CN109600710A - Multi-movement target monitoring method based on difference algorithm in a kind of video sensor network - Google Patents

Multi-movement target monitoring method based on difference algorithm in a kind of video sensor network Download PDF

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CN109600710A
CN109600710A CN201811501729.1A CN201811501729A CN109600710A CN 109600710 A CN109600710 A CN 109600710A CN 201811501729 A CN201811501729 A CN 201811501729A CN 109600710 A CN109600710 A CN 109600710A
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node
target
angle
monitoring
moment
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CN109600710B (en
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蒋波
蒋一波
王伟
郑旭峥
何成龙
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Hangzhou Incandescent Orange Digital Technology Co ltd
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Zhejiang University of Technology ZJUT
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/02—Services making use of location information
    • H04W4/023—Services making use of location information using mutual or relative location information between multiple location based services [LBS] targets or of distance thresholds
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/02—Services making use of location information
    • H04W4/025—Services making use of location information using location based information parameters
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/02—Services making use of location information
    • H04W4/029—Location-based management or tracking services
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management
    • H04W64/003—Locating users or terminals or network equipment for network management purposes, e.g. mobility management locating network equipment
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W84/00—Network topologies
    • H04W84/18—Self-organising networks, e.g. ad-hoc networks or sensor networks

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  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
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Abstract

A kind of multi-movement target monitoring method based on difference algorithm in video sensor network, comprising the following steps: (1) the multiple video sensor nodes of random placement in monitoring region；(2) mobile target is monitored；(3) position of lower a moment of mobile target is predicted；(4) can monitoring node all create maintenance one covering rotation information table；(5) monitoring node based on difference optimization algorithm rotates decision；(6) can monitoring node rotated according to finally more excellent solution.The position that the present invention can occur by predicting mobile target subsequent time, and accordingly using difference algorithm selection can overlay node progress perceived direction rotation.This invention as described the global optimization at each moment, can the maximum probability ground position that is likely to occur of coverage goal, improve the covering quality of multi-movement target.

Description

Multi-movement target monitoring method based on difference algorithm in a kind of video sensor network
Technical field
The present invention relates to video sensor network, multi-movement target is monitored in especially a kind of video sensor network The method of coverage optimization.
Technical background
This kind of appearance of completely new acquisition of information and target monitoring sensing equipment of video sensor, so that for mobile mesh Target state aware, information processing and tracing detection provide the solution of more convenient and efficient.It is made of this kind of sensor Video sensor network have to the acquisition of the multimedia messages such as data, image and video, transmission and processing capacity and it Visualizing monitor have conveniently, intuitive, informative the features such as.Various efficient abilities make it in industrial or agricultural, military, monitoring There is good development prospect in terms of security protection and environmental monitoring.
Target coverage control is an important research hot spot in video sensor network, however video sensor is different from Traditional sensors, since the ken by equipment of itself is limited, sensing range is one using node as the center of circle, and radius is perception The fan-shaped region of distance, so traditional target coverage optimization algorithm is not suitable for video sensor, it is in addition complicated to monitor region Node often using strategy is shed at random, deployment randomness also brings great challenge to the research of target coverage, in addition, Most target coverages are all directed to static object, then considerably less for the mobile target monitoring research for having more random character, depending on The real-time monitoring quality of video sensor network is also wait improve.Therefore, how to be moved using video sensor network in region more Realization of goal sufficiently efficiently monitors, and becomes a problem in the urgent need to address.
Solve the problems, such as that multi-movement target is monitored by video sensor network, there are many different schemes, a kind of methods It is to increase video sensor number of nodes, expands the coverage area of video sensor network, although this method being capable of essence Covering problem is solved, but its implementation is at high cost, and partial region may can not dispose more sensors due to features of terrain To reach covering demand.Another method is according to random placement video sensor node location and for target movement routine Prediction, design optimization algorithm strategy, carry out the perceived direction of real time rotation video sensor, accurate perception and monitor mobile mesh Its track is marked and covered, achievees the effect that telesecurity monitors.So path Accurate Prediction and node perceived direction it is reasonable It is scheduled to the key of problem, the two will directly affect the real-time and reliability of multi-movement target monitoring, a kind of good optimization Monitoring algorithm has great significance.
