Method and system for testing material performance based on low-temperature environment simulation

Abstract

The application provides a material performance test method and system based on low-temperature environment simulation, and relates to the technical field of material performance test. Firstly, the low-temperature characteristics of the test material are pre-analyzed, and dynamic stress benchmark signals containing multi-dimensional stress variables such as temperature change gradient variables and environmental medium interaction variables are generated in combination with dynamic low-temperature environment parameters of application scenarios. Based on the dynamic stress benchmark signals, a multi-dimensional performance coupling acquisition process is started, and multi-dimensional performance response data sets such as mechanical deformation response, physical property evolution and chemical stability are obtained. The multi-dimensional performance response data sets are coupled and associated with the benchmark signals, the performance response coupling coefficients are calculated, and a performance coupling association model is generated. According to the performance coupling association model, the dynamic stress threshold interval of the nonlinear change of the material performance is identified. Finally, a material low-temperature performance test execution scheme containing dynamic threshold control logic is generated based on the threshold interval and the model, and the stress variable combination can be adjusted according to the real-time performance response.

Description

Technical Field

[0001] This invention relates to the field of material performance testing, and more specifically, to a material performance testing method and system based on low-temperature environment simulation. Background Technology

[0002] In the field of materials science, testing the performance of materials in low-temperature environments is a crucial step in evaluating their applicability and reliability. With continuous advancements in technology, various materials are widely used in extreme low-temperature scenarios such as aerospace, polar exploration, and cryogenic electronics, which place stringent demands on the performance of materials under low-temperature conditions.

[0003] Traditional methods for testing the low-temperature performance of materials have several limitations. Firstly, existing methods often focus only on single-dimensional low-temperature stress factors, such as temperature reduction, neglecting the comprehensive impact of complex, multi-dimensional stress variables on material performance, including temperature gradients and interactions with environmental media in real-world low-temperature environments. For instance, in aerospace, aircraft experience rapid temperature changes during flight and are also affected by the interaction with rarefied gases at high altitudes; traditional methods cannot accurately simulate these complex environments. Secondly, traditional methods typically employ static testing, maintaining fixed low-temperature conditions, which fails to reflect performance changes in real-world dynamic low-temperature environments. Furthermore, traditional methods lack real-time feedback and dynamic adjustment mechanisms for material performance changes during testing, making it difficult to accurately determine the critical thresholds for nonlinear changes in material performance. Consequently, the test results cannot comprehensively and accurately reflect the material's performance in real-world low-temperature environments. Summary of the Invention

[0004] In view of the aforementioned problems, and in conjunction with the first aspect of the present invention, embodiments of the present invention provide a material performance testing method based on low-temperature environment simulation, the method comprising:

[0005] The low-temperature characteristics of the test material are pre-analyzed, and dynamic low-temperature environmental parameters of the material application scenario are combined to generate a dynamic stress reference signal containing multi-dimensional stress variables, including temperature change gradient variables and environmental medium interaction variables.

[0006] Based on the dynamic stress reference signal, a multi-dimensional performance coupling acquisition process is initiated to simultaneously capture the mechanical deformation response data, physical property evolution data, and chemical stability data of the test material under dynamic low temperature environment, thereby obtaining a multi-dimensional performance response dataset.

[0007] A coupling correlation analysis is performed on the multi-dimensional performance response dataset and the dynamic stress benchmark signal to calculate the performance response coupling coefficients corresponding to different combinations of stress variables and generate a performance coupling correlation model.

[0008] Based on the performance coupling correlation model, the dynamic stress threshold range when the performance of the test material undergoes nonlinear changes is identified;

[0009] Based on the dynamic stress threshold range and performance coupling correlation model, a material low-temperature performance test execution plan containing dynamic threshold adjustment logic is generated. The material low-temperature performance test execution plan can adjust the stress variable combination according to the real-time performance response.

[0010] In another aspect, embodiments of the present invention also provide a material performance testing system based on low-temperature environment simulation, including a processor and a machine-readable storage medium connected to the processor. The machine-readable storage medium is used to store programs, instructions, or code, and the processor is used to execute the programs, instructions, or code in the machine-readable storage medium to implement the above-described method.

[0011] Based on the above, this invention first performs a pre-analysis of the low-temperature characteristics of the test material and combines it with dynamic low-temperature environmental parameters of the application scenario to generate a dynamic stress reference signal containing multi-dimensional stress variables. This simulates the complex and variable stress conditions in actual low-temperature environments. Based on this dynamic stress reference signal, a multi-dimensional performance coupling acquisition process is initiated to simultaneously capture multi-dimensional performance response data such as mechanical deformation response, physical property evolution, and chemical stability. This allows for comprehensive acquisition of information on the material's performance changes under dynamic low-temperature environments. A coupling correlation analysis is performed on the multi-dimensional performance response dataset and the dynamic stress reference signal to generate a performance coupling correlation model. This model identifies the dynamic stress threshold range when the test material's performance exhibits non-linear changes. Finally, based on the dynamic stress threshold range and the performance coupling correlation model, a material low-temperature performance testing execution plan containing dynamic threshold control logic is generated. This enables dynamic adjustment of the stress variable combination according to real-time performance response during the testing process, making the testing more accurate and efficient, and enabling a comprehensive and accurate evaluation of the material's performance under real low-temperature environments. Attached Figure Description

[0012] Figure 1 This is a schematic diagram of the execution flow of the material performance testing method based on low-temperature environment simulation provided in the embodiments of the present invention.

[0013] Figure 2 This is a schematic diagram of exemplary hardware and software components of a material performance testing system based on low-temperature environment simulation provided in an embodiment of the present invention. Detailed Implementation

[0014] The present invention will now be described in detail with reference to the accompanying drawings. Figure 1This is a flowchart illustrating a material performance testing method based on low-temperature environment simulation according to an embodiment of the present invention. The following is a detailed description of this material performance testing method based on low-temperature environment simulation.

[0015] Step S110: Perform low-temperature characteristic pre-analysis on the test material, and generate a dynamic stress reference signal containing multi-dimensional stress variables by combining the dynamic low-temperature environmental parameters of the material application scenario. The multi-dimensional stress variables include temperature change gradient variables and environmental medium interaction variables.

[0016] In this embodiment, a certain type of aluminum alloy commonly used in the aerospace field is used as the test material. This test material is mainly used in the cryogenic fuel storage compartment of spacecraft and needs to maintain stable mechanical properties and chemical stability under extreme low-temperature environments. A preliminary low-temperature characteristic analysis of this aluminum alloy material is conducted. Known characteristic data at different temperatures are retrieved from a material database, and supplementary data obtained from previous basic experiments are combined to form a preliminary characteristic analysis report.

[0017] Dynamic cryogenic environmental parameters for the material's application scenarios were collected. These parameters were derived from environmental monitoring data of spacecraft operating in different orbits, including temperature fluctuations in space and the changing patterns of the surrounding medium (such as trace gases and radiation particles). Based on this information, multi-dimensional stress variables were constructed. The temperature gradient variable reflects the rate and magnitude of temperature change over time, while the environmental medium interaction variable covers factors such as medium composition, concentration, and mode of action. These variables were integrated and time-series planned to generate a dynamic stress baseline signal. This dynamic stress baseline signal serves as the basis for subsequent environmental simulations and performance tests, realistically reflecting the stress and impact states of the material in practical applications.

[0018] Step S111: Extract the basic physical property data and chemical composition information of the test material. The basic physical property data includes the thermal conductivity coefficient and thermal expansion coefficient of the material, and the chemical composition information includes the component proportions and types of easily reactive components of the material.

[0019] During the low-temperature characteristic pre-analysis of the aforementioned aluminum alloy material, basic physical property data and chemical composition information were extracted. The extraction of basic physical property data was accomplished through various experimental methods. For the thermal conductivity coefficient, the steady-state heat flow method was used for measurement. Under different initial temperature conditions, the temperature difference between the two ends of the material and the heat flow rate were recorded. The thermal conductivity coefficient data of the aluminum alloy material in different temperature ranges were calculated, forming a series of multiple numerical values ​​for the thermal conductivity coefficient.

[0020] The coefficient of thermal expansion is measured using laser interferometry. As the material undergoes temperature changes, a laser interferometer precisely captures the change in the material's length, and the coefficient of thermal expansion is calculated by combining this with the temperature change value, resulting in a sequence of multiple numerical values. Chemical composition information is extracted using methods such as spectral analysis and chemical titration. Spectroscopic analysis determines the types and approximate proportions of each element in the material, such as the proportions of aluminum, copper, and magnesium, forming multi-dimensional data on component proportions. Chemical titration is used to identify easily reactive components. For example, certain trace elements may react with specific media at low temperatures; the results of the titration reaction clarify the types of these easily reactive components.

[0021] Step S112: Obtain dynamic low-temperature environment parameters of the material application scenario. The dynamic low-temperature environment parameters include instantaneous temperature fluctuation data of the low-temperature environment in the scenario, dynamic change data of environmental medium composition, and distribution data of continuous stress duration.

[0022] To accurately generate dynamic stress reference signals and obtain dynamic low-temperature environmental parameters for the application scenario of this aluminum alloy material, instantaneous temperature fluctuation data is collected through a temperature sensor array onboard the spacecraft. These sensors are distributed on the surface of the cabin near the application location of the aluminum alloy material and can record temperature values ​​at different times in real time. The temperature values ​​change over time to form an instantaneous temperature fluctuation data sequence, which includes the temperature changes of the spacecraft in different operational phases such as low Earth orbit and high Earth orbit.

[0023] The dynamic change data of environmental medium composition comes from the spacecraft's medium analysis instrument, which can detect the concentration changes of various gas components in the surrounding environment, such as the changes in the concentration of helium and hydrogen at different orbital positions and operating periods, forming multi-dimensional dynamic change data of medium composition. The distribution data of continuous stress duration is obtained by statistically analyzing the spacecraft's residence time under different cryogenic environmental conditions, such as the duration of continuous cryogenic environment in the shadow region, the duration of continuous operation at specific medium concentrations, etc. This data is processed to form the distribution data of continuous stress duration.

