CN115980884A - Temperature anomaly detection method in high-altitude meteorological detection by secondary Bezier curve fitting - Google Patents
Temperature anomaly detection method in high-altitude meteorological detection by secondary Bezier curve fitting Download PDFInfo
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Abstract
The invention relates to a method for detecting temperature anomaly in high-altitude meteorological detection by secondary Bezier curve fitting, which comprises the following steps: acquiring high-altitude meteorological observation data; respectively forming arrays T and A according to the time data and the temperature data, and carrying out exception marking; constructing an array B for storing fitting temperature data, and carrying out Bezier fitting to obtain fitting temperature data; calculating the deviation between the temperature data and the fitting temperature data, and updating the abnormal mark; constructing an array D for storing the second fitting temperature data, and carrying out Bezier fitting based on the array B to obtain the second fitting temperature data; calculating the deviation between A and D, and updating the abnormal mark; the probability of each temperature data anomaly is calculated and a score array representing the probability of temperature anomalies is output. The invention enhances the data anomaly detection from coarse granularity to fine granularity step by step, provides anomaly data discrimination reference for data quality control personnel, is beneficial to unifying the discrimination standard of the anomaly data, avoids misjudgment and improves the working efficiency of the quality control personnel.
Description
Technical Field
The invention relates to a method for detecting air temperature abnormity in high-altitude meteorological detection by secondary Bezier curve fitting, and belongs to the technical field of high-altitude air temperature detection.
Background
The high-altitude meteorological detection means detecting meteorological elements such as temperature, air pressure, humidity, wind direction, wind speed and the like from the ground to 3 kilometers of altitude by means of an instrument. The high-altitude meteorological detection is one of basic services of meteorological observation, and is responsible for providing accurate and timely high-altitude meteorological information and data for weather forecast, climate analysis, scientific research and international exchange.
The high-altitude meteorological detection method is to utilize a balloon to carry a sonde to lift off for detection (commonly called balloon sounding), and has the advantages that detection data come from in-situ detection, the detection precision is high, the detection altitude is high, and the method is not influenced by weather. However, some abnormal data are often stored in the high altitude meteorological sounding data due to the failure of the sonde, the abnormality of wireless communication, the explosion of the sounding balloon when the sounding balloon rises to a certain altitude, and the like. If the abnormal data is directly utilized without being processed, the subsequent analysis and research will have larger deviation than the actual situation, and the reliability of the analysis and research results will be reduced.
The conventional quality control method for the high-altitude meteorological detection data is developed and matured, and mainly comprises the following steps: extreme value inspection, element-to-element consistency inspection (time consistency, space consistency, horizontal consistency), inverse temperature inspection, wind shear inspection and statics inspection. These quality control steps work well for rejecting significant outlier data, but are somewhat rough for refined outlier detection of second-class high-altitude weather probe data. On the basis of the above methods, a series of new quality control methods are derived, which comprise: improved statics inspection, super-adiabatic decrement rate inspection of temperature, rigor inspection, dual-weight outlier inspection, piecewise fitting inspection, radar original coordinate inspection, gaussian filter inspection, balloon acceleration inspection, monotonicity inspection, linear regression quality inspection and the like.
In high-altitude meteorological detection, data abnormity generally comprehensively considers data such as air temperature, air pressure, humidity, wind power, wind direction and the like, wherein the air temperature is the most main judgment basis of the data abnormity.
At present, a method for solving the anomaly of the high-altitude meteorological detection data by a meteorological bureau is to carry out manual screening, namely, workers with relevant experience analyze the acquired data and mark the acquired data with a label. However, the manual screening method not only wastes human resources, but also causes inconsistency of data abnormal determination results due to non-uniformity of the judgment standards of the personnel.
