WO2017211071A1

WO2017211071A1 - Temperature prediction method and apparatus thereof

Info

Publication number: WO2017211071A1
Application number: PCT/CN2016/113219
Authority: WO
Inventors: 高平东
Original assignee: 广州视源电子科技股份有限公司
Priority date: 2016-06-06
Filing date: 2016-12-29
Publication date: 2017-12-14
Also published as: CN106092371A; CN106092371B

Abstract

Disclosed are a temperature prediction method and apparatus. The method comprises: sampling a measured temperature of an object being measured at a fixed time frequency so as to obtain a measured temperature of the object being measured detected at each sampling point from the current sampling point to the M^th previous sampling point, and dividing the measured temperatures into N groups of measured temperature data (S1); for each group of measured temperature data, calculating a slope of a line of best fit of a temperature vs. time curve formed by the group of measured temperature data (S2); if the slopes of the lines of best fit of the N groups of measured temperature data meet a prediction condition, calculating a predicted incremental temperature of the object being measured at the current sampling point according to an increment prediction model (S3); and calculating a predicted temperature of the object being measured at the current sampling point according to a measured temperature and the predicted incremental temperature of the object being measured at the current sampling point as well as a predicted temperature at the previous sampling point (S4). The technical solution of the present invention can accelerate temperature measurement processes and has high interference resistance.

Description

Method and device for predicting temperature

Technical field

The invention relates to the field of temperature detection, and in particular to a method for predicting temperature and a device thereof.

Background technique

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.

Current electronic thermometers generally use the relationship between NTC (Negative Temperature Coefficient) thermistor and temperature to measure the temperature of the measured object, but the contact between the NTC probe and the measured object has a transmission medium or is incomplete. Contact, and the temperature rise of the NTC probe is slow, resulting in a decrease in the temperature measurement rate of the thermometer.

Summary of the invention

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.

In a first aspect, an embodiment of the present invention provides a method for predicting temperature, including:

Sampling the measured temperature of the measured object at a fixed frequency, and obtaining the measured temperature of the measured object sampled from each sampling point between the current sampling point and the past M sampling point, and dividing into N sets of measured temperature data; Where N≥3;

For each set of measured temperature data, calculate a fitted straight line slope of the temperature versus time curve composed of the set of measured temperature data;

When the slope of the fitted straight line of the N sets of measured temperature data satisfies the prediction condition, calculating the predicted incremental temperature of the measured object at the current sampling point according to the predicted incremental model;

Calculating 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 at the previous sampling point, and outputting a prediction of the current sampling point Temperature; wherein the predicted temperature of the last sampling point is based on the measured temperature and the predicted incremental temperature of the measured object at the previous sampling point, and the prediction of the previous sampling point at the previous sampling point Temperature calculated.

With reference to the first aspect, in the first implementation manner of the first aspect, the process of dividing the N pieces into the measured temperature data is specifically:

According to the change of the time axis, the time from the past Mth sampling point to the current sampling point is sequentially divided into N-1 Time period

Taking the measured temperature of all the sampling points of the measured object in the divided mth time period as the mth group measured temperature data, and placing the measured object between the current sampling point and the detecting sampling point The measured temperature of all the sampling points is taken as the Nth measured temperature data; wherein, 1 ≤ m ≤ N-1.

In conjunction with the first implementation of the first aspect, in a second implementation of the first aspect, the formula for calculating the slope of the fitted line is:

Where 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;

Then, 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; and,

One implementation manner of the predicted incremental model may be: ΔT=r×(−b _N /K), and

0<r<1; where b _N is the fitted linear slope of the Nth measured temperature data, and 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.

In conjunction with the first aspect, in a third implementation manner of the first aspect, the method further includes:

When the slope of the fitted straight line of the N sets of measured temperature data does not satisfy the prediction condition, the predicted incremental temperature of the measured object at the current sampling point is set to zero;

After calculating the predicted incremental temperature of the measured object at the current sampling point, the method further includes:

Determining whether the absolute value of the predicted incremental temperature of the measured object at the current sampling point is greater than 1;

If so, the predicted incremental temperature of the measured object at the current sampling point is revised to zero.

