Remote monitoring method and system for temperature control test box

Abstract

The invention relates to the technical field of data processing, in particular to a remote monitoring method and system for a temperature control test box, and the method comprises the steps: processing a temperature sequence through an improved filtering algorithm, and transmitting the processed temperature sequence to a remote monitoring center; when two continuous temperatures are judged as extreme points of the same type, taking the two temperatures as a group of temperature pairs, and classifying the temperature pairs as a first temperature point set; removing the temperature with the minimum authenticity in each group of temperature pairs in the first temperature point set, and combining the residual temperature in the first temperature point set with other temperatures which are not classified as the first temperature point set into a second temperature point set, all extreme points in the second temperature point set are local maximum value points or local minimum value points which are finally determined in the temperature sequence. According to the invention, the improved filtering algorithm is used to process the temperature sequence, the filtered temperature sequence is smoother and more accurate, and unnecessary data transmission quantity is reduced.

Description

Technical Field

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

[0002] The temperature control test chamber is a kind of experimental equipment used for precise temperature control, which is applied in scientific research, industrial production and product quality inspection. Its core function is to adjust the temperature in the chamber through the heating and cooling system so that it can be stably maintained within the target temperature range set by the user, thereby meeting the needs of different fields for sample preservation, standard temperature maintenance and testing under specific environments. In addition, the temperature control test chamber also has a variety of functions, such as temperature programming, data recording and exporting, remote monitoring, etc. These functions make the temperature control test chamber more intelligent and convenient, greatly improving the experimental efficiency and data processing capabilities.

[0003] Prior art, such as a patent application document with 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, judging whether the current temperature is not lower than a preset maximum temperature threshold, if the current temperature is too high, reducing the operating frequency of the central processing unit to cool down, if the current temperature is too low, increasing the operating frequency of the central processing unit to heat up, after adjusting the operating frequency, collecting the temperature again, repeating the judgment process, and then recording a certain amount of temperature data, and adjusting the operating frequency again according to the changing trend of these temperatures.

[0004] However, the control method of the above processor does not take into account the existence of external influencing factors such as electromagnetic interference, power supply fluctuations, load conditions, etc., which causes the collected temperature data to deviate from the actual value and makes it impossible to perform effective temperature control. Summary of the invention

[0005] In order 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 comprises: 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 eliminated, 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. 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.

[0007] The present invention preliminarily identifies the local maximum and minimum points in the temperature sequence. When two consecutive temperatures are judged to be extreme points of the same type, they are treated as a group, and the temperature point with the least authenticity in each group is eliminated based on the calculation of authenticity, thereby reducing the abnormal values ​​caused by measurement errors or equipment instability. After eliminating the abnormal values, the remaining extreme points can better reflect the actual temperature changes. The temperature sequence after filtering is smoother and more accurate, reducing unnecessary data transmission. For the remote monitoring center, this means that data can be received and processed more efficiently, reducing the cost and delay of data transmission.

[0008] Preferably, 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.

[0009] By constructing the actual local range of the target temperature, the system can capture the subtle features of the temperature changes around the target temperature. By replacing the target temperature with an interpolation algorithm and constructing a reference local range, it is actually simulating a hypothetical temperature change scenario. By comparing it with the actual local range, the system can evaluate the impact of the target temperature on the overall temperature change pattern.

[0010] By calculating the change gap and fitting error of the two fitting curves, the negative exponential power of the product of the change gap and the fitting error is used 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 impact of smaller gaps and errors in the form of negative exponential powers, making the authenticity assessment more sensitive and accurate.

[0011] Preferably, the change difference 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.

[0012] The average of the absolute differences between the fitting curve of the actual local range and the fitting curve of the reference local range in the local range of the target temperature is calculated by integration, thereby quantifying the degree of difference between the two.

[0013] Preferably, 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.

[0014] Large fitting errors may mean that there are abnormal data or 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.

[0015] Preferably, 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.

[0016] 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 anomalies in some temperature points, the overall authenticity assessment will not be greatly affected.

[0017] Preferably, the filtering algorithm is a CWF 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.

[0018] Preferably, after the processed temperature sequence is transmitted to the remote monitoring center, when the monitoring center detects an abnormal situation, a remote control instruction is sent through the remote monitoring center to adjust the parameters of the temperature control test box.

[0019] In a second aspect, a remote monitoring system for a temperature-controlled test chamber includes: a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the above-mentioned remote monitoring method for the temperature-controlled test chamber is implemented.

[0020] The beneficial effects of the present invention are: 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. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] 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 accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-limiting manner, and the same or corresponding reference numerals represent the same or corresponding parts, wherein: Figure 1 It is a method flow chart of step S1 to step S2 in a remote monitoring method for a temperature control test box according to an embodiment of the present invention.

[0022] Figure 2It is a method flow chart of steps S20 to S22 in a remote monitoring method for a temperature control test box according to an embodiment of the present invention. DETAILED DESCRIPTION

[0023] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0024] The application scenario of the present invention is: using the improved CWF algorithm to filter the temperature data of the temperature control test box, so that the processed temperature data is more accurate.

