High and low temperature environment test box thermal load verification method and system

Abstract

The invention discloses a high and low temperature environment test box thermal load verification method and system, and relates to the technical field of environment test, and the method comprises the steps: initializing a target temperature range, a cooling rate, a heating rate, a preset temperature fluctuation range and a thermal load demand, and carrying out the preliminary setting of a thermal load; adjusting a liquid nitrogen adjusting unit in real time by adopting a dynamic optimization algorithm, and adjusting internal thermal load parameters of the test box under the condition of no external thermal load to obtain reference data; based on a self-adaptive control algorithm, dynamically adjusting the matching relationship between the liquid nitrogen release rate and the internal thermal load of the test box, and verifying the operation state of the test box under different thermal load conditions; a self-adaptive thermal load adjusting algorithm is adopted, the relation between the liquid nitrogen release rate and thermal load change is adjusted in real time, and thermal load verification of the test box is carried out according to the states under different load conditions. According to the invention, through the dynamic heat exchange coefficient model and the high-order injection optimization algorithm, the thermal load verification precision of the high and low temperature environment test box is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental testing, and particularly to a method and system for verifying the thermal load of a high and low temperature environmental test chamber. Background Art

[0002] High and low temperature environmental test chambers are widely used in fields such as aerospace, electronic manufacturing, automotive industry, and materials science to evaluate the reliability of products under extreme temperature conditions. With the development of precision manufacturing and high-end technologies, the requirements for temperature control accuracy, thermal load adaptability, and response speed of test chambers are constantly increasing. Existing test chambers mostly adopt PID control or fixed parameter feedback control, but under complex thermal load conditions, traditional methods are difficult to meet the high-precision and fast-response requirements. In addition, the liquid nitrogen release rate directly affects temperature stability and thermal load calibration accuracy, so optimizing the liquid nitrogen control strategy becomes the key.

[0003] However, the existing temperature control has poor adaptability to thermal load changes and is difficult to maintain temperature stability when switching between high and low loads, affecting the reliability of experimental results. The liquid nitrogen release mostly uses fixed parameter settings and is difficult to adjust dynamically, resulting in excessive liquid nitrogen consumption or insufficient cooling rate, reducing experimental efficiency and increasing costs. In addition, the anti-disturbance ability of the test chamber under different thermal load conditions is weak and difficult to meet the high-precision test requirements. Therefore, there is an urgent need for a verification method that can dynamically optimize liquid nitrogen release, adaptively adjust thermal load, and improve temperature stability to enhance the adaptability and precise control ability of the test chamber. Summary of the Invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a method for verifying the thermal load of a high and low temperature environmental test chamber to solve the problems of insufficient temperature control accuracy, poor matching of liquid nitrogen release rate, and weak anti-disturbance ability of the high and low temperature environmental test chamber under different thermal load conditions.

[0006] To solve the above technical problems, the present invention provides the following technical solutions:

[0007] In the first aspect, the present invention provides a method for verifying the thermal load of a high and low temperature environmental test chamber, which includes initializing the target temperature range, cooling rate, heating rate, preset temperature fluctuation range, and thermal load requirements and performing a preliminary thermal load setting;

[0008] Using a dynamic optimization algorithm to adjust the liquid nitrogen adjustment unit in real time, and in the absence of an external thermal load, adjusting the internal thermal load parameters of the test chamber to obtain reference data;

[0009] Based on an adaptive control algorithm, dynamically adjusting the matching relationship between the liquid nitrogen release rate and the internal thermal load of the test chamber to verify the operating state of the test chamber under different thermal load conditions;

[0010] Adopt an adaptive thermal load regulation algorithm to adjust the relationship between the liquid nitrogen release rate and the change in thermal load in real time, and conduct thermal load calibration of the test chamber according to the states under different load conditions;

[0011] Through the linkage optimization calculation of efficient liquid nitrogen control and temperature reduction rate algorithm, verify the compressive parameters under different thermal load conditions.

[0012] As a preferred scheme of the thermal load calibration method for the high and low temperature environmental test chamber described in the present invention, wherein: initialize the target temperature range, cooling rate, heating rate, preset temperature fluctuation range and thermal load requirement and conduct preliminary thermal load setting. The specific steps are as follows.

[0013] Conduct preliminary correction on the target temperature range using a high-precision temperature sensor;

[0014] Based on the correction data, adjust the liquid nitrogen release rate to optimize the cooling uniformity control of the cooling temperature difference. Monitor the temperature at multiple points and record the cooling curve simultaneously, and calculate the cooling rate at the same time;

[0015] Use a segmented heating strategy to adjust the P control reference cooling curve to optimize the heating power control of the heating temperature difference, and record the heating response at the same time to calculate the heating rate;

[0016] Utilize the heating response to optimize the PID parameters, adopt feedforward-feedback composite control, adjust the liquid nitrogen and heating power in advance, control the temperature fluctuation range and record the fluctuation data at the same time;

[0017] Calculate the specific heat capacity of the test chamber to evaluate the internal thermal load of the test chamber, and adjust the heating and cooling rates to compensate for the initial thermal load setting.

[0018] As a preferred scheme of the thermal load calibration method for the high and low temperature environmental test chamber described in the present invention, wherein: adopt a dynamic optimization algorithm to adjust the liquid nitrogen adjustment unit in real time. In the case of no external thermal load, adjust the internal thermal load parameters of the test chamber to obtain reference data. The specific steps are as follows.

