A multi-material environment coupling data construction method for corrosion prediction

Abstract

The present application belongs to the field of material corrosion data engineering and corrosion prediction technology, and specifically relates to a multi-material environment coupling data construction method for corrosion prediction, which synchronously carries out multi-material corrosion experiments under unified controllable environmental conditions by constructing a controllable environmental test platform, collects equipment operation parameters, and forms a standardized multi-material corrosion data set through accurate data matching and fusion processing, thereby providing high-quality data support for corrosion behavior analysis and prediction model construction. The method follows the technical route of environment platform construction, parameter control, sample layout, experiment implementation, data acquisition, matching and fusion, and standardized construction. The present application has the advantages of realizing the synchronous acquisition of multi-material corrosion data under unified environmental conditions, establishing an accurate matching relationship between corrosion data and operation parameters, forming a standardized, structured and highly comparable corrosion data set, and having strong engineering practicality and promotion prospects.

Description

Technical Field

[0001] This invention belongs to the field of material corrosion data engineering and corrosion prediction technology. Specifically, it relates to a method for constructing multi-material environmental coupled data for corrosion prediction. In particular, it is a method for constructing a standardized and highly comparable corrosion dataset by building a unified and controllable environmental test platform, simultaneously acquiring multiple material corrosion data and equipment operating parameters, and establishing a precise matching relationship between the two. Background Technology

[0002] The corrosion behavior of materials during service is a complex result of the interaction between environmental factors and the intrinsic properties of the materials. Its evolution is influenced by multiple factors, including temperature, relative humidity, concentration of corrosive media, exposure time, and frequency of wet-dry alternation. Accurately obtaining data on the corrosion behavior of materials under different environmental conditions is a crucial foundation for conducting corrosion mechanism research, establishing corrosion prediction models, and developing protection strategies.

[0003] Currently, corrosion data acquisition mainly relies on two methods: accelerated corrosion testing in laboratories and exposure testing in natural environments. Accelerated laboratory testing offers advantages such as controllable environmental conditions, short testing cycles, and good data repeatability; however, the simulated environmental conditions are often idealized and simplified, resulting in deviations from actual service environments. Exposure testing in natural environments can accurately reflect the corrosion behavior of materials under specific conditions, but it has long testing cycles, uncontrollable environmental conditions, and significant data dispersion. More importantly, existing technologies suffer from the following prominent problems in the data acquisition process: First, there is a lack of a unified acquisition environment for multi-material corrosion data. Corrosion studies of different materials are often conducted independently at different times, locations, and under different environmental conditions, resulting in a lack of comparability of the acquired corrosion data. For example, corrosion data for aluminum alloys may come from coastal exposure tests, while corrosion data for carbon steel may come from accelerated laboratory tests. The two cannot be compared and analyzed under the same environmental benchmark, making it difficult to support corrosion modeling of multi-material systems.

[0004] Secondly, there is a lack of synchronization between corrosion data and equipment operating parameters. In actual engineering, equipment such as air conditioners continuously collect a large amount of parameter data reflecting environmental conditions and operating status through IoT systems. This data contains key information affecting the corrosion process. However, existing corrosion data acquisition methods are often independent of the equipment operating data acquisition system, and there is a lack of time synchronization and condition matching between the two, resulting in the inability to establish an effective correlation between valuable operating data and corrosion behavior.

[0005] Third, the data formats and standards are not uniform. Corrosion data from different sources vary significantly in terms of acquisition methods, representation, and precision scale. The lack of standardized processing procedures and unified storage structures makes it difficult to form high-quality standardized datasets, which limits the usability of the data in the construction of corrosion prediction models.

[0006] In summary, there is an urgent need to establish a multi-material environmental coupling data construction method for corrosion prediction. This method would acquire multi-material corrosion data synchronously under unified and controllable environmental conditions, establish a precise matching relationship with equipment operating parameters, and form a standardized and highly comparable corrosion dataset, thus providing a data foundation for highly reliable corrosion analysis and prediction. Summary of the Invention

[0007] The purpose of this invention is to overcome the technical shortcomings of existing technologies, such as inconsistent acquisition environments for multi-material corrosion data, lack of synchronous matching between corrosion data and operating parameters, and inconsistent data standards. This invention provides a method for constructing multi-material environmental coupled data for corrosion prediction. This method constructs a controllable environmental testing platform to simultaneously conduct multi-material corrosion experiments under unified and controllable environmental conditions, while simultaneously collecting equipment operating parameters. Through precise data matching and fusion processing, a standardized multi-material corrosion dataset is formed, providing high-quality data support for the construction of corrosion behavior analysis and prediction models.

