CN111123406A - Handheld meteorological instrument temperature data fitting method - Google Patents
Handheld meteorological instrument temperature data fitting method Download PDFInfo
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- CN111123406A CN111123406A CN201911356634.XA CN201911356634A CN111123406A CN 111123406 A CN111123406 A CN 111123406A CN 201911356634 A CN201911356634 A CN 201911356634A CN 111123406 A CN111123406 A CN 111123406A
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- G01—MEASURING; TESTING
- G01W—METEOROLOGY
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- G01W1/02—Instruments for indicating weather conditions by measuring two or more variables, e.g. humidity, pressure, temperature, cloud cover or wind speed
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Abstract
The invention provides a method for fitting temperature data of a handheld meteorological instrument, which adopts a multi-point observation technology and combines a data correction data fusion algorithm to realize accurate fitting of the temperature data of the handheld meteorological instrument. The fitting method comprises the following steps: collecting uncorrected observation values and environment true values of each temperature sensor in different temperature environments; step two, constructing a temperature fitting curve according to the uncorrected observation value and the environment true value in the step one, and correcting the current temperature observation value by using the temperature fitting curve; thirdly, determining the consistent reliability measure of all sensors at the current moment according to the corrected observation value of the temperature data of each sensor; and step four, obtaining a fusion result of all the observed values at the current moment by using the consistent reliability measure as a medium. The invention realizes the multi-point observation correction fusion technology on the handheld meteorological equipment for the first time, and utilizes the measured data of each observation point to the maximum extent, thereby more truly and accurately describing the importance degree of the observed data of each point in the observed environment, effectively avoiding the influence of subjective factors, and improving the temperature data measurement precision and the data reliability of the handheld measurement equipment.
Description
Technical Field
The invention relates to the field of handheld meteorological measurement, in particular to a temperature data fitting method for a handheld meteorological instrument.
Background
The handheld meteorological instrument as a new generation meteorological measuring device has the outstanding advantages of high measuring precision, stability, reliability, small volume, light weight, convenient carrying, direct handheld observation and the like, and can display and record data such as wind speed, wind direction, temperature, humidity, air pressure height, positioning and the like. However, due to the limitation of factors such as power consumption and size, the detection accuracy of the temperature sensor module changes slowly along with the temperature change of the working environment, namely, the drift phenomenon affects the detection accuracy. Therefore, the more accurate approximate fitting method of the temperature data is a key technology for realizing the rapid and accurate observation of the environmental data by the handheld meteorological equipment.
Disclosure of Invention
Aiming at the defects of the prior art, the invention adopts a multi-point observation technology and combines a data correction data fusion algorithm to realize the accurate fitting of the temperature data of the handheld meteorological equipment.
The fitting method of the temperature data of the handheld meteorological instrument comprises the following steps:
collecting uncorrected observation values and environment true values of each temperature sensor in different temperature environments;
step two, constructing a temperature fitting curve according to the uncorrected observation value and the environment true value in the step one, and correcting the current temperature observation value by using the temperature fitting curve;
thirdly, determining the consistent reliability measure of all sensors at the current moment according to the corrected observation value of the temperature data of each sensor;
and step four, obtaining a fusion result of all the observed values at the current moment by using the consistent reliability measure as a medium.
Further, the number of the multi-sensors is at least two.
Further, in step one, the recording process of the uncorrected observation value and the environmental true value of each sensor can be performed separately.
Further, in the step one, a thermostat is used for building standard test environments in different temperature states, and the temperature values of the test points at certain intervals in the full-temperature range are respectively read.
Further, in the second step, a temperature fitting curve is constructed, that is, the sensitivity, the slope and the correlation coefficient of the temperature sensor are obtained by performing linear fitting in a real temperature environment by using a least square method, a value which minimizes the sum of squares of the deviations of each measurement is selected as a reference parameter of the fitting curve, and then the corrected true value is read through the reconstructed fitting curve.
Furthermore, in the third and fourth steps, from the viewpoint of reliability of each observation point data, the actual measurement data of each observation point is mined, and finally, the correction fusion of the multi-point observation data is realized.
The main advantages of the invention are: the invention realizes the multi-point observation correction fusion technology on the handheld meteorological equipment for the first time, has the greatest advantages that the observation result is not limited by the controllable parameter, the influence of the subjective factor is effectively avoided, the measured data of each observation point is utilized to the greatest extent, thereby more truly and accurately describing the important degree of the observation data of each sensor point in the observed environment, effectively avoiding the influence of the subjective factor, and improving the temperature data measurement precision and the data reliability of the handheld measuring equipment.
Drawings
FIG. 1 is a flow chart of a method for fitting temperature data of a handheld meteorological instrument.
