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

Abstract

The invention relates to the field of environment simulation control, in particular to an environment simulation method and system for a high and low temperature test box. The method comprises the following steps: acquiring initial state monitoring parameters of a high and low temperature test box, performing regional environment parameter division and time point gradient change fitting, and constructing a plurality of regional temperature and humidity gradient change curves; identifying a test object in the high and low temperature test box; performing transient thermal change limit evolution excavation on the test object according to the plurality of regional temperature and humidity gradient change curves to generate a transient thermal change limit curve of the current test object; acquiring a preset test box environment simulation log, performing global temperature change cross period optimization according to the transient thermal change limit curve of the current test object, and constructing a global temperature change optimization simulation period; and executing environment simulation control operation according to the global temperature change optimization simulation period, and collecting high and low temperature simulation environment monitoring data. According to the invention, quick-response, accurate and stable environment simulation control is realized.

Description

Technical Field

[0001] The present invention relates to the field of environmental simulation control, and particularly 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 has an increasingly high demand for environmental control in the production process. Especially during the test and verification process under high and low temperature environmental conditions, high and low temperature test chambers have become indispensable important equipment. High and low temperature test chambers are mainly used to simulate the effects of extreme temperature conditions on objects in order to conduct tests on product reliability, performance stability, durability, etc. However, in practical applications, the environmental simulation of high and low temperature test chambers is often affected by various factors, such as non-uniformity of temperature change, instability of humidity control, and lag of system regulation. These factors may lead to a gap between the environmental simulation during the test process and the expected standard, affecting the accuracy of the experimental results.

[0003] Traditional environmental simulation methods for high and low temperature test chambers usually rely on simple temperature and humidity control, lacking comprehensive analysis of environmental state changes and real-time adjustment means. Existing systems often have difficulty in real-time monitoring and accurately adjusting the temperature and humidity distribution and air flow under complex operating environments, resulting in temperature or humidity deviations in certain areas, which in turn affect the state of the test object. This environmental control method is not only inefficient but also easily affected by external factors such as the number and type of test objects, resulting in unstable control effects.

[0004] With the development of intelligent technology, higher requirements are put forward for the environmental simulation method of high and low temperature test chambers. The traditional method relying on manual operation and simple control can no longer meet the test requirements of modern high precision, high efficiency, and high reliability. Therefore, there is an urgent need for an intelligent and high-precision environmental simulation method for high and low temperature test chambers, which can accurately simulate environmental changes under different temperature and humidity conditions through the combination of real-time data monitoring, dynamic adjustment, and intelligent algorithms, ensuring the stability and accuracy of the environment 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, including the following steps: Step S1: Obtain the initial state monitoring parameters of the high and low temperature test chamber, conduct regional environmental parameter division and time point gradient change fitting, and construct multiple regional temperature and humidity gradient change curves; Step S2: Identify the test object inside the high and low temperature test chamber; perform transient thermal change limit evolution mining on the test object according to the temperature and humidity gradient change curves of multiple regions to generate the 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 according to the transient thermal change limit curve of the current test object to construct a global temperature change optimization simulation cycle; Step S4: Execute the environment simulation control operation according to the global temperature change optimization simulation cycle, and collect the high and low temperature simulation environment monitoring data; make a humidity synchronization coordination control decision based on the high and low temperature simulation environment monitoring data to generate a humidity synchronization control strategy; Step S6: 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; Step S8: Perform transition delay temperature compensation calculation on the transition response delay area, and construct an intelligent environment simulation collaborative control engine based on the humidity synchronization control strategy.

[0007] The present invention ensures that the system can perform targeted thermal response analysis according to the materials and shapes of different objects by efficiently identifying the test objects, thereby avoiding interference caused by different objects to the test conditions and ensuring that the test conditions for each object meet its unique requirements. By analyzing the thermal change limit of the test object based on the temperature and humidity gradient change curves in different regions, the thermal response of the test object can be accurately predicted. Through the evolutionary mining technology, this method can accurately simulate the reaction of the object under transient temperature changes, thereby constructing a dynamic transient thermal change limit curve. This curve can reflect the thermal change characteristics of the object in real time, enabling the system to adjust the rate and amplitude of high and low temperature changes in real time and avoid excessive temperature shocks to the object. According to the transient thermal change limit curve of the test object, the system can provide personalized temperature control strategies for each object, ensuring that the impact of the experimental environment on the object is minimized and achieving a more accurate environmental simulation effect. Obtaining the initial state monitoring parameters (such as temperature, humidity, air flow, etc.) inside the test chamber and performing refined regional division of the interior of the test chamber based on these data can provide accurate basic data for subsequent temperature and humidity control. The temperature and humidity changes in each region are processed separately, ensuring the accuracy of the simulated environment. The temperature and humidity change curves of the regional environment are accurately modeled through fitting methods, enabling the trend of temperature and humidity changes to be accurately predicted. This change curve provides a reliable reference for the system during the high and low temperature alternation process, making the temperature and humidity changes during the test more in line with the requirements in actual applications. By obtaining and analyzing the preset environmental simulation log of the test chamber and combining it with the transient thermal change limit curve of the object, the cross cycle of temperature changes can be optimized in real time. This optimization not only ensures that the temperature changes more smoothly but also avoids interference or damage caused by excessive temperature fluctuations during the test. Through the global optimization of the high and low temperature cycles, the adaptability of the experimental object can be better adjusted, making the temperature changes during the entire test process conform to the thermal change response limit of the object, thereby improving the accuracy and reliability of the test. The optimized temperature change cycle is more flexible and adaptable, capable of meeting complex and changing test requirements. By executing the optimized global temperature change simulation cycle, the system can accurately control the high and low temperature environments inside the test chamber, avoiding the problem of uneven temperature and humidity changes that occur in traditional methods. This control can ensure that the test object is exposed to a more real and consistent environment. During the temperature change process, humidity is often a key factor affecting the reaction of the experimental object. By performing humidity synchronization and coordination control based on the high and low temperature environment monitoring data, the dynamic balance between humidity and temperature can be effectively ensured, avoiding the impact of unstable humidity on the test results. The system can generate humidity synchronization control strategies for each test object and adjust them according to real-time environmental data to ensure that the humidity change does not cause excessive interference to the test object, thereby improving the test accuracy. By performing response delay analysis based on the high and low temperature simulation environment monitoring data, the delay areas that may occur during the high and low temperature alternation process can be identified.This step can ensure that the system detects and corrects the hysteresis phenomenon during the temperature transition in real time, thus guaranteeing the efficiency of the experimental process. The system can automatically mark the regions with delayed transition responses and perform targeted compensation to prevent these regions from affecting the accuracy of temperature and humidity during the test. This process can significantly reduce the hysteresis effect of temperature response, ensuring a more stable environment in the entire test chamber. By calculating the transition delay temperature compensation for the regions with delayed transition responses, the error caused by the temperature response delay can be effectively reduced, ensuring a more accurate and timely high-low temperature alternation process in the environment. This step greatly improves the dynamic response speed of the test chamber environment. Based on the transition delay temperature compensation, the system combines the humidity synchronization control strategy to construct an intelligent environment simulation collaborative control engine, achieving the joint control optimization of temperature and humidity. This intelligent collaborative control can adjust the changes in high-low temperature and humidity in real time, making the entire test process smoother and more accurate. Through the intelligent environment simulation collaborative control engine, the system can globally optimize the temperature and humidity control according to all real-time data and feedback, further enhancing the system's adaptability and prediction ability to environmental changes, and ensuring that the test object can remain in an ideal test environment at all stages.

[0008] In this specification, an environment simulation system for a high-low temperature test chamber is provided, which is used to execute the environment simulation method of the high-low temperature test chamber as described above, including: A regional temperature and humidity module, which is used to obtain the initial state monitoring parameters of the high-low temperature test chamber, perform regional environmental parameter division and fitting of the time-point gradient change, and construct multiple regional temperature and humidity gradient change curves; A thermal change limit mining module, which is used to identify the test object in the high-low temperature test chamber; perform transient thermal change limit evolution mining on the test object according to multiple regional temperature and humidity gradient change curves, and generate the transient thermal change limit curve of the current test object; A temperature change cross-cycle module, which is used to obtain the preset test chamber environment simulation log, and perform global temperature change cross-cycle optimization according to the transient thermal change limit curve of the current test object, and construct a global temperature change optimization simulation cycle; A humidity synchronization module, which is used to execute the environment simulation control operation according to the global temperature change optimization simulation cycle, collect the high-low temperature simulation environment monitoring data; make a humidity synchronization coordination control decision according to the high-low temperature simulation environment monitoring data to generate a humidity synchronization control strategy; A response delay analysis module, which is used to perform high-low temperature transition response delay analysis according to the high-low temperature simulation environment monitoring data, and mark the regions with delayed transition responses; A collaborative control module, which is used to calculate the transition delay temperature compensation for the regions with delayed transition responses, and construct an intelligent environment simulation collaborative control engine based on the humidity synchronization control strategy.