[0019] Isolate the external heat source to make the test chamber free from heat source interference, record the current ambient temperature, set the target temperature T, calculate the internal thermal load, and adjust the internal heating through PWM control to make the thermal load reach a stable state;

[0020] Adopt a dynamic optimization algorithm to calculate the internal thermal load, and optimize the liquid nitrogen injection rate in real time to control the temperature error;

[0021] Calculate the thermal load of the optimized liquid nitrogen injection rate under the stable state of the test chamber, and adjust the internal heating rate;

[0022] According to the internal temperature fluctuation of the test chamber, a fuzzy adaptive algorithm is used to adjust the heat load parameters in real time, and at the same time, the liquid nitrogen release rate, the internal heat load of the test chamber, and the temperature curve under the stable state are recorded as reference data.

[0023] As a preferred embodiment of the heat load calibration method for the high and low temperature environmental test chamber of the present invention, wherein: based on the adaptive control algorithm, the matching relationship between the liquid nitrogen release rate and the internal heat load of the test chamber is dynamically adjusted to calibrate the operating state of the test chamber under different heat load conditions. The specific steps are as follows.

[0024] Based on the reference data, low heat load data, medium heat load data, and high heat load data are set, and the heat capacity under different heat load conditions is calculated using specific heat capacity.

[0025] The internal heating power is adjusted using the adaptive control algorithm to make the test chamber reach a steady state under different load conditions, and the steady state total load parameters are recorded.

[0026] The steady state total load is calculated using specific heat capacity, the cooling demand is calculated, and the liquid nitrogen release rate is dynamically adjusted using incremental PID control.

[0027] The temperature fluctuation is measured to calculate the heat load steady state time of the test chamber and the disturbance recovery time under different heat load conditions, and the operating state of the test chamber under different load conditions is calibrated.

[0028] As a preferred embodiment of the heat load calibration method for the high and low temperature environmental test chamber of the present invention, wherein: the adaptive heat load regulation algorithm is used to adjust the relationship between the liquid nitrogen release rate and the heat load change in real time, and the heat load of the test chamber is calibrated according to the state under different load conditions. The specific steps are as follows.

[0029] A filtering algorithm is used to preprocess the liquid nitrogen release rate, heat load, and temperature curve, and the basic operating parameters after preprocessing are calculated through time series analysis to extract heat transfer characteristic parameters.

[0030] For the heat transfer characteristic parameters, a dynamic heat transfer coefficient is used to calculate and update the new heat parameters in real time, and the heat transfer rate of each component of the test chamber is calculated in combination with the heat flux density data to form a dynamic coefficient model.

[0031] Through the non - linear heat balance equation, the calculation results of the heat transfer rate and the liquid nitrogen release rate are calculated, and the heat load modeling result is output using the non - linear regression model.

[0032] Based on the multi - order integral feedback control method, combined with the heat load modeling result, the heating power is adjusted in real time to keep the test chamber in a steady state under different load conditions and calculate the cooling demand.

[0033] According to the result of calculating the cooling demand based on the adaptive heat load, a non-linear liquid nitrogen injection control algorithm is adopted to adjust the liquid nitrogen release rate in real time and match the change of the internal heat load of the test chamber.

[0034] The perturbation test method is used to verify the steady-state time and perturbation recovery ability of the test chamber under different heat load conditions, record the liquid nitrogen release rate, heat load and temperature curves, and optimize the control parameters.

[0035] As a preferred scheme of the heat load calibration method for the high and low temperature environmental test chamber of the present invention, wherein: the linkage optimization is calculated through the high-efficiency liquid nitrogen control and temperature reduction rate algorithm, and the specific steps are as follows.

[0036] Based on the dynamic heat transfer coefficient model, calculate the internal heat capacity and heat transfer characteristics of the test chamber, establish a non-linear heat balance equation, and obtain the temperature step data of the heat load changing with time.

[0037] Based on the adaptive transformation heat regulation, calculate the temperature step data, and use the thermal stress feedback to optimize the heating power and liquid nitrogen injection rate in real time, and match the relationship between the liquid nitrogen release rate and the change of the heat load.

[0038] As a preferred scheme of the heat load calibration method for the high and low temperature environmental test chamber of the present invention, wherein: the compressive parameters under different heat load conditions are calibrated, and the specific steps are as follows.

[0039] For the real-time temperature error, liquid nitrogen flow rate and the heat load of the test chamber, use the high-order injection optimization model to dynamically calculate the optimal liquid nitrogen injection rate.

[0040] Use PWM to use small-flow high-frequency injection at low load and large-flow low-frequency injection at high load.

[0041] For the steady-state response data calculated by the liquid nitrogen injection optimization, use the standardized perturbation to conduct multiple calibration tests on the heat load response and compressive parameters of the test chamber under different temperature change rates.

[0042] Combined with the results of multiple calibrations, adjust the heating power and liquid nitrogen injection strategy to accurately control the temperature and adjust the compressive parameters of the test chamber in different temperature environments.

[0043] In a second aspect, the present invention provides a heat load calibration system for a high and low temperature environmental test chamber, including a target setting module, a reference data module, a state calibration module, a load adjustment module and a compressive parameter module.