In conjunction with the first aspect, in a fourth implementation manner of the first aspect, the process of calculating the predicted temperature of the measured object at a current sampling point is specifically:

Comparing the sum of the measured temperature and the predicted incremental temperature of the measured object at the current sampling point with the first coefficient, and after the multiplication, the predicted temperature of the measured object at the previous sampling point and the The product of the two coefficients is added to calculate a predicted temperature of the measured object at the current sampling point; wherein a sum of the first coefficient and the second coefficient is 1.

With reference to the second implementation manner of the first aspect, in a fifth implementation manner of the first aspect, after calculating the predicted temperature of the measured object at the current sampling point, the method further includes:

When it is judged that b _{N is} greater than zero and the predicted temperature of the measured object at the current sampling point is smaller than the predicted temperature at the previous sampling point, or b _{N is} less than zero and the predicted temperature of the measured object at the current sampling point is greater than When the predicted temperature of a sampling point is used, the predicted temperature of the measured object at the current sampling point is revised to the predicted temperature at the previous sampling point.

In a second aspect, 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.

With reference to the second aspect, in a first implementation manner of the second aspect, 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.

In conjunction with the first implementation of the second aspect, in a second implementation of the second aspect, the formula for calculating the slope of the fitted line is:

Where 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;

Then, 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;

With reference to the second aspect, in a third implementation manner of the second aspect, 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.

Embodiments of the present invention have the following beneficial effects:

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. When necessary, based on the predicted incremental model, the predicted incremental temperature of the measured object at the current sampling point is calculated based on the slope of the fitted straight line. And in combination with the measured temperature of the current sampling point and the predicted temperature of the previous sampling point, thereby speeding up the temperature measurement; since the calculated predicted temperature can take into account the current measured temperature and the predicted temperature of the previous sampling point, the resistance Dry; in addition, since the predicted temperature of each sampling point is predicted according to the above method, the collected data is dynamic and can dynamically predict and predict. temperature.

DRAWINGS

1 is a schematic flow chart of one embodiment of a method for predicting temperature provided by the present invention;

2 is a schematic diagram of a temperature versus time curve provided by the present invention;

3 is a schematic diagram showing a comparison of measured curves of measured temperature and predicted temperature provided by the present invention;

4 is a schematic view showing the slope of a fitted straight line as a function of temperature provided by the present invention;

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.

detailed description

The technical solutions in the embodiments of the present invention are clearly and completely described in the following with reference to the accompanying drawings in the embodiments of the present invention. It is obvious that the described embodiments are only a part of the embodiments of the present invention, but not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative efforts are within the scope of the present invention.

When 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)

Wherein, 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 provided by an embodiment of the present invention 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.

Deriving equation (1) leads to:

T'=T ₀ ke ^-kt (2)

From equation (1)(2)

4 shown as the formula (3) curve, when the derivative T 'is 0 stars is the object temperature T _target, the predicted temperature value is also provided by the present invention a method of the embodiment will be predicted temperature.

With reference to the above theory, the method for predicting temperature provided by the implementation of the present invention is as follows:

Referring to 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:

S1, sampling the measured temperature of the measured object at a fixed frequency, and obtaining the measured temperature of the measured object sampled from each sampling point between the current sampling point and the past M sampling point, and dividing into N sets of measured temperatures. Data; where N ≥ 2.

It should be noted that 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:

First, data acquisition is performed; when the 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.

Then, 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. And taking the measured temperature originally sampled to the sampling point as the last set of measured temperature data. 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.

In this embodiment, 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. In order to achieve a good effect, it is preferable that the value of M is 30, that is, 30 seconds. data.

S2, for each set of measured temperature data, calculating a fitted straight line slope of the temperature versus time curve composed of the set of measured temperature data;

In combination with the above description of the process of data acquisition and data grouping, the following is an example of calculating the slope of the fitted line of the measured temperature data of the third group to illustrate the calculation of the straight line slope of each set of measured temperature data:

As shown in FIG. 2, FIG. 2 is a schematic diagram of a temperature versus time curve provided by the present invention; assuming that the temperature versus time curve composed of the third set of measured temperature data is a segment in the middle curve of FIG. 2, the curve is Perform a linear fitting, that is, calculate the slope of the straight line as the slope of the curve by fitting the curve to the straight line; then assume that the straight line that fits the curve satisfies T=a+b*t The function, in order to minimize the error between the line and the curve, should be satisfied as follows:

When the medium is taken as the minimum value, 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:

Let the above two partial derivatives of equation (4) be equal to zero, calculate:

Calculated by equation (5)

Then 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. The fitted line slopes b ₁ and b _{2 of the} set and the second set of measured temperature data. Generally, 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. In addition, 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.

