CN102682573A - Time sequence analysis-based storm surge disaster early warning system - Google Patents

Time sequence analysis-based storm surge disaster early warning system Download PDF

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Publication number
CN102682573A
CN102682573A CN2012100930641A CN201210093064A CN102682573A CN 102682573 A CN102682573 A CN 102682573A CN 2012100930641 A CN2012100930641 A CN 2012100930641A CN 201210093064 A CN201210093064 A CN 201210093064A CN 102682573 A CN102682573 A CN 102682573A
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early warning
disaster
monitoring
historical
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CN102682573B (en
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黄冬梅
乔欢
何盛琪
王振华
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Shanghai Maritime University
Shanghai Ocean University
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Shanghai Maritime University
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Abstract

The invention discloses a time sequence analysis-based storm surge disaster early warning system, which comprises a database server, an early warning prediction model analysis module, an assistant decision making system, a Web server and a management user, wherein the database server stores historical monitoring data, real-time monitoring data, basic geographic information data, emergency plan data, related calculation model data and early warning data; the monitoring data and the early warning data are time sequence data; the early warning prediction model analysis module is mainly used for preprocessing the data, piecewise linearly expressing the time sequence data to fulfill dimension reduction and compression aims, performing similarity matching on the time sequence data and the historical monitoring data, and performing disaster early warning according to a similarity matching result; and the assistant decision making system displays assistant decision making information on the basis of geographical graphic information, and displays the assistant decision making information to a client of the management user through the deployed Web server.

Description

A kind of storm surge disaster early warning system based on time series analysis
Technical field
The present invention relates to a kind of ocean storm tide early warning system based on time series analysis, particularly a kind of make full use of historical Monitoring Data to present Monitoring Data analyze, the system and method for early warning.
Background technology
Along with the fast development of global economy, climate change is frequent, causes the global ocean acidifying, sea level rise, and Oceanic disasters take place frequently.Oceanic disasters mainly comprise: storm tide, tsunami, wave and coast erosion, red tide or the like; Wherein occupy the first at China's storm tide occurrence frequency and the disaster that causes; The loss that causes accounts for more than 60% of Oceanic disasters loss, aspect casualties, surpasses land disasters such as earthquake.
Present stage, the method that is used for the Oceanic disasters forecast adopts Numerical Prediction Method basically.Numerical Prediction Method mainly be utilize large-scale, robot calculator is found the solution the method that the system of equations of describing the Oceanic disasters phenomenon is made the Oceanic disasters forecast fast.The budget amount of its forecasting process is huge, length consuming time and consume more resource.In numerical forecasting; Since some small scales or approach small scale motion can't definite reflecting in forecast model; Add in the model of Numerical Prediction Method Determination of Parameters deficiency in objective method accurately, thereby the accuracy that has caused predicting the outcome reduces.
In Numerical Prediction Method, the foundation of model is based on historical disaster data simultaneously, and data are fewer, and it is very difficult that this single model of setting up based on the small sample data comes the Simulation of Complex system.Therefore need be on existing valuable historical summary, the computer technology of utilization science is carried out forecasting and warning to this disaster, for government decision technical support is provided better.
Summary of the invention
The purpose of this invention is to provide a kind of storm surge disaster early warning system, make full use of historical Monitoring Data based on time series analysis, to storm tide forecast, early warning.
In order to realize above purpose, the present invention adopts following technical scheme to realize:
A kind of storm surge disaster early warning system based on time series analysis, real-time receiving remote Monitoring Data, and the Monitoring Data sequence carried out early warning analysis, carry out early warning and aid decision making according to analysis result.It is characterized in that, comprise Database Systems, Early-warning Model analysis, aid decision-making system displaying, Web server and leading subscriber.Early warning forecast model analysis module mainly is after the observation data in the database is carried out pre-service; Time series data is carried out the purpose that piecewise linearity representes to reach the dimension compression; And then real-time Monitoring Data carried out similar coupling with historical monitoring materials data, and make disaster alarm according to similar matching result.If Monitoring Data reaches threshold value of warning, early warning forecast model analysis module stores observation data and the historical data information that is complementary in the early warning document data base into.Aid decision-making system is based on geographical figure information exhibition decision-making assistant information; Comprise and call that early warning data, emergency preplan data carry out simulation shows to disaster and emergency preplan generates; Calling the typhoon data of real-time monitoring shows; The simulated data of aid decision-making system and emergency preplan data also store in the early warning document data base, and through the Web server disposed with Map Services and data display client to leading subscriber.