[0024] Step S113: Establish a correlation mapping model between the basic properties of materials and dynamic environmental parameters, analyze the influence weight of different combinations of environmental parameters on material properties, and determine the key environmental parameter dimensions that affect material performance.

[0025] After obtaining the basic property data and dynamic environmental parameters of the aforementioned aluminum alloy materials, a correlation mapping model between the two was established. First, the basic material property data and dynamic environmental parameters were structured to enable correlation analysis of various data types in a unified format.

[0026] By designing multiple sets of controlled experiments and varying combinations of environmental parameters, the changes in the fundamental properties of the material were observed. For example, under different temperature fluctuation ranges and media composition combinations, the changes in the material's thermal conductivity and thermal expansion coefficient were measured, and the specific combinations of environmental parameters and the corresponding changes in material properties were recorded for each experiment. Based on these experimental data, correlation analysis was used to calculate the degree of correlation between different combinations of environmental parameters and changes in material properties, thereby obtaining the influence weight of each combination of environmental parameters on material properties.

[0027] Based on the magnitude of their influence weights, environmental parameter dimensions that have a significant impact on material properties are selected; these dimensions are then identified as key environmental parameter dimensions affecting material performance. For example, if the temperature gradient and the concentration change of a certain type of medium have high weights on the material's coefficient of thermal expansion, then these two parameter dimensions are determined as key environmental parameter dimensions.

[0028] Step S1131: Transform the basic physical property data and chemical composition information of the material into a standardized material property vector, where each dimension of the vector corresponds to a material property index.

[0029] The basic physical properties and chemical composition information of the aforementioned aluminum alloy materials are standardized and transformed into material property vectors. The thermal conductivity and thermal expansion coefficient sequences within the basic physical properties data are processed according to preset standardization rules to eliminate dimensional differences between different indicators.

[0030] The component percentage data in the chemical composition information also undergoes similar standardization processing, converting the percentage values ​​of each component into values ​​conforming to a vector format. In the processed material property vector, each dimension corresponds to a material property index, such as the first dimension corresponding to the thermal conductivity coefficient, the second dimension corresponding to the thermal expansion coefficient, and the third dimension corresponding to the aluminum content, forming a multi-dimensional material property vector, which facilitates subsequent correlation analysis with environmental parameter vectors.

[0031] Step S1132: Transform the dynamic low-temperature environmental parameters into a standardized environmental parameter vector, where each vector dimension corresponds to an environmental parameter index.

[0032] The collected dynamic low-temperature environmental parameters were standardized and transformed into an environmental parameter vector. The instantaneous temperature fluctuation data sequence was segmented according to time intervals, and the temperature fluctuation characteristic value of each segment was used as one dimension of the environmental parameter vector; the concentration changes of each medium in the dynamic change data of environmental medium composition were also used as different dimensions; and various duration parameters in the duration distribution data of continuous stress were also used as corresponding dimensions.

[0033] Through standardization, different types of environmental parameters are brought to the same numerical order of magnitude, eliminating the influence of dimensions. In the final environmental parameter vector, each dimension corresponds to an environmental parameter index, such as the temperature fluctuation range, the concentration of a certain medium, and the duration of continuous stress.

[0034] Step S1133: Construct a multi-input multi-output mapping analysis model, with environmental parameter vectors as inputs and changes in material property vectors as outputs.

[0035] A multi-input, multi-output mapping analysis model is constructed. The input of this model is the standardized environmental parameter vector mentioned above, and the output is the change in the material property vector. The model adopts a multilayer perceptron architecture, including an input layer, a hidden layer, and an output layer.

[0036] The number of neurons in the input layer is consistent with the dimension of the environmental parameter vector, and each neuron receives data from one dimension of the environmental parameter vector; the hidden layer has multiple neurons, which are used to perform nonlinear transformations and feature extraction on the input environmental parameter data; the number of neurons in the output layer is consistent with the dimension of the change in the material property vector, and each neuron outputs the change in the corresponding material property index.

[0037] The above model structure enables the analysis of the mapping relationship between combinations of environmental parameters and changes in material properties.

[0038] Step S1134: By controlling the variables, change any one dimension parameter in the environmental parameter vector one by one, observe the change range of the corresponding dimension of the material property vector, and record the change range data.

[0039] The controlled variable method is used to adjust the dimensional parameters in the environmental parameter vector one by one. While keeping other environmental parameter dimensions constant, the value of one dimension parameter is changed, and the change in each dimension of the material property vector is calculated through a mapping analysis model, thereby obtaining the magnitude of the change in material properties.

[0040] For example, keeping the medium composition and duration of continuous stress constant, the temperature fluctuation amplitude is varied, and the changes in the material's thermal conductivity and coefficient of thermal expansion are observed and recorded. A similar operation is performed on each dimension of the environmental parameter vector to obtain comprehensive amplitude data.

[0041] Step S1135: Based on the change amplitude data, calculate the influence weight value of each environmental parameter dimension on each dimension of the material property vector. The influence weight value is positively correlated with the change amplitude.

[0042] Based on the recorded change magnitude data, the influence weight value of each environmental parameter dimension on each dimension of the material property vector is calculated. The calculation method is to compare the change magnitude of material properties caused by each environmental parameter dimension with the total change magnitude caused by all environmental parameter dimensions to obtain the relative influence degree of that environmental parameter dimension. This relative influence degree is the influence weight value.

[0043] Since the influence weight value is positively correlated with the magnitude of change, the greater the magnitude of change in material properties caused by a certain environmental parameter dimension, the greater its corresponding influence weight value. Through the above calculation method, the influence weight values ​​of each environmental parameter dimension on different material property indicators are obtained, forming a weight matrix that clearly shows the influence relationships between the parameters.

[0044] Step S1136: Select environmental parameter dimensions whose influence weight values ​​are higher than the preset weight threshold and determine them as key environmental parameter dimensions that affect material properties.

[0045] A preset weight threshold is set, which is determined based on the importance of the material's application scenario and the requirements for its performance. The calculated influence weight value of each environmental parameter dimension is compared with this preset weight threshold, and environmental parameter dimensions with influence weight values ​​higher than the preset weight threshold are selected.

[0046] These selected environmental parameter dimensions are identified as key environmental parameter dimensions affecting material performance. For example, if a preset weight threshold is set to a certain value, when the influence weight values ​​of temperature change gradient and concentration of a certain medium exceed this threshold, these two environmental parameter dimensions are identified as key environmental parameter dimensions and will be given priority consideration in subsequent stress variable settings.

[0047] Step S114: Based on the key environmental parameter dimensions that affect material performance, set the initial range of change for multi-dimensional stress variables so that the initial range of change covers the extreme fluctuations of environmental parameters in the scenario.

[0048] After identifying the key environmental parameters affecting material performance, the initial range of variation for multi-dimensional stress variables is set based on the fluctuations of these dimensions in real-world application scenarios. For the key dimension of temperature gradient, the initial range of variation is determined by referring to temperature fluctuation data of spacecraft under extreme conditions. This initial range should include the maximum and minimum temperature gradients that may occur in the actual scenario.

[0049] For the key medium concentration dimension in the environmental medium interaction variables, an initial range of variation is set based on the extreme values ​​of the medium concentration in the actual scenario to ensure coverage of extreme fluctuations. In this way, the initial range of variation for the multi-dimensional stress variables can comprehensively reflect the various environmental stress conditions that the material may encounter in practical applications.

[0050] Step S115: Perform time series modeling on multi-dimensional stress variables to keep the change rhythm of each stress variable synchronized with the dynamic change pattern of environmental parameters in the scene, and generate a time series framework of dynamic stress reference signal.

[0051] Collect time-series data on the dynamic changes of environmental parameters in the scenario, and analyze the patterns of these changes, such as the periodicity of temperature changes and the trend of medium concentration changes. Based on these patterns, perform time-series modeling on multi-dimensional stress variables to determine the changing trends and rhythms of each stress variable at different time points.

[0052] For example, if the temperature in a scenario exhibits a pattern of rapid initial decrease followed by slow recovery over a certain period, then the temperature gradient variable among the stress variables is modeled using the same time series rhythm. This approach synchronizes the changing rhythm of each stress variable with the dynamic changes of environmental parameters in the scenario, thereby generating a time series framework for a dynamic stress baseline signal. This time series framework defines the changing patterns of each stress variable over time.

[0053] Step S116: Convert the initial range of each stress variable into a dimensionless standardized sequence, then embed the time series framework of the dynamic stress reference signal, and combine the constraints of material properties on the rate of change of variables to adjust the standardized change amplitude of each stress variable in the time series, thereby generating a dynamic stress reference signal containing multi-dimensional stress variables.

[0054] The initial ranges of each stress variable are standardized dimensionlessly, converting stress variable values ​​of different dimensions into dimensionless values ​​of the same order of magnitude, thus forming a dimensionless standardized sequence. For example, the initial ranges of the temperature gradient and the initial ranges of the medium concentration are converted into dimensionless sequences between 0 and 1 using standardization formulas.

[0055] The aforementioned dimensionless standardized sequences are embedded into the generated time series framework, ensuring that each stress variable has a corresponding standardized value at each node of the time series. Simultaneously, the standardized variation magnitude of each stress variable in the time series is adjusted by considering constraints on the rate of change of variables based on material properties, such as the potential embrittlement characteristics of materials under rapid temperature changes.

[0056] For example, if the material is sensitive to rapid temperature increases, the normalized variation amplitude of the temperature gradient over a certain time period can be appropriately reduced. After adjustment, a dynamic stress reference signal containing multi-dimensional stress variables is finally generated, which can accurately reflect the dynamic stress situation of the material in actual application scenarios.

