WO2017113875A1 - 预测温度的方法及其系统 - Google Patents

预测温度的方法及其系统 Download PDF

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WO2017113875A1
WO2017113875A1 PCT/CN2016/098236 CN2016098236W WO2017113875A1 WO 2017113875 A1 WO2017113875 A1 WO 2017113875A1 CN 2016098236 W CN2016098236 W CN 2016098236W WO 2017113875 A1 WO2017113875 A1 WO 2017113875A1
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temperature
value
fitting
prediction
current
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PCT/CN2016/098236
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French (fr)
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赵巍
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广州视源电子科技股份有限公司
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01K—MEASURING TEMPERATURE; MEASURING QUANTITY OF HEAT; THERMALLY-SENSITIVE ELEMENTS NOT OTHERWISE PROVIDED FOR
    • G01K1/00—Details of thermometers not specially adapted for particular types of thermometer
    • G01K1/02—Means for indicating or recording specially adapted for thermometers
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations

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  • the present invention relates to the field of temperature sensing, and more particularly to a method and system for predicting temperature.
  • the electronic thermometer Compared with the traditional mercury glass thermometer, the electronic thermometer has the advantages of convenient reading, harmless to the human body and the surrounding environment (no mercury), and is suitable for home use.
  • the wearable electronic thermometer takes a long time to reach the heat balance in some cases, and the stable temperature is measured.
  • the present invention provides a method of predicting temperature and a system thereof capable of accelerating temperature measurement.
  • the method for predicting temperature of the present invention has the following technical solutions, including:
  • the current temperature prediction value is obtained by the temperature fitting curve, and the temperature prediction value is output.
  • the invention also provides a system for predicting temperature, comprising:
  • the fitting data determining module is configured to collect the temperature value of the measured object, obtain a temperature sampling value, determine whether to suspend the temperature prediction according to the current temperature sampling value, and if not, obtain the historical temperature of the measured object. a sample value from which a plurality of temperature sample values are determined as fitting data of the predicted temperature;
  • a fitting parameter calculation module configured to calculate a fitting parameter of the temperature fitting curve by using the fitting data
  • a fitting parameter optimization module configured to determine an optimization parameter according to the historical temperature sampling value and the corresponding historical temperature prediction value, and optimize the fitting parameter by using the optimization parameter;
  • a time point prediction module configured to calculate a position of a current temperature sample value in the temperature fitting curve, and obtain a predicted time point according to the position and the optimization parameter
  • a temperature prediction module configured to obtain a current temperature prediction value by using the temperature fitting curve according to the optimized fitting parameter and the predicted time point, and output the temperature prediction value.
  • the method for predicting temperature of the present invention and the system thereof adopt a log-based temperature fitting curve, calculate a fitting parameter of a temperature fitting curve, and optimize the fitting parameter with an optimization parameter to calculate a current temperature sampling value.
  • a position in the temperature fitting curve, and a prediction time point is obtained according to the position and the optimization parameter, and the temperature is obtained by the temperature fitting curve according to the optimized fitting parameter and the predicted time point. Predict the value so that the temperature measurement can be accelerated.
  • 1 is a schematic flow chart of a method for predicting temperature of an embodiment
  • FIG. 2 is a schematic flow chart of a predicted temperature algorithm of a specific implementation manner
  • Fig. 3 is a schematic structural view of a system for predicting temperature of an embodiment.
  • S101 Collect a temperature value of the measured object, obtain a temperature sampling value, and determine whether to suspend the temperature prediction according to the current temperature sampling value.
  • this step by collecting the temperature value of the measured object, obtaining a temperature sampling value, and monitoring the temperature value, Whether to suspend the temperature prediction according to the current temperature sampling value, thereby quickly determining whether to enter the temperature prediction.
  • condition for setting the startup temperature prediction mode is further:
  • the current temperature sample value is greater than the second set value, and the first temperature derivative of the current temperature sample value and the previous preset number of historical temperature sample values are smaller than the first set value, and the second derivative is less than 0;
  • the temperature sample value determines whether the condition of the start temperature prediction mode is satisfied, and if so, starts the temperature prediction mode.
  • the temperature prediction mode By setting the condition for starting the temperature prediction mode in advance, if the condition that the startup temperature prediction mode is satisfied is detected, the temperature prediction mode is started; otherwise, the current temperature sampling value is directly output, thereby further improving the efficiency of temperature prediction.
  • the temperature prediction is continued. Selecting several temperature sampling values from the historical temperature sample values of the measured object as the fitting data of the predicted temperature, and appropriately increasing or decreasing the amount of data participating in the fitting according to the specific situation, thereby improving the fitting accuracy while improving the fitting accuracy. Reduce the hardware resources occupied by the fitting calculation and improve the efficiency of data fitting.
  • a plurality of temperature sample values may be determined from the acquired historical temperature sample values of the measured object as the fitting data of the predicted temperature: acquiring a historical temperature sample value of the measured object, according to the history The temperature sampling value obtains the mean and standard deviation of the first derivative of the historical temperature sampling value; and calculates the upper threshold and the lower threshold of the outlier determination according to the mean and the standard deviation, respectively:
  • Thre_upper k_mean+2.5*k_std
  • Thre_low k_mean-2.5*k_std
  • thre_upper is the upper threshold
  • thre_low is the lower threshold
  • k_mean is the average
  • k_std is the standard deviation
  • the historical temperature sample value is obtained according to the upper threshold and the lower threshold of the outlier determination.
  • a set number of temperature sample values whose first derivative is less than or equal to the upper limit of the threshold and greater than or equal to the lower limit of the threshold are selected, and the set number of temperature sample values are used as fitting data of the predicted temperature.