S3, when the fitted linear slope of the N sets of measured temperature data satisfies the prediction condition, the calculation is based on the predicted incremental model The predicted incremental temperature of the measured object at the current sampling point.

It should be noted that 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. That is to say, when the slope of the fitted straight line of the divided N sets of measured temperature data does not satisfy the prediction condition, the predicted incremental temperature of the measured object at the current sampling point is set to zero. That is, the prediction of the predicted incremental temperature is not performed. Therefore, 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.

After satisfying the above prediction conditions, the predicted incremental temperature of the current sampling point is predicted. Based on the foregoing theoretical derivation, 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.

Calculating the predicted incremental temperature: ΔT = r × (-b _N / K); thereby deriving the predicted incremental model ΔT = r × (-b _N / K) of the embodiment of the present invention, and

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. Since the division method of the plurality of measured temperature data is segmented, that is, 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.

Then, taking the above incremental model, taking the three sets of measured temperature data in the above example process as an example, calculating the predicted incremental temperature of the measured object at the current sampling point, as follows:

Referring to 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:

In order to improve the reliability of the predicted incremental temperature of the current sampling point, it is assumed in the third set of measured temperature data.

It is also a point in FIG. 4, and in order to prevent over-prediction, the value of r is set to 0.6 here; then the predicted incremental temperature of the measured object based on the above-mentioned three sets of measured temperature data at the current sampling point is: ΔT = 0.6 × (-b ₃ /K _T ).

In addition, in order to further prevent the above-mentioned predicted incremental temperature from being over-predicted, in the present embodiment, after calculating the predicted incremental temperature of the measured object at the current sampling point, it is necessary to perform the following revised incremental temperature correction operation:

S4. 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, and output the current sampling point. a predicted temperature; 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 a further sampling point at the previous sampling point The predicted temperature is calculated.

The process of calculating the predicted temperature of the measured object at the current sampling point may be specifically as follows:

It should be noted that 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. Preferably, the first coefficient is 0.2 and the second coefficient is 0.8. And 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.

In addition, after calculating the predicted temperature of the measured object at the current sampling point, the following revision operation of the predicted temperature of the current sampling point is further included, which can avoid the situation that the predicted predicted temperature is oscillated:

At the same time, after the above-mentioned revision operation of the predicted temperature of the current sampling point is completed, 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 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. When necessary, based on the predicted incremental model, the predicted incremental temperature of the measured object at the current sampling point is calculated based on the slope of the fitted straight line. In combination with the measured temperature of the current sampling point and the predicted temperature of the previous sampling point, 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. In addition, since the predicted temperature of each sampling point is predicted according to the above method, the collected data is dynamic and can dynamically predict the predicted temperature.

5 is a schematic structural diagram of an embodiment of a device for predicting temperature provided by the present invention; 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.

With reference to the second aspect, in a first implementation manner of the second aspect, as shown in 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.

In conjunction with the first implementation of the second aspect, in the second implementation of the second aspect, the fitting straight line is calculated The formula for the slope is:

Where 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.

With reference to the second implementation manner of the second aspect, in a third implementation manner of the second aspect,

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 predicted incremental model is: ΔT=r×(−b _N /K), and

In conjunction with the second aspect, in a fourth implementation manner of the second aspect, 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.

With reference to the second aspect, in a fifth implementation manner of the second aspect, 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.

With reference to the second aspect, in a sixth implementation manner of the second aspect, the process of calculating the predicted temperature of the measured object at the current sampling point is specifically:

With reference to the second implementation manner of the second aspect, in a seventh implementation manner of the second aspect, 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 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. When necessary, based on the predicted incremental model, the predicted incremental temperature of the measured object at the current sampling point is calculated based on the slope of the fitted straight line. In combination with the measured temperature of the current sampling point and the predicted temperature of the previous sampling point, 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. In addition, since the predicted temperature of each sampling point is predicted according to the above method, the collected data is dynamic and can dynamically predict the predicted temperature.

