Temperature fast switching method and device for hot and cold shock test chamber

Abstract

The present invention relates to the field of temperature control technology, and in particular to a method and device for rapid temperature switching of a hot and cold shock test chamber. The method comprises the following steps: obtaining the internal design drawing of the hot and cold shock test chamber and the hot and cold shock test log; performing multi-region three-dimensional point cloud modeling on the internal design drawing of the hot and cold shock test chamber, thereby constructing a multi-region three-dimensional point cloud model of the test chamber; obtaining the temperature monitoring parameters of the test chamber; performing position registration and time-series temperature fluctuation fitting on the multi-region three-dimensional point cloud model of the test chamber according to the temperature monitoring parameters of the test chamber, and constructing time-series temperature fluctuation curves for different regions; mining the hot and cold alternating demand of the hot and cold shock test log, and reconstructing the hot and cold shock chain of the whole process, and constructing a full-cycle hot and cold shock time window; and calculating the test temperature deviation according to the full-cycle hot and cold shock time window and the time-series temperature fluctuation curves of different regions. The present invention realizes accurate and rapid temperature switching of the test chamber.

Description

Technical Field

[0001] The present invention relates to the technical field of temperature control, and in particular to a method and device for quickly switching the temperature of a thermal shock test chamber. Background Art

[0002] With the continuous advancement of science and technology and the improvement of industrial automation level, thermal shock test chambers have been widely used in various industries, especially in the fields of aviation, electronics, automobiles and materials. As an important tool for testing the high and low temperature resistance of materials, components and equipment, thermal shock test chambers have the function of simulating an environment with rapid temperature changes. By simulating extreme hot and cold temperature changes, it helps scientific researchers and engineering technicians evaluate the temperature resistance and reliability of the tested samples to ensure that the products can withstand various environmental changes in actual use.

[0003] However, in traditional thermal shock tests, the switching speed, stability and accuracy of the test chamber temperature often face challenges. With the increasing test requirements, especially under the requirements of high precision and high efficiency, the traditional thermal shock test chamber temperature switching method can no longer meet the increasingly complex test requirements. Traditional temperature switching methods mainly rely on manual monitoring and mechanical adjustment, but these methods usually have problems such as slow response speed, inaccurate adjustment, and poor control system stability, which greatly affects the efficiency and accuracy of the test.

[0004] At the same time, with the continuous advancement of automation control technology and intelligent algorithms, the temperature switching method for hot and cold shock test chambers is gradually developing towards higher intelligence and precision. Traditional temperature control methods have gradually failed to meet the high requirements of modern production lines and R&D environments for hot and cold shock tests. Therefore, researching and developing an intelligent method for rapid temperature switching and precise control has become an urgent need in the current industry. Summary of the invention

[0005] In order to solve the above technical problems, the present invention proposes a method and device for quickly switching the temperature of a hot and cold shock test chamber to solve at least one of the above technical problems.

[0006] To achieve the above object, the present invention provides a method for quickly switching the temperature of a thermal shock test chamber, comprising the following steps:

[0007] Step S1: obtaining the internal design drawing of the hot and cold shock test chamber and the hot and cold shock test log; performing multi-region three-dimensional point cloud modeling on the internal design drawing of the hot and cold shock test chamber, thereby constructing a multi-region three-dimensional point cloud model of the test chamber;

[0008] Step S2: Acquire the temperature monitoring parameters of the test chamber; perform position registration and time-series temperature fluctuation fitting on the three-dimensional point cloud model of multiple regions of the test chamber according to the temperature monitoring parameters of the test chamber, and construct time-series temperature fluctuation curves of different regions;

[0009] Step S3: mining the hot and cold alternation demand of the hot and cold shock test log, reconstructing the hot and cold shock chain of the whole process, and building the hot and cold shock time window of the whole cycle;

[0010] Step S4: Calculate the test temperature deviation according to the full-cycle hot and cold shock time window and the time-series temperature fluctuation curves of different regions, so as to generate multi-time point temperature deviation values ​​for each region;

[0011] Step S5: performing shock temperature compensation calculation on the temperature deviation values ​​at multiple time points in each area, and performing dynamic temperature compensation control on the test box, and collecting hot and cold shock compensation monitoring parameters of the test box;

[0012] Step S6: Optimize the switching response delay according to the thermal shock compensation monitoring parameters, and construct an intelligent switching response optimization control strategy.

[0013] The present invention can accurately understand the structure, composition and functions of each area of ​​the test box by obtaining the internal design drawings and the hot and cold shock test logs of the test box. The design drawings are converted into a three-dimensional point cloud model to ensure the high restoration and accurate modeling of the spatial structure of the test box. The multi-region three-dimensional point cloud model can help to clearly divide the various functional areas (such as heating area, cooling area, homogenization area, etc.) in the test box, providing a basis for subsequent temperature monitoring, temperature control and compensation. This step provides accurate three-dimensional spatial data and design basis for subsequent temperature monitoring and simulation, temperature fluctuation analysis and other work, so that other steps can be carried out more efficiently and accurately. By obtaining the temperature monitoring parameters of each area of ​​the test box, the real-time temperature distribution in the test box can be fully grasped to ensure the accuracy of the temperature data. By performing position registration of the three-dimensional point cloud model of the test box with the temperature monitoring data, the temperature fluctuation data can be more accurately associated with the internal structure of the test box, ensuring the accurate mapping of temperature changes and spatial positions. Time series temperature fluctuation fitting enables real-time monitoring and generation of temperature fluctuation curves in different areas. Through these curves, the temperature change trend can be observed in real time, which helps to accurately predict the temperature changes in different areas, and facilitates subsequent temperature adjustment and compensation control. By mining the hot and cold alternation requirements of the test log, we can deeply understand the specific requirements of hot and cold alternation during the test, including the frequency, amplitude and timing of temperature changes. Ensure accurate grasp of the experimental design requirements. By reconstructing the entire process of hot and cold shock, we can clearly show each stage of the entire hot and cold shock process, ensure that the test meets the required time window requirements, and avoid the experiment deviating from the standard requirements. Constructing a full-cycle hot and cold shock time window helps the temperature changes in the experiment to always be within the controllable range, ensuring the consistency and repeatability of the experiment. Combining the full-cycle hot and cold shock time window and the time-series temperature fluctuation curves of different regions, the temperature deviation of each region can be accurately calculated. This deviation calculation will provide an accurate basis for subsequent temperature compensation. By generating multi-point temperature deviation values ​​for each region, it can help to monitor temperature changes more comprehensively and ensure that temperature control at each moment meets the experimental requirements. Real-time calculation of temperature deviation helps to identify temperature control deviations in advance during the test, ensure timely adjustments, and avoid deviation accumulation affecting the experimental results. By calculating shock temperature compensation through temperature deviation values, accurate temperature compensation can be performed for each region according to the actual temperature fluctuations, ensuring that the temperature of the test chamber is stable and reliable during hot and cold shocks. Dynamic temperature compensation control can adjust the heating and cooling systems of the test chamber in real time, quickly respond to temperature fluctuations, effectively reduce temperature transition time, and improve the efficiency of hot and cold shock experiments. During the compensation control process, compensation monitoring parameters can be collected in real time to provide data support for subsequent optimization analysis. These data will help evaluate the effect of compensation control and guide further adjustments.By analyzing the monitoring parameters of thermal shock compensation, we can deeply explore the delay problem of switching response, optimize the response time, and improve the system's fast switching capability. Based on the analysis results of response delay, we can build an intelligent optimization control strategy, dynamically adjust the control parameters according to the characteristics and requirements of the test chamber, and make the temperature switching of thermal shock tests faster, more accurate, and more efficient. Through delay optimization, unnecessary waiting time is reduced, making the test more efficient. Intelligent control strategies can automatically adjust parameters to meet different experimental requirements and improve the accuracy and stability of the overall test.

[0014] In this specification, a device for quickly switching the temperature of a thermal shock test chamber is provided, which is used to execute the method for quickly switching the temperature of a thermal shock test chamber as described above, comprising:

[0015] The three-dimensional modeling module is used to obtain the internal design drawings of the hot and cold shock test chamber and the hot and cold shock test logs; perform multi-region three-dimensional point cloud modeling on the internal design drawings of the hot and cold shock test chamber, thereby constructing a multi-region three-dimensional point cloud model of the test chamber;

[0016] The temperature fluctuation fitting module is used to obtain the temperature monitoring parameters of the test chamber; according to the temperature monitoring parameters of the test chamber, the position registration and time-series temperature fluctuation fitting of the three-dimensional point cloud model of the multi-region of the test chamber are performed to construct the time-series temperature fluctuation curves of different regions;

[0017] The hot and cold alternation demand module is used to mine the hot and cold alternation demand of the hot and cold shock test log, reconstruct the hot and cold shock chain of the whole process, and build the hot and cold shock time window of the whole cycle;

[0018] Deviation calculation module, used to calculate the test temperature deviation according to the full cycle hot and cold shock time window and the time series temperature fluctuation curves of different regions, so as to generate multi-time point temperature deviation values ​​for each region;

[0019] The compensation control module is used to perform shock temperature compensation calculations on the temperature deviation values ​​at multiple time points in each area, and to perform dynamic temperature compensation control on the test chamber, and collect the hot and cold shock compensation monitoring parameters of the test chamber;

[0020] The switching response optimization module is used to optimize the switching response delay according to the thermal shock compensation monitoring parameters and construct an intelligent switching response optimization control strategy.