  • the outlier point is a temperature sample value that is affected by external factors and causes a large change in the temperature sampling value, and the error is greater than the set value. Determining the fitted data of the predicted temperature by the above method can further improve the accuracy of data fitting.
  • the function of the temperature fitting curve is a logarithmic function.
  • the fitting of the logarithmic function can be converted into a fitting of a straight line function, as shown in the following formula:
  • the predicted temperature is calculated by the temperature fitting curve, so that the calculation takes up less hardware resources and can be operated under a lower hardware configuration, thereby saving hardware costs.
  • the fitting parameters k and b of the temperature fitting curve may be calculated by finding the optimal solution of the least squares method according to the fitting data, as the following formula Shown as follows:
  • y i is the temperature sample value of the i-th fit data
  • x i is the predicted time point of the i-th fit data
  • n is the number of fit data.
  • ⁇ is a preset optimization parameter
  • diff is the difference between the sum of the slopes of the preset number of historical temperature sample values and the slope of the corresponding historical temperature prediction value
  • fitting parameters k and b can be performed by the following formula optimization:
  • the fitting parameters k and b are optimized by using the preset optimization parameter ⁇ , and the fitting parameters k and b precision of the temperature fitting curve are improved, thereby further reducing the error of temperature prediction.
  • k is the slope of the current temperature sample value y 0 in the fitted curve
  • the predicted time point x is obtained according to the preset parameter ⁇ , the optimization parameter ⁇ , and the position x 0 :
  • the prediction time point is obtained according to the position and the optimization parameter, thereby providing accurate data for calculating the predicted temperature value of the current sampling point, and further improving Predict the accuracy of the temperature.
  • the temperature fitting curve After calculating all the variables of the temperature fitting curve, the temperature fitting curve can be substituted.
  • the current temperature prediction value is output and displayed to the current temperature prediction value.
  • the accuracy of the current temperature prediction value is further improved, and further, the original temperature prediction value can be obtained by using the temperature fitting curve;
  • the primary temperature value is optimized for the original temperature prediction value, and the optimized temperature value is used as the current temperature prediction value.
  • the original temperature prediction value is optimized by using the last temperature output value, and the optimized temperature value is used as the current temperature prediction value, including the previous temperature output value and the original temperature prediction.
  • the weighted average of the values gives the current temperature predictions:
  • y i+1 is the current temperature prediction value
  • y i is the previous temperature output value
  • y p is the original original temperature prediction value
  • w is the preset weight
  • the original temperature prediction value is optimized by using the previous temperature output value, and the accuracy of the current temperature prediction value is further improved on the one hand; on the other hand, the preset weight value is improved by adjusting the preset weight value.
  • Temperature predicts the speed, thereby increasing the efficiency of temperature prediction.
  • the value of the weight W is preset to 0.05.
  • the fitting parameter of the temperature fitting curve is calculated by using the log-fit curve based on the logarithm, and the fitting parameter is optimized by using the optimization parameter, and the current temperature sampling value is calculated.
  • a position in the temperature fitting curve, and a prediction time point is obtained according to the position and the optimization parameter, and the temperature is obtained by the temperature fitting curve according to the optimized fitting parameter and the predicted time point. Predict the value so that the temperature measurement can be accelerated.
  • Temperature sampling of the measured object determining whether the collected temperature data meets the following three conditions of the starting temperature prediction mode: (1) the current temperature sampling value is higher than 32 degrees; (2) the first derivative of the temperature sampling value is lower than 0.07; (3) The second derivative of the temperature sample value is less than zero.
  • the judgment objects of conditions (2) and (3) are the first 30 historical temperature sample values including the current temperature sample value. If the above three conditions are satisfied at the same time, the temperature prediction mode is started, and it is no longer judged whether to start the temperature prediction mode during the entire prediction period, otherwise the current temperature sample value is directly output for display.
  • the temperature prediction mode judges whether to suspend the execution of temperature prediction according to the following two conditions: (1)
  • the current temperature sampling value is one of the temperature sampling values compared with the previous temperature sampling value.
  • the absolute value of the order derivative is greater than 0.15; (2) whether the 10 historical temperature samples closest to the current temperature sample value are continuous drops, and the magnitude of the drop exceeds 0.1 degrees. If both of the above conditions are met, the temperature prediction is suspended and the current temperature sample value is directly output, otherwise the temperature prediction is performed.
  • an appropriate historical temperature sampling value is selected as the fitting data.
  • the historical temperature sample value of the appropriate length is selected for data fitting, and preferably, the first 25 historical temperature sample values including the current temperature sample value are selected as the fitting data, so that the fitting result has high precision and can Accurately reflect the trend of temperature changes.
  • the historical temperature sampling value may have a large change, and the historical temperature sampling value with large error is considered as an outlier, and the outliers are processed to further improve the fitting result. The accuracy.
  • the upper limit thre_upper and the lower limit thre_low of the judgment threshold of the outliers are respectively calculated according to the obtained mean and standard deviation, as shown in the following formula:
  • Thre_upper k_mean+2.5*k_std
  • Thre_low k_mean-2.5*k_std
  • the first derivative of the temperature sample is greater than the upper limit or less than the lower limit, it is considered an outlier and does not participate in the fit.
  • y i is the temperature sample value of the i-th fit data
  • x i is the predicted time point of the i-th fit data
  • n is the number of fit data.
  • the optimization parameters are determined according to the historical temperature sample values and the corresponding historical temperature prediction values, and the fitting parameters k and b are optimized with the optimization parameters. Specifically, the optimization parameter is determined according to the difference between the sum of the slopes of the preset number of historical temperature sample values and the slope of the corresponding historical temperature prediction value, and the formula is:
  • ⁇ is a preset optimization parameter
  • diff is the difference between the sum of the slopes of the preset number of historical temperature sample values and the slope of the corresponding historical temperature prediction value
  • fitting parameters k and b can be performed by the following formula optimization:
  • k is the slope of the current temperature sample value y 0 in the fitted curve
  • the predicted time point x is obtained according to the preset parameter ⁇ , the optimization parameter ⁇ , and the position x 0 :
  • the current temperature prediction value is obtained by the temperature fitting curve, and the temperature prediction value is output.