[0009] The present invention subdivides the interior of the test chamber into multiple regions and, combined with precise environmental monitoring, ensures that the temperature and humidity states of each region are separately recorded and analyzed. This partitioning method helps to precisely control the temperature and humidity changes in different regions and avoid differences in the uniformity of the test environment. By fitting the time-point gradient change curves of the regional temperature and humidity, the system can accurately capture the temperature and humidity change trends of each region at different time points. This helps the subsequent regulation and control system to make dynamic adjustments according to the actual changes. The environmental control of different regions can better meet the needs of different test objects. Especially when the test objects are highly sensitive to the environment, the detailed regional partitioning and change fitting will enhance the realism and accuracy of the simulation. Accurately identify and record information such as the material, size, and shape of the test object to ensure accurate thermal response analysis and temperature control of the test object. Through transient thermal change limit excavation, combined with the temperature and humidity changes in different regions, accurately calculate and generate the thermal change limit curve of each test object. This curve can reflect the maximum tolerance of the test object under different temperature change conditions and avoid object damage or adverse reactions caused by excessive temperature differences. With the transient thermal change limit curve, the system can automatically adjust the temperature change strategy of the test chamber based on the thermal response characteristics of the object to ensure that the test object is not adversely affected by rapid temperature changes. By analyzing historical test data and combining with the transient thermal change limit curve of the object, automatically optimize the cycle and speed of high and low temperature alternation to make the temperature change smoother and conform to the tolerance limit of the object. By precisely optimizing the temperature change crossover period, the system can avoid thermal shocks caused by sudden temperature rises or drops, thereby improving the survival environment of the experimental object and the reliability of the test. Through the optimization of the global temperature change cycle, the system can construct a more consistent simulation cycle for all test objects and environmental conditions to ensure the efficiency and consistency of the experiment. Humidity is an important factor affecting the test results in high and low temperature tests. The humidity synchronization module automatically adjusts the humidity by real-time monitoring and analyzing temperature changes to ensure the coordination of temperature and humidity changes. The humidity synchronization module can flexibly adjust the humidity according to the changes in environmental data during the test to avoid negative impacts on the test object due to too high or too low humidity and ensure the validity of the test results. Through the synchronous coordinated control of humidity and temperature, the system achieves more refined environmental regulation, can simulate more complex actual environmental conditions, and enhances the authenticity and reliability of the test. By real-time monitoring the temperature and humidity changes in the test environment, it is possible to accurately analyze and mark the possible delay regions during the high and low temperature transitions. By identifying the delay regions, the system can avoid temperature changes in these regions to prevent uneven thermal loads from affecting the test results. The marking of the high and low temperature transition response delay regions helps to predict in advance the possible errors in environmental changes, so as to take necessary compensation measures to ensure the stability of the test process. Delay analysis can significantly improve the data accuracy during the test process and ensure that the temperature and humidity changes during the high and low temperature transitions are more in line with the needs of the test object.By calculating the temperature compensation amount for the response delay area in real time, the error caused by the environmental temperature lag can be effectively reduced, ensuring a smoother high and low temperature transition process. The combination of the humidity synchronization control strategy and the 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 according to all real-time data for combined optimization control, making the environmental simulation more accurate and flexible. By introducing an intelligent collaborative control engine, the system can automatically adjust the environmental conditions according to the needs of different test objects, minimizing the interference of manual operations and improving the automation level of the experiment. Brief Description of the Drawings

[0010] Figure 1 It 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; Figure 2 It is a schematic detailed implementation step flow chart of step S1; Figure 3 It is a schematic detailed implementation step flow chart of step S2; Figure 4 It is a schematic detailed implementation step flow chart of step S3. Detailed Implementation Manner

[0011] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0012] The embodiments of the present application provide an environmental simulation method and system for a high and low temperature test chamber. The execution subjects of the environmental simulation method and system for the high and low temperature test chamber include, but are not limited to: mechanical equipment, data processing platforms, cloud server nodes, network upload devices, etc. that can be regarded as general computing nodes of the present 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.

[0013] Please refer to Figures 1 to 4 , the present invention provides an environmental simulation method for a high and low temperature test chamber, and the environmental simulation method for the high and low temperature test chamber includes the following steps: Step S1: Obtain the initial state monitoring parameters of the high and low temperature test chamber, perform regional environmental parameter division and time point gradient change fitting, and construct multiple regional temperature and humidity gradient change curves; 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 according to multiple regional temperature and humidity gradient change curves, and generate a transient thermal change limit curve of the current test object; Step S3: Obtain the preset environmental simulation log of the test chamber, and perform global temperature change cross-cycle optimization according to the transient thermal change limit curve of the current test object to construct a global temperature change optimization simulation cycle; Step S4: Optimize the simulation cycle execution environment simulation control job according to the global temperature change, and collect high and low temperature simulation environment monitoring data; make a humidity synchronization coordination control decision based on the high and low temperature simulation environment monitoring data to generate a humidity synchronization control strategy; Step S5: Conduct a high and low temperature transition response delay analysis based on the high and low temperature simulation environment monitoring data, and mark the transition response delay area; Step S6: Perform a transition delay temperature compensation calculation on the transition response delay area, and construct an intelligent environment simulation collaborative control engine based on the humidity synchronization control strategy.

[0014] The present invention ensures that the system can perform targeted thermal response analysis according to the materials and shapes of different objects by efficiently identifying the test objects, thereby avoiding interference caused by different objects to the test conditions and ensuring that the test conditions for each object meet its unique requirements. By analyzing the thermal change limit of the test object based on the temperature and humidity gradient change curves in different regions, the thermal response of the test object can be accurately predicted. Through the evolutionary mining technology, this method can accurately simulate the reaction of the object under transient temperature changes, thereby constructing a dynamic transient thermal change limit curve. This curve can reflect the thermal change characteristics of the object in real time, enabling the system to adjust the rate and amplitude of high and low temperature changes in real time and avoid excessive temperature shocks to the object. According to the transient thermal change limit curve of the test object, the system can provide personalized temperature control strategies for each object, ensuring that the impact of the experimental environment on the object is minimized and achieving a more accurate environmental simulation effect. Obtaining the initial state monitoring parameters (such as temperature, humidity, air flow, etc.) inside the test chamber and performing refined regional division of the interior of the test chamber based on these data can provide accurate basic data for subsequent temperature and humidity control. The temperature and humidity changes in each region are processed separately, ensuring the accuracy of the simulated environment. The temperature and humidity change curves of the regional environment are accurately modeled through fitting methods, enabling the trend of temperature and humidity changes to be accurately predicted. This change curve provides a reliable reference for the system during the high and low temperature alternation process, making the temperature and humidity changes during the test more in line with the requirements in actual applications. By obtaining and analyzing the preset environmental simulation log of the test chamber and combining it with the transient thermal change limit curve of the object, the cross cycle of temperature changes can be optimized in real time. This optimization not only ensures that the temperature changes more smoothly but also avoids interference or damage caused by excessive temperature fluctuations during the test. Through the global optimization of the high and low temperature cycles, the adaptability of the experimental object can be better adjusted, making the temperature changes during the entire test process conform to the thermal change response limit of the object, thereby improving the accuracy and reliability of the test. The optimized temperature change cycle is more flexible and adaptable, capable of meeting complex and changing test requirements. By executing the optimized global temperature change simulation cycle, the system can accurately control the high and low temperature environments inside the test chamber, avoiding the problem of uneven temperature and humidity changes that occur in traditional methods. This control can ensure that the test object is exposed to a more real and consistent environment. During the temperature change process, humidity is often a key factor affecting the reaction of the experimental object. By performing humidity synchronization and coordination control based on the high and low temperature environment monitoring data, the dynamic balance between humidity and temperature can be effectively ensured, avoiding the impact of unstable humidity on the test results. The system can generate a humidity synchronization control strategy for each test object and adjust it according to real-time environmental data to ensure that the humidity change does not cause excessive interference to the test object, thereby improving the test accuracy. By performing response delay analysis through the high and low temperature simulation environment monitoring data, the delay areas that may occur during the high and low temperature alternation process can be identified.This step can ensure that the system detects and corrects the hysteresis phenomenon during the temperature transition in real time, thus guaranteeing the efficiency of the experimental process. The system can automatically mark the regions with delayed transition responses and compensate them specifically to prevent these regions from affecting the accuracy of temperature and humidity during the test. This process can significantly reduce the hysteresis effect of temperature response, ensuring a more stable environment in the entire test chamber. By performing the conversion delay temperature compensation calculation on the regions with delayed transition responses, the error caused by the temperature response delay can be effectively reduced, ensuring a more accurate and timely high-low temperature alternating process in the environment. This step greatly improves the dynamic response speed of the test chamber environment. Based on the conversion delay temperature compensation, the system combines the humidity synchronization control strategy to construct an intelligent environment simulation collaborative control engine, achieving the joint control optimization of temperature and humidity. This intelligent collaborative control can adjust the change processes of high-low temperature and humidity in real time, making the entire test process smoother and more accurate. Through the intelligent environment simulation collaborative control engine, the system can globally optimize the temperature and humidity control according to all real-time data and feedback situations, further enhancing the system's adaptability and prediction ability to environmental changes, and ensuring that the test object can remain in the ideal test environment at all stages.