[0021] The present invention can accurately reproduce the spatial layout and functional areas inside the test box through three-dimensional modeling. This provides reliable basic data for subsequent temperature monitoring, temperature compensation, etc. The distribution of heat sources and cold sources in different regions is clearly distinguished, which is convenient for subsequent precise control. Through point cloud data, efficient CFD simulation can be performed to simulate the heat exchange process in different regions, providing theoretical support for subsequent temperature optimization. Through position registration and fitting, accurate mapping of temperature changes and specific regions can be achieved, ensuring that the temperature data of each region can accurately reflect its thermodynamic state. Provide real-time and accurate temperature fluctuation curves for subsequent temperature compensation and regulation, which is convenient for monitoring and adjusting temperature change trends. Through the time-series temperature curve, the temperature fluctuations in different regions of the test box can be observed in real time, providing data support for dynamic temperature adjustment. Through in-depth mining of the test log, the start and end time of hot and cold alternation can be accurately identified to ensure the accuracy of temperature switching. Full process reconstruction can help better understand the periodicity and experimental requirements of hot and cold shocks, and provide a clear time frame for subsequent control algorithms and temperature compensation strategies. Accurately dividing the time of hot and cold shock processes helps to better perform temperature compensation and dynamic control, and ensure the stability of the entire test process. By comparing the temperature fluctuation data at different time points with the target temperature, the temperature deviation can be obtained in real time. This is crucial for adjusting the temperature control system. Through the accurate calculation of the temperature deviation, data support can be provided for the control system, enabling it to dynamically adjust the output of the cold and heat sources to avoid unnecessary energy consumption and temperature fluctuations. The temperature deviation calculation can effectively identify the temperature fluctuations or anomalies in the test chamber, and adjust the control strategy in time to ensure that the temperature reaches the required accuracy. Through compensation calculation, the temperature can be quickly restored to the target state to ensure that the temperature fluctuation during the cold and hot alternation process does not exceed the set range. The system can dynamically adjust according to real-time data feedback to avoid temperature lag or error, thereby improving the test efficiency. Through accurate temperature compensation, the test chamber can achieve cold and hot alternation in the shortest time, thereby saving time and improving experimental efficiency. By optimizing the switching delay, the temperature response of the test chamber is ensured to be faster, reducing the time waste caused by delays during the test process. The intelligent control strategy can automatically adjust the various parameters during the cold and hot switching process, reduce manual intervention, and improve the degree of automation. The optimized intelligent control strategy not only improves the response speed, but also further improves the accuracy of temperature switching, making the test process more stable and reliable. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 A schematic flow chart of the steps of a method for rapid temperature switching of a hot and cold shock test chamber according to the present invention;

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

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

[0025] Figure 4 Detailed implementation flow chart of step S3. DETAILED DESCRIPTION

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

[0027] The present application example provides a method and device for rapid temperature switching of a hot and cold shock test chamber. The execution subjects of the method and device for rapid temperature switching of a hot and cold shock test chamber include but are not limited to the following: mechanical equipment, data processing platform, cloud server node, network upload device, etc. equipped with the system can be regarded as the general computing node of the present application, and the data processing platform includes but is not limited to: at least one of an audio and image management system, an information management system, and a cloud data management system.

[0028] See also Figures 1 to 4 The present invention provides a method for quickly switching the temperature of a hot and cold shock test chamber, and the method for quickly switching the temperature of a hot and cold shock test chamber comprises the following steps:

[0029] Step S1: obtaining the internal design drawing of the hot and cold shock test chamber and the hot and cold shock test log; performing multi-region three-dimensional point cloud modeling on the internal design drawing of the hot and cold shock test chamber, thereby constructing a multi-region three-dimensional point cloud model of the test chamber;

[0030] Step S2: Acquire the temperature monitoring parameters of the test chamber; perform position registration and time-series temperature fluctuation fitting on the three-dimensional point cloud model of multiple regions of the test chamber according to the temperature monitoring parameters of the test chamber, and construct time-series temperature fluctuation curves of different regions;

[0031] Step S3: mining the hot and cold alternation demand of the hot and cold shock test log, reconstructing the hot and cold shock chain of the whole process, and building the hot and cold shock time window of the whole cycle;

[0032] Step S4: Calculate the test temperature deviation according to the full-cycle hot and cold shock time window and the time-series temperature fluctuation curves of different regions, so as to generate multi-time point temperature deviation values ​​for each region;

[0033] Step S5: performing shock temperature compensation calculation on the temperature deviation values ​​at multiple time points in each area, and performing dynamic temperature compensation control on the test box, and collecting hot and cold shock compensation monitoring parameters of the test box;

[0034] Step S6: Optimize the switching response delay according to the thermal shock compensation monitoring parameters, and construct an intelligent switching response optimization control strategy.

[0035] The present invention can accurately understand the structure, composition and functions of each area of ​​the test box by obtaining the internal design drawings and the hot and cold shock test logs of the test box. The design drawings are converted into a three-dimensional point cloud model to ensure the high restoration and accurate modeling of the spatial structure of the test box. The multi-region three-dimensional point cloud model can help to clearly divide the various functional areas (such as heating area, cooling area, homogenization area, etc.) in the test box, providing a basis for subsequent temperature monitoring, temperature control and compensation. This step provides accurate three-dimensional spatial data and design basis for subsequent temperature monitoring and simulation, temperature fluctuation analysis and other work, so that other steps can be carried out more efficiently and accurately. By obtaining the temperature monitoring parameters of each area of ​​the test box, the real-time temperature distribution in the test box can be fully grasped to ensure the accuracy of the temperature data. By performing position registration of the three-dimensional point cloud model of the test box with the temperature monitoring data, the temperature fluctuation data can be more accurately associated with the internal structure of the test box, ensuring the accurate mapping of temperature changes and spatial positions. Time series temperature fluctuation fitting enables real-time monitoring and generation of temperature fluctuation curves in different areas. Through these curves, the temperature change trend can be observed in real time, which helps to accurately predict the temperature changes in different areas, and facilitates subsequent temperature adjustment and compensation control. By mining the hot and cold alternation requirements of the test log, we can deeply understand the specific requirements of hot and cold alternation during the test, including the frequency, amplitude and timing of temperature changes. Ensure accurate grasp of the experimental design requirements. By reconstructing the entire process of hot and cold shock, we can clearly show each stage of the entire hot and cold shock process, ensure that the test meets the required time window requirements, and avoid the experiment deviating from the standard requirements. Constructing a full-cycle hot and cold shock time window helps the temperature changes in the experiment to always be within the controllable range, ensuring the consistency and repeatability of the experiment. Combining the full-cycle hot and cold shock time window and the time-series temperature fluctuation curves of different regions, the temperature deviation of each region can be accurately calculated. This deviation calculation will provide an accurate basis for subsequent temperature compensation. By generating multi-point temperature deviation values ​​for each region, it can help to monitor temperature changes more comprehensively and ensure that temperature control at each moment meets the experimental requirements. Real-time calculation of temperature deviation helps to identify temperature control deviations in advance during the test, ensure timely adjustments, and avoid deviation accumulation affecting the experimental results. By calculating shock temperature compensation through temperature deviation values, accurate temperature compensation can be performed for each region according to the actual temperature fluctuations, ensuring that the temperature of the test chamber is stable and reliable during hot and cold shocks. Dynamic temperature compensation control can adjust the heating and cooling systems of the test chamber in real time, quickly respond to temperature fluctuations, effectively reduce temperature transition time, and improve the efficiency of hot and cold shock experiments. During the compensation control process, compensation monitoring parameters can be collected in real time to provide data support for subsequent optimization analysis. These data will help evaluate the effect of compensation control and guide further adjustments.By analyzing the monitoring parameters of thermal shock compensation, we can deeply explore the delay problem of switching response, optimize the response time, and improve the system's fast switching capability. Based on the analysis results of response delay, we can build an intelligent optimization control strategy, dynamically adjust the control parameters according to the characteristics and requirements of the test chamber, and make the temperature switching of thermal shock tests faster, more accurate, and more efficient. Through delay optimization, unnecessary waiting time is reduced, making the test more efficient. Intelligent control strategies can automatically adjust parameters to meet different experimental requirements and improve the accuracy and stability of the overall test.

[0036] In the embodiment of the present invention, refer to Figure 1 , is a schematic flow chart of the steps of a method for quickly switching the temperature of a hot and cold shock test chamber of the present invention. In this example, the steps of the method for quickly switching the temperature of a hot and cold shock test chamber include:

[0037] Step S1: obtaining the internal design drawing of the hot and cold shock test chamber and the hot and cold shock test log; performing multi-region three-dimensional point cloud modeling on the internal design drawing of the hot and cold shock test chamber, thereby constructing a multi-region three-dimensional point cloud model of the test chamber;

[0038] In this embodiment, the internal design drawings of the hot and cold shock test chamber are collected. These drawings should include detailed structure, materials, component layout and size information. The design drawings can be obtained from the engineering department, product manual or CAD file. After obtaining the drawings, they are reviewed to ensure their accuracy and completeness. Pay special attention to whether the relative position, size and connection method of the key components are indicated in the design drawings, which is crucial for subsequent modeling. The design drawings are digitized. If the original drawings are paper versions, they need to be scanned and converted into editable formats (such as PDF or DWG). Ensure that each part of the design drawing can be clearly identified for subsequent modeling. Analyze the drawings and extract key data from each area, including the names of different areas, the location of temperature monitoring points, the layout of cooling and heating equipment, etc. These data will provide basic information for modeling. Collect log data related to hot and cold shock tests. These data should include temperature change records, test parameter settings, start and end times, environmental conditions, etc. The test log is usually recorded automatically by the test equipment or manually entered by the operator. Ensure the integrity of the log data, covering data from multiple test cycles for subsequent analysis and model verification. Organize the test log data into a structured format for subsequent analysis. Data can usually be organized using spreadsheet software, including timestamps, temperature values, set points, and other relevant parameters. Analyze the test log to obtain temperature change patterns and impact characteristics to provide a reference for subsequent point cloud model verification and optimization. Determine the 3D modeling software to be used, such as Autodesk Revit, SketchUp, or Blender. These tools can generate detailed 3D models based on design drawings and support subsequent point cloud processing. Make sure that the selected software can handle multi-area construction and has good visualization capabilities for subsequent analysis and presentation. According to the design drawings, gradually build a 3D model of the test chamber in the modeling software. First create the basic geometry, and then add details, such as the location of temperature sensors, cooling and heating equipment, insulation materials, etc. in each area. During the modeling process, ensure that the size and layout of each area are consistent with the design. Figure 1 To ensure the accuracy of the model. The name and function of each area should be clearly marked to facilitate subsequent analysis. If actual measurement data (such as laser scanning or photogrammetry) is available, import it into the modeling software to compare the point cloud data with the model. This will help verify the accuracy of the model. After generating the 3D point cloud model, ensure that the model can be exported to a standard format (such as OBJ or PLY) for subsequent data analysis and visualization. Verify the generated multi-area 3D point cloud model to ensure that the model is consistent with the design drawings and actual measurement data. Visualization tools can be used to check the details of the model to ensure that there are no omissions or errors. Save the final 3D point cloud model and prepare to output it as a report or presentation material to ensure that it can be used for subsequent experimental analysis and research.

