A method and system for controlling the environmental temperature of a test chamber for high-temperature aging tests

Abstract

The present invention relates to the technical field of temperature control. More specifically, the present invention relates to a method and system for controlling the ambient temperature of a test chamber for high-temperature aging tests. The method includes: collecting the ambient temperature data of the test chamber and the power data of the heating accessories, calculating the deviation degree of the ambient temperature data, and setting an initial softening factor for the difference in the ambient temperature data using generalized predictive control; establishing a window to evaluate the local stationarity of the ambient temperature data within the window, and calculating the change rate of the power data of the test chamber; correcting the initial softening factor according to the change rate and the local stationarity to obtain a significant softening factor, and using the significant softening factor as a control weight to adjust the ambient temperature data of the test chamber at the next moment. The present invention improves the stability and reliability of ambient temperature control by adaptively adjusting the initial softening factor and dynamically adjusting the control weight.

Description

Technical Field

[0001] The present invention relates to the technical field of temperature control. More specifically, the present invention relates to a method and system for controlling the ambient temperature of a test chamber for high-temperature aging tests. Background Art

[0002] A high-temperature test chamber is a test in which a specimen is exposed to a high-temperature and air-dry environment, and its purpose is to determine the adaptability of military and civilian equipment for storage and operation under high-temperature conditions. It is used for screening, aging tests, life tests, accelerated life tests, evaluation tests of LEDs, components, and complete machines, etc., and also plays an important role in the verification of failure analysis. Among them, the high-temperature aging test chamber can simulate a high-temperature environment, discover potential faults in advance, improve the stability and reliability of products, and thus meet the market's demand for high-quality products. However, the temperature control of high-temperature aging test chambers faces many challenges.

[0003] The existing Chinese patent application document with the publication number CN116879144A discloses a control method for a climate test chamber and a climate test chamber. Specifically, the climate test chamber of the present invention includes a box body, a front door, a side door, a first fan, a second fan, and an irradiation light source. The front door has a first cavity, a first air inlet, and a first air outlet. The first fan is installed in the first cavity. The side door has a second cavity, a second air inlet, and a second air outlet. The second fan is installed in the second cavity. The control method includes: obtaining the target temperature Ts of the box body; obtaining the external ambient temperature Tw of the box body; determining whether the irradiation light source is turned on; and selectively starting the first fan or the second fan according to the target temperature Ts, the external ambient temperature Tw, and the determination result. The heat generated by the irradiation light source can be discharged through the air outlet on the door body.

[0004] According to the target temperature Ts, the external ambient temperature Tw, and the determination result, this application document selectively starts the first fan or the second fan, which is more efficient and more energy-saving. At present, there is often a large lag in the temperature response in high-temperature aging test chambers, that is, it takes a certain amount of time for the temperature change to be reflected in the control system. This lag may cause overshoot or insufficient adjustment when the control system adjusts the temperature. Moreover, in high-temperature aging tests, the ambient temperature of the test chamber may need to be rapidly increased or decreased within a short period of time. This rapid change requires the control system to be able to respond quickly and accurately control the temperature. At the same time, due to various uncertain factors during the test process, such as fluctuations in heating power and changes in the external environment, it is difficult to accurately predict the trajectory of temperature change. Summary of the Invention

[0005] To solve the problem of how to improve the control efficiency and accuracy of a high-temperature aging test chamber in the face of hysteresis, rapid temperature change requirements, and uncertain factors, the present invention provides solutions in the following aspects.

[0006] In the first aspect, collect the ambient temperature data of the test chamber and the power data of the heating accessories, calculate the deviation degree of the current ambient temperature data of the test chamber to reflect the difference in the current ambient temperature data, and set an initial softening factor for the difference in the ambient temperature data using generalized predictive control; take the current ambient temperature data of the test chamber as the end, establish a window with a preset length, and calculate the difference degree between adjacent ambient temperature data within the window to evaluate local stationarity; take the ratio of the difference between the power data of the test chamber at the current moment and the power data at other moments to the power data of the current test chamber to obtain the change rate of the power data of the test chamber; correct the initial softening factor according to the change rate and local stationarity to obtain a significant softening factor, and use the significant softening factor as the weight for controlling the ambient temperature data, thereby adjusting the ambient temperature data of the test chamber at the next moment; wherein, the significant softening factor satisfies the following relational expression: ; in the formula, represents the initial softening factor of the th ambient temperature data of the test chamber, represents the change rate of the power data of the heating accessories corresponding to the th ambient temperature data of the test chamber, represents the local stationarity of the th ambient temperature data of the test chamber, is the ceiling symbol.