[0008] To achieve the above-mentioned objectives, this invention provides a method for constructing multi-material environmental coupling data for corrosion prediction. This method follows a technical route encompassing environmental platform construction, parameter control, sample placement, experimental implementation, data acquisition, matching and fusion, and standardized construction, specifically including the following steps: S1: Experimental Environment Setup An adjustable environmental testing platform should be constructed to simulate the corrosive environment in which the materials are subjected. This platform should possess independent controllability of multiple parameters, enabling precise control of key environmental factors affecting the corrosion behavior of the materials, including but not limited to temperature, relative humidity, concentration of corrosive media (such as salt spray concentration and sulfur dioxide concentration), wet-dry cycle, and ultraviolet radiation intensity. The platform design should ensure the stability and uniformity of environmental parameters, providing consistent background conditions for simultaneous corrosion experiments on multiple materials.

[0009] S2: Environmental parameter control Based on the corrosive environmental characteristics of the target application scenario, the environmental parameters in the environmental testing platform are set and precisely controlled. These environmental parameters include at least the following dimensions: Temperature control: Set the temperature range and change mode (constant temperature or periodic change). Humidity control: Set the relative humidity level and the dry / wet cycle; Media condition control: Set the type, concentration, and application method of the corrosive medium (such as salt spray deposition rate and gas concentration). Action time control: Set the total experiment duration and sampling time nodes.

[0010] By precisely controlling the above parameters, stable and repeatable environmental conditions are formed, serving as a unified benchmark for multi-material corrosion experiments.

[0011] S3: Multi-material sample setup Samples of various engineering materials are simultaneously placed in the environmental testing platform to ensure that all samples are subjected to corrosion tests under identical environmental conditions. The materials cover commonly used engineering material categories in air conditioning systems and related equipment, including at least one or more of the following categories: Copper and copper alloys: used in air conditioning pipes, connectors, etc.; Aluminum and aluminum alloys: used for heat exchanger fins, shells, etc.; Steel and stainless steel: used for structural supports, fasteners, etc. Surface coating materials: Various organic or inorganic coatings used for the protection of heat exchangers and their outer shells.

[0012] When setting up samples, the spatial uniformity of the samples within the test platform should be considered to avoid local environmental deviations introduced by positional differences. Multiple parallel samples can be set up for each material category to assess the dispersion of the data.

[0013] S4: Corrosion Test Implementation Under set environmental conditions, corrosion experiments were simultaneously conducted on multiple material samples. During the experiment, the environmental testing platform operated automatically according to preset parameter curves, simulating the corrosion process under target environmental conditions. The experiment can be designed as a continuous or periodic operation mode to simulate corrosion mechanisms under different service conditions. The experimental cycle can be set according to the material corrosion rate and target prediction requirements, ranging from several days to several months.

[0014] S5: Corrosion Data Acquisition Corrosion data for each material sample was collected at preset time points during and after the experiment. The corrosion data included at least one or more of the following categories: Corrosion rate: quantified by mass loss method (weight loss per unit area per unit time) or thickness loss method (ratio of corrosion depth to time); Corrosion morphology: Macroscopic and microscopic morphological features of the corroded surface are obtained by optical microscopy, scanning electron microscopy or digital imaging, including pitting density, corrosion area ratio, crack distribution, etc. Corrosion grade: Based on the degree and morphology of corrosion, it is classified into qualitative or semi-quantitative grades such as slight corrosion, moderate corrosion, and severe corrosion according to preset standards.

[0015] Corrosion data collection should maintain synchronization across time points to ensure comparability of corrosion data for different materials under the same time reference.