[0057] Step S120: Based on the dynamic stress reference signal, initiate the multi-dimensional performance coupling acquisition process to simultaneously capture the mechanical deformation response data, physical property evolution data, and chemical stability data of the test material under dynamic low temperature environment, and obtain a multi-dimensional performance response dataset.

[0058] After obtaining the dynamic stress reference signal, a multi-dimensional performance coupling acquisition process is initiated based on it. First, a dynamic low-temperature environment simulation system that meets the requirements of the dynamic stress reference signal is built. This dynamic low-temperature environment simulation system can adjust parameters such as temperature change gradient and environmental medium composition according to the signal instructions to simulate the dynamic low-temperature environment of materials in practical applications.

[0059] After the simulation environment was set up, the aluminum alloy material samples were placed into the dynamic low-temperature environment simulation system. Simultaneously, a multi-dimensional performance acquisition device was activated. This device was used to capture the material's mechanical deformation response data, physical property evolution data, and chemical stability data. During the acquisition process, it was ensured that each acquisition device operated synchronously with the environmental simulation system, so that the acquired data could accurately correspond to different stress variable states.

[0060] Through continuous collection, a large amount of performance response data is obtained. After being organized and correlated, this data forms a multi-dimensional performance response dataset.

[0061] Step S121: Analyze the dynamic stress reference signal, extract the time series change patterns of multi-dimensional stress variables, determine the key change nodes of each stress variable, and use the key change nodes as the priority trigger nodes for performance acquisition.

[0062] The dynamic stress baseline signal is analyzed to extract time-series data of multi-dimensional stress variables. Data visualization and trend analysis are then used to identify the time-series variation patterns of each stress variable. For example, the rising and falling trends of the temperature change gradient variable over different time periods, as well as the fluctuation patterns of the concentrations of various media in the environmental media interaction variables, are analyzed.

[0063] Based on these patterns of change, the key change points for each stress variable are identified. These points are typically the times when the stress variable undergoes a significant change or when its trend reverses. Examples include the time when the temperature gradient changes from a slow increase to a rapid increase, or the time when the concentration of a certain medium suddenly reaches a peak.

[0064] By setting the aforementioned key change nodes as priority trigger nodes for performance acquisition, the performance acquisition device will increase the acquisition frequency at these nodes to more accurately capture the material's performance response when stress variables change significantly.

[0065] Step S122: Construct a multi-dimensional performance coupling acquisition matrix, which includes a mechanical deformation acquisition channel, a physical property acquisition channel, and a chemical stability acquisition channel. Each acquisition channel corresponds to a set of acquisition parameter settings.

[0066] The multi-dimensional performance coupling acquisition array integrates channels for acquiring different types of performance data. The mechanical deformation acquisition channel is equipped with devices such as strain gauges and laser displacement sensors to acquire mechanical deformation data such as strain and displacement of materials under different stress states. Its acquisition parameter settings include sampling frequency, measurement range, etc.

[0067] The physical property acquisition channel includes equipment such as thermal imagers and resistivity meters, which are used to collect data on the evolution of physical properties of materials, such as temperature distribution and resistivity. The acquisition parameters of this physical property acquisition channel are set according to the type of physical property, such as the resolution of the thermal imager and the measurement accuracy of the resistivity meter.

[0068] The chemical stability acquisition channel uses equipment such as infrared spectrometers and gas chromatographs to detect chemical changes that occur in materials during stress, such as the degree of surface oxidation and the composition of released gases. Its acquisition parameters include spectral scanning range and chromatographic analysis time.

[0069] By constructing this acquisition matrix, a systematic and comprehensive acquisition of multi-dimensional performance data of materials can be achieved.

[0070] Step S123: Based on the key change nodes of each stress variable, set the acquisition frequency of each channel in the multi-dimensional performance coupling acquisition matrix so that the acquisition frequency at the key change nodes is higher than that at non-key nodes.

[0071] Based on the identified key change nodes for each stress variable, the acquisition frequency of each channel in the multi-dimensional performance coupling acquisition matrix is ​​adjusted. During non-critical node periods, each channel uses a conventional acquisition frequency to collect data, in order to balance data volume and acquisition cost.

[0072] During the time periods before and after key change points, the acquisition frequency of each channel is increased, for example, from the usual once per second to multiple times per second, to ensure that the instantaneous performance response data of the material can be captured when the stress variable changes significantly.

[0073] By setting the different collection frequency as described above, the integrity of key data is ensured while avoiding the problem of excessive data volume in non-critical time periods, thus improving collection efficiency.

[0074] Step S124: Start the dynamic low temperature environment simulation device, adjust the environmental parameters according to the time series framework of the dynamic stress reference signal, and simultaneously start the multi-dimensional performance coupling acquisition matrix to synchronously acquire mechanical deformation response data, physical property evolution data and chemical stability data.

[0075] The dynamic low-temperature environment simulation device precisely adjusts parameters such as internal temperature gradient and environmental medium composition based on the time series framework of dynamic stress reference signal to create a dynamic low-temperature environment similar to the actual application scenario of the material.

[0076] After the environmental simulation device is started and running stably, the multi-dimensional performance coupling acquisition matrix is ​​activated, and each acquisition channel begins to work according to the set acquisition frequency. The mechanical deformation acquisition channel records data such as strain and displacement of the material in real time; the physical property acquisition channel monitors changes in the material's temperature, resistivity, and other physical properties; and the chemical stability acquisition channel analyzes changes in the chemical composition of the material under environmental influences.

[0077] Throughout the data acquisition process, the parameter adjustment of the environmental simulation device and the data acquisition of the acquisition matrix are strictly synchronized to ensure that each set of acquired data accurately corresponds to a specific environmental parameter state.

[0078] Step S125: During the data collection process, establish a real-time correlation mechanism between the collected data and stress variables, label the instantaneous values ​​of the corresponding stress variables for each set of collected data, and form an original dataset containing the stress-performance correspondence.

[0079] During data acquisition, a real-time correlation mechanism is established between the acquired data and stress variables. Using timestamp synchronization technology, the same timestamp is added to each instantaneous value of the stress variable recorded by the environmental simulation device and each set of performance data acquired by the acquisition matrix, enabling precise matching between the two based on the timestamp.

[0080] For each set of collected performance data, the instantaneous value of the corresponding stress variable is found based on its time stamp, and this instantaneous value is used as a label on the corresponding performance data. For example, if the time stamp of a certain set of mechanical deformation data is a specific moment, the temperature change gradient value and the medium concentration value at that moment are found from the records of the environmental simulation device using this time stamp, and these two values ​​are marked next to the set of mechanical deformation data.

[0081] The same labeling method was used for physical property evolution data and chemical stability data to ensure that each data set could be correlated one-to-one with the instantaneous value of the corresponding stress variable. Through this method, all collected performance data carried corresponding stress variable information. The labeled data were then arranged and integrated in chronological order to form a raw dataset containing the stress-performance correspondence. This raw dataset comprehensively records the performance response of the material under different stress states.

[0082] Step S1251: Install a stress variable real-time acquisition module on the dynamic low temperature environment simulation device. The stress variable real-time acquisition module is used to synchronously record the instantaneous values ​​of the temperature change gradient and the interaction between the dynamic low temperature environment simulation device and the environmental medium.

[0083] To ensure accurate acquisition of instantaneous values ​​of stress variables in the dynamic low-temperature environment simulation device, a real-time stress variable acquisition module was added to the device. This module includes components such as a temperature sensor and a medium concentration sensor. The temperature sensor monitors the temperature changes within the device in real time and calculates the instantaneous value of the temperature gradient. The medium concentration sensor detects the concentration of various media in the environment and generates instantaneous values ​​of environmental media interactions.

[0084] The real-time stress variable acquisition module is connected to the control system of the dynamic low-temperature environment simulation device, enabling it to synchronously acquire various parameters output by the device and ensure that the recorded instantaneous values ​​are consistent with the actual parameters output by the device. Simultaneously, this real-time stress variable acquisition module has high-frequency acquisition capabilities, allowing it to record instantaneous values ​​multiple times in a short period, meeting the high-precision acquisition requirements at key change points.

[0085] Step S1252: Install a time synchronization module on each acquisition channel in the multi-dimensional performance coupling acquisition matrix to ensure that the acquisition actions of each acquisition channel are consistent with the recording actions of the stress variable real-time acquisition module in terms of timestamp.

[0086] A time synchronization module is installed on each acquisition channel of the multi-dimensional performance coupling acquisition matrix. This time synchronization module uses a high-precision clock chip to provide a unified time reference. The time synchronization module is connected to the real-time stress variable acquisition module, and periodic calibration ensures that the time of the two remains synchronized. This ensures that the acquisition actions of each acquisition channel are precisely aligned with the recording actions of the real-time stress variable acquisition module, thereby guaranteeing that the acquired performance data and the instantaneous values ​​of stress variables have the same timestamp.

[0087] For example, when the time synchronization module of the mechanical deformation acquisition channel triggers the acquisition action, it can send a synchronization signal to the real-time stress variable acquisition module, enabling it to record the instantaneous value of the stress variable at the same moment, ensuring that the timestamps of the two are completely consistent. This method eliminates time discrepancies between different devices.

[0088] Step S1253: Each time the acquisition channel is started to collect performance data, the stress variable real-time acquisition module is triggered to record the instantaneous value of the stress variable at the current moment and generate a stress variable record entry containing a timestamp.

[0089] When a certain acquisition channel in the multi-dimensional performance coupling acquisition matrix starts acquiring performance data, the control unit of that acquisition channel sends a trigger signal to the stress variable real-time acquisition module. Upon receiving the trigger signal, the stress variable real-time acquisition module immediately records the instantaneous values ​​of the temperature change gradient and the environmental medium interaction at the current moment, and binds these instantaneous values ​​with the current timestamp to generate a stress variable record entry containing the timestamp, the instantaneous value of the temperature change gradient, and the instantaneous value of the environmental medium interaction.