The historical monitoring materials data of described database server stores, real-time monitoring materials data, basic geographical information material database, emergency preplan data, correlation computations model data and early warning data.Described monitoring materials data and early warning data are time series data.Described monitoring materials data and early warning data are time series data.Historical monitoring materials database comprises the historical typhoon title of storage, typhoon actual measurement and forecast data, the actual measurement of historical storm tide surge position, total water level and astronomical tide bit data, forecast data and disaster level data.The real-time Monitoring Data of the receiving remote of monitoring materials database timing in real time data acquisition system (DAS) transmission is provided with the database file automatic expansion and shrinks to adapt to the real-time typing of data with table.Geographical information material database storing basis, basis geographic information data comprises the remote sensing image data that spatial data, attribute data and the U.S. ESRI company of road and settlement place provide.Historical typhoon prediction scheme of emergency preplan data library storage and design prediction scheme, historical storm tide prediction scheme and design prediction scheme.Correlation computations model data library storage is used to choose disaster index, the simulation of storm tide the condition of a disaster, the model of disaster grade classification and the expertise that wherein relates to.The matching result data that the analysis of early warning data library storage Early-warning Model obtains, the condition of a disaster simulation assessment data, disaster grade classification data, the data in the early warning document data base store in the emergency preplan document data base after early warning is removed.
Described aid decision-making system show comprise this storm surge disaster the condition of a disaster simulation assessment, level evaluation, typhoon track is showed and according to the reasoning prediction scheme result of historical the condition of a disaster emergency preplan.The condition of a disaster simulation assessment is the information of returning to early warning; With this monitoring time sequence data is the displaying that boundary condition carries out the condition of a disaster simulation and floods scope, depth of the water submerging, and obtains this disaster level evaluation result according to the reasoning as a result of similarity measurement and send to leading subscriber.
Described Web server turns back to leading subscriber through Web Services transmission XML formatted data.
Described leading subscriber is after receiving early warning, to alert issue early warning report.Send to government website, mobile terminal of mobile telephone, offshore vessel mounted terminal and Decision Control center through Internet, the condition of a disaster is done trace analysis by leader expert.
Above-mentioned Early-warning Model analysis at first is that real-time Monitoring Data and historical Monitoring Data are carried out the data pre-service; In this process, to separate Monitoring Data and normal Monitoring Data when storm surge disaster takes place in the historical monitoring materials database, extract the tidal level data and analyze.For normal Monitoring Data, as case data, Monitoring Data when taking place for historical storm surge disaster is then all as case data according to the partial data of selecting storm tide wherein multiple season.Next is that historical Monitoring Data in the database and real-time Monitoring Data are carried out time series linear expression, realizes the dimensionality reduction and the compression of data.Be the method that adopts the Time Series Similarity coupling once more; Time series data in real-time Monitoring Data and the case database is carried out the similarity coupling; According to the distance value and the predefined judgment threshold of similarity measurement, judge and whether disaster is carried out early warning.
