CN108319664A - A kind of dam and the recognition methods of project security monitoring data error and system - Google Patents
A kind of dam and the recognition methods of project security monitoring data error and system Download PDFInfo
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- CN108319664A CN108319664A CN201810044079.6A CN201810044079A CN108319664A CN 108319664 A CN108319664 A CN 108319664A CN 201810044079 A CN201810044079 A CN 201810044079A CN 108319664 A CN108319664 A CN 108319664A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
- G06F16/2462—Approximate or statistical queries
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/21—Design, administration or maintenance of databases
- G06F16/215—Improving data quality; Data cleansing, e.g. de-duplication, removing invalid entries or correcting typographical errors
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
- G06F16/2474—Sequence data queries, e.g. querying versioned data
Abstract
The invention discloses a kind of dam and the recognition methods of project security monitoring data error and systems, include the following steps：Obtain certain dam and the observation data sequence { x of project security monitoring physical quantity1,x2,…,xnAnd its corresponding time of measuring sequence { t1,t2,…,tn}；New statistic d is constructed based on observation data and its corresponding time of measuringi, construct new data series：{d2,d3,…,dn‑1}；Calculate the arithmetic mean of instantaneous value of new data sequenceWith standard deviation Sd；To data x to be detectediOutliers identifying is carried out, data x to be detected is first obtainediCorresponding new statistic diIfThen think xiIt is rough error；IfThen think xiIt is normal value, k is the constant of setting.The present invention is based on observation data and its corresponding time of measuring to construct new data sequence, realize the automatic batch outliers identifying of magnanimity monitoring data, it has fully considered the gradual change feature of dam and project security monitoring data itself and time of measuring spacing difference that may be present, has been suitable in project security monitoring field.
Description
Technical field
The invention belongs to outliers identifying technical fields, and in particular to a kind of dam and the identification of project security monitoring data error
Method further relates to a kind of dam and project security monitoring data error identifying system.
Background technology
Rough error is to make a series of observation under identical observation condition, and absolute value is more than the measured deviation of limit difference, it is produced
Accuracy of instrument when raw most common reason is observation does not reach requirement, the design of technical specification and observation program are unreasonable, with
And observer's carelessness and instrument failure or technical carelessness etc..Measurement data containing rough error cannot use, it is necessary to make
Surely it operates effectively program and check method goes to find and rejected.
Outliers identifying is mainly by manual identified at present, by draw monitoring data time course line or monitoring effect quantity with
The relation line of influence factor checks that the cusp that peels off, incorporation engineering environmental data comparative analysis are made according to dam work theory and experience
Judge.This method is based primarily upon micro-judgment, therefore there are larger subjective factors, and is difficult to complete intelligence by computer
It can identify, especially when in face of mass data, data processing task is very heavy, will directly affect data-handling efficiency.
The present invention is in view of the above-mentioned problems, will provide one kind adapting to monitoring object itself gradual change, while considering measured value not
It is influenced with sampling interval duration, and the tightness for judging criterion can be with the dam and engineering safety of human intervention dynamic adjustment
Outliers identifying method is monitored, and realizes the automatic identification of rough error by computer programming.
Invention content
It is an object of the invention to overcome deficiency in the prior art, a kind of dam and project security monitoring data are provided
Outliers identifying method and system construct new data sequence based on observation data and its corresponding time of measuring, fully consider
The gradual change feature and time of measuring spacing difference that may be present of dam and project security monitoring data itself are suitable in engineering
Safety monitoring field.
