CN104504277B - A kind of ship conflict method for early warning - Google Patents
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Abstract
The present invention relates to a kind of ship conflict method for early warning, including the following steps, the real-time and historical position information of ship is obtained by sea radar first and does preliminary treatment；Then ship track data is pre-processed in each sampling instant, then ship track data is clustered in each sampling instant, parameter training is carried out using HMM to ship track data again and in each sampling instant, then in each sampling instant according to HMM parameter, hidden state q corresponding to current time observation is obtained using Viterbi algorithm, finally in each sampling instant by setting prediction time domain W, hidden state q based on ship current time, obtain the position prediction value O of future time period ship, and by establishing the dynamic behaviour implementing monitoring of safety regulation set pair ship and sending warning information in time.The present invention rolls and ship track is predicted in real time, effective early warning marine site conflict, improves the security of maritime traffic.
Description
Technical field
The present invention relates to a kind of marine site traffic control method, more particularly to a kind of ship conflict based on Rolling Planning strategy
Method for early warning.
Background technology
With the fast development of global shipping business, the traffic in the busy marine site in part is further crowded.It is close in vessel traffic flow
Collect complicated marine site, the regulation model allocated at artificial interval has still been combined not using sail plan for the collision scenario between ship
Adapt to the fast development of shipping business.To ensure the personal distance between ship, implement effective conflict early warning and just handed over as marine site
The emphasis of siphunculus system work.Ship conflict early warning is a key technology in navigational field, safe and efficient early warning scheme pair
In increase marine site ship flow and ensure that sea-freight safety is significant.
In order to improve the efficiency of navigation of ship, marine radar automatic plotter has been widely applied to ship monitor at present
In collision prevention, the equipment provides reference frame by extracting the judgement of collision scenario between ship of ship relevant information.And ship
Conflict early warning be based on the basis of the prediction to ship track, in ship real navigation, by meteorological condition, navigation equipment with
And the influence of the various factors such as driver's operation, its running status often not exclusively belong to a certain specific motion state, therefore
Prediction to ship track at present and ship conflict early warning are without accurate scheme.
The content of the invention
The technical problem to be solved in the present invention is to provide a kind of robustness preferable ship conflict method for early warning, this method
Ship trajectory predictions precision is higher, the ship conflict accuracy of early warning and ageing preferable.
Realize that the technical scheme of the object of the invention is to provide a kind of ship conflict method for early warning, including the following steps：
1. obtaining the real-time and historical position information of ship by sea radar, the positional information of each ship is discrete two-dimensional
Position sequence x '=[x1', x2' ..., xn'] and y '=[y1', y2' ..., yn'], by application wavelet transformation theory to original
Discrete two-dimensional position sequence x '=[x1', x2' ..., xn'] and y '=[y1', y2' ..., yn'] preliminary treatment is carried out, so as to obtain
Take the denoising discrete two-dimensional position sequence x=[x of ship1, x2..., xn] and y=[y1, y2..., yn]；
2. ship track data is pre-processed in each sampling instant, according to the acquired original discrete two-dimensional position of ship
Sequence x=[x1, x2..., xn] and y=[y1, y2..., yn], processing is carried out to it using first-order difference method and obtains new ship
Oceangoing ship discrete location sequence Δ x=[Δ x1, Δ x2..., Δ xn-1] and Δ y=[Δ y1, Δ y2..., Δ yn+1], wherein Δ xi=
xi+1-xi, Δ yi=yi+1-yi(i=1,2 ..., n-1)；
3. ship track data is clustered in each sampling instant, to ship discrete two-dimensional position sequence Δ new after processing
X and Δ y, number M ' is clustered by setting, it is clustered respectively using K-means clustering algorithms；
4. parameter training is carried out using HMM to ship track data in each sampling instant, by that will locate
Vessel motion track data Δ x and Δ y after reason are considered as the aobvious observation of hidden Markov models, by setting hidden state number
N and parameter renewal period τ ', is rolled according to the individual position detection values of nearest T ' and using B-W algorithms and obtains newest Hidden Markov
Model parameter λ '；
5. current time sight is obtained using Viterbi algorithm according to HMM parameter in each sampling instant
Hidden state q corresponding to measured value；
6. in each sampling instant, time domain W, the hidden state q based on ship current time are predicted by setting, obtains future
The position prediction value O of period ship, speculate so as to be rolled in each sampling instant to the track of ship in future time period；
7. in each sampling instant, the ship of running status and setting based on each ship needs to meet when running in marine site
Safety regulation collection, when being possible to be in the presence of to violate safety regulation between ship, to its dynamic behaviour implementing monitoring and be
Maritime traffic control centre provides timely warning information.