[0015] In the embodiment of the present invention, refer to Figure 1 , which is a schematic diagram of the step flow of the environment simulation method for a high-low temperature test chamber of the present invention. In this example, the steps of the environment simulation method for the high-low temperature test chamber include: Step S1: Obtain the initial state monitoring parameters of the high-low temperature test chamber, perform regional environmental parameter division and time-point gradient change fitting, and construct multiple regional temperature and humidity gradient change curves; In this embodiment, high-precision temperature and humidity sensors are installed inside the high and low temperature test chamber to ensure that the accuracy and response speed of the sensors can meet the experimental requirements. The selected sensors should have fast response characteristics, such as thermocouples or capacitive humidity sensors. Before the experiment starts, calibrate the sensors to ensure accurate readings. Perform preheating and precooling under different environmental conditions to eliminate the temperature lag phenomenon of the equipment. Develop a data acquisition plan and set the acquisition frequency of monitoring parameters. For example, record temperature and humidity data every minute to capture the details of environmental changes. At the same time, set the start time and end time of data recording to ensure the continuity and integrity of the data. Record the environmental parameters of the initial state, including the initial temperature, humidity, air pressure, etc. inside the test chamber for subsequent analysis. Ensure that the data storage format is unified, and CSV or database storage can be selected. According to the design of the test chamber and experimental requirements, divide the interior of the test chamber into multiple regions, such as upper left, upper right, lower left, lower right, etc. This division can help analyze the environmental characteristics of different regions and identify the laws of temperature and humidity changes. Determine the specific positions and ranges of each region, record the coordinates of each region for subsequent data analysis. The division of regions should be reasonably set according to the layout of the test chamber and the position of the test object. Group the collected monitoring parameters by region, and organize the temperature and humidity data of each region into the corresponding regional data set. Ensure that the data of each region contains timestamps for time series analysis. Record the initial state parameters of each region, including the initial temperature, humidity and their change ranges for subsequent analysis and comparison. Select a suitable gradient change fitting model. Common methods include linear regression, polynomial fitting or exponential fitting, etc. These models can effectively describe the trend of temperature and humidity changing with time. Determine the input parameters of the model, including the temperature and humidity data of each region and the timestamps to ensure the accuracy of the fitting process. Perform gradient change fitting on the temperature and humidity data of each region, and calculate the temperature and humidity change rates at different time points. Through fitting, the change curves of each region can be obtained, and the change trends and fluctuation amplitudes of temperature and humidity can be identified. Record the fitting results, including the fitting equation, correlation coefficient and fitting error, etc. to evaluate the accuracy of the fitting. Ensure that the fitting results of each region can truly reflect the characteristics of its temperature and humidity changes. According to the fitting results, generate the temperature and humidity gradient change curves of each region. These curves should clearly show the changes of temperature and humidity at different time points, and chart tools can be used for visualization. Ensure that the curve chart contains necessary annotations and legends so that the operator can quickly understand the temperature and humidity change characteristics of each region.

[0016] Step S2: Identify the test object inside the high and low temperature test chamber; perform transient thermal change limit evolution mining on the test object according to the temperature and humidity gradient change curves of multiple regions, and generate the transient thermal change limit curve of the current test object; In this embodiment, the test object is reasonably placed in the high and low temperature test chamber to ensure that its position is fixed and does not affect the temperature and humidity changes in the surrounding environment. The test object can be an electronic component, a material sample, or other objects to be tested, and its identification should be clear. Record the basic characteristics of the test object, including material type, size, weight, and thermal characteristics (such as thermal conductivity, specific heat capacity, etc.) for use in subsequent analysis. Install additional temperature and humidity sensors around the test object to accurately monitor the changes in the environment where the test object is located. These sensors should be placed at a certain distance from the test object and be able to capture the transient temperature changes on the object's surface. Ensure that all sensors are in good calibration status, and record the position of each sensor and its monitoring range for subsequent data analysis. During the test, collect the temperature and humidity data of the test object and its surrounding environment in real time. Ensure that the data collection frequency is high enough (such as once per second) to capture the details of transient changes. Organize the collected data by region and time to establish a corresponding database, including the temperature changes of the test object, the temperature and humidity data of the surrounding environment, etc. Select a suitable thermal change limit model. Commonly used models include the transient heat conduction model and the unsteady heat model. These models can describe the thermal response of the object during temperature changes. Determine the input parameters of the model, including the material characteristics of the test object, the initial temperature, the environmental temperature change rate, etc., to ensure the effectiveness of the model. Input the organized temperature and humidity data into the selected thermal change limit model to calculate the transient thermal change limit evolution. Identify the thermal response characteristics of the test object under different temperature and humidity conditions and generate a transient thermal change limit curve. Record the key data points of the transient thermal change limit curve, including the transient thermal change limit value, the change rate, and the corresponding time points. These data will help understand the thermal stability and safety of the test object. Generate a corresponding transient thermal change limit curve based on the calculated transient thermal change limit data. This curve should be able to reflect the thermal change limit of the test object under different environmental conditions. Use a suitable fitting method (such as polynomial fitting or linear fitting) to fit the transient thermal change limit data to improve the smoothness and readability of the curve and ensure that the curve can accurately reflect the actual situation.

[0017] Step S3: Obtain the preset test chamber environment simulation log, and perform global temperature change cross-cycle optimization according to the transient thermal change limit curve of the current test object to construct a global temperature change optimization simulation cycle; In this embodiment, a complete environmental simulation log is obtained from the control system of the high and low temperature test chamber. The log should include records of environmental parameters such as temperature, humidity, air pressure, etc. at various time points, as well as records of the equipment operating status and any abnormal events. Ensure that the time stamps of the log data are synchronized with the experimental cycle so that the environmental conditions at each time point can be accurately corresponded. Organize the collected environmental simulation log to remove duplicate data and outliers. Ensure the integrity and accuracy of the data for subsequent analysis. Classify the data according to different environmental parameters and establish a data framework, including temperature change curves, humidity change curves, and the change situations of other relevant environmental parameters. Analyze the key data in the environmental simulation log to identify the change trends, fluctuation ranges of temperature and humidity, and their impacts on the test object. For example, pay attention to the environmental stability in the high temperature and low temperature stages, and the humidity change situations in these stages. Record the analysis results and generate a report indicating the performance under different environmental conditions, providing data support for global temperature change optimization. Review the transient thermal change limit curve of the test object to identify the thermal stability and vulnerability of the object under different temperature conditions. In the curve, pay attention to observing the key turning points and limit values, which are crucial for optimizing the temperature change cycle. Record the important parameters in the curve, such as the thermal change limit value, change rate, and the corresponding time points for reference in subsequent optimization. Based on the transient thermal change limit curve, design a global temperature change cross-cycle optimization scheme. The scheme should include the duration of different temperature stages, the temperature change rate, and their alternating order. For example, it can be set that the high temperature stage is 60°C, lasting for 30 minutes, then quickly cooling down to -20°C, lasting for 30 minutes, and then returning to normal temperature. Consider the dynamic adjustment of humidity during the temperature alternation process to reduce the thermal stress of the test object. Ensure that at the end of each stage, the temperature and humidity parameters are within the safe range. Simulate the new global temperature change optimization cycle in the laboratory environment, and monitor the temperature and humidity changes of the test object in real time to test the effectiveness of the optimization scheme. Record the environmental parameters at each stage and their impacts on the test object. Analyze the experimental data to verify whether the optimization cycle can effectively reduce the thermal stress of the test object and ensure that the temperature and humidity changes meet the expected safety standards. Make necessary adjustments according to the experimental results to ensure the feasibility and effectiveness of the scheme.

