CN119861772A - Remote monitoring method and system for temperature control test box - Google Patents
Remote monitoring method and system for temperature control test box Download PDFInfo
- Publication number
- CN119861772A CN119861772A CN202510346030.6A CN202510346030A CN119861772A CN 119861772 A CN119861772 A CN 119861772A CN 202510346030 A CN202510346030 A CN 202510346030A CN 119861772 A CN119861772 A CN 119861772A
- Authority
- CN
- China
- Prior art keywords
- temperature
- point set
- local range
- remote monitoring
- authenticity
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D23/00—Control of temperature
- G05D23/19—Control of temperature characterised by the use of electric means
- G05D23/30—Automatic controllers with an auxiliary heating device affecting the sensing element, e.g. for anticipating change of temperature
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Automation & Control Theory (AREA)
- Control Of Temperature (AREA)
Abstract
本发明涉及数据处理技术领域，具体涉及一种用于温控试验箱的远程监控方法及系统，方法包括：利用改进的滤波算法对温度序列进行处理，将处理后的温度序列传输至远程监控中心；当连续两个温度被判断为相同类型的极值点时，将这两个温度作为一组温度对，并归为第一温度点集；将第一温度点集中每组温度对中真实性最小的温度剔除，将第一温度点集中剩余的温度与未被归为第一温度点集的其他温度组合为第二温度点集，第二温度点集中的所有极值点即为温度序列中最终确定的局部最大值点或局部最小值点。本发明使用改进后的滤波算法对温度序列进行处理，经过滤波处理后的温度序列更加平滑、准确，减少了不必要的数据传输量。
The present invention relates to the field of data processing technology, and in particular to a remote monitoring method and system for a temperature control test box, the method comprising: using an improved filtering algorithm to process a temperature sequence, and transmitting the processed temperature sequence to a remote monitoring center; when two consecutive temperatures are judged to be extreme points of the same type, the two temperatures are taken as a group of temperature pairs, and classified as a first temperature point set; the temperature with the least authenticity in each group of temperature pairs 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 as the first temperature point set into a second temperature point set, and all extreme points in the second temperature point set are the local maximum points or local minimum points finally determined in the temperature sequence. The present invention uses an improved filtering algorithm to process the temperature sequence, and the temperature sequence after filtering is smoother and more accurate, reducing unnecessary data transmission.
Description
Technical Field
The invention relates to the technical field of data processing. More particularly, the invention relates to a remote monitoring method and system for a temperature controlled test chamber.
Background
The temperature control test box is experimental equipment for accurately controlling temperature, and is applied to the fields of scientific research, industrial production, product quality detection and the like. The core function of the device is that the temperature in the box is regulated through the heating and refrigerating system, so that the device can be stably kept in a target temperature range set by a user, and the requirements of different fields on sample preservation, standard temperature maintenance and testing under specific environments are met. In addition, the temperature control test box also has multiple functions such as temperature programming, data recording and exporting, remote monitoring and the like, and the functions enable the temperature control test box to be more intelligent and convenient, so that the experimental efficiency and the data processing capacity are greatly improved.
The prior art discloses a temperature control method, a device and an electronic device of a central processing unit of a computer, for example, a patent application document with publication number CN115809182A, wherein the temperature control method of the processor comprises the steps of firstly collecting the current temperature of the central processing unit, judging whether the current temperature is not lower than a preset maximum temperature threshold, reducing the operation frequency of the central processing unit to reduce the temperature if the current temperature is too high, increasing the operation frequency of the central processing unit to raise the temperature if the current temperature is too low, adjusting the operation frequency, collecting the temperature again, repeating the judging process, recording a certain amount of temperature data, and adjusting the operation frequency again according to the change trend of the temperatures.
However, in the control method of the processor, the existence of external influencing factors such as electromagnetic interference, power supply fluctuation, load condition and the like is not considered, so that the acquired temperature data deviate from an actual value, and effective temperature control cannot be performed.
