Environmental simulation method and system for high and low temperature test chamber

Abstract

The present invention relates to the field of environmental simulation control, and in particular to an environmental simulation method and system for a high and low temperature test chamber. The method comprises the following steps: obtaining initial state monitoring parameters of the high and low temperature test chamber, performing regional environmental parameter division and point-in-time gradient change fitting, and constructing multiple regional temperature and humidity gradient change curves; identifying a test object within the high and low temperature test chamber; performing transient thermal change limit evolution mining on the test object based on the multiple regional temperature and humidity gradient change curves, and generating a transient thermal change limit curve for the current test object; obtaining a preset test chamber environmental simulation log, and performing global temperature change cross-cycle optimization based on the transient thermal change limit curve of the current test object, and constructing a global temperature change optimization simulation cycle; executing environmental simulation control operations according to the global temperature change optimization simulation cycle, and collecting high and low temperature simulation environmental monitoring data. The present invention achieves rapid response, precise and stable environmental simulation control.

Description

Technical Field

[0001] The present invention relates to the field of environmental simulation control, and in particular to an environmental simulation method and system for a high and low temperature test chamber. Background Art

[0002] With the rapid development of industrial technology, modern manufacturing is placing increasing demands on environmental control during production. This is especially true for testing and verifying high and low temperature environments, where high and low temperature test chambers have become indispensable equipment. High and low temperature test chambers are primarily used to simulate the effects of extreme temperatures on objects, enabling testing of product reliability, performance stability, and durability. However, in practice, environmental simulations in high and low temperature test chambers are often affected by various factors, such as uneven temperature variations, unstable humidity control, and lags in system regulation. These factors can cause the environmental simulation during testing to differ from the expected standard, impacting the accuracy of experimental results.

[0003] Traditional environmental simulation methods for high and low temperature test chambers typically rely on simple temperature and humidity control, lacking comprehensive analysis of environmental state changes and real-time adjustment capabilities. Existing systems often struggle to monitor and precisely adjust temperature, humidity, and airflow in complex operating environments. This can lead to temperature or humidity deviations in certain areas, which in turn can affect the state of the test objects. This environmental control approach is not only inefficient but also susceptible to external factors, such as the number and type of test objects, resulting in unstable control results.

[0004] The development of intelligent technology has placed higher demands on environmental simulation methods for high and low temperature test chambers. Traditional methods that rely on manual operation and simple control are no longer able to meet the high-precision, high-efficiency, and high-reliability testing requirements of modern times. Therefore, an intelligent, high-precision environmental simulation method for high and low temperature test chambers is urgently needed. By combining real-time data monitoring, dynamic adjustment, and intelligent algorithms, it can accurately simulate environmental changes under different temperature and humidity conditions, ensuring environmental stability and accuracy during the test process. Summary of the Invention

[0005] In order to solve the above technical problems, the present invention proposes an environmental simulation method and system for a high and low temperature test chamber to solve at least one of the above technical problems.

[0006] To achieve the above object, the present invention provides an environmental simulation method for a high and low temperature test chamber, comprising the following steps:

[0007] Step S1: Obtain the initial state monitoring parameters of the high and low temperature test chamber, divide the regional environmental parameters, and fit the gradient changes at the time points to construct temperature and humidity gradient change curves for multiple regions;

[0008] Step S2: Identify the test object in the high and low temperature test chamber; perform transient thermal change limit evolution mining on the test object based on the temperature and humidity gradient change curves of multiple regions to generate a transient thermal change limit curve of the current test object;

[0009] Step S3: Obtain the preset test chamber environment simulation log, and perform global temperature change cross-cycle optimization based on the transient thermal change limit curve of the current test object to construct a global temperature change optimization simulation cycle;

[0010] Step S4: Execute environmental simulation control operations according to the global temperature change optimization simulation cycle, collect high and low temperature simulation environment monitoring data; make humidity synchronization coordination control decisions based on the high and low temperature simulation environment monitoring data to generate a humidity synchronization control strategy;

[0011] Step S5: performing high and low temperature transition response delay analysis based on the high and low temperature simulation environment monitoring data, and marking the transition response delay area;

[0012] Step S6: Perform transition delay temperature compensation calculation on the transition response delay area, and build an intelligent environment simulation collaborative control engine based on the humidity synchronization control strategy.

[0013] This invention efficiently identifies test objects, enabling the system to perform targeted thermal response analysis based on their material and shape. This prevents interference with test conditions caused by different objects and ensures that each test condition meets its unique requirements. By analyzing the thermal limit of the test object based on the temperature and humidity gradient curves in different regions, the thermal response of the test object can be accurately predicted. This method, using evolutionary mining technology, accurately simulates the object's response to transient temperature changes, thereby constructing a dynamic transient thermal limit curve. This curve reflects the object's thermal characteristics in real time, enabling the system to adjust the rate and amplitude of high and low temperature fluctuations in real time to avoid excessive temperature shock to the object. Based on the transient thermal limit curve of the test object, the system can provide a personalized temperature control strategy for each object, minimizing the impact of the experimental environment on the object and achieving more accurate environmental simulation results. Initial state monitoring parameters within the test chamber (such as temperature, humidity, and airflow) are obtained and, based on this data, the chamber interior is finely divided into zones, providing accurate basic data for subsequent temperature and humidity control. Temperature and humidity changes in each zone are processed separately, ensuring the accuracy of the simulated environment. The temperature and humidity variation curve of the regional environment is accurately modeled using a fitting method, enabling precise prediction of temperature and humidity trends. This variation curve provides a reliable reference for the system during high and low temperature cycles, ensuring that temperature and humidity variations during testing more closely match actual application requirements. By acquiring and analyzing the test chamber's preset environmental simulation logs and combining them with the object's transient thermal limit curve, the cross-cycle of temperature variation can be optimized in real time. This optimization not only ensures smoother temperature variations but also avoids interference or damage caused by excessive temperature fluctuations during the test. Global optimization of the high and low temperature cycles allows for better adjustment of the test object's adaptability, ensuring that temperature variations throughout the test process meet the object's thermal response limits, thereby improving test accuracy and reliability. The optimized temperature variation cycle is more flexible and adaptable, capable of handling complex and changing testing requirements. By executing the optimized global temperature variation simulation cycle, the system can precisely control the high and low temperature environments within the test chamber, avoiding the uneven temperature and humidity variations that occur with traditional methods. This control ensures that the test object is exposed to a more realistic and consistent environment. Humidity is often a key factor influencing the object's response during temperature changes. By implementing synchronized humidity control based on high and low temperature environmental monitoring data, a dynamic balance between humidity and temperature can be effectively maintained, preventing the impact of unstable humidity on test results. The system generates a humidity synchronization control strategy tailored to each test object and adjusts it based on real-time environmental data to ensure that humidity fluctuations do not significantly interfere with the test object, thereby improving test accuracy. Response delay analysis using high and low temperature simulated environmental monitoring data can identify potential delay areas during alternating high and low temperature cycles.This step ensures that the system detects and corrects hysteresis during temperature transitions in real time, ensuring efficient testing. The system automatically identifies areas of delayed transition response and applies targeted compensation to prevent these areas from affecting temperature and humidity accuracy during testing. This process significantly reduces temperature response hysteresis, ensuring a more stable test chamber environment. By performing temperature compensation calculations for delayed transition response areas, errors caused by temperature response delays are effectively reduced, ensuring more accurate and timely high and low temperature transitions. This step significantly improves the dynamic response of the test chamber environment. Based on temperature compensation, the system incorporates a synchronous humidity control strategy to create an intelligent environmental simulation collaborative control engine, achieving joint optimization of temperature and humidity control. This intelligent collaborative control system adjusts temperature and humidity fluctuations in real time, making the entire testing process smoother and more accurate. Through this intelligent environmental simulation collaborative control engine, the system can globally optimize temperature and humidity control based on all real-time data and feedback, further enhancing the system's adaptability and predictive capabilities to environmental changes and ensuring that test objects remain in an ideal test environment at all stages.

[0014] In this specification, an environmental simulation system for a high and low temperature test chamber is provided, which is used to perform the environmental simulation method for the high and low temperature test chamber as described above, including:

[0015] The regional temperature and humidity module is used to obtain the initial state monitoring parameters of the high and low temperature test chamber, divide the regional environmental parameters, fit the gradient changes at the corresponding points in time, and construct multiple regional temperature and humidity gradient change curves;

[0016] The thermal change limit mining module is used to identify the test object in the high and low temperature test chamber; based on the temperature and humidity gradient change curves of multiple regions, the transient thermal change limit evolution of the test object is mined to generate the transient thermal change limit curve of the current test object;

[0017] The temperature change cross cycle module is used to obtain the preset test chamber environment simulation log, and perform global temperature change cross cycle optimization based on the transient thermal change limit curve of the current test object to construct a global temperature change optimization simulation cycle;

[0018] The humidity synchronization module is used to perform environmental simulation control operations according to the global temperature change optimization simulation cycle and collect high and low temperature simulation environment monitoring data; based on the high and low temperature simulation environment monitoring data, it makes humidity synchronization coordination control decisions to generate a humidity synchronization control strategy;

[0019] The response delay analysis module is used to analyze the high and low temperature transition response delay based on the high and low temperature simulation environment monitoring data and mark the transition response delay area;

[0020] The collaborative control module is used to perform transition delay temperature compensation calculation on the transition response delay area and build an intelligent environment simulation collaborative control engine based on the humidity synchronization control strategy.