Detailed Description
The technical solutions in the embodiments of the present invention will be described clearly and completely with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention provides an embodiment of a handheld meteorological instrument temperature data fitting method, which comprises the following steps:
collecting uncorrected observation values and environment true values of each temperature sensor in different temperature environments;
step two, constructing a temperature fitting curve according to the uncorrected observation value and the environment true value in the step one, and correcting the current temperature observation value by using the temperature fitting curve;
thirdly, determining the consistent reliability measure of all sensors at the current moment according to the corrected observation value of the temperature data of each sensor;
and step four, obtaining a fusion result of all the observed values at the current moment by using the consistent reliability measure as a medium.
In this preferred embodiment, there are at least two of the multiple sensors.
Specifically, the plurality of temperature sensors can adopt partially same specification models or completely different specification models; the recording of the uncorrected observed values and the environmental true values for each sensor is allowed to proceed separately.
In the preferred embodiment of this section, in step one, the recording of the uncorrected observed values and the environmental true values for each sensor can be performed separately.
In the preferred embodiment of the part, in the step one, a thermostat is used for building standard test environments in different temperature states, and the temperature values of the test points at certain intervals in the full-temperature range are respectively read.
Specifically, the certain interval may be set to a temperature value of 10 degrees, 5 degrees, 2 degrees, or the like according to actual conditions.
In this preferred embodiment, in step two, a temperature fitting curve is constructed, that is, the sensitivity, the slope, and the correlation coefficient of the temperature sensor obtained by performing linear fitting in a real temperature environment by using a least square method, a value that minimizes the sum of squares of deviations of measurements is selected as a reference parameter of the fitting curve, and a corrected true value is read through the reconstructed fitting curve.
Specifically, since the measurement characteristics of the temperature sensor are linear, a least square straight line fitting rule is applied.
According to multiple sets of measured values { xi,yiThe (i-1, 2, … n) defines an optimal linear equation (regression equation) y-ax + b, in which the two parameters a, b to be determined take their optimal estimated values
The experimental data distribution is ensured to be more densely close to the obtained straight line as far as possible, the obtained straight line is more consistent, namely, the fitting significance is maximum, and the temperature data obtained after linear fitting deviation correction is adopted is more accurate compared with the original observation data.
In the preferred embodiment of this section, in the third and fourth steps, from the viewpoint of reliability of each observation point data, the actual measurement data of each observation point is mined, and finally, the correction fusion of the multi-point observation data is realized.
Specifically, assume that the corrected observed value of a certain sensor i in the observation system at time k is xi(k) And i is 1,2, … n, and n is the number of sensors of the whole measuring system. The method is characterized in that the maximum and minimum closeness in fuzzy theory is used for nominally defining the closeness sigma of observed values of a sensor i and a sensor j at the moment kij(k) Then to σij(k) Carrying out weighted average calculation to obtain the consistency measure r of the sensor i at the moment k and other sensorsi(k)。
To react with ri(k) The reliability degree of the whole observation interval adopts the signal-to-noise ratio as the evaluation standard of the consistent reliability measure, and the consistent reliability measure of the sensor i at the moment k is
Wherein
And
are respectively consistent with the sensorMeasure of sexual activity ri(k) Mean and variance values at time k.
Measure of reliability wi(k) Normalization processing is carried out, so that the weight W of the sensor i at the moment k in all temperature sensing network data can be obtainedi(k) And then all the correction values collected until the moment k are weighted by Wi(k) The result of the weighted fusion is the current temperature fusion result (temperature value).
One specific example is given below:
the handheld meteorological instrument is used as a new generation meteorological measuring device, and can measure the current environmental temperature value by adopting a mode of combining an integrated built-in temperature and humidity sensor module and an external special temperature sensor in order to enlarge the temperature measuring range and measuring precision of a product.
Due to the limitation of factors such as power consumption and equipment shell size, different temperature sensors can feed back different measurement results at the same time. In this embodiment, a temperature sensing network based on three independent sensors is illustrated, which includes an integrated temperature and humidity sensing module built in a host and two same external dedicated temperature sensors. The specific implementation steps are as follows:
1) the method comprises the steps of setting up standard test environments in different temperature states by using an incubator, placing the handheld device into the incubator to collect observation values and true values of the temperature sensors in different temperature environments, and recording the observation values and the true values of the three temperature sensors to the current environment respectively after the standard test environments are stable.
2) And (3) collecting and reading the whole temperature range (-40-60 ℃) by taking 5 ℃ as a temperature interval (sampling point), and respectively recording the observed values of the three temperature sensors to the current environment and the true value of the constant temperature environment under different temperature environments.
3) For any set of parameters (21 test points) measured by the current three sets of sensors, an optimal linear equation (regression equation) y ═ ax + b, two of which are to be measured, can be determined by means of a least-squares linear fitting ruleThe optimal estimated values of the fixed parameters a and b are obtained
The following equation is taken as the minimum value.