[0007] The effect is that by collecting the ambient temperature data of the test chamber and the power data of the heating accessories, the system state can be monitored in real time, enabling the control strategy to adapt to the current working environment. Calculating the deviation degree of the ambient temperature data allows the control algorithm to adjust according to the difference between the actual and the desired values, thereby improving the control accuracy; by evaluating the local stationarity and the change rate of the power data, the system can identify and respond to external disturbances and internal changes, such as fluctuations in heating power and the influence of the external environment. This real-time feedback correction mechanism enhances the robustness of the system to uncertain factors; using generalized predictive control (GPC) to set the initial softening factor and adjusting the significant softening factor in real time according to the change of the system state makes the control output smoother, reduces overshoot and oscillation phenomena, helps to optimize the control performance, and improves the stability and response speed of the system.

[0008] Preferably, the deviation degree includes:

[0009] The deviation degree of the environmental temperature data of the test chamber is obtained according to the ratio between the difference and the actual value between the actual value and the expected value of the environmental temperature data of the current test chamber.

[0010] The effect is that by monitoring the deviation degree, the control system can reduce the control error caused by overshoot and oscillation, improve the stability of the system, and accurate temperature control can reduce the situation of overheating or overcooling, thereby optimizing energy consumption and improving energy efficiency.

[0011] Preferably, the deviation degree further includes:

[0012] Perform local least squares fitting on the current environmental temperature data of the test chamber, use the model obtained by least squares fitting to calculate the fitted value of the current test chamber temperature data, and use the negative correlation exponential function of the absolute value of the difference between the actual value and the fitted value as the deviation degree.

[0013] Preferably, the initial softening factor includes:

[0014] Take the reciprocal of the square root of the sum of the square of the deviation degree of the environmental temperature data of the test chamber plus 1 as the initial softening factor.

[0015] Preferably, the initial softening factor further includes:

[0016] Obtain the first temperature deviation according to the ratio between the difference and the actual value between the actual value and the expected value of the environmental temperature data of the current test chamber; perform local least squares fitting on the current environmental temperature data of the test chamber, use the model obtained by least squares fitting to calculate the fitted value of the current test chamber temperature data, and use the negative correlation exponential function of the absolute value of the difference between the actual value and the fitted value as the second temperature deviation;

[0017] Take the absolute value of the difference between the first temperature deviation and the second temperature deviation as the absolute difference, and take the square of the average value of the sum of the first temperature deviation and the second temperature deviation as the average difference; take the ratio between the absolute difference and the square root of the sum of the average difference plus 1 as the initial softening factor of the environmental temperature data of the current test chamber.

[0018] The effect is that the first temperature deviation reflects the difference between the actual temperature and the expected temperature, while the second temperature deviation reflects the difference between the actual temperature and the temperature predicted by the local least squares fitting model. The local least squares fitting model can help capture the trends and patterns of the temperature data, enabling the second temperature deviation to provide additional information about the temperature change trend, thereby improving the prediction accuracy.

[0019] Preferably, the local stationarity includes:

[0020] Calculate the absolute difference between two adjacent temperature data within the calculation window, and take the ratio between the sum of the absolute differences of all adjacent ambient temperature data within the window and one less than the window length as the average difference. Then use the exponential function to perform exponential decay on the average difference to obtain the local stationarity.

[0021] Its effect is as follows: By calculating the absolute difference between adjacent ambient temperature data within the window, the fluctuation of temperature data within the local time range can be quantified. The calculation of the average difference provides an index to measure the temperature stability. The larger the average difference, the greater the local fluctuation, and vice versa, indicating better local stability.