[0021] The present invention divides the interior of the test chamber into multiple zones and, combined with precise environmental monitoring, ensures that the temperature and humidity conditions of each zone are individually recorded and analyzed. This division facilitates precise control of temperature and humidity variations in different zones, avoiding variations in the uniformity of the test environment. By fitting the time-based gradient curves of regional temperature and humidity, the system accurately captures the temperature and humidity variation trends of each zone at different time points. This facilitates subsequent control and control systems, allowing dynamic adjustments based on actual changes. Environmental control in different zones can better adapt to the needs of different test subjects. Especially for test subjects with high environmental sensitivity, detailed zone division and variation fitting enhance the realism and accuracy of the simulation. Accurate identification and recording of test subject information such as material, size, and shape ensures accurate subsequent thermal response analysis and temperature control. By mining transient thermal limits and combining the temperature and humidity variations in different zones, a precise thermal limit curve is calculated and generated for each test subject. This curve reflects the test subject's maximum tolerance under varying temperature variations, preventing damage or adverse reactions caused by excessive temperature differences. With the transient thermal limit curve, the system automatically adjusts the test chamber's temperature change strategy based on the object's thermal response characteristics, ensuring that the test object is not adversely affected by rapid temperature changes. By analyzing historical test data and combining it with the object's transient thermal limit curve, the system automatically optimizes the cycle and rate of high and low temperature alternation, ensuring a smoother temperature change that meets the object's tolerance limits. By precisely optimizing the temperature change crossover cycle, the system avoids thermal shock caused by sudden temperature rises or falls, thereby improving the test object's survival environment and test reliability. By optimizing the global temperature change cycle, the system creates a more consistent simulation cycle for all test objects and environmental conditions, ensuring efficient and consistent testing. Humidity is a key factor affecting test results in high and low temperature tests. The humidity synchronization module monitors and analyzes temperature changes in real time and automatically adjusts humidity to ensure coordinated temperature and humidity changes. The humidity synchronization module flexibly adjusts humidity based on changes in environmental data during the test, preventing negative impacts on the test object caused by excessively high or low humidity and ensuring the validity of test results. Through synchronized and coordinated control of humidity and temperature, the system achieves more refined environmental regulation, simulating more complex real-world conditions and enhancing the authenticity and reliability of tests. By monitoring the temperature and humidity changes in the test environment in real time, it is possible to accurately analyze and mark potential delay areas during high-temperature transitions. By identifying delay areas, the system can avoid temperature changes within these areas, preventing uneven heat loads from affecting test results. Marking delay areas for high-temperature transition responses helps predict potential errors from environmental changes in advance, allowing necessary compensatory measures to ensure the stability of the test process. Delay analysis can significantly improve data accuracy during the test, ensuring that temperature and humidity changes during high-temperature transitions are more closely aligned with the needs of the test object.By calculating the temperature compensation amount in the transition response delay area in real time, the error caused by ambient temperature lag can be effectively reduced, ensuring a smoother high and low temperature transition process. The combination of humidity synchronization control strategy and temperature compensation strategy can provide a more intelligent control framework for the entire experiment. The system can automatically adjust parameters such as humidity and temperature based on all real-time data, and perform joint optimization control to make environmental simulation more accurate and flexible. By introducing an intelligent collaborative control engine, the system can automatically adjust environmental conditions according to the needs of different test objects to minimize interference from human operations and improve the degree of automation of the test. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 This is a schematic flow chart of the steps of an environmental simulation method for a high and low temperature test chamber according to the present invention;

[0023] Figure 2 Detailed implementation flow chart of step S1;

[0024] Figure 3 Detailed implementation flow chart of step S2;

[0025] Figure 4 Schematic diagram of the detailed implementation steps of step S3. DETAILED DESCRIPTION

[0026] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0027] This application provides a method and system for simulating the environment of a high- and low-temperature test chamber. The execution entities of this method and system include, but are not limited to, the following: mechanical equipment, data processing platforms, cloud server nodes, network upload devices, etc., which can be considered as general computing nodes of this application. The data processing platform includes, but is not limited to, at least one of an audio and image management system, an information management system, and a cloud data management system.

[0028] See also Figures 1 to 4 The present invention provides an environmental simulation method for a high and low temperature test chamber, the environmental simulation method for a high and low temperature test chamber comprising the following steps:

[0029] Step S1: Obtain the initial state monitoring parameters of the high and low temperature test chamber, divide the regional environmental parameters, and fit the gradient changes at the time points to construct temperature and humidity gradient change curves for multiple regions;

[0030] Step S2: Identify the test object in the high and low temperature test chamber; perform transient thermal change limit evolution mining on the test object based on the temperature and humidity gradient change curves of multiple regions to generate a transient thermal change limit curve of the current test object;

[0031] Step S3: Obtain the preset test chamber environment simulation log, and perform global temperature change cross-cycle optimization based on the transient thermal change limit curve of the current test object to construct a global temperature change optimization simulation cycle;

[0032] Step S4: Execute environmental simulation control operations according to the global temperature change optimization simulation cycle, collect high and low temperature simulation environment monitoring data; make humidity synchronization coordination control decisions based on the high and low temperature simulation environment monitoring data to generate a humidity synchronization control strategy;

[0033] Step S5: performing high and low temperature transition response delay analysis based on the high and low temperature simulation environment monitoring data, and marking the transition response delay area;

[0034] Step S6: Perform transition delay temperature compensation calculation on the transition response delay area, and build an intelligent environment simulation collaborative control engine based on the humidity synchronization control strategy.

[0035] This invention efficiently identifies test objects, enabling the system to perform targeted thermal response analysis based on their material and shape. This prevents interference with test conditions caused by different objects and ensures that each test condition meets its unique requirements. By analyzing the thermal limit of the test object based on the temperature and humidity gradient curves in different regions, the thermal response of the test object can be accurately predicted. This method, using evolutionary mining technology, accurately simulates the object's response to transient temperature changes, thereby constructing a dynamic transient thermal limit curve. This curve reflects the object's thermal characteristics in real time, enabling the system to adjust the rate and amplitude of high and low temperature fluctuations in real time to avoid excessive temperature shock to the object. Based on the transient thermal limit curve of the test object, the system can provide a personalized temperature control strategy for each object, minimizing the impact of the experimental environment on the object and achieving more accurate environmental simulation results. Initial state monitoring parameters within the test chamber (such as temperature, humidity, and airflow) are obtained and, based on this data, the chamber interior is finely divided into zones, providing accurate basic data for subsequent temperature and humidity control. Temperature and humidity changes in each zone are processed separately, ensuring the accuracy of the simulated environment. The temperature and humidity variation curve of the regional environment is accurately modeled using a fitting method, enabling precise prediction of temperature and humidity trends. This variation curve provides a reliable reference for the system during high and low temperature cycles, ensuring that temperature and humidity variations during testing more closely match actual application requirements. By acquiring and analyzing the test chamber's preset environmental simulation logs and combining them with the object's transient thermal limit curve, the cross-cycle of temperature variation can be optimized in real time. This optimization not only ensures smoother temperature variations but also avoids interference or damage caused by excessive temperature fluctuations during the test. Global optimization of the high and low temperature cycles allows for better adjustment of the test object's adaptability, ensuring that temperature variations throughout the test process meet the object's thermal response limits, thereby improving test accuracy and reliability. The optimized temperature variation cycle is more flexible and adaptable, capable of handling complex and changing testing requirements. By executing the optimized global temperature variation simulation cycle, the system can precisely control the high and low temperature environments within the test chamber, avoiding the uneven temperature and humidity variations that occur with traditional methods. This control ensures that the test object is exposed to a more realistic and consistent environment. Humidity is often a key factor influencing the object's response during temperature changes. By implementing synchronized humidity control based on high and low temperature environmental monitoring data, a dynamic balance between humidity and temperature can be effectively maintained, preventing the impact of unstable humidity on test results. The system generates a humidity synchronization control strategy tailored to each test object and adjusts it based on real-time environmental data to ensure that humidity fluctuations do not significantly interfere with the test object, thereby improving test accuracy. Response delay analysis using high and low temperature simulated environmental monitoring data can identify potential delay areas during alternating high and low temperature cycles.This step ensures that the system detects and corrects hysteresis during temperature transitions in real time, ensuring efficient testing. The system automatically identifies areas of delayed transition response and applies targeted compensation to prevent these areas from affecting temperature and humidity accuracy during testing. This process significantly reduces temperature response hysteresis, ensuring a more stable test chamber environment. By performing temperature compensation calculations for delayed transition response areas, errors caused by temperature response delays are effectively reduced, ensuring more accurate and timely high and low temperature transitions. This step significantly improves the dynamic response of the test chamber environment. Based on temperature compensation, the system incorporates a synchronous humidity control strategy to create an intelligent environmental simulation collaborative control engine, achieving joint optimization of temperature and humidity control. This intelligent collaborative control system adjusts temperature and humidity fluctuations in real time, making the entire testing process smoother and more accurate. Through this intelligent environmental simulation collaborative control engine, the system can globally optimize temperature and humidity control based on all real-time data and feedback, further enhancing the system's adaptability and predictive capabilities to environmental changes and ensuring that test objects remain in an ideal test environment at all stages.

