CN107229285A - Unmanned plane formation information distribution weight planing method, computer-readable recording medium - Google Patents
Unmanned plane formation information distribution weight planing method, computer-readable recording medium Download PDFInfo
- Publication number
- CN107229285A CN107229285A CN201710415126.9A CN201710415126A CN107229285A CN 107229285 A CN107229285 A CN 107229285A CN 201710415126 A CN201710415126 A CN 201710415126A CN 107229285 A CN107229285 A CN 107229285A
- Authority
- CN
- China
- Prior art keywords
- distributed
- bit stream
- mission bit
- information
- mrow
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 52
- 230000015572 biosynthetic process Effects 0.000 title claims abstract description 14
- 210000000349 chromosome Anatomy 0.000 claims description 60
- 238000004891 communication Methods 0.000 claims description 24
- 230000006870 function Effects 0.000 claims description 19
- 238000012545 processing Methods 0.000 claims description 19
- 108090000623 proteins and genes Proteins 0.000 claims description 19
- 238000004590 computer program Methods 0.000 claims description 9
- 230000005540 biological transmission Effects 0.000 claims description 8
- 230000002068 genetic effect Effects 0.000 claims description 8
- 238000012804 iterative process Methods 0.000 claims description 5
- 230000035772 mutation Effects 0.000 claims description 5
- 238000013459 approach Methods 0.000 claims description 4
- 230000008859 change Effects 0.000 claims description 3
- 230000002759 chromosomal effect Effects 0.000 claims description 3
- 238000005457 optimization Methods 0.000 claims description 3
- 230000008569 process Effects 0.000 description 8
- 238000013519 translation Methods 0.000 description 5
- 230000009286 beneficial effect Effects 0.000 description 3
- 238000013461 design Methods 0.000 description 3
- 238000004043 dyeing Methods 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 239000007787 solid Substances 0.000 description 3
- 230000006978 adaptation Effects 0.000 description 2
- 230000009467 reduction Effects 0.000 description 2
- 230000004044 response Effects 0.000 description 2
- 230000001174 ascending effect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 235000013399 edible fruits Nutrition 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000010365 information processing Effects 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000002834 transmittance Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/10—Simultaneous control of position or course in three dimensions
- G05D1/101—Simultaneous control of position or course in three dimensions specially adapted for aircraft
- G05D1/104—Simultaneous control of position or course in three dimensions specially adapted for aircraft involving a plurality of aircrafts, e.g. formation flying
Landscapes
- Engineering & Computer Science (AREA)
- Aviation & Aerospace Engineering (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The present invention relates to a kind of unmanned plane formation information distribution weight planing method, computer-readable recording medium and computer equipment, method, which is included in during unmanned plane is formed into columns, calls weight plan model；Attribute initialization to the mission bit stream to be distributed in task pool, obtains initial solution；Counterweight plan model is solved, and obtains weight programme；Distributed tasks information is treated according to weight programme to be distributed and transmit.Level Four mission bit stream of the present invention in the task pool of unmanned plane fleet system is distributed completion, or, prediction scheme is distributed based on initial information, when the income sum for not including three-level mission bit stream in the task pool of level Four mission bit stream is more than disturbance cost sum, call the heavy plan model pre-established, weight programme is determined using weight plan model, to ensure that the total revenue of the higher three-level mission bit stream of priority level and second task information in task pool is maximum, the reasonable arrangement to mission bit stream to be distributed in task pool is realized.
Description
Technical field
The present invention relates to unmanned plane-someone's machine information processing technology field, more particularly, to a kind of unmanned plane formation information
Distribution weight planing method, computer-readable recording medium and computer equipment.
Background technology
Nobody-have it is man-machine be performed in unison with task during, the different stages to required information type to be processed,
The quantity of information requirement, and the significance level of of information itself have certain difference, therefore are carried out effectively to mission bit stream
Distribution with transmittance process, it is necessary to consider that mission bit stream may be with different priority levels, such as mandatory mission bit stream, again
Want level mission bit stream, general level mission bit stream and low priority task information.Wherein：
Mandatory mission bit stream refers to the timing requirements because of itself, significance level or is entirely being performed in unison with task mistake
The mission bit stream that is played a key effect in journey, it is necessary to nobody-have and be distributed processing immediately in man-machine system, be priority level
Highest mission bit stream.
