CN104809350B - The differentiating method of ocean rubbish and bion - Google Patents
The differentiating method of ocean rubbish and bion Download PDFInfo
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- CN104809350B CN104809350B CN201510213209.0A CN201510213209A CN104809350B CN 104809350 B CN104809350 B CN 104809350B CN 201510213209 A CN201510213209 A CN 201510213209A CN 104809350 B CN104809350 B CN 104809350B
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Abstract
The present invention provides a kind of ocean rubbish and the differentiating method of bion includes：Establish study sample；Markov model study is carried out based on study sample；The classification that the Markov model obtained with study carries out detecting object ocean rubbish and bion judges.Wherein, the classification judgement that the Markov model obtained with study carries out detecting object ocean rubbish and bion specifically includes：Primary Location is carried out to detecting object；The position for tracking detecting object whithin a period of time, obtains the moving parameter of each monitoring moment detecting object, and forms moving parameter change sequence；The degree of agreement for calculating the moving parameter change sequence obtained and learning obtained Markov model；When calculating gained degree of agreement more than or equal to given threshold, judge detecting object for ocean rubbish.
Description
Technical field
The present invention relates to marine eco-environment detection technique fields, and more particularly to a kind of ocean rubbish and bion
Differentiating method.
Background technology
Quantity, type and the three kinds of technologies of distribution of assessment floating refuse at present：From periodical《Marine environment science》The
The article of the phase of volume 16 the 2nd《The monitoring method of floating refuse in marine environment》Describe following three kinds of technologies：
1. passing ships estimate the type and quantity of floating refuse to statistician by inquiry.This method is according to statistics people
The rubbish quantity that member daily jettisonings, after carrying out relevant mathematical statistics, obtains the quantity of the average annual floating refuse in certain marine site.Due to this
Method needs the statistician for collecting enough information and profession to be counted since the individual of statistician has differences
The conclusion come has larger difference.
2. determining the density and type of floating refuse by field investigation.(1) aircraft is used to float rubbish to a certain marine site
Rubbish carries out investigation observation, collects the data of related floating refuse distribution and quantity；(2) by other marine monitoring ships, pleasure-boat or
Freighter carries out jointly, and finite observation is carried out according to the course line of ship.Two ways be required to suitable sea situation (<3 grades) and it is good
Good visibility, the former confidence level is high and the latter's expense is low.
3. the situation of artificial beach observation monitoring floating refuse.Since the fraction floats rubbish in ocean is by wind direction and ocean current
Influence, not necessarily float to bank completely, this method keeps overall estimate amount relatively low.
To sum up, the detection of floating refuse also lacks economical and effective method；And sea low suspension rubbish and seabed rubbish
There is an urgent need for carry out relevant research and practice for detection.
Invention content
The present invention is in order to overcome existing ocean garbage detection technique to have much difficulty in healing the asking of weighing apparatus there are accuracy of detection and economic cost
Topic, provides a kind of differentiating method of economical and effective ocean rubbish and bion.
To achieve the goals above, the present invention provides a kind of ocean rubbish and the differentiating method of bion includes：
Establish study sample；
Markov model study is carried out based on study sample；
The classification that the Markov model obtained with study carries out detecting object ocean rubbish and bion judges have
Body includes：
Primary Location is carried out to detecting object；
The position for tracking detecting object whithin a period of time obtains the moving parameter of each monitoring moment detecting object, and shape
At moving parameter change sequence；
The degree of agreement for calculating the moving parameter change sequence obtained and learning obtained Markov model；
When calculating gained degree of agreement more than or equal to given threshold, judge detecting object for ocean rubbish.
In one embodiment of the invention, establishing the step of learning sample includes：
Primary Location is carried out to multiple sample objects within the scope of monitoring marine site；
Tracking detects the position of each sample object whithin a period of time, obtains the moving parameter of each monitoring moment object,
And form moving parameter change sequence sample；
Multiple sample objects within the scope of monitoring marine site are manually marked, multiple sample objects of identification are ocean rubbish
Rubbish or bion.
