A remote monitoring method and system for a temperature control test chamber

Abstract

The present invention relates to the technical field of data processing, and particularly relates to a remote monitoring method and system for a temperature control test chamber. The method includes: processing a temperature sequence by using an improved filtering algorithm, and transmitting the processed temperature sequence to a remote monitoring center; when two consecutive temperatures are determined to be extreme points of the same type, taking these two temperatures as a pair of temperatures and classifying them into a first temperature point set; removing the temperature with the smallest authenticity in each pair of temperatures in the first temperature point set, and combining the remaining temperatures in the first temperature point set with other temperatures that are not classified into the first temperature point set to form a second temperature point set. All the extreme points in the second temperature point set are the finally determined local maximum points or local minimum points in the temperature sequence. The present invention uses the improved filtering algorithm to process the temperature sequence. The temperature sequence after filtering processing is smoother and more accurate, reducing the unnecessary data transmission volume.

Description

Technical Field

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

[0002] A temperature control test chamber is an experimental device for precisely controlling temperature, and is applied to fields such as scientific research, industrial production, and product quality inspection. Its core function is to adjust the temperature inside the chamber through a heating and cooling system so that it can stably maintain within the target temperature range set by the user, thereby meeting the needs of sample preservation, standard temperature maintenance, and testing under specific environments in different fields. In addition, the temperature control test chamber also has various functions, such as temperature programming, data recording and export, remote monitoring, etc. These functions make the temperature control test chamber more intelligent and convenient, greatly improving the experimental efficiency and data processing ability.

[0003] The prior art, such as the patent application document with the publication number CN115809182A, discloses a temperature control method, device, and electronic device for a computer central processing unit. The temperature control method of the processor includes: first collecting the current temperature of the central processing unit, determining whether the current temperature is not lower than a preset maximum temperature threshold. If the current temperature is too high, then reducing the operating frequency of the central processing unit to cool down. If the current temperature is too low, then increasing the operating frequency of the central processing unit to heat up. After adjusting the operating frequency, collect the temperature again and repeat the determination process, and then record a certain number of temperature data and adjust the operating frequency again according to the change trend of these temperatures.

[0004] However, the above control method of the processor does not consider the existence of external influencing factors such as electromagnetic interference, power supply fluctuations, and load conditions, resulting in the collected temperature data deviating from the actual value and unable to perform effective temperature control. Summary of the Invention

[0005] To solve the technical problem that the collected temperature data deviates from the actual value and effective temperature control cannot be performed, the present invention provides solutions in the following aspects.

[0006] In a first aspect, a remote monitoring method for a temperature control test chamber includes:

[0007] Collect the temperature of the temperature control test chamber and construct a temperature sequence arranged in chronological order;

[0008] Process the temperature sequence using an improved filtering algorithm and transmit the processed temperature sequence to a remote monitoring center;

[0009] The improvement process includes:

[0010] Preliminarily identify local maximum points or local minimum points in the temperature sequence. When two consecutive temperatures are judged to be extreme points of the same type, these two temperatures are taken as a pair of temperatures and grouped into the first temperature point set.

[0011] Calculate the authenticity of each temperature in the first temperature point set.

[0012] Eliminate the temperature with the minimum authenticity in each pair of temperatures in the first temperature point set. Combine the remaining temperatures in the first temperature point set with other temperatures that have not been grouped into the first temperature point set to form the second temperature point set. All the extreme points in the second temperature point set are the finally determined local maximum points or local minimum points in the temperature sequence.

[0013] The present invention preliminarily identifies local maximum points and local minimum points in the temperature sequence. When two consecutive temperatures are judged to be extreme points of the same type, they are processed as a group. Based on the calculation of authenticity, the temperature point with the minimum authenticity in each group is eliminated, thereby reducing outliers caused by measurement errors or equipment instability. After eliminating the outliers, the remaining extreme points can better reflect the true temperature change. The temperature sequence after filtering processing is smoother and more accurate, reducing unnecessary data transmission volume. For the remote monitoring center, this means being able to receive and process data more efficiently, reducing the cost and delay of data transmission.

