Temperature control method and system for a natural convection constant temperature test chamber

Abstract

The present invention relates to the field of data processing, and particularly to a temperature control method and system for a natural convection constant temperature test chamber. The method includes: collecting the internal temperature of the test chamber and calculating the abnormality degree of each temperature; calculating the difference between all temperatures, using the difference as the error change rate, and using the product of the abnormality degrees of the two adjacent temperatures corresponding to the error change rate as the abnormality degree of the error change rate; adjusting the error change rate based on the abnormality degree of the error change rate to obtain an adjusted error change rate; obtaining the current internal temperature of the test chamber, calculating the adjusted error change rate corresponding to the current temperature, inputting the current temperature and the adjusted error change rate corresponding to the current temperature into a PID control algorithm, and controlling the temperature of the test chamber according to the output result of the PID control algorithm. By calculating the abnormality degree of the temperature and adjusting the error change rate, the present invention makes the control of the internal temperature of the test chamber more accurate.

Description

Technical Field

[0001] The present invention relates to the field of data processing. More specifically, the present invention relates to a temperature control method and system for a natural convection constant temperature test chamber. Background Art

[0002] A natural convection constant temperature test chamber is a test device used to simulate the actual indoor environmental temperature. Through natural convection test equipment and software, a constant temperature environment (natural convection) that does not require forced air circulation by a fan is generated, and relevant temperature detection and test integration are performed on the test product. The natural convection constant temperature test chamber is applied in industries such as electronics, electrical appliances, automobiles, and aerospace to test the performance and reliability of various electronic products or enclosed spaces under actual environmental temperatures.

[0003] The prior art, such as the patent application document with the publication number CN109375684A, discloses a PID control method. The PID control method includes: collecting the temperature and humidity inside the system; controlling the start time of the PTC heater, circulation water pump, and circulation fan in the system according to the set temperature and set humidity, and the currently collected temperature and current humidity; calculating the difference between the set temperature and the current temperature, and the difference between the set humidity and the current humidity, and using these differences and the incremental PID algorithm to adjust the output power of the PTC heater, circulation water pump, and circulation fan.

[0004] Although the above PID control method effectively regulates the system temperature and humidity to a certain extent, there are still some limitations. Especially during the process of collecting temperature data, external factors such as high-frequency noise in the measurement data and system dynamic changes will affect the calculation results of the PID controller, resulting in a decrease in the accuracy of temperature control. Summary of the Invention

[0005] To solve the technical problem of the decrease in the accuracy of temperature control caused by the interference of external factors, the present invention provides solutions in the following aspects.

[0006] In a first aspect, a temperature control method for a natural convection constant temperature test chamber includes:

[0007] Collecting the internal temperature of the test chamber and calculating the abnormality degree of each temperature;

[0008] Calculating the difference of all temperatures, using the difference as the error change rate, and taking the product of the abnormality degrees of two adjacent temperatures corresponding to the error change rate as the abnormality degree of the error change rate;

[0009] Adjusting the error change rate based on the abnormality degree of the error change rate to obtain an adjusted error change rate;

[0010] Obtain the current temperature inside the test chamber, calculate the adjusted error change rate corresponding to the current temperature, input the current temperature and the adjusted error change rate corresponding to the current temperature into the PID control algorithm, and control the temperature of the test chamber according to the output result of the PID control algorithm.

[0011] By collecting the temperature inside the test chamber and calculating the abnormality degree of each temperature, the present invention can accurately identify the abnormal points in the temperature data, thereby monitoring the temperature change more carefully and avoiding test errors caused by abnormal collected temperatures; taking the product of the abnormality degrees of two adjacent temperatures corresponding to the error change rate as the abnormality degree of the error change rate, and adjusting the error change rate based on this, making the error change rate closer to the actual temperature change situation, effectively reducing the problem of overestimated differential term caused by external factors in the PID control method, and improving the accuracy and stability of temperature control.

