CN103984872A - Recognition processing method and device for railway side wind speed data registration state characteristics - Google Patents
Recognition processing method and device for railway side wind speed data registration state characteristics Download PDFInfo
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- CN103984872A CN103984872A CN201410228461.4A CN201410228461A CN103984872A CN 103984872 A CN103984872 A CN 103984872A CN 201410228461 A CN201410228461 A CN 201410228461A CN 103984872 A CN103984872 A CN 103984872A
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Abstract
The invention discloses a recognition processing method and device for railway side wind speed data registration state characteristics. The method comprises performing preliminary judgment on wind speed sequences measured by two wind meters of a dual-redundancy system by threshold determining, correcting the wind speed sequences; for the corrected wind speed sequences, determining that the corresponding wind speed in the threshold range is the effective wind speed by comparing the measured tolerance threshold with the absolute value of the wind speed difference of the same moment of the two sequences and comparing the predicted tolerance threshold with the absolute value of the measured wind speed and predicted wind speed absolute value; determining whether information channels of the first wind meter and the second wind meter is failed by counting times that are not in the predicted tolerance range. According to the method and the device, the blank that dual-redundancy structure wind meter systems cannot determine the measured wind speed effectiveness in the prior art is filled, the accurate effective wind speed is provided for strong wind monitoring and early warning systems, and accurate monitoring and early warning are provided for the train operation particularly.
Description
Technical field
The invention belongs to track traffic safety technique field, be specifically related to a kind of wind speed along railway Registration of Measuring Data status flag identifying processing method and device.
Background technology
Current, the monitoring of Along Railway to air speed data registration state, particularly feature identification, generally adopts anemometer to carry out, for gale monitoring early warning system provides data and information.The error of anemometer information channel, fault can cause the wrong report of gale monitoring early warning system and fail to report, and have a strong impact on Train Dispatch & Command and traffic safety.For improving the reliability of air speed data registration status monitoring, wind speed wind direction sensor in gale monitoring early warning system can adopt the two redundancy structures of hardware, the validity that judges the air speed data that anemometer provides is improved the monitoring to wind speed, and by the validity of air speed data, judges the state of anemometer information channel.
The typical method that utilizes hardware redundancy structure to carry out the identification of air speed data registration status flag in air monitoring is voting method, but in the voting method of standard, at least to remove to measure same wind speed with three wind speed wind direction sensors of the same type, be not suitable for only having two redundant systems of two wind speed wind direction sensors.How at each ventilation measuring point place, by thering is the registration state of the air speed data that two redundancy structure decision anemometers of two cover anemometers provide, be the problem that the gale monitoring early warning system of existing pair of redundancy structure must solve.
Summary of the invention
The object of this invention is to provide a kind of wind speed along railway Registration of Measuring Data status flag identifying processing method and device, on the basis that wind speed is predicted, air speed data registration state is carried out to effective feature identification, correctly to show the state of anemometer information channel, for gale monitoring early warning system provides information accurately.
According to an aspect of the present invention, provide a kind of wind speed along railway Registration of Measuring Data status flag identifying processing method, described wind speed is measuring wind sequence { the wind speed S' in S'} of the first anemometer of same time measurement
jwith the wind speed S in the measuring wind sequence { S " } of the second anemometer "
j, described method comprises:
Step S1: the discrimination threshold D' of default the first anemometer
0discrimination threshold D with the second anemometer "
0; By S'
jwith discrimination threshold D'
0compare, by S "
jwith discrimination threshold D "
0compare: work as S'
j> D'
0time, by sequence (S'
j-1... S'
j-h...) in wind speed successively with D'
0compare, and first is less than to D'
0sequence (S'
j-1... S'
j-h...) in air speed value give S'
j; As S "
j> D "
0time, by sequence (S "
j-1... S "
j-h...) in wind speed successively with D "
0compare, and first is less than to D "
0sequence (S "
j-1... S "
j-h...) in air speed value give S "
j;
Step S2: default actual measurement tolerance threshold value D
r, according to T=∣ S'-S " ∣, calculate T
j; Work as T
j<D
rtime, compare S'
jand S "
j, larger in two values is effective wind speed; Work as T
j>D
rtime, adopt the wind speed forecasting method prediction of wind speed X' based on ARIMA
jand X "
j;
Step S3: the prediction tolerance threshold value D' of default the first anemometer
fprediction tolerance threshold value D with the second anemometer "
f, according to Y=∣ S-X ∣, calculate Y'
jand Y "
j, and compare Y'
jand D'
f, Y "
jand D "
f:; Work as Y'
j<D'
f, Y "
j<D "
ftime, compare S'
jand S "
j, larger in two values is effective wind speed; Work as Y'
j<D'
f, Y "
j>D "
ftime, S'
jfor effective wind speed; Work as Y'
j>D'
f, Y "
j<D "
ftime, S "
jfor effective wind speed; Work as Y'
j>D'
f, Y "
j>D "
ftime, execution step S4; Otherwise, skips steps S4;
Step S4: compare S'
jand S "
j, work as S'
j< S "
jtime, by S "
jvalue give S'
jto S'} wind series is revised, S "
jfor effective wind speed; Work as S'
j> S "
jtime, by S'
jvalue give S "
jto { S " } wind series is revised, S'
jfor effective wind speed; Count value Q adds 1;
Step S5: the Q in read step S4, if this Q reading is more 1 than the Q reading last time, preserves the Q that this reads; If this Q reading is identical with the Q reading last time, Q makes zero;
Step S6: when Q is greater than preset value, judge the first anemometer and the second anemometer information channel fault, S'
jand S "
jbe invalid wind speed.
