CN111865267B - Temperature measurement data prediction method and device - Google Patents
Temperature measurement data prediction method and device Download PDFInfo
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- H—ELECTRICITY
- H03—ELECTRONIC CIRCUITRY
- H03H—IMPEDANCE NETWORKS, e.g. RESONANT CIRCUITS; RESONATORS
- H03H17/00—Networks using digital techniques
- H03H17/02—Frequency selective networks
- H03H17/0248—Filters characterised by a particular frequency response or filtering method
- H03H17/0255—Filters based on statistics
- H03H17/0257—KALMAN filters
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Abstract
The embodiment of the invention provides a temperature measurement data prediction method and device, wherein the method comprises the following steps: constructing a temperature state equation and a temperature observation equation of a temperature measurement area based on a Kalman filtering algorithm; and predicting the actual temperature value of the temperature measuring region at the current moment by using the temperature state equation and the temperature observation equation according to the temperature measured value of the temperature measuring region at the moment before the current moment and the temperature measured value at the current moment. The embodiment of the invention greatly reduces the temperature measurement deviation and improves the accuracy of temperature measurement.
Description
Technical Field
The invention belongs to the technical field of temperature measurement optimization, and particularly relates to a temperature measurement data prediction method and device.
Background
During the measurement of the temperature, the measured temperature fluctuates slightly due to the influence of various factors.
For example, factors such as sky cloud state, sunlight irradiation intensity change, wind speed of a temperature measurement region and the like can cause random noise to be mixed in temperature measurement data, so that the temperature measurement data is distorted. The wrong temperature measurement data is used for making decisions on production and life, and huge losses are likely to be brought to production and life of people. Such as a temperature sensor in the kitchen for fire alarm.
Therefore, it is desirable to provide a method for predicting temperature measurement data to reduce or eliminate the influence of these noises on the temperature measurement data.
Disclosure of Invention
In order to solve the problem that the existing temperature measurement data is influenced by various factors to cause larger deviation of the temperature measurement data or at least partially solve the problem, an embodiment of the invention provides a temperature measurement data prediction method and device.
According to a first aspect of an embodiment of the present invention, there is provided a temperature measurement data prediction method, including:
constructing a temperature state equation and a temperature observation equation of a temperature measurement area based on a Kalman filtering algorithm;
and predicting the actual temperature value of the temperature measuring region at the current moment by using the temperature state equation and the temperature observation equation according to the temperature measured value of the temperature measuring region at the moment before the current moment and the temperature measured value at the current moment.
Specifically, a formula of a temperature state equation of the temperature measurement region is constructed by a Kalman filtering algorithm:
x k ＝A k x k-1 +w k ；
wherein x is k Representing the temperature measurement value, x, of the temperature measurement region at the kth time k-1 Representing the temperature measurement value of the temperature measurement region at the k-1 time, w k Representing the process noise of the measurement area at the kth time, A k Coefficients representing a temperature state equation of the measurement region at a kth time;
the formula for constructing the temperature observation equation of the temperature measurement area through the Kalman filtering algorithm is as follows:
y k ＝C k x k-1 +v k ；
wherein y is k Representing the actual temperature value of the measuring region at the kth time, C k Coefficients, v, representing the equation of state of observation of said measurement region at time k k Representing the measurement noise of the measurement area at the kth time.
Specifically, the step of predicting the actual temperature value of the temperature measurement region at the current time using the temperature state equation and the temperature observation equation according to the temperature measurement value of the temperature measurement region at the time immediately before the current time and the temperature measurement value at the current time includes:
adding the variance between the actual temperature value and the temperature measured value of the temperature measuring area at the previous moment and the variance of the process noise of the measuring area at the current moment to obtain the prediction deviation of the actual temperature value at the current moment;
calculating Kalman gain according to the prediction deviation and the measurement noise of the measurement region at the current moment;
and acquiring an actual temperature value of the temperature measuring region at the current moment according to the temperature measured value of the temperature measuring region at the previous moment, the temperature measured value at the current moment and the Kalman gain.
Specifically, the Kalman gain is calculated from the predicted deviation and the measurement noise of the measurement area at the current time by the following formula:
H k ＝P k ′/(P k ′+R k )；
wherein H is k Represents the Kalman gain, P, at time k k ' represents the predicted deviation of the actual temperature value at the kth time, R k Indicating the measurement noise at the kth time.