In to ocean seasonal effect in time series similarity analysis, at first there are two key issues to need to solve.One is how time series to be represented, another is how the seasonal effect in time series similarity to be measured.How these two key issues solve efficient and the accuracy that is determining algorithm.Time series data has characteristics such as higher-dimension property, complicacy, magnanimity property and noise, directly analyzes on the raw monitored sequence not only that difficulty is big, and influence is calculated and the performance of aspect is gone up in storage, but also influences algorithm accuracy.Therefore the present invention at first utilizes the main fluctuation characteristic of seasonal effect in time series method for expressing extraction time sequence, to yojan of time series dimension and compression, on higher level, time series is redescribed.The present invention representes that for time series method adopts the slope method for expressing based on unique point, and concrete steps are:
(1) preset time sequence If r i, r I+1For i record and i+1 record among the R, the numerical value the former with the latter among the R is subtracted each other, obtain new sequence P, as follows:
P i=r i+1-r i
(2) want in twos to take advantage of with before and after the numerical value among the time series P, obtain new sequence S, as follows:
S=P i*P i+1
(3) index position of negative value among the record S adds 1 with these index values, just can index value is corresponding with the sequence index value among the R, and corresponding point is the Local Extremum among the R;
(4) for amplification among the time series P greater than x and time difference point less than a; Also join in the sequence of unique point; Wherein x and a are according to expertise and actual monitoring time sequence calibration, the threshold parameter that optimization obtains afterwards, also can be imported by the user;
(5) seek among the sequential S zero index position, each adds 1 the index value that search is obtained, and judges to occur zero son section sequence continuously; If occur zero time number of times continuously greater than m; Then zero initial index position appears in record, and this point also is a unique point, and this point is added the set of people's unique point; Wherein m is that m also can be imported by the user according to expertise and actual monitoring time sequence calibration, the threshold parameter that optimization obtains afterwards;
(6) utilize above-mentioned algorithm, obtain the unique point set of a sequential, then this unique point set is divided into the n-1 section with time series, and n is the sum of unique point.At this moment, time series can be described by n straight-line segment, and its straight-line segment sequence representes with M, M be a length be k 3 yuan of vector M=MK, MX, ML}, the slope of i straight-line segment is by MK among the M iExpression, the horizontal ordinate of starting point is by MX iExpression, straight-line segment in the projected length of time shaft by ML iExpression.
In Early-warning Model was analyzed, the present invention sent early warning in two stages according to similarity The matching analysis result.Phase one is that Monitoring Data and the normal Monitoring Data of the historical same period are done the similarity coupling, if the distance value of similarity measurement all is no more than the phase one judgment threshold, does not then do early warning; If at least 1 record surpasses the phase one judgment threshold; Then get into subordinate phase; Just all disaster data are done the similarity coupling in Monitoring Data and the disaster prediction scheme storehouse; If the distance value of similarity measurement all is no more than the subordinate phase judgment threshold, then writes down these data and do early warning to the administrator; If there is at least one record to surpass the subordinate phase judgment threshold; Explain that then this Monitoring Data and historical disaster data are complementary; Monitoring Data and the historical disaster data that are complementary and the associated ratings of historical disaster data, the information such as position, astronomical tide, emergency preplan of surging are sent to the administrator, do early warning.
Compared with prior art; The present invention has made full use of historical Monitoring Data; Mate the rule information of finding to monitor at present sequence through the similarity of present Monitoring Data and historical data; And then utilize the correlativity of this real time data and historical data to carry out the storm surge disaster early warning, and relevant aid decision making service is provided simultaneously, in time send early warning information.
Description of drawings
Fig. 1 is a storm surge disaster early warning system structural drawing of the present invention.
The process flow diagram that Fig. 2 representes for invention time series piecewise linearity.
Fig. 3 is FSA E_studyState transition diagram.
Fig. 4 is an emergency preplan design route synoptic diagram.
Fig. 5 is the composition synoptic diagram figure of early warning alarm device of the present invention.
Embodiment
The invention discloses a kind of storm surge disaster early warning system, concrete embodiment is described below in conjunction with accompanying drawing based on time series analysis.
As shown in Figure 1, a kind of storm surge disaster early warning system based on time series analysis, real-time receiving remote Monitoring Data, and the Monitoring Data sequence carried out early warning analysis, carry out early warning and aid decision making according to analysis result.It is characterized in that, comprise Database Systems, Early-warning Model analysis, aid decision-making system displaying, Web server and leading subscriber.Early warning forecast model analysis module mainly is after the observation data in the database is carried out pre-service; Time series data is carried out the purpose that piecewise linearity representes to reach the dimension compression; And then real-time Monitoring Data carried out similar coupling with historical monitoring materials data, and make disaster alarm according to similar matching result.If Monitoring Data reaches threshold value of warning, early warning forecast model analysis module stores observation data and the historical data information that is complementary in the early warning document data base into.Aid decision-making system is based on geographical figure information exhibition decision-making assistant information; Comprise and call that early warning data, emergency preplan data carry out simulation shows to disaster and emergency preplan generates; Calling the typhoon data of real-time monitoring shows; The simulated data of aid decision-making system and emergency preplan data also store in the early warning document data base, and through the Web server disposed with Map Services and data display client to leading subscriber.