[0016] S6: Synchronous Acquisition of Operating Parameters While the corrosion experiment is being conducted, operating parameter data corresponding to the environmental conditions are collected using equipment or systems. These operating parameters reflect the operating characteristics of the actual equipment under similar environmental conditions and include at least one or more of the following categories: Environmental parameters: temperature, relative humidity, concentration of corrosive media, atmospheric pressure, etc. Operating parameters: equipment running time, number of start-stop cycles, load rate, operating frequency, etc. Heat exchange process parameters: inlet and outlet air temperature difference, refrigerant pressure and temperature, heat exchange efficiency, etc. Electrical and control parameters: input power, current, voltage, control valve opening, etc.

[0017] The acquisition frequency of operating parameters should be matched with the corrosion data acquisition nodes. A combination of continuous recording and node sampling can be used to ensure data integrity and time alignment.

[0018] S7: Data Matching and Fusion The corrosion data collected in step S5 is precisely matched with the operating parameters collected in step S6 based on time and environmental conditions to form a one-to-one correspondence, and then data fusion processing is performed. The matching and fusion include: Time alignment: Synchronize the time nodes of corrosion data acquisition with the time series of operating parameters to ensure that the environmental conditions corresponding to the corrosion data are precisely consistent with the environmental conditions reflected by the operating parameters in time; Condition matching: For corrosion data obtained in the laboratory, the actual environmental condition parameters are matched with the environmental state parameters in the operating parameters to verify the consistency between the experimental conditions and the actual working conditions. Data fusion: Corrosion data and operating parameters are integrated into a unified data record. Each record contains a timestamp, material identifier, corrosion data value, corresponding operating parameter value and environmental condition parameter value, forming a four-dimensional integrated data structure of "material-environment-corrosion-operation".

[0019] S8: Standardized Dataset Construction The matched and fused data are processed in a unified format and stored in a structured manner to construct a standardized multi-material corrosion dataset. The standardization process includes: Unified data format: Define unified data fields, data types, units and precision to convert data from different sources and in different formats into a standard format; Data Coding and Identification: Each piece of data is assigned a unique identification code, which includes metadata such as material type, experimental batch, environmental condition code, and time information; Data storage structure: A relational database or data lake architecture is adopted to establish a standardized data table structure, which supports efficient data retrieval, querying and export; Data quality labeling: Labeling the source, collection method, and credibility level of data to facilitate subsequent data screening and evaluation.

[0020] The resulting standardized multi-material corrosion dataset can serve as a unified data foundation for corrosion mechanism analysis, corrosion prediction model training, and material selection decisions.

[0021] Advantages of this invention: Compared with existing technologies, the multi-material environmental coupling data construction method for corrosion prediction provided by this invention has the following significant advantages: Achieving synchronous acquisition of corrosion data of multiple materials under unified environmental conditions: This invention constructs an adjustable environmental test platform to conduct corrosion experiments of multiple materials simultaneously under identical environmental conditions, so that the corrosion data of different materials have a unified environmental benchmark, solving the core problem of scattered data sources and lack of comparability of different materials in traditional methods.

[0022] Establishing a precise matching relationship between corrosion data and operating parameters: This invention simultaneously collects equipment operating parameters while corrosion experiments are being conducted, and establishes a correlation between corrosion data and operating parameters through precise time alignment and condition matching. This enables experimental data to be effectively linked with engineering operating data, providing a reliable data foundation for corrosion prediction based on operating data.

[0023] Forming a standardized, structured, and highly comparable corrosion dataset: Through a unified data format, coding identifier, and storage structure, this invention integrates multi-source heterogeneous corrosion data and operating parameters into a standardized dataset, significantly improving the availability, reusability, and traceability of the data, and supporting highly reliable corrosion analysis and modeling.

[0024] Support for the construction of high-precision corrosion prediction models: In the dataset constructed by this invention, corrosion data and operating parameters are precisely matched, and multi-material data are acquired in a unified environment, providing high-quality input-output pairing data for the training of corrosion prediction models, which helps to improve the prediction accuracy and generalization ability of the models.