[0022] Preferably, the local stationarity further includes:

[0023] Taking any ambient temperature data as the marked temperature, calculate the average value of all temperature data in the window corresponding to the marked temperature data. For each temperature data within the window, sum the squared differences between each temperature data and the average value as the sum of squared deviations. Calculate the ratio between the sum of squared deviations and one less than the window length to obtain the variance, and use the exponential function to perform exponential decay on the variance to obtain the local stationarity.

[0024] Its effect is as follows: By calculating the squared differences between each temperature data and the average value, the fluctuation magnitude of temperature data within the window can be measured more precisely. By calculating the variance, the distribution of temperature data around its average value can be quantified.

[0025] In a second aspect, a test chamber environmental temperature control system for high-temperature aging tests includes: a processor and a memory. The memory stores computer program instructions, and when the computer program instructions are executed by the processor, the above-mentioned test chamber environmental temperature control method for high-temperature aging tests is implemented.

[0026] The present invention has the following effects:

[0027] 1. By introducing an adaptive initial softening factor, the system can dynamically adjust the control weight according to the local stationarity and the change rate of power data, so as to better cope with uncertainties such as heating power fluctuations and external environment changes. This adaptive mechanism enhances the robustness of the system to various disturbances and ensures the stability and reliability of temperature control in a complex environment.

[0028] 2. By using local least squares fitting and exponential decay function to evaluate the local stationarity, the system can more accurately capture the local characteristics and change trends of temperature data, correct the initial softening factor to obtain a significant softening factor, and can achieve more refined control through the system, optimize the control strategy, reduce overshoot and under-regulation situations, thereby improving the overall control efficiency and accuracy.

[0029] 3. By collecting the ambient temperature data of the test chamber and the power data of the heating accessories in real time and calculating the deviation degree, the system can quickly identify temperature changes and adjust the control strategy in a timely manner. The real-time feedback mechanism enables the control system to quickly respond to temperature changes, reduce hysteresis, and improve the control accuracy and response speed. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] By referring to the following detailed description with reference to the accompanying drawings, the above and other objects, features, and advantages of the exemplary embodiments of the present invention will become readily understood. In the drawings, several embodiments of the present invention are shown by way of illustration and not limitation, and like or corresponding reference numerals indicate like or corresponding parts, wherein:

[0031] Figure 1 is a flowchart of the method for steps S1 - S4 in the method for controlling the ambient temperature of a test chamber for high-temperature aging test according to an embodiment of the present invention.

[0032] Figure 2 is a block diagram of the structure of a control system for the ambient temperature of a test chamber for high-temperature aging test according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0033] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0034] The following will describe in detail the specific embodiments of the present invention with reference to the accompanying drawings.

[0035] Refer to Figure 1 , a method for controlling the ambient temperature of a test chamber for high-temperature aging test includes steps S1 - S4, specifically as follows:

[0036] S1: Collect the ambient temperature data of the test chamber and the power data of the heating accessories, calculate the deviation degree of the ambient temperature data of the current test chamber to reflect the difference in the current ambient temperature data, and set an initial softening factor for the difference in the ambient temperature data using generalized predictive control.

[0037] It should be noted that a temperature sensor is installed at a suitable position inside the test chamber, the data acquisition frequency is set to 1 Hz, and the ambient temperature data is collected. The temperature sensor is connected to the data acquisition system, and the collected temperature data is transmitted to the data acquisition system.

[0038] That is to say, the degree of deviation reflects the precision of temperature control, namely, the proximity between the actual temperature and the target temperature. A larger degree of deviation requires a stronger control action to adjust the temperature to the desired value. Conversely, a smaller degree of deviation requires a weaker control action.

[0039] Based on the ratio between the difference and the actual value of the actual value and the desired value of the environmental temperature data of the current test chamber, the degree of deviation of the environmental temperature data of the test chamber is obtained.

[0040] Specifically, the degree of deviation satisfies the following relational expression:

[0041] ;

[0042] In the formula, represents the degree of deviation of the th environmental temperature data of the test chamber, represents the actual value of the th environmental temperature data of the test chamber, represents the desired value of the th environmental temperature data of the test chamber.