Disclosure of Invention
In order to solve the technical problem that the collected temperature data deviate from the actual value and cannot be effectively controlled, the invention provides the following aspects.
In a first aspect, a remote monitoring method for a temperature controlled test chamber includes:
collecting the temperature of a temperature control test box, and arranging and constructing a temperature sequence according to a time sequence;
Processing the temperature sequence by utilizing an improved filtering algorithm, and transmitting the processed temperature sequence to a remote monitoring center;
the improved process comprises the following steps:
Initially identifying local maximum points or local minimum points in the temperature sequence, wherein when two continuous temperatures are judged to be extreme points of the same type, the two temperatures are taken as a group of temperature pairs and are classified into a first temperature point set;
calculating the authenticity of each temperature in the first temperature point set;
and eliminating the temperature with the minimum authenticity in each group of temperature pairs in the first temperature point set, combining the rest temperature in the first temperature point set and other temperatures which are not classified into the first temperature point set into a second temperature point set, wherein all extreme points in the second temperature point set are local maximum points or local minimum points finally determined in a temperature sequence.
According to the method, the local maximum value point and the local minimum value point in the temperature sequence are initially identified, when two continuous temperatures are judged to be extreme points of the same type, the two extreme points are treated as a group, the temperature point with the minimum authenticity in each group is removed based on calculation of the authenticity, so that abnormal values caused by measurement errors or equipment instability are reduced, after the abnormal values are removed, the residual extreme points can reflect real temperature changes, the temperature sequence after filtering treatment is smoother and more accurate, and unnecessary data transmission quantity is reduced. This means that for a remote monitoring center, data can be received and processed more efficiently, reducing the cost and delay of data transmission.
Preferably, the obtaining of the authenticity includes:
selecting any one temperature in the first temperature point set as a target temperature, and taking the target temperature as a center to intercept a set number of temperatures to construct an actual local range of the target temperature;
Respectively obtaining fitting curves of an actual local range and a reference local range; calculating the variation gap and fitting error of the two fitting curves;
And taking the negative exponent power of the product of the variation gap and the fitting error as the authenticity of the target temperature.
By constructing the actual local range of the target temperature, the system can capture the subtle characteristics of the temperature change around the target temperature, and by utilizing an interpolation algorithm to replace the target temperature, a reference local range is constructed, and the system can evaluate the influence degree of the target temperature on the whole temperature change mode by comparing the actual local range with a hypothetical temperature change scene.
The method combines the information of the variation difference and the fitting error, and simultaneously amplifies the influence of smaller difference and error in the form of the negative exponent, so that the authenticity assessment is more sensitive and accurate.
Preferably, the difference between the two fitted curves satisfies the relation:
In the formula (I), in the formula (II), Is the firstThe variation gap of the fitting curve of the actual local range of the individual target temperatures from the reference local range,Is the firstFitting curves for the actual local ranges of the individual target temperatures,Is the firstFitting curves of the reference local ranges of the individual target temperatures,Is the amount of temperature data within a local range of the target temperature.
And calculating the average value of the absolute difference value of the fitting curve of the actual local range and the fitting curve of the reference local range in the target temperature local range through integration, thereby quantifying the difference degree between the two.
Preferably, the fitting error of the two fitting curves satisfies the relation:
In the formula (I), in the formula (II), Is the firstFitting errors of the fitting curves of the actual local ranges of the individual target temperatures and the reference local ranges,Is the firstWithin the actual local range of the target temperatureThe corresponding values of the individual temperature values on the fitted curve,Is the firstWithin the actual local range of the target temperatureThe actual value of the respective temperature is calculated,Is the firstWithin a reference local range of the target temperatureThe corresponding values of the individual temperature values on the fitted curve,Is the firstWithin a reference local range of the target temperatureThe actual value of the respective temperature is calculated,Is the amount of temperature data within a local range of the target temperature.
The fact that the fitting error is large may mean that abnormal data exist or that the fitting curve cannot capture the characteristics of the temperature data well, so that the abnormal data can be recognized and processed, and the data quality is improved.