[0025] It has good engineering application value and scalability: The method of this invention is not only applicable to data construction in laboratory environment, but can also be extended to field exposure test and online monitoring scenarios. It provides a standardized framework for the acquisition of corrosion data with different scales and different precision requirements, and has strong engineering practicality and promotion prospects. Attached Figure Description

[0026] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments: Figure 1 This is a schematic diagram of the overall process of the multi-material environmental coupling data construction method for corrosion prediction described in this invention, which shows the complete technical route from environmental platform construction to standardized dataset output. Figure 2 This is a schematic diagram of the simultaneous arrangement of multiple material samples in an environmental testing platform according to the present invention, showing the arrangement and spatial distribution of various material samples such as copper, aluminum, steel and coatings under unified environmental conditions; Figure 3 This is a schematic diagram of the data matching and fusion process described in this invention, illustrating the process of establishing a one-to-one correspondence between corrosion data and operating parameters through time alignment and condition matching, and integrating them into a unified data record. Detailed Implementation

[0027] The present invention will be further explained below with reference to specific implementation schemes, but it is not limited to the present invention. The structures, proportions, sizes, etc. shown in the accompanying drawings are only used to complement the content disclosed in the specification, so as to enable those skilled in the art to understand and read, and are not intended to limit the conditions under which the present invention can be implemented. Therefore, they have no substantial technical significance. Any modification of the structure, change of the proportion relationship or adjustment of the size, without affecting the effect and purpose that the present invention can produce, should still fall within the scope of the technical content disclosed in the present invention.

[0028] To make the objectives, technical solutions, and advantages of the present invention clearer, the technical solutions of the present invention will be described in detail below with reference to specific embodiments.

[0029] Example 1: Construction of Multi-Material Corrosion Data in Laboratory Environment S1: Experimental Environment Setup A multifunctional environmental test chamber was constructed, equipped with independent temperature control, humidity control, salt spray generation, and data acquisition systems. The chamber has an internal volume of 1 cubic meter, capable of accommodating multiple sample racks to ensure uniform distribution of environmental parameters.

[0030] S2: Environmental parameter control Setting experimental conditions to simulate a high-salt-fog coastal environment: Temperature: 35℃ ± 1℃; Relative humidity: 90% ± 5%; Salt spray concentration: 5% NaCl solution, sedimentation rate 1-2 mL / (80 cm³) 2 ·h); Experimental period: 30 days, with each sampling period lasting 7 days.

[0031] S3: Multi-material sample setup Four types of material samples were simultaneously placed inside the test chamber: Copper material: T2 pure copper, size 50mm×25mm×2mm, 3 parallel samples; Aluminum alloy: 3003 aluminum alloy, dimensions 50mm×25mm×0.3mm, 3 parallel samples; Carbon steel: Q235 carbon steel, dimensions 50mm×25mm×2mm, 3 parallel samples; Coating samples: An epoxy anti-corrosion coating was applied to an aluminum alloy substrate, with dimensions of 50mm×25mm×0.5mm, and 3 parallel samples were prepared.

[0032] All samples are suspended in the middle of the test chamber to ensure that there is no contact between the samples and that they are evenly distributed in space.

[0033] S4: Corrosion Test Implementation Start the environmental test chamber and run it for 30 days according to the preset parameters. During the experiment, the test chamber automatically records the real-time changes in environmental parameters to ensure the stability of experimental conditions.

[0034] S5: Corrosion Data Acquisition Parallel samples of each material were collected on days 7, 14, 21, and 30 for corrosion data acquisition. Corrosion rate: The weight loss method was used. The mass of the sample before and after corrosion was weighed using a precision balance (accuracy 0.1 mg). The relationship between the weight loss per unit area and the corrosion time was calculated to obtain the corrosion rate at each time point. Corrosion morphology: The surface morphology of the samples was photographed using a digital microscope, and the distribution of pitting corrosion and the ratio of corrosion area were recorded; Corrosion grade: The corrosion grade is determined based on the proportion of corrosion area and the depth of pitting corrosion, with reference to a preset standard.

[0035] S6: Synchronous Acquisition of Operating Parameters During the corrosion experiment, simulated operating parameters corresponding to the experimental conditions were collected simultaneously. In this embodiment, real-time environmental data (temperature, humidity, salt spray deposition) and simulated air conditioning operating parameters (the compressor operating time was set to 24 hours / day to simulate continuous operation) inside the test chamber were recorded through a data acquisition system.

[0036] S7: Data Matching and Fusion Corrosion data is matched with operating parameters at specific time points. For example, the corrosion rate data for day 7 is correlated with the average temperature, average humidity, cumulative salt spray deposition, and cumulative runtime from day 1 to day 7 to form a complete data record. Each record includes: time point, material type, corrosion rate, corrosion morphology description, corrosion level, average temperature, average humidity, cumulative salt spray deposition, and cumulative runtime.