Disclosure of Invention
In order to overcome the problems, the invention provides a method for detecting the abnormal air temperature in the high-altitude meteorological detection by secondary Bezier curve fitting, the method gradually enhances the abnormal detection of data from coarse granularity to fine granularity, comprehensively utilizes a Tag array and two Bezier fitting results to calculate an abnormal value array score representing the abnormal probability, provides abnormal data discrimination reference for data quality control personnel, is favorable for unifying the judgment standard of the quality control personnel on the abnormal data, avoids misjudgment and misjudgment, and can also greatly improve the working efficiency of the quality control personnel.
The technical scheme of the invention is as follows:
a method for detecting temperature anomaly in high-altitude meteorological detection by quadratic Bezier curve fitting comprises the following steps:
acquiring high-altitude meteorological observation data, wherein the high-altitude meteorological observation data comprise time data and temperature data;
arrays T and a are formed from the time data and the temperature data respectively,
carrying out extreme value-based exception marking based on the arrays T and A, and storing in a Tag array;
constructing an array B for storing fitting temperature data, carrying out Bezier fitting based on the array T and the array A to obtain fitting temperature data, and storing the fitting temperature data in the array B;
calculating the deviation of the temperature data and the fitting temperature data, and updating the abnormal mark in the Tag array according to the deviation and the threshold value;
constructing an array D for storing the second fitting temperature data, carrying out Bezier fitting based on the first Bezier fitting array B to obtain second fitting temperature data, and storing the second fitting temperature data in the array D;
calculating the deviation of the temperature data and the second fitting temperature data, and updating an abnormal mark in the Tag array according to the deviation and the threshold value;
the probability of each temperature data anomaly is calculated and a score array representing the probability of temperature anomalies is output.
Further, the high altitude meteorological observation data are obtained by the following method:
reading a source data file generated by an L-band high altitude meteorological detection system;
and analyzing the source data file, and extracting time and temperature data in the source data file.
Further, the length of the arrays T and A is n +2, and the data are stored under the subscripts of 1,2, \ 8230, where n is the position and the number of data points, and the head and tail elements of the arrays T and A are as follows:
A[0]＝GT 0 ；
T[0]＝T[1]-1；
A[n+1]＝A[n]+A[n]-A[n-1]；
T[n+1]＝T[n]+1；
wherein, GT 0 Is the ground temperature.
Further, carrying out extreme value-based exception marking on the arrays T and A, specifically:
acquiring a segmented extreme value of historical high-altitude meteorological observation data, wherein the segmented extreme value comprises a temperature maximum value max _ temp and a temperature minimum value min _ temp, and acquiring a range maximum value range _ max and a range minimum value range _ min of a measuring instrument;
tag labeling was performed for arrays T and A according to the following:
where Tag of 0 indicates normal temperature, and Tag of 1 indicates abnormal temperature.
According to A [1 ]]Temperature GT with the ground 0 Has a difference of Tag [1 ]]The assignment, the formula is as follows:
further, an array B for storing fitting temperature data is constructed, bezier fitting is carried out based on the array T and the array A, and fitting temperature data are obtained, and the method specifically comprises the following steps:
constructing an array B for storing fitting temperature data;
and (3) performing the following operation on each point A [ k ] to be determined by Bezier fitting, wherein k is more than or equal to 1 and less than or equal to n:
P1＝(A[k]，T[k])；
will P 0 、P 1 、P 2 As control points for Bezier curve calculation;
calculating an intermediate variable t:
calculating an intermediate variable P t ：
Wherein, P 0 [0]，P 1 [0]，P 2 [0]Temperature values of three control points respectively;
let B [ k ]]＝P t 。
Further, calculating the deviation between the temperature data and the fitting temperature data, and updating the abnormal mark in the Tag array according to the deviation and the threshold value according to the following formula:
wherein threshold1 is a threshold.
Further, the threshold1 is 0.122.
