CN108920429A - A kind of abnormal data analysis method of Water level trend monitoring - Google Patents
A kind of abnormal data analysis method of Water level trend monitoring Download PDFInfo
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Abstract
The invention discloses a kind of abnormal data analysis methods of Water level trend monitoring.The present invention is recorded first in certain water body section long term time, records water pressure data with short period of time, then be converted into relative water depth data；First therefrom reject obvious abnormal data, the increment for calculating the relative water depth data at two neighboring moment again forms increment sequence, the confidence interval of increment in increment sequence under specific confidence level is estimated using random statistical model (such as normal distribution curve), then the abnormal increment beyond confidence interval is filtered out, finally combine rainfall, the water temperature condition at moment locating for these abnormal increments, analyze and determine whether these abnormal increments can be received, to complete the analysis identification of abnormal data.Water level abnormality data analysing method provided by the invention is simple, scientific, reasonable, efficient, and whole process can be effectively reduced the time of data check consuming, improved the accuracy of monitoring data using electronic equipment calculating.
Description
Technical field
The invention belongs to hydrographic data processing technology field, especially a kind of abnormal data analysis side of Water level trend monitoring
Method.
Background technique
During hydrological observation test, gained waterlevel data result is to mankind's activity in hydrologic process and system change ten
Divide sensitivity, often there are a certain number of exceptional values.These exceptional value Producing reasons are largely artificially by measuring instrument
It is picked up from water, causes the data such as hydraulic pressure, relative water depth to occur abnormal.These exceptional values cannot all reflect hydrological variation mistake strictly according to the facts
Journey needs to be rejected using rational technique means.
In the prior art, screening is only resided within to the analysis judgement of monitoring abnormal data and rejects obviously abnormal number
The abnormal data of difficult local identification is often gradually identified in data use process, rejected according to the stage, workload
Greatly, time-consuming, low efficiency, uncertain strong, and the concentration analysis of high-volume monitoring data exceptional value is handled, phase not yet occurs
The scientific method or the like of the analysis and rejecting answered.
Summary of the invention
In order to invent a kind of method that the water level abnormality data analysis for certain water body is rejected, and it should guarantee analysis method
Science, the accuracy of judgement, also to improve data convenient for operation and check efficiency, the present invention provides a kind of Water level trend prison
The abnormal data analysis method of survey, is achieved through the following technical solutions.
A kind of abnormal data analysis method of Water level trend monitoring, includes the following steps：
S1, Precipitation Process of certain sampled point in certain extended time interval under basin water to be measured is continuously recorded；In the extended time interval
The interior time interval Δ t with specific duration, the continuous hydraulic pressure and water temperature data for recording sampled point, respectively obtains the sampled point at this
The continuous hydraulic pressure of extended time interval, water temperature data sequence；
S2, the relative water depth number that each water pressure data in the continuous water pressure data sequence is converted into the water surface to sampled point
According to obtaining sampled point in the continuous phase of the extended time interval to bathymetric data sequence；
S3, the Precipitation Process in conjunction with step S1 gained basin sampled point to be measured in extended time interval, remove the continuous phase
To the obvious abnormal data in bathymetric data sequence, several relative water depth data subsequences A is obtained；Each relative water depth
It include several relative water depth data a with same time interval of delta t in data subsequence A；
S4, it calculates in each relative water depth data subsequence A, the increment x of the relative water depth data a of every 2 adjacent moments,
Several increments subsequence X is obtained, then all increment subsequence X are merged to obtain increment sequence XX；
S5, the confidence interval for estimating increment x in increment sequence XX under specific confidence level, determine the upper and lower of the confidence interval
Limit threshold value, i.e., each received bound threshold value of increment；
S6, on the basis of the bound threshold value of the confidence interval, filter out and all in the increment sequence XX set beyond this
Believe the abnormal increment in section；
Precipitation Process in extended time interval of S7, the basin sampled point to be measured in conjunction with obtained by step S1, water temperature data sequence, into
One step analyzes the reasonability of each abnormal increment, rejects unreasonable abnormal increment.