In one embodiment of the invention, moving parameter is that movement speed and moving direction change.
In one embodiment of the invention, the acquisition step of moving parameter includes：
Track the position of detecting object or sample object after being tentatively set to whithin a period of time, measure detecting object or
Movement speed in the sample object unit interval；
Calculate the detecting object at each monitoring moment or the moving direction variation of sample object.
In one embodiment of the invention, when carrying out Markov model study based on study sample, including calculating each monitoring
Carve the transition probability of the transition probability and moving direction variation of detecting object movement speed.
In one embodiment of the invention, the Primary Location of Primary Location or sample object to detecting object is according to detection
Object or sample object signal wave, are positioned by solid region localization method.
In one embodiment of the invention, solid region localization method is the solid region positioning side based on " ellipsoidal cavity " model
Method.
In one embodiment of the invention, the solid region localization method based on " ellipsoidal cavity " model includes：
Any one node of ultrasonic sensor emits ultrasonic signal, other nodes receive the ultrasonic wave directly emitted and
Detecting object or the reflected ultrasonic wave of sample object；
Detecting object or sample object have been separately included away from launch point closest approach and farthest point and around launch point with arbitrarily connect
Sink line rotates two ellipsoids to be formed, and intersection forms " ellipsoidal cavity "；
Change receiving point and obtain more " ellipsoidal cavities ", intersection is carried out between " ellipsoidal cavity " and obtains barycenter；
The barycenter of gained forms barycenter group, finds out the barycenter of barycenter group, and the barycenter is detecting object or sample object
Position.
In conclusion ocean rubbish provided by the invention and the differentiating method of bion are compared with prior art, have
Following advantages：
Since the biology in ocean has autonomous mobility, their state change is that no rule is governed.So
And rubbish is that do not have autonomous mobility, therefore their state change is governed with certain rule in ocean.The present invention
By under the hydrological characteristics of specified sea areas, by constantly measuring sampling, each sample object is recorded in ocean in different moments
Under moving parameter, form moving parameter change sequence sample, the moving parameter change sequence sample of multiple samples forms
Sample is practised, and then Markov model study is carried out based on study sample.In subsequent detection, detected by measuring each moment
The moving parameter of object, and moving parameter change sequence is formed, by calculating the moving parameter change sequence formed in Ma Erke
The degree of agreement of husband's model judges that detecting object is ocean rubbish or bion.
Ocean rubbish provided by the invention and the differentiating method of bion are a kind of differentiations based on Markov model
Method has very high differentiation precision.Meanwhile differentiating method can be completed by means of computer, not only have very high differentiation
Efficiency, while having extremely low differentiation cost, effectively solves existing ocean rubbish detection method accuracy of detection and funds have much difficulty in healing weighing apparatus
The problem of.
For above and other objects of the present invention, feature and advantage can be clearer and more comprehensible, preferred embodiment cited below particularly,
And coordinate attached drawing, it is described in detail below.
Description of the drawings
Fig. 1 show the flow chart of the ocean rubbish and bion differentiating method of one embodiment of the invention offer.
Fig. 2 show the flow chart that study sample is established in Fig. 1.
Fig. 3 show in Fig. 1 and carries out ocean rubbish and biology to detecting object with the Markov model that study obtains
The flow chart that the classification of body judges.
Specific implementation mode
As shown in Figure 1, ocean rubbish provided in this embodiment and the differentiating method of bion include：
Step S10, study sample is established, including：
Step S11, Primary Location is carried out to multiple sample objects within the scope of monitoring marine site.
In this present embodiment, sample object is carried out using the solid region localization method based on " ellipsoidal cavity " model preliminary
Positioning.However, the present invention is not limited in any way this.In other embodiments, wireless sensor network node positioning can be used
Other positioning methods such as technology are positioned.