In such scheme, described method also comprises:
Step S7: work as S'
jduring for effective wind speed, output wind speed S'
jfor current measuring wind; As S "
jduring for effective wind speed, output wind speed S'
jfor current measuring wind.
In such scheme, the wind speed forecasting method prediction of wind speed X' of described employing based on ARIMA
jand X "
jfurther comprise:
From { choosing (S' S'} sequence
i..., S'
i+q..., S'
i+n, wherein, 0<q<n), from { S " } sequence, choose (S "
i..., S "
i+q..., S "
i+n, wherein, 0<q<n) be non-stationary time wind series sample data;
To (S'
i..., S'
i+q..., S'
i+n) and (S "
i..., S "
i+q..., S "
i+n) wind series carries out stationary test and pre-service;
According to (S'
i..., S'
i+q..., S'
i+n) and (S "
i..., S "
i+q..., S "
i+n) data in wind series set up respectively ARIMA model 1 and ARIMA model 2;
According to described ARIMA model 1, carry out the leading multi-step prediction iterative computation of wind speed, iterative computation j-i-n time, the result of calculating is the wind speed X' of prediction
j; According to described ARIMA model 2, carry out the leading multi-step prediction iterative computation of wind speed, iterative computation j-i-n time, the result of calculating is the wind speed X of prediction "
j.
In such scheme, the prediction tolerance threshold value D' of described default the first anemometer
fprediction tolerance threshold value D with the second anemometer "
ffurther comprise:
According to described ARIMA model 1, calculate the prediction of wind speed sequence (X' of the first anemometer
m..., X'
m+q..., X'
m+p, wherein, 0<q<p, m ≠ i); According to described ARIMA model 2 calculate the second anemometers prediction of wind speed sequence (X "
m..., X "
m+q..., X "
m+p, wherein, 0<q<p, m ≠ i);
From { choosing measuring wind sequence (S' S'} sequence
m..., S'
m+q..., S'
m+p, wherein, 0<q<p, m ≠ i); From { S " } sequence, choose measuring wind sequence (S "
m..., S "
m+q..., S "
m+p, wherein, 0<q<p, m ≠ i);
According to Y=∣ S-X ∣, calculate the prediction tolerance sequence (Y' of the first anemometer
1..., Y'
q..., Y'
p, wherein, 0<q<p), the prediction tolerance sequence of calculating the second anemometer (Y "
1..., Y "
q..., Y "
p, wherein, 0<q<p);
According to the prediction tolerance sequence (Y' of the first anemometer
1..., Y'
q..., Y'
p), by averaging method or least square method, calculate the prediction tolerance threshold value D' of the first anemometer
f, according to the prediction tolerance sequence of the second anemometer (Y " ..., Y "
q..., Y "
p), by averaging method or least square method, calculate the prediction tolerance threshold value D of the second anemometer "
f.
In such scheme, the described preset value in described step S6 is measuring wind speed number of times in Preset Time section.
According to another aspect of the present invention, also provide a kind of wind speed along railway Registration of Measuring Data status flag recognition process unit, described wind speed is measuring wind sequence { the wind speed S' in S'} of the first anemometer of same time measurement
jwith the wind speed S in the measuring wind sequence { S " } of the second anemometer "
j, described device comprises:
Just sentence correcting module, for the discrimination threshold D' of default the first anemometer
0discrimination threshold D with the second anemometer "
0, and by S'
jwith with discrimination threshold D'
0compare, by S "
jwith with discrimination threshold D "
0compare: work as S'
j> D'
0time, by sequence (S'
j-1... S'
j-h...) in wind speed successively with D'
0compare, and first is less than to D'
0sequence (S'
j-1... S'
j-h...) in air speed value give S'
j; As S "
j> D "
0time, by sequence (S "
j-1... S "
j-h...) in wind speed successively with D "
0compare, and first is less than to D "
0sequence (S "
j-1... S "
j-h...) in air speed value give S "
j;
Actual measurement difference comparison module, for default actual measurement tolerance threshold value D
r, according to T=∣ S'-S " ∣, calculate T
j; And comparison T
jand D
r: work as T
j<D
rtime, compare S'
jand S "
j, larger in two values is effective wind speed; Work as T
j>D
rtime, adopt the wind speed forecasting method prediction of wind speed X' based on ARIMA
jand X "
j;
Prediction difference comparison module, for the prediction tolerance threshold value D' of default the first anemometer
fprediction tolerance threshold value D with the second anemometer "
f, according to Y=∣ S-X ∣, calculate Y'
jand Y "
j, and compare Y'
jand D'
f, Y "
jand D "
f: work as Y'
j<D'
f, Y "
j<D "
ftime, compare S'
jand S "
j, larger in two values is effective wind speed; Work as Y'
j<D'
f, Y "
j>D "
ftime, S'
jfor effective wind speed; Work as Y'
j>D'
f, Y "
j<D "
ftime, S "
jfor effective wind speed; Work as Y'
j>D'
f, Y "
j>D "
ftime, compare S'
jand S "
j, work as S'
j< S "
jtime, by S "
jvalue give S'
jto S'} wind series is revised, S "
jfor effective wind speed; Work as S'
j> S "
jtime, by S'
jvalue give S "
jto { the fast sequence of S " } is revised, S'
jfor effective wind speed; Count value Q adds 1;
Counting control module, for the Q of read step S4, if this Q reading is more 1 than the Q reading last time, preserves the Q that this reads; If this Q reading is identical with the Q reading last time, Q makes zero;
Fault judge module, when Q is greater than preset value, judges the first anemometer and the second anemometer information channel fault, S'
jand S "
jbe invalid wind speed.