One of ordinary skill in the art can understand that all or part of the process of implementing the foregoing embodiments can be completed by a computer program to instruct related hardware, and the program can be stored in a computer readable storage medium. When executed, the flow of an embodiment of the methods as described above may be included. The storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

The above is a preferred embodiment of the present invention, and it should be noted that those skilled in the art can also make several improvements and retouchings without departing from the principles of the present invention. It is the scope of protection of the present invention.

Claims

A method for predicting temperature, comprising:

Sampling the measured temperature of the measured object at a fixed frequency, and obtaining the measured temperature of the measured object sampled from each sampling point between the current sampling point and the past M sampling point, and dividing into N sets of measured temperature data; Where N≥3;

For each set of measured temperature data, calculate a fitted straight line slope of the temperature versus time curve composed of the set of measured temperature data;

When the slope of the fitted straight line of the N sets of measured temperature data satisfies the prediction condition, calculating the predicted incremental temperature of the measured object at the current sampling point according to the predicted incremental model;

Calculating 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 at the previous sampling point, and outputting a prediction of the current sampling point Temperature; wherein the predicted temperature of the last sampling point is based on the measured temperature and the predicted incremental temperature of the measured object at the previous sampling point, and the prediction of the previous sampling point at the previous sampling point Temperature calculated.
The method for predicting temperature according to claim 1, wherein the process of dividing into N sets of measured temperature data is specifically:

According to the change of the time axis, the time from the past Mth sampling point to the current sampling point is sequentially divided into N-1 time segments;

Taking the measured temperature of all the sampling points of the measured object in the divided mth time period as the mth group measured temperature data, and placing the measured object between the current sampling point and the detecting sampling point The measured temperature of all the sampling points is taken as the Nth measured temperature data; wherein, 1 ≤ m ≤ N-1.
The method of predicting temperature according to claim 2, wherein the formula for calculating the slope of the fitted straight line is:

Where 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;

Then, the prediction condition is: 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 the fitting between any two of the N sets of measured temperature data The absolute value of the difference in the slope of the line is less than the preset threshold of the slope; and,

The predicted incremental model is: ΔT=r×(−b N /K), and
0 <r <1; wherein, B is a N-N group to fit the slope of the measured temperature data, B j-1 j-1 for the first group of the fitted line slope of the measured temperature data,
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 method of predicting temperature according to claim 1, wherein the method further comprises:

When the slope of the fitted straight line of the N sets of measured temperature data does not satisfy the prediction condition, the predicted incremental temperature of the measured object at the current sampling point is set to zero;

After calculating the predicted incremental temperature of the measured object at the current sampling point, the method further includes:

Determining whether the absolute value of the predicted incremental temperature of the measured object at the current sampling point is greater than 1;

If so, the predicted incremental temperature of the measured object at the current sampling point is revised to zero.
The method of predicting temperature according to claim 1, wherein the calculating the predicted temperature of the measured object at the current sampling point is specifically:

Comparing the sum of the measured temperature and the predicted incremental temperature of the measured object at the current sampling point with the first coefficient, and after the multiplication, the predicted temperature of the measured object at the previous sampling point and the The product of the two coefficients is added to calculate a predicted temperature of the measured object at the current sampling point; wherein a sum of the first coefficient and the second coefficient is 1.
The method of predicting temperature according to claim 3, further comprising: after calculating the output temperature of the measured object at the current sampling point, further comprising:

When it is judged that b N is greater than zero and the predicted temperature of the measured object at the current sampling point is smaller than the predicted temperature at the previous sampling point, or b N is less than zero and the predicted temperature of the measured object at the current sampling point is greater than When the predicted temperature of a sampling point is used, the predicted temperature of the measured object at the current sampling point is revised to the predicted temperature at the previous sampling point.
A device for predicting temperature, comprising:

The prediction data module is 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 Mth sampling point, and divide the measured temperature into N groups. 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 apparatus for predicting temperature according to claim 7, wherein the prediction data module comprises means 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 apparatus for predicting temperature according to claim 8, wherein the formula for calculating the slope of the fitted straight line is:

Where 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;

Then, the prediction condition is: 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 the fitting between any two of the N sets of measured temperature data The absolute value of the difference in the slope of the line is less than the preset threshold of the slope; and,

The predicted incremental model is: ΔT=r×(−b N /K), and
0<r<1; where b N is the fitted linear slope of the Nth measured temperature data, and 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 apparatus for predicting temperature according to claim 7, wherein the apparatus further comprises:

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.