  • the fitting parameter of the temperature fitting curve is calculated by using the log-fit curve based on the logarithm, and the fitting parameter is optimized by using the optimization parameter to calculate the current temperature sampling value. Calculating a predicted time point according to the position and the optimized parameter according to the position in the temperature fitting curve, and obtaining the current time by the temperature fitting curve according to the optimized fitting parameter and the predicted time point The temperature is predicted so that the temperature measurement can be accelerated.
  • the present invention also provides a system for predicting temperature, as shown in FIG. 3, including a fitting data determination module 301, a fitting parameter calculation module 302, a fitting parameter optimization module 303, a time point prediction module 304, and a temperature prediction module 305.
  • the fitting data determining module 301 is configured to collect a temperature value of the measured object, obtain a temperature sampling value, determine whether to suspend the temperature prediction according to the current temperature sampling value, and if not, obtain a historical temperature sampling value of the measured object, and determine therefrom Several temperature samples are taken as the fitted data for the predicted temperature.
  • the function of the temperature fitting curve is a logarithmic function. When calculating, the fitting of the logarithmic function can be converted into a fitting of a straight line function, and calculating the predicted temperature is less hardware resources, in a lower hardware configuration. It can also run, saving hardware costs.
  • the fitting parameter optimization module 303 is configured to determine an optimization parameter according to the historical temperature sampling value and the corresponding historical temperature prediction value, and optimize the fitting parameters k and b with the optimization parameter. By optimizing the fitting parameters k and b, the fitting parameters k and b precision of the temperature fitting curve are improved, thereby further reducing the error of temperature prediction.
  • the time point prediction module 304 is configured to calculate a position of the current temperature sample value in the temperature fitting curve, and obtain a predicted time point x according to the position and the optimization parameter.
  • the time point prediction module 304 calculates a predicted time point according to the position and the optimized parameter by calculating a position of the current temperature sample value in the temperature fitting curve, thereby providing an accurate calculation for the predicted temperature value of the current sampling point.
  • the data further improves the accuracy of the predicted temperature. as well as
  • the temperature prediction module 305 is configured to obtain the current temperature prediction value by using the temperature fitting curve according to the optimized fitting parameters k, b and the predicted time point x, and output the temperature prediction value.
  • the temperature prediction module 305 calculates all the variables of the temperature fitting curve, and substitutes the temperature fitting curve to obtain the predicted temperature value of the current sampling point.
  • the temperature prediction system of the embodiment adopts a log-based temperature fitting curve, calculates a fitting parameter of the temperature fitting curve, and optimizes the fitting parameter with an optimization parameter, and calculates a current temperature sampling value in the The temperature is fitted to the position in the curve, and the predicted time point is obtained according to the position and the optimized parameter, and the temperature prediction value is obtained by the temperature fitting curve according to the optimized fitting parameter and the predicted time point. , which can speed up the measurement of temperature.
  • the fitting data determining module 301 includes a fitting data determining sub-module for acquiring a historical temperature sampling value of the measured object, and obtaining a first-order historical temperature sampling value according to the historical temperature sampling value.
  • the mean value of the derivative and the standard deviation; the upper threshold and the lower threshold of the outlier determination based on the mean and the standard deviation are respectively:
  • Thre_upper k_mean+2.5*k_std
  • Thre_low k_mean-2.5*k_std
  • thre_upper is the upper threshold
  • thre_low is the lower threshold
  • k_mean is the average
  • k_std is the standard deviation
  • the historical temperature sample value is obtained according to the upper threshold and the lower threshold of the outlier determination.
  • a set number of temperature sample values whose first derivative is less than or equal to the upper limit of the threshold and greater than or equal to the lower limit of the threshold are selected, and the set number of temperature sample values are used as fitting data of the predicted temperature.
  • the outlier point is a temperature sample value that is affected by external factors and causes a large change in the temperature sampling value, and the error is greater than the set value. Determining the fitted data of the predicted temperature by this embodiment can further improve the accuracy of the data fit.
  • the fitting data determining module 301 further includes a temperature prediction pause sub-module, and the condition for setting the pause temperature prediction is: comparing the current temperature sampling value with the previous temperature sampling value. The value is greater than the first temperature set value; and the current temperature sample value and the previously preset number of temperature sample values are continuously decreased, and the magnitude of the decrease is greater than the second temperature set value.
  • Said Determining whether to suspend the temperature prediction according to the current temperature sampling value includes determining whether the condition of the pause temperature prediction is satisfied according to the current temperature sampling value, and if so, suspending the temperature prediction and outputting the current temperature sampling value. The accuracy of the pause temperature prediction can be improved by the condition of the pause temperature prediction set as described above.
  • the fitting data determining module 301 further includes a temperature prediction setting sub-module, and the condition for setting the starting temperature prediction mode is: the current temperature sampling value is greater than the second setting value, and the current temperature The first derivative of the sampled value and the previous preset number of historical temperature samples are smaller than the first set value, and the second derivative is less than zero. Whether or not the condition of the startup temperature prediction mode is satisfied is determined based on the current temperature sampling value, and if so, the temperature prediction mode is started.
  • the temperature prediction mode By setting the condition for starting the temperature prediction mode in advance, if the condition that the startup temperature prediction mode is satisfied is detected, the temperature prediction mode is started; otherwise, the current temperature sampling value is directly output, thereby further improving the efficiency of temperature prediction.