In such scheme, described device also comprises:
Wind speed output module, for working as S'
jduring for effective wind speed, output wind speed S'
jfor current measuring wind; As S "
jduring for effective wind speed, output wind speed S'
jfor current measuring wind.
In such scheme, described prediction difference comparison module comprises: forecasting wind speed module, and for adopting the wind speed forecasting method prediction of wind speed X' based on ARIMA
jand X "
j, concrete:
From { choosing (S' S'} sequence
i..., S'
i+q..., S'
i+n, wherein, 0<q<n), from { S " } sequence, choose (S "
i..., S "
i+q..., S "
i+n, wherein, 0<q<n) be non-stationary time wind series sample data;
To (S'
i..., S'
i+q..., S'
i+n) and (S "
i..., S "
i+q..., S "
i+n) wind series carries out stationary test and pre-service;
According to (S'
i..., S'
i+q..., S'
i+n) and (S "
i..., S "
i+q..., S "
i+n) data in wind series set up respectively ARIMA model 1 and ARIMA model 2;
According to described ARIMA model 1, carry out the leading multi-step prediction iterative computation of wind speed, iterative computation j-i-n time, the result of calculating is the wind speed X' of prediction
j; According to described ARIMA model 2, carry out the leading multi-step prediction iterative computation of wind speed, iterative computation j-i-n time, the result of calculating is the wind speed X of prediction "
j.
In such scheme, described prediction difference comparison module is also drawn together: prediction tolerance Threshold module, and for the prediction tolerance threshold value D' of default the first anemometer
fprediction tolerance threshold value D with the second anemometer "
f, concrete:
According to described ARIMA model 1, calculate the prediction of wind speed sequence (X' of the first anemometer
m..., X'
m+q..., X'
m+p, wherein, 0<q<p, m ≠ i); According to described ARIMA model 2 calculate the second anemometers prediction of wind speed sequence (X "
m..., X "
m+q..., X "
m+p, wherein, 0<q<p, m ≠ i);
From { choosing measuring wind sequence (S' S'} sequence
m..., S'
m+q..., S'
m+p, wherein, 0<q<p, m ≠ i); From { S " } sequence, choose measuring wind sequence (S "
m..., S "
m+q..., S "
m+p, wherein, 0<q<p, m ≠ i);
According to Y=∣ S-X ∣, calculate the prediction tolerance sequence (Y' of the first anemometer
1..., Y'
q..., Y'
p, wherein, 0<q<p), the prediction tolerance sequence of calculating the second anemometer (Y "
1..., Y "
q..., Y "
p, wherein, 0<q<p);
According to the prediction tolerance sequence (Y' of the first anemometer
1..., Y'
q..., Y'
p), by averaging method or least square method, calculate the prediction tolerance threshold value D' of the first anemometer
f, according to the prediction tolerance sequence of the second anemometer (Y " ..., Y "
q..., Y "
p), by averaging method or least square method, calculate the prediction tolerance threshold value D of the second anemometer "
f.
In such scheme, described fault output module also for: measuring wind speed number of times in Preset Time section is made as to described preset value.
Wind speed along railway Registration of Measuring Data status flag identifying processing method provided by the present invention and device, by discrimination threshold, two of two redundant systems wind series that anemometer is measured are tentatively identified, get rid of the wind speed obviously suddenling change in wind series and by the mode that the previous wind speed that meets judgment threshold is replaced to current wind speed, wind series revised, absolute value to revised wind series by the difference of the wind speed of actual measurement tolerance threshold value and two sequence synchronizations compares, the difference of wind speed in actual measurement tolerance threshold range wherein a larger wind speed be effective wind speed, otherwise, by prediction tolerance threshold value, judge, prediction tolerance threshold value is calculated based on ARIMA model, and calculate respectively two anemometers prediction tolerance threshold value of ventilation measuring point separately, in anemometer wind series separately, if the absolute value of the difference of actual measurement wind speed and prediction of wind speed is all in prediction tolerance threshold range, larger in two wind speed is effective wind speed, if one in the absolute value of the difference of actual measurement wind speed and prediction of wind speed in prediction range of tolerable variance, the corresponding wind speed in scope is effective wind speed, if the actual measurement of two anemometers and the absolute difference of prediction of wind speed be not all in prediction range of tolerable variance, the wind speed that temporary is larger is as effective wind speed, open counting simultaneously, when continuous counter surpasses preset value, judge the first anemometer and the second anemometer information channel fault, S'
jand S "
jbe invalid wind speed.The characteristic recognition method of air speed data registration state of the present invention, filled up the blank that of the prior art pair of redundancy structure anemometer system can not accurately judge measured air speed data registration state, further perfect wind measurement method, for gale monitoring early warning system provides effective wind speed more accurately, especially for train operation provides monitoring and early warning more accurately, make train in strong wind atmosphere, move safety and efficiently more.
Accompanying drawing explanation
Fig. 1 is the wind speed along railway Registration of Measuring Data status flag identifying processing method flow schematic diagram of the preferred embodiment of the present invention;
Fig. 2 is the schematic flow sheet of the employing ARIMA model prediction wind speed of the preferred embodiment of the present invention;
Fig. 3 is the schematic flow sheet of the default prediction of the employing ARIMA model tolerance threshold value of the preferred embodiment of the present invention;
Fig. 4 is the wind speed along railway Registration of Measuring Data status flag identifying processing method flow diagram of another preferred embodiment of the present invention;
Fig. 5 is the wind speed along railway Registration of Measuring Data status flag recognition process unit structural representation of the preferred embodiment of the present invention.