[0043] That is to say, represents the degree of difference between the th environmental temperature data of the test chamber and the th environmental temperature data. The greater the degree of difference between the actual value and the desired value of the th environmental temperature data of the test chamber, the greater the degree of deviation of the th environmental temperature data. Among them, the positive or negative sign of the degree of deviation of the th environmental temperature data of the test chamber indicates the direction. If the degree of deviation of the th environmental temperature data is positive, it indicates an upward deviation. If the degree of deviation of the th environmental temperature data of the test chamber is negative, it indicates a downward deviation.

[0044] In addition, in another embodiment, the degree of deviation can also be analyzed by least squares fitting. The specific steps are as follows:

[0045] Perform local least squares fitting on the environmental temperature data of the current test chamber. Using the model obtained by least squares fitting, calculate the fitted value of the temperature data of the current test chamber, and use the negative correlation exponential function of the absolute value of the difference between the actual value and the fitted value as the degree of deviation.

[0046] In the above two embodiments of the deviation degree, in the first embodiment, analysis is performed based on the expected value. In the second embodiment, local least squares fitting is used, which can more accurately describe the local trend of temperature data, thereby improving the control accuracy; it can adapt to the non-linear and time-varying characteristics of temperature data, enabling the control system to better adapt to system changes, and can be specifically selected according to the actual situation.

[0047] It should be noted that an initial softening factor can be obtained based on the deviation degree of the environmental temperature data of the test chamber. Then, analyze the possibility that this deviation is caused by external factor interference, and make a certain degree of correction to the initial softening factor to avoid the influence of the softening factor being too large or too small on the control efficiency. The specific steps to obtain the initial softening factor are as follows:

[0048] Take the reciprocal of the square root of the sum of the square of the deviation degree of the environmental temperature data of the test chamber plus 1 as the initial softening factor.

[0049] Specifically, the initial softening factor satisfies the following relational expression:

[0050] ;

[0051] In the formula, represents the initial softening factor corresponding to the th environmental temperature data of the test chamber, represents the deviation degree of the th environmental temperature data of the test chamber.

[0052] That is to say, when the deviation degree of the th environmental temperature data of the test chamber is large, a smaller softening factor is required to control the response speed of the system and improve the adjustment ability of the system.

[0053] In this embodiment, the softening factor is also called the smoothing factor and is applicable to generalized predictive control. Its role is to balance control performance and system response, reduce system overshoot and oscillation by adjusting the smoothness of the control signal, and improve the stability and robustness of the control. When the deviation degree is large, the softening factor is small, and the control system will respond to the deviation faster to reduce the deviation of the system; on the contrary, when the deviation degree is small, the softening factor is large, and the response of the control system will be smoother to avoid over-control.

[0054] In addition, in another embodiment, the initial softening factor can also be jointly obtained based on the results of the above two deviation degrees. The specific steps are as follows:

[0055] The first temperature deviation is obtained according to the ratio of the difference between the actual value and the expected value of the environmental temperature data of the current test chamber to the actual value; perform local least squares fitting on the environmental temperature data of the current test chamber, use the model obtained by least squares fitting to calculate the fitted value of the temperature data of the current test chamber, and use the negative correlation exponential function of the absolute value of the difference between the actual value and the fitted value as the second temperature deviation;

[0056] Take the absolute value of the difference between the first temperature deviation and the second temperature deviation as the absolute difference, and take the square of the average value of the sum of the first temperature deviation and the second temperature deviation as the average difference; take the ratio of the absolute difference to the square root of the average difference plus 1 as the initial softening factor of the environmental temperature data of the current test chamber.

[0057] Specifically, the initial softening factor satisfies the following relational expression:

[0058] ;

[0059] In the formula, represents the initial softening factor corresponding to the th environmental temperature data of the test chamber, represents the first temperature deviation of the th environmental temperature data of the test chamber, represents the second temperature deviation of the th environmental temperature data of the test chamber.

[0060] That is to say, is the mean value of the first temperature deviation and the second temperature deviation of the th environmental temperature data of the test chamber. The larger this value is, the greater the deviation degree of the temperature data. A smaller softening factor is required to control the response speed of the system and improve the adjustment ability of the system; is the difference between the first temperature deviation and the second temperature deviation of the th environmental temperature data, representing the credibility of the deviation degree of the th environmental temperature data of the test chamber. The greater the difference between the two, the higher the possibility that the environmental temperature data deviates.