[0018] Step S4: Execute the environmental simulation control operation according to the global temperature change optimization simulation cycle, and collect the high and low temperature simulation environment monitoring data; make a humidity synchronization coordination control decision based on the high and low temperature simulation environment monitoring data to generate a humidity synchronization control strategy; In this embodiment, the simulation period is optimized based on the global temperature change, and a detailed environmental simulation control plan is formulated. The plan should include the target temperature range, rate of change, alternating cycle and its duration. For example, set the high-temperature stage to 60°C for 30 minutes, and the low-temperature stage to -20°C for 30 minutes. Ensure that the temperature change in each stage conforms to the experimental requirements, avoid unnecessary thermal shock to the test object, and record the start and end times of each stage. Configure the temperature and humidity monitoring equipment of the high and low temperature test chamber to ensure that all sensors are working properly and have been calibrated. The monitoring equipment should include thermocouples, humidity sensors and data loggers. Set the data acquisition frequency. It is recommended to record the temperature and humidity data every minute or at a higher frequency (such as every 10 seconds) to ensure that subtle environmental changes can be captured. Start the high and low temperature test chamber and execute the temperature change control according to the formulated plan. Ensure that the system can automatically adjust according to the set temperature change cycle and monitor the current environmental status in real time. At the end of each stage, record the environmental parameters, including temperature, humidity and air pressure, etc., and compare them with the set values for subsequent analysis. During the environmental simulation process, monitor the data of the high and low temperature simulation environment in real time. Ensure that the data acquisition system can operate stably and record the changes in temperature and humidity in a timely manner. Organize the collected data according to the time stamp, establish a database to ensure the integrity and traceability of the data. Regularly check the collected data to ensure its accuracy and consistency. By comparing with the preset temperature and humidity targets, identify potential outliers. For the identified outliers, record them and analyze their possible causes, such as sensor failures, external interferences, etc., for necessary adjustments. Store the monitored data collected in a secure database to ensure the integrity and security of the data. Record the time stamp, temperature value, humidity value of each data point and its corresponding area parameters. Regularly back up the data to prevent data loss and provide a basis for subsequent data analysis. According to the monitoring data of the high and low temperature simulation environment, analyze the humidity change trend and identify the humidity change characteristics at different temperature stages. Record the initial value, target value and change range of humidity. Determine the need for humidity synchronization control. For example, in the high-temperature stage, whether the humidity needs to be maintained within a certain specific range to avoid affecting the test object. Based on the analysis results, formulate a humidity synchronization control strategy. This strategy should consider the current humidity level, target humidity range and the impact of temperature changes on humidity. For example, set the relative humidity to be controlled between 40% - 60% in the high-temperature stage. Record the specific parameters of humidity adjustment, including the rate of humidity change, target humidity value and adjustment method (such as humidification, dehumidification, etc.). While implementing the environmental simulation control, implement the humidity synchronization control strategy. Automatically adjust the humidity according to the real-time monitoring data to ensure that the humidity can be synchronized with the temperature change. Monitor the effect of humidity adjustment and record the difference between the actual humidity and the target humidity for dynamic adjustment to ensure the stability of the test environment.

[0019] Step S5: Perform high-low temperature transition response delay analysis based on the high-low temperature simulation environmental monitoring data, and mark the transition response delay regions; In this embodiment, obtain the complete environmental monitoring data from the high-low temperature test chamber to ensure that the data includes the temperature, humidity, and other relevant environmental parameters at each time point. The data should cover the entire experimental cycle for comprehensive analysis. Clean the data to remove outliers and noise. The moving average method or other smoothing techniques can be used to reduce the impact of instantaneous fluctuations and ensure the quality of the data for analysis. Determine the key parameters for analysis, including the temperature change rate, response time, and transition threshold. Set the standard for temperature change, such as when the temperature change exceeds 2°C, it is considered that a transition response has occurred. Record the initial state and target state of each region for subsequent comparison and analysis. Perform time series analysis on the cleaned data, and use statistical methods (such as autocorrelation analysis and Fourier transform) to identify the change trends of temperature and humidity. Pay attention to the temperature change rate during the transition stage and identify the time periods with faster or slower temperature changes. Calculate the temperature change rate at each time point and record the temperature change amplitude within a specific time period. Set the time threshold for response delay and identify the time periods during which the temperature change fails to respond in a timely manner. For each transition response, calculate the time difference from the start of the temperature change to reaching the set threshold, and record this time difference as the response delay. Use data visualization tools (such as line charts or heat maps) to display the relationship between temperature change and response delay, and clearly mark the delay time of each transition response. According to the analysis results, divide the different regions inside the high-low temperature test chamber and record the response delay situation of each region. The regions should be divided according to the changes in temperature and humidity, such as the upper left, upper right, lower left, and lower right regions. In the time series diagram, use different colors or symbols to mark the transition response delay regions. For example, use red to mark the regions where the response delay exceeds the set threshold to ensure that they are clearly visible in the diagram. Record the characteristics of the marked transition response delay regions, including the delay time, delay amplitude, and their corresponding temperature and humidity values. Ensure the integrity and traceability of the data for subsequent analysis and optimization. Generate an analysis report, which details the situation of the transition response delay regions, and points out the factors that may affect the delay, such as the equipment location, material thermal properties, etc.

[0020] Step S6: Perform transition delay temperature compensation calculation for the transition response delay regions, and construct an intelligent environmental simulation collaborative control engine based on the humidity synchronization control strategy.

[0021] In this embodiment, according to the previous analysis of the transition response delay, a suitable temperature compensation model is selected. These models should be able to reflect the impact of temperature changes under different environmental conditions on the test object. Commonly used models include linear compensation models and non-linear compensation models. Determine the input parameters of the model, which should include the response delay time, target temperature, current temperature, and characteristic data of the transition region, such as the rate of temperature change and humidity level. For each transition response delay region, calculate the required temperature compensation value. By analyzing the difference between the current temperature and the target temperature, determine the compensation temperature. For example, if the current temperature is 25°C, the target temperature is 30°C, and the response delay is 5 minutes, then the temperature value to be heated within these 5 minutes needs to be calculated. Record the results of the compensation calculation, including the compensation temperature, compensation amplitude, and time parameters required for implementation in each region. These data will provide the basis for subsequent control strategies. Implement the temperature compensation strategy in the high and low temperature test chamber, and monitor the temperature change of the test object in real time. By comparing the temperature curves before and after compensation, verify whether the effect of compensation reaches the expected goal. Record the temperature change situation after implementation, evaluate the effectiveness of the compensation strategy, and make necessary adjustments according to the feedback to ensure the stability of the test environment. According to the results of the transition delay temperature compensation calculation, design the architecture of the intelligent environment simulation collaborative control engine. The engine should include multiple functional modules, such as a data acquisition module, a control decision module, and an execution control module. Ensure that the engine can monitor environmental parameters in real time, automatically analyze the changes in temperature and humidity, and make adjustments according to the set compensation strategy and humidity synchronization control strategy. Configure high-precision sensors to collect environmental temperature, humidity, and other relevant parameters in real time. These data will serve as the basis for the engine's decision-making. Implement data preprocessing and cleaning to ensure the accuracy and consistency of the input data. Use machine learning algorithms to analyze historical data and identify the key factors affecting environmental stability. Based on the humidity synchronization control strategy, design a control decision algorithm. This algorithm should be able to automatically adjust the temperature and humidity according to the real-time monitoring data to ensure that the environmental conditions can be consistent with the set goals. During the execution control process, record the temperature and humidity changes in each adjustment for effect evaluation and strategy optimization. Ensure that the response time of the engine can meet the experimental requirements and adjust the environmental parameters in a timely manner. Implement the intelligent environment simulation collaborative control engine in the high and low temperature test chamber for system testing. Monitor the stability and response time of the system, and evaluate the effectiveness of the control strategy. Make optimization adjustments according to the 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.

[0022] In this embodiment, refer to Figure 2 , which is a schematic diagram of the detailed implementation steps of step S1. In this embodiment, the detailed implementation steps of step S1 include: Obtain the initial state monitoring parameters of the high and low temperature 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 regions; Conduct temporal and sequential change analysis on the temperature and humidity parameters of different regions to generate the temporal and sequential change characteristics of temperature and humidity in multiple regions; Perform multi-time-point gradient change fitting on the temporal and sequential change characteristics of temperature and humidity in multiple regions to construct the temperature and humidity gradient change curves of multiple regions.