Preferably, after obtaining the authenticity of the temperature, further comprising correcting the authenticity of the temperature, wherein the corrected authenticity satisfies the relation:
In the formula (I), in the formula (II), Is the firstThe authenticity of the corrected individual target temperatures,Is the firstThe authenticity of the individual target temperatures is determined,Concentration of the third temperature pointTemperature and the firstPearson correlation coefficients for the actual local ranges corresponding to the individual target temperatures,The total number of the temperatures contained in the third temperature point set is the total number of the temperatures contained in the third temperature point set, and the temperature data in the third temperature point set are all other extreme points which are not classified into the first temperature point set.
The correction process considers the correlation between a plurality of temperature points and the target temperature local range, so that the robustness of the authenticity assessment is enhanced, and even if errors or anomalies exist in some temperature points, the whole authenticity assessment is not greatly influenced.
Preferably, the filtering algorithm is a CWF algorithm, and when the maximum error between the temperature sequences obtained by two adjacent iterations is smaller than a preset threshold value, the iteration is stopped.
Preferably, after the processed temperature sequence is transmitted to the remote monitoring center, when the monitoring center monitors abnormal conditions, the remote monitoring center issues a remote control instruction to adjust parameters of the temperature control test box.
In a second aspect, a remote monitoring system for a temperature controlled test chamber includes a processor and a memory storing computer program instructions that when executed by the processor implement the remote monitoring method for a temperature controlled test chamber described above.
The beneficial effects of the invention are as follows:
The temperature data of the temperature control test box are collected, the temperature sequence is constructed, and the temperature sequence is processed by utilizing an improved filtering algorithm, so that abnormal temperature data can be more effectively identified and removed, and the accuracy of temperature monitoring is improved.
Drawings
The above, as well as additional purposes, features, and advantages of exemplary embodiments of the present invention will become readily apparent from the following detailed description when read in conjunction with the accompanying drawings. In the drawings, embodiments of the invention are illustrated by way of example and not by way of limitation, and like reference numerals refer to similar or corresponding parts and in which:
FIG. 1 is a flow chart of a method for steps S1-S2 in a remote monitoring method for a temperature controlled test chamber according to an embodiment of the present invention.
Fig. 2 is a flowchart of a method for remotely monitoring a temperature-controlled test chamber according to an embodiment of the present invention, from step S20 to step S22.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The temperature control test box and the temperature control method have the application scene that the temperature data of the temperature control test box are filtered by using an improved CWF algorithm, so that the processed temperature data are more accurate.
The embodiment of the invention discloses a remote monitoring method for a temperature control test box, which comprises the following steps of S1-S2 with reference to FIG. 1:
S1, collecting the temperature of a temperature control test box, and arranging and constructing a temperature sequence according to a time sequence.
The temperature sensor is arranged in the temperature control test box, temperature data in the temperature control test box are collected in real time, the collection frequency can be set to be 5 Hz, and then the collected temperature data are arranged according to the time sequence to construct a temperature sequence.
S2, processing the temperature sequence by utilizing an improved filtering algorithm, and transmitting the processed temperature sequence to a remote monitoring center.
The variable weight filtering algorithm (CWF) is a filtering method capable of effectively maintaining the detail characteristics of the time sequence. In the temperature control test, the trend of temperature change is very important information. The CWF algorithm can keep the overall change trend of the temperature data in the filtering process, and remove unnecessary noise and fluctuation, so that the temperature data is smoother and easy to analyze. Compared with the traditional fixed weight filtering method, the CWF algorithm can process detail features in temperature data more accurately by dynamically adjusting weights. This allows the CWF algorithm to have higher accuracy and better effect in temperature data filtering of the temperature controlled test box.
The basic steps of the CWF algorithm (since the CWF algorithm is in the prior art, detailed steps are not described in detail) are as follows:
preprocessing NDVI time sequence data, and identifying local maximum or minimum value points in a data sequence;
Secondly, generating new NDVI time series data by a 3-point variable weight filtering method;
thirdly, replacing local maximum or minimum value points;
fourth, updating the NDVI value;
and fifthly, outputting the filtered NDVI time sequence data under the iteration termination condition.