[0044] The target setting module is used to initialize the target temperature range, cooling rate, heating rate, preset temperature fluctuation range and heat load demand and perform preliminary heat load settings.

[0045] The reference data module is used to adjust the liquid nitrogen adjustment unit in real time by using a dynamic optimization algorithm, and to adjust the internal heat load parameters of the test box in the absence of an external heat load, so as to obtain reference data;

[0046] The state verification module is used to dynamically adjust the matching relationship between the liquid nitrogen release rate and the internal heat load of the test box based on an adaptive control algorithm, and verify the operating state of the test box under different heat load conditions;

[0047] The load adjustment module is used to use an adaptive thermal load adjustment algorithm to adjust the relationship between the liquid nitrogen release rate and the thermal load change in real time, and perform thermal load calibration of the test box according to the state under different load conditions;

[0048] The pressure resistance parameter module is used to verify the pressure resistance parameters under different heat load conditions through efficient liquid nitrogen control and temperature reduction algorithm calculation linkage optimization.

[0049] In a third aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of the high and low temperature environment test chamber heat load calibration method as described in the first aspect of the present invention is implemented.

[0050] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the high and low temperature environment test chamber heat load calibration method as described in the first aspect of the present invention is implemented.

[0051] The beneficial effects of the present invention are as follows: the internal heat capacity and heat transfer characteristics of the test chamber are accurately calculated through the dynamic heat transfer coefficient model, and a nonlinear heat balance equation is established to realize the real-time calculation of heat load and temperature step data. The adaptive transformation heat control method is adopted, combined with a high-order injection optimization model, to dynamically adjust the heating power and liquid nitrogen injection rate, accurately match the liquid nitrogen release rate and heat load changes, and at the same time, the temperature change and heat load response are repeatedly verified through the perturbation test method, and the control strategy is optimized, so that the test chamber can maintain stable operation under different environmental conditions, thereby improving the response speed, energy saving effect and reliability of experimental data. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0053] Figure 1 This is a flow chart of the heat load calibration method of the high and low temperature environment test chamber in Example 1.

[0054] Figure 2 It is a module diagram of the high and low temperature environmental test chamber heat load calibration system in Embodiment 1. Specific implementation manners

[0055] To make the above objects, features, and advantages of the present invention more apparent and understandable, the specific implementation manners of the present invention will be described in detail below with reference to the accompanying drawings of the specification.

[0056] In the following description, many specific details are set forth in order to fully understand the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0057] Secondly, the so-called "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation manner of the present invention. The "in one embodiment" that appears in different places in this specification does not all refer to the same embodiment, nor is it an embodiment that is separate or selectively mutually exclusive with other embodiments.

[0058] Embodiment 1, referring to Figure 1 and Figure 2 , is the first embodiment of the present invention. This embodiment provides a method for calibrating the heat load of a high and low temperature environmental test chamber, including the following steps:

[0059] S1. Initialize the target temperature range, cooling rate, heating rate, preset temperature fluctuation range, and heat load requirement, and perform a preliminary setting of the heat load.

[0060] Furthermore, through the use of a high-precision temperature sensor for the target temperature range, a preliminary correction is performed.

[0061] Specifically, the target temperature range refers to the temperature range that the test chamber needs to reach, and the preliminary correction is to calibrate environmental factors, sensor drift, and initial errors through a high-precision temperature sensor to ensure the accuracy of the measurement data. This process reduces the basic error of temperature control.

[0062] Based on the corrected data, adjust the liquid nitrogen release rate to optimize the cooling uniformity, control the cooling temperature difference, monitor the temperature at multiple points, record the cooling curve simultaneously, and calculate the cooling rate simultaneously.

[0063] It should be noted that by adjusting the liquid nitrogen release rate based on the correction data to optimize cooling uniformity, and adjusting the liquid nitrogen release rate in real time to control the temperature difference during cooling, the temperature control can rapidly and stably cool down without significant temperature differences. This process effectively avoids the errors caused by uneven cooling in traditional methods, and provides more refined real-time data through multi-point temperature monitoring and cooling curve recording, and then calculates and optimizes the cooling rate, ensuring the efficiency and consistency of the cooling process.

[0064] Use a segmented heating strategy to adjust the PWM control reference cooling curve to optimize the heating power and control the temperature difference during heating, and record the heating response to calculate the heating rate.

[0065] Specifically, adopting a segmented heating strategy and optimizing the heating power to control the temperature difference during heating by adjusting the PWM control reference cooling curve is another key step of the present invention. Traditional heating methods often neglect the temperature difference control during the heating process. The segmented heating can not only effectively avoid excessive fluctuations during the heating process, but also make the temperature change during the heating process smoother by optimizing the heating power to control the temperature difference, thereby improving the reliability and accuracy of the experiment. By recording the heating response and calculating the heating rate, the present invention provides a more accurate heating adjustment scheme, effectively avoiding the problems of too fast or too slow heating.

[0066] Preferably, the PWM control can accurately adjust the heating power, improve the controllability of the heating process, avoid temperature overshoot, and ensure the stability and predictability of the temperature rise process of the test chamber.