[0090] For example, when the physical property acquisition channel initiates the acquisition of material resistivity, it sends a trigger signal to the real-time stress variable acquisition module. This module then records the temperature gradient and medium concentration at that moment and generates corresponding stress variable record entries. Through this triggering mechanism, it is ensured that every performance data acquisition has a corresponding instantaneous stress variable value record, achieving precise linkage between acquisition and recording actions.

[0091] Step S1254: Bind the collected performance data with the stress variable record entries corresponding to the same timestamp to form a basic data unit containing performance data, stress variable values, and timestamps.

[0092] For each set of performance data acquired by the acquisition channel, its timestamp is extracted. Then, an entry with the same timestamp is searched in the stress variable record entries generated by the stress variable real-time acquisition module. After finding a matching entry, the performance data is bound to the stress variable values ​​(including instantaneous values ​​of temperature change gradient and instantaneous values ​​of environmental medium interaction) in that entry, forming a basic data unit containing performance data, stress variable values, and timestamps.

[0093] For example, the chemical stability acquisition channel collects data on the oxidation level of a material surface, with the timestamp being a specific moment. After finding the stress variable record entry at the same timestamp, the oxidation level data is bound to the temperature change gradient and medium concentration data in that entry, forming a basic data unit. Through this binding operation, the performance data and the corresponding stress variable values ​​form an inseparable whole.

[0094] Step S1255: Sort all basic data units containing performance data, stress variable values ​​and timestamps according to the order of timestamps to form an original dataset containing stress-performance correspondence. Each data node in the original dataset contains complete stress-performance correspondence information.

[0095] Collect all generated basic data units, extract the timestamp from each unit, and sort these basic data units in ascending order of timestamp. After sorting, integrate the above basic data units to form the original dataset containing the stress-performance correspondence.

[0096] In this original dataset, each data node is a basic data unit, containing performance data (such as mechanical deformation data, physical property data, or chemical stability data) at a specific time stamp, the corresponding stress variable values ​​(instantaneous values ​​of temperature change gradient and environmental medium interaction), and timestamp information. Through the above sorting and integration, the original dataset can clearly reflect the correspondence between stress variables that change over time and the material performance response.

[0097] Step S126: Perform validity screening on the original dataset containing the stress-performance correspondence, remove abnormal data caused by instantaneous fluctuations of the environmental simulation device, and integrate it into a multi-dimensional performance response dataset.

[0098] The original dataset is screened for validity. First, data validity criteria are established, including a reasonable range for performance data and limits on the fluctuation range of stress variable values. For example, based on the physical properties of the material, the maximum and minimum possible values ​​for mechanical deformation data are set, and data exceeding these ranges are considered outliers.

[0099] Data cleaning algorithms are used to examine each basic data unit in the original dataset, identifying data that does not meet the validity criteria. This data may be caused by transient fluctuations in the environmental simulation device (such as a brief malfunction of the temperature controller or pulsed fluctuations in the media supply system). The identified abnormal data is then removed from the original dataset, retaining only the valid basic data units.

[0100] The filtered valid data were categorized and integrated according to performance data type, forming three subsets: mechanical deformation response data, physical property evolution data, and chemical stability data. These three subsets were then combined to create a multi-dimensional performance response dataset. This multi-dimensional performance response dataset accurately reflects the performance response of materials under dynamic low-temperature environments.

[0101] Step S130: Perform coupling correlation analysis on the multi-dimensional performance response dataset and the dynamic stress benchmark signal, calculate the performance response coupling coefficients corresponding to different combinations of stress variables, and generate a performance coupling correlation model.

[0102] After acquiring the multidimensional performance response dataset and the dynamic stress baseline signal, a coupling correlation analysis between the two was conducted. First, the time series data of the multidimensional stress variables in the dynamic stress baseline signal were matched with the corresponding data in the multidimensional performance response dataset to ensure that each combination of stress variables could be mapped to the corresponding performance response data.

[0103] Statistical analysis and data mining methods are used to explore the intrinsic relationship between different combinations of stress variables and material performance responses, and to calculate the performance response coupling coefficient, which reflects the strength of this relationship. For example, the study analyzes the degree of interaction between the mechanical deformation, physical properties, and chemical stability of materials under specific temperature gradients and medium concentration combinations, as well as the strength of their correlation with stress variables.

[0104] Based on the calculated performance response coupling coefficient, a performance coupling correlation model is constructed. This model can predict the performance response coupling of materials according to the combination of input stress variables.

[0105] Step S131: Separate multiple data sequences from the multi-dimensional performance response dataset. The multiple data sequences include mechanical deformation response data sequences, physical property evolution data sequences, and chemical stability data sequences. Each data sequence contains performance data at multiple time points.

[0106] The multi-dimensional performance response dataset was subjected to data separation, and divided into three independent data sequences based on the type of performance data. The mechanical deformation response data sequence contains mechanical deformation data at all time points, such as strain values ​​and displacements at different times. Each data point corresponds to a time point and a corresponding combination of stress variables.

[0107] The physical property evolution data series consists of physical property data at various time points, such as temperature distribution data and resistivity data. Similarly, each data point is associated with a specific time point and stress variable combination. The chemical stability data series contains chemical change data of the material at different time points, such as the degree of surface oxidation and the concentration of released gas components.

[0108] By separating the data as described above, different types of performance data can be analyzed independently, while preserving their correlation with time points and combinations of stress variables.

[0109] Step S132: Extract time series data of multi-dimensional stress variables from the dynamic stress reference signal to form a stress variable sequence, wherein the stress variable sequence includes the temperature change gradient value and the environmental medium interaction value corresponding to each time node.

[0110] The dynamic stress baseline signal is analyzed to extract multi-dimensional stress variable time series data, including temperature change gradient values ​​and environmental media interaction values ​​(such as the concentration values ​​of various media) at each time point. These data are then arranged chronologically to form a stress variable sequence.

[0111] In the stress variable sequence, each time point contains a corresponding temperature change gradient value and environmental medium interaction value, consistent with the time points in the multi-dimensional performance response dataset, ensuring accurate matching and correlation analysis between the two. For example, the stress variable sequence at a certain time point includes information such as a certain temperature change gradient value, a certain helium concentration value, and a certain hydrogen concentration value at that moment.

[0112] Step S133: Establish a time node alignment mechanism between performance data and stress variables so that each performance data node can be matched with a unique corresponding stress variable node, forming multiple sets of stress-performance corresponding data pairs.

[0113] A time-node alignment mechanism for performance data and stress variables is established. First, the time nodes of the performance data sequence and stress variable sequence in the multi-dimensional performance response dataset are uniformly calibrated to ensure consistent temporal accuracy. For data nodes with slight time deviations, interpolation is used for time correction to ensure complete temporal alignment between performance data nodes and stress variable nodes.

[0114] After time node alignment, each performance data node is matched with its corresponding stress variable node to form a set of stress-performance data pairs. For example, the strain data at a certain time node in the mechanical deformation response data sequence, together with the temperature change gradient value and the environmental medium interaction value at the same time node in the stress variable sequence, form a set of stress-performance data pairs.

[0115] The alignment mechanism described above ensures that each performance data point can be accurately matched to a unique combination of stress variables.

[0116] Step S134: After standardizing the mechanical deformation data, physical property data, and chemical stability data in each stress-performance data pair, the degree of mutual influence between performance data is calculated using multivariate correlation analysis to obtain the internal coupling coefficient of performance.

[0117] For each stress-performance data pair, the mechanical deformation data, physical property data, and chemical stability data are standardized to eliminate dimensional differences between different types of performance data and bring them to the same numerical order of magnitude. For example, the strain values ​​in the mechanical deformation data, the resistivity values ​​in the physical property data, and the oxidation degree values ​​in the chemical stability data are converted into standardized values ​​between 0 and 1.

[0118] Multivariate correlation analysis methods, such as Pearson correlation coefficient analysis or partial least squares regression, are used to calculate the correlation between standardized mechanical deformation data and physical property data, the correlation between mechanical deformation data and chemical stability data, and the correlation between physical property data and chemical stability data. The numerical values ​​of these correlations are the internal coupling coefficients of performance, reflecting the degree of mutual influence between different performance data.

[0119] For example, if the correlation coefficient between mechanical deformation data and physical property data is high, it indicates a significant mutual influence between the two, and the corresponding internal coupling coefficient value is also large. By calculation, the internal coupling coefficient of performance for each stress-performance data pair is obtained, forming a sequence of internal coupling coefficients.

[0120] Step S135: Standardize the stress variables and each performance data separately, calculate their correlation strength, and obtain the stress-performance external coupling coefficient. The stress-performance external coupling coefficient reflects the degree to which the stress variables drive performance changes.

[0121] The temperature change gradient values ​​and environmental medium interaction values ​​in the stress variable sequence are standardized. At the same time, the mechanical deformation data, physical property data, and chemical stability data in the multi-dimensional performance response dataset are also standardized to ensure that the stress variables and performance data are of the same numerical order of magnitude, thus eliminating the influence of dimensions.

[0122] The correlation strength between the standardized temperature change gradient value and the standardized mechanical deformation data, the correlation strength between the temperature change gradient value and the physical property data, and the correlation strength between the temperature change gradient value and the chemical stability data are calculated. Similarly, the correlation strength between the environmental medium interaction value and each performance data is calculated. The values ​​of the above correlation strengths are the stress-performance external coupling coefficients, which reflect the degree to which the stress variable drives the performance change.

[0123] For example, if the correlation between the temperature change gradient value and the mechanical deformation data is high, it indicates that the temperature change gradient has a significant driving effect on the material's mechanical deformation, and the corresponding stress-performance external coupling coefficient value is also large. Through calculation, the stress-performance external coupling coefficient for each set of stress-performance data pairs is obtained, forming a stress-performance external coupling coefficient sequence.

[0124] Step S136: Integrate the internal performance coupling coefficient and the external stress-performance coupling coefficient to construct a performance coupling correlation model with stress variables as input and multi-dimensional performance coupling coefficients as output. The performance coupling correlation model with stress variables as input and multi-dimensional performance coupling coefficients as output includes the correspondence between different combinations of stress variables and coupling coefficients.