Specifically, the actual temperature value of the temperature measuring region at the current moment is obtained according to the temperature measured value of the temperature measuring region at the previous moment, the temperature measured value at the current moment and the Kalman gain through the following formula:
wherein,representing the actual temperature value, x, of the temperature measuring region at the kth moment k-1 Representing the temperature measurement value, x, of the temperature measurement region at the k-1 time k Representing the temperature measurement value H of the temperature measurement area at the kth time k The Kalman gain at time k is shown.
Specifically, the step of obtaining the actual temperature value of the temperature measurement area at the current moment further includes, according to the temperature measurement value of the temperature measurement area at the previous moment, the temperature measurement value at the current moment and the Kalman gain:
and updating the prediction deviation of the actual temperature value at the current moment according to the Kalman gain, and taking the updated result as the variance between the actual temperature value and the temperature measured value of the temperature measuring region at the current moment so as to predict the actual temperature value of the temperature measuring region at the next moment of the current moment according to the variance between the actual temperature value and the temperature measured value of the temperature measuring region at the current moment.
Specifically, the prediction deviation of the actual temperature value at the current moment is updated according to the Kalman gain by the following formula, and the updated result is used as the variance between the actual temperature value and the temperature measured value of the temperature measuring region at the current moment:
P k ＝(I-H k C k )P k ′；
wherein P is k Representing the variance between the actual temperature value and the temperature measured value of the temperature measuring region at the kth moment, wherein I is an all-1 matrix, H k Represents the Kalman gain, P, at time k k ' represents the predicted deviation of the actual temperature value at the kth time.
According to a second aspect of the embodiment of the present invention, there is provided a temperature measurement data prediction apparatus, including:
the construction module is used for constructing a temperature state equation and a temperature observation equation of the temperature measurement area based on a Kalman filtering algorithm;
and the prediction module is used for predicting the actual temperature value of the temperature measuring area at the current moment by using the temperature state equation and the temperature observation equation according to the temperature measured value of the temperature measuring area at the moment before the current moment and the temperature measured value at the current moment.
According to a third aspect of embodiments of the present invention, there is also provided an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor invoking the program instructions to be able to perform the method of predicting thermometry data provided by any of the various possible implementations of the first aspect.
According to a fourth aspect of embodiments of the present invention, there is also provided a non-transitory computer-readable storage medium storing computer instructions that cause the computer to perform the method of predicting thermometry data provided by any one of the various possible implementations of the first aspect.
The embodiment of the invention provides a temperature measurement data prediction method and a temperature measurement data prediction device, wherein a model is built for temperature state data and temperature observation data of a temperature measurement area by using a Kalman filtering algorithm, the actual temperature value of the temperature measurement area at the current moment is estimated based on the built model according to the temperature measurement value of the temperature measurement area at the previous moment and the temperature measurement value at the current moment, and compared with the temperature measurement by directly using a thermometer, the temperature measurement deviation is greatly reduced, and the temperature measurement accuracy is improved.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic overall flow chart of a temperature measurement data prediction method according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of the overall structure of a temperature measurement data prediction apparatus according to an embodiment of the present invention;
fig. 3 is a schematic diagram of an overall structure of an electronic device according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In one embodiment of the present invention, a method for predicting temperature measurement data is provided, and fig. 1 is a schematic overall flow chart of the method for predicting temperature measurement data provided in the embodiment of the present invention, where the method includes: s101, constructing a temperature state equation and a temperature observation equation of a temperature measurement area based on a Kalman filtering algorithm;
the Kalman filtering algorithm is an algorithm for optimally estimating the state of a system by utilizing a linear system state equation and inputting and outputting observation data through the system. The optimal estimate can also be seen as a filtering process, since the observed data includes the effects of noise and interference in the system. And in the stage of establishing a model by using the Kalman filtering algorithm, a large amount of data materials are collected for comparison, and redundancy values of the Kalman filtering algorithm are obtained.