Further, the step 1. in, by application wavelet transformation theory to original discrete two-dimensional position sequence x '=
[x1', x2' ..., xn'] and y '=[y1', y2' ..., yn'] preliminary treatment is carried out, so as to obtain the denoising discrete two-dimensional position of ship
Put sequence x=[x1, x2..., xn] and y=[y1, y2..., yn]：For given original two dimensional sequence data x '=[x1',
x2' ..., xn'], approximation is carried out to it respectively using the linear representation of following form：
Wherein：
F ' (x ') represents the function expression to being obtained after data smoothing processing, and ψ (x ') represents female ripple, and δ, J and K are small
Wave conversion constant, ψJ, K(x ') represents the transition form of female ripple, cJ, KRepresent the function coefficients obtained by wavelet transform procedure, its body
Wavelet ψ is showedJ, K(x ') to the weight size of whole approximation to function, if this coefficient very little, then it means wavelet ψJ, K(x′)
Weight it is also smaller, thus can be on the premise of not influence function key property, by wavelet ψ during approximation to functionJ, K
(x ') is removed；In real data processing procedure, " threshold transition " is implemented by given threshold χ, works as cJ, KDuring ＜ χ, setting
cJ, K=0；The selection of threshold function table uses the following two kinds mode：
With
For y '=[y1', y2' ..., yn'], denoising is also carried out using the above method.
Further, the step is 4. middle determines that flight path HMM parameter lambda '=process of (π, A, B) is as follows：
4.1) variable assigns initial value：Variable π is given using being uniformly distributedi, aijAnd bj(ok) assign initial valueWithAnd make
It meets constraints：WithThus obtain
λ0=(π0, A0, B0), wherein okRepresent a certain aobvious observation, π0、A0And B0It is by element respectivelyWithThe square of composition
Battle array, makes parameter l=0, o=(oT-T '+1,..., ot-1, ot) be current time t before the individual historical position observations of T '；
4.2) E-M algorithms are performed：
4.2.1) E- steps：By λlCalculate ξe(i, j) and γe(si)；
VariableSo
Wherein s represents a certain hidden state；
4.2.2) M- steps：WithEstimate respectively
Count πi, aijAnd bj(ok) and thus obtain λl+1；
4.2.3) circulate：L=l+1, E- steps and M- steps are repeated, until πi、aijAnd bj(ok) convergence, i.e.,
|P(o|λl+1)-P(o|λl) | ＜ ε, wherein parameter ε=0.00001, return to step 4.2.4)；
4.2.4)：Make λ '=λl+1, algorithm terminates.
Further, the step 5. it is middle determine the most preferably hidden status switch of ship track iterative process it is as follows：
5.1) variable assigns initial value：Make g=2, βT′(si(the s of)=1i∈ S), δ1(si)=πibi(o1), ψ1(si)=0, wherein,
,
Wherein variable ψg(sj) represent to make variable δg-1(si)aijTake the hidden state s of ship track of maximumi, parameter S expressions
The set of hidden state；
5.2) recursive process：
5.3) moment updates：G=g+1 is made, if g≤T ', return to step 5.2), otherwise iteration ends and go to step
5.4)；
5.4)Go to step 5.5)；
5.5) optimal hidden status switch obtains：
5.5.1) variable assigns initial value：Make g=T ' -1；
5.5.2) backward recursion：
5.5.3) moment renewal：G=g-1 is made, if g >=1, return to step 5.5.2), otherwise terminate.
