Material acceleration experiment environment parameter determination method and device, and environment simulation equipment

Abstract

The present application relates to the technical field of material testing, solves the technical problem that the existing technology fails to match the historical meteorological characteristics and extreme working conditions of the actual service area of the material in the accelerated experiment environment parameters, thereby resulting in insufficient reliability of the material accelerated experiment results, and provides a material accelerated experiment environment parameter determination method, device and environment simulation equipment, the method comprising: obtaining historical meteorological data according to a preset time limit and the service location of the material to be tested; preprocessing the historical meteorological data to obtain preprocessed meteorological data; performing cluster analysis on the preprocessed meteorological data to obtain target meteorological parameters for representing extreme working conditions; and calculating the environment parameters for material accelerated experiments according to the target meteorological parameters to obtain target experimental environment parameters. The present application improves the reliability of the material accelerated experiment results by constructing accelerated experiment environment parameters that match the actual service area of the material.

Description

Technical Field

[0001] This invention relates to the field of materials testing technology, and in particular to a method, apparatus, and environmental simulation equipment for determining environmental parameters of accelerated material testing. Background Technology

[0002] Accelerated aging testing (AAT) is widely used to evaluate the weather resistance of various engineering materials, including coatings, composites, building materials, and photovoltaic modules. By applying high temperatures, humidity, and radiation conditions in a laboratory setting, materials exhibit aging and degradation characteristics similar to those experienced during long-term service within a short period, allowing for the assessment of their durability, safety, and service life. AAT typically relies on standardized environmental settings, controlling temperature and humidity cycles, light radiation intensity, and other environmental factors to create repeatable accelerated aging environments, thus replacing long-term natural exposure tests.

[0003] Most existing accelerated aging techniques for materials use uniform temperature, humidity, and light intensity as experimental parameters, primarily referencing commonly used accelerated aging standards. However, materials in actual service are affected by real-world environments with variations in location, season, and extreme weather events. The temperature and humidity conditions specified in common accelerated aging standards are not applicable to all material service areas, leading to significant discrepancies between the experimental environment and actual operating conditions. Especially in areas with high temperatures and strong radiation, high humidity fluctuations, or frequent extreme weather events, the surface temperature and humidity response characteristics of materials often differ from standard experimental conditions. This makes it difficult for experimental results to accurately reflect the weather resistance of materials under real-world service conditions, resulting in decreased reliability of accelerated aging results and hindering effective support for material life prediction and engineering application evaluation.

[0004] Chinese patent CN110823291A discloses a method and system for monitoring indoor temperature and humidity in buildings based on the K-means clustering algorithm. The method includes: collecting and transmitting indoor temperature and humidity information; clustering the collected indoor temperature and humidity information using the K-means clustering algorithm to obtain multiple clustering results, wherein the data is divided into a training set and a test set; assigning operation instructions to the multiple clustering results; comparing the similarity between the collected new temperature and humidity information and the clustering results to obtain a decision result; and executing the decision result to adjust the indoor temperature and humidity. The aforementioned patented solution collects temperature and humidity data from multiple indoor locations, uses K-means clustering to classify the current indoor environmental state, and controls air conditioners, humidifiers, or dehumidifiers accordingly to maintain the indoor environment within a preset comfort range. Essentially, it solves the problems of complex wiring, high maintenance costs, and unintelligent automatic control in indoor environmental monitoring systems. However, this solution neither distinguishes the actual extreme working conditions experienced by material surfaces in different regions and service environments, nor can it equate these working conditions to the corresponding accelerated testing environment conditions. It remains at the level of indoor comfort control and cannot solve the technical problem that the uniform temperature and humidity environment in common accelerated testing standards is not applicable to the service areas of various materials, thus leading to a decrease in the reliability of accelerated testing results.

[0005] Therefore, how to construct accelerated experimental environment parameters that match the historical meteorological characteristics and extreme working conditions of the actual service areas of materials, thereby improving the reliability of accelerated experimental results, is an urgent technical problem to be solved. Summary of the Invention

[0006] In view of this, embodiments of the present invention provide a method, apparatus and environmental simulation equipment for determining the environmental parameters of accelerated material experiments, in order to solve the problem in the prior art that the environmental parameters of accelerated experiments fail to match the historical meteorological characteristics and extreme working conditions of the actual service areas of the materials, thereby resulting in insufficient reliability of the material accelerated experiment results.

[0007] In a first aspect, embodiments of the present invention provide a method for determining environmental parameters of accelerated material experiments, the method comprising: Historical meteorological data are obtained based on the preset time limit and the service location of the material to be tested; The historical meteorological data is preprocessed to obtain preprocessed meteorological data; Cluster analysis is performed on the preprocessed meteorological data to obtain target meteorological parameters for characterizing extreme working conditions; Based on the target meteorological parameters, the environmental parameters for the material acceleration experiment are calculated to obtain the target experimental environmental parameters.

[0008] Secondly, embodiments of the present invention provide a device for determining environmental parameters of accelerated material experiments, the device comprising: The historical meteorological data acquisition module is used to acquire historical meteorological data according to the preset time period and the service location of the material to be tested; The preprocessing module is used to preprocess the historical meteorological data to obtain preprocessed meteorological data; The clustering analysis module is used to perform clustering analysis on the preprocessed meteorological data to obtain target meteorological parameters for characterizing extreme working conditions; The experimental environment parameter calculation module is used to calculate the environmental parameters for the material acceleration experiment based on the target meteorological parameters, and obtain the target experimental environment parameters.

[0009] Thirdly, embodiments of the present invention provide a multifunctional environment simulation device, including: at least one processor, at least one memory, and computer program instructions stored in the memory, which, when executed by the processor, implement the method of the first aspect described above.

[0010] In summary, the beneficial effects of the present invention are as follows: The present invention provides a method, apparatus, and environmental simulation device for determining environmental parameters of accelerated material testing. The method includes: acquiring historical meteorological data based on a preset time period and the service location of the material to be tested; preprocessing the historical meteorological data to obtain preprocessed meteorological data; performing cluster analysis on the preprocessed meteorological data to obtain target meteorological parameters for characterizing extreme operating conditions; and calculating environmental parameters for accelerated material testing based on the target meteorological parameters to obtain target experimental environmental parameters. This invention first extracts historical meteorological data with regional and temporal characteristics from a meteorological database based on the service location and duration of the material under test, ensuring that subsequent analysis is based on the actual service environment. Then, the historical meteorological data undergoes threshold screening, consistency verification, and anomaly removal to ensure the reliability of the meteorological parameters used in the analysis. On this basis, cluster analysis is used to mine the processed meteorological data, extracting target meteorological parameters that represent typical extreme conditions in the material's service area through cluster centers. This avoids the distortion problems associated with traditional methods that rely solely on uniform standard temperature and humidity or simple extreme value selection. Finally, based on these target meteorological parameters and the material's thermal properties, the setpoints for temperature, humidity, and radiation in the experimental environment are calculated. This ensures that the accelerated experimental environment is statistically and physically equivalent to the extreme meteorological loads in the material's actual service area, thereby ensuring regional adaptability of the experimental conditions and significantly improving the accuracy and reliability of the accelerated experimental results. Attached Figure Description

[0011] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the embodiments of the present invention will be briefly introduced below. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort, and these are all within the protection scope of the present invention.

[0012] Figure 1 This is a schematic diagram of the overall process for determining the environmental parameters of accelerated material experiments in Embodiment 1 of the present invention; Figure 2 This is a schematic diagram of the process of performing cluster analysis on the preprocessed meteorological data in Embodiment 1 of the present invention; Figure 3 This is a schematic diagram of the K-medoids clustering distribution of the top 1% of extreme high-temperature samples in Embodiment 1 of the present invention; Figure 4 This is a schematic diagram of the K-medoids clustering distribution of the top 1% of the low-temperature extreme samples in Embodiment 1 of the present invention; Figure 5 This is a structural block diagram of the material acceleration experimental environment parameter determination device in Embodiment 2 of the present invention; Figure 6 This is a schematic diagram of the structure of the multifunctional environmental simulation device in Embodiment 3 of the present invention. Detailed Implementation

[0013] The features and exemplary embodiments of various aspects of the present invention will now be described in detail. To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only configured to explain the present invention and are not configured to limit the present invention. For those skilled in the art, the present invention can be practiced without some of these specific details. The following description of the embodiments is merely intended to provide a better understanding of the present invention by illustrating examples of the invention.