Embodiment
For making the object, technical solutions and advantages of the present invention more cheer and bright, below in conjunction with embodiment and with reference to accompanying drawing, the present invention is described in more detail.Should be appreciated that, these descriptions are exemplary, and do not really want to limit the scope of the invention.In addition, in the following description, omitted the description to known configurations and technology, to avoid unnecessarily obscuring concept of the present invention.
Fig. 1 is that Fig. 1 is the wind speed along railway Registration of Measuring Data status flag identifying processing method flow schematic diagram of the preferred embodiment of the present invention.
As shown in Figure 1, the wind speed along railway Registration of Measuring Data status flag identifying processing method of the embodiment of the present invention, comprises the steps:
Step S1, the discrimination threshold D' of default the first anemometer
0discrimination threshold D with the second anemometer "
0; By S'
jwith discrimination threshold D'
0compare, by S "
jwith discrimination threshold D "
0compare: work as S'
j> D'
0time, by sequence (S'
j-1... S'
j-h...) in wind speed successively with D'
0compare, and first is less than to D'
0sequence (S'
j-1... S'
j-h...) in air speed value give S'
j; As S "
j> D "
0time, by sequence (S "
j-1... S "
j-h...) in wind speed successively with D "
0compare, and first is less than to D "
0sequence (S "
j-1... S "
j-h...) in air speed value give S "
j.
Step S2, default actual measurement tolerance threshold value D
r, according to T=∣ S'-S " ∣, calculate T
j; Work as T
j<D
rtime, compare S'
jand S "
j, larger in two values is effective wind speed; Work as T
j>D
rtime, adopt the wind speed forecasting method prediction of wind speed X' based on ARIMA
jand X "
j.
Step S3, the prediction tolerance threshold value D' of default the first anemometer
fprediction tolerance threshold value D with the second anemometer "
f, according to Y=∣ S-X ∣, calculate Y'
jand Y "
j, and compare Y'
jand D'
f, Y "
jand D "
f:; Work as Y'
j<D'
f, Y "
j<D "
ftime, compare S'
jand S "
j, larger in two values is effective wind speed; Work as Y'
j<D'
f, Y "
j>D "
ftime, S'
jfor effective wind speed; Work as Y'
j>D'
f, Y "
j<D "
ftime, S "
jfor effective wind speed; Work as Y'
j>D'
f, Y "
j>D "
ftime, execution step S4; Otherwise, skips steps S4.
Step S4, relatively S'
jand S "
j, work as S'
j< S "
jtime, by S "
jvalue give S'
jto S'} wind series is revised, S "
jfor effective wind speed; Work as S'
j> S "
jtime, by S'
jvalue give S "
jto { S " } wind series is revised, S'
jfor effective wind speed; Count value Q adds 1.
Step S5, the Q in read step S4, if this Q reading is more 1 than the Q reading last time, preserves the Q that this reads; If this Q reading is identical with the Q reading last time, Q makes zero.
Step S6, when Q is greater than preset value, judges the first anemometer and the second anemometer information channel fault, S'
jand S "
jbe invalid wind speed.Here, described preset value can be measuring wind speed number of times in Preset Time section.Preset Time section can be adjusted accordingly according to different situations, as: 10s.
Optionally, said method can also comprise the steps:
Step S7, works as S'
jduring for effective wind speed, output wind speed S'
jfor current measuring wind; As S "
jduring for effective wind speed, output wind speed S'
jfor current measuring wind.
Here, described wind speed is measuring wind sequence { the wind speed S' in S'} of the first anemometer of same time measurement
jwith the wind speed S in the measuring wind sequence { S " } of the second anemometer "
j.
Fig. 2 is the schematic flow sheet of the employing ARIMA model prediction wind speed of the preferred embodiment of the present invention.
As shown in Figure 2, the embodiment of the present invention adopts the process of ARIMA model prediction wind speed, comprises the steps:
Step S21, from { choosing (S' S'} sequence
i..., S'
i+q..., S'
i+n, wherein, 0<q<n), from { S " } sequence, choose (S "
i..., S "
i+q..., S "
i+n, wherein, 0<q<n) be non-stationary time wind series sample data.
Step S22, to (S'
i..., S'
i+q..., S'
i+n) and (S "
i..., S "
i+q..., S "
i+n) wind series carries out stationary test and pre-service.
Step S23, according to (S'
i..., S'
i+q..., S'
i+n) and (S "
i..., S "
i+q..., S "
i+n) data in wind series set up respectively ARIMA model 1 and ARIMA model 2.
Step S24, carries out the leading multi-step prediction iterative computation of wind speed according to described ARIMA model 1, iterative computation j-i-n time, and the result of calculating is the wind speed X' of prediction
j; According to described ARIMA model 2, carry out the leading multi-step prediction iterative computation of wind speed, iterative computation j-i-n time, the result of calculating is the wind speed X of prediction "
j.
Fig. 3 is the schematic flow sheet of the default prediction of the employing ARIMA model tolerance threshold value of the preferred embodiment of the present invention.
As shown in Figure 3, the process of the default prediction of the employing ARIMA model of preferred embodiment of the present invention tolerance threshold value, comprises the steps:
Step S31, according to the prediction of wind speed sequence (X' of described ARIMA model 1 calculating the first anemometer
m..., X'
m+q..., X'
m+p, wherein, 0<q<p, m ≠ i); According to described ARIMA model 2 calculate the second anemometers prediction of wind speed sequence (X "
m..., X "
m+q..., X "
m+p, wherein, 0<q<p, m ≠ i).
Step S32, from { choosing measuring wind sequence (S' S'} sequence
m..., S'
m+q..., S'
m+p, wherein, 0<q<p, m ≠ i); From { S " } sequence, choose measuring wind sequence (S "
m..., S "
m+q..., S "
m+p, wherein, 0<q<p, m ≠ i).