In order to solve the above technical problems, the present invention provides a kind of dam and project security monitoring data error identification sides
Method, characterized in that include the following steps：
Step S1 obtains certain dam and the observation data sequence { x of project security monitoring physical quantity1,x2,…,xnAnd its it is right
Time of measuring sequence { the t answered1,t2,…,tn}；
Step S2 constructs new statistic d based on observation data and its corresponding time of measuringi, statistic diCalculating it is public
Formula is：
T in formulai-1、tiAnd ti+1Respectively observation data xi-1、xiAnd xi+1Corresponding time of measuring；
New data series are constructed according to above formula calculating：{d2,d3,…,dn-1}；
Step S3 calculates the arithmetic mean of instantaneous value of new data sequenceWith standard deviation Sd；
Step S4, to data x to be detectediOutliers identifying is carried out, data x to be detected is first obtainediCorresponding new statistic
di,
IfThen think xiIt is rough error；
IfThen think xiIt is normal value, i=2 in formula, 3 ... ..., n-1, k is the constant of setting.
Further, arithmetic mean of instantaneous value calculation formula is：
Further, standard deviation SdCalculation formula is
Further, the value range of k is 1 to 5.
Correspondingly, the present invention also provides a kind of dam and project security monitoring data error identifying systems, characterized in that
Including rough error parameter set unit, outlier analysis unit and preserve rough error marking unit；
Rough error parameter set unit, for rule of thumb setting rough error parameter k value；
Outlier analysis unit selects measurement point, outlier analysis is carried out to measurement point according to the rough error parameter value of setting, to know
Other rough error；
Rough error marking unit is preserved, it is whether reasonable for auditing outliers identifying result, rough error label is preserved if rationally.
Further, outlier analysis unit includes observation data acquisition module, new data sequence structure module and rough error
Identification module,
Data acquisition module is observed, certain dam and the observation data sequence { x of project security monitoring physical quantity are obtained1,
x2,…,xnAnd its corresponding time of measuring sequence { t1,t2,…,tn}；
New data sequence builds module, and new statistic d is constructed based on observation data and its corresponding time of measuringi, system
Measure diCalculation formula be：
T in formulai-1、tiAnd ti+1Respectively observation data xi-1、xiAnd xi+1Corresponding time of measuring；
New data series are constructed according to above formula calculating：{d2,d3,…,dn-1}；
Outliers identifying module calculates the arithmetic mean of instantaneous value of new data sequenceWith standard deviation Sd；To data x to be detectedi
Outliers identifying is carried out, data x to be detected is first obtainediCorresponding new statistic di,
IfThen think xiIt is rough error；
IfThen think xiIt is normal value, i=2 in formula, 3 ... ..., n-1, k is the constant of setting.
Further, judge that rough error is unreasonable if preserving in rough error marking unit, adjust k values and re-start rough error point
Analysis.
Further, the value range of k is 1~5.
Compared with prior art, the advantageous effect of the invention reached is：
(1) inventive algorithm is realized convenient for computer programming, can realize the automatic batch processing of magnanimity monitoring data, greatly
The efficiency for improving outliers identifying.
(2) present invention is by constructing new statistic, has fully considered dam and the project security monitoring data gradual changes of itself
Feature and time of measuring spacing difference that may be present are suitable for project security monitoring field.
(3) present invention is during outliers identifying, the case where not only allowing for measured value to be judged itself, and also while considering
Before and after the measured value the case where measured value, the case where having considered the current of measured value, history and following three kinds of states, thus from analysis
It is more reasonable compared with single status assessment in principle.
(4) present invention can be according to the actual conditions of different monitoring data series, and criterion is judged in dynamic adjustment, thus is actually answered
It is more flexible in, conveniently, it can be also used for carrying out outliers identifying contrast test, be conducive to improve the accurate of rough error intelligent recognition
Degree.
(5) mathematically principle understands computational methods of the invention, and physical meaning is clear in engineer application level, operating level
It is upper to be realized convenient for computer programming, there is certain innovation on theoretical method, there is higher engineering practical value.
Description of the drawings
Fig. 1 is the method for the present invention outliers identifying flow chart；
Fig. 2 is that rough error judges flow chart in the method for the present invention；
Fig. 3 is the outlier analysis parameter setting interface of present system；
Fig. 4 is outlier analysis result figure in the embodiment of the present invention；
Fig. 5 is measured value graph schematic diagram in the embodiment of the present invention.