Further, an array D for storing the second fitting temperature data is constructed, bezier fitting is carried out based on the first Bezier fitting array B, and the second fitting temperature data is obtained, specifically:
constructing an array D for storing second fitting temperature data;
the Bezier fitting is utilized to perform the following operations on each point B [ k ] to be determined:
P1＝(B[k]，T[k])；
calculating an intermediate variable t:
calculating an intermediate variable Pt:
let D [ k)]＝P t 。
Further, calculating the deviation between the temperature data and the second fitting temperature data, and updating the abnormal mark in the Tag array according to the deviation and the threshold value according to the following formula:
wherein threshold2 is a threshold.
Further, calculating the possibility of each temperature data anomaly, and outputting a score array representing the probability of the temperature anomaly, specifically:
and carrying out abnormal value labeling on the non-abnormal temperature data for representing the possibility of abnormality, wherein an abnormal value array score formula is as follows:
the invention has the following beneficial effects:
the invention enhances the data anomaly detection from coarse granularity to fine granularity step by step, firstly, the extreme value of the instrument range and the segmented extreme value of the historical data are utilized to filter the anomaly, and some obvious anomaly points are stripped; on the basis, carrying out anomaly judgment on discontinuous abnormal data points by using a first Bezier curve fitting mode; however, the first Bezier fitting is easily interfered by continuous abnormal points to generate misjudgment on the continuous abnormal points, so that the second Bezier curve fitting is carried out on the basis of the first Bezier judgment to avoid misjudgment of abnormal data caused by the continuous abnormal points. Finally, the method comprehensively utilizes the Tag marked array and the two Bezier fitting results to calculate the abnormal value array score representing the abnormal probability, provides abnormal data discrimination reference for data quality control personnel, is favorable for unifying the judgment standard of the quality control personnel on the abnormal data, avoids misjudgment and misjudgment, and can also greatly improve the working efficiency of the quality control personnel.
Drawings
FIG. 1 illustrates an anomaly detected using temperature range in accordance with an embodiment of the present invention.
FIG. 2 illustrates an abnormal point detected by using historical extrema between temperature partitions according to an embodiment of the present invention.
FIG. 3 is a diagram illustrating outliers obtained by performing a Bezier fit for the first time and then detecting the difference between the measured value and the fit value.
FIG. 4 shows anomaly points obtained by performing a second Bezier fitting and then detecting by subtracting the first fitting value from the current fitting value according to an embodiment of the present invention.
Detailed Description
The invention is described in detail below with reference to the figures and the specific embodiments.
A method for detecting temperature anomaly in high-altitude meteorological detection by quadratic Bezier curve fitting comprises the following steps:
acquiring high-altitude meteorological observation data, wherein the high-altitude meteorological observation data comprise time data and temperature data;
arrays T and a are formed from the time data and the temperature data respectively,
carrying out extreme value-based exception marking based on the arrays T and A, and storing in a Tag array;
constructing an array B for storing fitting temperature data, carrying out Bezier fitting based on the array T and the array A to obtain fitting temperature data, and storing the fitting temperature data in the array B;
calculating the deviation of the temperature data and the fitting temperature data, and updating the abnormal mark in the Tag array according to the deviation and the threshold value;
constructing an array D for storing the second fitting temperature data, carrying out Bezier fitting based on the first Bezier fitting array B to obtain second fitting temperature data, and storing the second fitting temperature data in the array D;
calculating the deviation of the temperature data and the second fitting temperature data, and updating the abnormal mark in the Tag array according to the deviation and the threshold value;
the possibility of abnormality of each temperature data is calculated, and a score array representing the probability of temperature abnormality is output.
In one embodiment, the high altitude meteorological observation data is obtained by:
reading a source data file generated by an L-band high altitude meteorological detection system;
and analyzing the source data file, and extracting time and temperature data in the source data file.
More specifically, the source data file (namely, the S file) generated by the domestic L-wave band high altitude weather detection system is read for analysis, and the time and temperature data in the source data file are extracted.