[0025] The embodiment of the present invention discloses a remote monitoring method for a temperature control test box, referring to Figure 1 , including steps S1-S2, which are as follows: S1: Collect the temperature of the temperature-controlled test chamber and construct a temperature sequence in chronological order.

[0026] A temperature sensor is set inside the temperature control test box to collect temperature data in the temperature control test box in real time. The collection frequency can be set to 5 Hz, and then the collected temperature data is arranged in chronological order to construct a temperature sequence.

[0027] S2: Use the improved filtering algorithm to process the temperature sequence and transmit the processed temperature sequence to the remote monitoring center.

[0028] The Changing-weight Filer (CWF) is a filtering method that can effectively maintain the detailed features of time series. In temperature control tests, the temperature change trend is very important information. The CWF algorithm can maintain the overall change 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 the traditional fixed weight filtering method, the CWF algorithm can more accurately process the detailed features in the temperature data by dynamically adjusting the weights. This makes the CWF algorithm have higher accuracy and better effect in the temperature data filtering of the temperature control test chamber.

[0029] The basic steps of the CWF algorithm (since the CWF algorithm is an existing technology, the detailed steps will not be repeated) are as follows: The first step is to preprocess the NDVI time series data and identify the local maximum or minimum points in the data series; The second step is to generate new NDVI time series data using the three-point variable weight filtering method; Step 3: Replace the local maximum or minimum point; Step 4: Update NDVI value; Step 5: Iterate the termination condition and output the filtered NDVI time series data.

[0030] It should be considered that in the original CWF algorithm, when encountering two consecutive points that 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 no longer change these extreme points in the subsequent filtering process. However, if the point with a larger value is a noise point, then this noise point will be mistakenly retained as an extreme point, which will lead to poor filtering effect, that is, filtering or under-filtering. In order to solve this problem, an embodiment of the present invention improves the original CWF algorithm, including analyzing the changing characteristics of the temperature series to calculate the authenticity of the temperature data, and when encountering consecutive extreme points of the same type, determining the final local extreme point based on the authenticity of these points.

[0031] Specifically, refer to Figure 2 The process of improving the CMF algorithm includes steps S20 to S22.

[0032] S20: 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 group of temperature pairs and classified as a first temperature point set.

[0033] S21: Calculate the authenticity of each temperature in the first temperature point set.

[0034] During the temperature collection process, the temperature sensor is susceptible to various noise interferences in the surrounding environment, such as electromagnetic interference, power supply fluctuations and other factors. Due to the influence of noise, abnormal values ​​or mutation points appear in the collected temperature sequence, which appears discontinuous or inconsistent compared with the surrounding temperature data. These abnormal values ​​or mutation points break the original change trend of the temperature sequence in the local range, making the temperature data no longer look smooth or continuous.

[0035] In order to evaluate the authenticity of the temperature data, the difference between each temperature data and its surrounding temperature data is further analyzed.

[0036] Specifically, any temperature in the first temperature point set is selected as the target temperature, and a set number of temperatures are intercepted with the target temperature as the center (10 temperature data can be collected according to the empirical value) to construct the actual local range of the target temperature; the temperature value after the target temperature is replaced is obtained by using the cubic spline interpolation method, and a reference local range of the target temperature is constructed with the replaced temperature value as the center (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; a coordinate system is established with the time series number in the actual local range as the horizontal coordinate and the temperature value as the vertical coordinate, and the least squares method is used to obtain the fitting curve of the actual local range. Similarly, the fitting curve of the reference local range can be obtained; the change gap and fitting error of the two fitting curves are calculated; the negative exponential power of the product of the change gap and the fitting error is used as the authenticity of the target temperature.

[0037] Exemplarily, the authenticity of the target temperature satisfies the relationship:

[0038] In the formula, For the The authenticity of the target temperature, 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 error between the actual local range of the target temperature and the fitting curve of the reference local range, is an exponential function.

[0039] If the change gap between the two fitting curves is large, it means that the target temperature is very different from the ambient temperature; if the target temperature deviates from its local range, then when fitting the curve, the curve may move away from the abnormal point, and after fitting the reference local range obtained by interpolation, the fitting curve may be closer to the fitting curve of the actual local range. Therefore, the greater the change in the fitting error, the greater the change gap between the two local ranges, that is, the more destructive the current data point is.

[0040] Among them, the absolute interpolation of the two fitting curves on the number of temperature data contained in the local range is calculated, and the absolute difference is integrated on the temperature number (that is, the area under the absolute difference curve is calculated). The integral result is divided by the number of temperature data in the local range to obtain the average absolute error. The average absolute error is used as the change difference between the actual local range of the target temperature and the fitting curve of the reference local range, that is, the relationship is satisfied:

[0041] 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, is the number of temperature data within the local range of the target temperature (ie, the number of temperature data within the actual or reference local range).