By selecting particular instrument to measure some sampled point in waters to be measured in extended time interval (such as 1 month, half a year, 1
Year, 3 years etc.) in a large amount of dynamic water-pressure data, these data are all in identical very short time interval (such as 5min, 10min
Deng) in measure.On this basis, the present invention is rejected using the screening that water level Incremental control completes abnormal data：The first step is
Obvious abnormal data are rejected in the lifting of water level when by judging rainfall in advance, i.e. step S1 to S3, this is easiest to use
Conventional means；Second step is to calculate the phase of adjacent moment by removing the relative water depth data subsequence obtained after abnormal data
To the increment (i.e. water level difference) of bathymetric data, merging forms the sampled point in the extended time interval with the increment of specified time interval
Sequence, it is assumed that these increments meet some probability distribution function, estimate confidence interval of the increment under specific confidence level, determine
Increment can received interval range, filter out each not in the abnormal increment of the range, finally abnormal increased according to each
The actual conditions such as precipitation, the water temperature at locating moment are measured, judge whether the exception increment can be received.
Preferably, in step S2, each water pressure data is converted into the water surface to sampled point in the continuous water pressure data sequence
The calculation formula of relative water depth data be：
H=(P-P0)/9.8；
Wherein, H is relative water depth data of the sampled point at the moment, unit m；P is sampled point water surface upper atmosphere pressure,
Unit is kPa；P0For sampled point hydraulic pressure, unit kPa.
Preferably, in step S3, specific judgement side of the continuous phase to the obvious abnormal data in bathymetric data sequence
Method is：When the relative water depth data at certain moment are 0 or negative value, then the relative water depth data at the moment are obvious abnormal data；
N relative water depth data subsequence A, each sub- sequence of relative water depth data are obtained after removing the obvious abnormal data
It arranges comprising the m relative water depth data a with same time interval of delta t in A, i.e.,
Ai={ ai,1,ai,2,…,ai,m, 1≤i≤n,
Wherein, AiRefer to i-th of relative water depth data subsequence, includes m relative water depth data, respectively ai,1,
ai,2,…,aim,。
Preferably, step S4 is specially：For each relative water depth data subsequence Ai={ ai,1,ai,2,…,ai,m, meter
Calculate ai,mAnd ai,m-1Between increment x, obtain increment subsequence Xi, each increment sub-series of packets is containing m-1 increment x, i.e.,
Xi={ xi,1,xi,2,…,xi,m-1, xi,m-1=ai,m-ai,m-1,
Wherein, xi,m-1For relative water depth data subsequence AiIn adjacent 2 relative water depth data ai,mAnd ai,m-1Increment；
Again by all increment subsequence XiMerging obtains increment sequence XX, XX={ X1,X2,…,Xn, i.e. increment sequence XX
Sampled point is contained to be in extended time interval with the increment of all relative water depth data of specified time interval Δ t.
Preferably, step S5 is specially：It is assumed that increment x contained by the increment sequence XX meets specific distribution function,
Estimate the confidence interval of the increment x under specific confidence level, determines that increment x is received up and down under the specific confidence level
Limit threshold value.
It is highly preferred that the specific distribution function is normal distribution, Gumbel distribution or t distribution in step S5.
It is highly preferred that the specific confidence level is 90%, 95% or 99% in step S5.
It is highly preferred that the specific confidence level is 95% in step S5.
Preferably, in step S7, the rational specific method of each abnormal increment obtained by further analytical procedure S6 is：
Junction closes Precipitation Process and water temperature data of the step S1 gained basin sampled point to be measured in extended time interval, when certain is different
Constant increment is less than the received lower threshold of increment x under specific confidence level, and water temperature variation this moment is obvious；Or when certain is different
Constant increment is greater than the received upper limit threshold of increment x under specified level, and water temperature variation this moment is obvious and when rainfall not occurring,
Then determine that the exception increment should reject, and then determines that the hydraulic pressure of corresponding record, relative water depth data should also reject；When certain increases extremely
Amount is greater than the received upper limit threshold of increment x under specified level, and water temperature is constant or small size variation, and when rainfall occurs, then
Determining the exception increment is caused by rainfall, and then thinks that the hydraulic pressure of corresponding record, relative water depth data can be received.