It should be considered that, in the original CWF algorithm, when two consecutive points are encountered and are judged to be extreme points of the same type (such as two local maxima or two local minima), the algorithm directly selects the point with the larger value (or smaller value) as the extreme point, and the extreme points are not changed in the subsequent filtering process. However, if the point with a larger value is a noise point, the noise point is erroneously kept as an extreme point, and thus the filtering effect is poor, i.e. the condition of over-filtering or under-filtering occurs. To solve this problem, embodiments of the present invention calculate the authenticity of temperature data by modifying the original CWF algorithm, including analyzing the varying features of the temperature sequence, and when successive extreme points of the same type are encountered, determining the final local extreme point based on the authenticity of these points.
Specifically, referring to fig. 2, the process of improving the CMF algorithm includes steps S20-S22.
And S20, primarily identifying local maximum points or local minimum points in the temperature sequence, wherein when two continuous temperatures are judged to be extreme points of the same type, the two temperatures are taken as a group of temperature pairs and are classified into a first temperature point set.
And S21, calculating the authenticity of each temperature in the first temperature point set.
In the temperature acquisition process, the temperature sensor is easy to be interfered by various noise in the surrounding environment, such as electromagnetic interference, power supply fluctuation and other factors, abnormal values or mutation points appear in the acquired temperature sequence due to the influence of the noise, the abnormal values or the mutation points are discontinuous or inconsistent compared with the surrounding temperature data, and the abnormal values or the mutation points break the original change trend of the temperature sequence in a local range, so that the temperature data are not smooth or continuous.
To evaluate the authenticity of the temperature data, the degree of difference between each temperature data and its surrounding temperature data is further analyzed.
Specifically, any one temperature in a first temperature point set is selected as a target temperature, a set number of temperatures (10 temperature data can be acquired according to an empirical value) are intercepted altogether by taking the target temperature as a center to construct an actual local range of the target temperature, a temperature value after target temperature replacement is obtained by using a cubic spline interpolation method, a reference local range of the target temperature (the number of the reference local range is the same as that of the temperature data in the actual local range) is constructed by taking the temperature value after replacement as the center, the edge data points in a sequence are required to be abandoned, a coordinate system is established by taking a time sequence serial number in the actual local range as an abscissa and taking the temperature value as an ordinate, a fitting curve of the actual local range is acquired by using a least square method, a fitting curve of the reference local range can be acquired in the same way, a variation difference and a fitting error of the two fitting curves are calculated, and a negative exponent of the product of the variation difference and the fitting error is taken as the authenticity of the target temperature.
Illustratively, the authenticity of the target temperature satisfies the relationship:
In the formula, Is the firstThe authenticity of the individual target temperatures is determined,Is the firstThe variation gap of the fitting curve of the actual local range of the individual target temperatures from the reference local range,Is the firstFitting errors of the fitting curves of the actual local ranges of the individual target temperatures and the reference local ranges,Is an exponential function.
If the target temperature deviates from the local range, the curve may be far away from the abnormal point when curve fitting is performed, and after the reference local range obtained by interpolation is fitted, the fitted curve may be closer to the fitted curve of the actual local range, so that the larger the change of the fitting error, the larger the change difference of the two local ranges, namely the larger the destructiveness of the current data point.
Calculating absolute interpolation of the two fitting curves on the number of temperature data contained in the local range, integrating the absolute difference on the number of temperatures (namely, calculating the area under the absolute difference curve), dividing the integrated result by the number of the temperature data in the local range to obtain an average absolute error, and taking the average absolute error as the variation gap of the fitting curve of the actual local range of the target temperature and the reference local range, wherein the variation gap satisfies the relation:
In the formula, Is the firstThe variation gap of the fitting curve of the actual local range of the individual target temperatures from the reference local range,Is the firstFitting curves for the actual local ranges of the individual target temperatures,Is the firstFitting curves of the reference local ranges of the individual target temperatures,Is the amount of temperature data within a local range of the target temperature (i.e., the amount of temperature data within an actual or reference local range).