  • the fitting parameter calculation module 302 includes a fitting parameter calculation sub-module for calculating a fitting parameter k of the temperature fitting curve by the optimal solution of the least squares method according to the fitting data.
  • y i is the temperature sample value of the i-th fit data
  • x i is the predicted time point of the i-th fit data
  • n is the number of fit data.
  • the point in time prediction module 304 includes a point in time calculation sub-module for calculating a position x 0 of the current temperature sample value y 0 in the fitted curve as:
  • the temperature prediction module 305 includes a temperature prediction sub-module and a temperature optimization sub-module.
  • the temperature prediction submodule is configured to obtain an original temperature prediction value by using the temperature fitting curve; optimize the original temperature prediction value by using a previous output temperature value, and use the optimized temperature value as This is the temperature prediction value for this time. According to this embodiment, the smoothing effect of the output display of the current temperature prediction value is improved, and the accuracy of the current temperature prediction value is further improved.
  • the original temperature prediction value is optimized by using the previous temperature output value, and the accuracy of the current temperature prediction value is further improved on the one hand; on the other hand, the preset weight value is improved by adjusting the preset weight value.
  • Temperature predicts the speed, thereby increasing the efficiency of temperature prediction.
  • the value of the weight W is preset to 0.05.

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Abstract

一种预测温度的方法及其系统，采用基于对数的温度拟合曲线，计算温度拟合曲线的拟合参数，并用优化参数对所述拟合参数进行优化；计算当前的温度采样值在所述温度拟合曲线中的位置，根据所述位置、优化参数得出预测时间点（S105）；根据优化后的拟合参数和所述预测时间点，通过所述温度拟合曲线得出本次的温度预测值（S106），从而能够加快温度的测量。

Description

预测温度的方法及其系统 技术领域
本发明涉及温度检测领域，特别是一种预测温度的方法及其系统。
背景技术
相比传统的水银玻璃温度计，电子温度计具有读数方便、对人体及周围环境无害(不含水银)的优点，适合于家庭使用。但是由于感温探头的尺寸限制，人体运动和用户佩戴的方式的不同，可穿戴式电子温度计在某些情况下需要较长的时间才能达到热平衡，测量出稳定温度。
发明内容
针对上述现有技术中存在的问题，本发明提供一种预测温度的方法及其系统，能够加快温度的测量。
本发明的预测温度的方法，技术方案如下，包括：
采集被测物体的温度值，得到温度采样值，根据当前的温度采样值判断是否暂停温度预测，若否，获取被测物体的历史温度采样值，从中确定出若干个温度采样值作为预测温度的拟合数据；
通过所述拟合数据计算温度拟合曲线的拟合参数；
根据历史温度采样值与对应的历史温度预测值确定优化参数，用所述优化参数对所述拟合参数进行优化；
计算当前的温度采样值在所述温度拟合曲线中的位置，根据所述位置、优化参数得出预测时间点；
根据优化后的拟合参数和所述预测时间点，通过所述温度拟合曲线得出本次的温度预测值，并输出所述温度预测值。
本发明还提供一种预测温度的系统，包括：
拟合数据确定模块，用于采集被测物体的温度值，得到温度采样值，根据当前的温度采样值判断是否暂停温度预测，若否，获取被测物体的历史温度采 样值，从中确定出若干个温度采样值作为预测温度的拟合数据；