[0067] Optimize the PID parameters using the heating response, adopt feedforward-feedback composite control, adjust the liquid nitrogen and heating power in advance, control the temperature fluctuation range and record the fluctuation data at the same time;

[0068] Calculate the specific heat capacity of the test chamber to evaluate the internal heat load of the test chamber, and adjust the heating and cooling rate compensation to initialize the heat load setting.

[0069] Specifically, the feedforward control is based on a prediction model to adjust the liquid nitrogen release and heating power before the temperature change occurs, while the feedback control detects the temperature deviation in real time and corrects it. This composite control method can effectively reduce the temperature fluctuation and improve the dynamic response ability of the test chamber. The temperature fluctuation range refers to the temperature deviation allowed by the temperature control at steady state. By optimizing the control strategy, the temperature fluctuation can be reduced to the minimum, improving the reliability of the test.

[0070] S2. Use a dynamic optimization algorithm to adjust the liquid nitrogen adjustment unit in real time. In the case of no external heat load, adjust the internal heat load parameters of the test chamber to obtain reference data.

[0071] Furthermore, by isolating external heat sources, the test chamber is free from heat source interference. Record the current ambient temperature, set the target temperature T, and calculate the internal heat load. Adjust the internal heating through PWM control to bring the heat load into a stable state.

[0072] Specifically, the target temperature T is the desired temperature inside the set test chamber, and the "internal heat load" refers to the influence of the heat capacity characteristics of the samples and the chamber itself inside the test chamber on temperature changes. PWM (Pulse Width Modulation) control is an efficient heating control method. Compared with traditional on-off control, PWM can precisely adjust the heating power, making the heat load more stable and improving the temperature control accuracy of the test chamber.

[0073] Use a dynamic optimization algorithm to calculate the internal heat load and optimize the liquid nitrogen injection rate in real time to control the temperature error.

[0074] Specifically, the dynamic optimization algorithm means adjusting the liquid nitrogen injection rate based on real-time temperature feedback to optimize the cooling process. The liquid nitrogen injection rate determines the rate and uniformity of temperature reduction. If the injection rate is too fast, it may cause the temperature to be too high, and if it is too slow, it may not meet the target temperature reduction requirements. Controlling the temperature error means making the temperature inside the test chamber as close as possible to the set target by optimizing the injection rate and reducing the temperature deviation.

[0075] Preferably, dynamically adjust the liquid nitrogen injection rate to keep the temperature inside the test chamber stable within the target range, reduce the temperature deviation, and improve the temperature control accuracy of the test chamber.

[0076] Calculate and optimize the heat load of the liquid nitrogen injection rate under the stable state of the test chamber, and adjust the internal heating rate;

[0077] According to the temperature fluctuations inside the test chamber, use a fuzzy adaptive algorithm to adjust the heat load parameters in real time, and simultaneously record the liquid nitrogen release rate, the internal heat load of the test chamber, and the temperature curve under the stable state as reference data.

[0078] It should be noted that during the operation of the test chamber, the test chamber records temperature data in real time through multiple temperature sensors (located at different measurement points) and generates a temperature change curve based on the time series. This curve can be used to analyze the heating and cooling rates, temperature stability, and heat load changes. The fuzzy adaptive algorithm is a control method based on fuzzy logic that can dynamically adjust control parameters in unknown or complex environments to improve the adaptability to uncertain factors. This algorithm can adjust the heat load parameters in real time according to the temperature fluctuations to keep the test chamber in a stable state. In addition, recording the reference data (liquid nitrogen release rate, test chamber heat load, temperature curve) will serve as the basis for model learning.

[0079] S3. Based on the adaptive control algorithm, dynamically adjust the matching relationship between the liquid nitrogen release rate and the internal heat load of the test chamber, and verify the operating state of the test chamber under different heat load conditions.

[0080] Furthermore, based on the reference data, set low heat load data, medium heat load data, and high heat load data, and use specific heat capacity to calculate the heat capacity under different heat load conditions.

[0081] Specifically, this data is used to establish a thermal characteristic model of the test chamber. The low heat load data, medium heat load data, and high heat load data respectively correspond to the test parameters under different heat load conditions. This hierarchical method helps to optimize the control strategy, enables the temperature control to adapt to different working conditions, and improves the control accuracy.

[0082] Adopt the adaptive control algorithm to adjust the internal heating power, so that the test chamber reaches a steady state under different load conditions, and record the steady-state total heat load parameters.

[0083] It should be noted that the specific heat capacity calculation refers to calculating the heat capacity based on the specific heat capacity of the material, where the heat capacity represents the energy required for the internal materials and samples of the test chamber to heat up and cool down. By calculating the heat capacity under different heat load conditions using specific heat capacity, the heating and cooling strategies can be optimized to ensure that the temperature control can operate efficiently under different load conditions.

[0084] Use specific heat capacity to calculate the steady-state total heat load, calculate the cooling demand, and adopt incremental PID control to dynamically adjust the liquid nitrogen release rate;

[0085] It should be noted that the adaptive control algorithm refers to dynamically adjusting the internal heating power according to the real-time temperature feedback of the test chamber, so that the test chamber can quickly reach a steady state under different heat load conditions. The steady-state total heat load parameter refers to the comprehensive heat load of the test chamber in the thermal equilibrium state, including factors such as heating power, environmental heat exchange, and sample heat capacity.