[0012] Automatically adjust the error change rate according to the change situation of the temperature inside the test chamber, so as to adapt to different temperature change rates. Whether it is a rapid temperature rise, a rapid temperature drop or a slowly changing temperature process, accurate control can be achieved through the coordinated action of the adjusted error change rate and the PID control algorithm, expanding the applicable range of temperature control.

[0013] Preferably, the process of obtaining the abnormality degree includes:

[0014] Calculate the similarity between any two temperatures;

[0015] Use a clustering algorithm to cluster all the collected temperatures to obtain multiple clusters;

[0016] Calculate the size of the data-free point area around each temperature in the cluster and the average distribution range of the temperatures in the cluster, and take the ratio of the size of the data-free point area around the temperature to the average distribution range as the abnormality degree of the corresponding temperature; wherein the size of the data-free point area around the temperature represents the area of a circle with any temperature in the cluster as the center point and the radius being the minimum value of the similarity between the center point and other temperatures, and there are no other temperatures in this area.

[0017] By calculating steps such as the similarity between temperatures, clustering, and calculating the abnormality degree, the abnormal points in the temperature data can be effectively identified. These abnormal points may be caused by noise in data collection and transmission, so these abnormal data can be marked to reduce their influence on the PID algorithm.

[0018] Preferably, the process of obtaining the abnormality degree further includes:

[0019] Use the exponential smoothing method to predict all the temperatures to obtain the predicted value corresponding to each temperature;

[0020] Calculate the standard deviation of all temperatures;

[0021] Take the ratio of the difference between the actual value and the predicted value of each temperature to the standard deviation as the predicted anomaly degree of the corresponding temperature;

[0022] Take the predicted anomaly degree as a correction factor, and take the product of the correction factor and the ratio of the size of the data - free point area around the temperature to the average distribution range as the final anomaly degree.

[0023] The introduction of the predicted anomaly degree is equivalent to providing a dynamically adjustable threshold for anomaly detection. When the temperature data changes relatively smoothly and the noise is small, the predicted anomaly degree is small, and the anomaly detection threshold is relatively low, which can more sensitively detect tiny anomaly changes; while when the data changes violently and the noise is large, the predicted anomaly degree is large, and the anomaly detection threshold is correspondingly increased, avoiding over - sensitivity and misjudgment caused by noise, so that the anomaly detection method can automatically adjust the detection strategy according to the dynamic characteristics of the data.

[0024] Preferably, the adjusted error change rate satisfies the relational expression:

[0025] ; where, represents the th weighted error change rate, represents the anomaly degree of the th error change rate, represents the value of the th error change rate, represents the sum of the reciprocals of the th error change rate to the th error change rate, represents the weighted sum of the first error change rates of the th error change rate.

[0026] By weighting the error change rate, the error change rate with a smaller anomaly degree (i.e., the data is relatively normal) has a greater weight in the calculation, while the error change rate with a larger anomaly degree has a smaller weight. This can reduce the influence of abnormal data on the overall error change rate, make the calculation result better reflect the error change trend under normal conditions, and thus improve the control accuracy.

[0027] In a temperature control system, noise is often introduced during the data acquisition and transmission process, resulting in fluctuations in the error change rate. By introducing the reciprocal of the anomaly degree as a weight, the influence of noise data on the calculation of the error change rate can be effectively reduced, making the control system more stable in the face of noise interference, reducing misoperations and unnecessary adjustments, and enhancing the robustness of the system.

[0028] Preferably, the output result of the PID control algorithm is:

[0029] ; where, is the control signal, is the proportional coefficient, is the integral coefficient, is the differential coefficient, is the error between the current temperature and the set temperature of the test chamber, is the weighted error change rate corresponding to the current temperature.

[0030] In the traditional PID control algorithm, the differential term mainly reflects the change trend of the error, which is used to predict the future error and adjust in advance to reduce the overshoot of the system and accelerate the response speed of the system. After introducing the weighted error change rate, the differential term can more accurately reflect the dynamic characteristics of the error change, because the weighted error change rate considers the weighted sum of the previous L error change rates and can better capture the trend and speed of the error change. This enables the control system to react more quickly to temperature changes, adjust the control signal in advance, thereby accelerating the response speed of the system and reducing the time required for the temperature to reach the set value.