The variation gapCan be used to measure the degree of similarity between the fitted curve of the actual local range and the fitted curve of the reference local range,The smaller the value of (c) indicates that the closer the two fitting curves are, the better the fitting effect.
And for the temperature data in the actual local range, calculating the average difference value between all the temperature values on the corresponding fitting curve and the actual temperature values, and similarly calculating the average difference value between all the temperature values on the fitting curve corresponding to the reference local range and the actual temperature values, wherein the difference value between the average difference value corresponding to the actual local range and the average difference value corresponding to the reference local range is taken as the fitting error of the fitting curve between the actual local range of the target temperature and the reference local range, namely, the formula is as follows:
In the formula, Is the firstFitting errors of the fitting curves of the actual local ranges of the individual target temperatures and the reference local ranges,Is the firstWithin the actual local range of the target temperatureThe corresponding values of the individual temperature values on the fitted curve,Is the firstWithin the actual local range of the target temperatureThe actual value of the respective temperature is calculated,Is the firstWithin a reference local range of the target temperatureThe corresponding values of the individual temperature values on the fitted curve,Is the firstWithin a reference local range of the target temperatureThe actual value of the respective temperature is calculated,Is the amount of temperature data within a local range of the target temperature.
The smaller the average difference between the fitted curve and the actual data, the smaller the corresponding fitting error, within the actual local range and the reference local range.
According to the above calculationThe authenticity of each target temperature can be calculated by the same principle as the authenticity of all temperatures in the first temperature point set.
In addition, it should be noted that the extreme points, whether they occur due to an increase or decrease in temperature, represent temperature change trends that are similar in nature, all caused by the same temperature control mechanism.
In order to further improve the calculation reliability of the temperature in the first temperature point set, firstly classifying all other extreme points which are not classified into the first temperature point set into a third temperature point set, analyzing the similarity degree of the first temperature point set and the third temperature point set on the shape or the change trend, namely analyzing the similarity degree of corresponding temperature data in the first temperature point set and the third temperature point set in an actual local range, and correcting the calculated temperature authenticity by utilizing the similarity degree.
Illustratively, still in the above-mentioned firstThe target temperature is exemplified byThe authenticity after the correction of each target temperature satisfies the relation:
In the formula, Is the firstThe authenticity of the corrected individual target temperatures,Is the firstThe authenticity of the individual target temperatures is determined,Concentration of the third temperature pointTemperature and the firstPearson correlation coefficients for the actual local ranges corresponding to the individual target temperatures,The total number of temperatures contained in the third set of temperature points. Wherein the third temperature point is concentrated toActual local range of the respective temperature and the firstThe construction manner of the actual local range corresponding to each target temperature is the same and will not be described in detail here.
The average structural similarity between the target temperature and the third set of temperature points is reflected, the higher the value of which the higher the authenticity of the target temperature.
S22, eliminating the temperature with the minimum authenticity in each group of temperature pairs in the first temperature point set, combining the rest temperature in the first temperature point set and other temperatures which are not classified into the first temperature point set into a second temperature point set, wherein all extreme points in the second temperature point set are local maximum points or local minimum points which are finally determined in a temperature sequence.
After eliminating the temperature data with smaller authenticity, the influence of noise and abnormal values on the identification of extreme points can be reduced, the rest temperature in the first temperature point set and other temperatures which are not classified into the first temperature point set are combined into a second temperature point set, and all extreme points in the second temperature point set are the local maximum value points or the local minimum value points which are finally determined in the temperature sequence.
And finally, the second temperature point set is the extreme point for the CWF algorithm.
Thus, the improvement of the CWF algorithm is completed.
And (3) carrying out filtering treatment on the temperature control test box data by using an improved CWF algorithm, wherein in the operation process of the algorithm, when the maximum error between temperature sequences obtained by two adjacent iterations is smaller than a preset threshold (the threshold is set to 0.36 according to experience), the iteration is stopped.