The historical monitoring materials data of described database server stores, real-time monitoring materials data, basic geographical information material database, emergency preplan data, correlation computations model data and early warning data.Described monitoring materials data and early warning data are time series data.Historical monitoring materials database comprises the historical typhoon title of storage, typhoon actual measurement and forecast data, the actual measurement of historical storm tide surge position, total water level and astronomical tide bit data, forecast data and disaster level data.The real-time Monitoring Data of the receiving remote of monitoring materials database timing in real time data acquisition system (DAS) transmission is provided with the database file automatic expansion and shrinks to adapt to the real-time typing of data with table.Geographical information material database storing basis, basis geographic information data comprises the remote sensing image data that spatial data, attribute data and the U.S. ESRI company of road and settlement place provide.Historical typhoon prediction scheme of emergency preplan data library storage and design prediction scheme, historical storm tide prediction scheme and design prediction scheme.Correlation computations model data library storage is used to choose disaster index, the simulation of storm tide the condition of a disaster, the model of disaster grade classification and the expertise that wherein relates to.The matching result data that the analysis of early warning data library storage Early-warning Model obtains, the condition of a disaster simulation assessment data, disaster grade classification data, the data in the early warning document data base store in the emergency preplan document data base after early warning is removed.
As shown in Figure 2, in said system, the early warning forecast model is a key modules, mainly realizes through following process:
(1) raw data is done pre-service, make part-time sequence (intercepted length should be more short-and-medium than disaster storehouse).
In this process, to separate Monitoring Data and normal Monitoring Data when storm surge disaster takes place in the historical monitoring materials database, extract the tidal level data and analyze.The data preprocessing process mainly is on time shaft, to get some the starting point as the designated length time series data according to fixed length, and according to following formula the seasonal effect in time series amplitude is carried out standardization processing, makes the part-time sequence:
x i=(x i-X MIN)/(X MAX-X MIN)
Wherein, x iFor carrying out normalized value, X MINBe seasonal effect in time series minimum value, X MAXBe the seasonal effect in time series maximal value.Use normalization method at this, the time series data after the stipulations has kept the integrality of raw data, and it is less to excavate required time and memory source, excavates more effectively, produces the analysis result of identical (or much at one).
(2) for normal Monitoring Data, as case data, Monitoring Data when taking place for historical storm surge disaster is then all as case data according to the partial data of selecting storm tide wherein multiple season.Historical Monitoring Data in the database and real-time Monitoring Data are carried out time series linear expression, realize the dimensionality reduction and the compression of data.The present invention representes that for time series method adopts the slope method for expressing based on unique point, and concrete steps are:
1) given length is the time series of n If r i, r I+1For i record and i+1 record among the R, the numerical value the former with the latter among the R is subtracted each other, obtain new sequence P, as follows:
P i=r i+1-r i
2) want in twos to take advantage of with before and after the numerical value among the time series P, obtain new sequence S, as follows:
S=P i*P i+1
3) index position of negative value among the record S; These index values are added 1; Just can index value is corresponding with the sequence index value among the R; Corresponding point is the Local Extremum among the R, joins in the arrangement set of unique point;
4) for amplification among the time series P greater than x and time difference point less than a; Also join among the set T of unique point; Wherein x and a are according to expertise and actual monitoring time sequence calibration, the threshold parameter that optimization obtains afterwards, also can be imported by the user.Definite method for parameter x and a adopts based on finite-state automata (Finite State Automaton, verification method FSA).FSA is a kind of important instrument, and in software design, checking, grammatical analysis, and field such as state recognition has extremely widely and uses.Concrete steps are following:
Define 1 cumulative errors E: after time series was segmented into the multistage subsequence, the summation E of the cumulative errors of each subsequence and former sequence was defined as
E = Σ i = 1 N e i 2 , e i = ( t i , x i ) - ( t j , - n i ) , T wherein i=t j, i=1 ... N, j=1 ... Q
Definition 2FSA E_study: FSA E_studyBe a quintuple form M=(Q, ∑, δ, S, F) state transitions is as shown in Figure 2.Wherein:
A) Q={S, q 1, q 2It is state set;
b)∑={E};
C) δ: Q * E → Q is a state transition function, and definition is like table 1.S begins by initial state, when cumulative errors satisfies precision, transfers to state q 1The time, computing stops; Otherwise, when transferring to state q 2The time, optimize segmentation parameter a and x, continue segmentation, and calculate the cumulative errors value;
D) S ∈ Q is an original state;
E) is the final state set, when satisfying E<θ (θ is given by expertise), finishes.