[0039] Step S2: Acquire the temperature monitoring parameters of the test chamber; perform position registration and time-series temperature fluctuation fitting on the three-dimensional point cloud model of multiple regions of the test chamber according to the temperature monitoring parameters of the test chamber, and construct time-series temperature fluctuation curves of different regions;

[0040] In this embodiment, it is ensured that the temperature monitoring equipment in the test box operates normally. These devices usually include multi-point temperature sensors, thermocouples or infrared thermometers, which can monitor the temperature changes in various areas of the test box in real time. Confirm the calibration status of each sensor to ensure that its measurement accuracy meets the experimental requirements. Calibration can be performed according to the equipment manual, and zero and span corrections can be performed if necessary. Set the acquisition frequency of the monitoring parameters, such as collecting temperature data once per second or once per minute. According to the experimental requirements, select a suitable time window to capture the temperature changes during cold and hot shocks. During the test, record the temperature data in real time and ensure the stability of the data storage system. Use data acquisition software to import the temperature data into the database in real time to form a structured data set. The collected temperature monitoring parameters need to be sorted to ensure the integrity and consistency of the data. Key data include sensor number, acquisition time, corresponding temperature value and its area. Preprocess the data to remove outliers and noise to ensure that the data used for analysis is of good quality. Statistical methods can be used to detect and correct outliers. Determine the method of position alignment. Commonly used techniques include feature-based alignment and alignment based on iterative closest point (ICP) algorithm. The selection of a suitable method depends on the characteristics of the data and the accuracy requirements of the alignment. Feature-based methods are suitable for models with obvious feature points, while ICP algorithms are suitable for situations where point cloud data is relatively dense. Use the acquired temperature monitoring parameters to compare the actual position of the sensor with the corresponding position in the 3D point cloud model to ensure that the accuracy of the model is consistent with the actual situation. Perform position registration by comparing the coordinates of the sensor with those in the model. If deviations are found, the model needs to be adjusted to ensure that the sensor position is consistent with the 3D model. Verify the accuracy of the registration results to ensure that the sensor position in each area matches the position in the model. You can use visualization tools to view the registered model to ensure that there are no obvious deviations. Record the adjustment parameters during the registration process for subsequent analysis and optimization. Determine a suitable time series temperature fluctuation fitting model. Commonly used models include linear regression, nonlinear regression, and time series analysis models (such as ARIMA or exponential smoothing). When selecting a model, the characteristics and actual needs of the data should be considered. Ensure that the fitting model can handle characteristics such as trends, seasonality, and random fluctuations in the data. Perform time series analysis on the temperature monitoring data of each area, and use the selected fitting model to perform fitting calculations for temperature fluctuations. Input temperature data and calculate fitting parameters and models. Record the fitting results, including the fitting curve, predicted values ​​and their corresponding time points, for subsequent analysis. Verify the fitting results and use historical data for comparison to evaluate the accuracy and fitting effect of the model. You can calculate the goodness of fit (such as R² value) to evaluate the effectiveness of the model. Visualize the fitting results and use charts to display the time-series temperature fluctuation curves of different regions to help technicians quickly identify the trends and patterns of temperature changes.

[0041] Step S3: mining the hot and cold alternation demand of the hot and cold shock test log, reconstructing the hot and cold shock chain of the whole process, and building the hot and cold shock time window of the whole cycle;

[0042] In this embodiment, log data of hot and cold shock tests are obtained and organized. These logs should include the timestamp, temperature setting, actual temperature, duration, and status (cooling or heating) of each test cycle. Ensure the integrity of the log data for subsequent analysis. Preprocess the log data to remove irrelevant information and outliers to ensure the accuracy and readability of the data. Data cleaning techniques can be used, such as removing duplicate records and filling missing values. Determine the requirements for hot and cold alternation and define what constitutes hot and cold alternation in the test. For example, set a temperature change threshold (such as 5°C), and when the temperature change exceeds this value, mark it as the starting point of hot and cold alternation. For each test cycle, identify the cooling and heating stages and record their start and end times. Use conditional statements or data filtering methods to analyze the log data line by line to extract paragraphs that meet the alternation conditions. Create a new data set to record the time, temperature value, and status of each hot and cold alternation process. Ensure that the data set contains detailed information on each alternation for subsequent analysis. For example, if a test starts cooling at 09:00, ends at 09:30, and then starts heating at 09:40, it will be recorded as "Cooling: 09:00-09:30, Heating: 09:40-09:50". Determine the structure of the cold and hot shock chain, including the start time, end time and duration of each stage. Set the key parameters of each shock chain, such as cooling and heating temperature, duration, etc. Identify the components of the chain, which usually include cold shock stage, hot shock stage and alternating stage to form a complete experimental process. Combine the extracted cold and hot alternating demand data with the test log to form a complete cold and hot shock chain data set. Ensure that the parameters of each stage (such as temperature, time, state) are clearly recorded. Analyze the data to verify whether the duration of each stage meets the set standards to ensure the validity of the experiment. Based on the sorted data, reconstruct the cold and hot shock chain and record the time series of each shock stage. For example, if it is found that the duration of the cold shock is 30 minutes and the duration of the hot shock is 20 minutes, record this information in sequence. During the reconstruction process, ensure the continuity of time and avoid time overlap or missing between data. Time series analysis methods can be used to ensure the accuracy of chain reconstruction. Based on the reconstructed hot and cold shock chain, construct a full-cycle hot and cold shock time window to ensure that the time window covers all hot and cold alternation processes. The start time can be set as the time point when the experiment starts, and the end time is the time point when the experiment ends. Record the time window of each stage to form a complete time view for subsequent data analysis and visualization. Record the reconstructed hot and cold shock chain data and the full-cycle time window in the database to ensure the traceability and accuracy of all information. Establish a good data management system for subsequent query and analysis. Ensure that the parameters, time and status of each stage are clearly marked to form a complete experimental record.

[0043] Step S4: Calculate the test temperature deviation according to the full-cycle hot and cold shock time window and the time-series temperature fluctuation curves of different regions, so as to generate multi-time point temperature deviation values ​​for each region;

[0044] In this embodiment, the starting point of the time window should be the time when the test starts, and the end point should be the time when the test ends. Record each key node of the time window for subsequent analysis. Ensure that all hot and cold shock stages within the time window are recorded in detail, including the duration of each stage, the temperature set point, and the actual temperature change. Extract data from the previously constructed time series temperature fluctuation curve to ensure that the temperature change data of each area is complete. These curves should reflect the real-time changes in temperature during the test, including the temperature response of the cooling and heating stages. Ensure that the data points of the time series temperature fluctuation curve match the time nodes in the full cycle time window for subsequent deviation calculation. Determine the calculation formula for temperature deviation, usually, temperature deviation = actual temperature − set temperature, the set temperature is the target temperature of each stage during the test, and the actual temperature is the monitoring data in the corresponding time period. Through this formula, you can intuitively understand whether the temperature response of each area meets expectations. According to the experimental requirements, set a reasonable threshold for temperature deviation. For example, the deviation can be controlled within ±2°C, and it is considered an abnormal situation if it exceeds this range. This threshold will be used for subsequent data analysis and anomaly detection. Match the full-cycle hot and cold shock time window with the time-series temperature fluctuation curve of the corresponding area to ensure that the actual temperature at each time point corresponds to the set temperature. For example, in the cooling stage, the set temperature is -40°C, and the actual monitored temperature at this time point is -35°C. Record the set temperature and actual temperature at each time point one by one to form a comprehensive table for subsequent deviation calculation. Calculate the temperature deviation at each time point, and use the defined formula to calculate the deviation value of each area in turn. Record the deviation results at each time point to form a new data set. For example, if the set temperature of a certain area at 09:10 is -30°C and the actual temperature is -28°C, the temperature deviation at this time point is 2°C. Record the temperature deviation values ​​of multiple time points in each area in the database, ensuring that each deviation value, time point, and area information are clearly marked. Ensure the traceability of the data for subsequent analysis. Statistics such as the average deviation, maximum deviation, and deviation frequency of each area provide a basis for subsequent analysis. Analyze the calculated temperature deviation values ​​at multiple time points to identify which areas have larger deviation values ​​and pay attention to possible abnormalities. Calculate the average temperature deviation of each area and compare it with the set threshold. Identify the time points and areas where the deviation values ​​exceed the threshold to provide a basis for subsequent optimization. For example, if the deviation values ​​of a certain area reach ±3°C at multiple time points, the temperature control system of this area needs to be paid special attention.

[0045] Step S5: performing shock temperature compensation calculation on the temperature deviation values ​​at multiple time points in each area, and performing dynamic temperature compensation control on the test box, and collecting hot and cold shock compensation monitoring parameters of the test box;

[0046] In this embodiment, a model for shock temperature compensation calculation is established to clarify the compensation target. The compensation parameter is usually proportional to the temperature deviation, and the compensation formula is set as: compensation parameter = K × temperature deviation, where K is the compensation coefficient, which needs to be adjusted according to the equipment characteristics and experimental requirements. By setting different K values, personalized compensation for different areas can be achieved. The multi-point temperature deviation values ​​of each area are combined with the compensation formula to calculate the compensation parameters of each area at different time points one by one. Ensure that the temperature deviation value used in the calculation process is the latest and can reflect the current test status. For example, if the temperature deviation of a certain area at 09:10 is 3°C, if the K value is set to 2, the compensation parameter at this time point is 6°C. The calculated compensation parameters of each area are recorded in the database to ensure that the compensation value, area information and corresponding temperature deviation at each time point are clearly marked. A new data set is formed to facilitate subsequent analysis and adjustment. The average compensation parameters and maximum compensation values ​​of each area are counted to help technicians quickly identify areas that need to be focused on. Ensure that the dynamic temperature control system of the test chamber can respond to the compensation parameters in real time. The system should include heaters and cooling equipment that can adjust the output power according to the calculated compensation parameters. Check the control system settings to ensure that it can accept external inputs (such as compensation parameters) and can make rapid adjustments. Test the response time of the equipment to ensure that it meets the experimental requirements. According to the calculated compensation parameters, adjust the output of the heating and cooling equipment of the test chamber in real time. For example, if the compensation parameter of a certain area is 6°C, set the corresponding heating power in the control system to ensure that the temperature is close to the set value. During the control process, monitor the temperature changes in each area to ensure that the system can respond to temperature fluctuations in time and make corresponding adjustments. The real-time data acquisition system should be able to record the real-time temperature and compensation set value of each area. While implementing dynamic temperature compensation control, collect the hot and cold shock compensation monitoring parameters of the test chamber in real time. These parameters include actual temperature, set temperature, compensation output power, sensor status, etc. to ensure data integrity. Record the compensation response of each area to ensure that the monitoring data matches the compensation control for subsequent analysis and optimization. Analyze the collected hot and cold shock compensation monitoring parameters, compare the difference between the actual temperature and the set temperature, and evaluate the effect of dynamic compensation control. Through statistical analysis, identify which areas have better temperature control effects and which areas have problems. For example, if the temperature fluctuation range of a certain area is reduced after compensation control, it means that the control effect is good; otherwise, it is necessary to focus on the control strategy of this area. Adjust the compensation control strategy according to the analysis results of the monitoring parameters. It may be necessary to reset the compensation coefficient K or optimize the control algorithm to improve the response speed and stability of the system. Through continuous iteration, optimize the dynamic temperature compensation control system to ensure that the temperature stability of each area can be maintained during the hot and cold shock test, thereby improving the reliability of the experiment.Record all monitoring parameters, compensation results and optimization suggestions in the experimental report to ensure data traceability. Form a complete experimental document to facilitate subsequent inspection and audit. Use charts and data visualization tools to display the effect of dynamic temperature compensation, helping technicians to intuitively understand the process and results of compensation control, and facilitate analysis and decision-making.