[0036] In the embodiment of the present invention, see Figure 1 , is a schematic flow chart of the steps of a method for simulating an environment of a high and low temperature test chamber according to the present invention. In this example, the steps of the method for simulating an environment of a high and low temperature test chamber include:

[0037] Step S1: Obtain the initial state monitoring parameters of the high and low temperature test chamber, divide the regional environmental parameters, and fit the gradient changes at the time points to construct temperature and humidity gradient change curves for multiple regions;

[0038] In this example, high-precision temperature and humidity sensors are installed in the high- and low-temperature test chamber to ensure that the sensor's accuracy and response speed meet the experimental requirements. Select sensors with fast response characteristics, such as thermocouples or capacitive humidity sensors. Before the experiment begins, calibrate the sensors to ensure accurate readings. Preheat and precool the equipment under different environmental conditions to eliminate temperature lag. Develop a data collection plan and set the frequency of monitoring parameter acquisition. For example, record temperature and humidity data every minute to capture detailed environmental changes. Also, set a start and end time for data recording to ensure data continuity and integrity. Record the initial environmental parameters, including the initial temperature, humidity, and air pressure within the chamber, for subsequent analysis. Ensure data storage is consistent in a CSV or database format. Based on the chamber design and experimental requirements, divide the chamber into multiple zones, such as upper left, upper right, lower left, and lower right. This division helps analyze the environmental characteristics of different zones and identify patterns in temperature and humidity changes. Determine the specific location and range of each zone and record the coordinates of each zone for subsequent data analysis. The zones should be appropriately divided based on the test chamber layout and the location of the test objects. Collected monitoring parameters should be grouped by zone, and the temperature and humidity data for each zone should be organized into corresponding zone datasets. Ensure that the data for each zone includes timestamps to facilitate time series analysis. Record the initial state parameters for each zone, including initial temperature, humidity, and their range, for subsequent analysis and comparison. Select an appropriate gradient fitting model. Common methods include linear regression, polynomial fitting, or exponential fitting. These models can effectively describe the temporal trends of temperature and humidity. Determine the model input parameters, including the temperature and humidity data for each zone, as well as timestamps, to ensure the accuracy of the fitting process. Perform a gradient fitting on the temperature and humidity data for each zone, calculating the rate of change of temperature and humidity at different time points. This fitting process generates a curve for each zone, identifying the trend and amplitude of temperature and humidity fluctuations. Record the fitting results, including the fitting equation, correlation coefficient, and fitting error, to assess the accuracy of the fitting. Ensure that the fitting results for each zone accurately reflect the characteristics of the temperature and humidity changes there. Based on the fitting results, generate a temperature and humidity gradient curve for each zone. These curves should clearly show how temperature and humidity change at different points in time. Graphical tools can be used for visualization. Ensure the graphs include necessary annotations and legends so that operators can quickly understand the temperature and humidity characteristics of each area.

[0039] Step S2: Identify the test object in the high and low temperature test chamber; perform transient thermal change limit evolution mining on the test object based on the temperature and humidity gradient change curves of multiple regions to generate a transient thermal change limit curve of the current test object;

[0040] In this embodiment, the test object is strategically placed within the high and low temperature test chamber, ensuring that it is fixed and does not affect temperature and humidity fluctuations in the surrounding environment. The test object can be an electronic component, material sample, or other object to be tested, and its identification must be clear. Record the basic characteristics of the test object, including material type, dimensions, weight, and thermal properties (such as thermal conductivity and specific heat capacity), for use in subsequent analysis. Install additional temperature and humidity sensors around the test object to accurately monitor changes in its environment. These sensors should be placed at a distance from the test object to capture transient temperature changes on the object's surface. Ensure that all sensors are properly calibrated, and record the location and monitoring range of each sensor for subsequent data analysis. During the test, collect real-time temperature and humidity data for the test object and its surroundings. Ensure that the data collection frequency is high enough (e.g., once per second) to capture details of transient changes. Organize the collected data by region and time, and establish a corresponding database that includes temperature changes of the test object and temperature and humidity data of the surrounding environment. Select an appropriate thermal limit model; commonly used models include transient heat conduction models and unsteady-state thermal models. These models can describe the thermal response of an object during temperature changes. Determine the model's input parameters, including the test object's material properties, initial temperature, and ambient temperature change rate, to ensure model validity. Input the organized temperature and humidity data into the selected thermal limit model to calculate the transient thermal limit evolution. Identify the thermal response characteristics of the test object under different temperature and humidity conditions and generate a transient thermal limit curve. Record key data points of the transient thermal limit curve, including the transient thermal limit value, change rate, and corresponding time points. This data will help understand the thermal stability and safety of the test object. Based on the calculated transient thermal limit data, generate a corresponding transient thermal limit curve. This curve should reflect the thermal limit of the test object under different environmental conditions. Use an appropriate fitting method (such as polynomial fitting or linear fitting) to fit the transient thermal limit data to improve the smoothness and readability of the curve and ensure that the curve accurately reflects the actual situation.

[0041] Step S3: Obtain the preset test chamber environment simulation log, and perform global temperature change cross-cycle optimization based on the transient thermal change limit curve of the current test object to construct a global temperature change optimization simulation cycle;

[0042] In this example, a complete environmental simulation log is obtained from the control system of the high- and low-temperature test chamber. This log should include records of environmental parameters such as temperature, humidity, and air pressure at each time point, as well as records of the equipment's operating status and any abnormal events. Ensure that the log data's timestamps are synchronized with the experimental cycle to accurately correspond to the environmental conditions at each time point. The collected environmental simulation logs are organized to remove duplicate data and outliers. This ensures data integrity and accuracy for subsequent analysis. The data is categorized by different environmental parameters, and a data framework is established, including temperature and humidity curves, as well as changes in other relevant environmental parameters. Key data in the environmental simulation logs is analyzed to identify temperature and humidity trends, fluctuations, and their impact on the test object. For example, focus on environmental stability during high and low temperature phases, as well as humidity changes during these phases. The analysis results are recorded and a report is generated, highlighting performance under different environmental conditions, providing data support for global temperature change optimization. The transient thermal limit curve of the test object is reviewed to identify the object's thermal stability and vulnerability under different temperature conditions. In the curve, key turning points and extreme values ​​are carefully observed; this information is crucial for optimizing the temperature change cycle. Record key parameters from the curve, such as the thermal transition limit, rate of change, and corresponding time points, for reference during subsequent optimization. Based on the transient thermal transition limit curve, design a global temperature change cross-cycle optimization plan. This plan should include the duration of each temperature stage, the temperature change rate, and the alternation sequence. For example, a high temperature stage could be set at 60°C for 30 minutes, followed by a rapid decrease to -20°C for 30 minutes before returning to room temperature. Consider dynamically adjusting the humidity during the temperature transition to reduce thermal stress on the test object. Ensure that temperature and humidity parameters are within safe ranges at the end of each stage. Simulate the new global temperature change optimization cycle in a laboratory environment and monitor the temperature and humidity changes of the test object in real time to verify the effectiveness of the optimization plan. Record the environmental parameters and their impact on the test object during each stage. Analyze the experimental data to verify whether the optimization cycle effectively reduces thermal stress on the test object and ensure that the temperature and humidity changes meet the expected safety standards. Make necessary adjustments based on the experimental results to ensure the feasibility and effectiveness of the plan.

[0043] Step S4: Execute environmental simulation control operations according to the global temperature change optimization simulation cycle, collect high and low temperature simulation environment monitoring data; make humidity synchronization coordination control decisions based on the high and low temperature simulation environment monitoring data to generate a humidity synchronization control strategy;

[0044] In this example, a detailed environmental simulation control plan is developed based on a global temperature change optimization simulation cycle. This plan should include the target temperature range, temperature change rate, alternating cycle, and its duration. For example, a high-temperature phase is set at 60°C for 30 minutes, and a low-temperature phase is set at -20°C for 30 minutes. Ensure that the temperature change in each phase meets the experimental requirements to avoid unnecessary thermal shock to the test object, and record the start and end times of each phase. Configure the high- and low-temperature test chambers with temperature and humidity monitoring equipment, ensuring that all sensors are functioning properly and calibrated. Monitoring equipment should include thermocouples, humidity sensors, and data loggers. Set a data collection frequency; it is recommended to record temperature and humidity data every minute or more (e.g., every 10 seconds) to ensure that subtle environmental changes are captured. Start the high- and low-temperature test chambers and execute temperature change control according to the developed plan. Ensure that the system automatically adjusts according to the set temperature change cycle and monitors the current environmental conditions in real time. At the end of each phase, record environmental parameters, including temperature, humidity, and air pressure, and compare them with the set values ​​for subsequent analysis. During the environmental simulation, collect monitoring data for the high- and low-temperature simulated environments in real time. Ensure the data acquisition system operates stably and records temperature and humidity changes in a timely manner. Organize collected data by timestamp and establish a database to ensure data integrity and traceability. Regularly review collected data to ensure accuracy and consistency. Identify potential outliers by comparing them against pre-set temperature and humidity targets. Any outliers discovered should be recorded and analyzed for possible causes, such as sensor failure or external interference, so that necessary adjustments can be made. Store collected monitoring data in a secure database to ensure data integrity and security. Record the timestamp, temperature, and humidity values, along with the corresponding regional parameters, for each data point. Back up data regularly to prevent data loss and provide a foundation for subsequent data analysis. Analyze humidity trends based on the monitoring data from the high and low temperature simulation environments, identifying the characteristics of humidity changes under different temperature stages. Record the initial and target humidity values, as well as their range. Determine the need for humidity synchronization control, for example, whether humidity needs to be maintained within a specific range during the high temperature stage to avoid impacting the test object. Based on the analysis results, develop a humidity synchronization control strategy. This strategy should consider the current humidity level, the target humidity range, and the impact of temperature changes on humidity. For example, during high-temperature periods, set the relative humidity between 40% and 60%. Record the specific parameters for humidity adjustment, including the rate of change, target humidity value, and adjustment method (such as humidification or dehumidification). While performing environmental simulation control, implement a synchronous humidity control strategy. Based on real-time monitoring data, automatically adjust the humidity to ensure it keeps pace with temperature changes. Monitor the effectiveness of humidity adjustments and record the difference between the actual humidity and the target humidity so that dynamic adjustments can be made to ensure the stability of the test environment.