The positioning principle of " ellipsoidal cavity " model is：
With 3 × 3 arraysFor：Using seashore horizontal line as Z axis positive direction, vertically to
Be Y-axis positive direction down perpendicular to the normal vector direction of emission array it is X-axis positive direction.Emitted with I points
For, I (x are taken first1,y1,z1), J (x2,y2,z2), K (x3,y3,z3), B (x4,y4,z4) it is reference point, O (x, y, z) is to wait for
Object is surveyed away from launch point I (x1,y1,z1) closest approach, P (x ', y ', z ') is launch point I (x1,y1,z1) farthest accessible point, J points
It is respectively t to receive the signal moment of the signal moment and O point reflections of the transmitting of I points back1And t1', K points receive the transmitting of I points
The signal moment that signal moment and O point reflections are returned is respectively t2And t2', B points receive the signal moment that I points emit and O points are anti-
It is respectively t to be emitted back towards the signal moment come3And t3′；Then：
dioj=v (t1′-t1)+dij,
diok=v (t2′-t2)+dik (1-1)
diob=v (t3′-t3)+dib
It is then focus with I, J, O is that any point can obtain ellipsoid TQ on ellipsoidIOJEquation is:
Similarly ellipsoid TQIOKWith ellipsoid TQIOBEquation is respectively：
Simultaneous equations (1-2), (1-3), (1-4) can obtain O (x, y, z) coordinate.
Similarly, for point I (x1,y1,z1), J (x2,y2,z2), K (x3,y3,z3), B (x4,y4,z4) it is reference point, farthest
Accessible point P (x ', y ', z ') is a bit on ellipsoid, when J points receive the signal of the signal moment and P point reflections of the transmitting of I points back
It is respectively t to carve4And t4', the signal moment that K points receive the signal moment of I points transmitting and P point reflections are returned is respectively t5With
t5', the signal moment that B points receive the signal moment of I points transmitting and P point reflections are returned is respectively t6And t6', then it can must reflect
Path length is respectively：
dipj=v (t4′-t4)+dij
dipk=v (t5′-t5)+dik (1-5)
dipb=v (t6′-t6)+dib
Based on 3 points of ellipsoid TQIPJ、TQIPKAnd TQIPBEquation is respectively：
Simultaneous equations (1-6), (1-7), (1-8) can obtain P (x ', y ', z ') coordinate.
Arbitrary focus is identical, and with O, the surfaces P are a little formed by two ellipsoidal surfaces as inside and outside ellipsoid, that is, form one
A " ellipsoidal cavity ", such as above-mentioned equation can be obtained three " ellipsoidal cavities " and be respectively：(TQIOJ, TQIPJ)、 (TQIOK, TQIPK) and
(TQIOB, TQIPB), then the intersection of three " ellipsoidal cavity " can be obtained, and find out barycenter, be denoted as Q (X1,Y1,Z1)。
Q(X1,Y1,Z1) be sample object position.
Step S12, the position for detecting each sample object is tracked whithin a period of time, obtains the shifting of each monitoring moment object
Dynamic parameter, and form moving parameter change sequence sample.In this present embodiment, moving parameter be sample object movement speed and
Moving direction changes.The step of obtaining the moving parameter be：
If three-dimensional coordinate of a certain sample object in different moments is Q (x, y, z).Such as T0The coordinate of the moment sample object
It is Q (x0,y0,z0), TiThe coordinate at moment is Q (xi,yi,zi).The sample object after being tentatively set to is tracked whithin a period of time
Position, measure the sample object unit interval in movement speed.Specifically, in Ti-1To TiThe displacement distance of time object
For：
Ti-1To TiThe average movement speed of time object O：
Vi-1,i=Di-1,i/(Ti-Ti-1) (2)
Known sample object is in average speed v in different time periods0,v1,v2... ....Known T0, T1And T2Moment sample contents
The coordinate of body then can indicate the moving direction of sample object with vector：
Two vectorial angles are：
Then the situation of change of the moving direction of sample object can be expressed with following formula in the unit interval：
Wherein t0=(T2-T0)/2 (5)
Step S13, multiple sample objects within the scope of monitoring marine site are manually marked, multiple sample objects of identification
It is ocean rubbish or bion.Each sample object forms moving parameter change sequence sample<(V1, V2..., Vn), (M1,
M2..., Mn)>, establish study sample.Wherein ViFor the movement speed of the i-th moment sample object, MiFor the i-th moment sample object
Moving direction variation.