Compared with prior art, the beneficial effects of the invention are as follows：
1, when having invented a kind of water lever fluctuating suitable for any water body, the identification of unusual fluctuations, screening, rejecting
Method, this method can be suitable for long-time, large batch of Data Analysis Services, and whole process can be in monitoring device or computer
Calculated result is immediately arrived at, data are effectively reduced and check the time expended, reduce the uncertainty of artificial treatment abnormal data；
2, estimated using specific statistical model (such as normal distribution curve) special using water level increment as control variable
The confidence interval of (such as 95%) water level increment is determined under confidence level, and the principle and foundation of judgement are relatively more scientific and reasonable；
3, the water level that may cause for the mankind's activities such as reading data, high-intensitive precipitation, water temperature variation and system change
Anomalous variation makes a concrete analysis of particular problem, improves the reliability of monitoring data.
Detailed description of the invention
Fig. 1 is a kind of flow chart of the abnormal data analysis method of Water level trend monitoring described in embodiment 1；
Fig. 2 is Precipitation Process figure of certain the basin sampled point of embodiment 1 in monitoring record on 1 day-July 31 July in 2017,
Record time interval 5min；
Fig. 3 is relative water depth number of certain the basin sampled point of embodiment 1 in monitoring record on 1 day-July 31 July in 2017
According to figure, time interval 5min is recorded；
Fig. 4 is relative water depth data of the embodiment 1 according to Fig. 3, calculates the incremental data figure of generation, records time interval
5min；
Fig. 5 be embodiment 1 according to fig. 2, the data of Fig. 4, it is raw after the corresponding abnormal relative water depth data of rejecting abnormalities increment
At acceptable relative water depth datagram.
Specific embodiment
Clear, complete description is carried out below with reference to technical solution of the attached drawing to embodiment in this patent, it is clear that is retouched
The embodiment stated is only a part of the embodiment of this patent, instead of all the embodiments.Embodiment based on this patent, this
Field those of ordinary skill obtained all other embodiment without making creative work belongs to this specially
The range that benefit is protected.
Embodiment 1
The present embodiment is the Water level trend prison carried out for a certain sampled point in certain waters on July 1st, 2017 to July 31
Abnormal data analysis is surveyed, following steps are specifically used：
S1, using HOBO pressure water-level gauge, continuously recording basin to be measured, (July one is whole on July 1st, 2017 to July 31
Month) Precipitation Process；And with 5min, (Δ t) continuously records the hydraulic pressure and water temperature number of sampled point for time interval within the period
According to respectively obtaining continuous hydraulic pressure of the sampled point in the period, water temperature data sequence；
S2, each water pressure data in the continuous water pressure data sequence of step S1 is passed through into calculation formula
H=(P-P0)/9.8
The relative water depth data of the water surface to sampled point are converted into, obtain sampled point in the continuous phase in July to bathymetric data
Sequence, wherein H is relative water depth data of the sampled point at the moment, unit m；P is sampled point water surface upper atmosphere pressure, single
Position is kPa；P0For sampled point hydraulic pressure, unit kPa.
S3, the Precipitation Process in conjunction with step S1 gained basin sampled point to be measured in July, when the phase at certain moment in July
When being 0 or negative value to bathymetric data, then the relative water depth data at the moment are obvious abnormal data.
N relative water depth data subsequence A, each sub- sequence of relative water depth data are obtained after removing these obvious abnormal datas
Time interval in column A comprising being recorded between each relative water depth data a in m relative water depth data a, each subsequence A
For 5min, i.e.,
Ai={ ai,1,ai,2,…,ai,m, 1≤i≤n,
Wherein, AiRefer to i-th of relative water depth data subsequence, includes m relative water depth data, respectively ai,1,
ai,2,…,ai,m；
S4, it calculates in each relative water depth data subsequence A, the increment x of the relative water depth data a of every 2 adjacent moments,
Increment subsequence X is obtained, then all increment subsequence X are merged to obtain increment sequence XX, specific step is as follows：
For each relative water depth data subsequence Ai={ ai,1,ai,2,…,ai,m, calculate ai,mAnd ai,m-1Between increasing
X is measured, increment subsequence X is obtainedi, each increment sub-series of packets is containing m-1 increment x, i.e.,
Xi=xi,1,xi,2,…,xi,m-1, xi,m-1=ai,m-ai,m-1,
Wherein, xi,m-1For relative water depth data subsequence AiIn adjacent 2 relative water depth data ai,mAnd ai,m-1Increment；
Again by all increment subsequence XiMerging obtains increment sequence XX, XX={ X1,X2,…,Xn, i.e. increment sequence XX
Sampled point is contained to be in extended time interval with the increment of all relative water depth data of specified time interval Δ t.