Assuming that the temperature of the temperature measuring area is about 26 ℃, the temperature in the temperature measuring area can be influenced by factors such as air circulation, sunlight and the like, and the temperature in the temperature measuring area can fluctuate slightly. The temperature in the temperature measuring region may be measured periodically in minutes, i.e. 1 minute for a sampling time. If the outside weather is cloudy and the sunlight irradiates, the temperature measurement is not completely sealed, and there is little exchange with the outside air, i.e. process noise w is introduced k The variance is Q k The size is assumed to be Q k =0.01. If the influence of process noise is not taken into account, i.e. the true temperature is constant, then Q k ＝0。
The measurement error of the thermometer can be known from the factory specifications of the thermometer. For example, when the measurement error of the thermometer is 0.5 ℃, the variance of the thermometer is 0.25. That is, the data of the kth measurement of the thermometer is not 100% accurate, it is with measurement noise v k And its variance R k ＝0.25。
In the embodiment, a state equation is constructed according to process noise introduced by the environment of the temperature measuring region. And constructing an observation equation according to the measurement error of a thermometer for measuring the temperature of the temperature measuring area.
S102, estimating the actual temperature value of the temperature measuring area at the current moment by using the temperature state equation and the temperature observation equation according to the temperature measured value of the temperature measuring area at the moment before the current moment and the temperature measured value at the current moment.
The present embodiment predicts the actual temperature value at time k from the temperature measurement value at time k-1 and the temperature measurement value at time k. In the prediction, the constructed state equation and the observation equation are used for prediction.
According to the embodiment, the Kalman filtering algorithm is used for establishing a model for the temperature state data and the temperature observation data of the temperature measuring area, the actual temperature value of the temperature measuring area at the current moment is estimated based on the established model according to the temperature measured value of the temperature measuring area at the previous moment and the temperature measured value of the temperature measuring area at the current moment, and compared with the method that the temperature measuring is directly carried out by using a thermometer, the temperature measuring deviation is greatly reduced, and the accuracy of temperature measurement is improved.
Based on the above embodiment, the formula of the temperature state equation for constructing the temperature measurement region by the Kalman filtering algorithm in this embodiment is as follows:
x k ＝A k x k-1 +w k ；
wherein x is k The temperature measurement value of the temperature measurement area at the kth moment is one-dimensional; x is x k-1 Representing a temperature measurement value of the temperature measurement region at the k-1 time; w (w) k Indicating the process noise of the measurement area at time k, w may be set k Variance Q of k ＝0.01；A k The coefficient representing the temperature state equation of the measurement region at the kth time can be set to A k ＝1；
The formula for constructing the temperature observation equation of the temperature measurement area through the Kalman filtering algorithm is as follows:
y k ＝C k x k-1 +v k ；
wherein y is k Representing the actual temperature value of the measuring region at the kth time, C k The coefficient representing the observation state equation of the measurement region at the kth time may be set to C k ＝1；v k Measurement noise, v, representing the measurement region at time k k Variance is R k R can be set according to the factory specification of the thermometer k ＝0.25。
Based on the above embodiment, the step of predicting the actual temperature value of the temperature measurement region at the current time by using the temperature state equation and the temperature observation equation according to the temperature measurement value of the temperature measurement region at the time immediately before the current time and the temperature measurement value at the current time in this embodiment includes: adding the variance between the actual temperature value and the temperature measured value of the temperature measuring area at the previous moment and the variance of the process noise of the measuring area at the current moment to obtain the prediction deviation of the actual temperature value at the current moment;
for example, assuming that the temperature measurement value of the temperature measurement region at the k-1 time is 24.9℃and the true temperature value of the temperature measurement region is 25℃then the deviation of the temperature measurement value is 0.1℃and the variance P k-1 ＝0.1 2 =0.01. The temperature measurement value of the temperature measurement area at the k moment is 25.5 ℃, the real temperature value of the temperature measurement area is 25.1 ℃, and the measurement deviation is 0.4 ℃. The present embodiment estimates the actual temperature value of the kth time temperature measurement region using the temperature measurement value of the kth time temperature measurement region of 25.5 ℃ and the temperature measurement value of the kth-1 time temperature measurement region of 24.9 ℃.
First, when the actual temperature value at the kth time is predicted using the temperature measurement value at the kth-1 time, the predicted deviation P thereof k ′＝P k-1 +Q k ＝0.02。
Calculating Kalman gain according to the prediction deviation and the measurement noise of the measurement region at the current moment;
then, a Kalman gain H is calculated k ＝P k ′/(P k ′+R k )＝0.0741。
And acquiring an actual temperature value of the temperature measuring region at the current moment according to the temperature measured value of the temperature measuring region at the previous moment, the temperature measured value at the current moment and the Kalman gain.