[0045] Step S5: performing high and low temperature transition response delay analysis based on the high and low temperature simulation environment monitoring data, and marking the transition response delay area;

[0046] In this example, complete environmental monitoring data is obtained from a high- and low-temperature test chamber, ensuring that the data includes temperature, humidity, and other relevant environmental parameters at each time point. The data should cover the entire experimental period for comprehensive analysis. The data is cleaned to remove outliers and noise. Moving averages or other smoothing techniques can be used to reduce the impact of transient fluctuations and ensure data quality. Key analysis parameters are determined, including the temperature change rate, response time, and transition threshold. A temperature change threshold is set; for example, a temperature change exceeding 2°C is considered a transition response. The initial and target states of each region are recorded for subsequent comparison and analysis. Time series analysis is performed on the cleaned data, using statistical methods (such as autocorrelation analysis and Fourier transform) to identify trends in temperature and humidity. Focus on the temperature change rate during the transition phase and identify time periods with faster or slower temperature changes. Calculate the temperature change rate at each time point and record the magnitude of the temperature change within a specific time period. Set a time threshold for response delay to identify time periods in which temperature changes are not responded to in a timely manner. For each transition response, calculate the time difference from the start of the temperature change to the time it reaches the set threshold and record this time difference as the response delay. Use data visualization tools (such as line charts or heat maps) to show the relationship between temperature changes and response delays, and clearly mark the delay time of each transition response. Based on the analysis results, divide the high and low temperature test chamber into different areas and record the response delay of each area. The areas should be divided according to the changes in temperature and humidity, such as the upper left, upper right, lower left and lower right areas. In the timing diagram, use different colors or symbols to mark the transition response delay areas. For example, use red to mark the area where the response delay exceeds the set threshold to ensure that it is clearly visible in the diagram. Record the characteristics of the marked transition response delay area, including the delay time, delay amplitude and its corresponding temperature and humidity values. Ensure the integrity and traceability of the data for subsequent analysis and optimization. Generate an analysis report that describes the situation of the transition response delay area in detail and points out the factors that may affect the delay, such as equipment location, material thermal properties, etc.

[0047] Step S6: Perform transition delay temperature compensation calculation on the transition response delay area, and build an intelligent environment simulation collaborative control engine based on the humidity synchronization control strategy.

[0048] In this embodiment, based on the previous analysis of the transition response delay, an appropriate temperature compensation model is selected. These models should be able to reflect the impact of temperature changes on the test object under different environmental conditions. Commonly used models include linear and nonlinear compensation models. The model's input parameters are determined. These parameters should include the response delay time, target temperature, current temperature, and characteristic data of the transition region, such as the temperature change rate and humidity level. For each transition response delay region, the required temperature compensation value is calculated. The compensation temperature is determined by analyzing the difference between the current and target temperatures. For example, if the current temperature is 25°C, the target temperature is 30°C, and the response delay is 5 minutes, the temperature value required for heating within these 5 minutes must be calculated. The compensation calculation results are recorded, including the compensated temperature, compensation amplitude, and implementation time parameters for each region. This data will provide the basis for subsequent control strategies. The temperature compensation strategy is implemented in a high and low temperature test chamber, and the temperature changes of the test object are monitored in real time. By comparing the temperature curves before and after compensation, it is verified that the compensation effect has achieved the desired results. The temperature changes after implementation are recorded to evaluate the effectiveness of the compensation strategy, and necessary adjustments are made based on the feedback to ensure the stability of the test environment. Based on the results of the transition delay temperature compensation calculation, the architecture of the intelligent environmental simulation collaborative control engine was designed. The engine should include multiple functional modules, including a data acquisition module, a control decision module, and an execution control module. The engine should monitor environmental parameters in real time, automatically analyze temperature and humidity changes, and make adjustments based on the specified compensation strategy and humidity synchronization control strategy. High-precision sensors should be deployed to collect real-time ambient temperature, humidity, and other relevant parameters. This data will serve as the basis for the engine's decision-making. Data preprocessing and cleaning should be implemented to ensure the accuracy and consistency of input data. Machine learning algorithms should be used to analyze historical data to identify key factors affecting environmental stability. Based on the humidity synchronization control strategy, a control decision algorithm should be designed. This algorithm should automatically adjust temperature and humidity based on real-time monitoring data to ensure that environmental conditions remain consistent with the specified targets. During the execution of the control, the temperature and humidity changes of each adjustment should be recorded for effectiveness evaluation and strategy optimization. The engine's response time should be ensured to meet experimental requirements, allowing for timely adjustments to environmental parameters. The intelligent environmental simulation collaborative control engine should be implemented in a high- and low-temperature test chamber for system testing. System stability and response time should be monitored to evaluate the effectiveness of the control strategy. Optimize and adjust based on test results to ensure that the engine can flexibly respond to environmental changes during actual operation and improve the reliability and accuracy of the experiment.

[0049] In this embodiment, refer to Figure 2 , is a flowchart of the detailed implementation steps of step S1. In this embodiment, the detailed implementation steps of step S1 include:

[0050] Obtain the initial status monitoring parameters of the high and low temperature test chamber;

[0051] Calculate real-time temperature and humidity parameters based on the initial state monitoring parameters;

[0052] Divide the real-time temperature and humidity parameters into regional environmental parameters to obtain the temperature and humidity parameters of different areas;

[0053] Analyze the time series changes of temperature and humidity parameters in different regions to generate the time series change characteristics of temperature and humidity in multiple regions;

[0054] The temperature and humidity time series change characteristics of multiple regions are fitted with multi-time point gradient changes to construct temperature and humidity gradient change curves for multiple regions.

[0055] In this example, before conducting the high and low temperature test, ensure that all monitoring equipment (such as temperature and humidity sensors, data loggers, etc.) is properly installed and calibrated. The calibration process should follow the equipment manufacturer's instructions to ensure measurement accuracy. Check the sensor sensitivity and response time. Ensure that the sensor can operate normally within the preset temperature range (e.g., -40°C to 100°C) and has an adequate humidity range (e.g., 10% to 95% relative humidity). Collect initial state monitoring parameters for the high and low temperature test chamber, including the initial temperature, humidity, air pressure, and other relevant environmental parameters within the chamber. It is recommended to monitor for at least 30 minutes before the test begins to obtain stable initial state data. Record each sensor reading and timestamp to ensure data integrity. Data should include temperature (unit: °C), relative humidity (unit: %RH), and air pressure (unit: hPa). During the operation of the high and low temperature test chamber, collect temperature and humidity data in real time. Set a data collection frequency, such as once every minute, to capture subtle environmental changes. Use data acquisition equipment to ensure that real-time data can be accurately transmitted to a computer or data processing system. Based on the collected real-time data, calculate the real-time temperature and humidity parameters within the test chamber. Use a simple weighted average or exponential smoothing method to smooth instantaneous readings to reduce fluctuations. Record real-time temperature and humidity changes and compare them with the initial state parameters to analyze environmental trends. Store the calculated real-time temperature and humidity parameters in a database, adding a timestamp to each data point. This ensures convenient access to subsequent analysis and processing. Categorize and organize real-time data, such as by time or region, to facilitate quick retrieval during subsequent analysis. Define zone division criteria based on the test chamber's structure and purpose. The test chamber can be divided into multiple zones (such as left, right, top, and bottom). Each zone should be based on the characteristics of the temperature and humidity distribution. Determine the criteria for each zone division, including but not limited to temperature and humidity fluctuations, equipment location, and air circulation conditions. Perform statistical analysis on the real-time temperature and humidity data within each zone, calculating the average temperature and humidity, as well as their fluctuation range. Use statistical metrics such as mean and standard deviation to describe the regional environmental conditions. Record the temperature and humidity parameters for each zone and generate a corresponding regional environmental parameter report. Organize the temperature and humidity data for each area in chronological order to ensure data timeliness. Use time series analysis methods to group data by minute or hour. Record time series data for each area to ensure its integrity and accuracy. Analyze the temperature and humidity time series data for each area to identify trends, cyclical fluctuations, and unexpected events. Use methods such as moving averages and Fourier transforms to analyze time series data. Generate time series change feature maps for each area to display the changing trends and correlations between temperature and humidity.Select an appropriate model to fit the gradient changes at multiple time points. Common methods include linear regression, polynomial fitting, and piecewise linear fitting. Consider the data's variation characteristics and fitting accuracy when selecting a model. Determine the model's input variables, including temperature and humidity parameters at different time points and the corresponding regional information. Perform a gradient fit based on the temporal variation characteristics of temperature and humidity in each region to generate temperature and humidity gradient curves for each region. Through the fitting process, identify the temperature and humidity variation patterns in each region. Record the fitting results for each region, including the fitted curve and goodness-of-fit (e.g., R² value), to assess the accuracy of the fit. Visualize the fitted temperature and humidity gradient curves and generate a chart to display the gradient variation characteristics of each region. Compare the changes in different regions using the chart to ensure the accuracy of the fitting results. Analyze the visualized results to identify the temperature and humidity variation patterns in each region, providing a basis for subsequent environmental control and optimization.