And transmitting the filtered temperature data to a remote monitoring center through a communication network, and after the remote monitoring center receives the data, displaying and storing the data in real time, wherein a user can check the running state and the data of the test box in real time through an interface of the monitoring center, and when abnormality is found or the parameters of the test box need to be adjusted, the operations such as stopping the test in the test box, adjusting the power supply setting or calibrating the sensor are executed.
The embodiment of the invention also discloses a remote monitoring system for the temperature control test box, which comprises a processor and a memory, wherein the memory stores computer program instructions, and the computer program instructions are executed by the processor to realize the remote monitoring method for the temperature control test box.
The system further comprises other components known to those skilled in the art, such as communication buses and communication interfaces, the arrangement and function of which are known in the art and therefore will not be described in detail herein.
In the context of this patent, the foregoing memory may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. For example, the computer-readable storage medium may be any suitable magnetic or magneto-optical storage medium, such as, for example, resistance change Memory RRAM (Resistive Random Access Memory), dynamic Random Access Memory DRAM (Dynamic Random Access Memory), static Random Access Memory SRAM (Static Random-Access Memory), enhanced dynamic Random Access Memory EDRAM (ENHANCED DYNAMIC Random Access Memory), high-Bandwidth Memory HBM (High-Bandwidth Memory), hybrid storage cube HMC (Hybrid Memory Cube), or the like, or any other medium that may be used to store the desired information and that may be accessed by an application, a module, or both. Any such computer storage media may be part of, or accessible by, or connectable to, the device.
In the description of the present specification, the meaning of "a plurality", "a number" or "a plurality" is at least two, for example, two, three or more, etc., unless explicitly defined otherwise.
While various embodiments of the present invention have been shown and described herein, it will be obvious to those skilled in the art that such embodiments are provided by way of example only. Many modifications, changes, and substitutions will now occur to those skilled in the art without departing from the spirit and scope of the invention. It should be understood that various alternatives to the embodiments of the invention described herein may be employed in practicing the invention.
Claims (8)
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202510346030.6A CN119861772B (en) | 2025-03-24 | 2025-03-24 | Remote monitoring method and system for temperature control test box |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202510346030.6A CN119861772B (en) | 2025-03-24 | 2025-03-24 | Remote monitoring method and system for temperature control test box |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN119861772A true CN119861772A (en) | 2025-04-22 |
| CN119861772B CN119861772B (en) | 2025-06-13 |
Family
ID=95392613
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202510346030.6A Active CN119861772B (en) | 2025-03-24 | 2025-03-24 | Remote monitoring method and system for temperature control test box |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN119861772B (en) |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2002009591A (en) * | 2000-06-20 | 2002-01-11 | Sony Corp | Method of setting FIR filter and its coefficient |
| US9500535B1 (en) * | 2013-06-27 | 2016-11-22 | Amazon Technologies, Inc. | Determining temperature using multiple sensors |
| KR101782796B1 (en) * | 2017-02-28 | 2017-09-28 | ㈜엠지에이치코리아 | Temperature controller for controlling spot heating of electric heating mat or electric heating bedding |
| WO2021092815A1 (en) * | 2019-11-13 | 2021-05-20 | 深圳市大疆创新科技有限公司 | Identification method, temperature measurement method, device and storage medium |
| CN114536100A (en) * | 2022-02-27 | 2022-05-27 | 重庆大学 | Temperature measuring method and device for milling cutting tool |
| CN117272594A (en) * | 2023-08-24 | 2023-12-22 | 贵州乌江水电开发有限责任公司 | Temperature module filtering optimization method and system applied to strong electromagnetic field |