拟合参数计算模块，用于通过所述拟合数据计算温度拟合曲线的拟合参数；
拟合参数优化模块，用于根据历史温度采样值与对应的历史温度预测值确定优化参数，用所述优化参数对所述拟合参数进行优化；
时间点预测模块，用于计算当前的温度采样值在所述温度拟合曲线中的位置，根据所述位置、优化参数得出预测时间点；以及
温度预测模块，用于根据优化后的拟合参数和所述预测时间点，通过所述温度拟合曲线得出本次的温度预测值，并输出所述温度预测值。
本发明的预测温度的方法及其系统，采用基于对数的温度拟合曲线，计算温度拟合曲线的拟合参数，并用优化参数对所述拟合参数进行优化，计算当前的温度采样值在所述温度拟合曲线中的位置，根据所述位置、优化参数得出预测时间点，根据优化后的拟合参数和所述预测时间点，通过所述温度拟合曲线得出本次的温度预测值，从而能够加快温度的测量。
附图说明
图1为一个实施例的预测温度的方法的流程示意图；
图2为一个具体实现方式的预测温度算法的流程示意图；
图3为一个实施例的预测温度的系统的结构示意图。
具体实施方式
为了使本发明的目的、技术方案和优点更加清楚，下面将结合附图对本发明作进一步地详细描述。
请参阅图1中一个实施例的预测温度的方法的流程示意图，包括步骤S101至步骤S106：
S101，采集被测物体的温度值，得到温度采样值，根据当前的温度采样值判断是否暂停温度预测。
该步骤通过采集被测物体的温度值，得到温度采样值，并监测所述温度值， 根据当前的温度采样值判断是否暂停温度预测，从而快速判断是否进入温度预测。
进一步地，在步骤S101之前，还包括设置启动温度预测模式的条件为：
当前的温度采样值大于第二设定值，并且当前的温度采样值及其之前预设数量的历史温度采样值的一阶导数均小于第一设定值、二阶导数均小于0；根据当前的温度采样值判断是否满足所述启动温度预测模式的条件，若是，则启动温度预测模式。
通过提前设置启动温度预测模式的条件，若检测到满足所述启动温度预测模式的条件，启动温度预测模式；否则，直接输出当前的温度采样值，从而进一步提高温度预测的效率。
进一步地，在所述根据当前的温度采样值判断是否暂停温度预测之前，还包括设置暂停温度预测的条件为：与上一次的温度采样值比较，当前的温度采样值的变化幅度大于第一温度设定值；并且当前的温度采样值及其之前预设数量的温度采样值为连续下降、且下降的幅度大于第二温度设定值。所述根据当前的温度采样值判断是否暂停温度预测，包括根据当前的温度采样值判断是否满足所述暂停温度预测的条件，若满足，则暂停温度预测，并输出当前的温度采样值。通过上述设置的暂停温度预测的条件，可提高暂停温度预测的准确度。
S102，若当前的温度采样值不满足暂停温度预测的条件，获取被测物体的历史温度采样值，从中确定出若干个温度采样值作为预测温度的拟合数据。
若当前的温度采样值不满足暂停温度预测的条件，继续进行温度预测。从被测物体的各历史温度采样值中选取若干个温度采样值作为预测温度的拟合数据，可以根据具体情况，适当增加或减少参与拟合的数据数量，从而在提高拟合精确度的同时，减少拟合计算占用的硬件资源，提高数据拟合的效率。
进一步地，可以通过以下方式从获取的被测物体的各历史温度采样值中确定出若干个温度采样值，作为预测温度的拟合数据：获取被测物体的历史温度采样值，根据所述历史温度采样值得到历史温度采样值的一阶导数的均值以及标准差；根据所述均值以及标准差计算离群点判别的阈值上限和阈值下限，分别为：
thre_upper＝k_mean+2.5*k_std，
thre_low＝k_mean-2.5*k_std，
其中，thre_upper为所述阈值上限，thre_low为所述阈值下限，k_mean为所述均值，k_std为所述标准差；根据所述离群点判别的阈值上限和阈值下限，从所述历史温度采样值中筛选出一阶导数小于等于所述阈值上限且大于等于所述阈值下限的、设定数量的温度采样值，将所述设定数量的温度采样值作为预测温度的拟合数据。
其中，所述离群点为受外界因素的影响而导致温度采样值有较大变化，误差大于设定值的温度采样值。通过上述方式确定预测温度的拟合数据可进一步提高数据拟合的精确度。
S103，通过所述拟合数据计算温度拟合曲线的拟合参数k和b，所述温度拟合曲线为：y＝k×ln(x)+b。
由上述公式可知，所述温度拟合曲线的函数为对数函数，在计算时，对数函数的拟合可以转化成直线函数的拟合，如以下公式所示：
y＝k×t+b,t＝ln(x)，
通过该温度拟合曲线计算预测温度，使得计算占用较少的硬件资源，在较低的硬件配置下亦能运行，从而节省硬件成本。
进一步地，为了提高计算拟合参数k和b的计算精确度，可根据所述拟合数据通过求最小二乘法的最优解来计算温度拟合曲线的拟合参数k和b，如以下公式所示：
Figure PCTCN2016098236-appb-000001
其中，yi为第i个拟合数据的温度采样值，xi为第i个拟合数据的预测时间点，n为拟合数据的数量。
S104，根据历史温度采样值与对应的历史温度预测值确定优化参数，用所述优化参数对所述拟合参数k和b进行优化。
具体地，根据预设数量的历史温度采样值斜率之和与对应的历史温度预测 值斜率之和的差，确定优化参数，公式为：
Figure PCTCN2016098236-appb-000002
其中，γ为预设的优化参数，diff为预设数量的历史温度采样值斜率之和与对应的历史温度预测值斜率之和的差，可通过以下公式对所述拟合参数k和b进行优化：
k'＝k×γ,b'＝b×γ，其中，k'、b'为优化后的拟合参数。
由于通过测量得到的温度采样值的二阶导数通常小于预测得到的温度预测值的二阶导数，亦即直接利用拟合参数k和b计算得到的温度预测值通常大于通过测量得到的实际温度值，因此，利用预设的优化参数γ对拟合参数k和b进行优化，提高了温度拟合曲线的拟合参数k和b精确度，从而进一步降低了温度预测的误差。
S105，计算当前的温度采样值在所述温度拟合曲线中的位置，根据所述位置、优化参数得出预测时间点x。
具体地，计算当前的温度采样值y0在拟合曲线中的位置x0为：
Figure PCTCN2016098236-appb-000003