[0056] In this embodiment, refer to Figure 3 , is a flowchart of the detailed implementation steps of step S2. In this embodiment, the detailed implementation steps of step S2 include:

[0057] Identify test objects in high and low temperature test chambers;

[0058] Performing real-time temperature change detection on the test object to generate temperature change detection parameters of the test object;

[0059] performing thermal inertia identification on the temperature change detection parameters to generate thermal inertia characteristics of the test object;

[0060] quantifying the temperature change hysteresis of the test object according to the temperature and humidity gradient change curves of the multiple regions to generate a heat capacity characteristic of the test object;

[0061] Perform thermal response state analysis on the thermal inertia characteristics and heat capacity characteristics of the test object to generate thermal response state data of the test object;

[0062] The transient thermal change limit evolution is mined based on the thermal response state data of the test object to generate the transient thermal change limit curve of the current test object.

[0063] In this embodiment, within the high and low temperature test chamber, first ensure that all test objects are properly arranged to avoid obstructing the sensors. Use a high-resolution camera or laser scanner to perform preliminary recognition of the test objects and ensure clear images. Clean and organize the interior of the test chamber to ensure that there is no dust or other material on the test objects that could affect the recognition results. This step improves recognition accuracy and reduces false positives. Image processing techniques are used to process the images captured by the camera to extract features such as the shape, color, and size of the test objects. Edge detection algorithms (such as Canny edge detection) can be used to identify object boundaries. Record the characteristic parameters of each test object, including size, weight, and material type, for subsequent analysis. Ensure that data is stored in a unified format for easy use. Apply machine learning or deep learning models (such as convolutional neural networks (CNNs)) for automatic recognition of test objects. These models are trained to accurately identify different types of test objects. Record recognition results, including object category, location coordinates, and recognition confidence, for subsequent verification and analysis. Install high-precision temperature sensors on or near the test objects to ensure that the sensors can monitor temperature changes in real time. Choose a sensor with a fast response time and high accuracy, such as a thermocouple or RTD sensor. Ensure the sensor is properly positioned to fully capture the test object's temperature changes and avoid data deviations caused by improper positioning. Set the temperature sensor's data acquisition frequency, for example, once per second, to ensure timely recording of temperature changes during high and low temperature environments. The sensor should be connected to a data logger for real-time data transmission. Record each temperature acquisition, including the timestamp and temperature value, for subsequent analysis. It is recommended to use data management software to monitor data in real time to ensure data accuracy and integrity. Select an appropriate thermal inertia identification model. Common methods include the heat conduction equation and specific heat capacity calculation. The model should consider the material properties and geometry of the test object to accurately reflect the thermal inertia characteristics. Determine the model's input parameters, such as the test object's temperature change data, the material's specific heat capacity, and density, to ensure accurate thermal inertia calculation. Calculate the test object's thermal inertia characteristics based on the real-time temperature change detection parameters. This can be achieved by analyzing the relationship between the rate of temperature change and the external temperature change to calculate the test object's thermal response time constant. Record thermal inertia characteristic results, including time constants and thermal inertia coefficients, for subsequent analysis. Analyze the temperature hysteresis of the test object based on temperature and humidity gradient curves across multiple regions. Identify hysteresis characteristics by comparing the time difference between the test object's temperature change and the ambient temperature change. Determine hysteresis measurement parameters and record the hysteresis time and corresponding temperature change for each test object to facilitate subsequent heat capacity calculation. Calculate the heat capacity characteristic of the test object based on the hysteresis time and temperature change. C = Q / ΔT, where C is the heat capacity, Q is the amount of heat absorbed or released, and ΔT is the temperature change.Record the thermal capacity characteristic results of each test object for subsequent analysis and comparison. Integrate the thermal inertia and thermal capacity characteristic data of the test object to facilitate thermal response state analysis. Ensure data structure and consistency to facilitate subsequent processing. Record the thermal response state parameters of each test object, including thermal inertia coefficient, heat capacity value, and temperature change rate, for comprehensive analysis. Analyze the integrated thermal response state data to identify the thermal response characteristics of the test object. This can be achieved by comparing the thermal response time and temperature change rate of different test objects. Record the thermal response state analysis results and generate a status report to help understand the performance of the test object under different temperature conditions. Collect transient thermal change data of the test object under different temperature conditions, including parameters such as temperature change rate, external ambient temperature, and time. This data will be used to mine transient thermal change limit evolution. Record the timestamp of each transient thermal change data point to ensure data timeliness and integrity. Select an appropriate transient thermal change limit evolution model. Common methods include nonlinear regression analysis and dynamic system models. These models can reflect the complex behavior of transient thermal change. Determine the model's input parameters, including transient thermal data and the thermal characteristics of the test object, to ensure model accuracy. Based on the collected transient thermal data, perform limit evolution mining to generate a transient thermal limit curve for the current test object. Analyze the thermal limit state by comparing temperature changes at different times. Record the characteristic parameters of the transient thermal limit curve, including the limit temperature and rate of change, for subsequent analysis and verification.

[0064] In this embodiment, refer to Figure 4 , is a flowchart of the detailed implementation steps of step S3. In this embodiment, the detailed implementation steps of step S3 include:

[0065] Get the preset test chamber environment simulation log;

[0066] Identify the high and low temperature crossover time of the preset test chamber environment simulation log to obtain the high and low temperature crossover change time;

[0067] Calculate the ambient temperature simulation cycle based on the high and low temperature cross-change time to generate a high and low temperature change simulation cycle;

[0068] According to the transient thermal change limit curve of the current test object, the high and low temperature change simulation cycle is globally optimized for the temperature change cross cycle, and a global temperature change optimization simulation cycle is constructed.

[0069] In this embodiment, an environmental monitoring system is configured in a high and low temperature test chamber to ensure that environmental parameters such as temperature, humidity, and air pressure can be recorded. High-precision sensors are selected and the data acquisition frequency is set, for example, recording data once every minute to capture environmental changes. Ensure that the logging system operates stably and regularly check the status of sensors and data loggers to avoid data loss or inaccuracy due to equipment failure. According to the experimental design, the preset environmental conditions of the test chamber are set, including high and low temperature ranges (such as from -40°C to 100°C) and humidity ranges (such as 20% to 80% relative humidity). The plan should include the temperature change rate and periodic change pattern to simulate the real environment. Record the preset conditions and their changes to ensure that the time and period characteristics of high and low temperature cross-changes can be subsequently analyzed. Preprocess the acquired environmental simulation logs to remove outliers and noise. The moving average method can be used to smooth the data to ensure data accuracy during the recognition process. Ensure that the timestamps of temperature and humidity data are consistent to facilitate subsequent analysis. Use a threshold method or change detection algorithm to identify the time of high and low temperature cross-changes. Set a temperature threshold (e.g., 0°C). When the temperature changes from above to below the threshold, record the time point as the high-low temperature crossover event. By traversing the simulation log, identify the start and end times of each crossover, and record the crossover time and duration for subsequent analysis. Organize the identified high-low temperature crossover events to generate a crossover timetable, recording the specific time and characteristics of each crossover. Analyze the distribution characteristics of the crossover times, including their frequency, duration, and impact on the test object, to provide a basis for subsequent simulation cycle calculations. Define the ambient temperature simulation cycle based on the identified high-low temperature crossover times. A simulation cycle should include the complete transition from high to low temperature and back again. Set the start and end times for the cycle to ensure accurate calculations. Record the duration of each simulation cycle to ensure that the temporal characteristics of the high-low temperature crossover are reflected. Use time series analysis to calculate the time interval between each high-low temperature crossover and generate a high-low temperature change simulation cycle based on this time interval. For example, use the average method to calculate the average period of multiple crossovers to ensure the stability of the results. Record the characteristic parameters of each simulation cycle, including the start time, end time, and duration, for subsequent optimization analysis. Select a suitable global temperature change cross-cycle optimization model. Common methods include genetic algorithms, particle swarm optimization, and linear programming. These models can optimize cycle settings while considering multiple constraints. Determine the input parameters of the model, including the high and low temperature change simulation cycle, the thermal characteristics of the test object, and environmental conditions, to ensure the accuracy of the optimization process. Based on the transient thermal change limit curve of the current test object, perform global optimization of the high and low temperature change simulation cycle. Input relevant parameters and calculate the optimal temperature change cross-cycle to ensure that the test object can achieve the best thermal response within the optimization cycle. Record the decision variables in the optimization process, including the optimized cycle length, crossover time point, etc., for subsequent verification and monitoring.Based on the optimization results, a global temperature change optimization simulation cycle is constructed. The cycle should include specific temperature change patterns, crossover time points, and duration to ensure effective application in the experiment. Record the detailed parameters of the constructed cycle, including high and low temperature settings, change rates, etc., for subsequent implementation and adjustment. Verify the effectiveness of the constructed global temperature change optimization simulation cycle in actual applications. Evaluate the accuracy and reliability of the optimization cycle by comparing actual results with theoretical expectations. Dynamically adjust the model based on the verification results to ensure that the optimization cycle can adapt to different experimental conditions and test object characteristics. Visualize the constructed global temperature change optimization simulation cycle and generate a cycle change graph to help the operator intuitively understand the characteristics of the optimized cycle. Summarize the various characteristics of the optimization cycle and analyze its impact on the thermal performance of the test object to provide basic data and theoretical support for subsequent research.