[0023] In this embodiment, before conducting the high and low temperature test, ensure that all monitoring devices (such as temperature and humidity sensors, data loggers, etc.) are correctly installed and calibrated. The calibration process should follow the guidance of the equipment manufacturer to ensure measurement accuracy. Check the sensitivity and response time of the sensors. Ensure that the sensors can work properly within the preset temperature range (e.g., -40°C to 100°C) and have a sufficient humidity range (e.g., 10% to 95% relative humidity). Collect the initial state monitoring parameters of the high and low temperature test chamber, including the initial temperature, humidity, air pressure, and other relevant environmental parameters inside the test chamber. It is recommended to conduct monitoring for at least 30 minutes before the test starts to obtain stable initial state data. Record the readings and timestamps of each sensor to ensure data integrity. The 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 the data collection frequency, for example, collect data once per minute to ensure that subtle environmental changes can be captured. Use data collection instruments 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 inside the test chamber. Simple weighted average method or exponential smoothing method can be used to smooth the instantaneous readings to reduce the impact of fluctuations. Record the changes in real-time temperature and humidity and compare them with the initial state parameters to analyze the trend of environmental changes. Store the real-time calculated temperature and humidity parameters in a database and add timestamps to each data point. Ensure that the data can be conveniently analyzed and processed later. Classify and organize the real-time data, such as storing it according to dimensions such as time and region, so that it can be quickly retrieved during subsequent analysis. According to the structure and purpose of the test chamber, set the area division criteria. The test chamber can be divided into multiple areas (such as left, right, upper, lower, etc.), and the division of each area should be based on the characteristics of temperature and humidity distribution. Determine the basis for the division of each area, including but not limited to changes in temperature and humidity, equipment location, air circulation conditions, etc. Conduct statistical analysis on the real-time temperature and humidity data in each divided area, and calculate the average temperature, humidity, and their fluctuation ranges in each area. Statistical indicators such as mean and standard deviation can be used to describe the environmental state of the area. Record the temperature and humidity parameters of each area and generate a corresponding area environmental parameter report. Organize the temperature and humidity data of each area in chronological order to ensure the timeliness of the data. Time series analysis methods can be used to group the data by minute or hour. Record the time series data of each area to ensure the integrity and accuracy of the time series data. Analyze the temperature and humidity time series data of each area to identify change trends, periodic fluctuations, and emergencies. Methods such as moving average method and Fourier transform can be used to analyze the time series data. Generate time series change characteristic diagrams for each area to show the change trends of temperature and humidity and their correlations.Select a suitable model for multi-time-point gradient change fitting. Common methods include linear regression, polynomial fitting, and piecewise linear fitting. When selecting, consider the change characteristics of the data and the fitting accuracy. Determine the input variables of the model, including the temperature and humidity parameters at different time points and the corresponding regional information. Perform gradient change fitting on the time series change characteristics of temperature and humidity in each region to generate temperature and humidity gradient change curves for multiple regions. Through the fitting process, identify the temperature and humidity change laws in each region. Record the fitting results for each region, including information such as the fitting curve and the goodness of fit (such as the R² value) to evaluate the accuracy of the fitting. Visualize the temperature and humidity gradient change curves obtained by fitting to generate charts showing the gradient change characteristics of each region. Compare the change situations of different regions through the charts to ensure the accuracy of the fitting results. Analyze the visualization results to identify the temperature and humidity change patterns in each region, providing a basis for subsequent environmental control and optimization.

[0024] In this embodiment, refer to Figure 3 , which is a schematic diagram of the detailed implementation steps of step S2. In this embodiment, the detailed implementation steps of step S2 include: Identify the test object in the high and low temperature test chamber; Conduct real-time temperature change detection on the test object to generate temperature change detection parameters of the test object; Conduct thermal inertia identification on the temperature change detection parameters to generate the thermal inertia characteristics of the test object; Quantify the temperature change lag of the test object according to the temperature and humidity gradient change curves of multiple regions to generate the heat capacity characteristics of the test object; Analyze the thermal response state of the thermal inertia characteristics and heat capacity characteristics of the test object to generate thermal response state data of the test object; Conduct transient thermal change limit evolution mining based on the thermal response state data of the test object to generate the transient thermal change limit curve of the current test object.

[0025] In this embodiment, inside the high and low temperature test chamber, first, it is necessary to ensure that the layout of all test objects is reasonable to avoid blocking the sensors. Use a high-resolution camera or a laser scanner to conduct a preliminary identification of the test objects and ensure that the images are clear. Clean and tidy the inside of the test chamber to ensure that there is no dust or other substances on the surface of the test objects that may affect the identification results. This step can ensure the improvement of the identification accuracy and reduce misidentifications. Utilize image processing technology to process the images captured by the camera and 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 the object boundaries. Record the characteristic parameters of each test object, including size, weight, and material type, etc., for subsequent analysis. Ensure that the data storage format is unified for subsequent use. Apply machine learning or deep learning models (such as convolutional neural network CNN) for the automatic identification of test objects. These models can accurately identify different types of test objects after training. Record the identification results, including information such as object category, position coordinates, and identification confidence, for subsequent verification and analysis. Install high-precision temperature sensors on or near the surface of the test objects to ensure that the sensors can monitor the temperature changes of the test objects in real time. Sensors of models with short response time and high precision should be selected, such as thermocouple or RTD sensors. Ensure that the installation positions of the sensors are reasonable to comprehensively capture the temperature changes of the test objects and avoid data deviation caused by improper positions. Set the data acquisition frequency of the temperature sensors, for example, once per second, to ensure that the temperature changes can be recorded in a timely manner when the high and low temperature environment changes. The sensors should be connected to a data recorder to transmit data in real time. Record the data of each temperature acquisition, including the timestamp and the temperature value, for subsequent analysis. It is recommended to use data management software to monitor the data in real time to ensure the accuracy and integrity of the data. Select a suitable thermal inertia identification model. Common methods include heat conduction equations and specific heat capacity calculations. The model should consider the material properties and geometric shapes of the test objects to accurately reflect the thermal inertia characteristics. Determine the input parameters of the model, such as the temperature change data of the test objects, the specific heat capacity and density of the materials, etc., to ensure accurate calculation of the thermal inertia. Calculate the thermal inertia characteristics of the test objects 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 thermal response time constant of the test objects. Record the results of the thermal inertia characteristics, including the time constant, thermal inertia coefficient, etc., for subsequent analysis. Based on the temperature and humidity gradient change curves in multiple regions, analyze the temperature change lag of the test objects. Identify the lag characteristics by comparing the time difference between the temperature change of the test objects and the environmental temperature change. Determine the lag measurement parameters and record the lag time and the corresponding temperature change values of each test object for subsequent calculation of the heat capacity. Calculate the heat capacity characteristics of the test objects based on the lag time and the temperature change. C = Q / ΔT, where C is the heat capacity, Q is the heat absorbed or released, and ΔT is the temperature change value.Record the results of the heat capacity characteristics of each test object for subsequent analysis and comparison. Integrate the heat inertia characteristics and heat capacity characteristic data of the test object for the analysis of the thermal response state. Ensure the structuring and consistency of the data for subsequent processing. Record the thermal response state parameters of each test object, including the heat inertia coefficient, heat capacity value, temperature change rate, etc., 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 times, temperature change rates, etc. of different test objects. Record the results of the thermal response state analysis, generate a state report to help understand the performance of the test object under different temperature conditions. Collect the transient thermal change data of the test object under different temperature conditions, including parameters such as temperature change rate, external environment temperature, and time. These data will be used for the mining of transient thermal change limit evolution. Record the timestamp of each transient thermal change data point to ensure the timeliness and integrity of the data. Select a suitable transient thermal change limit evolution model. Common methods include non-linear regression analysis and dynamic system models. These models can reflect the complex behavior of transient thermal changes. Determine the input parameters of the model, including transient thermal change data and the thermal characteristics of the test object, to ensure the accuracy of the model. Based on the collected transient thermal change data, conduct limit evolution mining to generate the transient thermal change limit curve of the current test object. Analyze the limit state of the thermal change by comparing the temperature changes at different times. Record the characteristic parameters of the transient thermal change limit curve, including the limit temperature, change rate, etc., for subsequent analysis and verification.

[0026] In this embodiment, refer to Figure 4 , which is a schematic diagram of the detailed implementation steps of step S3. In this embodiment, the detailed implementation steps of step S3 include: Obtain the preset test chamber environment simulation log; Identify the high and low temperature cross time for the preset test chamber environment simulation log to obtain the high and low temperature cross change time; Calculate the environmental temperature simulation period based on the high and low temperature cross change time to generate a high and low temperature change simulation period; Optimize the high and low temperature change simulation period globally according to the transient thermal change limit curve of the current test object to construct a globally optimized temperature change simulation period.