Finally, the actual temperature value at the kth time is predicted by using the temperature measured value at the kth timeIt can be seen that the temperature is compared to the temperature at the kth timeThe measured value is 24.9 ℃, and the predicted actual temperature value at the kth time is 24.94 ℃ which is closer to the actual temperature value of 25.1 ℃.
At this time, the prediction bias at the k time is updated to obtain P k ＝(I-H k C k )P k ' =0.0186, P k As the variance between the actual temperature value and the temperature measurement value of the temperature measurement region at the current moment. Then according to P k Continuing to calculate the actual temperature value y at the next moment of the current moment k+1 . The Kalman filtering algorithm estimates the actual temperature value at each instant by continuously recursing the variance.
The actual temperature value estimated in this embodiment greatly reduces the deviation from the temperature value directly measured by the thermometer, and it makes the state approach to the true value as much as possible although the Kalman filter error does not completely disappear. The variance can be analyzed and simulated by collecting more data under various complex environments such as strong wind, strong light and the like and data density, so that the filtering errors are more matched in environmental adaptability.
On the basis of the above embodiment, the present embodiment calculates a Kalman gain according to the prediction bias and the measurement noise of the measurement region at the current time by the following formula:
H k ＝P k ′/(P k ′+R k )；
wherein H is k Represents the Kalman gain, P, at time k k ' represents the predicted deviation of the actual temperature value at the kth time, R k Indicating the measurement noise at the kth time.
Based on the above embodiment, the present embodiment obtains the actual temperature value of the temperature measurement area at the current time according to the temperature measurement value of the temperature measurement area at the previous time, the temperature measurement value at the current time and the Kalman gain by the following formula:
wherein,representing the actual temperature value, x, of the temperature measuring region at the kth moment k-1 Representing the temperature measurement value, x, of the temperature measurement region at the k-1 time k Representing the temperature measurement value H of the temperature measurement area at the kth time k The Kalman gain at time k is shown.
On the basis of the foregoing embodiment, in this embodiment, after the step of obtaining the actual temperature value of the temperature measurement area at the current time according to the temperature measurement value of the temperature measurement area at the previous time, the temperature measurement value at the current time, and the Kalman gain, the method further includes: and updating the prediction deviation of the actual temperature value at the current moment according to the Kalman gain, and taking the updated result as the variance between the actual temperature value and the temperature measured value of the temperature measuring region at the current moment so as to predict the actual temperature value of the temperature measuring region at the next moment of the current moment according to the variance between the actual temperature value and the temperature measured value of the temperature measuring region at the current moment.
Based on the above embodiment, the present embodiment updates the prediction deviation of the actual temperature value at the current moment according to the Kalman gain by using the following formula, and uses the updated result as the variance between the actual temperature value and the temperature measured value of the temperature measuring region at the current moment:
P k ＝(I-H k C k )P k ′；
wherein P is k Representing the variance between the actual temperature value and the temperature measured value of the temperature measuring region at the kth moment, wherein I is an all-1 matrix, H k Represents the Kalman gain, P, at time k k ' represents the predicted deviation of the actual temperature value at the kth time.
In another embodiment of the present invention, a temperature measurement data prediction apparatus is provided, which is used to implement the method in the foregoing embodiments. Therefore, the descriptions and definitions in the foregoing embodiments of the thermometry data prediction method may be used for understanding the respective execution modules in the embodiments of the present invention. Fig. 2 is a schematic diagram of the overall structure of a temperature measurement data prediction apparatus according to an embodiment of the present invention, where the apparatus includes a construction module 201 and a prediction module 202, where:
the construction module 201 is configured to construct a temperature state equation and a temperature observation equation of the temperature measurement region based on a Kalman filtering algorithm;
the Kalman filtering algorithm is an algorithm for optimally estimating the state of a system by utilizing a linear system state equation and inputting and outputting observation data through the system. The optimal estimate can also be seen as a filtering process, since the observed data includes the effects of noise and interference in the system. And in the stage of establishing a model by using the Kalman filtering algorithm, a large amount of data materials are collected for comparison, and redundancy values of the Kalman filtering algorithm are obtained.