Step S33, according to Y=∣ S-X ∣, calculates the prediction tolerance sequence (Y' of the first anemometer
1..., Y'
q..., Y'
p, wherein, 0<q<p), the prediction tolerance sequence of calculating the second anemometer (Y "
1..., Y "
q..., Y "
p, wherein, 0<q<p).
Step S34, according to the prediction tolerance sequence (Y' of the first anemometer
1..., Y'
q..., Y'
p), by averaging method or least square method, calculate the prediction tolerance threshold value D' of the first anemometer
f, according to the prediction tolerance sequence of the second anemometer (Y " ..., Y "
q..., Y "
p), by averaging method or least square method, calculate the prediction tolerance threshold value D of the second anemometer "
f.
Fig. 4 is the wind speed along railway Registration of Measuring Data status flag identifying processing method flow diagram of another preferred embodiment of the present invention.
As shown in Figure 4, the present embodiment wind speed along railway Registration of Measuring Data status flag identifying processing method, comprises the steps:
Step S401, by the first anemometer monitoring wind speed, obtains the first anemometer wind series { S'}.
Step S402, the discrimination threshold D' of default the first anemometer
0.
Step S403, by S'
jwith discrimination threshold D'
0compare; Work as S'
j> D'
0time, execution step S404; Work as S'
j<D'
0time, directly perform step S409.
Step S404, by sequence (S'
j-1... S'
j-h...) in wind speed successively with D'
0compare, and first is less than to D'
0sequence (S'
j-1... S'
j-h...) in air speed value give S'
j.
Step S405, by the second anemometer monitoring wind speed, obtains the second anemometer wind series { S " }.
Step S406, the discrimination threshold D of default the second anemometer "
0.
Step S407, by S "
jwith discrimination threshold D "
0compare; As S "
j> D "
0time, execution step S408; As S "
j<D "
0time, directly perform step S409.
Step S408, by sequence (S "
j-1... S "
j-h...) in wind speed successively with D "
0compare, and first is less than to D "
0sequence (S "
j-1... S "
j-h...) in air speed value give S "
j.
Step S409, default actual measurement tolerance threshold value D
r, according to T=∣ S'-S " ∣, calculate T
j; Work as T
j<D
rtime, execution step S411; Work as T
j>D
rtime, perform step S412 and step S415 simultaneously.
Step S411, relatively S'
jand S "
j; Work as S'
j<S "
jtime, execution step S420; Work as S'
j>S "
jtime, execution step S421.
Step S412, from { choosing (S' S'} sequence
i..., S'
i+q..., S'
i+n, wherein, 0<q<n) be non-stationary time wind series sample data.
Step S413, to (S'
i..., S'
i+q..., S'
i+n) wind series carries out stationary test and pre-service.
Step S414, according to (S'
i..., S'
i+q..., S'
i+n) data in wind series set up ARIMA model 1, and carry out the leading multi-step prediction iterative computation of wind speed according to described ARIMA model 1, and iterative computation j-i-n time, the result of calculating is the wind speed X' of prediction
j, execution step S418.
Step S415, from { S " } sequence, choose (S "
i..., S "
i+q..., S "
i+n, wherein, 0<q<n) be non-stationary time wind series sample data.
Step S416, to (S "
i..., S "
i+q..., S "
i+n) wind series carries out stationary test and pre-service.
Step S417, according to (S "
i..., S "
i+q..., S "
i+n) data in wind series set up ARIMA model 2, and carry out the leading multi-step prediction iterative computation of wind speed according to described ARIMA model 2, iterative computation j-i-n time, the result of calculating is the wind speed X of prediction "
j, execution step S418.
Step S418, the prediction tolerance threshold value D' of default the first anemometer
fprediction tolerance threshold value D with the second anemometer "
f, according to Y=∣ S-X ∣, calculate Y'
jand Y "
j.
Step S419, relatively Y'
jand D'
f, Y "
jand D "
f: work as Y'
j<D'
f, Y "
j<D "
ftime, execution step S411; Work as Y'
j>D'
f, Y "
j>D "
ftime, execution step S411 performs step S422 simultaneously; Work as Y'
j<D'
f, Y "
j>D "
ftime, execution step S421; Work as Y'
j>D'
f, Y "
j<D "
ftime, execution step S420.
Step S420, by S "
jvalue give S'
jto S'} wind series is revised, output S "
jas effective wind speed.
Step S421, by S'
jvalue give S "
jto { S " } wind series is revised, output S'
jas effective wind speed.
Step S422, count value Q adds 1.
Step S423, the Q in read step S422, if this Q reading is more 1 than the Q reading last time, preserves the Q that this reads; If this Q reading is identical with the Q reading last time, Q makes zero.
Step S424, when Q is greater than preset value, judges the first anemometer and the second anemometer information channel fault, S'
jand S "
jbe invalid wind speed.
Fig. 5 is the wind speed along railway Registration of Measuring Data status flag recognition process unit structural representation of the preferred embodiment of the present invention.
As shown in Figure 5, the wind speed along railway Registration of Measuring Data status flag recognition process unit of the preferred embodiment of the present invention, comprising:
Just sentence correcting module 1, for the discrimination threshold D' of default the first anemometer
0discrimination threshold D with the second anemometer "
0, and by S'
jwith with discrimination threshold D'
0compare, by S "
jwith with discrimination threshold D "
0compare: work as S'
j> D'
0time, by sequence (S'
j-1... S'
j-h...) in wind speed successively with D'
0compare, and first is less than to D'
0sequence (S'
j-1... S'
j-h...) in air speed value give S'
j; As S "
j> D "
0time, by sequence (S "
j-1... S "
j-h...) in wind speed successively with D "
0compare, and first is less than to D "
0sequence (S "
j-1... S "
j-h...) in air speed value give S "
j.