[0061] Furthermore, when obtaining the correction degree of the initial softening factor of the environmental temperature data of the current test chamber, it is mainly through evaluating the possibility of the environmental temperature data of the current test chamber being interfered by the outside world. Because the greater the possibility of the environmental temperature data of the current test chamber being interfered by the outside world, the greater the possibility that the deviation degree of the environmental temperature data of the current test chamber is caused by this outside interference, the less accurate the initial softening factor obtained according to the deviation degree of the environmental temperature data of the current test chamber, and the greater the correction degree.

[0062] When evaluating the possibility of the current test chamber ambient temperature data being interfered by the outside world, it is mainly described by the local stability of the current test chamber ambient temperature data and the change in the corresponding power. Because the local stability of the current test chamber ambient temperature data can reflect the operating state of the system near the current moment. If the temperature data fluctuates violently in a short period of time, it may mean that the system is affected by external interference or internal faults.

[0063] Therefore, by comprehensively analyzing the correction degree of the initial softening factor through the local stability of the current test chamber ambient temperature data and the change in the corresponding power, the accuracy of the correction degree can be improved, and then the accuracy of the final softening factor can be improved. The specific steps are as follows:

[0064] S2: Taking the ambient temperature data of the current test chamber as the end, establish a window with a preset length, and calculate the difference degree between adjacent ambient temperature data within the window to evaluate the local stationarity.

[0065] The local stationarity includes:

[0066] Calculate the absolute difference between two adjacent temperature data within the window, and take the ratio of the sum of the absolute differences of all adjacent ambient temperature data within the window to the window length minus 1 as the average difference, and use the exponential function to perform exponential decay on the average difference to obtain the local stationarity.

[0067] Specifically, the local stationarity satisfies the following relational expression:

[0068] ;

[0069] In the formula, represents the local stationarity of the th ambient temperature data of the test chamber, represents the value of the th temperature data in the window corresponding to the th ambient temperature data of the test chamber, represents the value of the th temperature data in the window corresponding to the th ambient temperature data of the test chamber, represents the window length, represents the exponential function with the natural number as the base.

[0070] That is to say, among them, represents the average difference between two adjacent temperature data in the window corresponding to the th ambient temperature data of the test chamber. The larger this value is, the lower the local stationarity of the th ambient temperature data.

[0071] In addition, in another embodiment, it further includes:

[0072] Taking any environmental temperature data as the marked temperature, calculate the average value of all temperature data in the window corresponding to the marked temperature data. For each temperature data in the window, sum the square differences between each temperature data and the average value as the sum of squared deviations, calculate the ratio between the sum of squared deviations and the window length minus 1 to obtain the variance, and use the exponential function to perform exponential decay on the variance to obtain the local stationarity.

[0073] Specifically, the local stationarity satisfies the following relational expression:

[0074] ;

[0075] In the formula, represents the local stationarity of the th environmental temperature data of the test chamber, represents the th environmental temperature data of the test chamber, and represents the value of the th temperature data in the window corresponding to the th environmental temperature data of the test chamber, represents the average value of all temperature data in the window corresponding to the th environmental temperature data of the test chamber, represents the exponential function with the natural number

[0076] That is to say, represents the average value of the degree of deviation between the temperature data and the average value. A larger variance indicates greater fluctuations in the temperature data, and a smaller variance indicates smaller fluctuations in the temperature data. Considering the fluctuations of the temperature data within the local time range, the stability is quantified through the exponential decay function. The local stationarity can be an important parameter for the control system to adjust the control strategy to adapt to the local changes in the temperature data.

[0077] In addition, in another embodiment, it includes:

[0078] Taking any environmental temperature data as the marked temperature, obtain the maximum and minimum values of the temperature data in the window corresponding to the marked temperature of the test chamber, calculate the difference between the maximum and minimum values, calculate the average value of all temperature data in the window corresponding to the marked temperature data, and use the ratio between the difference and the average value as the local stationarity.