Further, the step 3. in, cluster number M ' value is 4.
Further, the step 4. in, state number N value is 3, and parameter renewal period τ ' is 30 seconds, and T ' is 10.
Further, the step 6. in, prediction time domain W is 300 seconds.
Further, the step 7. in dynamic behaviour implementing monitoring to each ship and carried for maritime traffic control centre
It is as follows for the detailed process of timely warning information：
7.1) the safety regulation collection D that need to meet when construction ship is run in marine sitemr(t)≥Dmin, wherein Dmr(t) represent
Any two ship m and ship r are in the distance of t, DminRepresent the minimum safe distance between ship；
7.2) according to the sampling time, establish by the observer Λ of the continuous running status of ship to discrete sampling state：Γ→
Ξ, wherein Γ represent the continuous running status of ship, and Ξ represents the discrete sampling state of ship；
7.3) as ship m and r observer ΛmAnd ΛrDiscrete observation numerical value ΞmAnd ΞrShow the vector not in t
When safety regulation is concentrated, i.e. relational expression Dmr(t)≥DminWhen invalid, alarm letter is sent to maritime traffic control centre at once
Breath.
The present invention has positive effect：(1) present invention during the real-time estimate of ship track, incorporated it is random because
The influence of element, used rolling track prediction scheme can extract the changing condition of extraneous enchancement factor in time, improve ship
The accuracy of oceangoing ship trajectory predictions.
(2) present invention is based on different performance index, and its ship track real-time estimate result can be in the presence of the multiple of conflict
Ship, which provides, frees trajectory planning scheme, improves the economy of vessel motion and the utilization rate of sea area resources.
(3) present invention is preferable to the early warning effect of ship conflict, can effectively, accurately and real-time predict the track of ship simultaneously
Ship conflict is predicted, effectively improves the security of marine site traffic.
Brief description of the drawings
Fig. 1 is the short-term Track Pick-up schematic flow sheet of vessel motion in the present invention；
Fig. 2 is that the vessel motion situation in the present invention monitors schematic flow sheet.
Embodiment
(embodiment 1)
See Fig. 1, a kind of ship conflict method for early warning of the present embodiment includes the following steps：
1. obtaining the real-time and historical position information of ship by sea radar, the positional information of each ship is discrete two-dimensional
Position sequence x '=[x1', x2' ..., xn'] and y '=[y1', y2' ..., yn'], by application wavelet transformation theory to original
Discrete two-dimensional position sequence x '=[x1', x2' ..., xn'] and y '=[y1', y2' ..., yn'] preliminary treatment is carried out, so as to obtain
Take the denoising discrete two-dimensional position sequence x=[x of ship1, x2..., xn] and y=[y1, y2..., yn]：Y=[y1, y2...,
yn]：For given original two dimensional sequence data x '=[x1', x2' ..., xn'], utilize the linear representation point of following form
It is other that approximation is carried out to it：
Wherein：
F ' (x ') represents the function expression to being obtained after data smoothing processing, and ψ (x ') represents female ripple, and δ, J and K are small
Wave conversion constant, ψJ, K(x ') represents the transition form of female ripple, cJ, KRepresent the function coefficients obtained by wavelet transform procedure, its body
Wavelet ψ is showedJ, K(x ') to the weight size of whole approximation to function, if this coefficient very little, then it means wavelet ψJ, K(x′)
Weight it is also smaller, thus can be on the premise of not influence function key property, by wavelet ψ during approximation to functionJ, K
(x ') is removed；In real data processing procedure, " threshold transition " is implemented by given threshold χ, works as cJ, KDuring ＜ χ, setting
cJ, K=0；The selection of threshold function table uses the following two kinds mode：
With
For y '=| y1', y2' ..., yn' |, denoising is also carried out using the above method；