[0125] By integrating the internal performance coupling coefficient and the stress-performance external coupling coefficient, and considering the needs of material application scenarios, a performance coupling correlation model is constructed. This performance coupling correlation model takes stress variables (temperature change gradient and environmental medium interaction value) as input and the integrated multi-dimensional performance coupling coefficient as output, which can reflect the correspondence between different combinations of stress variables and the coupling of material performance.

[0126] Through model training and optimization, this performance coupling correlation model can accurately output the corresponding multi-dimensional performance coupling coefficients based on the combination of input stress variables.

[0127] Step S1361: Based on the importance of each performance index in the material application scenario, set different weighting coefficients for the internal performance coupling coefficient and the stress-performance external coupling coefficient.

[0128] Based on the importance of various performance indicators of aluminum alloy materials in the application scenario of cryogenic fuel storage compartments for spacecraft, different weighting coefficients are set for the internal performance coupling coefficient and the stress-performance external coupling coefficient. For example, since mechanical deformation performance is directly related to the structural safety of the compartment, its corresponding internal performance coupling coefficient and stress-performance external coupling coefficient are set with higher weighting coefficients; while chemical stability performance, although important, has a relatively low priority in this scenario, and its corresponding weighting coefficient is set slightly lower.

[0129] The weighting coefficients were determined through a combination of expert evaluation and scenario requirements analysis to ensure that they accurately reflect the importance of each performance indicator in practical applications. Once set, a weighting coefficient matrix is ​​formed, corresponding to different internal performance coupling coefficients and stress-performance external coupling coefficients.

[0130] Step S1362: Based on the set weight coefficients, the internal coupling coefficient of performance and the external coupling coefficient of stress-performance corresponding to each group of stress variables are weighted and calculated to obtain the comprehensive coupling coefficient, which reflects the overall correlation between stress variables and multi-dimensional performance.

[0131] For each combination of stress variables, the internal performance coupling coefficient and the external stress-performance coupling coefficient are calculated using weighted averages according to predefined weighting factors. The calculation method involves multiplying each coupling coefficient by its corresponding weighting factor, and then summing all weighted coupling coefficients to obtain the overall coupling coefficient.

[0132] The comprehensive coupling coefficient takes into account both the internal interactions of performance and the driving effect of stress variables on performance, reflecting the overall correlation between stress variables and multi-dimensional performance. For example, if the comprehensive coupling coefficient obtained by weighting the internal coupling coefficient and the external coupling coefficient of stress-performance for a certain combination of stress variables is high, it indicates that the correlation between this combination of stress variables and the multi-dimensional performance of the material is strong.

[0133] Step S1363: Store all stress variable combinations and their corresponding comprehensive coupling coefficients in data format to form a stress-coupling coefficient sample dataset. Each sample in the stress-coupling coefficient sample dataset contains a stress variable input vector and a comprehensive coupling coefficient output value.

[0134] All stress variable combinations (represented as stress variable input vectors, including dimensions such as temperature change gradient values ​​and environmental medium interaction values) and their corresponding comprehensive coupling coefficient output values ​​are organized and stored in a unified data format to form a stress-coupling coefficient sample dataset.

[0135] In this sample dataset, each sample is a data pair consisting of a stress variable input vector and a comprehensive coupling coefficient output value. For example, a sample's stress variable input vector might be (temperature change gradient value 1, medium concentration value 1, medium concentration value 2), and the corresponding comprehensive coupling coefficient output value might be a specific numerical value.

[0136] Step S1364: Use a nonlinear regression algorithm to train the model on the stress-coupling coefficient sample dataset and construct a regression model with the stress variable input vector as the independent variable and the comprehensive coupling coefficient as the dependent variable.

[0137] Nonlinear regression algorithms, such as support vector regression and neural network regression, were used to train the model on the aforementioned stress-coupling coefficient sample dataset. During training, the stress variable input vector was used as the model's independent variable, and the combined coupling coefficient was used as the model's dependent variable. By adjusting the model's parameters, the model was made to predict the dependent variable as accurately as possible based on the independent variables.

[0138] For example, when using a neural network regression algorithm, a neural network model is constructed that includes an input layer, hidden layers, and an output layer. The number of neurons in the input layer is consistent with the dimension of the stress variable input vector, the number of neurons in the output layer is 1 (corresponding to the comprehensive coupling coefficient), and the number of neurons in the hidden layer is set according to the complexity of the sample data. By continuously optimizing the network weights and biases through the backpropagation algorithm, the error between the model's predicted output and the actual comprehensive coupling coefficient is gradually reduced, ultimately constructing a regression model with the stress variable input vector as the independent variable and the comprehensive coupling coefficient as the dependent variable.

[0139] Step S1365: Using cross-validation, the accuracy of the regression model with the stress variable input vector as the independent variable and the comprehensive coupling coefficient as the dependent variable is verified using a partial stress-coupling coefficient sample dataset. The deviation between the model prediction value and the actual comprehensive coupling coefficient is calculated. If the deviation exceeds the preset accuracy standard, the regression algorithm parameters of the regression model are adjusted.

[0140] The stress-coupling coefficient sample dataset is divided into a training set and a validation set. The training set is used for model training, and the validation set is used for model accuracy verification. A cross-validation method, such as k-fold cross-validation, is adopted. The training set is divided into k subsets. Each time, k-1 subsets are used for model training, and the remaining subset is used for validation. After repeating k times, the average deviation is taken as the accuracy reference of the model during the training process.

[0141] When validating the accuracy of the regression model, the stress variable input vector from the validation set is input into the model to obtain the model's predicted comprehensive coupling coefficient. The predicted values ​​are then compared with the corresponding actual comprehensive coupling coefficients in the validation set, and the deviation between the two is calculated. The deviation can be calculated using methods such as mean squared error or mean absolute error, which quantifies the difference between the predicted and actual values.

[0142] If the calculated deviation exceeds the preset accuracy standard, it indicates that the model's prediction accuracy has not met the requirements, and the regression algorithm parameters of the regression model need to be adjusted. For example, for the support vector regression algorithm, parameters such as the kernel function type and penalty coefficient can be adjusted; for the neural network regression algorithm, parameters such as the number of neurons in the hidden layer, the learning rate, and the number of iterations can be adjusted. After adjusting the parameters, retrain the model using the training set and verify the accuracy again using the validation set until the model's prediction deviation is lower than the preset accuracy standard.

[0143] Step S1366: The validated regression model with the stress variable input vector as the independent variable and the comprehensive coupling coefficient as the dependent variable is determined as the performance coupling correlation model. The performance coupling correlation model is used to output the corresponding multi-dimensional performance comprehensive coupling coefficient according to the combination of input stress variables.

[0144] When the regression model undergoes cross-validation and its prediction bias is lower than the preset accuracy standard, it indicates that the model can accurately reflect the relationship between the combination of stress variables and the comprehensive coupling coefficient. At this point, the regression model that has passed the validation is identified as the performance coupling correlation model.

[0145] This performance coupling correlation model is capable of receiving input combinations of stress variables, which are presented in vector form and include parameters from multiple dimensions such as temperature change gradient and interaction with the environmental medium. After processing the input vector, the model outputs the corresponding multi-dimensional performance comprehensive coupling coefficient, which comprehensively reflects the overall influence of the stress variable combination on the material's mechanical deformation, physical properties, and chemical stability.

[0146] Step S140: Based on the performance coupling correlation model, identify the dynamic stress threshold range when the performance of the test material undergoes nonlinear changes.

[0147] Based on the established performance coupling correlation model, the dynamic stress threshold range for nonlinear changes in the test material properties was identified. First, the model was used to generate a large number of stress variable combinations and their corresponding comprehensive coupling coefficients. This data covered the possible range of stress variable changes, forming a dataset for analysis.

[0148] By performing trend analysis and feature extraction on this dataset, we identified the patterns in the changes of the comprehensive coupling coefficient with stress variables, focusing on regions exhibiting significant changes. Combining this with the original performance data from the multi-dimensional performance response dataset, we determined whether these changes were non-linear, thereby identifying the corresponding range of stress variable values, i.e., the dynamic stress threshold interval.

[0149] Step S141: Analyze the performance coupling correlation model and extract the performance response coupling coefficient sequence corresponding to different combinations of stress variables. The performance response coupling coefficient sequence reflects the law of change of coupling coefficient with stress variables.

[0150] The performance coupling correlation model is analyzed by inputting a series of continuously changing stress variables to obtain the corresponding performance response coupling coefficients output by the model. Arranging these stress variable combinations in a certain order, such as from smallest to largest temperature gradient or from lowest to highest environmental medium concentration, yields a corresponding sequence of performance response coupling coefficients, known as the performance response coupling coefficient sequence.

[0151] Each element in the sequence corresponds to a specific combination of stress variables. By observing the changes in the coupling coefficient in the sequence, the pattern of the coupling coefficient changing with the stress variables can be clearly reflected, such as whether it grows linearly, fluctuates nonlinearly, or remains stable within a certain range.

[0152] Step S142: Perform nonlinear feature identification on the performance response coupling coefficient sequence, and use a trend change detection algorithm to locate stress variable nodes where the fluctuation rate of the coupling coefficient change rate is greater than a set threshold. The stress variable nodes are the preliminary threshold nodes.

[0153] A trend mutation detection algorithm is used to process the performance response coupling coefficient sequence, which can analyze the rate of change of the coupling coefficients in the sequence. The rate of change of the coupling coefficients is obtained by calculating the ratio of the change between two adjacent coupling coefficients to the change of the corresponding stress variable.

[0154] The calculated rate of change is compared with a set threshold. When the fluctuation of the rate of change exceeds the set threshold, it indicates that the trend of the coupling coefficient has changed abruptly at that location. The stress variable value corresponding to this abrupt change location is recorded and determined as the preliminary threshold node. The above preliminary threshold node is a potential location where the material properties may undergo nonlinear changes.