Step S20, it is based on study sample and carries out Markov model study.Specifically：
First, if VmaxFor maximum speed, VminFor minimum speed, VavgFor average speed.Meter state α1For high velocity α2For middling speed area α3It is low regime according to each
The record of ocean rubbish calculates the probability matrix of each state conversionWherein aijFor state αiTransfer is
αjProbability, such as a12For state α1It is transferred to state α2Probability.The present embodiment divides three velocity bands.However, the present invention couple
This is not construed as limiting.
Then, if MmaxFor maximum direction change, MminFor minimum direction change, MavgChange for mean direction.Count shape
State β1For quick variation zone β2For middling speed variation zone β3For
Low speed variation zone calculates the probability matrix of each state conversion according to the record of each ocean rubbishWherein bijFor state βiTransfer is βjProbability.The present embodiment divides three direction change regions.So
And this is not limited by the present invention.
Finally, sample object velocity series degree of agreement threshold value P is calculatedS1With direction change sequence degree of agreement threshold value PS2。
PS1Calculation it is as follows：
If Vi∈αk,Vj∈αl, then PV (Vi,Vj)=akl。 (6)
Velocity series degree of agreement threshold value
Wherein, k, l=1,2,3；PV(Vi,Vj) it is ViAffiliated state αkIt is transferred to VjAffiliated state αlProbability.
PS2Calculation it is as follows：
If Mi∈βk,Mj∈βl, then PM (Mi,Mj)=bkl。 (8)
Direction change sequence degree of agreement threshold value
Wherein, k, l=1,2,3；PM(Mi,Mj) it is MiAffiliated state βkIt is transferred to MjAffiliated state βlProbability.
Step S30, the Markov model obtained with study carries out ocean rubbish to detecting object and bion is divided
Class judges, specifically includes：
Step S31, Primary Location is carried out to detecting object.The step is identical as step S11, using based on " ellipsoidal cavity " mould
The solid region localization method of type positions detecting object, obtains QT(X1,Y1,Z1)。
Step S32, the position of detecting object is tracked whithin a period of time, obtains the mobile ginseng of each monitoring moment detecting object
Number, and form moving parameter change sequence.The step is identical as step S12, after formula (1)-(2)-(3)-(4)-(5) calculate
The moving parameter change sequence of formation is<(VT1, VT2..., VTn), (MT1, MT2..., MTn)>.Wherein VTiIt is detecting object T
The movement speed at i moment, MTiChange in the i-th moment moving direction for detecting object T.
Step S33, the moving parameter change sequence obtained is calculated<(VT1, VT2..., VTn), (MT1, MT2..., MTn) > with learn
Practise the degree of agreement of obtained Markov model, including velocity series degree of agreement PSpeed is coincideAnd PDirection change is coincide.Wherein PSpeed is coincideIt is
By sequence (VT1, VT2..., VTn) substitute into formula (6) and (7) obtain afterwards；PDirection change is coincideIt is by sequence (MT1, MT2..., MTn) substitute into
Formula (8) and (9) obtain afterwards.
Step S34, when calculating gained degree of agreement more than or equal to given threshold, judge detecting object for ocean rubbish
Rubbish.Specifically, working as PSpeed is coincide>=PS1And PDirection change is coincide>=PS2, then the object be judged as being ocean rubbish.
In conclusion ocean rubbish provided by the invention and the differentiating method of bion are compared with prior art, have
Following advantages：
Since the biology in ocean has autonomous mobility, their state change is that no rule is governed.So
And rubbish is that do not have autonomous mobility, therefore their state change is governed with certain rule in ocean.This hair
It is bright by under the hydrological characteristics of specified sea areas, by constantly measuring sampling, record in ocean each sample object when different
The moving parameter inscribed, forms moving parameter change sequence sample, and the moving parameter change sequence sample of multiple samples forms
Learn sample, and then Markov model study is carried out based on study sample.In subsequent detection, visited by measuring each moment
The moving parameter of object is surveyed, and forms moving parameter change sequence, by calculating the moving parameter change sequence formed in Ma Er
The degree of agreement of section's husband's model judges that detecting object is ocean rubbish or bion.