In one embodiment of the invention, the length of the arrays T and A is n +2, and the data are stored under the subscripts 1,2, \8230, where n is the position and n is the number of data points, and the head and tail elements of the arrays T and A are as follows:
A[0]＝GT 0 ；
T[0]＝T[1]-1；
A[n+1]＝A[n]+A[n]-A[n-1]；
T[n+1]＝T[n]+1；
wherein, GT 0 Is the ground temperature.
In an embodiment of the present invention, the extremum-based anomaly labeling is performed on the arrays T and a, specifically:
acquiring a segmented extreme value of historical high-altitude meteorological observation data, wherein the segmented extreme value comprises a temperature maximum value max _ temp and a temperature minimum value min _ temp, and acquiring a range maximum value range _ max and a range minimum value range _ min of a measuring instrument;
tag labeling was performed for arrays T and A according to the following:
wherein Tag of 0 indicates normal temperature, and Tag of 1 indicates abnormal temperature.
According to A [1 ]]GT temperature with the ground 0 Has a difference of Tag [1 ]]The assignment, the formula is as follows:
in one embodiment of the present invention, an array B for storing fitting temperature data is constructed, and Bezier fitting is performed based on the array T and the array a to obtain fitting temperature data, which specifically includes:
constructing an array B for storing fitting temperature data;
and (3) performing the following operation on each point A [ k ] to be determined by Bezier fitting, wherein k is more than or equal to 1 and less than or equal to n:
P 1 ＝(A[k]，T[k])；
will P 0 、P 1 、P 2 As control points for Bezier curve calculation;
calculating an intermediate variable t:
calculating an intermediate variable P t ：
Wherein, P 0 [0]，P 1 [0]，P 2 [0]Temperature values of three control points respectively;
let B [ k ] be]＝P t 。
Introducing intermediate variables t and P t Facilitating a simplified calculation process.
In one embodiment of the present invention, when calculating the deviation between the temperature data and the fitting temperature data and updating the abnormal flag in the Tag array according to the deviation and the threshold, the method proceeds according to the following formula:
wherein threshold1 is a threshold.
In particular, the threshold1 is 0.122.
In an embodiment of the present invention, an array D for storing second-time fitting temperature data is constructed, and Bezier fitting is performed based on the first-time Bezier fitting array B to obtain second-time fitting temperature data, which specifically includes:
constructing an array D for storing second fitting temperature data;
the Bezier fitting is utilized to perform the following operations on each point B [ k ] to be determined:
P1＝(B[k]，T[k])；
calculating an intermediate variable t:
calculating an intermediate variable P t ：
Let D [ k ] be]＝P t 。
In one embodiment of the present invention, when calculating the deviation between the temperature data and the second fitting temperature data and updating the abnormal flag in the Tag array according to the deviation and the threshold, the following steps are performed:
wherein threshold2 is a threshold.
In particular, the threshold2 is 0.076.
In one embodiment of the invention, the possibility of abnormality of each temperature data is calculated, a score array representing the probability of the temperature abnormality is output and used for representing the possibility of the temperature abnormality, the possibility is between 0 and 1, the larger the value of the score array is, the more likely the score array is to be abnormal data, and the formula of the abnormal value array score is as follows:
in specific use, the temperature node abnormity is checked by setting a threshold value.
The following is a description of the results of anomaly detection by using the sounding data of a certain sounding station in the method of the present invention.
Fig. 1 shows abnormal points detected by the sounding station using temperature range, where "x" is the abnormal point and "· is the normal point. (the two figures are time 1/7/2020 and time 2/5/19/2020). It can be seen from the figure that it is effective to determine the abnormality of the data point by using the range of the temperature measuring instrument, and the data point beyond the range of the instrument can be correctly marked.
FIG. 2 shows abnormal points detected by the exploration station using historical extreme values between temperature partitions, where "x" is an abnormal point and "· is a normal point. (the two figures are time 1/7/2020 and time 2/5/19/2020). As can be seen from the figure, the data point abnormity judgment is effectively carried out by utilizing the historical extreme values between the temperature partitions, and the marked abnormal points obviously deviate from the data track in the ascending process of the sounding balloon.