Actual measurement difference comparison module 2, for default actual measurement tolerance threshold value D
r, according to T=∣ S'-S " ∣, calculate T
j; And comparison T
jand D
r: work as T
j<D
rtime, compare S'
jand S "
j, larger in two values is effective wind speed; Work as T
j>D
rtime, adopt the wind speed forecasting method prediction of wind speed X' based on ARIMA
jand X "
j.
Prediction difference comparison module 3, for the prediction tolerance threshold value D' of default the first anemometer
fprediction tolerance threshold value D with the second anemometer "
f, according to Y=∣ S-X ∣, calculate Y'
jand Y "
j, and compare Y'
jand D'f, Y "
jand D "
f: work as Y'
j<D'
f, Y "
j<D "
ftime, compare S'
jand S "
j, larger in two values is effective wind speed; Work as Y'
j<D'
f, Y "
j>D "
ftime, S'
jfor effective wind speed; Work as Y'
j>D'
f, Y "
j<D "
ftime, S "
jfor effective wind speed; Work as Y'
j>D'
f, Y "
j>D "
ftime, compare S'
jand S "
j, work as S'
j< S "
jtime, by S "
jvalue give S'
jto S'} wind series is revised, S "
jfor effective wind speed; Work as S'
j> S "
jtime, by S'
jvalue give S "
jto { the fast sequence of S " } is revised, S'
jfor effective wind speed; Count value Q adds 1.
Counting control module 4, for the Q of read step S4, if this Q reading is more 1 than the Q reading last time, preserves the Q that this reads; If this Q reading is identical with the Q reading last time, Q makes zero.
Fault judge module 5, when Q is greater than preset value, judges the first anemometer and the second anemometer information channel fault, S'
jand S "
jbe invalid wind speed.
Optionally, described device also comprises:
Wind speed output module 6, for working as S'
jduring for effective wind speed, output wind speed S'
jfor current measuring wind; As S "
jduring for effective wind speed, output wind speed S'
jfor current measuring wind.
Here, described wind speed is measuring wind sequence { the wind speed S' in S'} of the first anemometer of same time measurement
jwith the wind speed S in the measuring wind sequence { S " } of the second anemometer "
j.
Should be understood that, above-mentioned embodiment of the present invention is only for exemplary illustration or explain principle of the present invention, and is not construed as limiting the invention.Therefore any modification of, making, be equal to replacement, improvement etc., within protection scope of the present invention all should be included in without departing from the spirit and scope of the present invention in the situation that.In addition, claims of the present invention are intended to contain whole variations and the modification in the equivalents that falls into claims scope and border or this scope and border.
Claims (10)
1. a wind speed along railway Registration of Measuring Data status flag identifying processing method, described wind speed is measuring wind sequence { the wind speed S' in S'} of the first anemometer of measuring of same time
jwith the wind speed S in the measuring wind sequence { S " } of the second anemometer "
j, it is characterized in that, described method comprises:
Step S1: the discrimination threshold D' of default the first anemometer
0discrimination threshold D with the second anemometer "
0; By S'
jwith discrimination threshold D'
0compare, by S "
jwith discrimination threshold D "
0compare: work as S'
j> D'
0time, by sequence (S'
j-1... S'
j-h...) in wind speed successively with D'
0compare, and first is less than to D'
0sequence (S'
j-1... S'
j-h...) in air speed value give S'
j; As S "
j> D "
0time, by sequence (S "
j-1... S "
j-h...) in wind speed successively with D "
0compare, and first is less than to D "
0sequence (S "
j-1... S "
j-h...) in air speed value give S "
j;
Step S2: default actual measurement tolerance threshold value D
r, according to T=∣ S'-S " ∣, calculate T
j; Work as T
j<D
rtime, compare S'
jand S "
j, larger in two values is effective wind speed; Work as T
j>D
rtime, adopt the wind speed forecasting method prediction of wind speed X' based on ARIMA
jand X "
j;
Step S3: the prediction tolerance threshold value D' of default the first anemometer
fprediction tolerance threshold value D with the second anemometer "
f, according to Y=∣ S-X ∣, calculate Y'
jand Y "
j, and compare Y'
jand D'
f, Y "
jand D "
f:; Work as Y'
j<D'
f, Y "
j<D "
ftime, compare S'
jand S "
j, larger in two values is effective wind speed; Work as Y'
j<D'
f, Y "
j>D "
ftime, S'
jfor effective wind speed; Work as Y'
j>D'
f, Y "
j<D "
ftime, S "
jfor effective wind speed; Work as Y'
j>D'
f, Y "
j>D "
ftime, execution step S4; Otherwise, skips steps S4;
Step S4: compare S'
jand S "
j, work as S'
j< S "
jtime, by S "
jvalue give S'
jto S'} wind series is revised, S "
jfor effective wind speed; Work as S'
j> S "
jtime, by S'
jvalue give S "
jto { S " } wind series is revised, S'
jfor effective wind speed; Count value Q adds 1;
Step S5: the Q in read step S4, if this Q reading is more 1 than the Q reading last time, preserves the Q that this reads; If this Q reading is identical with the Q reading last time, Q makes zero;
Step S6: when Q is greater than preset value, judge the first anemometer and the second anemometer information channel fault, S'
jand S "
jbe invalid wind speed.
2. method according to claim 1, is characterized in that, described method also comprises:
Step S7: work as S'
jduring for effective wind speed, output wind speed S'
jfor current measuring wind; As S "
jduring for effective wind speed, output wind speed S'
jfor current measuring wind.