[0070] In this embodiment, the global temperature change cross cycle optimization is performed on the high and low temperature change simulation cycle according to the transient thermal change limit curve of the current test object, and the specific steps of constructing the global temperature change optimization simulation cycle are:

[0071] Calculating the maximum range of high and low temperature changes based on the preset test chamber environment simulation log to obtain the peak range of high and low temperatures;

[0072] Based on the transient thermal change limit curve of the current test object, the thermal change limit risk is calculated for the high and low temperature peak range to obtain the transient thermal change limit risk;

[0073] Conduct thermal tolerance risk assessment on transient thermal change limit risk. When the transient thermal change limit risk exceeds the preset safety threshold, a transient thermal change limit warning signal is generated.

[0074] Make high and low temperature test adjustment decisions based on transient thermal change limit warning signals to obtain high and low temperature test adjustment plans;

[0075] Adaptively adjust the temperature variation amplitude of the high and low temperature peak range based on the high and low temperature test adjustment plan to generate adaptive temperature variation amplitude adjustment parameters;

[0076] Based on the adaptive temperature variation amplitude adjustment parameters, the global temperature variation cross-cycle optimization of the high and low temperature variation simulation cycle is performed to construct a global temperature variation optimization simulation cycle.

[0077] In this example, temperature data is extracted from a pre-set test chamber environmental simulation log to ensure data integrity and accuracy. Temperature changes throughout the test are recorded, including timestamps, maximum and minimum temperatures. Data is preprocessed to remove outliers and noise. A moving average method can be used to smooth the temperature data to reduce the impact of transient fluctuations and ensure the reliability of the calculation results. The peak range of high and low temperatures is calculated by finding the maximum and minimum values ​​in the entire temperature data set to obtain the maximum range of high and low temperature variations. Peak range = maximum temperature − minimum temperature. The calculated peak range is recorded and a report is generated containing the specific peak values ​​and corresponding time periods for subsequent analysis and decision-making. An appropriate thermal transition limit risk calculation model is selected. Common methods include heat conduction models and transient thermal transition models. The model should consider the material properties and geometry of the test object to accurately reflect the thermal transition limit risk. The model input parameters are determined, including the peak range of high and low temperatures, the thermal properties of the test object (such as specific heat capacity and thermal conductivity), and the current transient thermal transition limit curve. The transient thermal transition limit risk is calculated based on the selected model. Input the high and low temperature peak ranges and the transient thermal change limit data of the test object to calculate the thermal change limit risk value. By simulating the heat flux distribution, temperature points that may lead to thermal runaway can be identified. The calculation results, including the thermal change limit risk value and the corresponding temperature conditions, are recorded to provide a basis for subsequent decision-making. A safety threshold for thermal tolerance risk is set based on industry standards and the characteristics of the test object. This threshold should take into account the thermal stability of the material and the expected test conditions to ensure safety and reliability. The safety threshold is recorded and used as a basis for subsequent analysis. The calculated transient thermal change limit risk value is compared with the preset safety threshold to determine the risk status. If the risk exceeds the preset threshold, an early warning signal is triggered. The judgment results, including the risk value, threshold, and judgment time, are recorded, and a risk assessment report is generated to help decision-makers understand the current risk status. If the transient thermal change limit risk exceeds the preset safety threshold, a transient thermal change limit early warning signal is generated. This signal can be disseminated through an alarm system or data monitoring platform to ensure timely information dissemination to relevant personnel. Based on the transient thermal change limit early warning signal, decision criteria for adjusting high and low temperature tests are established. These standards should include how to adjust the amplitude, rate and cycle of temperature changes to ensure the safety of the test. Record the adjustment decision criteria, including specific adjustment parameters and implementation steps, to ensure the clarity of the decision. Construct a high and low temperature test adjustment plan based on the adjustment decisions made. For example, reduce the temperature change rate, shorten the high and low temperature alternation cycle, etc., to ensure that the test can be carried out within a safe range. Record the specific parameters of the adjustment plan, including the newly set temperature range, duration and change rate, etc., for subsequent implementation and monitoring. Based on the high and low temperature test adjustment plan, set adaptive temperature change amplitude adjustment parameters. These parameters should take into account the thermal characteristics and environmental changes of the current test object to ensure the rationality of the temperature change amplitude.Record the specific values ​​of the adjustment parameters, including the temperature ramp amplitude, duration, and adjustment rate, for subsequent use. During the high and low temperature test, perform temperature changes according to the adaptive adjustment parameters. For example, gradually adjust the temperature according to the set amplitude to avoid excessive thermal shock to the test object. Record the actual temperature changes during the adjustment process and compare them with the preset parameters to ensure the effectiveness of the adjustment. Select an appropriate global temperature ramp cross-cycle optimization model. Common methods include multi-objective optimization algorithms and dynamic programming. These models can optimize the temperature ramp cycle while considering multiple factors. Determine the model's input parameters, including the adaptive temperature ramp amplitude adjustment parameter, the high and low temperature peak range, and the thermal characteristics of the test object, to ensure the scientific nature of the optimization process. Based on the adaptive temperature ramp amplitude adjustment parameter, perform a global optimization of the high and low temperature simulation cycle. Input the relevant parameters and calculate the optimal temperature ramp cross-cycle to ensure the test object achieves the best thermal response within the optimized cycle. Record the decision variables during the optimization process, including the optimized cycle length and cross-cycle time points, for subsequent verification and monitoring. Record the optimized global temperature ramp cross-cycle to ensure that the results accurately reflect the thermal characteristics of the test object and environmental changes. Generate an optimization result report that compares the cycle characteristics before and after optimization. Verify the effectiveness of the optimization results and observe the effects of the optimization cycle through actual experiments to ensure the scientificity and rationality of the optimization process.

[0078] In this embodiment, step S4 includes the following steps:

[0079] Adjust equipment parameters in real time based on the global temperature change optimization simulation cycle, execute environmental simulation control operations, and detect high and low temperature simulation environment monitoring data;

[0080] Analyze the dynamic temperature changes in the test chamber based on the high and low temperature simulated environment monitoring data to generate dynamic temperature change characteristics;

[0081] Based on the dynamic temperature change characteristics, the humidity synchronization calculation inside the box is performed to obtain the humidity synchronization parameters inside the box;

[0082] Humidity synchronization coordination control decisions are made based on the humidity synchronization parameters in the box to generate a humidity synchronization control strategy.

[0083] In this embodiment, the various device parameters of the high- and low-temperature test chamber are set based on the results of a global temperature change optimization simulation cycle. These parameters include the target temperature, temperature change rate, alternation time, and control strategy. The target temperature should be determined based on the characteristics of the test object and the preset experimental conditions. The initial state of each device, including the current temperature, humidity, and other environmental conditions, is recorded for subsequent monitoring and comparison. The high- and low-temperature test chamber is started and the environmental simulation is performed according to the set optimization cycle and parameters. Ensure that the equipment is operating properly and monitor that all parameters are within the set ranges. Use temperature and humidity sensors to provide real-time feedback on environmental conditions to ensure that the system can automatically adjust based on real-time data. During operation, regularly check the equipment's operating status and fault alarms to ensure that no equipment anomalies occur and ensure the smooth progress of the experiment. Continuously monitor the temperature and humidity data within the test chamber and record changes in environmental parameters. Ensure that the data collection frequency is high enough (for example, once a minute) to capture subtle changes. Store the monitoring data in a database, recording the results of each data collection with a timestamp for subsequent analysis and comparison. The collected high- and low-temperature simulation environmental monitoring data is organized and preprocessed. Outliers and noise are removed to ensure data accuracy. Use a moving average method for smoothing to reduce data fluctuations. Ensure that temperature data and timestamps are consistent for subsequent dynamic analysis. Use statistical analysis methods to analyze the dynamic temperature change characteristics within the test chamber. Use time series analysis techniques to calculate the temperature change rate, fluctuation amplitude, and trend. Record analysis results, including the maximum, minimum, and average temperature values, as well as their rate of change, to provide a basis for subsequent humidity calculations. Based on the dynamic temperature change characteristics, select an appropriate humidity synchronization calculation model. Common models include the heat and moisture balance model and the relative humidity calculation model. These models dynamically calculate humidity changes based on temperature changes. Determine the model's input parameters, including the current temperature, the initial humidity within the test chamber, and the air pressure, to ensure model accuracy. Based on the selected model, input the dynamic temperature change data and perform humidity synchronization calculations. By calculating the impact of dynamic temperature changes on humidity, obtain real-time humidity parameters. Record the humidity synchronization parameters within each calculation cycle, including the calculated humidity value and its changes, for subsequent analysis. Develop a humidity synchronization coordinated control strategy based on the humidity synchronization parameters. These strategies should include how to adjust humidity, setting a target humidity range, and its rate of change to ensure the stability of the test environment. The specifics of the humidity control strategy, including the humidity range, target humidity value, and adjustment rate, should be recorded for subsequent implementation. In the high and low temperature test chamber, the environment should be adjusted according to the established humidity control strategy. Humidity within the chamber should be adjusted in real time using humidity control equipment (such as a humidifier or dehumidifier). The effectiveness of the humidity adjustment process should be monitored to ensure that humidity remains within the set range and is coordinated with temperature fluctuations.During synchronous humidity control, monitor humidity changes in real time and record the difference between actual and target humidity. This ensures effective implementation of humidity adjustment strategies and provides timely feedback on adjustment results. Dynamic adjustments are made based on monitoring results to ensure that humidity changes in sync with temperature, preventing adverse effects on test objects caused by humidity fluctuations.

[0084] In this embodiment, the specific steps of step S5 are:

[0085] Identify regional temperature distribution based on high and low temperature simulated environment monitoring data and generate regional temperature distribution maps;

[0086] Conduct local temperature difference analysis on the regional temperature distribution map and mark the local temperature difference features;

[0087] Calculate the high and low temperature transition rates based on the local temperature difference characteristics to generate high and low temperature transition rates;

[0088] The high and low temperature transition response delay analysis is performed on the high and low temperature transition rates according to the global temperature change optimization simulation cycle, and the transition response delay area is marked.