Summary of the invention
In order to overcome existing video sensor network to multi-movement target perception, monitoring effect is small, track covering quality Deficiency low, real-time is poor, the present invention provide the multi-movement target prison in a kind of video sensor network based on difference algorithm Survey method, the position that can occur by predicting mobile target subsequent time, and section can be covered using difference algorithm selection accordingly Point carries out the rotation of perceived direction.This invention as described the global optimization at each moment, can maximum probability coverage goal can The position that can occur, improves the covering quality of multi-movement target.
The technical solution adopted by the present invention to solve the technical problems is:
Multi-movement target monitoring method based on difference algorithm in a kind of video sensor network, the method include with Lower step:
(1) the multiple video sensor nodes of random placement in monitoring region, are numbered S=to video sensor node {Si| i=1,2 ..., n }, each video sensor node indicates S by five-tuplei=< Pi, R, α, θ, ω >, respectively indicate node Position, the perception radius perceive visual angle, perceived direction angle and angular velocity of rotation；
(2) mobile target is monitored, process is as follows:
(2.1) the mobile target for newly entering monitoring region is numbered, and is added to mobile target collection T={ Tj| J=1,2 ..., m } in；
(2.2) judge certain moment target TjBy video sensor node SiMonitoring the method covered is: And vectorDirection be located at [θi- α/2, θi+ α/2], whereinIndicate monitoring node SiIt is directed toward mobile target Tj's Vector field homoemorphism, i.e. vector absolute distance；
(3) position of lower a moment of mobile target is predicted, process is as follows:
(3.1) keep speed V constant in entire moving process according to mobile target, direction changes at random, predicts target It is using the position of t moment as the center of circle in the position that the t+1 moment can move into, radius is on the circumference of V Δ t, it may be assumed that
Tjt+1={ (Xjt+1, Yjt+1)|(Xjt+1-Xjt)2+(Yjt+1-Yjt(2=(V Δ t)2,=0,1,2 ...
In view of TjIn the moving direction V of t momentjt, then t+1 moment TjMost possible moving direction is Vjt, most can not The moving direction of energy is-Vjt, i.e. opposite direction；
It (3.2) is target TjDeviate V in t+1 moment moving directionjtAngle introduce probability density functionUnder target The moving direction at one moment be it is random, target moving direction deviation angle is stochastic variable, indicated with X,Indicate X's Probability density function, Φ (x) indicate that the distribution function of X, P (a≤X≤b) indicate X in the probability value of section [a, b]；Target can be with Moving direction is rotated clockwise or counter-clockwise, so X ∈ [- π, π], and meet:
And X is in the probability value of section [a, b] are as follows:
I.e. predicted position of the target at the t+1 moment is to fall in using the position of t moment as the center of circle, and radius is the circumference of V Δ t On, and mobile positive direction angle [a, b] is deviated with probability Φ (b)-Φ (a), target subsequent time, which is fallen in, in other words deviates VjtJust Probability on direction [a, b] circular arc is Φ (b)-Φ (a)；
Therefore node is bigger to the circular arc coverage area in target positive direction, and node may to the covering of target subsequent time Property is bigger；
(3.3) according to the prediction circumferential position of multiple mobile targets whether enter monitoring node can the area of coverage, determination can Monitoring node set Z={ Zi| i=1,2 ... n }, using node location as the center of circle, the perception radius is the border circular areas of R, referred to as node Can the area of coverage；
(4) can monitoring node all create one covering rotation information table of maintenance, process is as follows:
(4.1) each can monitoring node updating maintenance one open covering rotation information table, cover rotation information table content It include: " node S_id ", " mobile target T_id ", " required rotation angle A ", " whether activating " totally four；
(4.2) can all mobile targets that can occur in the area of coverage according to itself of each node in monitoring node set, To each mobile target TjCovering rotation angle is calculated, and constitutes rotation set Δ A={ Δ Aij| i=1,2 ... n, j=1, 2 ... m }, each can monitoring node according to itself perceived direction angle θi, calculate separately out and cover each mobile target in t+1 The angle delta A of rotation required for moment predicted position of the moving direction without offset, adds information in coverage information table；According to Node location PiPredicted position p with target subsequent time moving direction without offsetv, calculate vectorDeflection θk, calculate The perceived direction angle θ of nodeiWith θkAngle angle counterclockwise, if angle be less than π, node SiThe angle delta A for needing to rotate α/2=- angle+, otherwise α/2 Δ A=2 π-angle-；