Specific implementation mode
The invention will be further described below in conjunction with the accompanying drawings.Following embodiment is only used for clearly illustrating the present invention
Technical solution, and not intended to limit the protection scope of the present invention.
The dam of the present invention and project security monitoring data error recognition methods, as shown in Figure 1, including following procedure：
If the observation data sequence of certain dam and project security monitoring physical quantity is：{x1,x2,…,xn, observe data pair
The time of measuring answered is：{t1,t2,…,tn}。
1) data series { x to be detected is read1,x2,…,xn}。
2) new statistic d is constructed based on data sequence and its corresponding time of measuringi, calculate and obtain new data system
Arrange { d2,d3,…,dn-1}。
Pass through data to be tested xiWith its former and later two data xi-1、xi+1And the corresponding time of measuring of each data, it constructs
New statistic di,
T in formulai-1、tiAnd ti+1Respectively measured value xi-1、xiAnd xi+1Corresponding time of measuring.
New data series are constructed according to above formula calculating：
{d2,d3,…,dn-1}
3) data series { d is calculated2,d3,…,dn-1Arithmetic mean of instantaneous valueWith standard deviation Sd。
And SdThe calculating of two formulas is pressed respectively：
4) setting rough error judges the undetermined parameter k in criterion.
Rough error judges criterion, ifThen think xiFor rough error.
K is constant in formula, and it is 3 that k values are can use under default situations.In actual use it is contemplated that engineering practice, according to thick
Poor identical criterion tightness difference is adjusted into Mobile state, it usually needs considers dam and project security monitoring precision, the frequency etc.
The influence of factor if criterion more loosely can use larger k values, otherwise takes smaller k values, and through experiment, the value range of k is 1
~5.
5) to data x to be detected2、x3、……、xn-1It is judged, completes outliers identifying.
Specific Appraisal process is as follows, referring to Fig. 2：
1. calculating d2With arithmetic mean of instantaneous valueDeviation
2. judging data x2Whether it is rough error.
IfThen think x2It is rough error；
IfThen think x2It is normal value.
3. according to step 1. 2. in method calculate and judge successively data x3、……、xn-1Whether it is rough error.
IfThen think xiIt is rough error；
IfThen think xiIt is normal value, i=3 in formula, 4 ... ..., n-1.
Correspondingly, based on above-mentioned outliers identifying method, the present invention also provides a kind of dam and project security monitoring data are thick
Poor identifying system, including rough error parameter set unit, outlier analysis unit and preservation rough error marking unit.
Rough error parameter set unit, for rule of thumb setting rough error parameter k value；
Outlier analysis unit selects measurement point, outlier analysis is carried out to measurement point according to the rough error parameter value of setting, to know
Other rough error；
Rough error marking unit is preserved, it is whether reasonable for auditing outliers identifying result, rough error label is preserved if rationally.If
Unreasonable adjustment k values re-start outlier analysis.If criterion more loosely can use larger k values, otherwise take smaller k values, pass through
The value range of experiment, k is 1~5.
The processing procedure of outlier analysis unit described herein is 5 step mistakes of outliers identifying method described above
Journey；Module and outliers identifying module are built including observation data acquisition module, new data sequence,
Data acquisition module is observed, certain dam and the observation data sequence { x of project security monitoring physical quantity are obtained1,
x2,…,xnAnd its corresponding time of measuring sequence { t1,t2,…,tn}；
New data sequence builds module, and new statistic d is constructed based on observation data and its corresponding time of measuringi, system
Measure diCalculation formula be：
T in formulai-1、tiAnd ti+1Respectively observation data xi-1、xiAnd xi+1Corresponding time of measuring；
New data series are constructed according to above formula calculating：{d2,d3,…,dn-1}；
Outliers identifying module calculates the arithmetic mean of instantaneous value of new data sequenceWith standard deviation Sd；To data x to be detectedi
Outliers identifying is carried out, data x to be detected is first obtainediCorresponding new statistic di,
IfThen think xiIt is rough error；
IfThen think xiIt is normal value, i=2 in formula, 3 ... ..., n-1, k is the constant of setting.