2. ship track data is pre-processed in each sampling instant, according to the acquired original discrete two-dimensional position of ship
Sequence x=| x1, x2..., xn| and y=| y1, y2..., yn|, processing is carried out to it using first-order difference method and obtains new ship
Oceangoing ship discrete location sequence Δ x=| Δ x1, Δ x2..., Δ xn-1| and Δ y=| Δ y1, Δ y2..., Δ yn-1|, wherein Δ xi=
xi+1-xi, Δ yi=yi+1-yi(i=1,2 ..., n-1)；
3. ship track data is clustered in each sampling instant, to ship discrete two-dimensional position sequence Δ new after processing
X and Δ y, number M ' is clustered by setting, it is clustered respectively using K-means clustering algorithms；
4. parameter training is carried out using HMM to ship track data in each sampling instant, by that will locate
Vessel motion track data Δ x and Δ y after reason are considered as the aobvious observation of hidden Markov models, by setting hidden state number
N and parameter renewal period τ ', is rolled according to the individual position detection values of nearest T ' and using B-W algorithms and obtains newest Hidden Markov
Model parameter λ '；Determine that flight path HMM parameter lambda '=process of (π, A, B) is as follows：
4.1) variable assigns initial value：Variable π is given using being uniformly distributedi, aijAnd bj(ok) assign initial valueWithAnd
It is set to meet constraints：WithThus
To λ0=(π0, A0, B0), wherein okRepresent a certain aobvious observation, π0、A0And B0It is by element respectivelyWithThe square of composition
Battle array, makes parameter l=0, o=(ot-T′+1..., ot-1, ot) be current time t before the individual historical position observations of T '；
4.2) E-M algorithms are performed：
4.2.1) E- steps：By λlCalculate ξe(i, j) and γe(si)；
VariableSo
Wherein s represents a certain hidden state；
4.2.2) M- steps：WithEstimate respectively
Count πi, aijAnd bj(ok) and thus obtain λl+1；
4.2.3) circulate：L=l+1, E- steps and M- steps are repeated, until πi、aijAnd bj(ok) convergence, i.e.,
|P(o|λl+1)-P(o|λl) | ＜ ε, wherein parameter ε=0.00001, return to step 4.2.4)；
4.2.4)：Make λ '=λl+1, algorithm terminates.
5. current time sight is obtained using Viterbi algorithm according to HMM parameter in each sampling instant
Hidden state q corresponding to measured value：
5.1) variable assigns initial value：Make g=2, βT′(si(the s of)=1i∈ S), δ1(si)=πibi(o1), ψ1(si)=0, wherein,
,
Wherein variable ψg(sj) represent to make variable δg-1(si)aijTake the hidden state s of ship track of maximumi, parameter S expressions
The set of hidden state；
5.2) recursive process：
5.3) moment updates：G=g+1 is made, if g≤T ', return to step 5.2), otherwise iteration ends and go to step
5.4)；
5.4)Go to step 5.5)；
5.5) optimal hidden status switch obtains：
5.5.1) variable assigns initial value：Make g=T ' -1；
5.5.2) backward recursion：
5.5.3) moment renewal：G=g-1 is made, if g >=1, return to step 5.5.2), otherwise terminate..
6. in each sampling instant, time domain W, the hidden state q based on ship current time are predicted by setting, obtains future
The position prediction value O of period ship
Above-mentioned cluster number M ' value is 4, state number N value is 3, and parameter renewal period τ ' is 30 seconds, and T ' is 10,
It is 300 seconds to predict time domain W.
(application examples, navigation traffic control method)
The navigation traffic control method of the present embodiment includes the following steps：
Step A, the ship conflict method for early warning obtained according to embodiment 1 obtains what ship speculated in each sampling instant
The track of ship in future time period；
Step B, in each sampling instant, based on the current running status of ship and historical position observation sequence, sea is obtained
The numerical value of domain wind field variable, its detailed process are as follows：
B.1 the stop position of ship) is set as track reference coordinate origin；
B.2) when ship is in straight running condition and at the uniform velocity turning running status, marine site wind field linear filtering mould is built
Type；
B.3 the numerical value of wind field variable) is obtained according to constructed Filtering Model.