(4.3) each can monitoring node itself covering rotation information table is sent to server；
(5) monitoring node based on difference optimization algorithm rotates decision, and process is as follows:
(5.1) server is according to each coverage rotation information table received, initialization population individual A={ A1, A2..., ANP, wherein Ak=(Δ Ak1, Δ Ak2..., Δ Akn)TFor k-th of individual of population, NP is Population Size, Δ AkiGeneration Table monitoring node SiRotation angle, boundary conditionWherein Respectively indicate Δ Aki The upper bound of value range and lower bound are related to angular velocity of rotation ω；
(5.2) the sum of probability value corresponding to the arc section of coverage target predicted position should maximize, and design fitness Function F:
Wherein, node covers target T with probability g (x)jSubsequent time predicted position pj, cover probability g (x) are as follows:
Judge t moment node SiCover target TjPredicted position pjMethod beAnd vector's Direction is located at [θi- α/2, θi+α/2]；
(5.3) individual variation is realized by difference strategy, two different individuals in population is randomly selected, by its vector difference It is synthesized after scaling with to variation individual progress vector, i.e.,
vk(t+1)=Ar1(t)+G·(Ar2(t)-Ar3(t))j≠r1≠r2≠r3
Wherein, G is zoom factor, Ak(t) indicate that t is individual for k-th in population, in evolutionary process, in order to guarantee to solve Validity, intermediate need to be judged whether to meet boundary condition；
(5.4) crossover operation: to t for population { Ak(t) } and its variation intermediate { vk(t+1) } between progress individual Crossover operation generates test individual uk(t+1)；
Wherein, CR is crossover probability, irandFor the random integers of [1,2 ..., n], it is ensured that variation intermediate vk(t+1) in At least one " gene " is hereditary to the next generation；
(5.5) selection operation is carried out to test individual and former individual, using the big individual of greedy algorithm selection fitness into Enter next-generation population:
(5.6) from (5.3), the above steps are repeated up to reaching the number of iterations；
(6) can monitoring node rotated according to finally more excellent solution, process is as follows:
(6.1) after difference optimization algorithm, server obtains the more excellent individual that algorithm solves, and individual " gene position " is right Interdependent node is answered to rotate angle delta Aki；
(6.2) server will more excellent individual solution return to it is each can monitoring node, node looks for " gene position " of oneself, and Carry out respective angles rotation；
(6.3) it goes to step (2) after one time step of waiting to recalculate, until monitoring terminates.
Technical concept of the invention are as follows: some video sensor nodes of random placement in Target monitoring area, according to mesh It is constant to mark movement speed, offset direction uses probability density function, deduces mesh on accessibility circumferential position within the Δ t time The possible position of subsequent time is marked, and gives and how to be monitored using difference algorithm policy selection partial video sensor node Cover the process of mobile target.Can be calculated in this method can monitoring node cover mobile target maximum possible in-position Required rotation angle, and by server global calculation go out more excellent solution, be last transmitted to can monitoring node rotated certainly Plan, the invention can effectively improve the covering quality and covering success rate of mobile target, this monitoring optimizing approach application rail Mark prediction and difference algorithm, to realize that efficient multiple target real-time tracking covering provides possibility.
Beneficial effects of the present invention are mainly shown as: 1, target movement pattern is more acurrate；2, destination path covers matter Amount improves；3, speed of searching optimization is fast, and monitoring real-time is high.
Detailed description of the invention
Fig. 1 is the flow chart of the multi-movement target monitoring method in video sensor network based on difference algorithm.
Fig. 2 is the structure chart of the multi-movement target monitoring optimizing system in video sensor network based on difference algorithm.
Specific embodiment
The present invention is described further with reference to the accompanying drawing.