其中，k为当前的温度采样值y0在拟合曲线中的斜率；根据预设参数β、优化参数γ以及所述位置x0求得预测时间点x为：
x＝x0+β×γ，β＝1200。
本步骤通过计算当前的温度采样值在所述温度拟合曲线中的位置，根据所述位置、优化参数得出预测时间点，从而为计算当前采样点的预测温度值提供精确的数据，进一步提高预测温度的精确度。
S106，根据优化后的拟合参数k、b和所述预测时间点x，通过所述温度拟合曲线得出本次的温度预测值，并输出所述温度预测值。
通过计算得到温度拟合曲线的全部变量后，代入所述温度拟合曲线即可得 到本次的温度预测值，对所述本次的温度预测值进行输出显示。
为了提高对所述本次的温度预测值进行输出显示的平滑效果，进一步提高本次的温度预测值的精确度，进一步地，可以通过所述温度拟合曲线得出原始温度预测值；利用上一次的输出温度值对所述原始温度预测值进行优化，将优化后的温度值作为本次的温度预测值。
进一步地，所述利用上一次的温度输出值对所述原始温度预测值进行优化，将优化后的温度值作为本次的温度预测值，包括通过上一次的温度输出值和所述原始温度预测值的加权平均，得到本次的温度预测值：
yi+1＝yi×(1-w)+yp×w，
其中，yi+1为本次的温度预测值，yi为上一次的温度输出值，yp为本次的原始温度预测值，w为预设的权重。
因此，利用上一次的温度输出值对所述原始温度预测值进行优化，一方面进一步提高了本次的温度预测值的精确度；另一方面，通过调整所述预设的权重值，提高了温度预测的速度，从而提高温度预测的效率。优选地，权重W的值预设为0.05。
由上述实施例的预测温度的方法可知，采用基于对数的温度拟合曲线，计算温度拟合曲线的拟合参数，并用优化参数对所述拟合参数进行优化，计算当前的温度采样值在所述温度拟合曲线中的位置，根据所述位置、优化参数得出预测时间点，根据优化后的拟合参数和所述预测时间点，通过所述温度拟合曲线得出本次的温度预测值，从而能够加快温度的测量。
为了更好的理解上述实施例的预测温度方法，下面给出了一具体的实现方式，实现过程包括：
对被测物体进行温度采样，判断采集的温度数据是否满足以下三个启动温度预测模式的条件：(1)当前的温度采样值高于32度；(2)温度采样值的一阶导数低于0.07；(3)温度采样值的二阶导数小于0。条件(2)和(3)的判断对象为包含当前的温度采样值在内的前30个历史温度采样值。如果同时满足上述三个条件则启动温度预测模式，并且在整个预测周期内不再判断是否启动温度预测模式，否则直接输出当前的温度采样值进行显示。
如果温度预测模式已经被启动，在每一次温度采样后，根据以下两个条件判断是否暂停执行温度预测：(1)当前的温度采样值跟上一次的温度采样值相比，温度采样值的一阶导数的绝对值大于0.15；(2)距离当前的温度采样值最近的10个历史温度采样值是否为连续下降，且下降的幅度超过0.1度。如果同时满足上述两个条件，则暂停执行温度预测，并直接输出当前的温度采样值，否则执行温度预测。
以下为该实现方式中执行温度预测的具体算法，请参阅图2的温度预测算法的流程图，包括步骤S201至步骤S206：
S201，选择拟合数据。
根据各历史温度采样值，选择合适的历史温度采样值作为拟合数据。一方面，选择长度合适的历史温度采样值进行数据拟合，优选地，选择包括当前的温度采样值在内的前25个历史温度采样值作为拟合数据，使得拟合结果精确度高，能准确地反映温度变化的趋势。另一方面，由于外界因素的影响，历史温度采样值可能会有较大的变化，误差较大的历史温度采样值被认为是离群点，对离群点进行处理，进而进一步提高拟合结果的精确度。具体地，通过计算拟合数据的一阶导数的均值k_mean及标准差k_std，再根据得到的均值及标准差分别计算离群点的判断阈值的上限thre_upper和下限thre_low，如以下公式所示：
thre_upper＝k_mean+2.5*k_std，
thre_low＝k_mean-2.5*k_std，
如果温度采样值的一阶导数大于上限或者小于下限，则被视为离群点，不参与拟合。
S202，计算拟合参数。
通过所述拟合数据计算温度拟合曲线y＝k×ln(x)+b的拟合参数k和b。在选 择了拟合数据之后，通过求最小二乘法的最优解来计算温度拟合曲线的拟合参数k和b，如以下公式：
Figure PCTCN2016098236-appb-000004
其中，yi为第i个拟合数据的温度采样值，xi为第i个拟合数据的预测时间点，n为拟合数据的数量。
S203，优化拟合参数。
根据历史温度采样值与对应的历史温度预测值确定优化参数，用所述优化参数对所述拟合参数k和b进行优化。具体地，根据预设数量的历史温度采样值斜率之和与对应的历史温度预测值斜率之和的差，确定优化参数，公式为：
Figure PCTCN2016098236-appb-000005
其中，γ为预设的优化参数，diff为预设数量的历史温度采样值斜率之和与对应的历史温度预测值斜率之和的差，可通过以下公式对所述拟合参数k和b进行优化：
k'＝k×γ,b'＝b×γ，其中，k'、b'为优化后的拟合参数。
S204，计算需要预测的时间。
计算当前的温度采样值在所述温度拟合曲线中的位置，根据所述位置、优化参数得出预测时间点x。
具体地，计算当前的温度采样值y0在拟合曲线中的位置x0为：
Figure PCTCN2016098236-appb-000006
其中，k为当前的温度采样值y0在拟合曲线中的斜率；根据预设参数β、优化参数γ以及所述位置x0求得预测时间点x为：
x＝x0+β×γ，β＝1200。
S205，计算预测温度。
根据优化后的拟合参数k、b和所述预测时间点x，通过所述温度拟合曲线得出本次的温度预测值，并输出所述温度预测值。
由上述预测温度的方法的具体实现方式可知，采用基于对数的温度拟合曲线，计算温度拟合曲线的拟合参数，并用优化参数对所述拟合参数进行优化，计算当前的温度采样值在所述温度拟合曲线中的位置，根据所述位置、优化参数得出预测时间点，根据优化后的拟合参数和所述预测时间点，通过所述温度拟合曲线得出本次的温度预测值，从而能够加快温度的测量。