[0014] Preferably, the process of obtaining the authenticity includes:

[0015] Select any temperature in the first temperature point set as the target temperature. Intercept a set number of temperatures centered on the target temperature to construct the actual local range of the target temperature. Use the interpolation algorithm to obtain the temperature value after replacing the target temperature, and construct the reference local range of the target temperature centered on the replaced temperature value.

[0016] Respectively obtain the fitting curves of the actual local range and the reference local range. Calculate the change gap and fitting error between the two fitting curves.

[0017] Take the negative exponential power of the product of the change gap and the fitting error as the authenticity of the target temperature.

[0018] By constructing the actual local range of the target temperature, the system can capture the subtle features of the temperature change around the target temperature. Using the interpolation algorithm to replace the target temperature and then constructing the reference local range is actually simulating a hypothetical temperature change scenario. By comparing with the actual local range, the system can evaluate the impact degree of the target temperature on the overall temperature change pattern.

[0019] By calculating the change gap and fitting error of two fitting curves, taking the negative exponential power of the product of the change gap and the fitting error as the authenticity of the target temperature, this calculation method combines the information of the change gap and the fitting error, and at the same time amplifies the influence of smaller gaps and errors in the form of negative exponential power, making the authenticity evaluation more sensitive and accurate.

[0020] Preferably, the change gap between the two fitting curves satisfies the relational expression:

[0021] ; where is the change gap between the fitting curves of the actual local range and the reference local range of the th target temperature, is the fitting curve of the actual local range of the th target temperature, is the fitting curve of the reference local range of the th target temperature, is the number of temperature data within the local range of the target temperature.

[0022] By integrating, the average value of the absolute difference between the fitting curve of the actual local range and the fitting curve of the reference local range within the local range of the target temperature is calculated, thereby quantifying the degree of difference between the two.

[0023] Preferably, the fitting error between the two fitting curves satisfies the relational expression:

[0024] ; where is the fitting error between the fitting curves of the actual local range and the reference local range of the th target temperature, is the value corresponding to the th temperature value on the fitting curve within the actual local range of the th target temperature, is the actual value of the th temperature within the actual local range of the th target temperature, is the value corresponding to the th temperature value on the fitting curve within the reference local range of the th target temperature, is the actual value of the th temperature within the reference local range of the th target temperature, is the number of temperature data within the local range of the target temperature.

[0025] A large fitting error may indicate the existence of abnormal data or that the fitting curve fails to capture the characteristics of the temperature data well, which helps to identify and process abnormal data and improve data quality.

[0026] Preferably, after obtaining the authenticity of the temperature, it further includes correcting the authenticity of the temperature, and the corrected authenticity satisfies the relational expression:

[0027] ; In the formula, is the authenticity of the th target temperature after correction, is the authenticity of the th target temperature, is the Pearson correlation coefficient between the th temperature in the third temperature point set and the actual local range corresponding to the th target temperature, is the total number of temperatures included in the third temperature point set; the temperature data in the third temperature point set are all other extreme points that are not classified into the first temperature point set.

[0028] The correction process takes into account the correlation between multiple temperature points and the local range of the target temperature, thereby enhancing the robustness of the authenticity assessment. Even if there are errors or abnormalities in some temperature points, the overall authenticity assessment will not be overly affected.

[0029] Preferably, the filtering algorithm is the CWF algorithm, and when the maximum error between the temperature sequences obtained in two adjacent iterations is less than a preset threshold, the iteration stops.

[0030] Preferably, after transmitting the processed temperature sequence to the remote monitoring center, when the monitoring center detects an abnormal situation, a remote control instruction is issued through the remote monitoring center to adjust the parameters of the temperature control test chamber.