[0031] Preferably, controlling the temperature of the test chamber according to the output result of the PID control algorithm includes:

[0032] If the output result is positive, increase the heating power of the test chamber; otherwise, reduce the heating power of the test chamber.

[0033] Preferably, the clustering algorithm is the K-Means clustering algorithm.

[0034] In a second aspect, a temperature control system for a natural convection constant temperature test chamber includes: a processor and a memory, and the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the temperature control method of the natural convection constant temperature test chamber described above is implemented.

[0035] The beneficial effects of the present invention are:

[0036] Due to the existence of noise, the temperature data will fluctuate. The present invention accurately identifies abnormal points by calculating the abnormal degree of the internal temperature of the test chamber, improves the temperature monitoring accuracy, and reduces the test error. At the same time, it automatically adjusts according to the abnormal degree of the error change rate, combines with the PID control algorithm, realizes more accurate temperature control, adapts to different temperature change rates, and expands the applicable range of temperature control. Description of the Drawings

[0037] By reading 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 example and not limitation, and like or corresponding reference numerals denote like or corresponding parts, wherein:

[0038] Figure 1 It is a flowchart of the method from step S1 to step S4 in the temperature control method of a natural convection constant temperature test chamber according to an embodiment of the present invention. Specific Embodiments

[0039] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described 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 of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0040] The application scenario of the present invention is: using the PID control algorithm to regulate a natural convection constant temperature test chamber.

[0041] An embodiment of the present invention discloses a temperature control method for a natural convection constant temperature test chamber. Referring to Figure 1 , it includes steps S1 to S4, specifically as follows:

[0042] S1: Collect the internal temperature of the test chamber and calculate the abnormality degree of each temperature.

[0043] The PID algorithm is a commonly used control algorithm. It adjusts the input of the system by performing proportional, integral, and differential operations on the error between the system output and the set value, so that the system output reaches the expected value. However, when there are abnormal data in the system, these abnormal data will cause deviations in error calculation, thereby affecting the control effect of the PID algorithm.

[0044] Therefore, in order to improve the accuracy and stability of the PID algorithm, it is necessary to monitor the internal temperature of the test chamber in real time and calculate the abnormality degree of each temperature. By identifying and processing abnormal data, its influence on the PID algorithm can be reduced, thereby improving the performance of the control system.

[0045] Specifically, a temperature sensor is set inside the test chamber, and then the temperature sensor is used to collect the internal temperature of the test chamber in real time, that is, collect temperature data once a minute, at least 60 times, and then preprocess the collected temperature data, including data cleaning and data smoothing.

[0046] Furthermore, calculate the abnormality degree of the temperature data. The specific steps are as follows:

[0047] First, since temperature data usually has time dependence, that is, the temperature value is not only affected by time, and the temperature values at adjacent time points usually have a high correlation. Therefore, considering the similarity calculation of time positions can more accurately reflect the similarity between two temperature data.

[0048] Specifically, the similarity between two temperature data satisfies the relational expression:

[0049]

[0050] In the formula, is the similarity between the temperature collected at the th time and the temperature collected at the th time, is the temperature value collected at the th time, is the temperature value collected at the th time, is the result of normalizing the index value collected at the th time, is the result of normalizing the index value collected at the th time, is the total number of collections.

[0051] Among them, , directly reflect the temperature difference between the two collections, and reflect the position difference between the two collections in the time series. By combining these two factors, not only the difference in the temperature values themselves is considered, but also the position difference of these temperature values in the time series is considered, which helps to more comprehensively evaluate the similarity of two data points.

[0052] Then, use the adaptive K-Means clustering algorithm to cluster the temperature data to generate multiple clusters, because the traditional K-Means algorithm needs to specify the number of clusters in advance. However, in practical applications, it is not known in advance how many clusters should be divided is appropriate. The adaptive K-Means clustering can automatically determine the optimal number of clusters through iterative calculation and adjustment, thus avoiding the deviation that may be brought by the artificially set number of clusters.