S5, assume that increment x contained by the increment sequence XX meets normal distribution, estimation increases under 95% confidence level
The confidence interval of x is measured, determines the received bound threshold value of increment x under 95% confidence level；
S6, on the basis of the bound threshold value of the confidence interval, filter out in the increment sequence of step S4 own
Abnormal increment beyond confidence interval；
S7, Precipitation Process and water temperature data in conjunction with step S1 gained basin sampled point to be measured in extended time interval, when certain
Abnormal increment is less than the received lower threshold of increment x under 95% confidence level, and water temperature variation this moment is obvious；Or work as certain
Abnormal increment is greater than the received upper limit threshold of increment x under 95% confidence level, and water temperature variation this moment is obvious and does not occur
When rainfall, then determine that the exception increment should reject, and then determines that the hydraulic pressure of corresponding record, relative water depth data should also reject；When
Certain abnormal increment is greater than the received upper limit threshold of increment x under 95% confidence level, and water temperature is constant or small size variation, and sends out
When raw rainfall, then determine that the exception increment is caused by rainfall, and then thinks that the hydraulic pressure of corresponding record, relative water depth data can be by
Receive.
The analysis method flow chart of the present embodiment is as shown in Figure 1；Then Precipitation Process figure, the relative water depth number of formation are recorded
After moment locating for figure, incremental data figure and rejecting abnormalities increment accordingly abnormal relative water depth data, generation is subjected to
Relative water depth datagram as shown in Fig. 2~Fig. 5.
As shown in Figure 2, due to the influence of rainfall, in duration of raining there are it is multiple it is steep increase the variation dropped suddenly, these when
The observation at quarter may be exceptional value.But compare discovery by the combination of Fig. 2~Fig. 4, in the SEA LEVEL VARIATION of Fig. 3, Fig. 4 not with
The unmatched abnormal relative water depth data of Fig. 2 Precipitation Process.
The average value of water level increment in every interval 5min is obtained using the normfit function in Matlab according to step S5
And variance, the increment average value miu in the every 5min of the present embodiment are -1.2mm, standard deviation sigma is 158.6；Therefore it 95% sets
Believe that section is (miu-1.96*sigma, miu+1.96*sigma), i.e., (- 312.1,309.7).
Eventually by 95% confidence interval is compared, analyze and determine that July 1 to July 31 sets in every 5min beyond 95%
The reasonability for believing all abnormal increments in section, enumerates and summarizes abnormal relative water depth data, the following table 1 is made.
The abnormal relative water depth data summary table of 1 embodiment 1 of table
6 abnormal periods for including in table 1 are all in the case where no rainfall, and water level occurs substantially to fall in a short time
Fall behind and escalate in a short time again, temperature also shows as falling rapidly after sharp rising in the short time, can analyze disconnected
The increment of these fixed periods, relative water depth depth are exceptional value, need to reject.
Claims (9)
1. a kind of abnormal data analysis method of Water level trend monitoring, which is characterized in that include the following steps：
S1, Precipitation Process of certain sampled point in certain extended time interval under basin water to be measured is continuously recorded；In the extended time interval with
It is long-term at this to respectively obtain the sampled point for the time interval Δ t of specific duration, the continuous hydraulic pressure and water temperature data for recording sampled point
The continuous hydraulic pressure of period, water temperature data sequence；
S2, the relative water depth number that each water pressure data in the continuous water pressure data sequence is converted into the water surface to the sampled point
According to obtaining the sampled point in the continuous phase of the extended time interval to bathymetric data sequence；
S3, the Precipitation Process in conjunction with step S1 gained basin sampled point to be measured in extended time interval, remove the continuous phase to water
Obvious abnormal data in deep data sequence obtains several relative water depth data subsequences A；Each relative water depth data
It include several relative water depth data a with same time interval of delta t in subsequence A；
S4, it calculates in each relative water depth data subsequence A, the increment x of the relative water depth data a of every 2 adjacent moments is obtained
Several increments subsequence X, then all increment subsequence X are merged to obtain increment sequence XX；
S5, the confidence interval for estimating increment x in increment sequence XX under specific confidence level, determine the bound threshold of the confidence interval
Value, i.e., each received bound threshold value of increment；
S6, on the basis of the bound threshold value of the confidence interval, filter out all beyond the confidence area in the increment sequence XX
Between abnormal increment；
Precipitation Process in extended time interval of S7, the basin sampled point to be measured in conjunction with obtained by step S1, water temperature data sequence, further
The reasonability for analyzing each abnormal increment, rejects unreasonable abnormal increment.