[0031] In a second aspect, a remote monitoring system for a temperature control test chamber includes: a processor and a memory, and the memory stores computer program instructions, which implement the above-mentioned remote monitoring method for a temperature control test chamber when the computer program instructions are executed by the processor.

[0032] The beneficial effects of the present invention are:

[0033] By collecting the temperature data of the temperature control test chamber and constructing a temperature sequence, and then using an improved filtering algorithm to process the temperature sequence, abnormal temperature data can be more effectively identified and eliminated, thereby improving the accuracy of temperature monitoring. Description of the Drawings

[0034] 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 in an exemplary rather than restrictive manner, and the same or corresponding reference numerals represent the same or corresponding parts, wherein:

[0035] Figure 1 It is a flowchart of the method from step S1 to step S2 in a remote monitoring method for a temperature control test chamber according to an embodiment of the present invention.

[0036] Figure 2 It is a flowchart of the method from step S20 to step S22 in a remote monitoring method for a temperature control test chamber according to an embodiment of the present invention. Detailed Embodiments

[0037] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with 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. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative efforts fall within the scope of protection of the present invention.

[0038] The application scenario of the present invention is: using an improved CWF algorithm to filter the temperature data of the temperature control test chamber, making the processed temperature data more accurate.

[0039] An embodiment of the present invention discloses a remote monitoring method for a temperature control test chamber. Referring to Figure 1 , it includes steps S1 to S2, specifically as follows:

[0040] S1: Collect the temperature of the temperature control test chamber and construct a temperature sequence arranged in chronological order.

[0041] A temperature sensor is set inside the temperature control test chamber to collect the temperature data inside the temperature control test chamber in real time. The collection frequency can be set to 5 Hz. Then, the collected temperature data is arranged in chronological order to construct a temperature sequence.

[0042] S2: Process the temperature sequence using an improved filtering algorithm and transmit the processed temperature sequence to the remote monitoring center.

[0043] The Changing-weight Filer (CWF) algorithm is a filtering method that can effectively preserve the detailed features of time series. In temperature control experiments, the changing trend of temperature is very important information. The CWF algorithm can maintain the overall changing trend of temperature data during the filtering process, while removing unnecessary noise and fluctuations, making the temperature data smoother and easier to analyze. Compared with traditional fixed-weight filtering methods, the CWF algorithm can process the detailed features in temperature data more precisely by dynamically adjusting the weights. This makes the CWF algorithm have higher accuracy and better effects in filtering the temperature data of a temperature control test chamber.

[0044] Among them, the basic steps of the CWF algorithm (since the CWF algorithm is prior art, the detailed steps will not be elaborated here) are as follows:

[0045] Step 1: Preprocess the NDVI time series data to identify the local maximum or minimum points in the data series.

[0046] Step 2: Generate new NDVI time series data using the 3-point changing-weight filtering method.

[0047] Step 3: Replace the local maximum or minimum points.

[0048] Step 4: Update the NDVI value.

[0049] Step 5: Iteration termination condition, output the filtered NDVI time series data.

[0050] It should be noted that in the original CWF algorithm, when two consecutive points are judged to be extreme points of the same type (such as two local maxima or two local minima), the algorithm will directly select the point with a larger (or smaller) value as the extreme point and will not change these extreme points during the subsequent filtering process. However, if the point with a larger value is a noise point, then this noise point will be wrongly retained as an extreme point, resulting in poor filtering effects, that is, over-filtering or under-filtering occurs. To solve this problem, the embodiments of the present invention improve the original CWF algorithm by analyzing the changing characteristics of the temperature sequence to calculate the authenticity of temperature data, and when encountering consecutive extreme points of the same type, determining the final local extreme points based on the authenticity of these points.

[0051] Specifically, referring to Figure 2 , the process of improving the CMF algorithm includes Step S20 - Step S22.

[0052] S20: Initially identify the local maximum points or local minimum points in the temperature sequence. When two consecutive temperatures are judged to be extreme points of the same type, these two temperatures are taken as a pair of temperature points and classified into the first temperature point set.

