CN111121946B - Method for accurately determining abnormal value at multiple points in large dynamic range and large discrete single area - Google Patents
Method for accurately determining abnormal value at multiple points in large dynamic range and large discrete single area Download PDFInfo
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- CN111121946B CN111121946B CN201911307015.1A CN201911307015A CN111121946B CN 111121946 B CN111121946 B CN 111121946B CN 201911307015 A CN201911307015 A CN 201911307015A CN 111121946 B CN111121946 B CN 111121946B
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- G01—MEASURING; TESTING
- G01H—MEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC OR INFRASONIC WAVES
- G01H9/00—Measuring mechanical vibrations or ultrasonic, sonic or infrasonic waves by using radiation-sensitive means, e.g. optical means
- G01H9/004—Measuring mechanical vibrations or ultrasonic, sonic or infrasonic waves by using radiation-sensitive means, e.g. optical means using fibre optic sensors
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
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- H04B10/00—Transmission systems employing electromagnetic waves other than radio-waves, e.g. infrared, visible or ultraviolet light, or employing corpuscular radiation, e.g. quantum communication
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- H04B10/075—Arrangements for monitoring or testing transmission systems; Arrangements for fault measurement of transmission systems using an in-service signal
- H04B10/079—Arrangements for monitoring or testing transmission systems; Arrangements for fault measurement of transmission systems using an in-service signal using measurements of the data signal
- H04B10/0791—Fault location on the transmission path
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Abstract
The invention discloses a method for accurately determining abnormal values in a large dynamic range and a large discrete single area at multiple points. Collecting a single-area sample with a large dynamic range and a large dispersion, extracting the standard deviation and the dispersion difference of each point in the single-area sample, and carrying out threshold processing on the standard deviation and the dispersion difference; processing the standard deviation of each point in the single-area sample through standard deviation mean forward translation operation; calculating the confidence rates of all points in the single-region sample; and comparing the confidence rates of all the points with the threshold values of the confidence rates respectively, judging abnormal points in the sample, and accurately determining abnormal values by using the large dynamic range and the large discrete single-region multipoint. The invention overcomes the problems of large data volume and unobvious abnormality and can quickly and accurately determine the abnormal numerical value.
Description
Technical Field
The invention belongs to the technical field of computer data processing, and particularly relates to a method for determining abnormal values of large dynamic range and large discrete data.
Background
In computer modeling, it is important to clean up data samples to ensure that observations adequately represent problems. Sometimes, a data set may contain extreme values outside of the expected range, which are often referred to as outliers. By understanding and even removing these outliers, modeling and model skills can be improved.
Currently proposed methods of determining data outliers include distance-based methods, bias-based methods, and density-based methods, among others. Although the methods can find out abnormal values, the methods are complicated and require a large amount of machine learning, and particularly when the dynamic range is large and abnormal data are not obvious, the abnormal values are difficult to find out by the existing methods.
For example, in the distributed optical fiber vibration sensing positioning technology, the false alarm rate is a prominent problem, and when the system makes a false alarm, the vibration point measured by the system is different from the actual vibration point by hundreds of meters. Especially, when the transmission distance is long, the signal and the background noise cannot be distinguished because the back scattering signal is weak, and the false alarm of vibration positioning is caused, so a method for accurately positioning the vibration point is needed.
Disclosure of Invention
In order to solve the technical problems in the background art, the invention provides a method for accurately determining abnormal values at multiple points in a large dynamic range and a large discrete single area, which overcomes the problems of large data volume and unobvious abnormality and can quickly and accurately determine abnormal values.
In order to achieve the technical purpose, the technical scheme of the invention is as follows:
the method for accurately determining the abnormal value of the large dynamic range and the large discrete single area multipoint comprises the following steps:
(1) collecting a single-area sample with a large dynamic range and a large dispersion, extracting the standard deviation and the dispersion difference of each point in the single-area sample, and carrying out threshold processing on the standard deviation and the dispersion difference;
(2) processing the standard deviation of each point in the single-area sample through standard deviation mean forward translation operation;
(3) calculating the confidence rates of all points in the single-region sample;
(4) and comparing the confidence rates of all the points with the threshold values of the confidence rates respectively, judging abnormal points in the sample, and accurately determining abnormal values by using the large dynamic range and the large discrete single-region multipoint.
Further, in step (1), N single-region samples are collected at a time, and the average value of each point is calculatedStandard deviation sigmaiAnd a difference of dispersion dij:
Wherein x isijThe ith sample is represented as ith point value, i is 1,2, …, M is the number of sampling points, and j is 1,2, …, N.