[0027] In this embodiment, in a high and low temperature test chamber, an environmental monitoring system is configured 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, data is recorded once per minute to capture environmental changes. Ensure the stable operation of the logging system, regularly check the status of the sensors and data recorders, and avoid data loss or inaccuracy due to equipment failures. According to the experimental design, set the preset environmental conditions of the test chamber, 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 patterns to simulate the real environment. Record the preset conditions and their change processes to ensure that the time and cycle characteristics of high and low temperature cross-changes can be analyzed later. Preprocess the obtained 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 identification process. Ensure that the timestamps of temperature and humidity data are consistent for subsequent analysis. Use the threshold method or change detection algorithm to identify the high and low temperature cross-change times. Set a temperature threshold (such as 0°C). When the temperature changes from above the threshold to below the threshold, record this time point as the high and low temperature cross time. By traversing the simulation log, identify the start and end times of each cross, and record the cross-change time and its duration for subsequent analysis. Organize the identified high and low temperature cross-change times to generate a cross-time table, recording the specific time and its characteristics of each cross. Analyze the distribution characteristics of the cross times, including cross frequency, duration, and their impact on the test object, to provide a basis for subsequent simulation cycle calculation. According to the identified high and low temperature cross-change times, define the environmental temperature simulation cycle. The simulation cycle should include the complete process of temperature change from high to low and its reverse process. Set the start and end times of the cycle for accurate calculation. Record the time length of each simulation cycle to ensure that it can reflect the time characteristics of high and low temperature crosses. Use time series analysis methods to calculate the time intervals between each high and low temperature cross, and generate high and low temperature change simulation cycles accordingly. For example, use the average value method to calculate the average cycle of multiple crosses to ensure the stability of the results. Record the characteristic parameters of each simulation cycle, including start time, end time, duration, etc., 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 the cycle settings considering multiple constraints. Determine the input parameters of the model, including high and low temperature change simulation cycles, thermal characteristics of the test object, and environmental conditions, etc., to ensure the accuracy of the optimization process. Based on the transient thermal change limit curve of the current test object, globally optimize the high and low temperature change simulation cycles. 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 optimized cycle. Record the decision variables during the optimization process, including the optimized cycle length, cross time points, etc., for subsequent verification and monitoring.According to the optimization results, construct a global temperature change optimization simulation cycle. The cycle should include specific temperature change patterns, crossover time points, and durations 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 optimized cycle by comparing the actual results with the theoretical expectations. Make dynamic adjustments to the model according to the verification results to ensure that the optimized cycle can adapt to different experimental conditions and the characteristics of the test objects. Visualize the constructed global temperature change optimization simulation cycle to generate a cycle change diagram to help the operator intuitively understand the characteristics of the optimized cycle. Summarize the characteristics of the optimized cycle and analyze its impact on the thermal performance of the test object to provide basic data and theoretical support for subsequent research.

[0028] In this embodiment, the specific steps for optimizing the global temperature change crossover cycle of 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: Calculate the maximum change range of high and low temperatures according to the preset test chamber environment simulation log to obtain the high and low temperature peak ranges; Calculate the thermal change limit risk for the high and low temperature peak ranges based on the transient thermal change limit curve of the current test object to obtain the transient thermal change limit risk; Judge the thermal tolerance risk for the transient thermal change limit risk. When the transient thermal change limit risk exceeds the preset safety threshold, generate a transient thermal change limit warning signal; Make a decision on the adjustment of the high and low temperature tests according to the transient thermal change limit warning signal to obtain a high and low temperature test adjustment plan; Adjust the adaptive temperature change amplitude for the high and low temperature peak ranges based on the high and low temperature test adjustment plan to generate adaptive temperature change amplitude adjustment parameters; Optimize the global temperature change crossover cycle of the high and low temperature change simulation cycle based on the adaptive temperature change amplitude adjustment parameters to construct a global temperature change optimization simulation cycle.

[0029] In this embodiment, temperature data is extracted from the pre-set test chamber environment simulation log to ensure the integrity and accuracy of the data. Record the temperature changes during the entire test, including the time stamp, the highest temperature, and the lowest temperature. Preprocess the data to remove outliers and noise. The moving average method can be used to smooth the temperature data to reduce the impact of instantaneous fluctuations and ensure the reliability of the calculation results. Calculate the peak range of high and low temperatures. By finding the maximum and minimum values in the entire temperature dataset, obtain the maximum range of high and low temperature changes. Peak range = highest temperature - lowest temperature. Record the calculated peak range and generate a report containing the specific values of the peaks and the corresponding time periods for subsequent analysis and decision-making. Select a suitable calculation model for the thermal change limit risk. Common methods include the heat conduction model and the transient thermal change model. The model should consider the material properties and geometric shape of the test object to accurately reflect the thermal change limit risk. Determine the input parameters of the model, including the peak range of high and low temperatures, the thermal properties of the test object (such as specific heat capacity, thermal conductivity), and the current transient thermal change limit curve. Calculate the transient thermal change limit risk according to the selected model. Input the peak range of high and low temperatures and the transient thermal change limit data of the test object to calculate the thermal change limit risk value. The temperature points that may lead to thermal runaway can be identified by simulating the heat flow distribution. Record the calculation results, including the thermal change limit risk value and the corresponding temperature conditions, as a basis for subsequent judgment. Set a safety threshold for the thermal tolerance risk according to industry standards and the characteristics of the test object. This threshold should consider the thermal stability of the material and the expected test conditions to ensure safety and reliability. Record the safety threshold and use it as a judgment basis in subsequent analysis. Compare the calculated transient thermal change limit risk value with the pre-set safety threshold to judge the risk status. If the risk exceeds the pre-set threshold, a warning signal needs to be triggered. Record the judgment results, including the risk value, the threshold, and the judgment time, and generate a risk assessment report to help decision-makers understand the current risk status. If the transient thermal change limit risk exceeds the pre-set safety threshold, a transient thermal change limit warning signal is generated. This signal can be issued through an alarm system or a data monitoring platform to ensure that relevant personnel obtain information in a timely manner. According to the transient thermal change limit warning signal, formulate decision-making criteria for adjusting the high and low temperature test. These criteria should include how to adjust the amplitude, rate, and cycle of temperature changes to ensure the safety of the test. Record the adjustment decision-making criteria, including specific adjustment parameters and implementation steps, to ensure the clarity of the decision. According to the formulated adjustment decision, construct a high and low temperature test adjustment plan. 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 consider the current thermal properties of the test object and environmental changes to ensure the rationality of the temperature change amplitude.Record the specific values of the adjustment parameters, including the temperature change range, duration, adjustment rate, etc., for subsequent applications. During the high and low temperature tests, execute temperature changes according to the adaptive adjustment parameters. For example, gradually adjust the temperature according to the set range to avoid excessive thermal shock to the test object. Record the actual temperature change during the adjustment process and compare it with the preset parameters to ensure the effectiveness of the adjustment. Select a suitable global temperature change cross-cycle optimization model. Common methods include multi-objective optimization algorithms and dynamic programming. These models can optimize the temperature change cycle considering multiple factors. Determine the input parameters of the model, including the adaptive temperature change amplitude adjustment parameter, high and low temperature peak ranges, and the thermal characteristics of the test object, to ensure the scientific nature of the optimization process. Based on the adaptive temperature change amplitude adjustment parameter, globally optimize the high and low temperature change simulation cycle. Input the relevant parameters and calculate the optimal temperature change cross-cycle to ensure that the test object can achieve the best thermal response within the optimized cycle. Record the decision variables during the optimization process, including the optimized cycle length, cross time points, etc., for subsequent verification and monitoring. Record the optimized global temperature change cross-cycle to ensure that the results can accurately reflect the thermal characteristics of the test object and environmental changes. Generate an optimization result report to show the comparison of the cycle characteristics before and after optimization. Verify the effectiveness of the optimization results. Observe the effect of the optimized cycle through actual tests to ensure the scientific and reasonable nature of the optimization process.

[0030] In this embodiment, step S4 includes the following steps: Adjust the device parameters in real time according to the global temperature change optimization simulation cycle, execute the environmental simulation control operation, and detect the high and low temperature simulation environment monitoring data; Analyze the dynamic temperature changes in the test chamber based on the high and low temperature simulation environment monitoring data to generate dynamic temperature change characteristics; Perform in-chamber humidity synchronization calculation based on the dynamic temperature change characteristics to obtain in-chamber humidity synchronization parameters; Make a humidity synchronization coordination control decision according to the in-chamber humidity synchronization parameters to generate a humidity synchronization control strategy.