The temperature in the temperature measuring area can fluctuate slightly under the influence of factors such as air circulation, sunlight and the like. Assuming that the outside weather is cloudy when measuring temperature, sometimes not when shining in sunlight, and the temperature measurement is not completely sealed, there is a possibility of a small exchange with the outside air, i.e. introducing process noise. The measurement error of the thermometer can be known from the factory specifications of the thermometer. The construction module 201 constructs a state equation according to process noise introduced by the environment of the temperature measurement region. And constructing an observation equation according to the measurement error of a thermometer for measuring the temperature of the temperature measuring area.
The prediction module 202 is configured to predict an actual temperature value of the temperature measurement region at the current time using the temperature state equation and the temperature observation equation according to a temperature measurement value of the temperature measurement region at a time previous to the current time and a temperature measurement value at the current time.
The prediction module 202 predicts an actual temperature value at time k based on the temperature measurement at time k-1 and the temperature measurement at time k. In the prediction, the constructed state equation and the observation equation are used for prediction.
According to the embodiment, the Kalman filtering algorithm is used for establishing a model for the temperature state data and the temperature observation data of the temperature measuring area, the actual temperature value of the temperature measuring area at the current moment is estimated based on the established model according to the temperature measured value of the temperature measuring area at the previous moment and the temperature measured value of the temperature measuring area at the current moment, and compared with the method that the temperature measuring is directly carried out by using a thermometer, the temperature measuring deviation is greatly reduced, and the accuracy of temperature measurement is improved.
Based on the above embodiment, the formula of the temperature state equation of the temperature measurement region constructed by the construction module in this embodiment through the Kalman filtering algorithm is:
x k ＝A k x k-1 +w k ；
wherein x is k Representing the temperature measurement value, x, of the temperature measurement region at the kth time k-1 Representing the temperature measurement value of the temperature measurement region at the k-1 time, w k Representing the process noise of the measurement area at the kth time, A k Coefficients representing a temperature state equation of the measurement region at a kth time;
the formula for constructing the temperature observation equation of the temperature measurement area through the Kalman filtering algorithm is as follows:
y k ＝C k x k-1 +v k ；
wherein y is k Representing the actual temperature value of the measuring region at the kth time, C k Coefficients, v, representing the equation of state of observation of said measurement region at time k k Representing the measurement noise of the measurement area at the kth time.
On the basis of the above embodiment, the prediction module in this embodiment is specifically configured to: adding the variance between the actual temperature value and the temperature measured value of the temperature measuring area at the previous moment and the variance of the process noise of the measuring area at the current moment to obtain the prediction deviation of the actual temperature value at the current moment; calculating Kalman gain according to the prediction deviation and the measurement noise of the measurement region at the current moment; and acquiring an actual temperature value of the temperature measuring region at the current moment according to the temperature measured value of the temperature measuring region at the previous moment, the temperature measured value at the current moment and the Kalman gain.
On the basis of the above embodiment, the prediction module in this embodiment calculates the Kalman gain according to the prediction bias and the measurement noise of the measurement area at the current time by the following formula:
H k ＝P k ′/(P k ′+R k )；
wherein H is k Represents the Kalman gain, P, at time k k ' represents the predicted deviation of the actual temperature value at the kth time, R k Indicating the measurement noise at the kth time.
Based on the above embodiment, in this embodiment, the actual temperature value of the temperature measurement area at the current time is obtained according to the temperature measurement value of the temperature measurement area at the previous time, the temperature measurement value at the current time and the Kalman gain by the following formula:
wherein,representing the actual temperature value, x, of the temperature measuring region at the kth moment k-1 Representing the temperature measurement value, x, of the temperature measurement region at the k-1 time k Representing the temperature measurement value H of the temperature measurement area at the kth time k The Kalman gain at time k is shown.
Based on the above embodiment, the present embodiment further includes a recursion module, configured to update, according to the Kalman gain, a prediction deviation of the actual temperature value at the current time, and use an update result as a variance between the actual temperature value and the temperature measurement value of the temperature measurement region at the current time, so as to predict, according to the variance between the actual temperature value and the temperature measurement value of the temperature measurement region at the current time, the actual temperature value of the temperature measurement region at a next time of the current time.