Ocean rubbish provided by the invention and the differentiating method of bion are a kind of differentiations based on Markov model
Method has very high differentiation precision.Meanwhile differentiating method can be completed by means of computer, not only have very high differentiation
Efficiency, while having extremely low differentiation cost, effectively solves existing ocean rubbish detection method accuracy of detection and funds have much difficulty in healing weighing apparatus
The problem of.
Although the present invention is disclosed above by preferred embodiment, however, it is not intended to limit the invention, this any known skill
Skill person can make some changes and embellishment without departing from the spirit and scope of the present invention, therefore protection scope of the present invention is worked as
Subject to claims range claimed.
Claims (7)
1. a kind of differentiating method of ocean rubbish and bion, which is characterized in that including：
Establish study sample；
Markov model study is carried out based on study sample；
The classification that the Markov model obtained with study carries out detecting object ocean rubbish and bion judges, specific to wrap
It includes：
Primary Location is carried out to detecting object；
The position for tracking detecting object whithin a period of time, obtains the moving parameter of each monitoring moment detecting object, and forms shifting
Dynamic Parameters variation sequence；
The degree of agreement for calculating the moving parameter change sequence obtained and learning obtained Markov model；
When calculating gained degree of agreement more than or equal to given threshold, judge detecting object for ocean rubbish；
The moving parameter is that movement speed and moving direction change.
2. the differentiating method of ocean rubbish and bion according to claim 1, which is characterized in that the foundation study
The step of sample includes：
Primary Location is carried out to multiple sample objects within the scope of monitoring marine site；
Tracking detects the position of each sample object whithin a period of time, obtains the moving parameter of each monitoring moment object, and shape
At moving parameter change sequence sample；
Multiple sample objects within the scope of monitoring marine site are manually marked, identify that multiple sample objects are ocean rubbish
Rubbish or bion.
3. the differentiating method of ocean rubbish and bion according to claim 1 or 2, which is characterized in that the movement
The acquisition step of parameter includes：
The position of detecting object or sample object after being tentatively set to is tracked whithin a period of time, measures detecting object or sample
Movement speed in the object unit interval；
Calculate the detecting object at each monitoring moment or the moving direction variation of sample object.
4. the differentiating method of ocean rubbish and bion according to claim 1 or 2, which is characterized in that described to be based on
Learn sample and carry out Markov model study, including calculates transition probability and the shifting of each monitoring moment detecting object movement speed
The transition probability of dynamic direction change.
5. the differentiating method of ocean rubbish and bion according to claim 1 or 2, which is characterized in that detecting object
The Primary Location of body or the Primary Location of sample object are to be determined by solid region according to detecting object or sample object signal wave
Position method is positioned.
6. the differentiating method of ocean rubbish and bion according to claim 5, which is characterized in that the solid region
Localization method is the solid region localization method based on " ellipsoidal cavity " model.
7. the differentiating method of ocean rubbish and bion according to claim 6, which is characterized in that described based on " ellipse
The solid region localization method of spherical cavity " model includes：
Any one node of ultrasonic sensor emits ultrasonic signal, and other nodes receive the ultrasonic wave directly emitted and detection
Object or the reflected ultrasonic wave of sample object；
Detecting object or sample object have been separately included away from launch point closest approach and farthest point and around launch point and arbitrary receiving point
Line rotates two ellipsoids to be formed, and intersection forms " ellipsoidal cavity "；
Change receiving point and obtain more " ellipsoidal cavities ", intersection is carried out between " ellipsoidal cavity " and obtains barycenter；
The barycenter of gained forms barycenter group, finds out the barycenter of barycenter group, and the barycenter is the position of detecting object or sample object.
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