[0047] Step S6: Optimize the switching response delay according to the thermal shock compensation monitoring parameters, and construct an intelligent switching response optimization control strategy.

[0048] In this embodiment, the switching response delay data is collected from the hot and cold shock compensation monitoring system of the test box. These data should include the start time, end time, temperature change, compensation parameters and specific conditions of the system response of each hot and cold switch. Ensure the integrity and accuracy of the data for subsequent analysis. Record the specific time points of each hot and cold shock stage, such as the timestamps of starting cooling, ending cooling, starting heating and ending heating, and record the actual temperature and set temperature of each stage. Perform a preliminary analysis of the collected data to calculate the response delay in each hot and cold switching process. Response delay = actual response time − set response time, identify the switching process with large delays, and pay special attention to those time periods that exceed the preset threshold. This helps to identify the key areas and stages that need to be optimized. For example, if the response delay of a cooling stage exceeds the set value of 1 minute, it is necessary to focus on the cause. Use a chart tool to visualize the switching response delay data and display the response time and delay of different stages. This visualization process can help technicians quickly identify problem areas and provide an intuitive basis for subsequent optimization strategies. According to the results of the delay analysis, set specific goals for intelligent switching response optimization. These goals may include reducing response delays, improving temperature stability, and enhancing the system's adaptive capabilities. For example, the goal is to control the cooling response delay to less than 30 seconds while ensuring that the temperature fluctuation does not exceed ±2°C. Select an appropriate intelligent control algorithm, such as fuzzy control, adaptive control, or machine learning algorithm. These algorithms can dynamically adjust the control strategy based on real-time monitoring data to improve the flexibility and accuracy of the system response. When selecting an algorithm, consider the complexity and computing power of the system to ensure that the selected algorithm can process data in real time and respond quickly. Design a specific control strategy based on the selected control algorithm. For example, if fuzzy control is used, you can set the relationship rules between temperature deviation, response time, and compensation parameters to form a comprehensive control strategy. Determine the adjustment range of the control parameters, such as increasing the output power of heating or cooling when the temperature deviation is large; when the temperature is close to the set value, reduce the output power to prevent overshoot. Implement the designed intelligent control strategy into the control system of the test chamber. Ensure that the control system can receive data from the temperature monitoring system in real time and adjust it according to the optimization strategy. During implementation, monitor the response of the system to ensure that it can respond quickly to temperature changes and adjust the control parameters based on real-time data. Conduct a series of experiments to verify the effectiveness of the optimized control strategy. Record the response delay, temperature fluctuation, and system stability data for each test and compare them with the data before optimization. Evaluate the effectiveness of the optimization strategy through statistical analysis. For example, if the cooling response delay is reduced from 60 seconds to 30 seconds after optimization, and the temperature fluctuation is controlled within ±1.5°C, the optimization strategy is effective. Continue to optimize the control strategy based on the test results and data feedback.Continuously adjust the parameters and control rules of the intelligent algorithm to improve the system's response speed and temperature control accuracy. Record the effect and improvement measures of each optimization to form a complete optimization process document to facilitate further optimization of subsequent experiments and control strategies.

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

[0050] Step S11: Obtaining the internal design drawing of the thermal shock test chamber and the thermal shock test log;

[0051] Step S12: visually identifying the components of the internal design drawing of the thermal shock test chamber, and marking multiple internal components of the test chamber;

[0052] Step S13: performing functional type analysis on multiple internal components of the test chamber to obtain the functional type of each component;

[0053] Step S14: dividing the test chamber area functions according to the functional type of each component to generate multiple internal functional areas;

[0054] Step S15: visually measuring the geometric dimensions of multiple internal functional areas to generate geometric parameters of each area;

[0055] Step S16: Perform multi-region three-dimensional point cloud modeling according to the geometric parameters of each region, thereby constructing a multi-region three-dimensional point cloud model of the test box.

[0056] In this embodiment, after obtaining authorization, contact the engineering design department to obtain the internal design drawings of the hot and cold shock test box. These drawings usually include the layout, size and material information of each component. The design drawings can be CAD format files to ensure their clarity and accuracy. Make sure to obtain the latest version of the design drawings to avoid using outdated materials to ensure the accuracy of subsequent analysis. Obtain log records of hot and cold shock tests from the laboratory or production department. These logs usually include information such as parameter settings, test time, ambient temperature, humidity changes, etc. for each test. Record key data of the test, such as the number of tests, test purpose, equipment operating status and fault records. This information will provide background and data support for subsequent analysis. Sort and integrate the obtained design drawings and test logs. A database can be created to associate the various parts of the design drawings with the relevant data in the test log for quick query and retrieval. Perform data preprocessing to ensure the integrity and consistency of all data. Check and mark the outliers in the test log to eliminate interference during subsequent analysis. Record the design drawings and test logs in the project management system for easy access and sharing by team members. Use visualization tools to display the key parameter trends of the test log to help team members better understand the test process. Perform a detailed analysis of the obtained design drawings to understand the overall structure of the test chamber and the layout of each component. All key components need to be identified, such as cooling systems, heating systems, control panels, sensors, etc. Prepare graphics processing software (such as Adobe Illustrator or AutoCAD) for component labeling and identification. Use graphics processing software in the design drawings to identify and label the components one by one. Add labels to each component to explain its name and function. For example, label "cooling fan", "heating element", "temperature sensor", etc. For complex components, multiple levels of identification may be required to ensure that all internal details are accurately labeled. During the labeling process, analyze the relationship and interaction between the various components. For example, the coordinated work between the cooling system and the heating system, or the communication between the sensor and the control panel. Use different colors or icons to indicate the functional type of the component to help quickly identify it during subsequent analysis. Save the marked design drawings in a shareable format (such as PDF or PNG) and record the detailed information of each component, including its location, function, and related parameters. This information provides the basis for subsequent analysis and functional division. According to the working principle and design requirements of the test chamber, set the component function classification standard. The components can be divided into main function components (such as heating, cooling) and auxiliary function components (such as monitoring, control). Determine the specific functions of each type of component and develop descriptive standards for each function for subsequent analysis. Analyze the marked components one by one to identify the main function of each component. In this process, refer to the data in the design drawings and test logs to ensure the accuracy of identification.Record the functional type of each component, for example, cooling elements belong to the cooling system, control panels belong to the control system, etc. Double-confirm the identified functional types, and invite experts in related fields to proofread the results to ensure the accuracy and consistency of the functional classification. If there are disputes or ambiguities in the functional classification, further discussion and adjustment can be carried out. Record the functional types of all components in the database to form a complete component functional analysis report. The report should include the component name, location, functional type and their relationship to each other for subsequent area division. According to the functional type and relationship of the components, set the area division standards inside the test chamber. The test chamber can be divided into heating area, cooling area, control area and monitoring area, etc. Ensure that each area division has clear functional boundaries for subsequent analysis and management. Perform area division on the marked design drawing and group components with similar functions into the same area. For example, all cooling-related components (such as cooling fans and cooling ducts) are divided into cooling areas. For components that need to work in coordination, ensure that they are properly laid out in space to effectively achieve their functions. Provide a functional description for each divided area, recording its main functions and related components. For example, the description of the cooling area can include "responsible for cooling the inside of the test chamber, including cooling fans and coolant circulation system". Ensure that the functional description of the area is clear and easy to understand for subsequent analysis. Record the divided area information in the database and generate a visual chart to show the distribution of the internal functional areas of the test chamber. These visualizations will help technicians quickly understand the structure and function of the test chamber. According to the design drawings, select appropriate measuring tools (such as laser rangefinders, calipers, etc.) to measure the geometric dimensions. Ensure that the accuracy of the tools can meet the design requirements. Determine the measurement method, which can be direct measurement or calculation based on drawings, and ensure that the selected method is applicable to each functional area. Measure the geometric dimensions of each functional area and record its length, width, and height. Ensure the accuracy and consistency of the measurement to avoid inaccurate regional parameters due to measurement errors. During the measurement process, pay attention to record the specific location and shape characteristics of each area for subsequent analysis. Organize and calculate the measured dimensional data to generate the geometric morphological parameters of each functional area. This includes the volume, surface area, etc. of each area. Select appropriate 3D modeling software (such as AutoCAD, Blender or SketchUp) for 3D point cloud modeling according to needs. Ensure that the software supports multi-region modeling and can handle complex geometries. Use the geometric parameters of each region to build 3D models one by one in the modeling software. Accurately set the shape and position of each region based on the recorded dimensional data. During the modeling process, ensure that the relationship and connection between the various regions meet the actual design requirements. Convert the constructed 3D model into point cloud data for subsequent analysis and visualization. Point cloud data can effectively represent the spatial characteristics and structural information of each region.Use appropriate point cloud generation methods to ensure that the density and accuracy of the point cloud meet the analysis requirements. Verify the generated 3D point cloud model and check its consistency with the design drawing. Ensure the accuracy and completeness of the model by comparing the model with the design drawing. Make necessary adjustments and optimizations based on the verification results to ensure that the model can truly reflect the internal structure and function of the test chamber.

[0057] In this embodiment, refer to Figure 3 , is a flowchart of detailed implementation steps of step S2. In this embodiment, the detailed implementation steps of step S2 include:

[0058] Step S21: Perform real-time monitoring sampling based on multiple sensor nodes to obtain the temperature monitoring parameters of the test chamber;

[0059] Step S22: Calculate the positions of multiple sensor nodes one by one to obtain sensor node position positioning data;

[0060] Step S23: performing real-time temperature change calculation on the temperature monitoring parameters of the test chamber to generate real-time temperature change characteristics;

[0061] Step S24: performing position registration processing on the multi-region three-dimensional point cloud model of the test box according to the sensor node position positioning data, so as to obtain the regional position of each sensor node;

[0062] Step S25: Perform time-series temperature fluctuation fitting on the real-time temperature change characteristics according to the regional position of each sensor node, and construct time-series temperature fluctuation curves for different regions.