Based on the above embodiment, the recursion module in this embodiment updates the prediction deviation of the actual temperature value at the current time according to the Kalman gain by using the following formula, and uses the updated result as the variance between the actual temperature value and the temperature measurement value of the temperature measurement region at the current time:
P k ＝(I-H k C k )P k ′；
wherein P is k Representing the variance between the actual temperature value and the temperature measured value of the temperature measuring region at the kth moment, wherein I is an all-1 matrix, H k Represents the Kalman gain, P, at time k k ' represents the predicted deviation of the actual temperature value at the kth time.
Fig. 3 illustrates a physical schematic diagram of an electronic device, as shown in fig. 3, where the electronic device may include: processor 301, communication interface (Communications Interface) 302, memory (memory) 303 and communication bus 304, wherein processor 301, communication interface 302, memory 303 accomplish the communication between each other through communication bus 304. The processor 301 may call logic instructions in the memory 303 to perform the following method: constructing a temperature state equation and a temperature observation equation of a temperature measurement area based on a Kalman filtering algorithm; and predicting the actual temperature value of the temperature measuring region at the current moment by using the temperature state equation and the temperature observation equation according to the temperature measured value of the temperature measuring region at the moment before the current moment and the temperature measured value at the current moment.
Further, the logic instructions in the memory 303 may be implemented in the form of software functional units and stored in a computer readable storage medium when sold or used as a stand alone product. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The present embodiment provides a non-transitory computer readable storage medium storing computer instructions that cause a computer to perform the methods provided by the above-described method embodiments, for example, including: constructing a temperature state equation and a temperature observation equation of a temperature measurement area based on a Kalman filtering algorithm; and predicting the actual temperature value of the temperature measuring region at the current moment by using the temperature state equation and the temperature observation equation according to the temperature measured value of the temperature measuring region at the moment before the current moment and the temperature measured value at the current moment.
Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the above method embodiments may be implemented by hardware associated with program instructions, where the foregoing program may be stored in a computer readable storage medium, and when executed, the program performs steps including the above method embodiments; and the aforementioned storage medium includes: various media that can store program code, such as ROM, RAM, magnetic or optical disks.
The apparatus embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
From the above description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or of course may be implemented by means of hardware. Based on this understanding, the foregoing technical solution may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a computer readable storage medium, such as ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method described in the respective embodiments or some parts of the embodiments.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (7)
1. A method of predicting thermometry data, comprising:
constructing a temperature state equation and a temperature observation equation of a temperature measurement area based on a Kalman filtering algorithm;
according to the temperature measured value of the temperature measuring area at the moment before the current moment and the temperature measured value at the current moment, predicting the actual temperature value of the temperature measuring area at the current moment by using the temperature state equation and the temperature observation equation;
the equation of the temperature state equation of the temperature measuring region is constructed by a Kalman filtering algorithm:
x k ＝A k x k-1 +w k ；
wherein x is k Representing the temperature measurement value, x, of the temperature measurement region at the kth time k-1 Representing the temperature measurement value of the temperature measurement region at the k-1 time, w k Process noise representing the measurement region at the kth time, A k Coefficients representing a temperature state equation of the measurement region at a kth time;
the formula for constructing the temperature observation equation of the temperature measurement area through the Kalman filtering algorithm is as follows:
y k ＝C k x k-1 +v k ；
wherein y is k Representing the actual temperature value of the measuring region at the kth time, C k Coefficients, v, representing the equation of state of observation of said measurement region at time k k Represent the firstMeasuring noise of the measuring area at the moment k;
the step of predicting the actual temperature value of the temperature measuring area at the current moment by using the temperature state equation and the temperature observation equation according to the temperature measured value of the temperature measuring area at the moment before the current moment and the temperature measured value at the current moment comprises the following steps:
adding the variance between the actual temperature value and the temperature measured value of the temperature measuring area at the previous moment and the variance of the process noise of the measuring area at the current moment to obtain the prediction deviation of the actual temperature value at the current moment;
calculating Kalman gain according to the prediction deviation and the measurement noise of the measurement region at the current moment;
acquiring an actual temperature value of the temperature measuring region at the current moment according to the temperature measured value of the temperature measuring region at the previous moment, the temperature measured value at the current moment and the Kalman gain;
acquiring an actual temperature value of the temperature measuring region at the current moment according to the temperature measured value of the temperature measuring region at the previous moment, the temperature measured value at the current moment and the Kalman gain by the following formula:
wherein,representing the actual temperature value, x, of the temperature measuring region at the kth moment k-1 Representing the temperature measurement value, x, of the temperature measurement region at the k-1 time k Representing the temperature measurement value H of the temperature measurement area at the kth time k The Kalman gain at time k is shown.