[0031] In this embodiment, according to the results of optimizing the simulation cycle based on global temperature changes, various equipment parameters of the high and low temperature test chamber are set. These parameters include the target temperature, temperature change rate, alternating time, and control strategy, etc. The target temperature should be determined according to the characteristics of the test object and the preset experimental conditions. Record the initial state of each device, including the current temperature, humidity, and other environmental conditions for subsequent monitoring and comparison. Start the high and low temperature test chamber and perform environmental simulation according to the set optimization cycle and parameters. Ensure the normal operation of the equipment and monitor whether each parameter is within the set range. Use temperature and humidity sensors to provide real-time feedback on the environmental state to ensure that the system can automatically adjust according to real-time data. During operation, regularly check the operation status and fault alarms of the equipment to ensure that there are no abnormal situations with the equipment and guarantee the smooth progress of the experiment. Continuously monitor the temperature and humidity data in the test chamber and record the changes in environmental parameters. Ensure that the data acquisition frequency is high enough (e.g., once per minute) to capture subtle changes. Store the monitored data in a database and record the results of each data acquisition according to the timestamp for subsequent analysis and comparison. Organize and preprocess the monitored data of the high and low temperature simulation environment. Remove outliers and noise to ensure the accuracy of the data. The moving average method can be used for smoothing to reduce data fluctuations. Ensure that the temperature data is consistent with the timestamp for subsequent dynamic analysis. Use statistical analysis methods to analyze the dynamic temperature change characteristics in the test chamber. Time series analysis techniques can be adopted to calculate the rate of temperature change, fluctuation amplitude, and change trend. Record the analysis results, including the maximum, minimum, average values of temperature change and their change rates, etc., to provide a basis for subsequent humidity calculation. According to the dynamic temperature change characteristics, select a suitable humidity synchronous calculation model. Common models include the heat and moisture balance model and the relative humidity calculation model. These models can dynamically calculate humidity changes based on temperature changes. Determine the input parameters of the model, including the current temperature, initial humidity in the test chamber, air pressure, etc., to ensure the accuracy of the model. According to the selected model, input the dynamic temperature change data and perform synchronous calculation of humidity. By calculating the impact of dynamic temperature changes on humidity, obtain real-time humidity parameters. Record the humidity synchronous parameters in each calculation cycle, including the calculated humidity values and their changes for subsequent analysis. According to the humidity synchronous parameters, formulate a humidity synchronous coordination control strategy. These strategies should include how to adjust humidity, set the target humidity range and its change rate to ensure the stability of the test environment. Record the specific content of the humidity control strategy, including the humidity change range, target humidity value, and adjustment rate, etc., for subsequent implementation. In the high and low temperature test chamber, perform environmental adjustment according to the formulated humidity control strategy. Real-time adjust the humidity in the test chamber through humidity adjustment equipment (such as humidifiers or dehumidifiers). Monitor the actual effect during the humidity adjustment process to ensure that the humidity can change within the set range and be coordinated with the temperature change.During the implementation of humidity synchronization control, monitor the humidity change in real time and record the difference between the actual humidity and the target humidity. Ensure that the humidity adjustment strategy can be effectively implemented and timely feedback the adjustment effect. Make dynamic adjustments according to the monitoring results to ensure that the humidity can change synchronously with the temperature, and avoid adverse effects on the test object due to humidity fluctuations.

[0032] In this embodiment, the specific steps of step S5 are as follows: Identify the regional temperature distribution of the high and low temperature simulation environment monitoring data to generate a regional temperature distribution map; Conduct a local temperature difference analysis on the regional temperature distribution map and mark the local temperature difference characteristics; Calculate the high and low temperature transition rate based on the local temperature difference characteristics to generate the high and low temperature transition rate; Conduct a high and low temperature transition response delay analysis on the high and low temperature transition rate according to the global temperature change optimized simulation period, and mark the transition response delay area.

[0033] In this embodiment, complete environmental monitoring data, including temperature records of each area, are obtained from the high and low temperature test chamber. Each area should be clearly defined, such as the left side, right side, upper part, and lower part. Ensure that the temperature data of each area cover the entire experimental cycle for comprehensive analysis. Preprocess the monitoring data to remove outliers and noise to ensure data quality. The moving average method can be used to smooth the data to reduce the impact of instantaneous fluctuations. According to the design of the test chamber, different areas are divided, and the temperature data are classified by area. The temperature data of each area should record the corresponding timestamp for subsequent analysis. Use data analysis tools to organize the temperature data of different areas and generate temperature statistical data for each area, including the maximum value, minimum value, average value, and temperature distribution range. Use data visualization tools to generate a regional temperature distribution map. Forms such as heat maps can be selected to represent the temperature data of each area with different shades of color to intuitively reflect the temperature distribution. Ensure that the temperature distribution map contains necessary legends and annotations for the operator to quickly understand the temperature status of each area. Record the generated temperature distribution map and archive it together with the experimental data for subsequent analysis and verification. Analyze the generated regional temperature distribution map to identify areas with obvious temperature differences. Focus on areas with large temperature changes and local hot spots, and record the temperature values and positions of these areas. Calculate the temperature differences between areas to determine which areas have temperature differences exceeding a preset threshold (e.g., 5°C) and mark them as local temperature difference characteristic areas. Mark the local temperature difference characteristic areas on the regional temperature distribution map and highlight them with different colors or symbols. These marks should clearly indicate the specific location and degree of temperature difference. Record the marked local temperature difference characteristics, including specific temperature values, position coordinates, and relevant time information for subsequent analysis. Define the high and low temperature transition, which usually refers to the rate of temperature change from one extreme to another. Set the start and end points of the transition time and divide them according to the regional temperature change. Record the timestamp, start temperature, and end temperature of each transition for accurate rate calculation. The transition rate = ΔT / Δt, where ΔT is the temperature change (end temperature - start temperature) and Δt is the time required for the transition. Calculate the corresponding high and low temperature transition rates for each local temperature characteristic area and record the calculation results for each area.

[0034] Define the transition response delay, which generally refers to the time delay required for the temperature change to reach a certain threshold. A response threshold needs to be set (for example, the time when the temperature change exceeds 2°C). Record the specific time points of the temperature change to ensure that the response of each transition can be accurately captured. Optimize the data of the simulation period according to the global temperature change, analyze the relationship between the high and low temperature transition rates and the response time, and calculate the transition response delay of each region. The time series analysis method can be used to identify the time points when the temperature reaches the set threshold. Mark the response delay situation of each region, record 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, intuitively showing the response delay situations of different regions. Ensure that the chart contains necessary explanations and annotations to help the operator understand the reasons for the delay. Summarize the results of the transition response delay analysis, identify the factors that may affect the response delay, and provide suggestions for subsequent optimization.

[0035] In this embodiment, the specific steps of step S6 are as follows: Infer the attribution of the response delay for the transition response delay region to obtain the response delay factors; Calculate the transition delay temperature compensation for the transition response delay region according to the response delay factors to generate a transition delay temperature compensation strategy; Based on the humidity synchronization control strategy and the transition delay temperature compensation strategy, conduct intelligent collaborative simulation optimization to construct an intelligent environment simulation collaborative control engine.

[0036] In this embodiment, data is obtained from the previous transition response delay analysis, including the temperature change curve, transition response time, and environmental conditions of each region. These data provide a basis for attribution inference. Focus on the characteristic data of the transition response delay region, analyze its temperature change and response time under different conditions, and identify key factors that may affect the response delay, such as equipment location, material properties, wind speed, etc. Select a suitable attribution inference model, common ones include multiple regression analysis and decision tree models, etc. These models can handle multiple variables and analyze the influence degree of each factor on the response delay. Determine the input parameters of the model, including the temperature change rate, humidity level, equipment performance indicators, etc., to ensure the accuracy of attribution inference. Apply the selected model to conduct attribution inference on the transition response delay region and identify the contribution degree of each response delay factor. For example, by analyzing the model output, determine which factors have the greatest impact on the transition delay. Record the results of the attribution analysis, including the contribution rate, influence direction of each factor and their interactions, so as to provide a basis for subsequent compensation calculations. According to the results of attribution inference, formulate a temperature compensation strategy for transition delay. This strategy should take into account the influence of different response delay factors on temperature changes and aim to compensate for the delay by adjusting the temperature settings. Set a compensation threshold to clarify under what circumstances temperature compensation is required, such as when the response delay exceeds the preset safety range or when there are obvious local temperature differences. Select a suitable temperature compensation calculation method, common methods include linear compensation models and non-linear compensation models. The compensation calculation should be based on the actual temperature change curve and response delay characteristics. Determine the input parameters, including the response delay time, target temperature and its change range, to ensure the accuracy of the compensation calculation. According to the formulated compensation strategy, use the selected calculation method to perform temperature compensation calculations. Calculate the compensated target temperature to ensure rapid heating or cooling can be achieved within the response delay region. Record the results of the compensation calculation, including the compensated target temperature, compensation amplitude and corresponding time parameters, for subsequent implementation and verification. Design an intelligent environmental simulation collaborative control engine to integrate the humidity synchronization control strategy and the transition delay temperature compensation strategy. The engine should be able to monitor environmental parameters in real time and make automatic adjustments according to the set strategy. Determine the functional modules of the engine, including data acquisition, real-time monitoring, decision-making and execution control, etc., to ensure the intelligence and automation of the system. Implement the intelligent control engine in a high and low temperature test chamber to collect temperature and humidity data in real time. The system should be able to automatically adjust the temperature and humidity according to the current environmental conditions and preset strategies to achieve collaborative control. Monitor the operation status of the system to ensure that the engine executes according to the set parameters and record the effects of each adjustment for subsequent analysis. Evaluate the operation effect of the intelligent collaborative control engine to analyze whether the expected control objectives have been achieved.The evaluation metrics include the stability of temperature and humidity, response time, and its impact on the test object. Optimization is carried out according to the evaluation results, and the control strategy and parameter settings are adjusted to ensure that the system can flexibly respond under different environmental conditions, improving the reliability and accuracy of the experiment.