Fig. 3 shows anomaly points obtained by performing detection by using the difference between the measured value and the fitted value after the first Bezier fitting of the data of the sounding station, wherein "+" is the anomaly point and "·" is the normal point. (the time of the two figures are 2020, 2-month, 3-day, 7 and 2020, 1-month, 2-day, 7, respectively). As can be seen, the first Bezier fitting method can effectively detect it as an anomaly for a small number of offset outliers in the 8 th and 58 th seconds in fig. 3 (a), 3072 th and 3074 th seconds in fig. 3 (b).
Fig. 4 shows anomaly points obtained by performing a second Bezier fitting on the sounding station data and then detecting the difference between the first fitting value and the current fitting value, where "+" is an anomaly point and "·" is a normal point. (1/5/7 in 2020). As can be seen from the graph, the continuous sharp change from 3345 seconds to 3350 seconds of FIG. 4, the first Bezier fitting result is easily affected by the continuous outliers, thereby misjudging the correct point (the temperature corresponding to 3346 seconds). On the basis of the first Bezier judgment, the 3344 second corresponding point temperature is selected as a reference, so that the 3344 second corresponding point can be recovered, and the error judgment of the abnormity is reduced.
The above description is only an embodiment of the present invention, and not intended to limit the scope of the present invention, and all equivalent structures made by using the contents of the specification and the drawings of the present invention or directly or indirectly applied to other related technical fields are included in the scope of the present invention.
Claims (10)
1. A method for detecting temperature anomaly in high-altitude meteorological detection by secondary Bezier curve fitting is characterized by comprising the following steps:
acquiring high-altitude meteorological observation data, wherein the high-altitude meteorological observation data comprises time data and temperature data;
arrays T and a are formed from the time data and the temperature data respectively,
carrying out extreme value-based exception marking based on the arrays T and A, and storing in a Tag array;
constructing an array B for storing fitting temperature data, carrying out Bezier fitting based on the array T and the array A to obtain fitting temperature data, and storing the fitting temperature data in the array B;
calculating the deviation of the temperature data and the fitting temperature data, and updating the abnormal mark in the Tag array according to the deviation and the threshold value;
constructing an array D for storing the second fitting temperature data, carrying out Bezier fitting based on the first Bezier fitting array B to obtain second fitting temperature data, and storing the second fitting temperature data in the array D;
calculating the deviation of the temperature data and the second fitting temperature data, and updating an abnormal mark in the Tag array according to the deviation and the threshold value;
the probability of each temperature data anomaly is calculated and a score array representing the probability of temperature anomalies is output.
2. The method for detecting air temperature anomaly in quadratic Bezier curve-fitted high altitude meteorological sounding according to claim 1, wherein the high altitude meteorological observation data is obtained by:
reading a source data file generated by an L-waveband high-altitude meteorological detection system;
and analyzing the source data file, and extracting time and temperature data in the source data file.
3. The method for detecting the air temperature abnormality in the high altitude meteorological exploration with the quadratic Bezier curve fitting as claimed in claim 1, wherein the length of the arrays T and A is n +2, the data are stored in the arrays with subscripts of 1,2, \ 8230, the position of n is the number of data points, and the head and tail elements of the arrays T and A are as follows:
A[0]＝GT 0 ；
T[0]＝T[1]-1；
A[n+1]＝A[n]+A[n]-A[n-1]；
T[n+1]＝T[n]+1；
wherein, GT 0 Is the ground temperature.