[0089] In this example, complete environmental monitoring data is obtained from the high and low temperature test chamber, including temperature records for each zone. Each zone should be clearly defined, such as left, right, top, and bottom. Ensure that the temperature data for each zone covers the entire experimental period for comprehensive analysis. Preprocess the monitoring data to remove outliers and noise to ensure data quality. A moving average method can be used to smooth the data to reduce the impact of transient fluctuations. Based on the design of the test chamber, different zones are divided and the temperature data is classified by zone. The temperature data for each zone should be recorded with a corresponding timestamp for subsequent analysis. Use data analysis tools to organize the temperature data from different zones and generate temperature statistics for each zone, including maximum, minimum, average, and temperature distribution range. Use data visualization tools to generate regional temperature distribution maps. You can choose a form such as a heat map to display the temperature data for each zone in shades of color to intuitively reflect the temperature distribution. Ensure that the temperature distribution map includes the necessary legends and annotations to enable the operator to quickly understand the temperature status of each zone. Record the generated temperature distribution map and archive it with the experimental data for subsequent analysis and verification. Analyze the generated regional temperature distribution map to identify areas with significant temperature differences. Focus on areas with significant temperature variations and local hotspots, recording the temperature values ​​and locations of these areas. Calculate the temperature differences between regions and identify those with temperature differences exceeding a preset threshold (e.g., 5°C). These areas are marked as characteristic local temperature differences. Mark these characteristic local temperature differences on the regional temperature distribution map, highlighting them with different colors or symbols. These markings should clearly indicate the specific location and extent of the temperature differences. Record the marked local temperature difference characteristics, including the specific temperature values, location coordinates, and associated time information, for subsequent analysis. Define a high-low temperature transition, typically referring to the rate at which the temperature changes from one extreme to the other. Set the start and end points for the transition time, dividing the transition into zones based on the regional temperature variation. Record the timestamp, start temperature, and end temperature of each transition to facilitate accurate rate calculation. Transition rate = ΔT / Δt, where ΔT is the temperature change (end temperature minus start temperature) and Δt is the time required for the transition. Calculate the corresponding high-low temperature transition rate for each characteristic local temperature region and record the calculated results for each region.

[0090] Define transition response delay, typically the time delay required for a temperature change to reach a certain threshold. A response threshold needs to be set (for example, the time it takes for a temperature change to exceed 2°C). Record the specific time points of the temperature change to ensure accurate capture of the response of each transition. Based on the data from the global temperature change optimization simulation cycle, analyze the relationship between the high and low temperature transition rates and response times, and calculate the transition response delay for each region. Use timing analysis to identify the time points when the temperature reaches the set threshold. Mark the response delay for each region, recording the delay time and the corresponding temperature change for subsequent analysis. Visualize the marked transition response delay regions to generate a response delay distribution map to intuitively display the response delays in different regions. Ensure that the necessary explanations and annotations are included in the chart to help operators understand the cause of the delay. Summarize the results of the transition response delay analysis, identify factors that may affect the response delay, and provide recommendations for subsequent optimization.

[0091] In this embodiment, the specific steps of step S6 are:

[0092] Perform response delay attribution inference on the transition response delay area to obtain the response delay factor;

[0093] Performing a transformation delay temperature compensation calculation on the transformation response delay region according to the response delay factor, and generating a transformation delay temperature compensation strategy;

[0094] Based on the humidity synchronization control strategy and the transition delay temperature compensation strategy, intelligent collaborative simulation optimization is carried out to build an intelligent environmental simulation collaborative control engine.

[0095] In this embodiment, data from a previous transition response delay analysis is obtained, including temperature change curves, transition response times, and environmental conditions for each area. This data provides the basis for attribution inference. Focusing on the characteristic data of the transition response delay area, the temperature changes and response times under different conditions are analyzed to identify key factors that may affect the response delay, such as device location, material properties, and wind speed. An appropriate attribution inference model is selected, with common examples including multivariate regression analysis and decision tree models. These models can handle multiple variables and analyze the impact of each factor on the response delay. The model's input parameters, including temperature change rate, humidity level, and device performance indicators, are determined to ensure the accuracy of the attribution inference. The selected model is used to perform attribution inference on the transition response delay area, identifying the contribution of each response delay factor. For example, by analyzing the model output, the factors with the greatest impact on the transition delay are determined. The attribution analysis results, including the contribution rate of each factor, the direction of influence, and their interactions, are recorded to provide a basis for subsequent compensation calculations. Based on the attribution inference results, a transition delay temperature compensation strategy is developed. This strategy should consider the impact of different response delay factors on temperature changes and aim to compensate for the delay by adjusting temperature settings. Set compensation thresholds to clearly define when temperature compensation is required, such as when response delay exceeds a preset safety range or when local temperature differences are significant. Select an appropriate temperature compensation calculation method, with common methods including linear and nonlinear compensation models. The compensation calculation should be based on the actual temperature curve and response delay characteristics. Determine input parameters, including response delay time, target temperature, and its range, to ensure the accuracy of the compensation calculation. Based on the established compensation strategy, use the selected calculation method to perform temperature compensation. Calculate the compensated target temperature to ensure rapid temperature increase or decrease within the response delay range. Record the compensation calculation results, including the compensated target temperature, compensation amplitude, and corresponding time parameters, for subsequent implementation and verification. Design an intelligent environmental simulation collaborative control engine that integrates a humidity synchronization control strategy and a transition delay temperature compensation strategy. The engine should be able to monitor environmental parameters in real time and automatically adjust according to the established strategies. Define the engine's functional modules, including data acquisition, real-time monitoring, decision-making, and execution control, to ensure the system's intelligence and automation. Implement the intelligent control engine in a high and low temperature test chamber to collect real-time temperature and humidity data. The system should be able to automatically adjust temperature and humidity based on current environmental conditions and pre-set policies to achieve coordinated control. Monitor the system's operating status to ensure the engine is executing according to set parameters and record the effects of each adjustment for subsequent analysis. Evaluate the performance of the intelligent coordinated control engine to determine whether the desired control objectives have been achieved.Evaluation indicators include temperature and humidity stability, response time, and their impact on the test object. Based on the evaluation results, optimization is carried out, and the control strategy and parameter settings are adjusted to ensure that the system can respond flexibly to different environmental conditions and improve the reliability and accuracy of the experiment.

[0096] In this embodiment, an environmental simulation system for a high and low temperature test chamber is provided, which is used to perform the environmental simulation method for the high and low temperature test chamber as described above, including:

[0097] The regional temperature and humidity module is used to obtain the initial state monitoring parameters of the high and low temperature test chamber, divide the regional environmental parameters, fit the gradient changes at the corresponding points in time, and construct multiple regional temperature and humidity gradient change curves;

[0098] The thermal change limit mining module is used to identify the test object in the high and low temperature test chamber; based on the temperature and humidity gradient change curves of multiple regions, the transient thermal change limit evolution of the test object is mined to generate the transient thermal change limit curve of the current test object;

[0099] The temperature change cross cycle module is used to obtain the preset test chamber environment simulation log, and perform global temperature change cross cycle optimization based on the transient thermal change limit curve of the current test object to construct a global temperature change optimization simulation cycle;

[0100] The humidity synchronization module is used to perform environmental simulation control operations according to the global temperature change optimization simulation cycle and collect high and low temperature simulation environment monitoring data; based on the high and low temperature simulation environment monitoring data, it makes humidity synchronization coordination control decisions to generate a humidity synchronization control strategy;

[0101] The response delay analysis module is used to analyze the high and low temperature transition response delay based on the high and low temperature simulation environment monitoring data and mark the transition response delay area;

[0102] The collaborative control module is used to perform transition delay temperature compensation calculation on the transition response delay area and build an intelligent environment simulation collaborative control engine based on the humidity synchronization control strategy.