[0053] S21: Calculate the authenticity of each temperature in the first temperature point set.

[0054] During the temperature acquisition process, the temperature sensor is vulnerable to various noise interferences in the surrounding environment, such as electromagnetic interference, power fluctuations and other factors. Due to the influence of noise, abnormal values or mutation points appear in the acquired temperature sequence. Compared with the surrounding temperature data, they appear discontinuous or inconsistent, and these abnormal values or mutation points break the original change trend of the temperature sequence within a local range, making the temperature data no longer look smooth or continuous.

[0055] To evaluate the authenticity of the temperature data, further analyze the degree of difference between each temperature data and its surrounding temperature data.

[0056] Specifically, select any one temperature in the first temperature point set as the target temperature, and intercept a set number of temperatures centered on the target temperature (10 temperature data can be collected according to empirical values) to construct the actual local range of the target temperature; use the cubic spline interpolation method to obtain the temperature value after replacing the target temperature, and construct the reference local range of the target temperature centered on the replaced temperature value (the number of temperature data in the reference local range is the same as that in the actual local range). It should be noted that the edge data points in the sequence are discarded; take the time series number in the actual local range as the abscissa and the temperature value as the ordinate to establish a coordinate system, and use the least squares method to obtain the fitting curve of the actual local range. Similarly, the fitting curve of the reference local range can be obtained; calculate the change gap and fitting error of the two fitting curves; take the negative exponential power of the product of the change gap and the fitting error as the authenticity of the target temperature.

[0057] Exemplarily, the authenticity of the target temperature satisfies the relational expression:

[0058]

[0059] In the formula, is the authenticity of the th target temperature, is the change gap between the fitting curves of the actual local range and the reference local range of the th target temperature, is the fitting error between the fitting curves of the actual local range and the reference local range of the th target temperature, is an exponential function.

[0060] If the variation gap between two fitted curves is large, it indicates that there is a significant difference between the target temperature and the surrounding temperature; if the target temperature deviates from its local range, then during curve fitting, this curve may move away from this abnormal point, and after fitting the reference local range obtained by interpolation, the fitted curve may be closer to the fitted curve of the actual local range. Therefore, the greater the change in the fitting error, the greater the variation gap between the two local ranges, that is, the greater the destructiveness of the current data point.

[0061] Among them, calculate the absolute interpolation of the temperature data quantities included in the local ranges of the two fitted curves, integrate this absolute difference with respect to the temperature quantity (that is, find the area under the absolute difference curve), divide the integration result by the temperature data quantity within the local range to obtain the mean absolute error, and take this mean absolute error as the variation gap between the fitted curves of the actual local range and the reference local range of the target temperature, that is, the relationship is satisfied as:

[0062]

[0063] In the formula, is the variation gap between the fitted curves of the actual local range and the reference local range of the th target temperature, is the fitted curve of the actual local range of the th target temperature, is the fitted curve of the reference local range of the th target temperature, is the temperature data quantity within the local range of the target temperature (that is, the temperature data quantity within the actual or reference local range).

[0064] The above variation gap can be used to measure the similarity degree between the fitted curve of the actual local range and the fitted curve of the reference local range. The smaller the

[0065] value, the closer the two fitted curves are and the better the fitting effect. For the temperature data within the actual local range, calculate the average difference between all the temperature values on the corresponding fitted curve and the actual temperature values; similarly, calculate the average difference between all the temperature values on the fitted curve corresponding to the reference local range and the actual temperature values, and take the difference between the average difference corresponding to the actual local range and the average difference corresponding to the reference local range as the fitting error between the fitted curves of the actual local range and the reference local range of the target temperature, that is, the relationship is satisfied as:

[0066]

[0067] In the formula, is the The fitting error of the fitting curve between the actual local range and the reference local range of a target temperature is the value corresponding to the th temperature value on the fitting curve within the actual local range of the th target temperature, and is the actual value of the th temperature within the actual local range of the th target temperature; is the value corresponding to the th temperature value on the fitting curve within the reference local range of the th target temperature, and is the actual value of the th temperature within the reference local range of the target temperature;

[0068] Within the actual local range and the reference local range, the smaller the average difference between the fitting curve and the actual data, the smaller the corresponding fitting error.