Embodiment
Certain engineering temperature record rough error is more, and artificial rough error processing is relatively complicated, and dam is developed based on the method for the present invention
And project security monitoring data error identifying system (computer programming in the prior art can be used to realize this system), it realizes
The automatic identification of rough error.
First, outlier analysis parameter is set, is normally set up k=3, the parameter setting interface of system is in detail as shown in Figure 3.
Secondly, outliers identifying method using the present invention carries out outlier analysis, rough error is identified, with tables of data and graph two
Kind form displaying outlier analysis is as a result, in detail as shown in Figure 4.
Finally, it is audited by tables of data and graph, it is believed that automatic identification result is reasonable, preserves rough error label, slightly
Measured value graph after difference is as shown in Figure 5.
In addition, k values are dynamically adapted, if thinking, criterion more loosely can use larger k values, otherwise take smaller k values.It is logical
Cross the accuracy that rough error intelligent recognition can be improved in adjustment k values.
The method of the present invention considers the influence of the gradual change law and different sampling stages interval of monitoring data itself, is dam
And the rejecting of project security monitoring data error provides a kind of accurately and efficiently computational methods, convenient for passing through computer programming reality
Existing intelligent recognition and separating and measuring rough error.It can greatly improve the efficiency of measurement data outliers identifying using the present invention, there is standard
The advantages of exactness is high, highly practical, convenient for promoting, can efficiently identification and excluding gross error from mass data, enable measured value
More accurately reflect the changing rule of works.
The above is only a preferred embodiment of the present invention, it is noted that for the ordinary skill people of the art
For member, without departing from the technical principles of the invention, several improvements and modifications, these improvements and modifications can also be made
Also it should be regarded as protection scope of the present invention.
Claims (8)
1. a kind of dam and project security monitoring data error recognition methods, characterized in that include the following steps：
Step S1 obtains certain dam and the observation data sequence { x of project security monitoring physical quantity1,x2,…,xnAnd its it is corresponding
Time of measuring sequence { t1,t2,…,tn}；
Step S2 constructs new statistic d based on observation data and its corresponding time of measuringi, statistic diCalculation formula
For：
T in formulai-1、tiAnd ti+1Respectively observation data xi-1、xiAnd xi+1Corresponding time of measuring；
New data series are constructed according to above formula calculating：{d2,d3,…,dn-1}；
Step S3 calculates the arithmetic mean of instantaneous value of new data sequenceWith standard deviation Sd；
Step S4, to data x to be detectediOutliers identifying is carried out, data x to be detected is first obtainediCorresponding new statistic di,
IfThen think xiIt is rough error；
IfThen think xiIt is normal value, i=2 in formula, 3 ... ..., n-1, k is the constant of setting.
2. a kind of dam according to claim 1 and project security monitoring data error recognition methods, characterized in that arithmetic
Mean value calculation formula is：
3. a kind of dam according to claim 2 and project security monitoring data error recognition methods, characterized in that standard
Poor SdCalculation formula is
4. a kind of dam according to claim 1 and project security monitoring data error recognition methods, characterized in that k's
Value range is 1 to 5.
5. a kind of dam and project security monitoring data error identifying system, characterized in that including rough error parameter set unit, slightly
Poor analytic unit and preservation rough error marking unit；
Rough error parameter set unit, for rule of thumb setting rough error parameter k value；
Outlier analysis unit selects measurement point, and outlier analysis is carried out to measurement point according to the rough error parameter value of setting, thick to identify
Difference；
Rough error marking unit is preserved, it is whether reasonable for auditing outliers identifying result, rough error label is preserved if rationally.
