptive environment test scenario sequence includes: A test scenario decision space is constructed with environmental stress tolerance residual, test resource constraints, and failure risk level as optimization objectives. Initialize the root node of the test scenario decision tree, and traverse the action space of environmental factors to generate multi-level child node branches; The Monte Carlo tree search algorithm is used to select the branch node with the highest confidence upper limit starting from the root node, and to expand the unexplored combination of environmental factors and actions. Simulate the equipment performance degradation path after executing a selected combination of environmental factors, calculate the test cost-benefit function, and backtrack to update the decision tree node weights. When the rate of change of the test cost-benefit function is lower than the convergence threshold, the optimal environmental test scenario sequence and the corresponding load allocation strategy are output. The Monte Carlo tree search algorithm performs selective expansion on the decision tree. The search process starts from the root node and calculates the upper confidence limit of the child nodes at each decision point. ； in, Indicates the upper confidence limit. This indicates the child node currently being evaluated. Indicates its parent node, This is the cumulative test revenue value of this node. Parent node visit count, Record the number of times the node is accessed. To explore the coefficients used to balance the weights of development and exploration, the algorithm prioritizes extending downwards from the branch with the highest confidence upper limit. When encountering an insufficiently explored node, a new child node is created. The initial access count of the new node is set to one, and the test benefit value is estimated by interpolation based on the historical data of the parent node.

2. The comprehensive environmental adaptability testing system for key performance indicators according to claim 1, characterized in that, The specific process for performing normalization preprocessing includes: It receives raw environmental data collected by temperature sensors, vibration sensors, electromagnetic sensors and humidity sensors, groups the data by sensor type, and aligns the sampling timestamps of each group of data through a time sliding window. Calculate the covariance matrix of each group of environmental data, determine the principal component direction of each group of data based on the covariance eigenvalue decomposition, and perform standardization scaling along the principal component direction to eliminate dimensional differences. Traverse each set of standardized data, calculate its Mahalanobis distance distribution, and mark data points that exceed three times the standard deviation as environmental outliers. Missing data points were filled by local weighted regression, and environmental outliers were handled by median replacement. Finally, range scaling was used to map each group of data to the zero-to-one interval.

3. The comprehensive environmental adaptability testing system for key performance indicators according to claim 2, characterized in that, The specific process for constructing a multi-dimensional environment association map by the dynamic environmental scene modeling module includes: Time series features of preprocessed temperature, vibration, electromagnetic interference, and humidity data are extracted to construct an environmental factor relationship matrix; Historical environmental stress data, equipment performance degradation records, and environmental factor coupling relationships are used as initial nodes of the graph, and the environmental correlation strength between nodes is calculated through a graph attention mechanism. The weight coefficients of the environmental factor relationship matrix are updated based on the environmental data stream, and the adjacency matrix of the environmental correlation strength is reconstructed through a graph convolutional network to generate a dynamic environmental stress propagation network.

4. The comprehensive environmental adaptability testing system for key performance indicators according to claim 3, characterized in that, The specific process of dynamically adjusting the combination of test environment chamber parameters by the environmental scenario simulation control module includes: Analyze the temperature gradient, vibration spectrum, electromagnetic intensity, and humidity variation curves in the optimal environmental test scenario sequence; Adjust the heater power, vibration table frequency, electromagnetic field generator intensity, and humidifier output of the test environment chamber according to the environmental factor coupling relationship matrix; Real-time acquisition of performance response data of the device under test under environmental stress sequence, and feedback to the environmental scene dynamic modeling module to update the multi-dimensional environmental correlation map; If the deviation between the performance response data and the expected degradation curve exceeds the control threshold, the test scheme optimization module will be re-triggered to generate an alternative environment test scenario sequence.

5. The comprehensive environmental adaptability testing system for key performance indicators according to claim 4, characterized in that, The specific process by which the failure mode prediction module generates a set of potential failure modes includes: Based on the node connection strength of the environmental stress propagation network, identify highly correlated environmental factor combination chains; The stress accumulation trajectory of each environmental factor combination chain is predicted by a temporal convolutional network, and the failure probability density at the trajectory intersection is calculated. By aggregating the environmental factor combination chains whose failure probability density exceeds a critical threshold, a potential failure mode coding table sorted by failure risk is generated.

6. The comprehensive environmental adaptability testing system for key performance indicators according to claim 1, characterized in that, It also includes an environment scene classification module, used to manage the input data of the environment scene simulation control module: Receive real-time environment test scenario request data and extract its environmental factor combination feature vector; Calculate the cosine similarity between the feature vector and the feature library of historical environment test scenarios. If the similarity is higher than the scenario matching threshold, it is classified as a compliant environment test scenario. If the similarity is lower than the scene matching threshold, the environmental scene dynamic modeling module is invoked to construct a temporary environmental association map and the newly added environmental test scene features are output to the historical feature library.

7. The comprehensive environmental adaptability testing system for key performance indicators according to claim 1, characterized in that, The specific process for the environmental tolerance analysis module to output tolerance residuals includes: The baseline value of tolerance predicted by the virtual model of environmental stress tolerance is compared with the measured performance degradation index by point-by-point difference calculation. The difference results are smoothed by Gaussian filtering to generate tolerance residual curves and transmit them to the test scheme optimization module. When the slope of the tolerance residual curve changes beyond the warning threshold, the environmental scenario simulation control module is triggered to pause the current test sequence and initiate the safety protocol.

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