[0063] In this embodiment, a multi-sensor network is built to ensure that temperature sensors are arranged in key areas inside the test chamber. These sensors should be high-precision temperature sensors, such as thermocouples or RTDs (platinum resistance thermometers) to ensure data accuracy and reliability. When selecting sensors, consider their response time and measurement range to ensure that they can cover all temperature change requirements of the test chamber. Configure the data acquisition system and set the sampling frequency of each temperature sensor, usually once per second, to capture temperature changes in a timely manner. Ensure that the data acquisition system can receive and store sensor data in real time. Use data acquisition equipment (such as data acquisition cards or microcontrollers) to transmit sensor data to a central data processing unit wirelessly or wired. Through the data acquisition system, the temperature data of each sensor is recorded in real time and timestamped to form time series data of temperature monitoring parameters. These data will provide a basis for subsequent analysis. Ensure the integrity and accuracy of the data, regularly check the status of the sensors and the operation of the data acquisition system to prevent data loss or errors. Visualize the real-time temperature monitoring parameters and use charts to show the temperature change trends at different sensor locations so that technicians can quickly identify abnormal situations. For example, use a line chart to show the real-time temperature changes of each sensor to help monitor the temperature distribution. Inside the test chamber, plan the installation locations of sensors according to design requirements and heat conduction characteristics. Ensure that sensors are evenly distributed and cover all key areas of the test chamber to fully monitor temperature. The temperature changes at different locations can be predicted through simulation analysis software to select the optimal sensor location. Calibrate the position of each sensor and record its coordinate data in three-dimensional space (such as X, Y, and Z coordinates). These coordinate data can be obtained through measurement tools (such as laser rangefinders) to ensure accuracy. When recording, ensure that the installation location of each sensor corresponds to its temperature data to form an association between position and temperature data. Integrate the location information of all sensors into a database to form a sensor node location positioning data set. This data set should include the unique identifier, coordinate information, and installation date of each sensor. Perform data verification to ensure the accuracy and consistency of all location data to avoid subsequent analysis deviations caused by incorrect sensor location. Use 3D modeling software (such as AutoCAD or Blender) to visualize the location information of the sensors and show the sensor layout inside the test chamber. This visualization helps with subsequent analysis and alignment processing, allowing technicians to clearly view the sensor location. Determine the calculation indicators of temperature change characteristics, such as the average value, rate of change, maximum and minimum values ​​of temperature. These indicators will be used to quantify the characteristics of temperature change. Select a suitable time window, such as every minute or every second, and calculate the temperature change characteristics within the time window to capture short-term and long-term temperature fluctuations. Analyze the real-time temperature monitoring parameters one by one and calculate the temperature change characteristics of each sensor within the set time window.Calculate the temperature change rate of each sensor within one minute: change rate = (current temperature − previous temperature) / time interval, and record the temperature change characteristics of each sensor, including change rate, fluctuation range and trend, for subsequent analysis. Integrate the calculated real-time temperature change characteristics into the database and generate a visual chart to show the temperature change trend of different sensors. Use a line chart or heat map to show the temperature change, which helps to quickly identify abnormal areas. Prepare a multi-region 3D point cloud model of the test chamber, which should include all functional areas and components inside the test chamber. Ensure the accuracy and completeness of the model to lay the foundation for subsequent registration processing. Use 3D modeling software to confirm the geometry and spatial layout of the model to avoid registration problems caused by model errors. Select a suitable position registration algorithm, such as the iterative closest point algorithm (ICP) or feature matching algorithm, to achieve the registration of the sensor node position with the 3D point cloud model. Ensure that the selected algorithm can handle the noise and incompleteness of the point cloud data to ensure the accuracy of the registration. Use the location information of the sensor node to register its data with the 3D point cloud model. Use the algorithm to calculate the corresponding regional position of each sensor node in the 3D model. During the registration process, the registration error of each sensor is recorded for subsequent adjustment and optimization. The registration results are verified to ensure that the position of each sensor node matches the functional area of ​​the 3D model. If deviations are found, necessary adjustments and optimizations are made to ensure the accuracy of the registration. The registration results, including the regional position and registration error of each sensor node, are recorded to provide a basis for subsequent analysis. To determine the method for fitting the time series temperature fluctuation curve, linear regression, smoothing spline curve, or Fourier transform can be selected. The characteristics of the data and the purpose of fitting should be considered when selecting. Ensure that the selected method can adapt to the nonlinear characteristics of temperature changes and provide an accurate fluctuation curve. The real-time temperature change characteristics of each sensor node are sorted according to the regional position to ensure that the temperature data of each region is complete and the time series is consistent. Record the sensor nodes of each region to ensure that the data can be accurately extracted during subsequent fitting. Perform time series fluctuation fitting on the temperature data of each region to generate a temperature fluctuation curve. Calculate the temperature fluctuation characteristics of each region through the fitting method and record the fitting parameters. For example, the linear regression method is used to fit the time series temperature data to obtain the temperature change trend and fluctuation range of each region. The fitted temperature fluctuation curve is visualized, and the temperature change trend of different areas is displayed in a chart. By comparing the fluctuation curves of different areas, the effectiveness of temperature control and the differences between areas can be identified. Based on the analysis results, optimization suggestions are put forward, such as adjusting the temperature control strategy for certain areas to improve the overall performance of the test chamber.

[0064] In this embodiment, the specific steps of step S25 are:

[0065] According to the regional position of each sensor node, the real-time temperature change characteristics are interpolated dynamically in the test box to generate temperature change distribution maps of multiple regions;

[0066] Calculate the temperature change rate of each area according to the temperature change distribution diagram of multiple areas;

[0067] Calculating the temperature change amplitude according to the temperature change rate, thereby obtaining the temperature change amplitude of each area;

[0068] Based on the real-time temperature change characteristics, the temperature fluctuation periodic law is evolved to generate the temperature fluctuation periodic characteristics;

[0069] The time-series temperature fluctuation fitting is performed according to the temperature change amplitude and temperature fluctuation period characteristics of each area, and the time-series temperature fluctuation curves of different areas are constructed.

[0070] In this embodiment, a suitable interpolation method is selected according to the location of the sensor node and the real-time temperature change characteristics. Common interpolation methods include linear interpolation, inverse distance weighting (IDW) and Kriging interpolation (Kriging). The distribution characteristics and target accuracy of the data should be considered when selecting. For example, Kriging interpolation is suitable for temperature data with spatial autocorrelation and can provide a smoother and more accurate temperature distribution estimate. The regional location of each sensor node and the corresponding real-time temperature monitoring parameters are sorted to ensure the integrity and consistency of the data. The coordinates (X, Y, Z) of each sensor and its real-time temperature value are recorded. Ensure that the sensors are evenly distributed to avoid interpolation errors caused by sparse nodes. If there are fewer sensors in some areas, consider adding virtual nodes for interpolation. Use the selected interpolation method to interpolate the temperature change of each area and generate a temperature change distribution map. During the interpolation process, the temperature estimates of each area are calculated using the temperature values ​​of the surrounding sensors. Record the interpolation results, including the temperature distribution characteristics and interpolation errors of each area, for subsequent analysis. Set the calculation standard of the temperature change rate, which is usually expressed as the temperature change per unit time, such as degrees Celsius / minute (°C / min). Set the calculation time window, such as every minute or every second. Analyze the temperature change distribution map of each area and calculate the temperature change within the set time window. For each area, record the temperature values ​​at the beginning and end of the time window. Record the temperature change rate of each area in the database for subsequent analysis and reference. Form a temperature change rate data set for each area, including the area name, change rate and time information. Determine the definition of the temperature change amplitude, which is usually the difference between the maximum temperature and the minimum temperature in a specific time window, that is: temperature change amplitude = maximum temperature − minimum temperature. Analyze the temperature change distribution map of each area to find the maximum and minimum temperature values ​​within the set time window. These values ​​can be extracted from real-time monitoring data or read from the temperature change distribution map. Ensure the accuracy of the data. If there are abnormal values, they need to be screened and corrected. Record the temperature change amplitude of each area in the database to form a temperature change amplitude data set. Ensure that the data set includes the area name, maximum temperature, minimum temperature and its change amplitude. Determine the definition of the temperature fluctuation periodicity characteristics, which usually involves the periodicity and frequency of temperature changes. In the analysis, consider using Fourier transform or wavelet transform to identify the periodic characteristics of temperature fluctuations. Use the temperature change characteristics of real-time monitoring to perform time series analysis. Calculate the autocorrelation function of the temperature data to determine its periodic characteristics. Use Fourier transform to convert the temperature time series to the frequency domain, identify significant frequency components, and then derive the period of temperature fluctuation. Record the calculated temperature fluctuation period characteristics in the database to form a temperature fluctuation period characteristic data set. These data should include the period length, main frequency and corresponding temperature change characteristics of each area.Select a suitable fitting model based on the temperature variation amplitude and period characteristics of each region. For example, a sine wave fitting model or a polynomial fitting model can be used to describe the time series temperature fluctuation curve. Determine model parameters, such as the fitting period, amplitude, and phase, to accurately reflect the temperature fluctuation characteristics. Arrange the temperature variation amplitude and fluctuation period characteristics of each region to ensure that the data format is consistent for the subsequent fitting process. Clean the data and remove outliers to ensure the accuracy of the fitting. Perform time series fitting on the temperature data of each region and calculate the fitting parameters. Use methods such as the least squares method to optimize the fitting results to ensure that the fitting curve can effectively reflect the actual temperature fluctuation. Record the fitting results of each region, including the fitting curve equation, goodness of fit (such as R² value), etc., to evaluate the fitting effect. Visualize the time series temperature fluctuation curve obtained by fitting to help technicians understand the temperature fluctuation characteristics of different regions. A line chart can be used to show the comparison between the fitting curve and the actual temperature data to facilitate the identification of the accuracy and trend of the fitting.