3. method according to claim 1, is characterized in that, the wind speed forecasting method prediction of wind speed X' of described employing based on ARIMA
jand X "
jfurther comprise:
From { choosing (S' S'} sequence
i..., S'
i+q..., S'
i+n, wherein, 0<q<n), from { S " } sequence, choose (S "
i..., S "
i+q..., S "
i+n, wherein, 0<q<n) be non-stationary time wind series sample data;
To (S'
i..., S'
i+q..., S'
i+n) and (S "
i..., S "
i+q..., S "
i+n) wind series carries out stationary test and pre-service;
According to (S'
i..., S'
i+q..., S'
i+n) and (S "
i..., S "
i+q..., S "
i+n) data in wind series set up respectively ARIMA model 1 and ARIMA model 2;
According to described ARIMA model 1, carry out the leading multi-step prediction iterative computation of wind speed, iterative computation j-i-n time, the result of calculating is the wind speed X' of prediction
j; According to described ARIMA model 2, carry out the leading multi-step prediction iterative computation of wind speed, iterative computation j-i-n time, the result of calculating is the wind speed X of prediction "
j.
4. method according to claim 3, is characterized in that, the prediction tolerance threshold value D' of described default the first anemometer
fprediction tolerance threshold value D with the second anemometer "
ffurther comprise:
According to described ARIMA model 1, calculate the prediction of wind speed sequence (X' of the first anemometer
m..., X'
m+q..., X'
m+p, wherein, 0<q<p, m ≠ i); According to described ARIMA model 2 calculate the second anemometers prediction of wind speed sequence (X "
m..., X "
m+q..., X "
m+p, wherein, 0<q<p, m ≠ i);
From { choosing measuring wind sequence (S' S'} sequence
m..., S'
m+q..., S'
m+p, wherein, 0<q<p, m ≠ i); From { S " } sequence, choose measuring wind sequence (S "
m..., S "
m+q..., S "
m+p, wherein, 0<q<p, m ≠ i);
According to Y=∣ S-X ∣, calculate the prediction tolerance sequence (Y' of the first anemometer
1..., Y'
q..., Y'
p, wherein, 0<q<p), the prediction tolerance sequence of calculating the second anemometer (Y "
1..., Y "
q..., Y "
p, wherein, 0<q<p);
According to the prediction tolerance sequence (Y' of the first anemometer
1..., Y'
q..., Y'
p), by averaging method or least square method, calculate the prediction tolerance threshold value D' of the first anemometer
f, according to the prediction tolerance sequence of the second anemometer (Y " ..., Y "
q..., Y "
p), by averaging method or least square method, calculate the prediction tolerance threshold value D of the second anemometer "
f.
5. according to the method described in claim 1 to 4 any one, it is characterized in that, the described preset value in described step S6 is measuring wind speed number of times in Preset Time section.
6. a wind speed along railway Registration of Measuring Data status flag recognition process unit, described wind speed is measuring wind sequence { the wind speed S' in S'} of the first anemometer of measuring of same time
jwith the wind speed S in the measuring wind sequence { S " } of the second anemometer "
j, it is characterized in that, described device comprises:
Just sentence correcting module, for the discrimination threshold D' of default the first anemometer
0discrimination threshold D with the second anemometer "
0, and by S'
jwith with discrimination threshold D'
0compare, by S "
jwith with discrimination threshold D "
0compare: work as S'
j> D'
0time, by sequence (S'
j-1... S'
j-h...) in wind speed successively with D'
0compare, and first is less than to D'
0sequence (S'
j-1... S'
j-h...) in air speed value give S'
j; As S "
j> D "
0time, by sequence (S "
j-1... S "
j-h...) in wind speed successively with D "
0compare, and first is less than to D "
0sequence (S "
j-1... S "
j-h...) in air speed value give S "
j;
Actual measurement difference comparison module, for default actual measurement tolerance threshold value D
r, according to T=∣ S'-S " ∣, calculate T
j; And comparison T
jand D
r: work as T
j<D
rtime, compare S'
jand S "
j, larger in two values is effective wind speed; Work as T
j>D
rtime, adopt the wind speed forecasting method prediction of wind speed X' based on ARIMA
jand X "
j;
Prediction difference comparison module, for the prediction tolerance threshold value D' of default the first anemometer
fprediction tolerance threshold value D with the second anemometer "
f, according to Y=∣ S-X ∣, calculate Y'
jand Y "
j, and compare Y'
jand D'
f, Y "
jand D "
f: work as Y'
j<D'
f, Y "
j<D "
ftime, compare S'
jand S "
j, larger in two values is effective wind speed; Work as Y'
j<D'
f, Y "
j>D "
ftime, S'
jfor effective wind speed; Work as Y'
j>D'
f, Y "
j<D "
ftime, S "
jfor effective wind speed; Work as Y'
j>D'
f, Y "
j>D "
ftime, compare S'
jand S "
j, work as S'
j< S "
jtime, by S "
jvalue give S'
jto S'} wind series is revised, S "
jfor effective wind speed; Work as S'
j> S "
jtime, by S'
jvalue give S "
jto { the fast sequence of S " } is revised, S'
jfor effective wind speed; Count value Q adds 1;
Counting control module, for the Q of read step S4, if this Q reading is more 1 than the Q reading last time, preserves the Q that this reads; If this Q reading is identical with the Q reading last time, Q makes zero;
Fault judge module, when Q is greater than preset value, judges the first anemometer and the second anemometer information channel fault, S'
jand S "
jbe invalid wind speed.
7. device according to claim 6, is characterized in that, described device also comprises:
Wind speed output module, for working as S'
jduring for effective wind speed, output wind speed S'
jfor current measuring wind; As S "
jduring for effective wind speed, output wind speed S'
jfor current measuring wind.