本发明还提供一种预测温度的系统，如图3所示，包括拟合数据确定模块301、拟合参数计算模块302、拟合参数优化模块303、时间点预测模块304以及温度预测模块305。
所述拟合数据确定模块301用于采集被测物体的温度值，得到温度采样值，根据当前的温度采样值判断是否暂停温度预测，若否，获取被测物体的历史温度采样值，从中确定出若干个温度采样值作为预测温度的拟合数据。
所述拟合参数计算模块302用于通过所述拟合数据计算温度拟合曲线的拟合参数k和b，所述温度拟合曲线为：y＝k×ln(x)+b。所述温度拟合曲线的函数为对数函数，在计算时，对数函数的拟合可以转化成直线函数的拟合，计算预测温度是占用较少的硬件资源，在较低的硬件配置下亦能运行，从而节省硬件成本。
所述拟合参数优化模块303用于根据历史温度采样值与对应的历史温度预测值确定优化参数，用所述优化参数对所述拟合参数k和b进行优化。通过对所述拟合参数k和b进行优化，提高了温度拟合曲线的拟合参数k和b精确度，从而进一步降低了温度预测的误差。
所述时间点预测模块304用于计算当前的温度采样值在所述温度拟合曲线中的位置，根据所述位置、优化参数得出预测时间点x。所述时间点预测模块304通过计算当前的温度采样值在所述温度拟合曲线中的位置，根据所述位置、优化参数得出预测时间点，从而为计算当前采样点的预测温度值提供精确的数据，进一步提高预测温度的精确度。以及
所述温度预测模块305用于根据优化后的拟合参数k、b和所述预测时间点x，通过所述温度拟合曲线得出本次的温度预测值，并输出所述温度预测值。所述温度预测模块305通过计算得到温度拟合曲线的全部变量后，代入所述温度拟合曲线即可得到当前采样点的预测温度值。
本实施例的预测温度的系统，采用基于对数的温度拟合曲线，计算温度拟合曲线的拟合参数，并用优化参数对所述拟合参数进行优化，计算当前的温度采样值在所述温度拟合曲线中的位置，根据所述位置、优化参数得出预测时间点，根据优化后的拟合参数和所述预测时间点，通过所述温度拟合曲线得出本次的温度预测值，从而能够加快温度的测量。
在其中一个实施例中，所述拟合数据确定模块301包括拟合数据确定子模块，用于获取被测物体的历史温度采样值，根据所述历史温度采样值得到历史温度采样值的一阶导数的均值以及标准差；根据所述均值以及标准差计算离群点判别的阈值上限和阈值下限，分别为：
thre_upper＝k_mean+2.5*k_std，
thre_low＝k_mean-2.5*k_std，
其中，thre_upper为所述阈值上限，thre_low为所述阈值下限，k_mean为所述均值，k_std为所述标准差；根据所述离群点判别的阈值上限和阈值下限，从所述历史温度采样值中筛选出一阶导数小于等于所述阈值上限且大于等于所述阈值下限的、设定数量的温度采样值，将所述设定数量的温度采样值作为预测温度的拟合数据。
其中，所述离群点为受外界因素的影响而导致温度采样值有较大变化，误差大于设定值的温度采样值。通过该实施例确定预测温度的拟合数据可进一步提高数据拟合的精确度。
在其中一个实施例中，所述拟合数据确定模块301还包括温度预测暂停子模块，用于设置暂停温度预测的条件为：与上一次的温度采样值比较，当前的温度采样值的变化幅度大于第一温度设定值；并且当前的温度采样值及其之前预设数量的温度采样值为连续下降、且下降的幅度大于第二温度设定值。所述 根据当前的温度采样值判断是否暂停温度预测，包括根据当前的温度采样值判断是否满足所述暂停温度预测的条件，若满足，则暂停温度预测，并输出当前的温度采样值。通过上述设置的暂停温度预测的条件，可提高暂停温度预测的准确度。
在其中一个实施例中，所述拟合数据确定模块301还包括温度预测设置子模块，用于设置启动温度预测模式的条件为：当前的温度采样值大于第二设定值，并且当前的温度采样值及其之前预设数量的历史温度采样值的一阶导数均小于第一设定值、二阶导数均小于0。根据当前的温度采样值判断是否满足所述启动温度预测模式的条件，若是，则启动温度预测模式。
通过提前设置启动温度预测模式的条件，若检测到满足所述启动温度预测模式的条件，启动温度预测模式；否则，直接输出当前的温度采样值，从而进一步提高温度预测的效率。
在其中一个实施例中，所述拟合参数计算模块302包括拟合参数计算子模块，用于根据所述拟合数据通过最小二乘法的最优解计算温度拟合曲线的拟合参数k和b：
Figure PCTCN2016098236-appb-000007
其中，yi为第i个拟合数据的温度采样值，xi为第i个拟合数据的预测时间点，n为拟合数据的数量。
在其中一个实施例中，所述时间点预测模块304包括时间点计算子模块，用于计算当前的温度采样值y0在拟合曲线中的位置x0为：
Figure PCTCN2016098236-appb-000008
根据预设参数β、优化参数γ以及所述位置x0求得预测时间点x为：x＝x0+β×γ。
在其中一个实施例中，所述温度预测模块305包括温度预测子模块和温度优化子模块。
所述温度预测子模块用于通过所述温度拟合曲线得出原始温度预测值；利用上一次的输出温度值对所述原始温度预测值进行优化，将优化后的温度值作 为本次的温度预测值。通过该实施例，提高了对所述本次的温度预测值进行输出显示的平滑效果，进一步提高本次的温度预测值的精确度。
所述温度优化子模块用于通过上一次的温度输出值和所述原始温度预测值的加权平均，得到本次的温度预测值：yi+1＝yi×(1-w)+yp×w，其中，yi+1为本次的温度预测值，yi为上一次的温度输出值，yp为本次的原始温度预测值，w为预设的权重。
可知，利用上一次的温度输出值对所述原始温度预测值进行优化，一方面进一步提高了本次的温度预测值的精确度；另一方面，通过调整所述预设的权重值，提高了温度预测的速度，从而提高温度预测的效率。优选地，权重W的值预设为0.05。
以上所述实施例的各技术特征可以进行任意的组合，为使描述简洁，未对上述实施例中的各个技术特征所有可能的组合都进行描述，然而，只要这些技术特征的组合不存在矛盾，都应当认为是本说明书记载的范围。