Table 1 FSA E_studyState transition function
5) seek among the sequential S zero index position, each adds 1 the index value that search is obtained, and judges to occur zero son section sequence continuously; If occur zero time number of times continuously greater than m; Then zero initial index position appears in record, and this point also is a unique point, and this point is added the set of people's unique point.Wherein m is according to expertise and actual monitoring time sequence calibration, the threshold parameter that optimization obtains afterwards, also can be imported by the user;
6) utilize above-mentioned algorithm, obtain the unique point set of a sequential, then this unique point set is divided into the n-1 section with time series, and n is the sum of unique point.At this moment, time series can be described by n straight-line segment, and its straight-line segment sequence representes with M, M be a length be k 3 yuan of vector M=MK, MX, ML}, the slope of i straight-line segment is by MK among the M iExpression, the horizontal ordinate of starting point is by MX iExpression, straight-line segment in the projected length of time shaft by ML iExpression.After time series data represented according to top method, can computing time the pattern distance of sequence, sequence is carried out similarity measurement:
Define 3 slope pattern distances: the linear segmented of given sequence is represented M i={ MK i, MX i, ML iAnd M j={ MK j, MX j, ML j, M then iWith M jPattern distance be defined as:
d ( M i , M j ) = ( | MX i - MX j | + | ML i - ML j | | ML i + ML j | + | MK i - MK j | | MK i + MK j | ) / 3
Definition 4 dynamic mode distances based on the slope pattern:
D warp ( M i , M j ) = d ( M i , M j ) + min D warp ( M i , rest ( M j ) ) D warp ( rest ( M i ) , M j ) D warp ( rest ( M i ) , rest ( M j ) )
D wherein Warp(M i, M j) be the DTW distance of pattern, rest (M j) expression sequence M jRemove end element sequence afterwards, d (M i, M j) be the slope pattern distance in the definition 1.
Aid decision-making system show comprise this storm surge disaster the condition of a disaster simulation assessment, level evaluation, typhoon track is showed and according to the reasoning prediction scheme result of historical the condition of a disaster emergency preplan.
Described the condition of a disaster simulation assessment is the information of returning to early warning; With this monitoring time sequence data is the displaying that boundary condition carries out the condition of a disaster simulation and floods scope, depth of the water submerging, and obtains this disaster level evaluation result according to the reasoning as a result of similarity measurement and send to leading subscriber.
Described typhoon track displaying is visited basic geographic information data and typhoon track data by system through Web server; Show that at the leading subscriber end form that comprises pattern exhibiting, the typhoon data of typhoon track shows and the rose diagram of typhoon wind direction, grade.
As shown in Figure 4; Be emergency preplan design route synoptic diagram; Emergency preplan shows it is to calculate engineering and the managerial measure that should take according to historical storm surge disaster analysis and the analysis of design storm surge disaster; To the devastated, carry out the evacuation path analysis simultaneously based on basic geographic information datas such as house, road, rivers.
Web server turns back to leading subscriber through Web Services transmission XML formatted data, also can the request of receiving management person's user's data obtain to turn back to leading subscriber after the data from database.