[0086] Measure the temperature fluctuation to calculate the steady-state time of the test chamber heat load and the disturbance recovery time under different heat load conditions, and verify the operating state of the test chamber under different load conditions.

[0087] Specifically, the temperature fluctuation measurement is to record the temperature fluctuation amplitude of the test chamber under steady-state conditions to evaluate its temperature stability. The disturbance recovery time refers to the time required for the test chamber to recover to a steady state after being disturbed under different heat load conditions. This data is used to verify the dynamic response ability of the test chamber and optimize the control strategy.

[0088] Preferably, measuring the temperature fluctuation and the disturbance recovery time can accurately evaluate the temperature stability of the test chamber, optimize the temperature control parameters, enable it to quickly recover to a steady state under different load conditions, and improve the adaptability and reliability of the test chamber.

[0089] S4. An adaptive thermal load regulation algorithm is adopted to adjust the relationship between the liquid nitrogen release rate and the thermal load change in real time, and the thermal load of the test chamber is calibrated according to the states under different load conditions.

[0090] Furthermore, a filtering algorithm is used to preprocess the liquid nitrogen release rate, thermal load, and temperature curve, and the basic operating parameters after preprocessing are calculated through time series analysis to extract heat transfer characteristic parameters.

[0091] Specifically, the filtering algorithm is used to preprocess the liquid nitrogen release rate, thermal load, and temperature curve. This step helps to eliminate noise interference and abnormal data, ensuring the smoothness and accuracy of the input data. Subsequently, based on time series analysis, the basic operating parameters after preprocessing are calculated to extract heat transfer characteristic parameters. Time series analysis can identify potential periodic and trend changes in the data, accurately capturing the change law of heat transfer performance.

[0092] For the heat transfer characteristic parameters, a dynamic heat transfer coefficient is used to calculate and update the new heat parameters in real time, and the heat transfer rate of each component of the test chamber is calculated by combining the heat flux density data to form a dynamic coefficient model.

[0093] It should be noted that the heat parameters are calculated and updated in real time through the dynamic heat transfer coefficient, and the heat transfer rate of each component of the test chamber is calculated by combining the heat flux density data to form a dynamic coefficient model. The heat flux density data refers to the heat transfer amount per unit area, while the dynamic heat transfer coefficient reflects the heat exchange efficiency of the test chamber components under different working states. Combining the two can adjust the heat transfer model in real time and improve the accuracy of heat transfer prediction.

[0094] Preferably, traditional methods often assume that the heat transfer coefficient is constant, but in actual applications, the heat transfer coefficient will change with factors such as time, temperature, and environment. By dynamically adjusting the heat transfer coefficient, the actual changes in the heat transfer process can be more accurately reflected, ensuring the best heat transfer performance under different loads. This makes the thermal load control more flexible and accurate, especially being able to respond quickly when dealing with load fluctuations.

[0095] Through the nonlinear heat balance equation, the calculation results of the heat transfer rate and the liquid nitrogen release rate are calculated, and a nonlinear regression model is used to output the thermal load modeling result.

[0096] Specifically, the heat transfer rate and the liquid nitrogen release rate are calculated through the nonlinear heat balance equation, and a nonlinear regression model is used to output the thermal load modeling result. The nonlinear heat balance equation takes into account the complex relationships between variables such as heat flow, temperature, and time, and the nonlinear regression model can capture these complex nonlinear dependencies.

[0097] The specific formula of the nonlinear heat balance equation is as follows:

[0098]

[0099] Among them, is the rate of change of the internal temperature of the test chamber, P h is to increase the heating rate for heating, P c is to increase the cooling rate for cooling, C e is the effective heat capacity of the test chamber, h d is the dynamic heat transfer coefficient of the test chamber, (T - T a ) represents the temperature difference between the internal temperature of the test chamber and the ambient temperature, h represents the heat transfer coefficient, d represents the rate of change of temperature with time, a represents the external temperature of the test chamber, and e represents the total internal heat capacity of the test chamber;

[0100] Preferably, in view of the limitations of the traditional linear model, this step provides a modeling method that can handle complex thermodynamic relationships. Nonlinear regression can better adapt to complex behaviors under unsteady and perturbed conditions, thus providing higher accuracy when predicting the relationship between liquid nitrogen release rate and heat load. In this way, the heat load demand under different working conditions can be accurately estimated, providing more reliable data support for subsequent control optimization.

[0101] Based on the multi-order integral feedback control method and combined with the heat load modeling results, the heating power is adjusted in real time to keep the test chamber in a steady state under different load conditions and calculate the cooling demand.

[0102] It should be noted that the multi-order integral feedback control method adjusts the heating power by continuously accumulating error feedback information, so as to ensure that the test chamber maintains a steady state under different load conditions. This method can quickly make adjustments when facing large-range load fluctuations, reduce the steady-state error, and calculate the cooling demand according to the real-time heat load modeling results.

[0103] Preferably, it solves the problems of slow response, large error accumulation, and poor adaptability of traditional thermal control. By dynamically feedback-adjusting the load fluctuations, this method can effectively improve the steady-state maintenance ability and temperature control accuracy of the test chamber under different load conditions, greatly improving the operation efficiency and reliability. In addition, the combination with other technologies such as heat load modeling and cooling demand calculation makes this method have higher accuracy and adaptability.