Further, in step (1), the method of thresholding the standard deviation and the discrete deviation is as follows:
setting a threshold t1And t2;
Standard deviation sigma of the ith sample pointiSatisfy sigmai≤t1When, will σiIs set to t1;
Discrete difference d of ith sampling point of jth sampleijSatisfy dij≤t2When d is greater than dijIs set to 0.
Further, the process of step (2) is as follows:
setting T1And T2The ith-T1~i-T2The standard deviation of all the internal sampling points is weighted and averaged to be used as the standard deviation sigma of the ith sampling pointi。
Further, in step (3), the confidence rates D of the respective points are calculated as followsij:
Dij=dij-g*σi
Wherein g is a preset multiple; if D isijIf greater than 0, let DijIf not, let Dij=0;
Will DijStored in an array D, DijI.e. the element in the ith row and the jth column of the array D.
Further, in step (4), a confidence threshold D is set, and the sum D of each row element of the array D is calculatediIf d isiIf > d, the ith sampling point is considered as an abnormal point.
Drawings
FIG. 1 is a flow chart of an embodiment distributed optical fiber vibration sensing positioning method;
FIG. 2 is a graph of signal versus disturbance location at 10km of disturbance provided by an embodiment;
FIG. 3 is a graph of standard deviation at 10km of perturbation versus perturbation position provided by an embodiment;
FIG. 4 is a graph of the dispersion difference at 10km of perturbation versus the location of the perturbation provided by the embodiment;
FIG. 5 is a ring diagram of the criterion and disturbance position at 10km of disturbance provided by the embodiment;
FIG. 6 is a graph of confidence rate at 10km versus disturbance location for an embodiment;
FIG. 7 is a graph of location dispersion at a perturbation of 10km provided by an embodiment;
FIG. 8 is a diagram illustrating the relationship between the maximum localization dispersion and the perturbation position according to the embodiment.
Detailed Description
The technical scheme of the invention is explained in detail in the following with the accompanying drawings.
In reality, many scenes need to locate abnormal values of large data, such as the distributed optical fiber vibration sensing location technology mentioned in the aforementioned background art. In this embodiment, a method for accurately determining an abnormal value at multiple points in a large dynamic range and a large discrete single area is applied to a distributed optical fiber vibration sensing positioning technology, so as to obtain a distributed optical fiber vibration sensing positioning method, as shown in fig. 1, the steps of which are as follows:
step 1: taking N curves collected by the distributed optical fiber vibration sensing system as a group, calculating the average value, the standard deviation and the dispersion difference of M points on the N curves, and carrying out threshold processing on the standard deviation and the dispersion difference.
The following preferred scheme is adopted to realize the step 1:
average value of each pointStandard deviation sigmaiAnd a difference of dispersion dijThe calculation formula of (a) is as follows:
xijthe ith point value in the jth curve is represented, i is 1,2, …, M is the number of sampling points, and j is 1,2, …, N. N-20, M-65536.
The method for thresholding the standard deviation and the dispersion deviation is as follows:
setting a threshold t1And t2Standard deviation σ of the ith sample pointiSatisfy sigmai≤t1When, will σiIs set to t1(ii) a Discrete difference d of ith sampling point of jth sampleijSatisfy dij≤t2When d is greater than dijIs set to 0. Further, set t1=30,t2=72。
Step 2: and processing the standard deviation of each sampling point through a standard deviation mean forward translation operation.
The following preferred scheme is adopted to realize the step 2:
setting T1And T2The ith-T1~i-T2The standard deviation of all the internal sampling points is weighted and averaged to be used as the standard deviation sigma of the ith sampling pointi. Further, T is set1=1000,T2When 500, then:
and step 3: the confidence rates of all the sample points are calculated.
The following preferred scheme is adopted to realize the step 3:
the confidence rate D of each point is calculated according to the following formulaij:
Dij=dij-g*σi
Wherein g is a preset multiple; if D isijIf greater than 0, let DijIf not, let Dij=0;
Will DijStored in an array D, DijI.e. the element in the ith row and the jth column of the array D.
In distributed fiber optic vibration sensing, the preferred value of the multiple g is 2.4.
And 4, step 4: and comparing the confidence rates of all the points with threshold values of the confidence rates respectively, thereby judging the vibration points.
The following preferred scheme is adopted to realize the step 4:
setting a confidence rate threshold value D, and calculating the sum D of each row element of the array DiIf d isiIf the sampling point is greater than d, the ith sampling point is considered to be possibly disturbed, the number of all the possible disturbed points is counted to be t, and the average of all the possible disturbed points is taken as the real vibrated point. Further, d is set to 6.
To verify the present invention, the following experiment was performed.