2. a kind of abnormal data analysis method of Water level trend monitoring according to claim 1, which is characterized in that step S2
In, each water pressure data is converted into the calculating public affairs of relative water depth data of the water surface to sampled point in the continuous water pressure data sequence
Formula is：
H=(P-P0)/9.8；
Wherein, H is relative water depth data of the sampled point at the moment, unit m；P is sampled point water surface upper atmosphere pressure, unit
For kPa；P0For sampled point hydraulic pressure, unit kPa.
3. a kind of abnormal data analysis method of Water level trend monitoring according to claim 2, which is characterized in that step S3
In, the continuous phase is to the specific judgment method of the obvious abnormal data in bathymetric data sequence：When the opposite water at certain moment
When deep data are 0 or negative value, then the relative water depth data at the moment are obvious abnormal data；
N relative water depth data subsequence A, each relative water depth data subsequence A are obtained after removing the obvious abnormal data
In relative water depth data a comprising m with same time interval of delta t, i.e.,
Ai={ aI, 1, aI, 2..., aI, m, 1≤i≤n,
Wherein, AiRefer to i-th of relative water depth data subsequence, includes m relative water depth data, respectively aI, 1, aI, 2...,
aI, m。
4. a kind of abnormal data analysis method of Water level trend monitoring according to claim 3, which is characterized in that step S4
Specially：For each relative water depth data subsequence Ai={ aI, 1, aI, 2..., aI, m, calculate aI, mAnd aI, m-1Between increasing
X is measured, increment subsequence X is obtainedi, each increment sub-series of packets is containing m-1 increment x, i.e.,
Xi={ xI, 1, xI, 2..., xI, m-1, xI, m-1=aI, m-aI, m-1,
Wherein, xI, m-1For relative water depth data subsequence AiIn adjacent 2 relative water depth data aI, mAnd aI, m-1Increment；
Again by all increment subsequence XiMerging obtains increment sequence XX, XX={ X1, X2..., Xn, i.e. increment sequence XX includes
Sampled point is in extended time interval with the increment of all relative water depth data of specified time interval Δ t.
5. a kind of abnormal data analysis method of Water level trend monitoring according to claim 4, which is characterized in that step S5
Specially：It is assumed that increment x contained by the increment sequence XX meets specific distribution function, estimation increases under specific confidence level
The confidence interval of x is measured, determines the received bound threshold value of increment x under the specific confidence level.
6. a kind of abnormal data analysis method of Water level trend monitoring according to claim 5, which is characterized in that step S5
In, the specific distribution function is normal distribution, Gumbel distribution or t distribution.
7. a kind of abnormal data analysis method of Water level trend monitoring according to claim 5, which is characterized in that step S5
In, the specific confidence level is 90%, 95% or 99%.
8. a kind of abnormal data analysis method of Water level trend monitoring according to claim 7, which is characterized in that step S5
In, the specific confidence level is 95%.
9. a kind of abnormal data analysis method of Water level trend monitoring according to claim 5,6 or 7, which is characterized in that
In step S7, the rational specific method of each abnormal increment obtained by further analytical procedure S6 is：
In conjunction with Precipitation Process and water temperature data of the step S1 gained basin sampled point to be measured in extended time interval, when certain abnormal increment
Less than the received lower threshold of increment x under specific confidence level, and water temperature variation this moment is obvious；Or when certain abnormal increment
Greater than the received upper limit threshold of increment x under specified level, and water temperature variation this moment is obvious and when rainfall not occurring, then determines
The exception increment should reject, and then determine that the hydraulic pressure of corresponding record, relative water depth data should also reject；When certain exception increment is greater than
The received upper limit threshold of increment x under specified level, and water temperature is constant or small size variation, and when rainfall occurs, then determining should
Abnormal increment is caused by rainfall, and then thinks that the hydraulic pressure of corresponding record, relative water depth data can be received.
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