[0071] In this embodiment, refer to Figure 4 , is a flowchart of detailed implementation steps of step S3. In this embodiment, the detailed implementation steps of step S3 include:

[0072] Step S31: mining the hot and cold alternation demand on the hot and cold shock test log to extract the hot and cold alternation demand data;

[0073] Step S32: identifying the hot and cold alternating intervals of the hot and cold alternating demand data, and generating hot and cold alternating interval data;

[0074] Step S33: performing alternating interval change analysis according to the hot-cold alternating interval data to generate a hot-cold alternating cycle pattern;

[0075] Step S34: Calculate the duration of the shock mode according to the cold and hot shock test log, so as to obtain the cold shock time length and the hot shock time length;

[0076] Step S35: reconstruct the cold shock time length and the hot shock time length of the whole process cold and hot shock chain based on the cold and hot alternating interval data, and construct a full-cycle cold and hot shock time window.

[0077] In this embodiment, the definition of the hot and cold alternation demand is clarified, that is, the alternation of the cooling and heating stages during the test. It is necessary to identify the turning point of the temperature change, such as the moment when the temperature changes from the heating state to the cooling state. In order to effectively extract these demands, a temperature change threshold can be set. For example, when the temperature change exceeds a certain set value (such as 5°C), it is marked as the starting point of the hot and cold alternation. The time series analysis method is used to analyze the sorted test logs. The log data is scanned line by line, and the time points that meet the hot and cold alternation conditions are recorded to extract the hot and cold alternation demand data. For example, if the temperature drops from 60°C to 25°C at a certain time point, it is recorded as the demand of "hot shock ends and cold shock begins". The extracted hot and cold alternation demand data is saved as a new data set, and the time, temperature value and state (heating or cooling) of each alternation are recorded. This data set will provide a basis for subsequent analysis. Generate a visual chart to show the frequency and timing changes of hot and cold alternation to help understand the pattern of hot and cold alternation demand during the test. Determine the definition of the hot and cold alternation interval, that is, the time interval from the end of a cold shock to the beginning of the next hot shock. This interval helps analyze the frequency and regularity of hot and cold alternation. Add a new field to the data set to record the time interval between each hot and cold alternation. According to the extracted hot and cold alternation demand data, analyze the time points of each alternation one by one and calculate the time difference between adjacent hot and cold alternation states. For example, if the cold shock ends at 10:00 and the hot shock starts at 10:05, the recording interval is 5 minutes. Use simple loops or data processing tools (such as Python's Pandas library) to perform this process to improve efficiency and accuracy. Record the calculated hot and cold alternation interval data in a new data set to ensure that the start and end time and interval time of each alternation process are clearly recorded. Generate statistical data, including the total number of alternations, average alternation interval, etc., for subsequent analysis. Clarify the definition of the alternation cycle pattern, that is, the regularity and characteristics of the hot and cold alternation cycle. Analyze the alternation interval data to identify common alternation patterns. Set analysis criteria, such as the range of cycle length, which usually ranges from a few minutes to a few hours. Use statistical analysis methods to perform descriptive statistics on the hot and cold alternation interval data and calculate its mean, standard deviation, and coefficient of variation. These indicators will help identify the stability and regularity of the alternating intervals. Use frequency distribution analysis to identify common values ​​and outliers in the alternating intervals and understand the distribution characteristics of the data. Use time series analysis methods, such as the autoregressive moving average model (ARIMA), to fit the hot and cold alternating interval data to identify potential periodic patterns. Record the analysis results, including the cycle length, frequency, and its corresponding temperature change characteristics to provide data support. Clarify the definition of the duration of cold shocks and hot shocks, which usually refers to the time period from the start of heating or cooling to reaching the set temperature or end state. Add a new field to the test log data to record the duration of each shock.According to the test log, analyze the start and end time of each cold shock and heat shock one by one. Calculate the duration of each shock and record the corresponding time data. For example, if the cold shock starts at 09:00 and ends at 09:30, the duration is 30 minutes. This process can be simplified using data processing tools. Record the calculated cold shock time length and heat shock time length in a new data set to ensure that the duration and type of each shock are clearly visible. Generate statistical data, including the average duration and total number of cold and heat shocks, to provide a basis for subsequent analysis. Define the reconstruction process of the cold and heat shock chain, that is, combine the duration of each cold and heat shock with the alternating interval data to form a complete cold and heat shock process. Determine the start and end time of each stage in the shock chain so that the entire cold and heat shock process is clearly recorded. According to the cold and heat shock time lengths obtained in the previous steps, as well as the cold and heat alternating interval data, combine the time periods of each shock one by one to form a complete cold and heat shock chain. Calculate the start and end time of each shock stage. For example, if the cold shock starts at 09:00 and lasts for 30 minutes with an alternating interval of 10 minutes, the hot shock starts at 09:40. The reconstructed cold and hot shock chain is recorded in a new dataset to form a complete full-cycle cold and hot shock time window. This dataset should contain information such as the time, type, and duration of each shock stage. Generate statistical data, including the total duration of each cycle, the proportion of each stage, etc., to provide support for subsequent analysis. Use charts to display the full-cycle cold and hot shock time window to help technicians intuitively understand the overall process and time distribution of cold and hot shocks. Gantt charts and other forms can be used to display the time schedule of different stages to facilitate the identification of shock patterns and time characteristics.

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

[0079] Step S41: Calculate the required temperature of hot and cold shock according to the hot and cold alternating demand data to obtain the specified temperature value of each hot and cold shock;

[0080] Step S42: extracting multiple hot and cold shock switching time points according to the full-cycle hot and cold shock time window;

[0081] Step S43: Based on the multiple hot and cold shock switching time points, the temperature trend of the time series temperature fluctuation curves of different regions is predicted at corresponding time points, so as to obtain the predicted value of the temperature trend at the shock time point of each region;

[0082] Step S44: Based on the designated temperature value of each hot and cold shock, the experimental temperature deviation calculation is performed on the temperature trend prediction value at the shock time point of each region, so as to generate multi-time point temperature deviation values ​​of each region.

[0083] In this embodiment, the temperature range is set, for example, the hot shock can be set to 60°C, and the cold shock can be set to -40°C, to ensure that these two temperature values ​​meet the test standards. Collect the hot and cold alternation demand data and integrate it with the specified target temperature. Ensure that the hot and cold temperature values ​​required for each alternation are accurately recorded for subsequent calculations. For each alternation process, record the start time, end time and its corresponding target temperature. This information can be obtained by analyzing the hot and cold alternation demand data extracted previously. According to the demand for each hot and cold alternation, perform temperature calculations to ensure that the target temperature of each shock stage is accurately reflected in the data set. For example, if the cold shock starts at 09:00 and the target temperature is -40°C, it is recorded as "cold shock: -40°C". Store these calculation results in a structured manner for subsequent analysis and query. Clarify the scope of the full-cycle hot and cold shock time window, which should cover the start and end times of all hot and cold shock processes. Ensure the integrity of the data so that the accurate switching time point can be extracted. Set the starting and ending points of the time window, for example, from the first second to the last second of the test. According to the demand data of hot and cold shocks, analyze the start and end time of each shock one by one to identify the key switching time points. This process can be completed by traversing the recorded time data. For example, if the start time of the cold shock is 09:00, the end time is 09:30, and the start time of the hot shock is 09:40, then record 09:00, 09:30 and 09:40 as the switching time points. Record the extracted switching time points in a new data set, ensuring that the type (cold or hot) of each time point and its corresponding time information are clearly marked. Generate statistical information, including the total number of switches, the average time interval of each switch, etc., to provide a basis for subsequent analysis. Determine a prediction model suitable for time series temperature fluctuation data. Commonly used models include linear regression, ARIMA model or machine learning model (such as support vector regression). When selecting a model, the characteristics of the data and the purpose of prediction should be considered. Ensure that the model can handle the nonlinear change characteristics of temperature data and provide accurate temperature trend prediction. Arrange the time series temperature fluctuation curve data of each region to ensure that the data is complete and in a consistent format. Each time series data set should include timestamps, temperature values ​​and their change characteristics. Associate the extracted switching time points with the corresponding time-series temperature fluctuation curves for subsequent prediction. Use the selected prediction model to predict the temperature trend of each area at the switching time point. Input the data at the time point and calculate the predicted value. Record the predicted temperature trend values ​​of each area at different switching time points to form a new data set. Verify the prediction results, compare with historical data, and calculate the accuracy and error rate of the prediction. Ensure the effectiveness of the prediction model. Visualize the prediction results and use charts to show the comparison between the predicted temperature trend values ​​and the actual observed values ​​in different areas, so that technicians can quickly understand the accuracy and trend of the prediction. Clarify the definition of temperature deviation, that is, the difference between the predicted value and the target temperature value.Temperature deviation = predicted temperature value − specified temperature value. Set the threshold of temperature deviation to facilitate subsequent data analysis and anomaly detection. Calculate the deviation of the predicted temperature trend value at each time point in each region. Compare the predicted value of each impact time point with the corresponding specified temperature value one by one, and record the deviation results. For example, if the predicted temperature of a region is -35°C and the specified temperature is -40°C, the temperature deviation is 5°C. Record the calculated multi-time point temperature deviation values ​​in a new data set to ensure that the deviation value, time point and its corresponding target temperature of each region are clearly marked. Generate statistical data, including the average deviation, maximum deviation and deviation frequency of each region, to provide a basis for subsequent analysis.

[0084] In this embodiment, step S5 includes the following steps:

[0085] Step S51: performing shock temperature compensation calculation on the temperature deviation values ​​at multiple time points of each region, thereby generating a cold and hot shock compensation parameter for each region;

[0086] Step S52: Calculating the temperature control power based on the thermal shock compensation parameters of each area, thereby generating the temperature control power parameters of each area;

[0087] Step S53: Perform dynamic temperature compensation control on the test box based on the dynamic temperature control power parameters of each zone, and collect the thermal shock compensation monitoring parameters of the test box in real time.