[0104] According to the result of calculating the cooling demand based on the adaptive heat load, a nonlinear liquid nitrogen injection control algorithm is adopted to adjust the liquid nitrogen release rate in real time and match the change of the internal heat load of the test chamber.

[0105] It should be noted that according to the result calculated from the adaptive heat load, a nonlinear liquid nitrogen injection control algorithm is used to adjust the liquid nitrogen release rate in real time. This algorithm automatically adjusts the liquid nitrogen injection rate according to the real-time monitored change of the heat load to ensure that the liquid nitrogen cooling can dynamically match the change of the heat load.

[0106] Preferably, the adaptive algorithm can adjust the liquid nitrogen injection rate according to the real-time changing load and environmental conditions, avoiding the problems of overcooling or insufficient cooling. Compared with traditional control methods, this adaptive control can significantly improve flexibility and precision, ensuring that the liquid nitrogen cooling process always matches the load demand.

[0107] Adopt the perturbation test method to verify the steady-state time and perturbation recovery ability of the test chamber under different thermal load conditions, record the liquid nitrogen release rate, thermal load and temperature curve, and optimize the control parameters.

[0108] Specifically, adopt the perturbation test method to verify the steady-state time and perturbation recovery ability of the test chamber under different thermal load conditions, and optimize the control parameters. The perturbation test simulates the sudden change of the environmental load to test the response and recovery ability in the face of abnormal fluctuations.

[0109] S5. Through the linkage optimization of high-efficiency liquid nitrogen control and temperature reduction rate algorithm, verify the compressive parameters under different thermal load conditions.

[0110] Furthermore, based on the dynamic heat transfer coefficient model, calculate the internal heat capacity and heat transfer characteristics of the test chamber, establish a nonlinear heat balance equation, and obtain the temperature step data of the thermal load changing with time.

[0111] Specifically, it is used to accurately calculate the specific heat capacity and heat transfer characteristics inside the test chamber. By establishing a nonlinear heat balance equation, the temperature step data of the thermal load changing with time can be comprehensively evaluated. This process ensures that the temperature change of the test chamber under different thermal load conditions can be accurately predicted and calculated.

[0112] Based on the adaptive transformation heat regulation, calculate the temperature step data, and use the thermal stress feedback to optimize the heating power and liquid nitrogen injection rate in real time to match the relationship between the liquid nitrogen release rate and the change of the thermal load.

[0113] It should be noted that the temperature step data refers to the curve of the temperature gradually changing with time during the test process. The adaptive transformation heat regulation method dynamically optimizes the heating power and liquid nitrogen injection rate through real-time feedback of the temperature change and the thermal load state, so as to ensure the accurate matching of the relationship between the liquid nitrogen release rate and the change of the thermal load.

[0114] Preferably, it can adjust the heating and cooling processes of the test chamber under different loads in real time, ensure that the temperature control is not affected by external disturbances, and maintain stable operation. Through adaptive adjustment, it can automatically learn the relationship between the load and the temperature, accurately predict the future change of the thermal load, and make a rapid response.

[0115] For the real-time temperature error, liquid nitrogen flow rate and the thermal load of the test chamber, use the high-order injection optimization model to dynamically calculate the optimal liquid nitrogen injection rate.

[0116] Use PWM to perform small-flow high-frequency injection at low loads and large-flow low-frequency injection at high loads;

[0117] Specifically, adopt a high-order injection optimization model, dynamically calculate according to the real-time temperature error, liquid nitrogen flow rate and the heat load of the test chamber, and output the optimal liquid nitrogen injection rate. Under different load conditions, through PWM control, small-flow high-frequency injection is adopted at low loads and large-flow low-frequency injection is adopted at high loads, which can make the liquid nitrogen injection more refined and avoid the energy waste and temperature control instability caused by too much or too little liquid nitrogen.

[0118] For the steady-state response data of the optimized calculation of liquid nitrogen injection, use standardized perturbations to conduct multiple calibration tests on the heat load response and compressive parameters of the test chamber at different temperature change rates;

[0119] Combined with the results of multiple calibrations, adjust the heating power and liquid nitrogen injection strategy to accurately control the temperature and adjust the compressive parameters of the test chamber in different temperature environments.

[0120] It should be noted that the perturbation test method is used to verify the heat load response and compressive capacity of the test chamber at different temperature change rates. By performing multiple calibrations on different temperature environments through standardized perturbations, it is possible to record and analyze the response data of the liquid nitrogen release rate, temperature curve and heat load in real time, so as to further optimize the control parameters.

[0121] Preferably, the beneficial effect of the perturbation test is to ensure that the test chamber has strong compressive capacity under different temperature change conditions by simulating the heat load fluctuations in the actual environment. Multiple calibrations can help optimize the heating power and liquid nitrogen injection strategy to ensure stable operation in complex load changes. Compared with the traditional single calibration, the repetitive calibration can enhance the reliability and adaptability of the test results.