Step C, needed in each sampling instant, the ship of running status and setting based on each ship when being run in marine site
The safety regulation collection of satisfaction, when being possible to be in the presence of violating safety regulation between ship, to its dynamic behaviour implementing monitoring
And provide timely warning information for maritime traffic control centre；
Step D, when warning information occurs, on the premise of ship physical property and marine site traffic rules is met, pass through
Set optimizing index function and incorporate wind field variable value, ship collision avoidance track is entered using Model Predictive Control Theory method
Row Rolling Planning, and program results is transferred to each ship and performed, its detailed process is as follows：
D.1 the termination reference point locations P, collision avoidance policy control time domain Θ, trajectory predictions of ship collision avoidance trajectory planning) are set
Time domain γ；
D.2 on the premise of) being set in given optimizing index function, based on cooperative collision avoidance trajectory planning thought, by
Each ship assigns different weights and incorporates real-time wind field variable filtering numerical value, obtains the collision avoidance track of each ship and keeps away
Hit control strategy and program results is transferred to each ship and perform, and each ship only implements its first in Rolling Planning interval
Optimal Control Strategy；
D.3) in next sampling instant, repeat step is D.2) until each ship reaches it and frees terminal.
Above-mentioned termination reference point locations P is set as next navigation channel point of vessel position conflict point, during collision avoidance policy control
Domain Θ is 300 seconds；Trajectory predictions time domain γ is 300 seconds.
7. Fig. 2 is seen, in each sampling instant, when the ship of running status and setting based on each ship is run in marine site
The safety regulation collection that need to meet, when being possible to be in the presence of violating safety regulation between ship, its dynamic behaviour is implemented to supervise
Control and provide timely warning information for maritime traffic control centre, its detailed process is as follows：
7.1) the safety regulation collection D that need to meet when construction ship is run in marine sitemr(t)≥Dmin, wherein Dmr(t) represent
Any two ship m and ship r are in the distance of t, DminRepresent the minimum safe distance between ship；
7.2) according to the sampling time, establish by the observer Λ of the continuous running status of ship to discrete sampling state：Γ→
Ξ, wherein Γ represent the continuous running status of ship, and Ξ represents the discrete sampling state of ship；
7.3) as ship m and r observer ΛmAnd ΛrDiscrete observation numerical value ΞmAnd ΞrShow the vector not in t
When safety regulation is concentrated, i.e. relational expression Dmr(t)≥DminWhen invalid, alarm letter is sent to maritime traffic control centre at once
Breath.
Obviously, above-described embodiment is only intended to clearly illustrate example of the present invention, and is not to the present invention
The restriction of embodiment.For those of ordinary skill in the field, it can also be made on the basis of the above description
Its various forms of changes or variation.There is no necessity and possibility to exhaust all the enbodiments.And these belong to this hair
Among the obvious changes or variations that bright spirit is extended out is still in protection scope of the present invention.