以上所述实施例仅表达了本发明的几种实施方式，其描述较为具体和详细，但并不能因此而理解为对本发明专利范围的限制。应当指出的是，对于本领域的普通技术人员来说，在不脱离本发明构思的前提下，还可以做出若干变形和改进，这些都属于本发明的保护范围。因此，本发明专利的保护范围应以所附权利要求为准。

Claims (10)

  1. 一种预测温度的方法，其特征在于，包括：
    采集被测物体的温度值，得到温度采样值，根据当前的温度采样值判断是否暂停温度预测，若否，获取被测物体的历史温度采样值，从中确定出若干个温度采样值作为预测温度的拟合数据；
    通过所述拟合数据计算温度拟合曲线的拟合参数；
    根据历史温度采样值与对应的历史温度预测值确定优化参数，用所述优化参数对所述拟合参数进行优化；
    计算当前的温度采样值在所述温度拟合曲线中的位置，根据所述位置、优化参数得出预测时间点；
    根据优化后的拟合参数和所述预测时间点，通过所述温度拟合曲线得出本次的温度预测值，并输出所述温度预测值。
  2. 根据权利要求1所述的预测温度的方法，其特征在于，所述温度拟合曲线为：
    y＝k×ln(x)+b，
    其中，x为预测时间点，y为温度采样值，k和b为所述拟合参数；
    所述通过所述拟合数据计算温度拟合曲线的拟合参数，包括：
    根据所述拟合数据通过最小二乘法的最优解计算温度拟合曲线的拟合参数：
    Figure PCTCN2016098236-appb-100001
    其中，yi为第i个拟合数据的温度采样值，xi为第i个拟合数据的预测时间点，n为拟合数据的数量，k和b为所述拟合参数。
  3. 根据权利要求1所述的预测温度的方法，其特征在于，所述计算当前的温度采样值在所述温度拟合曲线中的位置，根据所述位置、优化参数得出预测时间点x，包括：
    计算当前的温度采样值y0在拟合曲线中的位置x0为：
    Figure PCTCN2016098236-appb-100002
    根据预设参数β、优化参数γ以及所述位置x0求得预测时间点x为：
    x＝x0+β×γ。
  4. 根据权利要求1所述的预测温度的方法，其特征在于，所述通过所述温度拟合曲线得出本次的温度预测值，包括：
    通过所述温度拟合曲线得出原始温度预测值；
    利用上一次的输出温度值对所述原始温度预测值进行优化，将优化后的温度值作为本次的温度预测值。
  5. 根据权利要求4所述的预测温度的方法，其特征在于，所述利用上一次的温度输出值对所述原始温度预测值进行优化，将优化后的温度值作为本次的温度预测值，包括：
    通过上一次的温度输出值和所述原始温度预测值的加权平均，得到本次的温度预测值：
    yi+1＝yi×(1-w)+yp×w，
    其中，yi+1为本次的温度预测值，yi为上一次的温度输出值，yp为本次的原始温度预测值，w为预设的权重。
  6. 根据权利要求1所述的预测温度的方法，其特征在于，所述获取被测物体的历史温度采样值，从中确定出若干个温度采样值作为预测温度的拟合数据，包括：
    获取被测物体的历史温度采样值，根据所述历史温度采样值得到历史温度采样值的一阶导数的均值以及标准差；
    根据所述均值以及标准差计算离群点判别的阈值上限和阈值下限，分别为：
    thre_upper＝k_mean+2.5*k_std，
    thre_low＝k_mean-2.5*k_std，
    其中，thre_upper为所述阈值上限，thre_low为所述阈值下限，k_mean为所述均值，k_std为所述标准差；
    根据所述离群点判别的阈值上限和阈值下限，从所述历史温度采样值中筛选出一阶导数小于等于所述阈值上限且大于等于所述阈值下限的、设定数量的温度采样值，将所述设定数量的温度采样值作为预测温度的拟合数据。
  7. 根据权利要求1所述的预测温度的方法，其特征在于，所述根据当前的温度采样值判断是否暂停温度预测之前，包括：
    设置暂停温度预测的条件为：
    与上一次的温度采样值比较，当前的温度采样值的变化幅度大于第一温度设定值；并且当前的温度采样值及其之前预设数量的温度采样值为连续下降、且下降的幅度大于第二温度设定值；
    所述根据当前的温度采样值判断是否暂停温度预测，包括：
    根据当前的温度采样值判断是否满足所述暂停温度预测的条件，若满足，则暂停温度预测，并输出当前的温度采样值。
  8. 根据权利要求1所述的预测温度的方法，其特征在于，所述采集被测物体的温度值，得到温度采样值，根据当前的温度采样值判断是否暂停温度预测之前，还包括：
    设置启动温度预测模式的条件为：
    当前的温度采样值大于第二设定值，并且当前的温度采样值及其之前预设数量的历史温度采样值的一阶导数均小于第一设定值、二阶导数均小于0；
    根据当前的温度采样值判断是否满足所述启动温度预测模式的条件，若是，则启动温度预测模式。
  9. 一种预测温度的系统，其特征在于，包括：
    拟合数据确定模块，用于采集被测物体的温度值，得到温度采样值，根据当前的温度采样值判断是否暂停温度预测，若否，获取被测物体的历史温度采样值，从中确定出若干个温度采样值作为预测温度的拟合数据；
    拟合参数计算模块，用于通过所述拟合数据计算温度拟合曲线的拟合参数；
    拟合参数优化模块，用于根据历史温度采样值与对应的历史温度预测值确定优化参数，用所述优化参数对所述拟合参数进行优化；
    时间点预测模块，用于计算当前的温度采样值在所述温度拟合曲线中的位置，根据所述位置、优化参数得出预测时间点；以及
    温度预测模块，用于根据优化后的拟合参数和所述预测时间点，通过所述温度拟合曲线得出本次的温度预测值，并输出所述温度预测值。
  10. 根据权利要求9所述的预测温度的系统，其特征在于，所述温度预测模块包括：
    温度预测子模块，用于通过所述温度拟合曲线得出原始温度预测值；利用上一次的输出温度值对所述原始温度预测值进行优化，将优化后的温度值作为本次的温度预测值。
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