[0079] Specifically, the local stationarity satisfies the following relational expression:

[0080] ;

[0081] In the formula, represents the The local stationarity of the ambient temperature data represents the maximum value of the temperature data in the window corresponding to the th ambient temperature data of the test chamber, represents the minimum value of the temperature data in the window corresponding to the th ambient temperature data of the test chamber, represents the average value of all the temperature data in the window corresponding to the th ambient temperature data of the test chamber.

[0082] That is to say, the difference between the maximum and minimum values of the temperature data in the window reflects the fluctuation range of the temperature data, and the average value represents the central tendency of the temperature data within the window. By dividing the difference by the average value, a relative fluctuation range can be obtained, which helps to compare the stability at different temperature levels.

[0083] S3: Obtain the change rate of the power data of the test chamber by taking the ratio of the difference between the power data of the test chamber at the current moment and the power data at other moments to the power data of the current test chamber.

[0084] Specifically, the change rate satisfies the following relational expression:

[0085] ;

[0086] In the formula, represents the change rate of the power data of the heating accessory of the test chamber corresponding to the th ambient temperature data of the test chamber, represents the power data of the heating accessory of the test chamber corresponding to the th ambient temperature data of the test chamber, represents the power data of the heating accessory of the test chamber corresponding to the th ambient temperature data of the test chamber.

[0087] That is to say, is the degree of change of the power data of the heating accessory of the test chamber corresponding to the th ambient temperature data of the test chamber compared to the power data of the heating accessory of the test chamber corresponding to the th ambient temperature data of the test chamber, that is, the change rate of the power data of the heating accessory of the test chamber corresponding to the th ambient temperature data of the test chamber.

[0088] It should be noted that a larger change rate means that the power data has changed significantly in a short period of time, which may indicate that the system is affected by external energy input, such as fluctuations in heating power. When the change rate is large, more frequent or stronger control actions may be required to maintain the temperature stability.

[0089] S4: Modify the initial softening factor according to the change rate and local stationarity to obtain a significant softening factor, and use the significant softening factor as the weight for controlling the environmental temperature data, so as to adjust the environmental temperature data of the test chamber at the next moment.

[0090] Among them, the significant softening factor satisfies the following relational expression:

[0091] ;

[0092] In the formula, represents the significant softening factor of the th environmental temperature data of the test chamber, represents the initial softening factor of the th environmental temperature data of the test chamber, represents the change rate of the power data of the heating accessory corresponding to the th environmental temperature data of the test chamber, represents the local stationarity of the th environmental temperature data of the test chamber, is the ceiling symbol.

[0093] That is to say, is the correction degree of the initial softening factor corresponding to the th environmental temperature data of the test chamber. The greater the change rate of the power data of the heating accessory corresponding to the th environmental temperature data of the test chamber, and the lower the local stationarity of the th environmental temperature data of the test chamber, it indicates that the current environmental temperature data of the test chamber is more likely to be affected by the outside world. The deviation degree of the th environmental temperature data of the test chamber is very likely to be caused by external interference. At this time, the initial softening factor obtained according to the deviation degree of the th environmental temperature data of the test chamber is inaccurate and needs to be further corrected according to the possibility of this deviation degree being affected by external interference to ensure the accuracy of the initial softening factor. At the same time, the further correction also avoids the over-softening caused by too small softening factor and affects the accurate response of the control.

[0094] Controlling the environmental temperature data of the test chamber at the next moment according to the significant softening factor includes:

[0095] Set a control module in the environmental temperature control system of the test chamber for high-temperature aging tests, input the environmental temperature data into the control module, construct a temperature prediction model, apply the softening factor corresponding to the current environmental temperature data of the test chamber to the weight of the control input, formulate a control strategy for the environmental temperature of the test chamber at the next moment, convert the formulated control strategy into specific control instructions, and send them to the control system of the test chamber.

[0096] The present invention also provides a test chamber environmental temperature control system for high-temperature aging tests. As Figure 2 shown, the system includes a processor and a memory, and the memory stores computer program instructions. When the computer program instructions are executed by the processor, a test chamber environmental temperature control method for high-temperature aging tests according to the first aspect of the present invention is implemented.

[0097] The system also includes other components well known to those skilled in the art, such as a communication bus and a communication interface. Their settings and functions are known in the art, so they will not be described herein again.