Claims (1)
- The method for early warning 1. a kind of ship conflicts, it is characterised in that including the following steps：1. obtaining the real-time and historical position information of ship by sea radar, the positional information of each ship is discrete two-dimensional position Sequence x'=[x1',x2',...,xn'] and y'=[y1',y2',...,yn'], by application wavelet transformation theory to original discrete Two-dimensional position sequence x'=[x1',x2',...,xn'] and y'=[y1',y2',...,yn'] preliminary treatment is carried out, so as to obtain ship The denoising discrete two-dimensional position sequence x=[x of oceangoing ship1,x2,...,xn] and y=[y1,y2,...,yn]；2. ship track data is pre-processed in each sampling instant, according to acquired ship denoising discrete two-dimensional position sequence X=[x1,x2,...,xn] and y=[y1,y2,...,yn], it is carried out using first-order difference method processing obtain new ship from Dissipate position sequence Δ x=[Δ x1,Δx2,...,Δxn-1] and Δ y=[Δ y1,Δy2,...,Δyn-1], wherein Δ xi=xi+1- xi,Δyi=yi+1-yi, i=1,2 ..., n-1；3. ship track data is clustered in each sampling instant, to ship discrete two-dimensional position sequence Δ x new after processing and Δ y, number M' is clustered by setting, it is clustered respectively using K-means clustering algorithms；4. parameter training is carried out using HMM to ship track data in each sampling instant, after it will handle Vessel motion track data Δ x and Δ y be considered as the aobvious observations of hidden Markov models, by set hidden state number N and Parameter updates period τ ', is rolled according to T' nearest position detection value and using B-W algorithms and obtains newest Hidden Markov mould Shape parameter λ '；5. current time observation is obtained using Viterbi algorithm according to HMM parameter in each sampling instant Corresponding hidden state q；6. in each sampling instant, time domain W, the hidden state q based on ship current time are predicted by setting, obtains future time period The position prediction value O of ship, speculate so as to be rolled in each sampling instant to the track of ship in future time period；7. in the peace that each sampling instant, the ship of running status and setting based on each ship need to meet when being run in marine site Full rule set, to its dynamic behaviour implementing monitoring and it is sea when being possible to be in the presence of violating safety regulation between ship Traffic control center provides timely warning information；The step 5. it is middle using Viterbi algorithm obtain current time observation corresponding to hidden state q process it is as follows：5.1) variable assigns initial value：Make g=2, βT′(si)=1, si∈ S, δ1(si)=πibi(o1), ψ1(si)=0, wherein,Wherein variable ψg(sj) represent to make variable δg-1(si)aijTake the hidden state s of ship track of maximumi, the hidden shape of parameter S expressions The set of state；5.2) recursive process：5.3) moment updates：G=g+1 is made, if g≤T', return to step 5.2), otherwise iteration ends and go to step 5.4)；5.4)Go to step 5.5)；5.5) optimal hidden status switch obtains：5.5.1) variable assigns initial value：Make g=T'-1；5.5.2) backward recursion：5.5.3) moment renewal：G=g-1 is made, if g >=1, return to step 5.5.2), otherwise terminate.
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| CN201711492998.1A CN108182342A (en) | 2014-12-30 | 2014-12-30 | A kind of ship conflict method for early warning based on Rolling Planning strategy |
| CN201711492948.3A CN108108588A (en) | 2014-12-30 | 2014-12-30 | A kind of ship conflict method for early warning of Rolling Planning |
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| CN109543715A (en) * | 2018-10-23 | 2019-03-29 | 武汉理工大学 | A kind of ship air route is extracted and the method for track deviation detection |
| CN112578679B (en) * | 2019-09-30 | 2024-02-13 | 北京国双科技有限公司 | Control system and method, storage medium and electronic device |
| CN111145595B (en) * | 2020-02-20 | 2021-06-25 | 智慧航海（青岛）科技有限公司 | Method for confirming key avoidance of autonomous driving ship based on projection pursuit method |
| CN113128826B (en) * | 2021-03-03 | 2022-08-30 | 武汉理工大学 | Navigation risk prediction method, navigation risk prediction device and storage medium |
| CN113283153B (en) * | 2021-06-15 | 2023-08-25 | 大连海事大学 | Ship vibration equipment service life prediction method under variable working conditions |
| CN113626929B (en) * | 2021-07-07 | 2024-01-23 | 武汉理工大学 | Multi-stage multi-topology ship traffic complexity measurement method and system |
| CN115410419B (en) * | 2022-08-23 | 2024-02-02 | 交通运输部天津水运工程科学研究所 | Ship mooring early warning method, system, electronic equipment and storage medium |
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| CN108108588A (en) | 2018-06-01 |
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