Referring to Figures 1 and 2, the multi-movement target monitoring method in a kind of video sensor network based on difference algorithm, packet Include following steps:
Step 1: the random placement video sensor node in Target monitoring area, is compiled to video sensor node Number S={ Si| i=1,2 ..., n }；
Whether monitor that new mobile target occurs in region step 2: judging to monitor, the 4th is jumped to if not Step；
Step 3: processing is numbered to the mobile target newly monitored, it is added to mobile object set T={ Tj| j=1, 2 ..., m } in；
Step 4: predicting the position of lower a moment of mobile target；Speed V is kept in entire moving process according to mobile target Constant, direction changes at random, then target is the radius using the position of t moment as the center of circle in the position that the t+1 moment can move into On circumference for V Δ t, it may be assumed that
Tjt+1={ (Xjt+1, Yjt+1)|(Xjt+1-Xjt)2+(Yjt+1-Yjt)2=(V Δ t)2,=0,1,2 ...
In view of TjIn the moving direction V of t momentjt, then t+1 moment TjMost possible moving direction is Vjt, most can not The moving direction of energy is-Vjt, i.e. opposite direction；
It is target T according to actual conditionsjDeviate moving direction V at the t+1 momentjtAngle introduce probability density functionThe moving direction of target subsequent time be it is random, target direction deviation angle is stochastic variable, indicated with X, Indicate X probability density function, Φ (x) indicate X distribution function, P (a≤X≤b) indicate X section [a, b] probability value, Target can rotate clockwise or counter-clockwise moving direction, so X ∈ [- π, π], and meet:
And X is in the probability value of section [a, b] are as follows:
I.e. predicted position of the target at the t+1 moment is to fall in using the position of t moment as the center of circle, and radius is the circumference of V Δ t On, and mobile positive direction angle [a, b] is deviated with probability Φ (b)-Φ (a), target subsequent time, which is fallen in, in other words deviates VjtJust Probability on direction [a, b] circular arc is Φ (b)-Φ (a)；
Node is bigger to the arc section coverage area in target positive direction, covering possibility of the node to target subsequent time It is bigger；
Step 5: according to the prediction circumferential position of multiple mobile targets whether enter monitoring node can the area of coverage, determine It can monitoring node set Z={ Zi| i=1,2 ... n }, using node location as the center of circle, the perception radius is the border circular areas of R, is referred to as saved That puts can the area of coverage；
Step 6: each can monitoring node updating maintenance one open covering rotation information table, covering rotation information table it is interior Whether appearance includes: " node S_id ", " mobile target T_id ", " required rotation angle A ", " activating " totally four；
Step 7: can all mobile mesh that can occur in the area of coverage according to itself of each node in monitoring node set Mark, to each mobile target TjCovering rotation angle is calculated, and constitutes rotation set Δ A={ Δ Aij| i=1,2 ... n, j= 1,2 ... m }, each can monitoring node according to itself perceived direction angle θi, calculate separately out and cover each mobile target in t+ The angle delta A of rotation required for 1 moment predicted position of the moving direction without offset, adds information in coverage information table；Root According to node location PiPredicted position p with target subsequent time moving direction without offsetv, calculate vectorDeflection θk, meter The perceived direction angle θ of operator nodeiWith θkAngle angle counterclockwise, if angle be less than π, node SiThe angle for needing to rotate α/2 Δ A=-angle+, otherwise α/2 Δ A=2 π-angle-；
Step 8: each can monitoring node itself covering rotation information table is sent to server；
Step 9: the monitoring node based on difference optimization algorithm rotates decision, server is covered according to each node received Lid rotation information table, initialization population individual A={ A1, A2..., ANP, wherein Ak=(Δ Ak1, Δ Ak2..., Δ Akn)TFor kind K-th of individual of group, NP is Population Size, Δ AkiRepresent monitoring node SiRotation angle, boundary conditionWherein Respectively indicate Δ AkiThe upper bound of value range and lower bound, with rotation angle speed It is related to spend ω；
Step 10: calculating fitness value to individual according to fitness function F.Coverage target predicted position arc section institute The sum of corresponding probability value should maximize, so:
Wherein, node covers target T with probability g (x)jSubsequent time predicted position pj, cover probability g (x) are as follows:
Judge t moment node SiCover target TjPredicted position pjMethod beAnd vector's Direction is located at [θi- α/2, θi+α/2]；
Step 11: by difference strategy realize individual variation, randomly select two different individuals in population, by its to It is synthesized after amount difference scaling with to variation individual progress vector, i.e.,
vk(t+1)=Ar1(t)+G·(Ar2(t)-Ar3(t))j≠r1≠r2≠r3
Wherein, G is zoom factor, Ak(t) indicate that t is individual for k-th in population, in evolutionary process, in order to guarantee to solve Validity, intermediate need to be judged whether to meet boundary condition；
Step 12: crossover operation: to t for population { Ak(t) } and its variation intermediate { vk(t+1) } individual is carried out Between crossover operation, generate test individual uk(t+1),
Wherein, CR is crossover probability, irandFor the random integers of [1,2 ..., n], it is ensured that variation intermediate vk(t+1) in At least one " gene " is hereditary to the next generation；
Step 13: selection operation is carried out to test individual and former individual, using big of greedy algorithm selection fitness Body enters next-generation population:
Step 14: from ten steps, the above steps are repeated up to reaching the number of iterations；
Step 15: server obtains more excellent individual according to ideal adaptation angle value after difference optimization algorithm, can monitor Node is rotated according to finally more excellent individual solution；
Step 16: server will more excellent individual solution return to it is each can monitoring node, node looks for the " gene of oneself Position ", and rotate respective angles Δ Aji；
It is continued to execute step 17: jumping to second step after waiting a time step, until monitoring terminates.