[0053] Each of the above clusters has a center, which represents the average temperature and the average collection index of all temperature data in the cluster.

[0054] For the th cluster, if the distance between a temperature data and the center of the cluster is large, this may mean that this temperature data has a large difference from other temperature data in the cluster.

[0055] To quantify this difference, first find the minimum and maximum values of the temperature data within the th cluster, as well as the minimum and maximum values of the acquisition indices. For the th temperature data in the th cluster, calculate the minimum similarity between this temperature data and other temperature data in its cluster.

[0056] Furthermore, calculate the degree of abnormality of the th temperature data in the th cluster, that is, the relationship is satisfied as:

[0057]

[0058] In the formula, is the degree of abnormality of the th temperature data in the th cluster, is the pi, is the minimum value of the similarity between the th temperature data and other temperature data in the th cluster, is the maximum temperature value in the th cluster, is the minimum temperature value in the th cluster, is the maximum value of the acquisition index in the th cluster, is the minimum value of the acquisition index in the th cluster, is the total number of temperature data in the th cluster.

[0059] Among them, the numerator represents the size of the data-free point area around the th temperature data in the th cluster, that is, taking the th temperature data in the th cluster as the center, and the radius is of the area of the circle. There is no other data in this area; if there is a large data-free point area around a temperature data, it means that the difference between this data and other data is large, then this data is more likely to be abnormal. The denominator represents the average distribution range of the temperature data in the th cluster.

[0060] The larger is, the more it indicates that the One temperature data is very discrete compared to other temperature data in its cluster.

[0061] According to the above The anomaly degrees of all other temperatures are calculated in the same way.

[0062] In addition, it should be considered that temperature data is usually time - series data, that is, the data changes over time. Calculating similarity and performing clustering analysis based solely on the static characteristics of data points (such as temperature values and acquisition indices) may not fully capture the dynamic characteristics of data changing over time. Through the calculation of predicted anomaly degrees, we can use time - series prediction techniques such as exponential smoothing to predict temperature data and evaluate the difference between the actual value and the predicted value. This difference reflects the degree of data change over time and helps to identify those anomaly points that deviate significantly from the expected trend.

[0063] Specifically, use exponential smoothing to predict all the collected temperature data to obtain the predicted value corresponding to each temperature data, and then calculate the standard deviation of all temperature data.

[0064] For each temperature data, calculate the difference between its actual value and the predicted value, and divide this difference by the standard deviation to obtain the predicted anomaly degree of this temperature data. This predicted anomaly degree reflects the deviation degree between the actual temperature and the predicted temperature. The greater the deviation degree, the more likely this temperature data is to be abnormal.

[0065] Furthermore, take the calculated predicted anomaly degree as a correction factor, and multiply it by the ratio of the size of the data - free area around the temperature to the average distribution range obtained above to get the final anomaly degree, that is, the relationship is satisfied as:

[0066]

[0067] In the formula, is the final anomaly degree of the th temperature, is the anomaly degree of the th temperature without adding the correction factor, is the correction factor of the th temperature.

[0068] This final anomaly degree comprehensively considers the predicted anomaly of temperature data in time series and the degree of discreteness in space, so as to more comprehensively reflect the anomaly of temperature data.

[0069] S2: Calculate the differences of all temperatures, take the differences as the error change rates, and take the product of the anomaly degrees of two adjacent temperatures corresponding to the error change rates as the anomaly degree of this error change rate.

[0070] First, calculate the differences of all temperatures, which can be expressed as: , where is the th difference value (i.e., the th error change rate), is the th temperature value, is the th temperature value.

[0071] can be used as the error change rate, indicating the change of temperature data at adjacent moments. The error change rate is the basis of the derivative term in the PID control algorithm and is used to reflect the change trend of temperature error.

[0072] In the above S1, the abnormality degree of each temperature data has been calculated (comprehensively calculated based on information such as the similarity of temperature data, clustering results, and prediction errors, and used to measure whether each temperature data is affected by noise or other abnormal factors). In order to reduce the influence of abnormal data on the error change rate, the product of the abnormality degrees of the two temperatures corresponding to the above is used as the abnormality degree, that is, the relational expression is satisfied as:

[0073]

[0074] In the formula, is the th abnormality degree of the error change rate, is the th abnormality degree of the temperature, is the th abnormality degree of the temperature.