[0069] According to the above calculation of the authenticity of the th target temperature, the authenticity of all temperatures in the first temperature point set can be calculated in the same way.

[0070] In addition, it should be noted that whether the extreme points are caused by temperature increase or decrease, the temperature change trends they represent are essentially similar and are caused by the same temperature control mechanism.

[0071] To further improve the calculation reliability of the authenticity of the temperatures in the first temperature point set, first classify all other extreme points that are not classified into the first temperature point set into the third temperature point set, analyze the similarity in shape or change trend between the first temperature point set and the third temperature point set, that is, analyze the similarity of the corresponding temperature data in the actual local range between the first temperature point set and the third temperature point set, and use this similarity to correct the authenticity of the temperature obtained from the above calculation.

[0072] Exemplarily, still taking the th target temperature as an example above, the corrected authenticity of the th target temperature satisfies the relational expression:

[0073]

[0074] In the formula, is the corrected authenticity of the th target temperature, is the authenticity of the th target temperature, is the Pearson correlation coefficient between the th temperature in the third temperature point set and the actual local range corresponding to the th target temperature. is the total number of temperatures included in the third temperature point set. Among them, the construction method of the actual local range of the th temperature in the third temperature point set is the same as that of the actual local range corresponding to the th target temperature, which will not be elaborated here.

[0075] reflects the average structural similarity between the target temperature and the third temperature point set. The higher its value, the higher the authenticity of the target temperature.

[0076] S22: Remove the temperature with the minimum authenticity in each group of temperature pairs in the first temperature point set, and combine the remaining temperatures in the first temperature point set with other temperatures not classified into the first temperature point set to form a second temperature point set. All extreme points in the second temperature point set are the finally determined local maximum points or local minimum points in the temperature sequence.

[0077] After removing the temperature data with relatively low authenticity, the influence of noise and outliers on the identification of extreme points can be reduced. Combine the remaining temperatures in the first temperature point set with other temperatures not classified into the first temperature point set to form a second temperature point set. All extreme points in the second temperature point set are the finally determined local maximum points or local minimum points in the temperature sequence.

[0078] The finally determined second temperature point set is the extreme points for the CWF algorithm.

[0079] So far, the improvement of the CWF algorithm has been completed.

[0080] Use the improved CWF algorithm to filter the data of the temperature control test chamber. During the operation of the algorithm, when the maximum error between the temperature sequences obtained from two adjacent iterations is less than a preset threshold (according to experience, this threshold is set to 0.36), stop the iteration.

[0081] Transmit the temperature data after filtering to the remote monitoring center through the communication network. After receiving the data, the remote monitoring center performs real-time display and storage. Users can view the operation status and data of the test chamber in real time through the interface of the monitoring center. When abnormalities are found or the parameters of the test chamber need to be adjusted, operations such as stopping the test in the test chamber, adjusting the power supply settings, or calibrating the sensors are performed.

[0082] An embodiment of the present invention also discloses a remote monitoring system for a temperature control test chamber, which includes a processor and a memory. The memory stores computer program instructions, and when the computer program instructions are executed by the processor, the remote monitoring method for the temperature control test chamber according to the present invention is implemented.

[0083] The system further includes other components well-known to those skilled in the art, such as a communication bus and a communication interface, and their settings and functions are known in the art, so they will not be described herein again.

[0084] 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 device. For example, a computer-readable storage medium can be any suitable magnetic storage medium or magneto-optical storage medium, such as, for example, resistive random access memory (RRAM), dynamic random access memory (DRAM), static random access memory (SRAM), enhanced dynamic random access memory (EDRAM), high-bandwidth memory (HBM), 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.