6. a kind of dam according to claim 5 and project security monitoring data error identifying system, characterized in that rough error
Analytic unit includes observing data acquisition module, new data sequence structure module and outliers identifying module,
Data acquisition module is observed, certain dam and the observation data sequence { x of project security monitoring physical quantity are obtained1,x2,…,xn}
And its corresponding time of measuring sequence { t1,t2,…,tn}；
New data sequence builds module, and new statistic d is constructed based on observation data and its corresponding time of measuringi, statistic di
Calculation formula be：
T in formulai-1、tiAnd ti+1Respectively observation data xi-1、xiAnd xi+1Corresponding time of measuring；
New data series are constructed according to above formula calculating：{d2,d3,…,dn-1}；
Outliers identifying module calculates the arithmetic mean of instantaneous value of new data sequenceWith standard deviation Sd；To data x to be detectediIt carries out
Outliers identifying first obtains data x to be detectediCorresponding new statistic di,
IfThen think xiIt is rough error；
IfThen think xiIt is normal value, i=2 in formula, 3 ... ..., n-1, k is the constant of setting.
7. a kind of dam according to claim 5 and project security monitoring data error identifying system, characterized in that preserve
If judging that rough error is unreasonable in rough error marking unit, adjusts k values and re-start outlier analysis.
8. a kind of dam according to claim 5 and project security monitoring data error identifying system, characterized in that k's
Value range is 1~5.
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| CN111177218A (en) * | 2019-12-25 | 2020-05-19 | 深圳市东深电子股份有限公司 | Dam safety analysis method based on big data analysis |
| CN111222095A (en) * | 2019-12-19 | 2020-06-02 | 国网电力科学研究院有限公司 | Gross error discrimination method, device and system in dam deformation monitoring |
| CN111709465A (en) * | 2020-06-04 | 2020-09-25 | 中国电建集团华东勘测设计研究院有限公司 | Intelligent identification method for rough difference of dam safety monitoring data |
| CN114492168A (en) * | 2021-12-28 | 2022-05-13 | 大唐水电科学技术研究院有限公司 | Method for identifying gross errors of dam safety monitoring data under dynamic system |
| CN116401535A (en) * | 2023-06-05 | 2023-07-07 | 中国电建集团西北勘测设计研究院有限公司 | Time sequence data coarse and fine recognition method and system based on difference method |
| CN117609710A (en) * | 2024-01-24 | 2024-02-27 | 中国电建集团西北勘测设计研究院有限公司 | Method and device for preventing normal jump of monitoring data from being removed |
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| CN111222095A (en) * | 2019-12-19 | 2020-06-02 | 国网电力科学研究院有限公司 | Gross error discrimination method, device and system in dam deformation monitoring |
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| CN111709465B (en) * | 2020-06-04 | 2022-12-20 | 中国电建集团华东勘测设计研究院有限公司 | Intelligent identification method for rough difference of dam safety monitoring data |
| CN114492168A (en) * | 2021-12-28 | 2022-05-13 | 大唐水电科学技术研究院有限公司 | Method for identifying gross errors of dam safety monitoring data under dynamic system |
| CN116401535A (en) * | 2023-06-05 | 2023-07-07 | 中国电建集团西北勘测设计研究院有限公司 | Time sequence data coarse and fine recognition method and system based on difference method |
| CN116401535B (en) * | 2023-06-05 | 2023-09-22 | 中国电建集团西北勘测设计研究院有限公司 | Time sequence data coarse and fine recognition method and system based on difference method |
| CN117609710A (en) * | 2024-01-24 | 2024-02-27 | 中国电建集团西北勘测设计研究院有限公司 | Method and device for preventing normal jump of monitoring data from being removed |
| CN117609710B (en) * | 2024-01-24 | 2024-04-12 | 中国电建集团西北勘测设计研究院有限公司 | Method and device for preventing normal jump of monitoring data from being removed |
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Application publication date: 20180724 |
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