[0103] The present invention divides the interior of the test chamber into multiple zones and, combined with precise environmental monitoring, ensures that the temperature and humidity conditions of each zone are individually recorded and analyzed. This division facilitates precise control of temperature and humidity variations in different zones, avoiding variations in the uniformity of the test environment. By fitting the time-based gradient curves of regional temperature and humidity, the system accurately captures the temperature and humidity variation trends of each zone at different time points. This facilitates subsequent control and control systems, allowing dynamic adjustments based on actual changes. Environmental control in different zones can better adapt to the needs of different test subjects. Especially for test subjects with high environmental sensitivity, detailed zone division and variation fitting enhance the realism and accuracy of the simulation. Accurate identification and recording of test subject information such as material, size, and shape ensures accurate subsequent thermal response analysis and temperature control. By mining transient thermal limits and combining the temperature and humidity variations in different zones, a precise thermal limit curve is calculated and generated for each test subject. This curve reflects the test subject's maximum tolerance under varying temperature variations, preventing damage or adverse reactions caused by excessive temperature differences. With the transient thermal limit curve, the system automatically adjusts the test chamber's temperature change strategy based on the object's thermal response characteristics, ensuring that the test object is not adversely affected by rapid temperature changes. By analyzing historical test data and combining it with the object's transient thermal limit curve, the system automatically optimizes the cycle and rate of high and low temperature alternation, ensuring a smoother temperature change that meets the object's tolerance limits. By precisely optimizing the temperature change crossover cycle, the system avoids thermal shock caused by sudden temperature rises or falls, thereby improving the test object's survival environment and test reliability. By optimizing the global temperature change cycle, the system creates a more consistent simulation cycle for all test objects and environmental conditions, ensuring efficient and consistent testing. Humidity is a key factor affecting test results in high and low temperature tests. The humidity synchronization module monitors and analyzes temperature changes in real time and automatically adjusts humidity to ensure coordinated temperature and humidity changes. The humidity synchronization module flexibly adjusts humidity based on changes in environmental data during the test, preventing negative impacts on the test object caused by excessively high or low humidity and ensuring the validity of test results. Through synchronized and coordinated control of humidity and temperature, the system achieves more refined environmental regulation, simulating more complex real-world conditions and enhancing the authenticity and reliability of tests. By monitoring the temperature and humidity changes in the test environment in real time, it is possible to accurately analyze and mark potential delay areas during high-temperature transitions. By identifying delay areas, the system can avoid temperature changes within these areas, preventing uneven heat loads from affecting test results. Marking delay areas for high-temperature transition responses helps predict potential errors from environmental changes in advance, allowing necessary compensatory measures to ensure the stability of the test process. Delay analysis can significantly improve data accuracy during the test, ensuring that temperature and humidity changes during high-temperature transitions are more closely aligned with the needs of the test object.By calculating the temperature compensation amount in the transition response delay area in real time, the error caused by ambient temperature lag can be effectively reduced, ensuring a smoother high and low temperature transition process. The combination of humidity synchronization control strategy and temperature compensation strategy can provide a more intelligent control framework for the entire experiment. The system can automatically adjust parameters such as humidity and temperature based on all real-time data, and perform joint optimization control to make environmental simulation more accurate and flexible. By introducing an intelligent collaborative control engine, the system can automatically adjust environmental conditions according to the needs of different test objects to minimize interference from human operations and improve the degree of automation of the test.

[0104] The present invention is therefore intended to be illustrative and non-restrictive in all respects, with the scope of the invention being defined by the appended claims rather than the foregoing description, and all changes that come within the meaning and range of equivalents of the application documents are intended to be embraced therein.

[0105] The foregoing description is intended only to provide specific embodiments of the present invention, which will enable those skilled in the art to understand and implement the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown herein, but is to be construed in the widest possible manner consistent with the principles and novel features disclosed herein.

Claims

1. A method for simulating the environment of a high and low temperature test chamber, characterized in that: The following steps are involved: Step S1: Obtain the initial state monitoring parameters of the high and low temperature test chamber, divide the regional environmental parameters, and perform multi-point gradient change fitting to construct temperature and humidity gradient change curves for multiple regions; Step S2: Identify the test object in the high and low temperature test chamber; perform transient thermal change limit evolution mining on the test object based on the temperature and humidity gradient change curves of multiple regions to generate a transient thermal change limit curve of the current test object; Step S3: Obtain the preset test chamber environment simulation log, and perform global temperature change cross-cycle optimization based on the transient thermal change limit curve of the current test object to construct a global temperature change optimization simulation cycle; Step S4: Execute environmental simulation control operations according to the global temperature change optimization simulation cycle, collect high and low temperature simulation environment monitoring data; make humidity synchronization coordination control decisions based on the high and low temperature simulation environment monitoring data to generate a humidity synchronization control strategy; Step S5: performing high and low temperature transition response delay analysis based on the high and low temperature simulation environment monitoring data, and marking the transition response delay area; Step S6: performing a transition delay temperature compensation calculation on the transition response delay area, and constructing an intelligent environment simulation collaborative control engine based on the humidity synchronization control strategy; Among them, the specific steps of step S1 are: Acquire initial state monitoring parameters of the high and low temperature test chamber, wherein the initial state monitoring parameters of the high and low temperature test chamber include initial temperature, humidity, and air pressure inside the test chamber; Calculate real-time temperature and humidity parameters based on the initial state monitoring parameters; Divide the real-time temperature and humidity parameters into regional environmental parameters to obtain the temperature and humidity parameters of different areas; Analyze the time series changes of temperature and humidity parameters in different regions to generate the time series change characteristics of temperature and humidity in multiple regions; Perform multi-point gradient change fitting on the time series change characteristics of temperature and humidity in multiple regions to construct temperature and humidity gradient change curves for multiple regions; Among them, the specific steps of step S2 are: Identify test objects in high and low temperature test chambers; Performing real-time temperature change detection on the test object to generate temperature change detection parameters of the test object; performing thermal inertia identification on the temperature change detection parameters to generate thermal inertia characteristics of the test object; quantifying the temperature change hysteresis of the test object according to the temperature and humidity gradient change curves of the multiple regions to generate a heat capacity characteristic of the test object; Perform thermal response state analysis on the thermal inertia characteristics and heat capacity characteristics of the test object to generate thermal response state data of the test object; Based on the thermal response state data of the test object, transient thermal change limit evolution mining is performed to generate the transient thermal change limit curve of the current test object; Among them, the specific steps of step S3 are: Get the preset test chamber environment simulation log; Identify the high and low temperature crossover time of the preset test chamber environment simulation log to obtain the high and low temperature crossover change time; Calculate the ambient temperature simulation cycle based on the high and low temperature cross-change time to generate a high and low temperature change simulation cycle; According to the transient thermal change limit curve of the current test object, the global temperature change cross cycle optimization of the high and low temperature change simulation cycle is performed to construct a global temperature change optimization simulation cycle; The specific steps of performing global temperature change cross cycle optimization on the high and low temperature change simulation cycle according to the transient thermal change limit curve of the current test object and constructing the global temperature change optimization simulation cycle are as follows: Calculating the maximum range of high and low temperature changes based on the preset test chamber environment simulation log to obtain the peak range of high and low temperatures; Based on the transient thermal change limit curve of the current test object, the thermal change limit risk is calculated for the high and low temperature peak range to obtain the transient thermal change limit risk; Conduct thermal tolerance risk assessment on transient thermal change limit risk. When the transient thermal change limit risk exceeds the preset safety threshold, a transient thermal change limit warning signal is generated. Make high and low temperature test adjustment decisions based on transient thermal change limit warning signals to obtain high and low temperature test adjustment plans; Adaptively adjust the temperature variation amplitude of the high and low temperature peak range based on the high and low temperature test adjustment plan to generate adaptive temperature variation amplitude adjustment parameters; Based on the adaptive temperature variation amplitude adjustment parameters, global temperature variation cross-cycle optimization is performed on the high and low temperature variation simulation cycle.

2. The environmental simulation method of the high and low temperature test chamber according to claim 1, characterized in that: The specific steps of step S4 are: Adjust equipment parameters in real time based on the global temperature change optimization simulation cycle, execute environmental simulation control operations, and detect high and low temperature simulation environment monitoring data; Analyze the dynamic temperature changes in the test chamber based on the high and low temperature simulated environment monitoring data to generate dynamic temperature change characteristics; Based on the dynamic temperature change characteristics, the humidity synchronization calculation inside the box is performed to obtain the humidity synchronization parameters inside the box; Humidity synchronization coordination control decisions are made based on the humidity synchronization parameters in the box to generate a humidity synchronization control strategy.

3. The environmental simulation method of the high and low temperature test chamber according to claim 1, characterized in that: The specific steps of step S5 are: Identify regional temperature distribution based on high and low temperature simulated environment monitoring data and generate regional temperature distribution maps; Conduct local temperature difference analysis on the regional temperature distribution map and mark the local temperature difference features; Calculate the high and low temperature transition rates based on the local temperature difference characteristics to generate high and low temperature transition rates; The high and low temperature transition response delay analysis is performed on the high and low temperature transition rates according to the global temperature change optimization simulation cycle, and the transition response delay area is marked.

4. The environmental simulation method of a high and low temperature test chamber according to claim 1, characterized in that: The specific steps of step S6 are: Perform response delay attribution inference on the transition response delay area to obtain the response delay factor; Performing a transformation delay temperature compensation calculation on the transformation response delay region according to the response delay factor, and generating a transformation delay temperature compensation strategy; Based on the humidity synchronization control strategy and the transition delay temperature compensation strategy, intelligent collaborative simulation optimization is carried out to build an intelligent environmental simulation collaborative control engine.

5. An environmental simulation system for a high and low temperature test chamber, characterized in that: The method for performing the environmental simulation of the high and low temperature test chamber according to claim 1 comprises: The regional temperature and humidity module is used to obtain the initial state monitoring parameters of the high and low temperature test chamber, divide the regional environmental parameters, fit the gradient changes at the corresponding points in time, and construct multiple regional temperature and humidity gradient change curves; The thermal change limit mining module is used to identify the test object in the high and low temperature test chamber; based on the temperature and humidity gradient change curves of multiple regions, the transient thermal change limit evolution of the test object is mined to generate the transient thermal change limit curve of the current test object; The temperature change cross cycle module is used to obtain the preset test chamber environment simulation log, and perform global temperature change cross cycle optimization based on the transient thermal change limit curve of the current test object to construct a global temperature change optimization simulation cycle; The humidity synchronization module is used to perform environmental simulation control operations according to the global temperature change optimization simulation cycle and collect high and low temperature simulation environment monitoring data; based on the high and low temperature simulation environment monitoring data, it makes humidity synchronization coordination control decisions to generate a humidity synchronization control strategy; The response delay analysis module is used to analyze the high and low temperature transition response delay based on the high and low temperature simulation environment monitoring data and mark the transition response delay area; The collaborative control module is used to perform transition delay temperature compensation calculation on the transition response delay area and build an intelligent environment simulation collaborative control engine based on the humidity synchronization control strategy.

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