[0122] The present embodiment also provides a high and low temperature environment test chamber heat load verification system, including: a target setting module, a reference data module, a state verification module, a load adjustment module and a pressure resistance parameter module; the target setting module is used to initialize the target temperature range, the cooling rate, the heating rate, the preset temperature fluctuation range and the heat load demand and perform preliminary settings of the heat load; the reference data module is used to use a dynamic optimization algorithm to adjust the liquid nitrogen adjustment unit in real time, and adjust the internal heat load parameters of the test chamber in the absence of an external heat load to obtain reference data; the state verification module is used to dynamically adjust the matching relationship between the liquid nitrogen release rate and the internal heat load of the test chamber based on an adaptive control algorithm, and verify the operating state of the test chamber under different heat load conditions; the load adjustment module is used to use an adaptive heat load adjustment algorithm to adjust the relationship between the liquid nitrogen release rate and the heat load change in real time, and perform heat load verification of the test chamber according to the state under different load conditions; the pressure resistance parameter module is used to verify the pressure resistance parameters under different heat load conditions through efficient liquid nitrogen control and temperature reduction algorithm calculation linkage optimization.

[0123] This embodiment also provides a computer device, which is suitable for the case of a high and low temperature environment test chamber heat load calibration method, including: a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the high and low temperature environment test chamber heat load calibration method proposed in the above embodiment.

[0124] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0125] This embodiment also provides a storage medium, on which a computer program is stored. When the program is executed by a processor, it implements the method for verifying the thermal load of a high and low temperature environmental test chamber as proposed in the above embodiment; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (abbreviated as SRAM), electrically erasable programmable read-only memory (abbreviated as EEPROM), erasable programmable read-only memory (abbreviated as EPROM), programmable read-only memory (abbreviated as PROM), read-only memory (abbreviated as ROM), magnetic memory, flash memory, magnetic disk or optical disc.

[0126] In summary, the present invention accurately calculates the internal heat capacity and heat transfer characteristics of the test chamber through a dynamic heat transfer coefficient model, and establishes a non-linear heat balance equation to achieve real-time calculation of thermal load and temperature step data. By adopting an adaptive transformation heat regulation method and combining a high-order injection optimization model, the heating power and liquid nitrogen injection rate are dynamically adjusted to accurately match the liquid nitrogen release rate with the change of thermal load. At the same time, the temperature change and thermal load response are verified multiple times by the perturbation test method to optimize the control strategy, so that the test chamber can maintain stable operation under different environmental conditions, improving the response speed, energy-saving effect and reliability of experimental data.

[0127] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

Claims

1. A method for verifying the thermal load of a high and low temperature environmental test chamber, characterized in that: Include Initialize the target temperature range, cooling rate, heating rate, preset temperature fluctuation range, and heat load demand, and perform preliminary heat load settings Use a dynamic optimization algorithm to adjust the liquid nitrogen regulating unit in real time. Without external heat load, adjust the internal heat load parameters of the test chamber to obtain reference data Based on the adaptive control algorithm, dynamically adjust the matching relationship between the liquid nitrogen release rate and the internal heat load of the test chamber, and verify the operating status of the test chamber under different heat load conditions Use an adaptive heat load adjustment algorithm to adjust the relationship between the liquid nitrogen release rate and the heat load change in real time, and perform heat load verification on the test chamber according to the status under different load conditions Through the calculation and linkage optimization of the efficient liquid nitrogen control and temperature reduction rate algorithm, verify the compressive parameters under different heat load conditions 2. The method for verifying the thermal load of a high and low temperature environmental test chamber according to claim 1, characterized in that: The steps for initializing the target temperature range, cooling rate, heating rate, preset temperature fluctuation range, and heat load demand, and performing preliminary heat load settings are as follows Perform preliminary correction on the target temperature range using a high-precision temperature sensor Based on the corrected data, adjust the liquid nitrogen release rate to optimize the cooling uniformity control of the temperature difference. Monitor the temperature at multiple points and record the cooling curve simultaneously, and calculate the cooling rate Use a segmented heating strategy to adjust the PWM control reference cooling curve to optimize the heating power control of the temperature difference, and record the heating response simultaneously to calculate the heating rate Optimize the PID parameters using the heating response, adopt feedforward-feedback composite control, adjust the liquid nitrogen and heating power in advance, control the temperature fluctuation range, and record the fluctuation data simultaneously Calculate the specific heat capacity of the test chamber, evaluate the internal heat load of the test chamber, and adjust the cooling and heating rates to compensate for the initial heat load settings 3. The method for verifying the thermal load of a high and low temperature environmental test chamber according to claim 2, wherein: The steps for using a dynamic optimization algorithm to adjust the liquid nitrogen regulating unit in real time, adjusting the internal heat load parameters of the test chamber without external heat load, and obtaining reference data are as follows Isolate the external heat source to make the test chamber free from heat source interference. Record the current ambient temperature, set the target temperature T, calculate the internal heat load, and adjust the internal heating through PWM control to make the heat load reach a stable state Use a dynamic optimization algorithm to calculate the internal heat load, and optimize the liquid nitrogen injection rate in real time to control the temperature error Calculate the heat load of the optimized liquid nitrogen injection rate under the stable state of the test chamber, and adjust the internal heating rate According to the internal temperature fluctuation of the test chamber, use a fuzzy adaptive algorithm to adjust the heat load parameters in real time, and record the liquid nitrogen release rate, internal heat load of the test chamber, and temperature curve under the stable state as reference data 4. The thermal load calibration method for the high and low temperature environmental test chamber according to claim 3, wherein: The steps for dynamically adjusting the matching relationship between the liquid nitrogen release rate and the internal heat load of the test chamber based on the adaptive control algorithm, and verifying the operating status of the test chamber under different heat load conditions are as follows Based on the reference data, set low heat load data, medium heat load data, and high heat load data, and calculate the heat capacity under different heat load conditions using the specific heat capacity Use the adaptive control algorithm to adjust the internal heating power to make the test chamber reach a steady state under different load conditions, and record the steady state total load parameters The steady-state total load is calculated using the specific heat capacity, the cooling demand is calculated, and the liquid nitrogen release rate is dynamically adjusted using incremental PID control; The steady-state time of the thermal load of the test chamber and the disturbance recovery time under different thermal load conditions are measured by calculating the temperature fluctuation, and the operating state of the test chamber under different load conditions is verified.