To minimize S, the S pairs should be minimized
The first partial derivative of (a) is zero and the second partial derivative is greater than zero. In practice, since S is always greater than zero, there must be a minimum. The first partial derivative is zero and is obtained after the arrangement:
4) taking the best estimated values of a and b
And substituting the equation y as ax + b to obtain a regression equation of the sensor, and ensuring that the experimental data are closely distributed near the obtained straight line as far as possible by the method. The temperature data obtained after the linear fitting deviation correction is adopted is more accurate compared with the original observation data. Three reconstructed temperature fitting curves can be obtained by adopting the same calculation mode for the data of the other two groups of sensors, and the observation value of any temperature point in the full temperature range of the sensor can be corrected (compensated) by utilizing the temperature fitting curves.
5) According to the reconstructed temperature fitting curve of each sensor, the corrected observed value x of the three temperature sensors in the current observation state at any moment can be further calculated1，x2，x3. Calculating the consistency measure of the sensor i at the current moment and other sensors as follows:
although at a certain observation instant the consistency measure riCan be very large, but cannot be used as a measure of the reliability of the whole observation interval, and the consistency measure r is requirediOn the basis, the mean value and the variance of the observation consistency at the moment k are calculated as follows:
6) and adopting the signal-to-noise ratio as an evaluation standard of the consistent reliability measure, wherein the consistent reliability measure of the sensor i at the moment k is as follows:
after normalization, the method comprises the following steps:
Wi(k) the weight of the sensor i at the current moment in the total temperature sensing network data is given.
7) Weighting W all the collected correction observation values at the k momenti(k) The weighted fusion of (1) thus achieves the result of obtaining the fusion of all the modified observations at the time k using the measure of uniform reliability as a medium
And T is the temperature fusion result (temperature value) at the current moment.
The above-mentioned embodiments are merely illustrative of the preferred embodiments of the present invention, and do not limit the scope of the present invention, and various modifications and improvements made to the technical solution of the present invention by those skilled in the art without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.
Claims (6)
1. The method for fitting the temperature data of the handheld meteorological instrument is characterized by comprising the following steps of:
collecting uncorrected observation values and environment true values of each temperature sensor in different temperature environments;
step two, constructing a temperature fitting curve according to the uncorrected observation value and the environment true value in the step one, and correcting the current temperature observation value by using the temperature fitting curve;
thirdly, determining the consistent reliability measure of all sensors at the current moment according to the corrected observation value of the temperature data of each sensor;
and step four, obtaining a fusion result of all the observed values at the current moment by using the consistent reliability measure as a medium.
2. The method of fitting hand-held weather instrument temperature data of claim 1, wherein there are at least two of the multiple sensors.
3. The method of claim 1, wherein in step one, the recording of the uncorrected observation values and the environmental truth values for each sensor is performed separately.
4. The method for fitting the temperature data of the handheld meteorological instrument according to claim 1, wherein in the first step, a thermostat is used for building standard test environments in different temperature states, and the temperature values of the test points at certain intervals in the full-temperature measuring range are respectively read.
5. The method for fitting temperature data of a handheld meteorological instrument according to claim 1, wherein in the second step, the temperature fitting curve is constructed, that is, the sensitivity, the slope and the correlation coefficient of the temperature sensor are obtained by performing linear fitting under a real temperature environment by using a least square method, a value which minimizes the sum of squares of deviation of each measurement is selected as a reference parameter of the fitting curve, and a corrected true value is read through the reconstructed fitting curve.
6. The method for fitting temperature data of a handheld meteorological instrument according to claim 1, characterized in that in the third step and the fourth step, from the viewpoint of reliability of data of each observation point, the actual measurement data of each observation point is mined, and finally correction fusion of multi-point observation data is realized.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112362358A (en) * | 2020-11-06 | 2021-02-12 | 上海汽车集团股份有限公司 | Method and device for determining physical value of vehicle signal |
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| CN102252770A (en) * | 2011-04-29 | 2011-11-23 | 中冶赛迪工程技术股份有限公司 | Temperature-measurement compensating method and corrective type high-accuracy thermometer |
| CN108618789A (en) * | 2018-05-15 | 2018-10-09 | 江南大学 | Driver fatigue monitor system based on opencv technologies |
| CN108960334A (en) * | 2018-07-12 | 2018-12-07 | 中国人民解放军陆军炮兵防空兵学院郑州校区 | A kind of multi-sensor data Weighted Fusion method |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102252770A (en) * | 2011-04-29 | 2011-11-23 | 中冶赛迪工程技术股份有限公司 | Temperature-measurement compensating method and corrective type high-accuracy thermometer |
| CN108618789A (en) * | 2018-05-15 | 2018-10-09 | 江南大学 | Driver fatigue monitor system based on opencv technologies |
| CN108960334A (en) * | 2018-07-12 | 2018-12-07 | 中国人民解放军陆军炮兵防空兵学院郑州校区 | A kind of multi-sensor data Weighted Fusion method |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112362358A (en) * | 2020-11-06 | 2021-02-12 | 上海汽车集团股份有限公司 | Method and device for determining physical value of vehicle signal |
| CN112362358B (en) * | 2020-11-06 | 2023-08-22 | 上海汽车集团股份有限公司 | Method and device for determining physical value of whole vehicle signal |
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Application publication date: 20200508 |