[0037] In this embodiment, an environmental simulation system for a high and low temperature test chamber is provided, which is used to execute the environmental simulation method of the high and low temperature test chamber as described above, and includes: A regional temperature and humidity module, which is used to obtain the initial state monitoring parameters of the high and low temperature test chamber, perform regional environmental parameter division and time-point gradient change fitting, and construct multiple regional temperature and humidity gradient change curves; A thermal change limit mining module, which is used to identify the test object in the high and low temperature test chamber; perform transient thermal change limit evolution mining on the test object according to multiple regional temperature and humidity gradient change curves, and generate a transient thermal change limit curve of the current test object; A temperature change cross-cycle module, which is used to obtain the preset environmental simulation log of the test chamber, and perform global temperature change cross-cycle optimization according to the transient thermal change limit curve of the current test object, and construct a global temperature change optimization simulation cycle; A humidity synchronization module, which is used to 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; A response delay analysis module, which is used to perform high and low temperature transition response delay analysis based on the high and low temperature simulation environment monitoring data, and mark the transition response delay area; A collaborative control module, which is used to perform transition delay temperature compensation calculation on the transition response delay area, and construct an intelligent environmental simulation collaborative control engine based on the humidity synchronization control strategy.

[0038] The present invention subdivides the interior of the test chamber into multiple regions and, in combination with precise environmental monitoring, ensures that the temperature and humidity conditions of each region are separately recorded and analyzed. This partitioning method helps to precisely control the temperature and humidity changes in different regions and avoid differences in the uniformity of the test environment. By fitting the time-point gradient change curves of the regional temperature and humidity, the system can accurately capture the temperature and humidity change trends of each region at different time points. This helps to subsequently adjust and control the system and make dynamic adjustments according to the actual changes. The environmental control of different regions can better meet the requirements of different test objects. Especially when the test object is highly sensitive to the environment, the detailed regional partitioning and change fitting will enhance the realism and accuracy of the simulation. Accurately identify and record information such as the material, size, and shape of the test object to ensure accurate thermal response analysis and temperature control of the test object. Through transient thermal change limit excavation, in combination with the temperature and humidity changes in different regions, accurately calculate and generate the thermal change limit curve of each test object. This curve can reflect the maximum tolerance of the test object under different temperature change conditions and avoid damage or adverse reactions to the object caused by excessive temperature differences. With the transient thermal change limit curve, the system can automatically adjust the temperature change strategy of the test chamber based on the thermal response characteristics of the object to ensure that the test object is not adversely affected by rapid temperature changes. By analyzing historical test data and combining with the transient thermal change limit curve of the object, automatically optimize the cycle and speed of high and low temperature alternation to make the temperature change smoother and conform to the tolerance limit of the object. By precisely optimizing the temperature change crossover period, the system can avoid thermal shocks caused by sudden temperature rises or drops, thereby improving the survival environment of the experimental object and the reliability of the test. Through the optimization of the global temperature change cycle, the system can construct a more consistent simulation cycle for all test objects and environmental conditions to ensure the efficiency and consistency of the experiment. Humidity is an important factor affecting the test results in high and low temperature tests. The humidity synchronization module automatically adjusts the humidity by real-time monitoring and analyzing temperature changes to ensure the coordination of temperature and humidity changes. The humidity synchronization module can flexibly adjust the humidity according to the changes in environmental data during the test to avoid negative impacts on the test object caused by too high or too low humidity and ensure the effectiveness of the test results. Through the synchronous coordinated control of humidity and temperature, the system achieves more refined environmental regulation, can simulate more complex actual environmental conditions, and enhances the authenticity and reliability of the test. By real-time monitoring the temperature and humidity changes in the test environment, it is possible to accurately analyze and mark the possible delay regions during the high and low temperature transition process. By identifying the delay regions, the system can avoid temperature changes in these regions to prevent uneven thermal loads from affecting the test results. The marking of the high and low temperature transition response delay regions helps to predict in advance the possible errors in environmental changes, so as to take necessary compensation measures to ensure the stability of the test process. Delay analysis can significantly improve the data accuracy during the test process and ensure that the temperature and humidity changes during the high and low temperature transition process are more in line with the requirements of the test object.By calculating the temperature compensation amount of the response delay area in real time, the error caused by the environmental temperature lag can be effectively reduced, ensuring a smoother high-low temperature transition process. The combination of the humidity synchronization control strategy and the 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 according to all real-time data for joint optimization control, making the environmental simulation more accurate and flexible. By introducing an intelligent collaborative control engine, the system can automatically adjust the environmental conditions according to the needs of different test objects to minimize the interference of manual operations and improve the automation level of the experiment.

[0039] Therefore, in any regard, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Thus, all changes falling within the meaning and scope of the equivalent elements of the application document are intended to be encompassed within the present invention.

[0040] As described above are only specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather to the broadest scope consistent with the principles and novel features invented 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 fit the gradient changes at the time points to construct multiple regional temperature and humidity gradient change curves; 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 according to 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 a preset test chamber environment simulation log, and perform global temperature change cross-cycle optimization according to 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 according to 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: 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.

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 S1 are: Obtain the initial state monitoring parameters of the high and low temperature test chamber; Calculate real-time temperature and humidity parameters according to 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 areas, and generate the time series change characteristics of temperature and humidity in multiple areas; 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.

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 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 the heat capacity characteristics 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; The transient thermal change limit evolution is mined according to the thermal response state data of the test object to generate the transient thermal change limit curve of the current test object.

4. The environmental simulation method of the high and low temperature test chamber according to claim 1, characterized in that: 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 according to 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 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.

5. The environmental simulation method of the high and low temperature test box according to claim 4, characterized in that: 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 according to the preset test chamber environment simulation log to obtain the high and low temperature peak range; 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 judgment 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; Based on the high and low temperature test adjustment scheme, the high and low temperature peak range is adaptively adjusted to generate adaptive temperature change amplitude adjustment parameters; Based on the adaptive temperature change amplitude adjustment parameters, the global temperature change cross-cycle optimization of the high and low temperature change simulation cycle is carried out to construct a global temperature change optimization simulation cycle.

6. 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 according to the global temperature change optimization simulation cycle, perform environmental simulation control operations, and detect high and low temperature simulation environment monitoring data; According to the high and low temperature simulation environment monitoring data, the dynamic temperature change analysis in the test chamber is carried out to generate dynamic temperature change characteristics; Based on the dynamic temperature change characteristics, the humidity synchronization calculation in the box is performed to obtain the humidity synchronization parameters in the box; Humidity synchronization coordination control decisions are made according to the humidity synchronization parameters in the box to generate a humidity synchronization control strategy.

7. 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 the regional temperature distribution of high and low temperature simulation environment monitoring data and generate regional temperature distribution map; Conduct local temperature difference analysis on the regional temperature distribution map and mark the local temperature difference features; The high and low temperature transition rates are calculated based on the local temperature difference characteristics to generate the high and low temperature transition rates; According to the global temperature change optimization simulation cycle, the high and low temperature transition rate is analyzed for the high and low temperature transition response delay, and the transition response delay area is marked.

8. The environmental simulation method of the high and low temperature test box 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 transformation delay temperature compensation calculation on the transformation response delay area 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.

9. 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 as claimed in 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 time point, 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; according to 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 according to the transient thermal change limit curve of the current test object to build 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; make humidity synchronization coordination control decisions based on the high and low temperature simulation environment monitoring data 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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