[0098] In the present invention, the aforementioned memory can be any tangible medium that contains or stores a program, and this program can be used by or in combination with an instruction execution system, device, or component. For example, a computer-readable storage medium can be any suitable magnetic storage medium or magneto-optical storage medium, such as a resistive random access memory (RRAM), a dynamic random access memory (DRAM), a static random access memory (SRAM), an enhanced dynamic random access memory (EDRAM), a high-bandwidth memory (HBM), a hybrid memory cube (HMC), etc., or any other medium that can be used to store the required information and can be accessed by an application, a module, or both. Any such computer storage medium can be part of the device or accessible or connectable to the device. Any application or module described in the present invention can be implemented by computer-readable / executable instructions stored or otherwise held by such a computer-readable medium.

[0099] In the description of this specification, the meanings of "a plurality" and "several" are at least two, for example, two, three, or more, etc., unless otherwise clearly and specifically defined.

[0100] Although this specification has shown and described multiple embodiments of the present invention, it is obvious to those skilled in the art that such embodiments are provided only by way of example. Those skilled in the art will think of many changes, alterations, and alternative ways without departing from the spirit and concept of the present invention. It should be understood that various alternative solutions to the embodiments of the present invention described herein can be adopted in the process of practicing the present invention.

Claims

1. A method for controlling the ambient temperature of a test chamber for a high temperature aging test, characterized in that: include: Collect the ambient temperature data of the test chamber and the power data of the heating accessories, calculate the deviation degree of the current ambient temperature data of the test chamber to reflect the difference of the current ambient temperature data, and use generalized predictive control to set the initial softening factor for the difference of the ambient temperature data; Taking the ambient temperature data of the current test chamber as the terminal data, a window of preset length is established, and the difference between adjacent ambient temperature data in the window is calculated to evaluate local stability; The rate of change of the power data of the test box is obtained by taking the ratio of the difference between the power data of the test box at the current moment and the power data at any other moment and the power data of the current test box at the current moment; The initial softening factor is modified according to the change rate and local stability to obtain a significant softening factor, and the significant softening factor is used as a control weight to adjust the ambient temperature data of the test box at the next moment; Among them, the significant softening factor Satisfies the following relationship: ; In the formula, Indicates the test chamber The initial softening factor of the ambient temperature data is Indicates the test chamber The rate of change of the power data of the heating accessories of the test chamber corresponding to the ambient temperature data is Indicates the test chamber The local stationarity of the ambient temperature data, is the rounding symbol; The initial softening factor includes: taking the reciprocal of the square root of the square of the deviation degree of the ambient temperature data of the test chamber plus 1 as the initial softening factor; Local stability includes: calculating the absolute difference between two adjacent temperature data in the window, taking the ratio between the sum of the absolute differences of all adjacent ambient temperature data in the window and the window length minus 1 as the average difference, and using an exponential function to exponentially decay the average difference to obtain local stability; Or local stationarity includes: taking any ambient temperature data as the marked temperature, calculating the average value of all temperature data in the window corresponding to the marked temperature data, for each temperature data in the window, summing the square difference between each temperature data and the average value as the sum of squared deviations, calculating the ratio between the sum of squared deviations and the window length minus 1 to obtain the variance, and using an exponential function to exponentially decay the variance to obtain local stationarity.

2. A test chamber environment temperature control method for high temperature aging test according to claim 1, characterized in that: The degree of deviation includes: The deviation degree of the ambient temperature data of the test box is obtained according to the ratio of the difference between the actual value and the expected value of the ambient temperature data of the current test box and the actual value.

3. A test chamber environment temperature control method for high temperature aging test according to claim 1, characterized in that: The degree of deviation also includes: Perform local least squares fitting on the current test chamber ambient temperature data, use the model obtained by least squares fitting to calculate the fitting value of the current test chamber temperature data, and use the negative correlation exponential function of the absolute value of the difference between the actual value and the fitting value as the degree of deviation.

4. A test chamber environment temperature control system for high temperature aging test, characterized in that: include: A processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, a test chamber environment temperature control method for high temperature aging test according to any one of claims 1 to 3 is implemented.

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