As shown in Figure 5, leading subscriber is after receiving early warning, to alert issue early warning report.Send to government website, mobile terminal of mobile telephone, offshore vessel mounted terminal and Decision Control center through Internet, the condition of a disaster is done trace analysis by leader expert.Real-time release disaster data information service simultaneously.
In sum; The present invention is a kind of storm surge disaster early warning system based on time series analysis; Mate the rule information of finding to monitor at present sequence through the similarity of present Monitoring Data and historical data; And then utilize the correlativity of this real time data and historical data to carry out the storm surge disaster early warning, and relevant aid decision making service is provided simultaneously, in time send early warning information through number of ways.
The above is merely illustrative, but not is restricted.This description should not be construed as limitation of the present invention, and any spirit of the present invention and category of not breaking away from all should be contained within the application range.

Claims (6)

1. storm surge disaster early warning system based on time series analysis, receiving remote Monitoring Data in real time, and the Monitoring Data sequence carried out early warning analysis, carry out early warning and aid decision making according to analysis result.It is characterized in that, comprise Database Systems, Early-warning Model analysis, aid decision-making system displaying, Web server and leading subscriber.Described Database Systems comprise historical monitoring materials database, real-time monitoring materials database, basic geographical information material database, emergency preplan document data base, correlation computations model document data base and early warning document data base.After described early warning forecast model analysis module carries out pre-service to the observation data in the database; Time series data is carried out the purpose that piecewise linearity representes to reach the dimension compression; And then real-time Monitoring Data carried out similar coupling with historical monitoring materials data, and make disaster alarm according to similar matching result.If Monitoring Data reaches threshold value of warning, early warning forecast model analysis module stores observation data and the historical data information that is complementary in the early warning document data base into.Described aid decision-making system is based on geographical figure information exhibition decision-making assistant information; Comprise and call that early warning data, emergency preplan data carry out simulation shows to disaster and emergency preplan generates; Calling the typhoon data of real-time monitoring shows; The simulated data of aid decision-making system and emergency preplan data also store in the early warning document data base, and through the Web server disposed with Map Services and data display client to leading subscriber.
Described historical monitoring materials database comprises storage historical typhoon title, typhoon actual measurement and forecast data, the actual measurement of historical storm tide surge position, total water level and astronomical tide bit data, forecast data and disaster level data.
Described real-time monitoring materials database is the real-time Monitoring Data of receiving remote data acquisition system (DAS) transmission regularly, the database file automatic expansion is set shrinks to adapt to the real-time typing of data with table.
Geographical information material database storing basis, described basis geographic information data comprises the remote sensing image data that spatial data, attribute data and the U.S. ESRI company of road and settlement place provide.
Historical typhoon prediction scheme of described emergency preplan data library storage and design prediction scheme, historical storm tide prediction scheme and design prediction scheme.
Described correlation computations model data library storage is used to choose disaster index, the simulation of storm tide the condition of a disaster, the model of disaster grade classification and the expertise that wherein relates to.
The matching result data that the analysis of described early warning data library storage Early-warning Model obtains, the condition of a disaster simulation assessment data, disaster grade classification data, the data in the early warning document data base store in the emergency preplan document data base after early warning is removed.
The analysis of described early warning forecast model mainly is after data are carried out pre-service; Time series data is carried out the purpose that piecewise linearity representes to reach the dimension compression; And then carry out similar coupling, and make disaster alarm according to similar matching result with historical monitoring materials data.
Described aid decision-making system is based on geographical figure information exhibition decision-making assistant information, and is shown to the leading subscriber client through the Web server of disposing.
2. a kind of storm surge disaster early warning system according to claim 1 based on time series analysis; It is characterized in that, described aid decision-making system show comprise this storm surge disaster the condition of a disaster simulation assessment, level evaluation, typhoon track is showed and according to the reasoning prediction scheme result of historical the condition of a disaster emergency preplan.
Described the condition of a disaster simulation assessment is the information of returning to early warning; With this monitoring time sequence data is the displaying that boundary condition carries out the condition of a disaster simulation and floods scope, depth of the water submerging, and obtains this disaster level evaluation result according to the reasoning as a result of similarity measurement and send to leading subscriber.