4. The method for detecting the abnormal air temperature in the high-altitude meteorological sounding by quadratic Bezier curve fitting according to claim 3, wherein the extreme value-based abnormal marking is carried out on the arrays T and A, and specifically comprises the following steps:
acquiring a segmented extreme value of historical high-altitude meteorological observation data, wherein the segmented extreme value comprises a temperature maximum value max _ temp and a temperature minimum value min _ temp, and acquiring a range maximum value range _ max and a range minimum value range _ min of a measuring instrument;
tag labeling was performed for arrays T and A according to the following:
wherein Tag of 0 indicates normal temperature, and Tag of 1 indicates abnormal temperature.
According to A [1 ]]GT temperature with the ground 0 Has a difference of Tag [1 ]]The assignment, the formula is as follows:
5. the method for detecting temperature anomaly in high-altitude meteorological sounding by quadratic Bezier curve fitting according to claim 4, wherein an array B for storing fitting temperature data is constructed, bezier fitting is performed based on the array T and the array A, and the fitting temperature data is obtained, and specifically:
constructing an array B for storing fitting temperature data;
and (3) performing the following operation on each point A [ k ] to be determined by Bezier fitting, wherein k is more than or equal to 1 and less than or equal to n:
P 0 ＝(A[i],T[i])i<k and Tag[i]＝0and
P 1 ＝(A[k],T[4])；
P 2 ＝(A[j],T[j])k<j and Tag[j]＝0and
will P 0 、P 1 、P 2 As control points for Bezier curve calculation;
calculating an intermediate variable t:
calculating an intermediate variable P t ：
Wherein, P 0 [0],P 1 [0],P 2 [0]Temperature values of three control points respectively;
let B [ k ]]＝P t 。
6. The method for detecting air temperature anomaly during high altitude meteorological sounding with quadratic Bezier curve fitting according to claim 5, wherein the deviation between the temperature data and the fitted temperature data is calculated, and the anomaly flag in the Tag array is updated according to the deviation and the threshold value according to the following formula:
wherein threshold1 is a threshold.
7. The method for detecting temperature anomaly in high altitude meteorological sounding by quadratic Bezier curve fitting according to claim 6, wherein the threshold1 is 0.122.
8. The method for detecting temperature anomaly in high-altitude meteorological sounding by quadratic Bezier curve fitting according to claim 6, wherein an array D for storing second-time fitting temperature data is constructed, bezier fitting is performed based on the first-time Bezier fitting array B to obtain second-time fitting temperature data, and the method specifically comprises the following steps:
constructing an array D for storing second fitting temperature data;
and (3) performing the following operation on each point B [ k ] to be judged by using Bezier fitting:
P 0 ＝(B[i],T[i])i<k and Tag[i]＝0and
P 1 ＝(B[k],T[k])；
P 2 ＝(B[j],T[j])k<j and Tag[j]＝0and
calculating an intermediate variable t:
calculating an intermediate variable P t ：
Let D [ k ] be]＝P t 。
9. The method for detecting air temperature abnormality in the high altitude meteorological sounding by quadratic Bezier curve fitting according to claim 7, wherein the deviation between the temperature data and the secondarily fitted temperature data is calculated, and when the abnormality flag in the Tag array is updated according to the deviation and the threshold value, the method is performed according to the following formula:
wherein threshold2 is a threshold.
10. The method for detecting temperature anomaly in high-altitude meteorological sounding by quadratic Bezier curve fitting according to claim 4, wherein the probability of each temperature data anomaly is calculated, and a score array representing the probability of temperature anomaly is output, specifically:
and carrying out abnormal value labeling on the non-abnormal temperature data for representing the possibility of abnormality, wherein an abnormal value array score formula is as follows:
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| EP0421566A2 (en) * | 1989-10-04 | 1991-04-10 | Stanley Electric Co., Ltd. | System for generating approximate curves and system for memorizing curves |
| CN103528585A (en) * | 2013-09-26 | 2014-01-22 | 中北大学 | Path planning method of passable area divided at unequal distance |
| CN108829878A (en) * | 2018-06-26 | 2018-11-16 | 北京理工大学 | A kind of industry experiment data abnormal point detecting method and device |
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