Referring to Fig. 2, the multi-movement target based on difference algorithm is monitored in the video sensor network realized using this method Optimization system, specifically include that mobile target trajectory prediction module, difference algorithm node decision-making module, can monitoring node rotating mould Block and user interface module.
(1) it mobile target trajectory prediction module: according to mobile target velocity is constant and probability density function, predicts next The target position at moment.
(2) difference algorithm node decision-making module: establishment can monitoring node collection, maintenance sends respective covering rotation information table To server, server is based on difference algorithm and carries out global optimization.
(3) can monitoring node rotary module: can more excellent individual solution of the monitoring node according to server passback, accurately seek position, It is rotated according to respective angles.
(4) user interface module: the related monitoring environmental parameter of configuration such as monitors area size, video sensor section Point quantity, node location, the perception radius, initial perceived direction angle, perception visual angle, time interval etc. change environmental parameter, carry out More Experimental comparisons.

Claims (1)

1. the multi-movement target monitoring method in a kind of video sensor network based on difference algorithm, which is characterized in that described Method the following steps are included:
(1) the multiple video sensor nodes of random placement in monitoring region, are numbered S={ S to video sensor nodei|i =1,2 ..., n }, each video sensor node indicates S by five-tuplei=< Pi, R, α, θ, ω >, node location is respectively indicated, The perception radius perceives visual angle, perceived direction angle and angular velocity of rotation；
(2) mobile target is monitored, process is as follows:
(2.1) the mobile target for newly entering monitoring region is numbered, and is added to mobile target collection T={ Tj| j=1, 2 ..., m } in；
(2.2) judge certain moment target TjBy video sensor node SiMonitoring the method covered is:And to AmountDirection be located at [θi- α/2, θi+ α/2], whereinIndicate monitoring node SiIt is directed toward mobile target TjVector Mould, i.e. vector absolute distance；
(3) position of lower a moment of mobile target is predicted, process is as follows:
(3.1) keep speed V constant in entire moving process according to mobile target, direction changes at random, predicts target in t+1 The position that moment can move into is using the position of t moment as the center of circle, and radius is on the circumference of V Δ t, it may be assumed that
Tjt+1={ (Xjt+1, Yjt+1)|(Xjt+1-Xjt)2+(Yjt+1-Yjt)2=(V Δ t)2, t=0,1,2 ...