[0075] S3: Adjust the error change rate based on the abnormality degree of the error change rate to obtain the adjusted error change rate.

[0076] After calculating the abnormality degrees of each error change rate in the above S2, further reduce the influence of abnormal values on the calculation of the error change rate by the method of weighted summation, so that the finally obtained weighted error change rate is more stable and reliable.

[0077] Specifically, intercept the first L error change rates before the th error change rate and perform weighted average to obtain the th weighted error change rate.

[0078] The error change rate reflects the trend of data variation over time. By considering multiple previous error change rates, the changes in this trend can be better captured and understood. For example, if the recent error change rate shows a gradually increasing trend, it may indicate that the system is undergoing some changes or adjustments. Through weighting, this trend can be more accurately quantified, and more informed decisions can be made based on it.

[0079] Exemplarily, for the th error change rate, intercept the previous 20 error change rates, and calculate the sum of the reciprocals of the abnormality degrees of these error change rates; for the th error change rate and the previous 20 error change rates, take the reciprocal of the corresponding abnormality degree as its weight, and then calculate the weighted sum of these 20 error change rates; divide the weighted sum by the sum of the reciprocals of the abnormality degrees to obtain the th weighted error change rate. That is, the relational expression is satisfied as:

[0080]

[0081] In the formula, represents the th weighted error change rate, represents the abnormality degree of the th error change rate, represents the value of the th error change rate, represents the sum of the reciprocals from the th error change rate to the th error change rate, represents the weighted sum of the first error change rates of the th error change rate.

[0082] By the above method, the th weighted error change rate is obtained, which takes into account the abnormality degrees of the previous error change rates, thereby reducing the influence of abnormal data on the error change rate.

[0083] S4: Obtain the current temperature inside the test chamber, calculate the adjusted error change rate corresponding to the current temperature, input the current temperature and the adjusted error change rate corresponding to the current temperature into the PID control algorithm, and control the temperature of the test chamber according to the output result of the PID control algorithm.

[0084] Collect the current temperature data through a temperature sensor, compare the collected current temperature data with the set temperature of the test chamber to obtain an error value; and recalculate the weighted error change rate corresponding to the current temperature according to the method of calculating the weighted error change rate described above.

[0085] The current temperature and the weighted error change rate of the current temperature are passed as input parameters to the PID control algorithm. The PID control algorithm calculates a control signal based on the input temperature error and the weighted error change rate, and adjusts the heating power of the test chamber according to the calculated control signal. The larger the output value of the control signal, the more the heating power needs to be increased; the smaller the control output value, the more the heating power needs to be decreased. For example, if the control output is positive, increase the heating power; if the control output is negative, decrease the heating power.

[0086] Among them, the control signal satisfies the relational expression:

[0087]

[0088] In the formula, is the control signal, is the proportionality coefficient, is the integral coefficient, is the differential coefficient, is the error between the current temperature and the set temperature of the test chamber, is the weighted error change rate corresponding to the current temperature.

[0089] Among them, the weighted temperature error change rate is used to replace the traditional error change rate for PID control. By using the weighted temperature error change rate, the influence of temperature data with a large degree of abnormality on the PID control result can be reduced. This is because the weighting process ensures that data with a higher degree of abnormality (i.e., data affected by more noise) will be given a smaller weight, while data with a lower degree of abnormality (i.e., more reliable data) will be given a larger weight. In this way, the weighted error change rate can better reflect the true change trend of the temperature data and reduce the influence of abnormal data on the control result.

[0090] To sum up, in the process of the PID algorithm controlling the temperature of the natural convection constant temperature test chamber, when there is a situation that is significantly inconsistent with the conventional error change rate and makes the derivative term in the PID algorithm inflated, by introducing historical error data and error change rates, calculating the degree of abnormality of each error data, and calculating the weights of the recent multiple errors according to the degree of abnormality for weighting operations, a more accurate derivative term of the discrete temperature data can be obtained, so as to obtain a more accurate PID calculation result.