Continuously applying disturbance at 10km, continuously acquiring 2000 curves, and performing method processing on the acquired 2000 curves, wherein fig. 2 is an acquired signal original curve diagram, and because a disturbance signal is small and a dynamic range is large, it is difficult to directly obtain a disturbance position from fig. 2. By adopting the invention, the original data of FIG. 2 is subjected to feature extraction, FIG. 3 is a standard deviation graph thereof, and the standard deviation of the disturbance position and the subsequent point can be obviously changed; FIG. 4 is a graph of the dispersion difference of 20 samples taken, from above, it can be seen that the dispersion difference of the disturbance position and the point after the disturbance position has a sudden change; FIG. 5 is a graph of 20 samples taken after each point criterion, and it can be seen from the graph that only the criterion of the disturbance point position is distinguished from other position points, and the characteristics are obvious; FIG. 6 shows the confidence rates of 20 samples taken at each point, and it can be seen that the confidence rate at the perturbed point is greater than 6, as distinguished from the un-perturbed point. Fig. 7 shows the positioning dispersion of 100 sets of data at 10km of disturbance points by using the method of the present invention, and it can be seen that the maximum dispersion is 4 and the minimum dispersion is 0.
In addition, in this embodiment, only the disturbance position is changed under the condition that the laboratory conditions are kept unchanged, and the data of 2000 curves at different positions are collected and processed, so as to obtain the maximum dispersion at 2.2km, 5km, 10km, 15km and 20 km. Fig. 8 is a relationship diagram between the positioning dispersion and the disturbance position, and it can be seen that, along with the increase of the disturbance position, the positioning dispersion is always kept within 4.8 meters, which indicates that the positioning method can achieve accurate positioning, i.e. the abnormal point can be accurately found.
The above experiment shows that the method for accurately determining the abnormal value at the large dynamic range and the large discrete single area and the multiple points is applied to the distributed optical fiber vibration sensing system for locating the disturbance point, when the optical fiber is subjected to perturbation, the signal at the disturbance position is not obviously changed, the threshold value comparison method cannot determine the vibration point, but the standard deviation, the discrete difference and the confidence rate at the position are obviously changed due to the small change of the signal, so that the abnormal value can be accurately determined through the method, and the possibility of misjudgment is reduced.
The embodiments are only for illustrating the technical idea of the present invention, and the technical idea of the present invention is not limited thereto, and any modifications made on the basis of the technical scheme according to the technical idea of the present invention fall within the scope of the present invention.
Claims (6)
1. A distributed optical fiber vibration sensing positioning method is characterized by comprising the following steps:
(1) taking N curves collected by the distributed optical fiber vibration sensing system as a group, calculating the average value, the standard deviation and the dispersion difference of M points on the N curves, and carrying out threshold processing on the standard deviation and the dispersion difference;
average value of each pointStandard deviation sigmaiAnd a difference of dispersion dijIs calculated as follows:
wherein x isijThe ith point value in the jth curve is represented, i is 1,2, …, M is the number of sampling points, j is 1,2, …, N;
the method for thresholding the standard deviation and the dispersion deviation is as follows:
setting a threshold t1And t2Standard deviation σ of the ith sample pointiSatisfy sigmai≤t1When, will σiIs set to t1(ii) a Discrete difference d of ith sampling point of jth sampleijSatisfy dij≤t2When d is greater than dijSet to 0;
(2) and processing the standard deviation of each sampling point through standard deviation mean forward translation operation:
setting T1And T2The ith-T1~i-T2The standard deviation of all the internal sampling points is weighted and averaged to be used as the standard deviation sigma of the ith sampling pointi;
(3) Calculating the confidence rates of all sampling points:
the confidence rate D of each point is calculated according to the following formulaij:
Dij=dij-g*σi
Wherein g is a preset multiple; if D isijIf greater than 0, let DijIf not, let Dij=0;
Will DijStored in an array D, DijThe element is the element of the ith row and the jth column in the array D;
(4) comparing the confidence rates of all the points with threshold values of the confidence rates respectively, thereby judging vibration points:
setting a confidence rate threshold value D, and calculating the sum D of each row element of the array DiIf d isiIf the sampling point is greater than d, the probability of disturbance of the ith sampling point is considered, the number of all the points with the probability of disturbance is counted to be t, and all the points are averaged to be used as real vibration points.
2. The distributed optical fiber vibration sensing and positioning method according to claim 1, wherein in step (1), N-20 and M-65536.
3. The distributed optical fiber vibration sensing and positioning method according to claim 1, wherein in step (1), t is set1=30,t2=72。
4. The distributed optical fiber vibration sensing and positioning method according to claim 1, wherein in step (2), T is set1=1000,T2=500。
5. The distributed optical fiber vibration sensing and positioning method according to claim 1, wherein in step (3), the multiple g is 2.4.
6. The distributed optical fiber vibration sensing and positioning method according to claim 1, wherein in step (4), a confidence rate threshold value d-6 is set.
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