3. a kind of storm surge disaster early warning system based on time series analysis according to claim 1 is characterized in that, described leading subscriber is after receiving early warning, to alert issue early warning report.Send to government website, mobile terminal of mobile telephone, offshore vessel mounted terminal and Decision Control center through Internet, the condition of a disaster is done trace analysis by leader expert.
4. a kind of storm surge disaster early warning system according to claim 1 based on time series analysis; It is characterized in that; Described Early-warning Model analysis at first is that real-time Monitoring Data and historical Monitoring Data are carried out the data pre-service; In this process, to separate Monitoring Data and normal Monitoring Data when storm surge disaster takes place in the historical monitoring materials database, extract the tidal level data and analyze.For normal Monitoring Data, as case data, Monitoring Data when taking place for historical storm surge disaster is then all as case data according to the partial data of selecting storm tide wherein multiple season.Next is that historical Monitoring Data in the database and real-time Monitoring Data are carried out time series linear expression, realizes the dimensionality reduction and the compression of data.Be the method that adopts the Time Series Similarity coupling once more; Time series data in real-time Monitoring Data and the case database is carried out the similarity coupling; According to the distance value and the predefined judgment threshold of similarity measurement, judge and whether disaster is carried out early warning.
5. a kind of storm surge disaster early warning system based on time series analysis according to claim 2 is characterized in that, described time series representes that method adopts the slope method for expressing based on unique point, and concrete steps are:
(1) preset time sequence If r i, r I+1For i record and i+1 record among the R, the numerical value the former with the latter among the R is subtracted each other, obtain new sequence P, as follows:
P i=r i+1-r i
(2) want in twos to take advantage of with before and after the numerical value among the time series P, obtain new sequence S, as follows:
S=P i*P i+1
(3) index position of negative value among the record S adds 1 with these index values, just can index value is corresponding with the sequence index value among the R, and corresponding point is the Local Extremum among the R;
(4) for amplification among the time series P greater than x and time difference point less than a; Also join in the set of unique point; Wherein x and a threshold parameter that to be system obtain with actual monitoring time sequence calibration, after optimizing according to expertise also can be imported by the user;
(5) seek among the sequential S zero index position, each adds 1 the index value that search is obtained, and judges to occur zero son section sequence continuously; If occur zero time number of times continuously greater than m; Then zero initial index position appears in record, and this point also is a unique point, and this point is added the set of people's unique point; Wherein m is that m also can be imported by the user according to expertise and actual monitoring time sequence calibration, the threshold parameter that optimization obtains afterwards;
(6) utilize above-mentioned algorithm, obtain the unique point set of a sequential, then this unique point set is divided into the n-1 section with time series, and n is the sum of unique point.At this moment, time series can be described by n straight-line segment, and its straight-line segment sequence representes with M, M be a length be k 3 yuan of vector M=MK, MX, ML}, the slope of i straight-line segment is by MK among the M iExpression, the horizontal ordinate of starting point is by MX iExpression, straight-line segment in the projected length of time shaft by ML iExpression.
6. a kind of storm surge disaster early warning system based on time series analysis according to claim 2 is characterized in that, described real-time Monitoring Data behind the dimensionality reduction and the time series data in the case database are carried out the similarity coupling is to carry out in two stages.Phase one is that Monitoring Data and the normal Monitoring Data of the historical same period are done the similarity coupling, if the distance value of similarity measurement all is no more than the phase one judgment threshold, does not then do early warning; If at least 1 record surpasses the phase one judgment threshold; Then get into subordinate phase; Just all disaster data are done the similarity coupling in Monitoring Data and the disaster prediction scheme storehouse; If the distance value of similarity measurement all is no more than the subordinate phase judgment threshold, then writes down these data and do early warning to the administrator; If there is at least one record to surpass the subordinate phase judgment threshold; Explain that then this Monitoring Data and historical disaster data are complementary; Monitoring Data and the historical disaster data that are complementary and the associated ratings of historical disaster data, the information such as position, astronomical tide, emergency preplan of surging are sent to the administrator, do early warning.
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