2. The method according to claim 1, wherein the Kalman gain is calculated from the prediction bias and the measurement noise of the measurement region at the current time by the following formula:
H k ＝P k ′/(P k ′+R k )；
wherein H is k Represents the Kalman gain, P, at time k k ' represents the predicted deviation of the actual temperature value at the kth time, R k Indicating the measurement noise at the kth time.
3. The method according to claim 1, wherein the step of obtaining the actual temperature value of the temperature measurement region at the current time further comprises, based on the temperature measurement value of the temperature measurement region at the previous time, the temperature measurement value at the current time, and the Kalman gain:
and updating the prediction deviation of the actual temperature value at the current moment according to the Kalman gain, and taking the updated result as the variance between the actual temperature value and the temperature measured value of the temperature measuring region at the current moment so as to predict the actual temperature value of the temperature measuring region at the next moment of the current moment according to the variance between the actual temperature value and the temperature measured value of the temperature measuring region at the current moment.
4. The method according to claim 3, wherein the prediction bias of the actual temperature value at the current time is updated according to the Kalman gain by using the following formula, and the updated result is used as the variance between the actual temperature value and the temperature measured value of the temperature measuring region at the current time:
P k ＝(I-H k C k )P k ′；
wherein P is k Representing the variance between the actual temperature value and the temperature measured value of the temperature measuring region at the kth moment, wherein I is an all-1 matrix, H k Represents the Kalman gain, P, at time k k ' represents the predicted deviation of the actual temperature value at the kth time.
5. A thermometry data prediction apparatus comprising:
the construction module is used for constructing a temperature state equation and a temperature observation equation of the temperature measurement area based on a Kalman filtering algorithm;
the prediction module is used for predicting the actual temperature value of the temperature measuring area at the current moment by using the temperature state equation and the temperature observation equation according to the temperature measured value of the temperature measuring area at the moment before the current moment and the temperature measured value at the current moment;
the construction module constructs a formula of a temperature state equation of the temperature measuring region through a Kalman filtering algorithm, wherein the formula is as follows:
x k ＝A k x k-1 +w k ；
wherein x is k Representing the temperature measurement value, x, of the temperature measurement region at the kth time k-1 Representing the temperature measurement value of the temperature measurement region at the k-1 time, w k Process noise representing the measurement region at the kth time, A k Coefficients representing a temperature state equation of the measurement region at a kth time;
the construction module constructs a formula of a temperature observation equation of the temperature measurement region through a Kalman filtering algorithm, wherein the formula is as follows:
y k ＝C k x k-1 +v k ；
wherein y is k Representing the actual temperature value of the measuring region at the kth time, C k Coefficients, v, representing the equation of state of observation of said measurement region at time k k Representing measurement noise of the measurement area at the kth time;
the prediction module is specifically configured to:
adding the variance between the actual temperature value and the temperature measured value of the temperature measuring area at the previous moment and the variance of the process noise of the measuring area at the current moment to obtain the prediction deviation of the actual temperature value at the current moment;
calculating Kalman gain according to the prediction deviation and the measurement noise of the measurement region at the current moment;
acquiring an actual temperature value of the temperature measuring region at the current moment according to the temperature measured value of the temperature measuring region at the previous moment, the temperature measured value at the current moment and the Kalman gain;
the prediction module obtains an actual temperature value of the temperature measuring region at the current moment according to the temperature measured value of the temperature measuring region at the previous moment, the temperature measured value at the current moment and the Kalman gain by the following formula:
wherein,representing the actual temperature value, x, of the temperature measuring region at the kth moment k-1 Representing the temperature measurement value, x, of the temperature measurement region at the k-1 time k Representing the temperature measurement value H of the temperature measurement area at the kth time k The Kalman gain at time k is shown.
6. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the thermometry data prediction method of any one of claims 1 to 4 when the program is executed by the processor.
7. A non-transitory computer readable storage medium having stored thereon a computer program, which when executed by a processor performs the steps of the thermometry data prediction method according to any one of claims 1 to 4.
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