[0155] Step S143: For each preliminary threshold node, extract the corresponding multi-dimensional performance data and analyze whether the mechanical deformation, physical properties, and chemical stability data show nonlinear changes at the same time. If nonlinear changes are all observed, then the preliminary threshold node is confirmed as a valid threshold node.

[0156] For each initial threshold node, mechanical deformation data, physical property data, and chemical stability data corresponding to the timestamp of that node are extracted from the multi-dimensional performance response dataset to form a complete set of performance data.

[0157] Nonlinear variation analysis was performed on each of the three sets of data to determine whether nonlinear variation occurred in each set. Only when nonlinear variation occurred in all three sets of data could the preliminary threshold node be confirmed as the critical point where significant nonlinear changes in material properties occurred, and it was marked as a valid threshold node.

[0158] Step S1431: Extract mechanical deformation data, physical property data and chemical stability data corresponding to the timestamps of the preliminary threshold nodes from the multi-dimensional performance response dataset to form a threshold node performance data group.

[0159] Within the multi-dimensional performance response dataset, a search is performed based on the timestamp of the initial threshold node to find all performance data recorded at that timestamp. This includes mechanical deformation data, such as material strain values ​​and bending degrees; physical property data, such as measured values ​​of thermal conductivity and resistivity; and chemical stability data, such as detected values ​​of surface oxidation degree and concentration of released gas components.

[0160] The above data is compiled and organized to form a threshold node performance data set, which fully includes all the performance indicators of the material at the initial threshold node time.

[0161] Step S1432: Perform nonlinearity judgment on the mechanical deformation data, calculate the deformation change rate of the corresponding data point and the adjacent data points, and if the change rate exceeds the preset deformation nonlinearity threshold, it is determined that the mechanical deformation data has a nonlinear change.

[0162] Select the data point corresponding to the initial threshold node and several adjacent data points. Calculate the mechanical deformation difference between this data point and the previous data point, as well as the mechanical deformation difference between this data point and the next data point. Divide these differences by the corresponding time interval to obtain the deformation rate.

[0163] The calculated deformation rate is compared with a preset deformation nonlinearity threshold. If the rate of change exceeds the threshold, it indicates that the mechanical deformation rate of the material has become significantly abnormal at the initial threshold node, and the mechanical deformation data is determined to have a nonlinear change.

[0164] Step S1433: Perform nonlinearity judgment on the physical characteristic data, calculate the deviation rate of the physical characteristic index corresponding to the corresponding data point from the average value of the previous time period, and if the deviation rate exceeds the preset physical nonlinearity threshold, it is determined that the physical characteristic data has a nonlinear change.

[0165] Define a preceding time period, such as the time period consisting of multiple consecutive data points before the initial threshold node, and calculate the average value of the physical characteristic index within this time period. Subtract this average value from the physical characteristic index value of the data point corresponding to the initial threshold node to obtain the deviation value.

[0166] Divide the deviation value by the average value to obtain the deviation rate. If the deviation rate exceeds the preset physical nonlinearity threshold, it indicates that the physical characteristic index at that node has deviated significantly from the previous average level, and it is determined that the physical characteristic data has undergone nonlinear changes.

[0167] Step S1434: Perform nonlinearity judgment on the chemical stability data, calculate the rate of change of the chemical index corresponding to the data point with respect to the initial state, and if the rate of change exceeds the preset chemical nonlinearity threshold, it is determined that the chemical stability data has undergone nonlinear change.

[0168] Acquire chemical index data of the test material in its initial state (i.e., before stress), such as the initial degree of surface oxidation and the initial gas release. Calculate the difference between the chemical index of the data point corresponding to the preliminary threshold node and the chemical index of the initial state. Divide this difference by the chemical index of the initial state to obtain the rate of change.

[0169] If the rate of change exceeds the preset chemical nonlinearity threshold, it indicates that the chemical state of the material at this node has changed significantly compared with the initial state, and the chemical stability data is determined to have a nonlinear change.

[0170] Step S1435: Statistically determine the nonlinear changes of the three performance data: mechanical deformation data, physical property data, and chemical stability data. If all three are determined to have nonlinear changes, then mark the preliminary threshold node as a valid threshold node.

[0171] The nonlinear change judgment results of mechanical deformation data, physical property data, and chemical stability data are summarized, and each data point is checked one by one to see if a nonlinear change has occurred. Only when the judgment results of all three types of performance data show nonlinear changes can the preliminary threshold node be confirmed as a key node where the overall material properties show nonlinear changes, and at this time, the preliminary threshold node is marked as a valid threshold node.

[0172] Step S144: Divide the stress variable values ​​corresponding to adjacent effective threshold nodes into intervals to obtain multiple stress variable intervals. Each stress variable interval corresponds to a range in which the coupling coefficient changes smoothly.

[0173] All valid threshold nodes are arranged in ascending order of their corresponding stress variable values. Then, the stress variable values ​​corresponding to two adjacent valid threshold nodes are used as the endpoints of the intervals to divide the intervals into multiple stress variable intervals.

[0174] Each stress variable interval contains a series of continuous stress variable values. By analyzing the performance response coupling coefficient sequence within these intervals, it can be seen that each interval corresponds to a range where the coupling coefficient changes relatively smoothly. That is, within this interval, the coupling coefficient does not show significant nonlinear abrupt changes with the stress variable.

[0175] Step S145: Verify the trend of coupling coefficient change within each stress variable interval, calculate the fluctuation amplitude of coupling coefficient within the stress variable interval, and if the fluctuation amplitude is less than the preset stability standard, then confirm that the stress variable interval is a dynamic stress threshold interval.

[0176] For each defined stress variable interval, extract all performance response coupling coefficient values ​​within that interval, and calculate the difference between the maximum and minimum values ​​of these coupling coefficients. This difference represents the fluctuation range of the coupling coefficient within that interval.

[0177] The fluctuation range is compared with the preset stability standard. If the fluctuation range is less than the preset stability standard, it indicates that the change of the coupling coefficient is in a stable state within the stress variable range and no obvious nonlinear fluctuations have occurred. This confirms that the stress variable range is the dynamic stress threshold range.

[0178] Step S146: Integrate all confirmed dynamic stress threshold intervals to form a set of threshold intervals containing the critical change range of each stress variable, and label the corresponding nonlinear performance change characteristics of each dynamic stress threshold interval.

[0179] All verified dynamic stress threshold intervals are integrated and arranged in order of stress variable value to form a complete set of threshold intervals. Each dynamic stress threshold interval in this set is labeled with corresponding nonlinear performance characteristics, such as the nonlinear changes in material mechanical deformation, the abrupt changes in physical properties, and the changing trends in chemical stability within that interval.

[0180] The above annotations clearly demonstrate the nonlinear changes in material properties within different stress variable ranges.

[0181] Step S150: Based on the dynamic stress threshold range and performance coupling correlation model, generate a material low-temperature performance test execution plan containing dynamic threshold adjustment logic. The material low-temperature performance test execution plan can adjust the stress variable combination according to the real-time performance response.

[0182] By combining dynamic stress threshold ranges and performance coupling correlation models, a test execution plan for the material's low-temperature performance is generated. This test execution plan needs to clearly define the test process, the objectives of each stage, and the adjustment rules for stress variables. The core of the plan is the inclusion of dynamic threshold control logic, which enables the test process to automatically adjust the combination of stress variables based on the material's real-time performance response, ensuring that the test can comprehensively and accurately reflect the material's performance under different low-temperature environments.

[0183] For example, step S151: parse the set of threshold intervals containing the critical change range of each stress variable, extract the critical range of stress variable and the nonlinear change characteristics of performance corresponding to each dynamic stress threshold interval, and convert them into threshold monitoring indicators in the testing process.

[0184] The threshold interval set is analyzed, and the critical range of the stress variable for each dynamic stress threshold interval is extracted, i.e., the maximum and minimum values ​​of the stress variable corresponding to that interval. At the same time, the nonlinear performance change characteristics of each interval are extracted, such as the abrupt change point of mechanical deformation and the abnormal change type of physical properties.

[0185] The critical range of stress variables and the nonlinear change characteristics of performance are transformed into specific threshold monitoring indicators. For example, the critical range of stress variables is transformed into upper and lower limit alarm values ​​in the monitoring system, and the nonlinear change characteristics of performance are transformed into threshold values ​​for performance parameters that need to be monitored. This enables the material performance status to be monitored in real time during the testing process through the above indicators.

[0186] Step S152: Based on the performance coupling correlation model, determine the range of the target comprehensive coupling coefficient for different test stages of the material's low-temperature performance test, so that the adjustment of stress variables in each test stage is guided by achieving the target comprehensive coupling coefficient.

[0187] Using a performance coupling correlation model, the changes in the comprehensive coupling coefficient under different combinations of stress variables are simulated. Based on the overall test objectives and requirements, different stages of the material's low-temperature performance test are divided, such as the initial adaptation stage, the gradual loading stage, and the extreme test stage.

[0188] A target comprehensive coupling coefficient range is determined for each testing phase. This range is set based on the testing focus of each phase and the potential performance characteristics of the material. During testing, the adjustment of stress variables in each phase is guided by achieving the target comprehensive coupling coefficient for that phase. By adjusting the combination of stress variables, the comprehensive coupling coefficient is stabilized within the target range to achieve the testing objectives of each phase.

[0189] Step S153: Construct dynamic threshold control logic, which includes: when the comprehensive coupling coefficient corresponding to the real-time performance response is close to the upper limit of the target comprehensive coupling coefficient range, reducing the intensity of the stress variable according to a preset rule; when the comprehensive coupling coefficient is lower than the lower limit of the target comprehensive coupling coefficient range, increasing the intensity of the stress variable according to a preset rule; and maintaining the stability of the stress variable when the comprehensive coupling coefficient is within the target comprehensive coupling coefficient range.

[0190] The dynamic threshold control logic determines the adjustment method of the stress variable based on the comparison between the comprehensive coupling coefficient corresponding to the real-time performance response and the target comprehensive coupling coefficient range.

[0191] When the real-time integrated coupling coefficient approaches the upper limit of the target range, it indicates that the material may be about to enter a critical state of nonlinear performance change. At this time, the intensity of the stress variable is reduced according to the preset rules, such as reducing the temperature change gradient or reducing the concentration of a specific medium, in order to avoid abrupt changes in the material performance.

[0192] When the real-time integrated coupling coefficient is lower than the lower limit of the target range, it indicates that the current stress variable intensity is insufficient and fails to fully stimulate the material's performance response. The stress variable intensity should be increased according to the preset rules, such as increasing the temperature change gradient or increasing the medium concentration.

[0193] When the real-time integrated coupling coefficient is within the target range, it indicates that the material performance response is stable. The current stress variable combination remains unchanged in order to obtain performance data under steady-state conditions.

[0194] Step S154: Divide the low-temperature performance testing of materials into stages, which include a pre-stress stage, a main testing stage, and a threshold verification stage. Each stage sets a corresponding target comprehensive coupling coefficient range and threshold monitoring indicators during the testing process.

[0195] The low-temperature performance testing of materials is divided into three stages: pre-stress stage, main testing stage, and threshold verification stage. The pre-stress stage is mainly used to allow the material to gradually adapt to the low-temperature environment and avoid abnormal material performance caused by sudden low-temperature stress. This pre-stress stage sets a low target comprehensive coupling coefficient range and a relatively lenient threshold monitoring index.

[0196] The main testing phase is the core phase of the test. It requires a comprehensive examination of the material's performance under different combinations of stress variables, setting a moderate to high range of target comprehensive coupling coefficients and strict threshold monitoring indicators.

[0197] The threshold verification stage specifically tests the dynamic stress threshold range to verify whether the material's performance changes within the threshold range are consistent with expectations. It also sets the target comprehensive coupling coefficient range and targeted threshold monitoring indicators corresponding to the dynamic stress threshold range.

[0198] Step S155: Configure corresponding environmental simulation parameter adjustment rules and performance acquisition parameter rules for each stage in the material low-temperature performance testing process, so that environmental simulation and performance acquisition are linked with dynamic threshold control logic.

[0199] For the pre-stress phase, environmental simulation parameter adjustment rules are configured, such as gradually increasing the temperature gradient and gradually increasing the medium concentration. The performance acquisition parameter rules are set to a low acquisition frequency to reduce the amount of data.

[0200] The environmental simulation parameter adjustment rules in the main testing phase are adjusted in real time according to the dynamic threshold control logic, and the performance acquisition parameter rules are set to a high acquisition frequency, especially to improve acquisition accuracy when approaching the threshold monitoring indicators.

[0201] During the threshold verification phase, the environmental simulation parameter adjustment rules are finely adjusted around the dynamic stress threshold range, such as making small fluctuations within the threshold range. The performance acquisition parameter rules are set to the highest acquisition frequency and accuracy to ensure that subtle performance changes can be captured.

[0202] By configuring the above rules, environmental simulation and performance acquisition can be linked with dynamic threshold control logic to achieve automation and precision in the testing process.

[0203] Step S156: Integrate the phased process of material low-temperature performance testing, the range of target comprehensive coupling coefficients, threshold monitoring indicators during the testing process, dynamic threshold control logic, environmental simulation parameter adjustment rules, and performance acquisition parameter rules to form a material low-temperature performance testing execution plan that includes a real-time control mechanism.

[0204] This paper integrates the phased process of material low-temperature performance testing, the range of the comprehensive coupling coefficient of each phase, the threshold monitoring indicators, the dynamic threshold control logic, and the parameter rules for environmental simulation and performance acquisition. The results are arranged according to the order and logical relationship of the tests to form a complete material low-temperature performance testing execution plan.

[0205] The low-temperature performance testing scheme for this material includes a real-time control mechanism that can automatically adjust the combination of stress variables and the collected parameters based on the real-time performance response of the material during the test, ensuring that the test can comprehensively and accurately evaluate the performance of the material in a low-temperature environment.

[0206] Figure 2 The illustration shows exemplary hardware and software components of a material performance testing system 100 based on low-temperature environment simulation, which can implement the ideas of this application, according to some embodiments of this application. For example, a processor 120 can be used in the material performance testing system 100 based on low-temperature environment simulation and to perform the functions in this application.

[0207] The material performance testing system 100 based on low-temperature environment simulation can be a general-purpose server or a special-purpose server; both can be used to implement the material performance testing method based on low-temperature environment simulation of this application. Although only one server is shown in this application, for convenience, the functions described in this application can be implemented in a distributed manner on multiple similar platforms to balance the load.

[0208] For example, a material performance testing system 100 based on cryogenic environment simulation may include a network port 110 connected to a network, one or more processors 120 for executing program instructions, a communication bus 130, and various forms of storage media 140, such as a disk, ROM, or RAM, or any combination thereof. Exemplarily, the material performance testing system 100 based on cryogenic environment simulation may also include program instructions stored in ROM, RAM, or other types of non-transitory storage media, or any combination thereof. The methods of this application can be implemented according to these program instructions. The material performance testing system 100 based on cryogenic environment simulation also includes an I / O interface 150 between the computer and other input / output devices.

[0209] For ease of explanation, only one processor is described in the material performance testing system 100 based on cryogenic environment simulation. However, it should be noted that the material performance testing system 100 based on cryogenic environment simulation in this application may also include multiple processors. Therefore, the steps performed by one processor described in this application may also be performed jointly by multiple processors or individually. For example, if the processor of the material performance testing system 100 based on cryogenic environment simulation performs steps A and B, it should be understood that steps A and B may also be performed jointly by two different processors or individually by one processor. For example, the first processor performs step A, the second processor performs step B, or the first processor and the second processor jointly perform steps A and B.

[0210] Furthermore, this embodiment of the invention also provides a readable storage medium, wherein computer-executable instructions are preset in the readable storage medium, and when the processor executes the computer-executable instructions, the above-mentioned material performance testing method based on low-temperature environment simulation is implemented.

[0211] It should be noted that, in order to simplify the description of the present invention and thus help to understand one or more embodiments of the invention, multiple features may sometimes be grouped into one embodiment, drawing or description thereof in the foregoing description of the embodiments of the present invention.

Claims

1. A material performance testing method based on low-temperature environment simulation, characterized in that, The method includes: The low-temperature characteristics of the test material are pre-analyzed, and dynamic low-temperature environmental parameters of the material application scenario are combined to generate a dynamic stress reference signal containing multi-dimensional stress variables, including temperature change gradient variables and environmental medium interaction variables. Based on the dynamic stress reference signal, a multi-dimensional performance coupling acquisition process is initiated to simultaneously capture the mechanical deformation response data, physical property evolution data, and chemical stability data of the test material under dynamic low temperature environment, thereby obtaining a multi-dimensional performance response dataset. A coupling correlation analysis is performed on the multi-dimensional performance response dataset and the dynamic stress benchmark signal to calculate the performance response coupling coefficients corresponding to different combinations of stress variables and generate a performance coupling correlation model. Based on the performance coupling correlation model, the dynamic stress threshold range when the performance of the test material undergoes nonlinear changes is identified; Based on the dynamic stress threshold range and performance coupling correlation model, a material low-temperature performance test execution plan containing dynamic threshold adjustment logic is generated. The material low-temperature performance test execution plan can adjust the stress variable combination according to the real-time performance response. The process involves pre-analyzing the low-temperature properties of the test material, combining this with dynamic low-temperature environmental parameters from the material's application scenario, to generate a dynamic stress baseline signal containing multi-dimensional stress variables, including: Extract the basic physical property data and chemical composition information of the test material. The basic physical property data includes the thermal conductivity coefficient and thermal expansion coefficient of the material. The chemical composition information includes the component proportions and types of easily reactive components of the material. The dynamic low-temperature environment parameters of the material application scenario are obtained. The dynamic low-temperature environment parameters include instantaneous temperature fluctuation data, dynamic change data of environmental medium composition, and distribution data of continuous stress duration in the low-temperature environment of the scenario. Establish a correlation mapping model between basic material properties and dynamic environmental parameters, analyze the influence weight of different combinations of environmental parameters on material properties, and determine the key environmental parameter dimensions that affect material performance; Based on the key environmental parameters that affect material performance, an initial range of variation for multi-dimensional stress variables is set so that the initial range of variation covers extreme fluctuations in environmental parameters in the scenario. Time series modeling of multidimensional stress variables is performed to keep the change rhythm of each stress variable synchronized with the dynamic change pattern of environmental parameters in the scene, and a time series framework of dynamic stress benchmark signal is generated. The initial range of each stress variable is converted into a dimensionless standardized sequence, and then embedded into the time series framework of the dynamic stress reference signal. Combined with the constraints of material properties on the rate of change of variables, the standardized change amplitude of each stress variable in the time series is adjusted to generate a dynamic stress reference signal containing multi-dimensional stress variables.

2. The material performance testing method based on low-temperature environment simulation according to claim 1, characterized in that, The process involves establishing a correlation mapping model between fundamental material properties and dynamic environmental parameters, analyzing the influence weights of different combinations of environmental parameters on material properties, and determining the key environmental parameter dimensions affecting material performance, including: The basic physical properties and chemical composition information of materials are transformed into standardized material property vectors, where each dimension of the vector corresponds to a material property index. The dynamic low-temperature environmental parameters are transformed into a standardized environmental parameter vector, where each vector dimension corresponds to an environmental parameter index. Construct a multi-input, multi-output mapping analysis model, with environmental parameter vectors as inputs and changes in material property vectors as outputs; By using the controlled variable method, we changed any one of the dimension parameters in the environmental parameter vector one by one, observed the change range of the corresponding dimension of the material property vector, and recorded the change range data. Based on the change amplitude data, the influence weight value of each environmental parameter dimension on each dimension of the material property vector is calculated, and the influence weight value is positively correlated with the change amplitude. Environmental parameter dimensions with influence values ​​higher than the preset weight threshold are identified as key environmental parameter dimensions affecting material properties.