[0037] S8: Standardized Dataset Construction The merged data was then organized into a standard dataset using a unified format. The data table structure includes: Data ID (automatically generated unique code), Material Type, Sampling Time (days), Corrosion Rate (mm / year), Corrosion Area Percentage (%), Corrosion Grade, Average Temperature (°C), Average Humidity (%), and Cumulative Salt Spray Amount (mg / cm³). 2 The data includes the cumulative runtime (hours), data source (laboratory), and experimental batch identifier. A total of 12 sets of data were collected (4 materials × 3 sampling time points), forming a usable standardized dataset.

[0038] Example 2: Verification of Fusion of Operating Parameters Based on Example 1, the matching ability of the dataset constructed by the method of the present invention with the actual running data is further verified.

[0039] S1 to S5: Same as above S6: Collect actual air conditioning operating parameters We selected air conditioning equipment actually in operation in a coastal area and collected its operating data for the past year, including: outdoor ambient temperature, outdoor relative humidity, compressor operating time, and system start-up and shutdown frequency. We extracted data from the operating period that most closely resembled laboratory experimental conditions (daily average temperature 32-36℃, daily average humidity 85-95%, continuous operation for 30 days) and calculated the average temperature, average humidity, and cumulative operating time for that period.

[0040] S7: Data Matching and Fusion Corrosion data obtained from the laboratory was correlated and matched with actual air conditioning operating parameters. For example, the corrosion rate obtained from a 30-day corrosion experiment under laboratory conditions was correlated with the average environmental parameters and cumulative operating time over a 30-day actual operation. The matching results showed that the laboratory environmental conditions and the actual operating conditions had good consistency (temperature deviation ±2℃, humidity deviation ±5%).

[0041] S8: Standardized Dataset Construction Laboratory corrosion data was fused with actual operating parameters for the corresponding time periods to form an extended dataset containing both laboratory corrosion data and actual operating parameters. This dataset includes material type, corrosion data, laboratory environmental parameters, and actual operating parameters, providing a training data foundation for subsequent corrosion prediction based on operating parameters.

[0042] Example 3: Data Validation and Application Data consistency verification Consistency verification was performed on the standardized dataset constructed in Example 1. The analysis results show: Under the same environmental conditions, the corrosion rates of the four materials were ranked as follows: carbon steel > aluminum alloy > copper > coating, which is consistent with known corrosion patterns. The corrosion rate changes with experimental time in accordance with the law of corrosion kinetics (faster in the early stage and slower in the later stage). The data dispersion between parallel samples was small (coefficient of variation <15%), indicating that the experimental data had good repeatability.

[0043] Model training applications Using the standardized dataset described above, a corrosion prediction model was trained with operating parameters (temperature, humidity, and runtime) as input and the aluminum alloy corrosion rate as output. The model's goodness of fit R on the training set was [value missing]. 2 The accuracy reached 0.89, with a prediction error of 12.3% on the independent test set, validating the dataset's effective support for corrosion prediction modeling.

[0044] Example 4: Data Extension under Multiple Environmental Conditions To cover a wider range of application scenarios, the method of this invention can be extended to repeated experiments under multiple environmental conditions.

[0045] Parameter settings Three sets of typical environmental conditions were set up: Condition A: Low temperature and low humidity (25℃, 60% RH), simulating a mild inland environment; Condition B: Medium temperature and humidity (30℃, 75% RH), simulating a typical humid environment; Condition C: High temperature and high humidity (35℃, 95% RH), simulating a coastal high humidity environment.

[0046] Experiment Implementation For each set of environmental conditions, the experimental procedure of Example 1 was repeated to obtain corrosion data of the four materials under the three environmental conditions and match them with the corresponding operating parameters.

[0047] Dataset Construction The data from the three sets of experiments were integrated to form an extended standardized dataset covering multiple environmental conditions. This dataset contains 36 corrosion data records for 4 materials × 3 environmental conditions × 3 time points, providing a rich data foundation for corrosion prediction and material selection under multiple environmental conditions.

[0048] Matters not covered in this invention are common knowledge.