In view of TjIn the moving direction V of t momentjt, then t+1 moment TjMost possible moving direction is Vjt, most unlikely Moving direction is-Vjt, i.e. opposite direction；
It (3.2) is target TjDeviate V in t+1 moment moving directionjtAngle introduce probability density functionUnder target for the moment The moving direction at quarter be it is random, target moving direction deviation angle is stochastic variable, indicated with X,Indicate that the probability of X is close Function is spent, Φ (x) indicates that the distribution function of X, P (a≤X≤b) indicate X in the probability value of section [a, b]；Target can be clockwise Or moving in rotation direction counterclockwise, so X ∈ [- π, π], and meet:
And X is in the probability value of section [a, b] are as follows:
I.e. predicted position of the target at the t+1 moment is to fall in using the position of t moment as the center of circle, radius be V Δ t circumference on, and Mobile positive direction angle [a, b] is deviated with probability Φ (b)-Φ (a), target subsequent time, which is fallen in, in other words deviates VjtPositive direction Probability on [a, b] circular arc is Φ (b)-Φ (a)；
Therefore node is bigger to the circular arc coverage area in target positive direction, and node gets over the covering possibility of target subsequent time Greatly；
(3.3) according to the prediction circumferential position of multiple mobile targets whether enter monitoring node can the area of coverage, determination can monitor Node set Z={ Zi| i=1,2 ... n }, using node location as the center of circle, the perception radius be R border circular areas, referred to as node can The area of coverage；
(4) can monitoring node all create one covering rotation information table of maintenance, process is as follows:
(4.1) each can monitoring node updating maintenance one open covering rotation information table, covering rotation information table content include: " node S_id ", " mobile target T_id ", " required rotation angle A ", " whether activating " totally four；
(4.2) can all mobile targets that can occur in the area of coverage according to itself of each node in monitoring node set, to every A mobile target TjCovering rotation angle is calculated, and constitutes rotation set Δ A={ Δ Aij| i=1,2 ... n, j=1,2 ... m }, Each can monitoring node according to itself perceived direction angle θi, calculate separately out and cover each mobile target and moved at the t+1 moment The angle delta A of rotation required for dynamic predicted position of the direction without offset, adds information in coverage information table；According to node position Set PiPredicted position p with target subsequent time moving direction without offsetv, calculate vectorDeflection θk, calculate node Perceived direction angle θiWith θkAngle angle counterclockwise, if angle be less than π, node SiThe angle delta A=- for needing to rotate α/2 angle+, otherwise α/2 Δ A=2 π-angle-；
(4.3) each can monitoring node itself covering rotation information table is sent to server；
(5) monitoring node based on difference optimization algorithm rotates decision, and process is as follows:
(5.1) server is according to each coverage rotation information table received, initialization population individual A={ A1, A2..., ANP, wherein Ak=(Δ Ak1, Δ Ak2..., Δ Akn)TFor k-th of individual of population, NP is Population Size, Δ AkiRepresent monitoring Node SiRotation angle, boundary conditionWherein Respectively indicate Δ AkiValue model The upper bound enclosed and lower bound are related to angular velocity of rotation ω；
(5.2) the sum of probability value corresponding to the arc section of coverage target predicted position should maximize, and design fitness function F:
Wherein, node covers target T with probability g (x)jSubsequent time predicted position pj, cover probability g (x) are as follows:
Judge t moment node SiCover target TjPredicted position pjMethod beAnd vectorDirection Positioned at [θi- α/2, θi+α/2]；
(5.3) individual variation is realized by difference strategy, randomly selects two different individuals in population, its vector difference is scaled It is synthesized afterwards with to variation individual progress vector, i.e.,
vk(t+1)=Ar1(t)+G·(Ar2(t)-Ar3(t))j≠r1≠r2≠r3
Wherein, G is zoom factor, Ak(t) indicate that t is individual for k-th in population, in evolutionary process, in order to guarantee the effective of solution Property, intermediate need to be judged whether to meet boundary condition；
(5.4) crossover operation: to t for population { Ak(t) } and its variation intermediate { vk(t+1) } intersection between individual is carried out Operation generates test individual uk(t+1)；
Wherein, CR is crossover probability, irandFor the random integers of [1,2 ..., n], it is ensured that variation intermediate vk(t+1) at least There is one " gene " to be hereditary to the next generation；
(5.5) selection operation is carried out to test individual and former individual, under entering using the big individual of greedy algorithm selection fitness Generation population:
(5.6) from (5.3), the above steps are repeated up to reaching the number of iterations；
(6) can monitoring node rotated according to finally more excellent solution, process is as follows:
(6.1) after difference optimization algorithm, server obtains the more excellent individual that algorithm solves, individual " gene position " corresponding phase Artis rotates angle delta Aki；
(6.2) server will more excellent individual solution return to it is each can monitoring node, node looks for oneself " gene position ", and carries out Respective angles rotation；
(6.3) it goes to step (2) after one time step of waiting to recalculate, until monitoring terminates.
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CN113784361A (en) * 2021-05-13 2021-12-10 中国地质大学（武汉） Sensor node dynamic adjustment method, device and storage medium

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