[0091] The embodiment of the present invention also discloses a temperature control system for a natural convection constant temperature test chamber, including a processor and a memory. The memory stores computer program instructions, and when the computer program instructions are executed by the processor, the temperature control method for the natural convection constant temperature test chamber according to the present invention is implemented.

[0092] 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 and will not be elaborated herein.

[0093] In the present invention, the aforementioned memory can be any tangible medium that contains or stores a program, which can be used by or in conjunction with an instruction execution system, apparatus, or device. 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 program, module, or both. Any such computer storage medium can be part of the device or accessible or connectable to the device.

[0094] In the description of this specification, the meanings of "a plurality of" and "several" are at least two, such as two, three, or more, etc., unless otherwise specifically defined.

[0095] 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 by way of example only. Those skilled in the art will think of many changes, alterations, and alternative ways without departing from the spirit and idea 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 practice of the present invention.

Claims

1. A temperature control method for a natural convection constant temperature test chamber, characterized in that, Including: Collecting the internal temperature of the test chamber; Calculating the abnormality degree of each temperature, including: calculating the similarity between any two temperatures; using a clustering algorithm to cluster all the collected temperatures to obtain multiple clusters; calculating the size of the data-free point area around each temperature in the cluster and the average distribution range of the temperatures in the cluster, and taking the ratio of the size of the data-free point area around the temperature to the average distribution range as the abnormality degree of the corresponding temperature; wherein the size of the data-free point area around the temperature represents the area of a circle with any temperature in the cluster as the center point and the minimum value of the similarity between the center point and other temperatures as the radius, and there are no other temperatures in this area; using the exponential smoothing method to predict all the temperatures to obtain the predicted value corresponding to each temperature; calculating the standard deviation of all the temperatures; taking the ratio of the difference between the actual value and the predicted value of each temperature to the standard deviation as the predicted abnormality degree of the corresponding temperature; taking the predicted abnormality degree as a correction factor, and taking the product of the correction factor and the ratio of the size of the data-free point area around the temperature to the average distribution range as the final abnormality degree; Calculating the difference of all the temperatures, taking the difference as the error change rate, and taking the product of the abnormality degrees of two adjacent temperatures corresponding to the error change rate as the abnormality degree of the error change rate; Adjust the error change rate based on the degree of abnormality of the error change rate to obtain the adjusted error change rate, and the satisfied relational expression is: ; In the formula, represents the th weighted error change rate, represents the degree of abnormality of the th error change rate, represents the value of the th error change rate, represents the sum of the reciprocals of the th error change rate to the th error change rate, represents the weighted sum of the first error change rates of the th error change rate; Obtaining the current temperature inside the test chamber, calculating the adjusted error change rate corresponding to the current temperature, inputting the current temperature and the adjusted error change rate corresponding to the current temperature into the PID control algorithm, and controlling the temperature of the test chamber according to the output result of the PID control algorithm.

2. The temperature control method of a natural convection constant temperature test chamber according to claim 1, characterized in that, The output result of the PID control algorithm is: ; where, is the control signal, is the proportional coefficient, is the integral coefficient, is the differential coefficient, is the error between the current temperature and the set temperature of the test chamber, is the weighted error change rate corresponding to the current temperature.

3. The temperature control method of a natural convection constant temperature test chamber according to claim 2, characterized in that, Controlling the temperature of the test chamber according to the output result of the PID control algorithm includes: If the output result is positive, increasing the heating power of the test chamber; otherwise, decreasing the heating power of the test chamber.

4. The temperature control method of a natural convection constant temperature test chamber according to claim 1, characterized in that, The clustering algorithm is the K-Means clustering algorithm.

5. A temperature control system for a natural convection constant temperature test chamber, characterized in that, Including: A processor and a memory, the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the temperature control method of the natural convection constant temperature test chamber according to any one of claims 1-4 is implemented.

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