[0088] In this embodiment, the goal of shock temperature compensation is to adjust the control system of the test box to ensure that the temperature of each area reaches the set value by calculating the temperature deviation value. The compensation value should be large enough to offset the temperature deviation caused by environmental changes or equipment performance fluctuations. The formula for determining the compensation calculation, usually the temperature compensation can be expressed as: compensation parameter = K × temperature deviation, where K is the compensation coefficient, which is set according to the system response characteristics. Collect the temperature deviation values ​​of multiple time points in each area to ensure that the data is complete and the format is consistent. Each area should have a corresponding time point and deviation value to facilitate subsequent calculations. For different areas, different compensation coefficients K may need to be set according to experimental conditions and characteristics to ensure the accuracy of compensation. For each time point in each area, the above formula is used to calculate the compensation parameters. Record the compensation parameters of each area at different time points to form a new data set. For example, if the temperature deviation of a certain area is 5°C and the compensation coefficient K is set to 2, the compensation parameter of the area is 10°C. The calculated cold and hot shock compensation parameters are recorded in the database to ensure that the compensation parameters, time points and corresponding deviation values ​​of each area are clearly marked. Use charts to show the changes in compensation parameters for each area to help technicians quickly identify which areas need stronger compensation so that they can adjust the control strategy later. Clarify the calculation method of temperature control power. Usually, power is proportional to compensation parameters. Set the power calculation formula, control power = Pbase + Pcomp × compensation parameter, where Pbase is the basic power and Pcomp is the compensation power coefficient. Collect the cold and hot shock compensation parameters for each area to ensure the accuracy and completeness of the data. It is also necessary to prepare basic power data to ensure the accuracy of power calculation. For different areas, different compensation power coefficients may need to be set according to equipment characteristics and heat load conditions. For each area, use the above power calculation formula to calculate the temperature control power parameters one by one. Make sure to record the power data of each area at different time points to form a new power data set. For example, if the compensation parameter of a certain area is 10°C, the basic power is 100W, and the compensation power coefficient is 2, then the control power is 120W. Record the calculated temperature control power parameters in the database to ensure that the power parameters, time points and their corresponding compensation parameters of each area are clearly marked. Configure the control system of the test chamber to achieve dynamic temperature compensation control. Ensure that the system can adjust the output of the heating and cooling equipment in real time according to the calculated control power parameters. Set up a feedback mechanism to ensure that the temperature sensors can monitor the temperature changes in each area in real time and feed the data back to the control system. Adjust the power output of the heating and cooling equipment of the test chamber according to the dynamic temperature control power parameters of each area. For example, if the control power of a certain area is 120W, adjust the output of the heater accordingly to ensure that the temperature is maintained at the set value. During the control process, continuously monitor the temperature changes in each area to ensure that the system can quickly respond to temperature fluctuations and make appropriate adjustments.Use the data acquisition system to record the thermal shock compensation monitoring parameters of the test chamber in real time. These parameters include current temperature, set temperature, power output, etc., to form a complete data set. Set an appropriate sampling frequency, such as collecting data once per second, to ensure that rapidly changing temperature conditions can be captured. Analyze the monitoring parameters collected in real time to evaluate the effect of dynamic compensation control. Compare the deviation between the actual temperature and the set temperature to identify the response time and stability of the system. According to the analysis results, adjust the compensation control strategy and power parameters to ensure that the temperature control of the test chamber in the thermal shock test achieves the best effect.

[0089] In this embodiment, step S6 includes the following steps:

[0090] Step S61: performing a comprehensive evaluation of the thermal shock effect according to the thermal shock compensation monitoring parameters to generate a plurality of thermal shock stage effect evaluation values;

[0091] Step S62: performing real-time switching response analysis of the test chamber on the thermal shock compensation monitoring parameters, and extracting real-time switching response data;

[0092] Step S63: calculating the impact phase response rate of the real-time switching response data to obtain multiple cold and hot impact phase response rates;

[0093] Step S64: calculating the hot and cold switching response delay of each hot and cold shock stage response rate according to the multiple hot and cold shock stage effect evaluation values, thereby obtaining the hot and cold switching stage response delay data;

[0094] Step S65: Optimize the switching response delay according to the response delay data of the hot-cold switching stage, and construct an intelligent switching response optimization control strategy.

[0095] In this embodiment, the indicators for evaluating the effect of cold and hot shock are clarified, including the time for the temperature to reach the set value, the temperature fluctuation range, the temperature deviation and the system response time. Setting these indicators helps to quantify the effect of cold and hot shock. For example, the time for the set temperature to reach the set value should be less than the predetermined threshold (such as 1 minute), and the temperature fluctuation range should be controlled within ±2°C. Collect and organize the cold and hot shock compensation monitoring parameters, including the temperature change, compensation parameters and actual response of each stage. Ensure the integrity and consistency of the data for subsequent analysis. Integrate these data into a comprehensive data set for easy calculation and analysis. According to the set evaluation indicators, a comprehensive evaluation is performed on each cold and hot shock stage. Analyze the data of each stage one by one, and calculate the effect evaluation value of each stage. For example, if the time for the temperature of a certain stage to reach the set value is 40 seconds and the fluctuation range is ±1.5°C, the effect evaluation value of this stage can be obtained by weighted method. The calculated effect evaluation values ​​of multiple cold and hot shock stages are recorded in the database to ensure that the evaluation value, time and corresponding parameters of each stage are clearly marked. Use charts to display the effect evaluation values ​​of each stage to help technicians quickly identify the performance of different stages, which is convenient for subsequent optimization and adjustment. Determine the goal of the real-time switching response analysis, which is to evaluate the temperature response time and stability of the test chamber during the hot and cold shock switching process. This analysis will provide a basis for subsequent optimization. Set the calculation method for response time, usually the time from the start of the switch to the temperature reaching the set value. Collect hot and cold shock compensation monitoring parameters, especially temperature data before and after the switch time point. Ensure that the data is complete and covers all relevant switching processes. Record the temperature changes before and after the switch for subsequent analysis and calculation. Analyze the collected monitoring parameters and extract the temperature data before and after each switch. Record the temperature changes before and after the switch to calculate the response time and stability. For example, if the temperature changes from 20°C to 25°C after the switch, record the switching time and the corresponding temperature change. Record the extracted real-time switching response data in a new data set, ensuring that the time, temperature changes before and after each switch, and its response are clearly marked. Use charts to show the trend of the switching response data to help technicians identify potential problems in the switching process. Define the calculation method for response rate, usually the rate of temperature change per unit time, such as degrees Celsius per minute (°C / min). This indicator can reflect the response speed of the test chamber to hot and cold shock switching. From the extracted real-time switching response data, collect the temperature change and response time of each impact stage. Ensure the accuracy and completeness of the data for subsequent calculations. For each impact stage, record the starting and ending values ​​of the temperature change, as well as the corresponding response time. For each impact stage, use the above formula to calculate the response rate, record the response rate value of each stage, and form a new data set. For example, if the temperature change in a certain stage is 10°C and the response time is 2 minutes, the response rate is 5°C / min.Record the calculated response rates of multiple hot and cold shock stages in the database, and ensure that the response rate, time and corresponding temperature changes of each stage are clearly marked. Use charts to show the changes in response rates at different stages to help technicians quickly identify the pros and cons of response performance. Clarify the calculation method of hot and cold switching response delay, that is, the delay time from the start of switching to the actual temperature reaching the set value. This value can reflect the reaction sensitivity of the system. Collect the effect evaluation value and response rate data of each hot and cold shock stage to ensure that the data is complete and the format is consistent. Record the expected response time and actual response time of each stage for delay calculation. For example, if the expected response time is 1 minute and the actual response time is 1.5 minutes, the response delay is 0.5 minutes. For each hot and cold shock stage, use the defined formula to calculate the response delay, record the delay value of each stage, and form a new data set. Ensure that the relevant time information and corresponding evaluation values ​​are recorded for subsequent analysis. Record the calculated hot and cold switching stage response delay data in the database, and ensure that the delay value, time and corresponding evaluation information of each stage are clearly marked. Use charts to show the response delay at different stages to help technicians quickly identify delay issues and facilitate subsequent optimization. Determine the goal of response delay optimization, that is, to reduce the delay time during the hot and cold switching process and improve the response speed of the system. Set clear optimization indicators, such as controlling the response delay within a predetermined range (such as less than 1 minute). Set evaluation criteria for the optimization strategy to ensure the effectiveness of the optimization measures. Analyze the collected response delay data to identify the main causes of the delay. Possible factors include equipment performance, control algorithms, environmental factors, etc. Through data comparison, find out the hot and cold switching stages with poor performance and record related operating parameters for in-depth analysis. Based on the analysis results, design a switching response optimization control strategy. This may include adjusting the control algorithm, optimizing equipment parameters, and improving the performance of heating and cooling equipment. Set a new control strategy, such as using an adaptive control algorithm, so that the system can dynamically adjust the control parameters based on real-time monitoring data to achieve faster response. Implement the optimization strategy in the control system of the test chamber and monitor it in real time. Record the adjusted response delay data to evaluate the optimization effect. Compare the changes in response delay before and after optimization to ensure the effectiveness of the optimization measures and further adjust the strategy to achieve the best results.

[0096] In this embodiment, a device for quickly switching the temperature of a thermal shock test chamber is provided, which is used to execute the method for quickly switching the temperature of a thermal shock test chamber as described above, including:

[0097] The three-dimensional modeling module is used to obtain the internal design drawings of the hot and cold shock test chamber and the hot and cold shock test logs; perform multi-region three-dimensional point cloud modeling on the internal design drawings of the hot and cold shock test chamber, thereby constructing a multi-region three-dimensional point cloud model of the test chamber;

[0098] The temperature fluctuation fitting module is used to obtain the temperature monitoring parameters of the test chamber; according to the temperature monitoring parameters of the test chamber, the position registration and time-series temperature fluctuation fitting of the three-dimensional point cloud model of the multi-region of the test chamber are performed to construct the time-series temperature fluctuation curves of different regions;

[0099] The hot and cold alternation demand module is used to mine the hot and cold alternation demand of the hot and cold shock test log, reconstruct the hot and cold shock chain of the whole process, and build the hot and cold shock time window of the whole cycle;

[0100] Deviation calculation module, used to calculate the test temperature deviation according to the full cycle hot and cold shock time window and the time series temperature fluctuation curves of different regions, so as to generate multi-time point temperature deviation values ​​for each region;

[0101] The compensation control module is used to perform shock temperature compensation calculations on the temperature deviation values ​​at multiple time points in each area, and to perform dynamic temperature compensation control on the test chamber, and collect the hot and cold shock compensation monitoring parameters of the test chamber;

[0102] The switching response optimization module is used to optimize the switching response delay according to the thermal shock compensation monitoring parameters and construct an intelligent switching response optimization control strategy.