[0042] The above change gap It can be used to measure the similarity between the fitting curve of the actual local range and the fitting curve of the reference local range. The smaller the value of , the closer the two fitting curves are and the better the fitting effect is.

[0043] For the temperature data within the actual local range, the average difference between all temperature values ​​on the corresponding fitting curve and the actual temperature value is calculated; similarly, the average difference between all temperature values ​​on the fitting curve corresponding to the reference local range and the actual temperature value is calculated, and the difference between the average difference corresponding to the actual local range and the average difference corresponding to the reference local range is taken as the fitting error of the fitting curve of the actual local range of the target temperature and the reference local range, that is, the relationship is satisfied:

[0044] 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.

[0045] In 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.

[0046] According to the above calculation The authenticity of the target temperature can be calculated by the same logic as the authenticity of all temperatures in the first temperature point set.

[0047] In addition, it should be noted that no matter 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.

[0048] In order to further improve the calculation reliability of the authenticity of the temperature in the first temperature point set, firstly, all other extreme points that are not classified as the first temperature point set are classified as the third temperature point set, and the similarity between the first temperature point set and the third temperature point set in shape or change trend is analyzed, that is, the similarity between the corresponding temperature data in the first temperature point set and the third temperature point set in the actual local range is analyzed, and the authenticity of the temperature calculated above is corrected using the similarity.

[0049] For example, the above For example, the target temperature The authenticity of the target temperature after correction satisfies the relationship:

[0050] 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 contained in the third temperature point set. The actual local range of the temperature is The construction method of the actual local range corresponding to each target temperature is the same and will not be repeated here.

[0051] It reflects the average structural similarity between the target temperature and the third temperature point set. The higher the value, the higher the authenticity of the target temperature.

[0052] S22: Eliminate the temperature with the smallest authenticity in each temperature pair in the first temperature point set, and combine the remaining temperatures in the first temperature point set with other temperatures that are not classified into the first temperature point set into a second temperature point set, and all extreme value points in the second temperature point set are the local maximum value points or local minimum value points finally determined in the temperature sequence.

[0053] After eliminating the temperature data with less authenticity, the influence of noise and outliers on the identification of extreme points can be reduced. The remaining temperatures in the first temperature point set are combined with other temperatures that are not classified into the first temperature point set into a second temperature point set. All extreme points in the second temperature point set are the local maximum points or local minimum points finally determined in the temperature sequence.

[0054] The second temperature point set finally determined is the extreme point used for the CWF algorithm.

[0055] At this point, the improvement of the CWF algorithm has been completed.

[0056] The improved CWF algorithm is used to filter the temperature control test chamber data. During the operation of the algorithm, when the maximum error between the temperature sequences obtained by two adjacent iterations is less than a preset threshold (based on experience, the threshold is set to 0.36), the iteration is stopped.

[0057] The filtered temperature data is transmitted to the remote monitoring center through the communication network. After receiving the data, the remote monitoring center displays and stores it in real time. Users can view the operating status and data of the test chamber in real time through the interface of the monitoring center. When an abnormality is found or the test chamber parameters need to be adjusted, operations such as stopping the test in the test chamber, adjusting the power settings or calibrating the sensor can be performed.

[0058] An embodiment of the present invention further discloses a remote monitoring system for a temperature-controlled test box, including a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, a remote monitoring method for a temperature-controlled test box according to the present invention is implemented.

[0059] The system also includes other components familiar to those skilled in the art, such as a communication bus and a communication interface. The configuration and functions of these components are known in the art and will not be described in detail here.

[0060] In the present invention, the aforementioned memory may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, apparatus or device. For example, a computer-readable storage medium may be any appropriate magnetic storage medium or magneto-optical storage medium, such as a resistive random access memory RRAM (Resistive Random Access Memory), a dynamic random access memory DRAM (Dynamic Random Access Memory), a static random access memory SRAM (Static Random-Access Memory), an enhanced dynamic random access memory EDRAM (Enhanced Dynamic Random Access Memory), a high-bandwidth memory HBM (High-Bandwidth Memory), a hybrid memory cube HMC (Hybrid Memory Cube), 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 may be part of a device or accessible or connectable to a device.

[0061] In the description of this specification, "plurality" or "several" means at least two, such as two, three or more, etc., unless otherwise clearly and specifically defined.

[0062] Although this specification has shown and described a number of embodiments of the present invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Those skilled in the art will conceive of many modifications, changes and alternatives without departing from the ideas and spirit of the present invention. It should be understood that in the practice of the present invention, various alternatives to the embodiments of the present invention described herein may be employed.

Claims

1. A remote monitoring method for a temperature control test box, 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 truth 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 eliminated, 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. 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.

2. A remote monitoring method for a temperature control test box according to claim 1, characterized in that: 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.

3. A remote monitoring method for a temperature control test box according to claim 2, 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.

4. A remote monitoring method for a temperature control test box according to claim 3, 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.

5. A remote monitoring method for a temperature control test box according to claim 4, 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.

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

7. 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.

8. 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 7 is implemented.

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