[0049] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for constructing multi-material environmental coupling data for corrosion prediction, characterized in that, Includes the following steps: S1: Construct a controllable environmental testing platform to simulate the corrosive environment in which materials are subjected; S2: Set and control the environmental parameters in the environmental test platform, including at least temperature, relative humidity, corrosive media conditions and exposure time; S3: Simultaneously place samples of various engineering materials in the environmental test platform. The materials include at least two of the following: copper and copper alloys, aluminum and aluminum alloys, steel and stainless steel, or surface coating materials. Ensure that all samples are under the same environmental conditions. S4: Under the set environmental conditions, corrosion tests are carried out simultaneously on the deployed multi-material samples; S5: During the corrosion experiment, corrosion data of each material sample is collected according to the preset time nodes. The corrosion data includes corrosion rate, corrosion morphology or corrosion level. S6: While the corrosion experiment is being conducted, the operating parameter data corresponding to the environmental conditions are collected by the equipment or system. The operating parameters include at least one of the following: environmental state parameters, operating condition parameters, heat exchange process parameters, or electrical and control parameters. S7: Accurately match the corrosion data collected in step S5 with the operating parameters collected in step S6 in terms of time and environmental conditions, establish a one-to-one correspondence, and perform data fusion processing to form a fused data structure of "material-environment-corrosion-operation". S8: Perform unified format processing and structured storage on the matched and fused data to construct a standardized multi-material corrosion dataset.

2. The method for constructing multi-material environmental coupling data for corrosion prediction according to claim 1, characterized in that, The adjustable environmental test platform described in step S1 has the ability to independently adjust temperature, relative humidity, concentration of corrosive media, dry-wet alternation cycle and ultraviolet radiation intensity, ensuring the stability and uniformity of environmental parameters.

3. The method for constructing multi-material environmental coupling data for corrosion prediction according to claim 1, characterized in that, The environmental parameter control described in step S2 includes: temperature control (setting the temperature range and change mode), humidity control (setting the relative humidity level and dry-wet alternation cycle), media condition control (setting the type, concentration, and application method of the corrosive medium), and action time control (setting the total experimental duration and sampling time nodes).

4. The method for constructing multi-material environmental coupling data for corrosion prediction according to claim 1, characterized in that, The engineering materials mentioned in step S3 also include organic or inorganic coating materials for the protection of air conditioning heat exchangers; when setting up the samples, multiple parallel samples are set up for each material category, and the spatial uniformity of the samples within the test platform is ensured.

5. The method for constructing multi-material environmental coupling data for corrosion prediction according to claim 1, characterized in that, The corrosion rate mentioned in step S5 is quantified by the mass loss method or the thickness loss method; the corrosion morphology includes pitting density, corrosion area ratio or crack distribution; the corrosion level includes slight corrosion, moderate corrosion or severe corrosion.

6. The method for constructing multi-material environmental coupling data for corrosion prediction according to claim 1, characterized in that, The environmental state parameters mentioned in step S6 include temperature, relative humidity, or concentration of corrosive media; the operating condition parameters include equipment running time, number of start-stop cycles, load rate, or operating frequency; the heat exchange process parameters include inlet and outlet air temperature difference, refrigerant pressure, or refrigerant temperature; and the electrical and control parameters include input power, current, voltage, or control valve opening.

7. The method for constructing multi-material environmental coupling data for corrosion prediction according to claim 1, characterized in that, The data matching and fusion described in step S7 includes: time alignment of the time nodes of corrosion data acquisition with the time series of operating parameters, condition matching to verify the consistency between experimental conditions and actual working conditions, and data fusion of integrating corrosion data and operating parameters into a unified data record.

8. The method for constructing multi-material environmental coupling data for corrosion prediction according to claim 1, characterized in that, The standardization process described in step S8 includes: defining unified data fields, data types, and unified data formats with uniform units and precision; assigning a unique identifier to each data entry; using a relational database or data lake architecture for data storage; and data quality labeling that marks the data source, collection method, and reliability level.

9. The method for constructing multi-material environmental coupling data for corrosion prediction according to claim 1, characterized in that, The method is applicable to laboratory accelerated corrosion experiments and natural environment exposure tests. By repeatedly executing steps S1 to S8 under multiple environmental conditions, an extended standardized dataset covering a variety of environmental conditions can be constructed.

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