| CN117848438A (en) * | 2024-03-07 | 2024-04-09 | 威海纳尔仪表科技有限公司 | High-precision measuring method for gas flow and thermal gas mass flowmeter |
-
2025
- 2025-03-24 CN CN202510346030.6A patent/CN119861772B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2002009591A (en) * | 2000-06-20 | 2002-01-11 | Sony Corp | Method of setting FIR filter and its coefficient |
| US9500535B1 (en) * | 2013-06-27 | 2016-11-22 | Amazon Technologies, Inc. | Determining temperature using multiple sensors |
| KR101782796B1 (en) * | 2017-02-28 | 2017-09-28 | ㈜엠지에이치코리아 | Temperature controller for controlling spot heating of electric heating mat or electric heating bedding |
| WO2021092815A1 (en) * | 2019-11-13 | 2021-05-20 | 深圳市大疆创新科技有限公司 | Identification method, temperature measurement method, device and storage medium |
| CN114536100A (en) * | 2022-02-27 | 2022-05-27 | 重庆大学 | Temperature measuring method and device for milling cutting tool |
| CN117272594A (en) * | 2023-08-24 | 2023-12-22 | 贵州乌江水电开发有限责任公司 | Temperature module filtering optimization method and system applied to strong electromagnetic field |
| CN117848438A (en) * | 2024-03-07 | 2024-04-09 | 威海纳尔仪表科技有限公司 | High-precision measuring method for gas flow and thermal gas mass flowmeter |
Non-Patent Citations (2)
| Title |
|---|
| RONGFENG ZHANG ET AL: "A Temperature Filtering Algorithm for Cement Burning Belt Based on Local Weighted Regression", 2017 4TH INTERNATIONAL CONFERENCE ON INFORMATION SCIENCE AND CONTROL ENGINEERING, 23 July 2017 (2017-07-23), pages 821 - 824, XP033254859, DOI: 10.1109/ICISCE.2017.175 * |
| 胡昂 等: "图像处理在可见光测温中的应用", 工业控制计算机, 30 April 2019 (2019-04-30), pages 101 - 105 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN119861772B (en) | 2025-06-13 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN119511105B (en) | Battery health status comprehensive evaluation method, system, device and storage medium | |
| CN119244805B (en) | Precise control method of solenoid valve based on dynamic flow regulation | |
| CN118500577B (en) | Financial warehouse supervision method based on Internet of things technology | |
| CN119620624B (en) | Intelligent adjustment method and system for fluid control of microfluidic chip | |
| CN119002240B (en) | A crystal furnace pressure intelligent control method and system | |
| CN118173825B (en) | Fuel cell temperature control method and device, fuel cell and automobile | |
| CN118795203B (en) | Large-current test data processing method and system for explosion-proof electric appliance | |
| CN119200712B (en) | A method for degrading vomitoxin in corn deep processing | |
| CN118471865B (en) | Wafer bonding stability control method and system based on dynamic adjustment | |
| CN119311061A (en) | Ultra-low temperature preservation and processing control method for aquatic products | |
| CN118733852A (en) | Visualization system and method for the whole production process of MVR scraper crystallizer | |
| CN119620814B (en) | Intelligent control system is drawed to chinese-medicinal material | |
| CN119861772B (en) | Remote monitoring method and system for temperature control test box | |
| CN118296013B (en) | Data storage method and system based on industrial Internet | |
| CN120029062A (en) | A method and system for adaptive control of photoelectric sensor in extreme environment | |
| CN120029199A (en) | A high-precision emulsification control method and device | |
| CN118427576B (en) | Warehouse management method and system based on pressure signal decomposition and filtering | |
| CN118350974B (en) | Comprehensive health care monitoring method and system for intelligent communities | |
| CN111266405A (en) | Plate and strip hot rolling process control method and control device | |
| CN117348635A (en) | Intelligent wine cabinet temperature control system and control method thereof | |
| CN119916869B (en) | Temperature control method and system of natural convection constant temperature test box | |
| CN119311042B (en) | Valve intelligent control method and system based on Internet of Things | |
| CN118367839B (en) | Energy-saving motor operation optimization analysis method, system and storage medium | |
| CN120354242B (en) | Data cleaning and steady-state detection method and system based on robust time series modeling | |
| CN120447649A (en) | Packaging temperature control method and system for LED braiding machine |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |