WO2017211071A1 - Procédé de prédiction de température et appareil asocié - Google Patents

Procédé de prédiction de température et appareil asocié Download PDF

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Publication number
WO2017211071A1
WO2017211071A1 PCT/CN2016/113219 CN2016113219W WO2017211071A1 WO 2017211071 A1 WO2017211071 A1 WO 2017211071A1 CN 2016113219 W CN2016113219 W CN 2016113219W WO 2017211071 A1 WO2017211071 A1 WO 2017211071A1
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Prior art keywords
temperature
measured
sampling point
predicted
current sampling
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Chinese (zh)
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高平东
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Guangzhou Shiyuan Electronics Thecnology Co Ltd
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Guangzhou Shiyuan Electronics Thecnology Co Ltd
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01K—MEASURING TEMPERATURE; MEASURING QUANTITY OF HEAT; THERMALLY-SENSITIVE ELEMENTS NOT OTHERWISE PROVIDED FOR
    • G01K13/00—Thermometers specially adapted for specific purposes

Definitions

  • the invention relates to the field of temperature detection, and in particular to a method for predicting temperature and a device thereof.
  • the electronic thermometer (or thermometer) is in contact with the object to be measured through the heat conducting device, so that the temperature of the heat conducting device is slowly changed to the temperature of the object to be measured, and then the temperature of the heat conducting device is obtained by the temperature sensor, thereby obtaining the temperature of the object to be measured.
  • NTC Negative Temperature Coefficient
  • the embodiment of the invention provides a method and a device for predicting temperature, which can speed up the measurement of temperature and has strong anti-interference ability.
  • an embodiment of the present invention provides a method for predicting temperature, including:
  • the process of dividing the N pieces into the measured temperature data is specifically:
  • the time from the past Mth sampling point to the current sampling point is sequentially divided into N-1 Time period
  • the measured temperature of all the sampling points of the measured object in the divided mth time period is taken as the mth group measured temperature data; wherein, 1 ⁇ m ⁇ N-1.
  • the formula for calculating the slope of the fitted line is:
  • b j is the fitted linear slope of the measured temperature data of the jth group, 1 ⁇ j ⁇ N; n is the number of sampling points included in the measured temperature data of the jth group; t i is the number of the measured temperature data of the jth group The time point corresponding to the i sampling points; T i is the measured temperature of the measured object at the i-th sampling point in the j-th set of measured temperature data;
  • an implementation manner of the prediction condition may be: the absolute values of the fitted linear slopes of the N sets of measured temperature data are all within a preset linear slope interval, and any of the N sets of measured temperature data.
  • the absolute value of the difference between the slopes of the fitted straight lines between the two groups is less than the preset threshold of the slope;
  • the method further includes:
  • the predicted incremental temperature of the measured object at the current sampling point is set to zero
  • the method further includes:
  • the process of calculating the predicted temperature of the measured object at a current sampling point is specifically:
  • the method further includes:
  • an embodiment of the present invention further provides an apparatus for predicting temperature, including:
  • a prediction data module configured to sample the measured temperature of the measured object at a fixed frequency, and obtain the measured temperature of the measured object sampled from each sampling point between the current sampling point and the past M sampling point, and divide into N group measured temperature data; wherein, N ⁇ 3;
  • a slope calculation module configured, for each set of measured temperature data, calculating a fitted straight line slope of a temperature versus time curve composed of the set of measured temperature data
  • a prediction calculation module configured to calculate a predicted incremental temperature of the measured object at a current sampling point according to the predicted incremental model when a slope of a fitted straight line of the N sets of measured temperature data satisfies a prediction condition
  • a predicted temperature calculation module configured to calculate a predicted temperature of the measured object at a current sampling point according to the measured temperature and the predicted incremental temperature of the measured object at the current sampling point, and the predicted temperature of the previous sampling point, And outputting a predicted temperature of the current sampling point; wherein the predicted temperature of the previous sampling point is based on the measured temperature and the predicted incremental temperature of the measured object at the previous sampling point, and at the previous sampling point Calculated by the predicted temperature of the previous sampling point.
  • the prediction data module includes a unit for dividing into N sets of measured temperature data, specifically:
  • a time dividing unit configured to sequentially divide the time of the past Mth sampling point to the current sampling point into N-1 time segments according to a change of the time axis
  • a data dividing unit configured to use the measured temperature of all the sampling points of the measured object in the mth time segment as the mth group measured temperature data, and the measured object at the current sampling point
  • the measured temperature of all sampling points between the detection sampling points is taken as the Nth group measured temperature data; wherein, 1 ⁇ m ⁇ N-1.
  • the formula for calculating the slope of the fitted line is:
  • b j is the fitting linear slope of the j-th set of measured temperature data, 1 ⁇ j ⁇ N; n is the number of sampling points included in the j-th measured temperature data; t i is the number in the j-th measured temperature data The time point corresponding to the i sampling points; T i is the measured temperature of the measured object at the i-th sampling point in the j-th set of measured temperature data;
  • an implementation manner of the prediction condition may be: the absolute values of the fitted linear slopes of the N sets of measured temperature data are all within a preset linear slope interval, and any two of the N sets of measured temperature data. The absolute value of the difference between the slopes of the fitted straight lines between the groups is less than the preset threshold of the slope;
  • the device further includes:
  • a prediction adjustment module configured to: when the slope of the fitted straight line of the N sets of measured temperature data does not satisfy the prediction condition, set the predicted incremental temperature of the measured object at the current sampling point to zero;
  • a prediction judging module configured to determine whether an absolute value of the predicted incremental temperature of the measured object at the current sampling point is greater than 1 after calculating the predicted incremental temperature of the measured object at the current sampling point;
  • a prediction revision module configured to: when the absolute value of the predicted incremental temperature of the measured object at the current sampling point is greater than 1, the revised incremental temperature of the measured object at the current sampling point is revised to zero;
  • a temperature adjustment module configured to: after calculating the predicted temperature of the measured object at the current sampling point, when determining that b N is greater than zero and the predicted temperature of the measured object at the current sampling point is less than the prediction at the previous sampling point The temperature, or b N is less than zero, and the predicted temperature of the measured object at the current sampling point is greater than the predicted temperature of the previous sampling point, and the predicted temperature of the measured object at the current sampling point is revised to the previous sampling. The predicted temperature of the point.
  • the method for predicting temperature provided by the embodiment of the present invention divides the obtained measured temperature from the current sampling point to the Mth sampling point in the past into a plurality of sets of measured temperature data, and then uses the temperature of each set of measured temperature data to build time with time.
  • the slope of the fitted curve of the curve is used to determine whether the output of the current measured temperature needs to be adjusted.
  • the predicted incremental temperature of the measured object at the current sampling point is calculated based on the slope of the fitted straight line.
  • the collected data is dynamic and can dynamically predict and predict. temperature.
  • FIG. 1 is a schematic flow chart of one embodiment of a method for predicting temperature provided by the present invention
  • FIG. 2 is a schematic diagram of a temperature versus time curve provided by the present invention.
  • FIG. 3 is a schematic diagram showing a comparison of measured curves of measured temperature and predicted temperature provided by the present invention
  • FIG. 4 is a schematic view showing the slope of a fitted straight line as a function of temperature provided by the present invention.
  • FIG. 5 is a schematic structural view of an embodiment of a device for predicting temperature provided by the present invention.
  • FIG. 6 is a schematic structural diagram of an embodiment of a prediction data module of a temperature prediction apparatus provided by the present invention.
  • the electronic thermometer detects the measured temperature of the measured object through the probe
  • the detected measured temperature is usually directly displayed on the display interface of the thermometer, but the displayed temperature is detected from the start of the detection to the detection of the final actual temperature of the measured object.
  • the ascending process is too slow, in order to speed up the display of the temperature, and to ensure the accuracy of the actual temperature detected by the measured object is detected, the present invention provides a predicted temperature based on the temperature versus time curve satisfying the exponential relationship.
  • the method is performed by an electronic thermometer, which is as follows:
  • the electronic thermometer detects the measured temperature curve of the measured object as shown in the measured temperature curve shown in FIG. 3, and FIG. 3 is a schematic diagram of the comparison between the measured temperature and the predicted temperature provided by the present invention; wherein the measured temperature curve satisfies:
  • T T target- T 0 e -kt (1)
  • T is the measured temperature of the current sampling point
  • the target temperature T target is the measured temperature of the object (i.e., t is infinite)
  • (T target -T 0) is the temperature difference between the target temperature and the onset temperature (i.e., t is At time zero
  • k can be equivalent to the temperature transfer coefficient and t is time.
  • the method of predicting temperature aims to speed up the measurement of temperature. No prediction is made for the temperature change itself is very fast, and for the temperature change is very slow, it is considered that the stable temperature is not predicted.
  • the predicted temperature value is also provided by the present invention a method of the embodiment will be predicted temperature.
  • the method for predicting temperature provided by the implementation of the present invention is as follows:
  • FIG. 1 it is a schematic flowchart of an embodiment of a method for predicting temperature provided by the present invention.
  • the method for predicting temperature is performed by a thermometer, and includes steps S1 to S4, as follows:
  • the fixed frequency can be set according to the needs of the thermometer sampling.
  • the following is an example of the process of data acquisition and data grouping during the prediction of the predicted temperature of the current sampling point:
  • thermometer starts detecting the temperature and detects the measured temperature of the measured object through the probe
  • the thermometer samples the measured temperature of the measured object at a fixed frequency of 1 Hz, and takes the time t as the current sampling point, from the current sampling.
  • Point t starts counting back to the Mth sampling point t-M+1 (ie, the above-mentioned detection sampling point), and obtains the measured temperature of the above M sampling points, that is: ⁇ (t-M+1, T(t-M) +1)), (t-M+2, T(t-M+2)), (t-M+3, T(t-M+3)).
  • t,T(( t)) ⁇ a total of M sampling points.
  • data grouping is performed; according to the change of the time axis, the time from the Mth sampling point t-M+1 to the current sampling point t is sequentially divided into two time segments in order; it should be noted that the time segment may not be divided.
  • the number of equally divided and divided segments can also be set as needed, here only as an example; then, the measured object is from the sampling point t-M+1 to the sampling point in the first time period divided.
  • the measured temperature of all sampling points in tM/2+1 is taken as the first set of measured temperature data; the measured object is in the second time period divided from the sampling point tM/2+1 to the sampling point t
  • the measured temperature of all sampling points is taken as the second set of measured temperature data, and the measured temperature of all the sampling points of the measured object between the current sampling point t and the Mth sampling point t-M+1 is taken as the third group.
  • Measured temperature data If the time period of the division is more than three, the measured temperature of all the sampling points included in the subsequent time period may be used as the data in the measurement data group with the same serial number of the time period, similar to the above-mentioned measured temperature data group group mode.
  • the above group number is set only for the convenience of subsequent grouping.
  • the group number setting can also be other forms. It only needs to ensure that the adjacent two sets of data are basically not coincident, and the time of the temperature data in the group is continuously changed. Just fine.
  • the sampled M is not too small, if the data volume is too small, the accuracy of the linear fitting calculation performed later is insufficient, and the anti-interference is weak; if the value of M is too large, the sampling time is long, and the prediction is long.
  • the real-time nature of the predicted temperature of the current sampling point becomes weak, and it is easy to predict an error when the temperature suddenly changes.
  • the value of M is 30, that is, 30 seconds. data.
  • the function in order to minimize the error between the line and the curve, should be satisfied as follows:
  • the parameters of a and b minimize the error between the fitted straight line and the curve. Then, according to the binary grading method, the partial derivatives of the parameters of a and b are respectively obtained for the above formula:
  • this formula is a fitting straight line slope formula, so the third set of measured temperature data is substituted into the fitted straight line slope formula, and the fitted linear slope b 3 of the third set of measured temperature data can be obtained, and the first can be calculated first.
  • any set of measured temperature data can calculate the fitted straight line slope of the set of measured temperature data by the above-mentioned fitted straight line slope formula; wherein n is the number of sampling points included in the measured temperature data of the set; t i is The time point corresponding to the i-th sampling point in the measured temperature data of the group; T i is the measured temperature of the measured object at the i-th sampling point in the measured temperature data of the group.
  • the linear slope is used to calculate the slope of the line, the calculation amount is small, and the anti-interference ability can be improved by multi-point fitting.
  • the curve of the measured temperature of the measured object by the thermometer can refer to the measured temperature curve of FIG. 3, and the measured temperature curve can be used to know that if the measured temperature rises relatively fast and the predicted temperature rise speed is equal or greater, that is, the fitting If the absolute value of the slope of the line is relatively large, it is considered that the transmission medium of the thermometer probe is not good for prediction and the predicted temperature is calculated; if the temperature changes very slowly, that is, the absolute value of the slope of the fitted line is relatively small, the detected temperature is considered to be stable. It is not necessary to make predictions and calculate the predicted temperature; if the slope of the fitted straight line of any two sets of data is too large, it is considered that there is a sudden change in temperature without prediction.
  • the above prediction condition may be set as follows: the absolute values of the fitted straight line slopes of the N sets of measured temperature data divided into the preset linear slope ranges, and any two groups in the N sets of measured temperature data.
  • the absolute value of the difference between the slopes of the fitted straight lines is less than the preset slope threshold; preferably, the slope of the straight line is (0.0001, 0.28), and the threshold of the slope is 0.004;
  • the difference threshold is not limited to the above value, and can be adjusted according to actual conditions.
  • the predicted incremental temperature of the current sampling point is predicted.
  • the fitted linear slope b j of each set of measured temperature data and the average measured temperature of all sampling points in the group are known.
  • Value constitutes a coordinate point Can approximate the coordinate point falling on Figure 4, then the slope of the line in Figure 4 is Further, one of N coordinate points composed of N sets of measured temperature data may be selected, where it is preferably a coordinate point composed of the Nth set of measured temperature data.
  • b N is the fitted linear slope of the Nth measured temperature data
  • b j-1 is the fitted straight line slope of the measured temperature data of the j-1th group.
  • the average value of the measured temperatures of all the sampling points in the measured temperature data of the jth group The average of the measured temperatures of all the sampling points in the measured temperature data of the j-1th group.
  • the above-mentioned predicted incremental model is a plurality of sets of measured temperature data for fitting.
  • the predicted incremental model is a relationship between the true variation of the slope and the temperature change by the piecewise linear fitting.
  • the predicted incremental temperature can be dynamically adjusted; and the predicted incremental model can be applied to a thermometer in which the probe is a plurality of types of delivery media.
  • FIG. 4 it is a schematic diagram of the slope of the fitted straight line as a function of temperature provided by the present invention; in the measured temperature data of the first group and the second group
  • the two points in Figure 4 can be approximated, and the slope of the line in Figure 4 is:
  • the process of calculating the predicted temperature of the measured object at the current sampling point may be specifically as follows:
  • the sum of the measured temperature and the predicted incremental temperature of the measured object at the current sampling point may be used as the predicted temperature of the current sampling point, but if the sum of the two is directly As the predicted temperature of the current sampling, the curve formed by the predicted temperature of the adjacent sampling points will be too abrupt, so that the predicted temperature of the previous sampling point can be excessively smoothed, and the predicted temperature of the previous sampling point is added as a parameter. calculation process.
  • the first coefficient is 0.2 and the second coefficient is 0.8.
  • the prediction process of the previous sampling point is basically consistent with the prediction process of the current sampling point, and will not be described here.
  • the predicted temperature is output, that is, the thermometer displays the predicted temperature on the display interface.
  • the prediction process of the predicted temperature of each subsequent sampling point can repeat the above steps as the current sampling point of the sampling point.
  • S1 to S4 calculate the predicted temperature of the sampling point.
  • the method for predicting temperature divides the obtained measured temperature from the current sampling point to the Mth sampling point in the past into a plurality of sets of measured temperature data, and then uses the temperature of each set of measured temperature data to build time with time.
  • the slope of the fitted curve of the curve is used to determine whether the output of the current measured temperature needs to be adjusted.
  • the predicted incremental temperature of the measured object at the current sampling point is calculated based on the slope of the fitted straight line.
  • the calculated predicted temperature can take into account the change of the current measured temperature and the predicted temperature of the previous sampling point, and the dry resistance is strong.
  • the collected data is dynamic and can dynamically predict the predicted temperature.
  • the device for predicting temperature can perform all the processes of the method for predicting temperature, and specifically includes:
  • the prediction data module 10 is configured to sample the measured temperature of the measured object at a fixed frequency, acquire the measured temperature of the measured object sampled from each sampling point between the current sampling point and the past M sampling point, and divide N group measured temperature data; wherein, N ⁇ 3;
  • the slope calculation module 20 is configured to calculate, for each set of measured temperature data, a fitted straight line slope of a temperature versus time curve composed of the set of measured temperature data;
  • a prediction calculation module 30 configured to calculate, according to the predicted incremental model, a predicted incremental temperature of the measured object at a current sampling point when a slope of a fitted straight line of the N sets of measured temperature data satisfies a prediction condition;
  • the predicted temperature calculation module 40 is configured to calculate a predicted temperature of the measured object at the current sampling point according to the measured temperature and the predicted incremental temperature of the measured object at the current sampling point and the predicted temperature of the previous sampling point. Wherein the predicted temperature of the previous sampling point is based on the measured temperature and the predicted incremental temperature of the measured object at the previous sampling point, and the predicted temperature of the last previous sampling point of the previous sampling point. computational.
  • FIG. 6 is a schematic structural diagram of an embodiment of a prediction data module of a temperature prediction apparatus provided by the present invention, the prediction data module 10 Including the unit for dividing into N sets of measured temperature data, specifically:
  • the time dividing unit 11 is configured to sequentially divide the time of the past Mth sampling point to the current sampling point into N-1 time segments according to the change of the time axis;
  • a data dividing unit 12 configured to use the measured temperature of all the sampling points of the measured object in the mth time segment as the mth group measured temperature data, and the measured object to be at the current sampling point
  • the measured temperature of all the sampling points between the detection sampling points is taken as the Nth group measured temperature data; wherein, 1 ⁇ m ⁇ N-1.
  • the fitting straight line is calculated
  • the formula for the slope is:
  • b j is the fitted linear slope of the measured temperature data of the jth group, 1 ⁇ j ⁇ N; n is the number of sampling points included in the measured temperature data of the jth group; t i is the number of the measured temperature data of the jth group The time point corresponding to i sampling points; T i is the measured temperature of the measured object at the i-th sampling point in the j-th set of measured temperature data.
  • the prediction condition is that: the absolute values of the fitted linear slopes of the N sets of measured temperature data are within a preset linear slope interval, and the fitted straight line slope between any two of the N sets of measured temperature data.
  • the absolute value of the difference is less than the preset slope threshold;
  • the device further includes:
  • the prediction adjustment module 50 is configured to set the predicted incremental temperature of the measured object at the current sampling point to zero when the slope of the fitted straight line of the N sets of measured temperature data does not satisfy the predicted condition.
  • the device further includes:
  • the prediction determining module 60 is configured to determine, after calculating the predicted incremental temperature of the measured object at the current sampling point, whether the absolute value of the predicted incremental temperature of the measured object at the current sampling point is greater than 1;
  • the prediction revision module 70 is configured to revise the predicted incremental temperature of the measured object at the current sampling point to zero when the absolute value of the predicted incremental temperature of the measured object at the current sampling point is greater than 1.
  • the process of calculating the predicted temperature of the measured object at the current sampling point is specifically:
  • the device further includes:
  • the temperature adjustment module 80 is configured to: after calculating the predicted temperature of the measured object at the current sampling point, when determining that b N is greater than zero and the predicted temperature of the measured object at the current sampling point is less than the previous sampling point Predicting the temperature, or b N is less than zero and the predicted temperature of the measured object at the current sampling point is greater than the predicted temperature of the previous sampling point, and the predicted temperature of the measured object at the current sampling point is revised to be the previous one. The predicted temperature of the sample point.
  • the device for predicting temperature divides the obtained measured temperature from the current sampling point to the Mth sampling point in the past into a plurality of sets of measured temperature data, and then uses the temperature of each set of measured temperature data to build time with time.
  • the slope of the fitted curve of the curve is used to determine whether the output of the current measured temperature needs to be adjusted.
  • the predicted incremental temperature of the measured object at the current sampling point is calculated based on the slope of the fitted straight line.
  • the calculated predicted temperature can take into account the change of the current measured temperature and the predicted temperature of the previous sampling point, and the dry resistance is strong.
  • the collected data is dynamic and can dynamically predict the predicted temperature.
  • the storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

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Abstract

L'invention concerne un procédé et un appareil de prédiction de température. Le procédé comprend les étapes suivantes : échantillonner une température mesurée d'un objet mesuré à une fréquence temporelle fixée de manière à obtenir une température mesurée de l'objet mesuré détectée à chaque point d'échantillonnage, depuis le point d'échantillonnage actuel jusqu'au Mième point d'échantillonnage précédent, et diviser les températures mesurées en N groupes de données de températures mesurées (S1) ; pour chaque groupe de données de températures mesurées, calculer une pente d'une ligne de meilleur ajustement d'une courbe de température en fonction du temps formée par le groupe de données de températures mesurées (S2) ; si les pentes des lignes de meilleur ajustement des N groupes de données de températures mesurées satisfont à une condition de prédiction, calculer une température incrémentale prédite de l'objet mesuré au point d'échantillonnage actuel en fonction d'un modèle de prédiction incrémental (S3) ; et calculer une température prédite de l'objet mesuré au point d'échantillonnage actuel en fonction d'une température mesurée et de la température incrémentale prédite de l'objet mesuré au point d'échantillonnage actuel ainsi qu'une température prédite au point d'échantillonnage précédent (S4).<sp /> La solution technique de la présente invention peut accélérer des processus de mesure de température et présente une résistance aux interférences élevée.
PCT/CN2016/113219 2016-06-06 2016-12-29 Procédé de prédiction de température et appareil asocié Ceased WO2017211071A1 (fr)

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Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2003503694A (ja) * 1999-06-23 2003-01-28 エリアフ ルビンスタイン、 高速応答温度計
CN101115979A (zh) * 2005-03-29 2008-01-30 西铁城控股株式会社 电子体温计
CN101199414A (zh) * 2006-12-11 2008-06-18 深圳迈瑞生物医疗电子股份有限公司 一种快速体温测量装置及其温度测量方法
US20150185086A1 (en) * 2014-01-02 2015-07-02 King Abdullah International Medical Research Center Thermometer using differential temperature measurements
CN105286812A (zh) * 2015-12-02 2016-02-03 广东宝莱特医用科技股份有限公司 一种体温测量方法和装置
CN106092371A (zh) * 2016-06-06 2016-11-09 广州视源电子科技股份有限公司 预测温度的方法及其装置

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN100516687C (zh) * 2007-09-17 2009-07-22 深圳和而泰智能控制股份有限公司 动态空调温度控制方法
CN105547499B (zh) * 2015-12-29 2018-05-25 广州视源电子科技股份有限公司 预测温度的方法及其系统

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2003503694A (ja) * 1999-06-23 2003-01-28 エリアフ ルビンスタイン、 高速応答温度計
CN101115979A (zh) * 2005-03-29 2008-01-30 西铁城控股株式会社 电子体温计
CN101199414A (zh) * 2006-12-11 2008-06-18 深圳迈瑞生物医疗电子股份有限公司 一种快速体温测量装置及其温度测量方法
US20150185086A1 (en) * 2014-01-02 2015-07-02 King Abdullah International Medical Research Center Thermometer using differential temperature measurements
CN105286812A (zh) * 2015-12-02 2016-02-03 广东宝莱特医用科技股份有限公司 一种体温测量方法和装置
CN106092371A (zh) * 2016-06-06 2016-11-09 广州视源电子科技股份有限公司 预测温度的方法及其装置

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111962101A (zh) * 2020-08-13 2020-11-20 中国铝业股份有限公司 一种铝电解质温度、初晶温度及过热度的测算方法
CN115923554A (zh) * 2022-10-26 2023-04-07 宁波惠康实业有限公司 一种空调的变频控制方法

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