[0085] In the description of this specification, the meanings of "a plurality" and "several" are at least two, such as two, three, or more, etc., unless otherwise specifically and clearly defined.

[0086] 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 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 process of practicing the present invention.

Claims

1. A remote monitoring method for a temperature control test chamber, characterized in that: include: Collect the temperature of the temperature-controlled test chamber and construct a temperature sequence in chronological order; The temperature sequence is processed by using an improved filtering algorithm and the processed temperature sequence is transmitted to a remote monitoring center; The improved process includes: Preliminarily identifying a local maximum point or a local minimum point in the temperature sequence, wherein when two consecutive temperatures are judged to be extreme points of the same type, the two temperatures are regarded as a set of temperature pairs and classified as a first temperature point set; Calculate the authenticity of each temperature in the first temperature point set; The temperature with the smallest authenticity in each temperature pair in the first temperature point set is removed, and the remaining temperatures in the first temperature point set are combined with other temperatures that are not classified into the first temperature point set to form a second temperature point set, and all extreme value points in the second temperature point set are the local maximum points or local minimum points finally determined in the temperature sequence; The authenticity acquisition process includes: Select any temperature in the first temperature point set as the target temperature, intercept a set number of temperatures with the target temperature as the center to construct the actual local range of the target temperature; use the interpolation algorithm to obtain the temperature value after the target temperature is replaced, and construct the reference local range of the target temperature with the replaced temperature value as the center; Obtaining fitting curves of the actual local range and the reference local range respectively; calculating the change gap and fitting error of the two fitting curves; The negative exponential power of the product of the variation gap and the fitting error is taken as the authenticity of the target temperature.

2. A remote monitoring method for a temperature control test box according to claim 1, characterized in that: The change gap between the two fitting curves satisfies the relationship: ; In the formula, For the The difference between the actual local range of the target temperature and the fitting curve of the reference local range, For the The fitting curve of the actual local range of the target temperature, For the The fitting curve of the reference local range of the target temperature, The number of temperature data within the local range of the target temperature.

3. A remote monitoring method for a temperature control test box according to claim 2, characterized in that: The fitting errors of the two fitting curves satisfy the relationship: ; In the formula, For the The fitting error between the actual local range of the target temperature and the fitting curve of the reference local range, For the The actual local range of the target temperature The value corresponding to the temperature value on the fitting curve is, For the The actual local range of the target temperature The actual value of the temperature, For the The reference local range of the target temperature The value corresponding to the temperature value on the fitting curve is, For the The reference local range of the target temperature The actual value of the temperature, The number of temperature data within the local range of the target temperature.

4. A remote monitoring method for a temperature control test box according to claim 3, characterized in that: After obtaining the authenticity of the temperature, the authenticity of the temperature is corrected, and the corrected authenticity satisfies the relationship: ; In the formula, For the The corrected authenticity of the target temperature, For the The authenticity of the target temperature, The third temperature point is concentrated The temperature and The Pearson correlation coefficient of the actual local range corresponding to the target temperature, is the total number of temperatures included in the third temperature point set; the temperature data in the third temperature point set are all other extreme value points that are not included in the first temperature point set.

5. A remote monitoring method for a temperature control test box according to claim 1, characterized in that: The filtering algorithm is a variable weight filtering algorithm, and the iteration is stopped when the maximum error between the temperature sequences obtained by two adjacent iterations is less than a preset threshold.

6. A remote monitoring method for a temperature control test box according to claim 1, characterized in that: After the processed temperature sequence is transmitted to the remote monitoring center, when the monitoring center detects an abnormal situation, a remote control command is issued through the remote monitoring center to adjust the parameters of the temperature control test chamber.

7. A remote monitoring system for a temperature control test chamber, 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, the remote monitoring method for a temperature control test box according to any one of claims 1 to 6 is implemented.

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