[0103] The present invention can accurately reproduce the spatial layout and functional areas inside the test box through three-dimensional modeling. This provides reliable basic data for subsequent temperature monitoring, temperature compensation, etc. The distribution of heat sources and cold sources in different regions is clearly distinguished, which is convenient for subsequent precise control. Through point cloud data, efficient CFD simulation can be performed to simulate the heat exchange process in different regions, providing theoretical support for subsequent temperature optimization. Through position registration and fitting, accurate mapping of temperature changes and specific regions can be achieved, ensuring that the temperature data of each region can accurately reflect its thermodynamic state. Provide real-time and accurate temperature fluctuation curves for subsequent temperature compensation and regulation, which is convenient for monitoring and adjusting temperature change trends. Through the time-series temperature curve, the temperature fluctuations in different regions of the test box can be observed in real time, providing data support for dynamic temperature adjustment. Through in-depth mining of the test log, the start and end time of hot and cold alternation can be accurately identified to ensure the accuracy of temperature switching. Full process reconstruction can help better understand the periodicity and experimental requirements of hot and cold shocks, and provide a clear time frame for subsequent control algorithms and temperature compensation strategies. Accurately dividing the time of hot and cold shock processes helps to better perform temperature compensation and dynamic control, and ensure the stability of the entire test process. By comparing the temperature fluctuation data at different time points with the target temperature, the temperature deviation can be obtained in real time. This is crucial for adjusting the temperature control system. Through the accurate calculation of the temperature deviation, data support can be provided for the control system, enabling it to dynamically adjust the output of the cold and heat sources to avoid unnecessary energy consumption and temperature fluctuations. The temperature deviation calculation can effectively identify the temperature fluctuations or anomalies in the test chamber, and adjust the control strategy in time to ensure that the temperature reaches the required accuracy. Through compensation calculation, the temperature can be quickly restored to the target state to ensure that the temperature fluctuation during the cold and hot alternation process does not exceed the set range. The system can dynamically adjust according to real-time data feedback to avoid temperature lag or error, thereby improving the test efficiency. Through accurate temperature compensation, the test chamber can achieve cold and hot alternation in the shortest time, thereby saving time and improving experimental efficiency. By optimizing the switching delay, the temperature response of the test chamber is ensured to be faster, reducing the time waste caused by delays during the test process. The intelligent control strategy can automatically adjust the various parameters during the cold and hot switching process, reduce manual intervention, and improve the degree of automation. The optimized intelligent control strategy not only improves the response speed, but also further improves the accuracy of temperature switching, making the test process more stable and reliable.

[0104] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0105] The above is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for rapid temperature switching of a hot and cold shock test chamber, characterized in that: The following steps are involved: Step S1: obtaining the internal design drawing of the hot and cold shock test chamber and the hot and cold shock test log; performing multi-region three-dimensional point cloud modeling on the internal design drawing of the hot and cold shock test chamber, thereby constructing a multi-region three-dimensional point cloud model of the test chamber; Step S2: Acquire the temperature monitoring parameters of the test chamber; perform position registration and time-series temperature fluctuation fitting on the three-dimensional point cloud model of multiple regions of the test chamber according to the temperature monitoring parameters of the test chamber, and construct time-series temperature fluctuation curves of different regions; Step S3: mining the hot and cold alternation demand of the hot and cold shock test log, reconstructing the hot and cold shock chain of the whole process, and constructing the hot and cold shock time window of the whole cycle; the hot and cold shock time window of the whole cycle specifically includes the time, type and duration of each shock stage, including the total duration of each cycle and the proportion of each stage; Step S4: Calculate the test temperature deviation according to the full-cycle hot and cold shock time window and the time-series temperature fluctuation curves of different regions, so as to generate multi-time point temperature deviation values ​​for each region; Step S5: performing shock temperature compensation calculation on the temperature deviation values ​​at multiple time points in each area, and performing dynamic temperature compensation control on the test box, and collecting hot and cold shock compensation monitoring parameters of the test box; Step S6: Optimize the switching response delay according to the thermal shock compensation monitoring parameters, and construct an intelligent switching response optimization control strategy.

2. The method for rapid temperature switching of a thermal shock test chamber according to claim 1, characterized in that: The specific steps of step S1 are: Step S11: Obtaining the internal design drawing of the thermal shock test chamber and the thermal shock test log; Step S12: visually identifying the components of the internal design drawing of the thermal shock test chamber, and marking multiple internal components of the test chamber; Step S13: performing functional type analysis on multiple internal components of the test chamber to obtain the functional type of each component; Step S14: dividing the test chamber area functions according to the functional type of each component to generate multiple internal functional areas; Step S15: visually measuring the geometric dimensions of multiple internal functional areas to generate geometric parameters of each area; Step S16: Perform multi-region three-dimensional point cloud modeling according to the geometric parameters of each region, thereby constructing a multi-region three-dimensional point cloud model of the test box.

3. The method for rapid temperature switching of a thermal shock test chamber according to claim 1, characterized in that: The specific steps of step S2 are: Step S21: Perform real-time monitoring sampling based on multiple sensor nodes to obtain the temperature monitoring parameters of the test chamber; Step S22: Calculate the positions of multiple sensor nodes one by one to obtain sensor node position positioning data; Step S23: performing real-time temperature change calculation on the temperature monitoring parameters of the test chamber to generate real-time temperature change characteristics; Step S24: performing position registration processing on the multi-region three-dimensional point cloud model of the test box according to the sensor node position positioning data, so as to obtain the regional position of each sensor node; Step S25: Perform time-series temperature fluctuation fitting on the real-time temperature change characteristics according to the regional position of each sensor node, and construct time-series temperature fluctuation curves for different regions.

4. The method for rapid temperature switching of a thermal shock test chamber according to claim 3, characterized in that: The specific steps of step S25 are: According to the regional position of each sensor node, the real-time temperature change characteristics are interpolated dynamically in the test box to generate temperature change distribution maps of multiple regions; Calculate the temperature change rate of each area according to the temperature change distribution diagram of multiple areas; Calculating the temperature change amplitude according to the temperature change rate, thereby obtaining the temperature change amplitude of each area; Based on the real-time temperature change characteristics, the temperature fluctuation periodic law is evolved to generate the temperature fluctuation periodic characteristics; The time-series temperature fluctuation fitting is performed according to the temperature change amplitude and temperature fluctuation period characteristics of each area, and the time-series temperature fluctuation curves of different areas are constructed.

5. The method for rapid temperature switching of a thermal shock test chamber according to claim 1, characterized in that: The specific steps of step S3 are: Step S31: mining the hot and cold alternation demand on the hot and cold shock test log to extract the hot and cold alternation demand data; Step S32: identifying the hot and cold alternating intervals of the hot and cold alternating demand data, and generating hot and cold alternating interval data; Step S33: performing alternating interval change analysis according to the hot-cold alternating interval data to generate a hot-cold alternating cycle pattern; Step S34: Calculate the duration of the shock mode according to the cold and hot shock test log, so as to obtain the cold shock time length and the hot shock time length; Step S35: reconstruct the cold shock time length and the hot shock time length of the whole process cold and hot shock chain based on the cold and hot alternating interval data, and construct a full-cycle cold and hot shock time window.

6. The method for rapid temperature switching of a thermal shock test chamber according to claim 1, characterized in that: The specific steps of step S4 are: Step S41: Calculate the required temperature of hot and cold shock according to the hot and cold alternating demand data to obtain the specified temperature value of each hot and cold shock; Step S42: extracting multiple hot and cold shock switching time points according to the full-cycle hot and cold shock time window; Step S43: Based on the multiple hot and cold shock switching time points, the temperature trend of the time series temperature fluctuation curves of different regions is predicted at corresponding time points, so as to obtain the predicted value of the temperature trend at the shock time point of each region; Step S44: Based on the designated temperature value of each hot and cold shock, the experimental temperature deviation calculation is performed on the temperature trend prediction value at the shock time point of each region, so as to generate multi-time point temperature deviation values ​​of each region.

7. The method for rapid temperature switching of a thermal shock test chamber according to claim 1, characterized in that: The specific steps of step S5 are: Step S51: performing shock temperature compensation calculation on the temperature deviation values ​​at multiple time points of each region, thereby generating a cold and hot shock compensation parameter for each region; Step S52: Calculating the temperature control power based on the thermal shock compensation parameters of each area, thereby generating the temperature control power parameters of each area; Step S53: Perform dynamic temperature compensation control on the test box based on the dynamic temperature control power parameters of each zone, and collect the thermal shock compensation monitoring parameters of the test box in real time.

8. The method for rapid temperature switching of a thermal shock test chamber according to claim 1, characterized in that: The specific steps of step S6 are: Step S61: performing a comprehensive evaluation of the thermal shock effect according to the thermal shock compensation monitoring parameters to generate a plurality of thermal shock stage effect evaluation values; Step S62: performing real-time switching response analysis of the test chamber on the thermal shock compensation monitoring parameters, and extracting real-time switching response data; Step S63: calculating the impact phase response rate of the real-time switching response data to obtain multiple cold and hot impact phase response rates; Step S64: calculating the hot and cold switching response delay of each hot and cold shock stage response rate according to the multiple hot and cold shock stage effect evaluation values, thereby obtaining the hot and cold switching stage response delay data; Step S65: Optimize the switching response delay according to the response delay data of the hot-cold switching stage, and construct an intelligent switching response optimization control strategy.

9. A temperature rapid switching device for a hot and cold shock test chamber, characterized in that: The method for quickly switching the temperature of a thermal shock test chamber according to claim 1 comprises: The three-dimensional modeling module is used to obtain the internal design drawings of the hot and cold shock test chamber and the hot and cold shock test logs; perform multi-region three-dimensional point cloud modeling on the internal design drawings of the hot and cold shock test chamber, thereby constructing a multi-region three-dimensional point cloud model of the test chamber; The temperature fluctuation fitting module is used to obtain the temperature monitoring parameters of the test chamber; according to the temperature monitoring parameters of the test chamber, the position registration and time-series temperature fluctuation fitting of the three-dimensional point cloud model of the multi-region of the test chamber are performed to construct the time-series temperature fluctuation curves of different regions; The hot and cold alternation demand module is used to mine the hot and cold alternation demand of the hot and cold shock test log, reconstruct the hot and cold shock chain of the whole process, and build the hot and cold shock time window of the whole cycle; the hot and cold shock time window of the whole cycle includes the time, type and duration of each shock stage, including the total duration of each cycle and the proportion of each stage; Deviation calculation module, used to calculate the test temperature deviation according to the full cycle hot and cold shock time window and the time series temperature fluctuation curves of different regions, so as to generate multi-time point temperature deviation values ​​for each region; The compensation control module is used to perform shock temperature compensation calculations on the temperature deviation values ​​at multiple time points in each area, and to perform dynamic temperature compensation control on the test chamber, and collect the hot and cold shock compensation monitoring parameters of the test chamber; The switching response optimization module is used to optimize the switching response delay according to the thermal shock compensation monitoring parameters and construct an intelligent switching response optimization control strategy.

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