[0014] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes said element.

[0015] It should be noted that all actions involving the acquisition of signals, information, or data in this invention are carried out in compliance with the relevant data protection laws and regulations of the locality and with authorization from the owner of the relevant device.

[0016] Example 1 Please see Figure 1 This invention provides a method for determining environmental parameters in accelerated material experiments, the method comprising: Historical meteorological data are obtained based on the preset time limit and the service location of the material to be tested; Specifically, the time frame corresponds to the material's design life or the service stage of interest, and the service location corresponds to a specific geographical region or coordinate point, such as a coastal city, a plateau region, or a frigid region. The service location includes the latitude and longitude corresponding to the material's service location, and the historical meteorological data is a sequence of meteorological elements such as temperature, humidity, and solar radiation recorded in that region within the stated time frame. For example, the historical meteorological data includes weather data from the past 5 years, containing a sequence of meteorological elements such as temperature, humidity, and solar radiation. By retrieving original meteorological records from a pre-set meteorological database based on the service location and filtering them by time frame, for example, the meteorological database includes the open-source meteorological data website OpenMeteo, a complete dataset covering many years of seasonal changes and extreme weather events is obtained. This allows subsequent analysis to be based on a climate foundation that reflects the characteristics of the actual service environment, helping to avoid accelerating experiments using a uniform standard environment that is out of touch with actual operating conditions, and improving the relevance and reliability of the experimental environment setting from the source.

[0017] The historical meteorological data is preprocessed to obtain preprocessed meteorological data; Specifically, historical meteorological data often contains issues such as sensor malfunctions, missing data, incorrect backfilling, or abnormal spikes. Directly using these data in analysis can lead to deviations from reality in the identification of extreme operating conditions. Preprocessing can be improved by setting thresholds based on the physical reasonable range of meteorological parameters. Records exceeding this range are marked and removed. Internal consistency is verified through the correlation between different meteorological elements; for example, the combination of high radiation and extremely low temperature can be identified as an anomaly. Furthermore, outlier detection methods based on interquartile ranges are used to identify statistically significant outliers. Missing data is corrected using strategies such as interpolation, resampling, or direct deletion. The resulting preprocessed meteorological data is cleaner, more continuous, and more reliable. It effectively reduces the interference of noisy data on extreme sample selection and cluster boundaries, making the subsequent representative extreme operating conditions more closely resemble the actual service environment.

[0018] Cluster analysis is performed on the preprocessed meteorological data to obtain target meteorological parameters for characterizing extreme working conditions; Specifically, a limited number of representative extreme environment combinations are extracted from massive meteorological records. The preprocessed meteorological data contains multi-dimensional features such as temperature, humidity, and solar shortwave radiation. Directly using maximum values ​​or simple quantiles is insufficient to fully reflect the diversity of extreme operating conditions. By proportionally screening and standardizing extreme samples at both high and low temperatures, a K-medoids clustering method is used to group meteorological records with similar extreme characteristics into one category. Each category is characterized by a set of temperature, humidity, and radiation parameters corresponding to its cluster center, representing a typical extreme operating condition. The number of clusters can be adaptively selected within the candidate range using indicators such as the silhouette coefficient, thus achieving a balance between characterization capability and complexity. The resulting target meteorological parameters are no longer abstract statistical extremes, but rather typical operating condition points representing the actual extreme weather patterns in the material's service area, which is beneficial for subsequently establishing accelerated experimental environment settings that match real environmental loads.

[0019] Based on the target meteorological parameters, the environmental parameters for the material acceleration experiment are calculated to obtain the target experimental environmental parameters.

[0020] Specifically, statistically extreme weather conditions are transformed into feasible temperature, humidity, and radiation setpoints for laboratory testing. Target meteorological parameters provide external operating condition information such as air temperature, ambient humidity, and solar shortwave radiation intensity. These can be combined with the thermophysical parameters of the material under test, such as convective heat transfer coefficient, material surface absorptivity, and emissivity, to establish a thermal balance relationship between solar radiation absorption, convective heat transfer, and surface radiative heat dissipation. The corresponding material surface temperature is then calculated and used as the temperature setpoint for the accelerated testing. Humidity from the target meteorological parameters is directly used as the experimental humidity setpoint. If necessary, the radiation intensity is amplified or scaled based on equipment capabilities and safety margins. Through this computational mapping from target meteorological parameters to experimental environment parameters, the accelerated environment constructed in the laboratory can be equivalent to the extreme operating conditions of the material's service area in terms of thermal and humidity load. This makes the accelerated testing conditions closer to the actual service environment, thereby significantly improving the reliability of the accelerated testing results in predicting actual lifespan and failure behavior.

[0021] In an optional embodiment, obtaining historical meteorological data based on a preset time period and the service location of the material to be tested includes: Based on the service location, initial meteorological data corresponding to the service location is obtained from a preset meteorological database; Specifically, in terms of spatial dimension, the application scenario of the material is precisely located in a specific area. The service location refers to the actual installation or use site of the material under test in the engineering project. This could be a city, a factory area, or even a specific region specified by latitude and longitude coordinates. The pre-set meteorological database is a data source that has been connected or configured in advance, such as historical observation databases published by national or regional meteorological departments or data interfaces of commercial meteorological service platforms. The initial meteorological data is a collection of raw meteorological records collected by the database near the service location, generally including multiple elements such as temperature, relative humidity, solar radiation, and wind speed. The purpose of this step is to avoid using general climate data unrelated to the actual use environment of the material, and instead to obtain targeted meteorological information matching the service location of the material. In practice, the nearest meteorological station or grid data can be selected from the database using the administrative region name or latitude and longitude coordinates corresponding to the service location. Daily or hourly observation records for that area over the entire time span can be retrieved in batches as the initial data source for subsequent analysis. This approach ensures that the extreme working conditions and experimental environments constructed subsequently are based on the climatic background of the material's actual service area, fully reflecting the differences in temperature, humidity, and radiation conditions in different regions, thus laying the foundation for improving the regional adaptability of accelerated experimental environments.

[0022] Based on the stated time period, the initial meteorological data is filtered, and the meteorological data within the stated time period is selected as the historical meteorological data.

[0023] Specifically, in terms of spatial dimension, the application scenario of the material is precisely located in a specific area. The service location refers to the actual installation or use site of the material under test in the engineering project. This could be a city, a factory area, or even a specific region specified by latitude and longitude coordinates. The pre-set meteorological database is a data source that has been connected or configured in advance, such as historical observation databases published by national or regional meteorological departments or data interfaces of commercial meteorological service platforms. The initial meteorological data is a collection of raw meteorological records collected by the database near the service location, generally including multiple elements such as temperature, relative humidity, solar radiation, and wind speed. The purpose of this step is to avoid using general climate data unrelated to the actual use environment of the material, and instead to obtain targeted meteorological information matching the service location of the material. In practice, the nearest meteorological station or grid data can be selected from the database using the administrative region name or latitude and longitude coordinates corresponding to the service location. Daily or hourly observation records for that area over the entire time span can be retrieved in batches as the initial data source for subsequent analysis. This approach ensures that the extreme working conditions and experimental environments constructed subsequently are based on the climatic background of the material's actual service area, fully reflecting the differences in temperature, humidity, and radiation conditions in different regions, thus laying the foundation for improving the regional adaptability of accelerated experimental environments.

[0024] In an optional embodiment, the preprocessing of the historical meteorological data to obtain preprocessed meteorological data includes: Based on preset meteorological parameter thresholds, the historical meteorological data is filtered by thresholds to obtain the first meteorological data; Specifically, by performing preliminary filtering on the physical rationality of the original meteorological records, meteorological parameter thresholds are upper and lower limits set for different meteorological elements. For example, meteorological records of a coastal city over many years show that the temperature is usually between several degrees below zero and over forty degrees Celsius, the relative humidity is between ten percent and close to saturation, and the solar shortwave radiation intensity also has a reasonable range that matches the local latitude and climate type. The purpose of setting these thresholds is to use meteorological common sense and empirical data to eliminate records that obviously violate physical laws or observational patterns, such as temperatures abnormally high above eighty degrees Celsius or low below fifty degrees Celsius, or humidity values ​​that are negative or far above one hundred. In the implementation process, allowable ranges for each element can be given based on multi-year statistical results or authoritative standards for different regions. Records falling outside the range are filtered out in the program or marked as invalid values, and data falling within the reasonable range are retained as the primary meteorological data. Through this processing, subsequent analysis is no longer affected by extreme erroneous measurement points or data entry errors, which helps to improve the overall authenticity and stability of meteorological data.

[0025] The first meteorological data is subjected to an internal consistency correlation check to obtain the second meteorological data; Specifically, the correlations between different meteorological elements are used to further identify potential anomalous combinations. Internal consistency correlation verification involves checking whether there are combinations of elements such as temperature, humidity, and radiation that significantly violate empirical patterns within the same timeframe or period. For example, persistently extremely low temperatures under strong solar radiation, or abnormally low relative humidity recorded during rainfall. The aim is to identify records where a single element value seems reasonable, but when combined with multiple elements, they clearly do not conform to the meteorological logic of the same period. This can be implemented using simple empirical rules, or by establishing multivariate correlation intervals based on historical large-sample statistics. For example, typical humidity and radiation ranges corresponding to a certain temperature range can be defined, and records exceeding these multivariate correlation intervals can be marked and removed. Alternatively, correlation coefficients, clustering, and other methods can be used to identify points that do not conform to mainstream patterns. The second meteorological data obtained after internal consistency verification shows more coordinated correlations between elements, providing more reliable multidimensional meteorological characteristics for subsequent extreme condition identification.

[0026] The second meteorological data is subjected to outlier detection and removal to obtain the preprocessed meteorological data.

[0027] Specifically, outlier detection further eliminates isolated outliers in a statistical sense to ensure the representativeness of the data distribution. Outlier detection can employ statistical methods such as the interquartile range (IQR). By calculating the first and third quartiles of a given element, the IQR is obtained, and records exceeding a certain multiple of the IQR range are considered outliers. Alternatively, multidimensional distance metrics or clustering results can be combined to identify data deviating from the main group. The aim is to process samples that, while not exceeding physical thresholds and not necessarily showing significant anomalies in element association, differ greatly from the majority of records in statistical distribution, preventing these isolated points from causing bias in subsequent extreme sample screening and cluster analysis. In practice, anomaly detection can be performed separately or jointly for key elements such as temperature, humidity, and radiation. Data identified as anomalous is removed or marked separately and excluded from extreme condition calculations. The preprocessed meteorological data obtained after outlier detection and removal exhibits better continuity and statistical representativeness, reducing noise interference while ensuring data authenticity, making the extreme condition parameters obtained from subsequent cluster analysis more robust and reliable.

[0028] In an optional embodiment, the step of performing outlier detection and removal on the second meteorological data to obtain the preprocessed meteorological data includes: Each meteorological parameter in the second meteorological data is classified to obtain a set of meteorological parameters; Specifically, the second meteorological data consists of historical meteorological records that have undergone physical threshold screening and internal consistency verification. Each record typically contains multiple meteorological parameters, such as temperature, relative humidity, and solar shortwave radiation intensity. Classifying the meteorological parameters in the second meteorological data involves extracting parameters with the same physical meaning from the multidimensional records, forming separate sets such as temperature, humidity, and solar shortwave radiation parameters. This method decomposes the originally mixed multidimensional meteorological information into several one-dimensional parameter sequences, facilitating independent statistical analysis and outlier identification for each type of meteorological parameter. It avoids the interference of numerical differences between different physical dimensions on the accuracy of anomaly detection, providing a clear data foundation for subsequent statistical feature calculations based on interquartile ranges.

[0029] The meteorological parameter sets are arranged according to their numerical values, and the first and third quartiles of each meteorological parameter are obtained based on the arrangement results. Specifically, arranging the meteorological parameter set according to its numerical value is to obtain the distributional position of the parameters within the entire sample, thereby calculating key statistics used to characterize the distribution. Specifically, after sorting the temperature parameter set from smallest to largest, the value at approximately the 25th percentile can be determined as the first quartile, and the value at approximately the 75th percentile as the third quartile; the humidity parameter set and the solar shortwave radiation parameter set are processed similarly. The first quartile reflects the upper bound of the low-value region in the sample, and the third quartile reflects the lower bound of the high-value region. These two positions together characterize the range of the middle 50% of the data. Through this step, robust quantiles for each meteorological parameter can be extracted without relying on specific distribution assumptions, providing a direct basis for constructing outlier intervals.

[0030] Based on the first quartile and the third quartile, the interquartile range corresponding to each meteorological parameter is calculated; Specifically, the interquartile range (IQR) is the difference between the third quartile and the first quartile, used to describe the dispersion of the middle portion of the dataset. In this step, for each meteorological parameter, the IQR is obtained by subtracting the first quartile from the third quartile obtained in the previous step. The IQR depends only on the middle 50% of the data and is not significantly affected by maximum or minimum values, thus exhibiting good robustness even in the presence of potential outliers. By calculating the IQR for each meteorological parameter, a scale reflecting the normal fluctuation range of that parameter can be obtained, providing a quantitative width reference for subsequently defining normal intervals and outlier thresholds, thereby avoiding the bias caused by simply relying on the mean plus or minus a certain number of standard deviations.

[0031] Based on the interquartile range, an outlier determination interval for each meteorological parameter is constructed. The lower limit of the outlier determination interval is the product of the first quartile minus a preset multiple and the interquartile range, and the upper limit of the outlier determination interval is the product of the third quartile plus the preset multiple and the interquartile range. Specifically, constructing outlier determination intervals using interquartile ranges (ICMs) explicitly applies the statistically common ICM method to anomaly detection for meteorological parameters. Specifically, for each meteorological parameter, the lower limit of its normal value is obtained by subtracting a preset multiple and the ICM from the first quartile; the upper limit of its normal value is obtained by adding the preset multiple and the ICM to the third quartile. The preset multiple can be set according to the sensitivity requirements of the actual application, for example, using one and a half ICMs or other empirical coefficients to control the width of the anomaly determination interval. In this way, the normal value interval covers most typical meteorological conditions while also exposing extreme points that significantly deviate from the overall distribution, thus providing statistically significant outlier determination boundaries for each meteorological parameter.

[0032] The parameter values ​​of each meteorological parameter in the second meteorological data are compared with the corresponding anomaly determination interval to obtain the anomaly determination result of each meteorological parameter; Specifically, each parameter value in each meteorological data record in the second meteorological data set is compared one by one with the corresponding outlier determination interval. If a parameter value in a record falls outside the pre-constructed normal value interval, the value of that parameter in that record is marked as outlier; if all parameter values ​​fall within the normal interval, the record is considered normal in that parameter dimension. By comparing multiple parameters such as temperature, humidity, and solar shortwave radiation, the outlier determination results for each meteorological data record in each parameter dimension can be obtained. This process transforms abstract statistical intervals into outlier identifiers on specific data records, realizing the mapping from statistical characteristics to single-point data, and providing a clear basis for subsequent data removal or retention based on outlier identifiers.

[0033] Based on the anomaly determination result, the second meteorological data is removed to obtain the preprocessed meteorological data.

[0034] Specifically, based on the anomaly assessment results obtained in the previous step, the second meteorological data is processed by removing data records deemed abnormal from the meteorological dataset or marking them as invalid data unusable for subsequent analysis, according to a pre-set data cleaning strategy. In practice, removal conditions can be set as needed; for example, a record can be removed entirely only if it shows anomalies in multiple key meteorological parameters simultaneously, or records showing anomalies in a single parameter can be processed individually. This removal process effectively eliminates non-physical anomalies caused by acquisition errors, sensor malfunctions, or data transmission problems, making the remaining preprocessed meteorological data more realistic and reliable. This provides a high-quality data foundation for subsequent extreme sample screening, cluster analysis, and calculation of environmental parameters for accelerated materials experiments based on the preprocessed data.

[0035] In an alternative embodiment, please refer to Figure 2 The cluster analysis performed on the preprocessed meteorological data to obtain target meteorological parameters for characterizing extreme operating conditions includes: The preprocessed meteorological data is filtered according to a preset ratio coefficient to obtain initial meteorological parameters; Specifically, in the process of extracting extreme sample intervals from the overall data distribution, the proportion coefficient can be understood as a parameter used to limit the proportion of extreme samples at the high-temperature and low-temperature ends. For example, taking 1% or 5% of the temperature records at the beginning and end corresponds to the typical situation of the material under extreme high-temperature and extreme-low-temperature environments in the region. The initial meteorological parameters are these extreme temperatures and their corresponding humidity, solar radiation, and other multi-dimensional elements screened from the preprocessed meteorological data. The purpose of this is not to simply take the maximum or minimum value, but to use a proportional range to characterize the statistical characteristics of extreme operating conditions, avoiding being dominated by a single abnormally high value while retaining enough extreme samples for subsequent cluster analysis. In implementation, the temperature can be sorted from high to low, the quantile positions corresponding to the high-temperature and low-temperature ends can be determined according to the proportion coefficient, corresponding intervals can be truncated at both ends of the sequence, and the humidity and radiation corresponding to these times can be retrieved to form the initial meteorological parameter set. Through this extreme sample screening, the subsequent cluster analysis can focus on the meteorological conditions with the greatest risk to the material, improving the targeting of the extracted operating conditions.

[0036] The initial meteorological parameters are standardized to obtain standardized meteorological parameters; Specifically, to address the issue of significant differences in the dimensions and numerical ranges of various meteorological elements, the units and magnitudes of initial meteorological parameters such as temperature, humidity, and solar shortwave radiation are inconsistent. Temperature may vary within tens of degrees Celsius, and radiation intensity may range from hundreds to thousands. If the original values ​​are directly used for clustering, elements with larger values ​​will dominate distance calculations, masking the impact of other elements on extreme conditions. Standardized meteorological parameters refer to mapping the values ​​of each element to a set of parameters with uniform dimensions and similar numerical scales through linear transformation. For example, zero-mean unit variance standardization or normalization by maximum and minimum values ​​can be used to make each dimension comparable during clustering. In implementation, the mean and standard deviation can be calculated for each meteorological element, and the corresponding terms in the initial meteorological parameters can be transformed to obtain a new set of parameters with a mean of approximately zero and a variance of one, i.e.: in, Represents standardized meteorological parameters. Indicates the initial meteorological parameters. This represents the mean. The standard deviation is represented by the standard deviation. Through standardization, the clustering algorithm can simultaneously consider the relative differences in temperature, humidity, and radiation, improving its ability to identify multidimensional extreme working conditions and avoiding clustering results being dominated by a single numerical scale.

[0037] The number of candidate clusters in the preset set of candidate cluster numbers is evaluated, and the target number of clusters is determined based on the evaluation results. Specifically, the candidate cluster number set is a pre-defined set of possible clustering values ​​by the user or system, such as an integer range from 2 to several clusters. The target cluster number is the one selected from this set of candidate values ​​that best reflects the degree of subdivision of the extreme operating condition mode. The purpose of this step is to avoid the problem of overly coarse or fragmented division of operating conditions caused by subjectively specifying too large or too small a cluster number. The optimal number of clusters is automatically determined through quantitative clustering quality indicators. In implementation, pre-clustering can be performed once for each candidate cluster number. The clustering results are evaluated using indicators such as the silhouette coefficient, which comprehensively considers intra-cluster compactness and inter-cluster separation. The corresponding evaluation value is obtained, and the cluster number with the highest score that exceeds the preset evaluation threshold is selected as the target cluster number. If the overall evaluation results are too low, a fallback rule can be used to select a smaller cluster number to avoid overfitting. Through such evaluation and screening, extreme operating conditions are divided into several classes with appropriate numbers and significant differences, providing a reliable basis for subsequent selection of representative operating conditions.

[0038] Based on the target number of clusters, and combined with the preset K-medoids clustering algorithm, the standardized meteorological parameters are clustered to obtain the target meteorological parameters.

[0039] Specifically, cluster analysis of the standardized meteorological parameters is the process of extracting a finite number of representative typical operating condition points from extreme samples. K-medoids clustering is a clustering algorithm that uses real sample points as cluster centers. Compared with the K-means algorithm, which uses the mean as the center, it is more robust to outliers and suitable for processing extreme meteorological data of limited scale but sensitive to individual samples. The standardized meteorological parameters serve as the algorithm input, and the target number of clusters determines the number of operating condition categories to be divided. The clustering process iteratively adjusts the cluster centers and sample affiliation relationships to make the meteorological records within each category as similar as possible in terms of characteristics such as temperature, humidity, and radiation. Finally, the set of temperature, humidity, and solar shortwave radiation values ​​corresponding to the center record of each category constitutes the target meteorological parameters, used to characterize different types of extreme operating conditions. The target meteorological parameters obtained in this way not only retain the multidimensional characteristics of extreme samples, but also ensure that each target point corresponds to an actual observation record, facilitating direct use in subsequent experimental environment setting. This approach helps to cover the main extreme climate patterns of the material's service area with a small number of representative operating conditions, improving the representativeness and rationality of the accelerated experimental environment selection.

[0040] In one specific embodiment, such as Figure 3As shown, for the top 1% of high-temperature extreme samples selected based on the first temperature threshold, a two-dimensional feature distribution map is constructed for each sample, with solar shortwave radiation as the abscissa and relative humidity as the ordinate. Air temperature levels are represented by color mapping, thus visually demonstrating the distribution of high-temperature extreme events in a multi-dimensional meteorological feature space. After Z-score standardization, the K-medoids clustering algorithm is used to perform pattern recognition on this high-temperature extreme sample set. The algorithm automatically selects k=2 within the candidate range to achieve the optimal division of the high-temperature extreme event structure. Points marked with red pentagrams in the figure represent the medoids calculated for each cluster, which are real historical sample points, representing the typicality of the cluster in terms of temperature, relative humidity, and solar shortwave radiation combinations. Points marked with solid stars represent the most representative high-temperature extreme events; their temperature, humidity, and radiation parameters are used to subsequently calculate the material surface temperature through the heat balance equation and serve as the basis for setting the high-temperature operating conditions in the accelerated experiment. As can be seen from the figure, high-temperature extreme events show a significant concentration in the range of high radiation intensity, and the samples corresponding to the central elements have good closeness and representativeness with the surrounding samples, proving that the clustering results can effectively reflect the real extreme weather patterns at the material's service location.

[0041] like Figure 4 As shown, the top 1% of low-temperature extreme samples selected based on the second temperature threshold were plotted as scatter points in the solar shortwave radiation-relative humidity feature space in the same manner, with color representing temperature levels to observe the clustering characteristics of low-temperature extreme events under different humidity and radiation conditions. After Z-score normalization, K-medoids clustering was used to perform cluster analysis on the low-temperature samples. The silhouette coefficient evaluation results led the algorithm to automatically select k=3 to obtain the optimal cluster division. In the figure, the points marked with hollow stars are the central elements of each cluster, all of which are real low-temperature extreme events, representing the typical combination of temperature, humidity, and solar shortwave radiation parameters for that cluster; the samples marked with solid stars are the most representative low-temperature extreme events, and their corresponding temperature and humidity parameters can be used as the basis for setting the environment for low-temperature accelerated experiments. It can also be observed in the figure that most low-temperature extreme samples are concentrated in the lower solar radiation range and form obvious multi-cluster structures with differences in humidity levels, verifying that cluster analysis can reveal the multi-mode characteristics of low-temperature extreme events in terms of humidity and radiation, providing statistical support for the scientific setting of experimental conditions.

[0042] In an optional embodiment, the step of filtering the preprocessed meteorological data according to a preset proportional coefficient to obtain initial meteorological parameters includes: The temperature values ​​in the preprocessed meteorological data are sorted sequentially to obtain a sorted temperature value sequence. Specifically, an ordered foundation is established in the overall data for subsequent quantile selection. The preprocessed temperature values ​​have already eliminated obvious outliers. By arranging them in ascending or descending order, the low-temperature, normal-temperature, and high-temperature ranges can be naturally partitioned within the sequence, providing an accurate index for subsequent location-based statistical quantile determination. In practice, the temperature fields for all time points can be extracted into a one-dimensional array, sorted using conventional sorting algorithms or data analysis libraries, and the index relationship between the sorted temperature values ​​and the original records is preserved. This approach makes the extraction of extreme temperature ranges more intuitive and repeatable, ensuring the rigor and traceability of extreme sample selection.

[0043] Obtain the first and second statistical quantile positions corresponding to the proportionality coefficient; Specifically, the requirement for extreme sample proportions is transformed into specific positional markers within an ordered sequence. The proportion coefficient represents the desired percentage of samples from the high and low temperature ends, for example, 1% for one end. The statistical quantile position is the index point in the sorted temperature value sequence corresponding to these proportions, used to delineate the boundaries of extreme intervals. In implementation, the corresponding sequence number can be directly calculated based on the total number of temperature values ​​and the proportion coefficient, rounded down or up to obtain the integer position, which serves as the boundary between the high and low temperature ends, respectively. In this way, the selection of extreme temperatures does not rely on manually determining specific temperature values, but rather on determining intervals based on the relative positions of the data distribution, improving the comparability and adaptability between different regions and time periods.

[0044] Based on the first statistical quantile position and the second statistical quantile position, a first temperature threshold and a second temperature threshold are determined in the temperature value sequence, wherein the first temperature threshold is greater than the second temperature threshold; Specifically, the abstract quantile positions are further solidified into concrete temperature limits. The first temperature threshold can be considered as the lower limit temperature of the high-temperature end, and the second temperature threshold can be considered as the upper limit temperature of the low-temperature end. These two temperature values ​​are obtained by reading the values ​​at the corresponding positions in the sorted sequence. In this way, the extreme high-temperature range can be defined as all temperature records above the first temperature threshold, and the extreme low-temperature range can be defined as all temperature records below the second temperature threshold. This process is implemented simply by reading the temperature values ​​according to the quantile positions in the sorted array and recording them as threshold parameters. For example, suppose the temperature value sequence T is {T1, T2, ... T}. n} The first statistical quantile is 0.99, and the second statistical quantile is 0.01, then the first temperature threshold is... = (T), second temperature threshold = (T). By determining the threshold based on the data's own distribution, the range of extreme samples can be automatically adjusted according to the regional climate characteristics, avoiding the underestimation or overestimation of extreme operating conditions in some areas due to the use of fixed temperature standards.

[0045] Each temperature value in the temperature value sequence is compared with the first temperature threshold and the second temperature threshold to obtain the comparison result; Specifically, comparing each temperature value in the temperature value sequence with the first temperature threshold and the second temperature threshold to obtain the comparison result is the process of determining the extreme nature of each temperature record. The comparison result can be represented as one of three categories: above the first temperature threshold, below the second temperature threshold, or in between, used to mark which records belong to extreme high temperatures, which belong to extreme low temperatures, and which are normal operating conditions. In implementation, when traversing the sorted temperature sequence or the original temperature records, an interval judgment can be performed on each temperature value, and the judgment result can be attached to the corresponding record in the form of a flag or category label. Through this classification labeling, when screening extreme samples later, data only needs to be extracted according to the flag, without repeating the threshold comparison. It also facilitates statistical analysis of the number and distribution of extreme samples, and helps to verify whether the proportional coefficient and threshold settings are reasonable.

[0046] Based on the comparison results, each temperature value in the temperature value sequence is filtered to obtain the filtered target temperature value. Specifically, based on the comparison results, records classified as extreme high and low temperatures are extracted to form an extreme temperature sample set. The target temperature value set includes records that are statistically located at both ends of the temperature distribution, representing the most severe temperature conditions the material may experience in its service area. In implementation, based on the flags generated in the previous step, all temperature entries marked as being above a first temperature threshold or below a second temperature threshold are selected, and their corresponding time indices or record numbers are retained. Through this filtering method, subsequent cluster analysis will focus on pattern recognition of extreme temperature conditions, reducing interference from general operating conditions, thus making the finally extracted target meteorological parameters more focused on the temperature range that has the greatest impact on material performance.

[0047] Based on the target temperature value, the corresponding target humidity value and target solar radiation value are selected from the preprocessed meteorological data to obtain the initial meteorological parameters.

[0048] Specifically, other key meteorological elements are supplemented to the extreme temperature records on the timeline. Each target temperature value corresponds to a specific moment in the historical meteorological record, at which time information such as humidity and shortwave solar radiation also exists. These corresponding humidity and radiation values ​​are extracted together to form an initial meteorological parameter sample containing the three elements of temperature, humidity, and radiation. In implementation, the timestamp or index of the target temperature record can be used to perform a correlation search in the preprocessed multidimensional meteorological data table, merging the humidity and radiation fields at the same time point into the same record to form a unified feature vector. The initial meteorological parameters obtained in this way not only reflect the extreme temperature itself, but also retain the humidity background and radiation level at the time the temperature occurred, providing a complete description of the extreme conditions for subsequent multidimensional clustering analysis. This helps to consider the combined effects of temperature, humidity, and solar radiation on materials in the experimental environment setting.

[0049] In an optional embodiment, evaluating the number of candidate clusters in a preset set of candidate cluster numbers and determining the target number of clusters based on the evaluation results includes: Based on the total number of meteorological data records in the standardized meteorological parameters, and combined with the preset first cluster number and second cluster number, the candidate cluster number set is determined; Specifically, a reasonable search range is defined for the number of clusters under the constraint of sample size. The total number of meteorological data records can be understood as the number of sample entries participating in the extreme condition analysis. The first and second cluster numbers correspond to the minimum and maximum cluster numbers, respectively, for example, from 2 to 6 clusters. This ensures that at least different types of extreme conditions can be distinguished, while avoiding the number of clusters being too large than the sample size, resulting in too few samples per cluster. In implementation, the feasible upper limit of the maximum number of clusters can be determined first based on the total number of records, and then combined with the preset first and second cluster numbers to generate a discrete set of candidate cluster numbers. This approach ensures that subsequent cluster quality evaluation is only performed within a statistically reasonable range of cluster numbers, improving algorithm efficiency while avoiding the selection of cluster numbers that are difficult to interpret in engineering or that are not supported by sufficient samples.

[0050] Based on the number of each candidate cluster in the candidate cluster set, pre-cluster analysis is performed on the standardized meteorological parameters to obtain the pre-cluster results corresponding to each candidate cluster number; Specifically, the data structure is tentatively partitioned under different clustering number assumptions. Standardized meteorological parameters are multidimensional samples that have been unified in dimensions and scale. Each candidate clustering number corresponds to a "preset number of categories" partitioning scheme. The purpose of pre-clustering analysis is not to directly obtain the final extreme operating conditions, but to observe how the samples are clustered under different numbers of categories. In implementation, the K-medoids clustering process can be called once for each candidate clustering number, performing several rounds of iteration to obtain the category label and corresponding cluster center of each meteorological data record, forming a set of pre-clustering results. Through this multi-clustering number pre-simulation method, complete cluster partitioning information can be provided for subsequent clustering quality assessment, allowing the selection of the target clustering number to be based on real clustering behavior rather than relying on empirical judgment.

[0051] Based on the pre-clustering results, the silhouette coefficient of each meteorological data record is calculated, and the silhouette coefficients of all meteorological data records under the same candidate cluster number are statistically analyzed to obtain the silhouette coefficient evaluation value corresponding to each candidate cluster number. Specifically, in quantifying the differences in clustering performance under different cluster number configurations, the silhouette coefficient is an indicator of sample clustering quality, taking into account both intra-cluster compactness and inter-cluster separation. A value closer to 1 indicates a more suitable sample for the current cluster, while a value close to 0 or negative indicates poor partitioning performance. In practice, based on each pre-clustering result, the average distance of each meteorological data record within its current cluster and its average distance to the nearest other clusters can be calculated. The silhouette coefficient is then obtained using a standard formula. Finally, the average or median of all silhouette coefficients under the same candidate cluster number is calculated, and this statistical value is used as the silhouette coefficient evaluation value for that cluster number. This approach compresses the complex clustering structure into a comparable quantitative indicator, allowing the quality of different cluster numbers to be judged on a unified scale, helping to avoid subjective bias caused by visually observing the cluster distribution shape.

[0052] The contour coefficient evaluation values ​​are compared, and the maximum contour coefficient evaluation value is taken as the target contour coefficient evaluation value based on the comparison results. Specifically, the configuration with the best clustering quality is selected from all candidate cluster numbers. The silhouette coefficient evaluation values ​​corresponding to each candidate cluster number constitute a set of comparable scores. A higher value means that the overall cluster structure is clearer, the samples within the cluster are more concentrated, and the boundaries between clusters are more obvious under that cluster number. In implementation, a simple maximum search can be performed on this set of evaluation values, and the highest value is determined as the target silhouette coefficient evaluation value, while the corresponding cluster number is recorded for later use. Through this selection method based on the maximum silhouette coefficient, the determination of the target cluster number no longer relies on subjective experience, but is driven by the clustering behavior of the data itself. This helps to match the final number of extreme condition categories with the inherent structure of actual meteorological data, thereby improving the rationality and stability of the target meteorological parameter division.

[0053] The number of candidate clusters corresponding to the target contour coefficient evaluation value is obtained as the initial number of clusters, and the target contour coefficient evaluation value is compared with a preset evaluation threshold. Specifically, after scoring the effects of different cluster numbers, a candidate optimal cluster number is given, and it is determined whether the cluster quality reaches an acceptable level. The target silhouette coefficient evaluation value is the score with the largest silhouette coefficient evaluation value among all candidate cluster numbers. The corresponding candidate cluster number is the number of categories that perform best under data-driven principles, and the initial cluster number is a provisional adoption of this candidate value. The evaluation threshold can be understood as the minimum acceptable line for cluster quality. For example, when the silhouette coefficient is lower than a certain value, the boundaries between clusters are considered unclear. In implementation, the candidate cluster number corresponding to the maximum silhouette coefficient can be recorded first and used as the initial cluster number. At the same time, the maximum silhouette coefficient is compared with the pre-set threshold. Through this step, a quality threshold can be added while respecting the data adaptive results, preventing the mechanical adoption of the cluster number corresponding to the maximum value when the overall clustering effect is poor, which helps to improve the robustness of the selection of the target cluster number.

[0054] If the target contour coefficient evaluation value is greater than the evaluation threshold, then the initial cluster number is used as the target cluster number; Specifically, after confirming that the clustering effect reaches the expected quality level, the optimal number of clusters driven by data is formally adopted. The target number of clusters is the parameter used in subsequent formal clustering analysis. A number greater than the threshold indicates that, at this initial number of clusters, there is good separation and intra-cluster consistency among various extreme operating conditions, and the cluster structure is sufficiently clear. In implementation, when the comparison result is greater than the target number, no further adjustment is needed; the initial number of clusters is directly written into the algorithm configuration for subsequent K-medoids clustering of standardized meteorological parameters. In this way, the classification of extreme operating conditions is derived from objective data analysis and satisfies quality constraints. This ensures the granularity of operating condition classification while avoiding excessive splitting or merging, thus more accurately characterizing different extreme environmental patterns.

[0055] If the target contour coefficient evaluation value is less than or equal to the evaluation threshold, then the preset number of clusters is used as the target number of clusters.

[0056] Specifically, when the overall clustering quality is weak, a pre-planned conservative number of clusters is used. The preset number of clusters is usually a fixed value specified based on engineering experience and application requirements. For example, two clusters are used to distinguish between high-temperature and low-temperature extreme conditions, or a few clusters are used to simplify the analysis. If the target profile coefficient evaluation value is not met, it means that under the current data conditions, it is difficult to form a particularly clear multi-class structure regardless of which candidate cluster number is used. Continuing to rely on the configuration with the maximum evaluation value may only result in a barely acceptable division of noise. In implementation, when the comparison result is less than or equal to, the initial number of clusters is ignored, and the preset number of clusters is written into the target number of clusters parameter. This fallback mechanism can still output stable and easily interpretable condition classification results even when the sample size is insufficient, the data structure is ambiguous, or the differences between extreme conditions themselves are limited. It avoids frequent fluctuations in the number of clusters due to small changes in the evaluation value, and improves the robustness and engineering usability of the entire method under complex real-world data.

[0057] In an optional embodiment, the step of calculating the environmental parameters for the materials acceleration experiment based on the target meteorological parameters to obtain the target experimental environmental parameters includes: Based on the target meteorological parameters, obtain the target humidity, target air temperature, and target solar shortwave radiation intensity; Specifically, the extreme operating condition center points obtained from the previous clustering are broken down into several key boundary conditions required for subsequent calculations. Target meteorological parameters can be understood as a combination of information representing a certain type of extreme weather, including elements such as air temperature, relative humidity, and the intensity of solar shortwave radiation received by the ground surface or component surface under that operating condition. Here, humidity, air temperature, and solar shortwave radiation intensity are extracted separately as the basic inputs describing the external environmental heat and humidity load. In implementation, a structured record can be established for each set of target meteorological parameters, and the corresponding temperature, humidity, and radiation values ​​can be directly read by field name, or extracted sequentially by operating condition number when multiple operating conditions exist. This approach ensures that subsequent calculations are always based on meteorological conditions that statistically represent the extreme environment of the service area, rather than using uniform standard values ​​unrelated to specific regions. This ensures that the experimental setup is based on external meteorological parameters that match real operating conditions from the outset.

[0058] Obtain the preset convective heat transfer coefficient, material absorptivity, and material emissivity corresponding to the material to be tested; Specifically, in addition to environmental parameters, the inherent thermal properties of the material are introduced. The convective heat transfer coefficient describes the material's ability to exchange heat with the surrounding air via convection, and is typically related to factors such as wind speed and surface morphology. The material absorptivity characterizes the proportion of short-wave solar radiation absorbed by the material surface, determining how much radiant energy is converted into surface heat. The material emissivity describes the efficiency of heat dissipation from the material surface in the form of long-wave radiation. These parameters can be determined experimentally, specified in material specifications, or provided by relevant standards, or typical values ​​can be preset for different material types. By clarifying these material parameters before calculation, subsequent heat balance analysis is no longer limited to environmental meteorological conditions, but can reflect the actual heating and cooling behavior of the material under specific surface properties, making the final experimental temperature setpoint material-specific rather than a one-size-fits-all copy of the environmental temperature.

[0059] Thermal balance calculations are performed on the target air temperature, target solar shortwave radiation intensity, convective heat transfer coefficient, material absorptivity, and material emissivity to obtain the surface temperature of the material under test. Specifically, by unifying external meteorological conditions and material thermal properties within a single energy conservation framework, the equivalent temperature conditions truly acting on the material are obtained. Thermal balance calculations can be understood as constructing an energy balance relationship between solar shortwave radiation absorption, convective heat transfer on the material surface, and radiative heat dissipation. Under steady-state or quasi-steady-state assumptions, the surface temperature at which energy input equals output is determined. By substituting the target air temperature and solar radiation intensity into the corresponding conversion expressions, and using the convective heat transfer coefficient, absorptivity, and emissivity to weight the fluxes, the material surface temperature corresponding to this extreme condition can be obtained numerically. In practical implementation, a simple one-dimensional steady-state thermal balance model can be used, with software or programs automatically performing numerical iterations. Through this thermal balance calculation, the temperature set in the laboratory is the material surface temperature equivalent to the actual service extreme conditions in terms of thermal load, thus significantly improving the fit between the accelerated experimental temperature setting and the real service thermal environment.

[0060] Based on the target humidity and the surface temperature, the accelerated experimental environment parameters are set to obtain the target experimental environment parameters.

[0061] Specifically, after completing the physical equivalence conversion, the calculation results are applied to the control parameters that the experimental equipment can execute. The target humidity is directly derived from the target meteorological parameters, reflecting the humidity state of the surrounding air under the material's extreme operating conditions. The surface temperature is the equivalent surface temperature of the material obtained from the thermal balance calculation. Together, they constitute the key environmental conditions that need to be simulated in the experiment. When setting the experimental environmental parameters, the surface temperature can be used as the target value of the air temperature inside the constant temperature chamber or environmental chamber. If necessary, the actual surface temperature of the material can be adjusted in conjunction with the radiation source to make it close to the calculated result. At the same time, the relative humidity value corresponding to the target humidity is input into the humidification and dehumidification control system to form a temperature and humidity linkage control strategy. The final target experimental environmental parameters can be directly used to develop experimental programs and set equipment, so that the temperature and humidity conditions in the accelerated experiment are consistent with the extreme operating conditions of the material's service area in both statistical and thermodynamic terms, thereby improving the reliability of the accelerated experiment results in predicting actual service life and failure risk.

[0062] In an optional embodiment, the step of performing thermal balance calculations on the target air temperature, target solar shortwave radiation intensity, convective heat transfer coefficient, material absorptivity, and material emissivity to obtain the surface temperature of the material under test includes: Obtain a pre-defined heat balance equation to characterize the energy balance relationship between solar shortwave radiation absorption, convective heat transfer, and surface radiative heat transfer; Specifically, the pre-defined heat balance equation can be understood as the energy balance ledger of the material surface. One side represents the heat flux received after the material absorbs solar shortwave radiation, while the other side represents the heat flux lost through convective heat transfer with the air and radiative heat dissipation from the material itself. Under steady-state or quasi-steady-state conditions, the sum of these two should be equal. This equation is typically derived based on classical heat transfer models and includes terms related to solar shortwave radiation intensity, convective heat transfer coefficient, material absorptivity, material emissivity, and the surface temperature to be determined. During implementation, the one-dimensional steady-state heat balance model or a appropriately simplified energy balance expression can be determined during the method design phase, taking into account the material application scenario and engineering experience. This simplified expression is then fixed as the pre-defined heat balance equation for subsequent numerical calculations. By pre-defining this equation, each subsequent temperature solution follows the same energy conservation relationship, ensuring a consistent physical basis for surface temperature calculations under different operating conditions. This helps guarantee the rigor and comparability of the accelerated experimental environment settings in terms of equivalent thermal loads.

[0063] The amount of solar shortwave radiation absorbed is calculated based on the target solar shortwave radiation intensity and the material absorptivity. Specifically, solar shortwave radiation absorption quantifies the actual heat flux received by a material surface from solar radiation on the energy input side. The target solar shortwave radiation intensity is derived from target meteorological parameters, representing the solar shortwave radiation energy received per unit area per unit time. The material absorptivity reflects the proportion of incident radiation absorbed by the material surface. Multiplying the two yields the radiation absorption per unit area. In practice, the target solar shortwave radiation intensity can be converted to a uniform power density unit, and multiplied by the material absorptivity to obtain a numerical heat flux term, which is then written as a fixed input into the heat balance equation. The radiation absorption calculated in this way considers both the regional differences in environmental radiation intensity and the influence of the optical properties of the material surface. Compared to simply using radiation intensity, it is closer to the actual energy input experienced by the material surface, providing a reliable energy source term for accurately solving the surface temperature.

[0064] Calculate the convective heat transfer based on the target air temperature and the convective heat transfer coefficient; Specifically, convective heat transfer characterizes the heat transferred between a material surface and the surrounding air via convection on the energy exchange side. The target air temperature represents the thermal state of the ambient air mass, while the convective heat transfer coefficient, which integrates factors such as wind speed, surface roughness, and flow state, determines the heat transfer capacity per unit area per unit temperature difference. In heat balance analysis, convective heat transfer is usually proportional to the difference between the surface temperature and the air temperature, and is expressed as the product of the convective heat transfer coefficient and this temperature difference. In implementation, an expression for convective heat transfer can be established in the calculation module, with the target air temperature as a known quantity and the surface temperature as an unknown quantity retained in the formula. Together with the convective heat transfer coefficient, this constitutes the convection term, forming the heat flux component dependent on the surface temperature. By introducing this convective heat transfer, the model can reflect the impact of wind speed changes, installation location, etc., on the material's cooling efficiency, making the calculated surface temperature closer to the heat dissipation behavior under actual service conditions.

[0065] Based on the emissivity of the material, construct the radiative heat dissipation related to the surface temperature to be solved; Specifically, radiative heat dissipation refers to the portion of heat lost by a material to the outside world through its own infrared radiation on the energy output side. Material emissivity describes the material's surface radiation capacity relative to an ideal blackbody; a higher value indicates stronger radiative heat dissipation. Theoretically, radiative heat dissipation is related to the fourth power of the surface temperature and is a key parameter characterizing important heat dissipation channels under high-temperature conditions. In practice, based on thermal radiation theory, a radiative heat dissipation expression incorporating surface temperature can be constructed using material emissivity as a coefficient. This expression is then included as a term in the heat balance equation, working together with solar radiation absorption and convective heat transfer to solve the energy balance problem. By explicitly incorporating radiative heat dissipation into the model, surface temperature calculation no longer relies solely on convective heat transfer paths. This allows for a more accurate reflection of the material's ability to release heat through radiation under extreme high-temperature conditions, resulting in a more reasonable equivalent temperature setting and avoiding underestimation or overestimation of the material's temperature response in real-world environments.

[0066] Substituting the solar shortwave radiation absorption, the convective heat transfer, and the radiative heat dissipation into the heat balance equation, the surface temperature that satisfies the energy balance condition is calculated.

[0067] Specifically, by substituting solar radiation absorption as the energy input and convective heat transfer and radiative heat dissipation as the energy output into a pre-defined heat balance equation, a set of equations concerning surface temperature can be obtained. Under steady-state assumptions, the surface temperature value that equalizes energy input and output can be solved numerically or analytically. This can be achieved using iterative calculations, nonlinear equation solvers, or simplified linearization methods. Based on given target air temperature, solar radiation intensity, and material thermal parameters, the energy balance point is automatically found. Through this solution process, the final surface temperature is no longer a subjectively defined empirical value, but rather an equivalent temperature derived by comprehensively considering environmental radiation, air convection, and material radiative heat dissipation behavior. This temperature can be directly used as the temperature setpoint in accelerated experiments, ensuring that the experimental environment, in terms of thermal load, is highly consistent with the extreme operating conditions of the material's actual service area, thereby improving the reliability and engineering interpretability of the experimental results.

[0068] Example 2 Please see Figure 5 This invention provides a device for determining environmental parameters in accelerated material experiments, the device comprising: The historical meteorological data acquisition module is used to acquire historical meteorological data according to the preset time period and the service location of the material to be tested; The preprocessing module is used to preprocess the historical meteorological data to obtain preprocessed meteorological data; The clustering analysis module is used to perform clustering analysis on the preprocessed meteorological data to obtain target meteorological parameters for characterizing extreme working conditions; The experimental environment parameter calculation module is used to calculate the environmental parameters for the material acceleration experiment based on the target meteorological parameters, and obtain the target experimental environment parameters.

[0069] It should be noted that each module and unit in the material acceleration experimental environment parameter determination device in this embodiment corresponds one-to-one with each step in the material acceleration experimental environment parameter determination method in the aforementioned embodiment. Therefore, the specific implementation of this embodiment can refer to the implementation of the aforementioned material acceleration experimental environment parameter determination method, and will not be repeated here.

[0070] Example 3 In addition, combined Figure 1 The method for determining the environmental parameters of accelerated material experiments described in this embodiment of the invention can be implemented using a multifunctional environmental simulation device. Figure 6 A schematic diagram of the hardware structure of the multifunctional environment simulation device provided in an embodiment of the present invention is shown.

[0071] A multi-functional environment simulation device may include a processor and a memory storing computer program instructions.

[0072] Specifically, the processor may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement embodiments of the present invention.

[0073] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0074] Computer-readable media include both permanent and non-permanent, removable and non-removable media, which can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient media, such as modulated communication signals and carrier waves.

[0075] The processor reads and executes computer program instructions stored in the memory to implement any of the methods for determining the experimental environment parameters of materials acceleration experiments in the above embodiments.

[0076] In one example, the multi-functional environment simulation device may also include a communication interface and a bus. For example, Figure 6 As shown, the processor 401, memory 402, and communication interface 403 are connected through bus 410 and complete communication with each other.

[0077] The communication interface is mainly used to enable communication between various modules, devices, units and / or equipment in the embodiments of the present invention.

[0078] A bus, including hardware, software, or both, couples components of a multi-functional environment simulation device together. For example, and not limitingly, a bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a Microchannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or other suitable buses, or combinations of two or more of these. Where appropriate, a bus may include one or more buses. While specific buses are described and illustrated in embodiments of the invention, the invention contemplates any suitable bus or interconnect.