5. The thermal load calibration method for the high and low temperature environmental test chamber according to claim 4, characterized in that: The adaptive thermal load regulation algorithm is adopted to adjust the relationship between the liquid nitrogen release rate and the change of the thermal load in real time, and the thermal load of the test chamber is verified according to the state under different load conditions. The specific steps are as follows. A filtering algorithm is used to preprocess the liquid nitrogen release rate, the thermal load, and the temperature curve, and the basic operating parameters after preprocessing are calculated by time series analysis to extract the heat transfer characteristic parameters; For the heat transfer characteristic parameters, a dynamic heat transfer coefficient is used to calculate and update the new thermal parameters in real time, and the heat transfer rate of each component of the test chamber is calculated by combining the heat flux density data to form a dynamic coefficient model; The heat transfer rate calculation result and the liquid nitrogen release rate are calculated through a non-linear heat balance equation, and the heat load modeling result is output using a non-linear regression model; Based on the multi-order integral feedback control method, combined with the heat load modeling result, the heating power is adjusted in real time to keep the test chamber in a steady state under different load conditions and calculate the cooling demand; According to the cooling demand result calculated by the adaptive thermal load, a non-linear liquid nitrogen injection control algorithm is adopted to adjust the liquid nitrogen release rate in real time and match the change of the internal thermal load of the test chamber; The disturbance test method is used to verify the steady-state time and disturbance recovery ability of the test chamber under different thermal load conditions, record the liquid nitrogen release rate, the thermal load, and the temperature curve, and optimize the control parameters.

6. The thermal load calibration method for a high and low temperature environmental test chamber according to claim 5, characterized in that: The linkage optimization is calculated through the efficient liquid nitrogen control and temperature reduction rate algorithm. The specific steps are as follows. Based on the dynamic heat transfer coefficient model, the internal heat capacity and heat transfer characteristics of the test chamber are calculated, a non-linear heat balance equation is established, and the temperature step data of the thermal load changing with time is obtained; Based on the adaptive transformation heat regulation, the temperature step data is calculated, and the heating power and the liquid nitrogen injection rate are optimized in real time using the thermal stress feedback to match the relationship between the liquid nitrogen release rate and the change of the thermal load.

7. The method for calibrating the heat load of a high and low temperature environmental test chamber according to claim 6, characterized in that: The compressive parameters under different thermal load conditions are verified. The specific steps are as follows. For the real-time temperature error, the liquid nitrogen flow rate, and the thermal load of the test chamber, a high-order injection optimization model is used to dynamically calculate the optimal liquid nitrogen injection rate; PWM is used to perform small-flow high-frequency injection at low loads and large-flow low-frequency injection at high loads; For the steady-state response data calculated by the liquid nitrogen injection optimization, standardized disturbances are used to perform multiple verifications on the thermal load response and compressive parameters of the test chamber under different temperature change rates; Combined with the results of multiple verifications, the heating power and the liquid nitrogen injection strategy are adjusted to accurately control the temperature and adjust the compressive parameters of the test chamber in different temperature environments.

8. A thermal load calibration system for a high and low temperature environmental test chamber, based on the thermal load calibration method for a high and low temperature environmental test chamber according to any one of claims 1 to 7, characterized in that: It includes a target setting module, a reference data module, a state verification module, a load adjustment module, and a compressive parameter module; The target setting module is used to initialize the target temperature range, the cooling rate, the heating rate, the preset temperature fluctuation range, and the thermal load demand and perform a preliminary setting of the thermal load; The reference data module is used to adjust the liquid nitrogen adjustment unit in real time by using a dynamic optimization algorithm, and to adjust the internal heat load parameters of the test box in the absence of an external heat load, so as to obtain reference data; The state verification module is used to dynamically adjust the matching relationship between the liquid nitrogen release rate and the internal heat load of the test box based on an adaptive control algorithm, and verify the operating state of the test box under different heat load conditions; The load adjustment module is used to use an adaptive thermal load adjustment algorithm to adjust the relationship between the liquid nitrogen release rate and the thermal load change in real time, and perform thermal load calibration of the test box according to the state under different load conditions; The pressure resistance parameter module is used to verify the pressure resistance parameters under different heat load conditions through efficient liquid nitrogen control and temperature reduction algorithm calculation linkage optimization.

9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that: When the processor executes the computer program, the steps of the high and low temperature environment test chamber heat load calibration method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the high and low temperature environment test chamber heat load calibration method according to any one of claims 1 to 7 are implemented.

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