8. device according to claim 6, is characterized in that, described prediction difference comparison module comprises: forecasting wind speed module, and for adopting the wind speed forecasting method prediction of wind speed X' based on ARIMA
jand X "
j, concrete:
From { choosing (S' S'} sequence
i..., S'
i+q..., S'
i+n, wherein, 0<q<n), from { S " } sequence, choose (S "
i..., S "
i+q..., S "
i+n, wherein, 0<q<n) be non-stationary time wind series sample data;
To (S'
i..., S'
i+q..., S'
i+n) and (S "
i..., S "
i+q..., S "
i+n) wind series carries out stationary test and pre-service;
According to (S'
i..., S'
i+q..., S'
i+n) and (S "
i..., S "
i+q..., S "
i+n) data in wind series set up respectively ARIMA model 1 and ARIMA model 2;
According to described ARIMA model 1, carry out the leading multi-step prediction iterative computation of wind speed, iterative computation j-i-n time, the result of calculating is the wind speed X' of prediction
j; According to described ARIMA model 2, carry out the leading multi-step prediction iterative computation of wind speed, iterative computation j-i-n time, the result of calculating is the wind speed X of prediction "
j.
9. device according to claim 8, is characterized in that, described prediction difference comparison module also comprises: prediction tolerance Threshold module, and for the prediction tolerance threshold value D' of default the first anemometer
fprediction tolerance threshold value D with the second anemometer "
f; Wherein
According to described ARIMA model 1, calculate the prediction of wind speed sequence (X' of the first anemometer
m..., X'
m+q..., X'
m+p, wherein, 0<q<p, m ≠ i); According to described ARIMA model 2 calculate the second anemometers prediction of wind speed sequence (X "
m..., X "
m+q..., X "
m+p, wherein, 0<q<p, m ≠ i);
From { choosing measuring wind sequence (S' S'} sequence
m..., S'
m+q..., S'
m+p, wherein, 0<q<p, m ≠ i); From { S " } sequence, choose measuring wind sequence (S "
m..., S "
m+q..., S "
m+p, wherein, 0<q<p, m ≠ i);
According to Y=∣ S-X ∣, calculate the prediction tolerance sequence (Y' of the first anemometer
1..., Y'
q..., Y'
p, wherein, 0<q<p), the prediction tolerance sequence of calculating the second anemometer (Y "
1..., Y "
q..., Y "
p, wherein, 0<q<p);
According to the prediction tolerance sequence (Y' of the first anemometer
1..., Y'
q..., Y'
p), by averaging method or least square method, calculate the prediction tolerance threshold value D' of the first anemometer
f, according to the prediction tolerance sequence of the second anemometer (Y " ..., Y "
q..., Y "
p), by averaging method or least square method, calculate the prediction tolerance threshold value D of the second anemometer "
f.
10. according to the device described in claim 6 to 9 any one, it is characterized in that, described fault output module also for: measuring wind speed number of times in Preset Time section is made as to described preset value.
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104951798A (en) * | 2015-06-10 | 2015-09-30 | 上海大学 | Method for predicting non-stationary fluctuating wind speeds by aid of LSSVM (least square support vector machine) on basis of EMD (empirical mode decomposition) |
| CN106372731A (en) * | 2016-11-14 | 2017-02-01 | 中南大学 | Strong-wind high-speed railway along-the-line wind speed space network structure prediction method |
| CN107024601A (en) * | 2017-04-30 | 2017-08-08 | 中南大学 | A kind of the Along Railway wind measurement method and control system of control of intelligently being continued a journey based on unmanned aerial vehicle group |
| CN107121566A (en) * | 2017-04-30 | 2017-09-01 | 中南大学 | A kind of train monitoring method and system measured in real time based on bodywork surface wind speed unmanned plane |
| CN108760066A (en) * | 2018-06-04 | 2018-11-06 | 中车青岛四方机车车辆股份有限公司 | A kind of train temperature detection method, apparatus and system |
| CN109584585A (en) * | 2018-12-10 | 2019-04-05 | 中南大学 | Highway based on cloud storage big wind pre-warning method in real time |
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| CN103033648A (en) * | 2012-12-31 | 2013-04-10 | 北京佳讯飞鸿电气股份有限公司 | Method for testing validity of output data of air sensor |
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| CN103033648A (en) * | 2012-12-31 | 2013-04-10 | 北京佳讯飞鸿电气股份有限公司 | Method for testing validity of output data of air sensor |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104951798A (en) * | 2015-06-10 | 2015-09-30 | 上海大学 | Method for predicting non-stationary fluctuating wind speeds by aid of LSSVM (least square support vector machine) on basis of EMD (empirical mode decomposition) |
| CN106372731A (en) * | 2016-11-14 | 2017-02-01 | 中南大学 | Strong-wind high-speed railway along-the-line wind speed space network structure prediction method |
| CN107024601A (en) * | 2017-04-30 | 2017-08-08 | 中南大学 | A kind of the Along Railway wind measurement method and control system of control of intelligently being continued a journey based on unmanned aerial vehicle group |
| CN107121566A (en) * | 2017-04-30 | 2017-09-01 | 中南大学 | A kind of train monitoring method and system measured in real time based on bodywork surface wind speed unmanned plane |
| CN108760066A (en) * | 2018-06-04 | 2018-11-06 | 中车青岛四方机车车辆股份有限公司 | A kind of train temperature detection method, apparatus and system |
| CN109584585A (en) * | 2018-12-10 | 2019-04-05 | 中南大学 | Highway based on cloud storage big wind pre-warning method in real time |
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Application publication date: 20140813 |