[0079] Example 4 Furthermore, in conjunction with the methods for determining material acceleration experimental environment parameters in the above embodiments, this invention can be implemented using a computer-readable storage medium. This computer-readable storage medium stores computer program instructions; when executed by a processor, these computer program instructions implement any of the material acceleration experimental environment parameter determination methods in the above embodiments.

[0080] In summary, the embodiments of the present invention provide a method, apparatus, and environmental simulation equipment for determining environmental parameters of accelerated material experiments.

[0081] It should be clarified that the present invention is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present invention is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of the present invention.

[0082] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0083] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0084] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1The function specified in one or more boxes.

[0085] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0086] It should also be noted that the exemplary embodiments mentioned in this invention describe methods or systems based on a series of steps or apparatus. However, this invention is not limited to the order of the steps described above; that is, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0087] The above description is merely a specific embodiment of the present invention. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, modules, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. It should be understood that the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the protection scope of the present invention.

Claims

1. A method for determining environmental parameters in accelerated materials experiments, characterized in that, The method includes: Historical meteorological data are obtained based on the preset time limit and the service location of the material to be tested; The historical meteorological data is preprocessed to obtain preprocessed meteorological data; Cluster analysis is performed on the preprocessed meteorological data to obtain target meteorological parameters for characterizing extreme working conditions; Based on the target meteorological parameters, the environmental parameters for the material acceleration experiment are calculated to obtain the target experimental environmental parameters; The calculation of environmental parameters for the materials acceleration experiment based on the target meteorological parameters, resulting in the target experimental environmental parameters, includes: Based on the target meteorological parameters, obtain the target humidity, target air temperature, and target solar shortwave radiation intensity; Obtain the preset convective heat transfer coefficient, material absorptivity, and material emissivity corresponding to the material to be tested; Thermal balance calculations are performed on the target air temperature, target solar shortwave radiation intensity, convective heat transfer coefficient, material absorptivity, and material emissivity to obtain the surface temperature of the material under test. Based on the target humidity and the surface temperature, the accelerated experimental environment parameters are set to obtain the target experimental environment parameters.

2. The material-accelerated experimental environment parameter determination method according to claim 1, characterized by, The process of acquiring historical meteorological data based on a preset timeframe and the service location of the material to be tested includes: Based on the service location, initial meteorological data corresponding to the service location is obtained from a preset meteorological database; Based on the stated time period, the initial meteorological data is filtered, and the meteorological data within the stated time period is selected as the historical meteorological data.

3. The material-accelerated experimental environment parameter determination method of claim 1, wherein, The preprocessing of the historical meteorological data to obtain preprocessed meteorological data includes: Based on preset meteorological parameter thresholds, the historical meteorological data is filtered by thresholds to obtain the first meteorological data; The first meteorological data is subjected to an internal consistency correlation check to obtain the second meteorological data; The second meteorological data is subjected to outlier detection and removal to obtain the preprocessed meteorological data.

4. The material accelerated laboratory environmental parameter determination method of claim 1, wherein, The cluster analysis performed on the preprocessed meteorological data yields target meteorological parameters for characterizing extreme operating conditions, including: The preprocessed meteorological data is filtered according to a preset ratio coefficient to obtain initial meteorological parameters; The initial meteorological parameters are standardized to obtain standardized meteorological parameters; The number of candidate clusters in the preset set of candidate cluster numbers is evaluated, and the target number of clusters is determined based on the evaluation results. Based on the target number of clusters, and combined with the preset K-medoids clustering algorithm, the standardized meteorological parameters are clustered to obtain the target meteorological parameters.

5. The material-accelerated experimental environment parameter determination method according to claim 4, characterized by, The step of filtering the preprocessed meteorological data according to a preset ratio coefficient to obtain initial meteorological parameters includes: The temperature values ​​in the preprocessed meteorological data are sorted sequentially to obtain a sorted temperature value sequence. Obtain the first and second statistical quantile positions corresponding to the proportionality coefficient; Based on the first statistical quantile position and the second statistical quantile position, a first temperature threshold and a second temperature threshold are determined in the temperature value sequence, wherein the first temperature threshold is greater than the second temperature threshold; Each temperature value in the temperature value sequence is compared with the first temperature threshold and the second temperature threshold to obtain the comparison result; Based on the comparison results, each temperature value in the temperature value sequence is filtered to obtain the filtered target temperature value. Based on the target temperature value, the corresponding target humidity value and target solar radiation value are selected from the preprocessed meteorological data to obtain the initial meteorological parameters.

6. The material-accelerated experimental environment parameter determination method according to claim 4, wherein, The process of evaluating each candidate cluster number in the preset candidate cluster number set and determining the target cluster number based on the evaluation results includes: Based on the total number of meteorological data records in the standardized meteorological parameters, and combined with the preset first cluster number and second cluster number, the candidate cluster number set is determined; Based on the number of each candidate cluster in the candidate cluster set, pre-cluster analysis is performed on the standardized meteorological parameters to obtain the pre-cluster results corresponding to each candidate cluster number; Based on the pre-clustering results, the silhouette coefficient of each meteorological data record is calculated, and the silhouette coefficients of all meteorological data records under the same candidate cluster number are statistically analyzed to obtain the silhouette coefficient evaluation value corresponding to each candidate cluster number. The contour coefficient evaluation values ​​are compared, and the maximum contour coefficient evaluation value is taken as the target contour coefficient evaluation value based on the comparison results. The number of candidate clusters corresponding to the target contour coefficient evaluation value is obtained as the initial number of clusters, and the target contour coefficient evaluation value is compared with a preset evaluation threshold. If the target contour coefficient evaluation value is greater than the evaluation threshold, then the initial cluster number is used as the target cluster number; If the target contour coefficient evaluation value is less than or equal to the evaluation threshold, then the preset number of clusters is used as the target number of clusters.

7. The material accelerated laboratory environmental parameter determination method of claim 1, wherein, The process of performing thermal balance calculations on the target air temperature, target solar shortwave radiation intensity, convective heat transfer coefficient, material absorptivity, and material emissivity to obtain the surface temperature of the material under test includes: Obtain a pre-defined heat balance equation to characterize the energy balance relationship between solar shortwave radiation absorption, convective heat transfer, and surface radiative heat transfer; The amount of solar shortwave radiation absorbed is calculated based on the target solar shortwave radiation intensity and the material absorptivity. Calculate the convective heat transfer based on the target air temperature and the convective heat transfer coefficient; Based on the emissivity of the material, construct the radiative heat dissipation related to the surface temperature to be solved; Substituting the solar shortwave radiation absorption, the convective heat transfer, and the radiative heat dissipation into the heat balance equation, the surface temperature that satisfies the energy balance condition is calculated.

8. A material acceleration experiment environment parameter determination apparatus, characterized by, The device includes: The historical meteorological data acquisition module is used to acquire historical meteorological data according to the preset time period and the service location of the material to be tested; The preprocessing module is used to preprocess the historical meteorological data to obtain preprocessed meteorological data; The clustering analysis module is used to perform clustering analysis on the preprocessed meteorological data to obtain target meteorological parameters for characterizing extreme working conditions; The experimental environment parameter calculation module is used to calculate the environmental parameters for the material acceleration experiment based on the target meteorological parameters, and obtain the target experimental environment parameters. The calculation of environmental parameters for the materials acceleration experiment based on the target meteorological parameters, resulting in the target experimental environmental parameters, includes: Based on the target meteorological parameters, obtain the target humidity, target air temperature, and target solar shortwave radiation intensity; Obtain the preset convective heat transfer coefficient, material absorptivity, and material emissivity corresponding to the material to be tested; Thermal balance calculations are performed on the target air temperature, target solar shortwave radiation intensity, convective heat transfer coefficient, material absorptivity, and material emissivity to obtain the surface temperature of the material under test. Based on the target humidity and the surface temperature, the accelerated experimental environment parameters are set to obtain the target experimental environment parameters.

9. A multi-functional environmental simulation apparatus, characterized by comprising: include: At least one processor, at least one memory, and computer program instructions stored in the memory, which, when executed by the processor, implement the method as described in any one of claims 1-7.

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