3. The material performance testing method based on low-temperature environment simulation according to claim 1, characterized in that, Based on the dynamic stress reference signal, a multi-dimensional performance coupling acquisition process is initiated to simultaneously capture the mechanical deformation response data, physical property evolution data, and chemical stability data of the test material under dynamic low-temperature environment, resulting in a multi-dimensional performance response dataset, including: The dynamic stress baseline signal is analyzed to extract the time series variation patterns of multi-dimensional stress variables, determine the key change nodes of each stress variable, and use the key change nodes as the priority trigger nodes for performance acquisition. A multi-dimensional performance coupling acquisition matrix is ​​constructed, which includes a mechanical deformation acquisition channel, a physical property acquisition channel, and a chemical stability acquisition channel. Each acquisition channel corresponds to a set of acquisition parameter settings. Based on the key change nodes of each stress variable, the acquisition frequency of each channel in the multi-dimensional performance coupling acquisition matrix is ​​set so that the acquisition frequency at the key change nodes is higher than that at non-key nodes. The dynamic low-temperature environment simulation device is activated, and the environmental parameters are adjusted according to the time series framework of the dynamic stress reference signal. At the same time, the multi-dimensional performance coupling acquisition matrix is ​​activated to synchronously acquire mechanical deformation response data, physical property evolution data and chemical stability data. During the data collection process, a real-time correlation mechanism between the collected data and stress variables is established, and the instantaneous values ​​of the corresponding stress variables are labeled for each set of collected data to form an original dataset containing the correspondence between stress and performance. The original dataset containing the stress-performance correspondence was filtered for validity, and abnormal data caused by instantaneous fluctuations of the environmental simulation device were removed, and the dataset was integrated into a multi-dimensional performance response dataset.

4. The material performance testing method based on low-temperature environment simulation according to claim 3, characterized in that, During the data collection process, a real-time correlation mechanism between the collected data and stress variables is established. Each set of collected data is labeled with the corresponding instantaneous value of the stress variable, forming an original dataset containing the stress-performance correspondence, including: A stress variable real-time acquisition module is added to the dynamic low temperature environment simulation device. The stress variable real-time acquisition module is used to synchronously record the instantaneous values ​​of the temperature change gradient and the interaction between the dynamic low temperature environment simulation device and the environmental medium. Add a time synchronization module to each acquisition channel in the multi-dimensional performance coupling acquisition matrix to ensure that the acquisition actions of each acquisition channel are consistent with the recording actions of the stress variable real-time acquisition module in terms of timestamps; Each time the acquisition channel is started to collect performance data, the stress variable real-time acquisition module is triggered to record the instantaneous value of the stress variable at the current moment and generate a stress variable record entry containing a timestamp. The collected performance data is bound to the stress variable record entries corresponding to the same timestamp to form a basic data unit containing performance data, stress variable values, and timestamps; Based on the chronological order of timestamps, all basic data units containing performance data, stress variable values, and timestamps are sorted to form an original dataset containing stress-performance correspondences. Each sequence node in the original data sequence contains complete stress-performance correspondence information.

5. The material performance testing method based on low-temperature environment simulation according to claim 1, characterized in that, The step of performing coupling correlation analysis on the multi-dimensional performance response dataset and the dynamic stress benchmark signal, calculating the performance response coupling coefficients corresponding to different combinations of stress variables, and generating a performance coupling correlation model includes: Multiple data sequences are separated from the multi-dimensional performance response dataset. These multiple data sequences include mechanical deformation response data sequences, physical property evolution data sequences, and chemical stability data sequences. Each data sequence contains performance data at multiple time points. Time series data of multi-dimensional stress variables are extracted from dynamic stress reference signals to form a stress variable sequence, which includes temperature change gradient value and environmental medium interaction value corresponding to each time node; Establish a time node alignment mechanism between performance data and stress variables so that each performance data node can be matched with a unique corresponding stress variable node, forming multiple sets of stress-performance data pairs; After standardizing the mechanical deformation data, physical property data, and chemical stability data in each stress-performance data pair, multivariate correlation analysis was used to calculate the degree of mutual influence between performance data and obtain the internal coupling coefficient of performance. The stress variables and performance data are standardized separately, and their correlation strength is calculated to obtain the stress-performance external coupling coefficient, which reflects the degree to which the stress variables drive performance changes. By integrating the internal performance coupling coefficient and the external stress-performance coupling coefficient, a performance coupling correlation model is constructed with stress variables as input and multi-dimensional performance coupling coefficients as output. The performance coupling correlation model with stress variables as input and multi-dimensional performance coupling coefficients as output includes the correspondence between different combinations of stress variables and coupling coefficients.

6. The material performance testing method based on low-temperature environment simulation according to claim 5, characterized in that, The integrated performance internal coupling coefficient and stress-performance external coupling coefficient are used to construct a performance coupling correlation model with stress variables as input and multi-dimensional performance coupling coefficients as output, including: Based on the importance of each performance index in the material application scenario, different weighting coefficients are set for the internal performance coupling coefficient and the stress-performance external coupling coefficient. Based on the set weight coefficients, the internal coupling coefficient of performance and the external coupling coefficient of stress-performance corresponding to each group of stress variables are weighted and calculated to obtain the comprehensive coupling coefficient, which reflects the overall correlation between stress variables and multi-dimensional performance. All stress variable combinations and their corresponding comprehensive coupling coefficients are stored digitally to form a stress-coupling coefficient sample dataset. Each sample in the stress-coupling coefficient sample dataset contains a stress variable input vector and a comprehensive coupling coefficient output value. A nonlinear regression algorithm is used to train the model on the stress-coupling coefficient sample dataset to construct a regression model with the stress variable input vector as the independent variable and the comprehensive coupling coefficient as the dependent variable. The accuracy of a regression model with stress variable input vector as independent variable and comprehensive coupling coefficient as dependent variable is verified by using a partial stress-coupling coefficient sample dataset. The deviation between the model prediction value and the actual comprehensive coupling coefficient is calculated. If the deviation exceeds the preset accuracy standard, the regression algorithm parameters of the regression model are adjusted. The validated regression model with the stress variable input vector as the independent variable and the comprehensive coupling coefficient as the dependent variable is identified as the performance coupling correlation model. The performance coupling correlation model is used to output the corresponding multi-dimensional performance comprehensive coupling coefficient based on the combination of input stress variables.

7. The material performance testing method based on low-temperature environment simulation according to claim 1, characterized in that, The step of identifying the dynamic stress threshold range when the test material's properties exhibit nonlinear changes based on the performance coupling correlation model includes: The performance coupling correlation model is analyzed, and the performance response coupling coefficient sequence corresponding to different combinations of stress variables is extracted. The performance response coupling coefficient sequence reflects the law of change of coupling coefficient with stress variables. Nonlinear feature identification is performed on the performance response coupling coefficient sequence. A trend change detection algorithm is used to locate the stress variable node where the fluctuation of the rate of change of the coupling coefficient is greater than a set threshold. The stress variable node is the preliminary threshold node. For each preliminary threshold node, extract the corresponding multi-dimensional performance data and analyze whether the mechanical deformation, physical properties, and chemical stability data show nonlinear changes at the same time. If nonlinear changes are all observed, the preliminary threshold node is confirmed as a valid threshold node. The stress variable values ​​corresponding to adjacent effective threshold nodes are divided into intervals to obtain multiple stress variable intervals. Each stress variable interval corresponds to a range in which the coupling coefficient changes smoothly. The trend of coupling coefficient change within each stress variable interval is verified, and the fluctuation amplitude of coupling coefficient within the stress variable interval is calculated. If the fluctuation amplitude is less than the preset stability standard, the stress variable interval is confirmed as a dynamic stress threshold interval. All confirmed dynamic stress threshold intervals are integrated to form a set of threshold intervals containing the critical change range of each stress variable. Each dynamic stress threshold interval is labeled with the corresponding nonlinear performance change characteristics.

8. The material performance testing method based on low-temperature environment simulation according to claim 7, characterized in that, For each preliminary threshold node, corresponding multi-dimensional performance data is extracted, and the mechanical deformation, physical properties, and chemical stability data are analyzed to see if nonlinear changes occur simultaneously. If nonlinear changes occur in all three, the preliminary threshold node is confirmed as a valid threshold node, including: Mechanical deformation data, physical property data, and chemical stability data corresponding to the timestamps of the initial threshold nodes are extracted from the multi-dimensional performance response dataset to form a threshold node performance data group. Nonlinearity is assessed on the mechanical deformation data. The deformation rate of the corresponding data point and its adjacent data points is calculated. If the rate of change exceeds the preset nonlinearity threshold, the mechanical deformation data is determined to have a nonlinear change. Nonlinearity is assessed on the physical characteristic data. The deviation rate of the physical characteristic index corresponding to the data point from the average value of the previous time period is calculated. If the deviation rate exceeds the preset physical nonlinearity threshold, the physical characteristic data is determined to have undergone nonlinear changes. Nonlinearity is assessed on the chemical stability data. The rate of change of the chemical index corresponding to the data point from the initial state is calculated. If the rate of change exceeds the preset chemical nonlinearity threshold, the chemical stability data is determined to have a nonlinear change. If the nonlinear changes of the three types of performance data—statistical mechanical deformation data, physical property data, and chemical stability data—are all determined to be nonlinear, then the preliminary threshold node is marked as a valid threshold node.

9. A material performance testing system based on low-temperature environment simulation, characterized in that, The device includes a processor and a memory, the memory and the processor being connected. The memory is used to store programs, instructions or code, and the processor is used to execute the programs, instructions or code in the memory to implement the material performance testing method based on low-temperature environment simulation as described in any one of claims 1-8.

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