Importance level mission bit stream refers to have whole collaborative processes material impact, task to complete income apparently higher than general
The mission bit stream of level and low priority task, for example：Reconnaissance mission.Battle reconnaissance determines the tendency of war in modern war,
Precisely timely battle field information is capable of the success or failure of left and right war, because unmanned plane performs the reconnaissance mission wind that has that no one was injured
The features such as danger, deployment are flexibly, response is timely, enjoy various countries to pay close attention to, reconnaissance mission is turned into the currently the most important task of unmanned plane
One of pattern.
General level mission bit stream refers to the routinely assignment instructions that requirement forecasting and charge center are sent, for example：It is aerial pre-
Alert task.Unmanned plane is deployed in advance and transmitted close to the overhead of enemy, then the information that unmanned plane is obtained by communication link
To being parked in, having for safety area is man-machine, then by have it is man-machine pass information to control centre in good time, carry out interception task.
Whether low priority task refer to the deadline and perform influence little task for this collaborative processes efficiency,
Such as daily cruise task dispatching.
Nobody-have it is man-machine be performed in unison with task during, different phase is probably change, thing to the demand of task
Preceding programme is likely difficult to adapt to protean task environment, and the preferential distribution to mandatory mission bit stream and biography
Pass and certain influence is had on original programme.In such cases it need the information distributor in being formed into columns to unmanned plane
Case carries out weight-normality and drawn, and information is distributed programme and is more rationally already adapted to currently perform task environment.
At present, nobody-have it is man-machine be performed in unison with task process, can be to be distributed in task pool without a kind of scheme
Mission bit stream carry out rational weight-normality and draw to arrange, form optimal information distribution and translation sequence.
The content of the invention
(1) technical problem solved
The present invention provides a kind of unmanned plane formation information distribution weight method and device for planning, can solve not having in the prior art
There is a kind of scheme to carry out rational weight-normality to mission bit stream to be distributed in unmanned plane formation task pool and draw arrangement, formed most
Excellent information distribution and translation sequence.
(2) technical scheme
In a first aspect, the unmanned plane formation information distribution weight planing method that the present invention is provided includes：
When the level Four mission bit stream in task pool is distributed completion, or, prediction scheme is distributed based on initial information, not included
When the income sum of three-level mission bit stream is more than disturbance cost sum in the task pool of level Four mission bit stream, call what is pre-established
Weight plan model；The optimization aim of the heavy plan model is the three-level task in maximizing task pool under default constraints
The total revenue of information and second task information；
The distribution of the mission bit stream to be distributed in task pool is initialized with transitive attribute using coding method, obtains initial
Solution；
Based on the initial solution, the heavy plan model is solved using genetic algorithm, obtained to described to be distributed
Mission bit stream distributes the heavy programme with transmission；
The mission bit stream to be distributed is distributed and transmitted according to the heavy programme；
Wherein, the significance level of the level Four mission bit stream, the three-level mission bit stream and the second task information according to
Secondary reduction, and the level Four mission bit stream is mandatory mission bit stream.
Optionally, the initial information distribution prediction scheme is each in the task pool for not including level Four mission bit stream to maximize
The distribution approach that the weighted value sum of individual mission bit stream to be distributed is set up for target.
Optionally, distribution and transitive attribute of the use coding method to the mission bit stream to be distributed in task pool are initial
Change, obtain initial solution, including：
The weight-normality, which is drawn solution to model and is encoded on chromosome, the chromosome, using coding method includes and task pool
In the one-to-one gene of mission bit stream to be distributed；
First mark of each gene on chromosome is set to 1, the first mark for being set to 1 characterizes that the gene is corresponding to be treated point
Hair mission bit stream is that can be distributed and transmit；
Destination node, priority value, financial value and the disturbance value at cost of each mission bit stream to be distributed are obtained, and for each
Individual mission bit stream to be distributed generates a source node different from its destination node at random；
Judge whether each mission bit stream to be distributed needs forwarding；Mission bit stream to be distributed for needing forwarding, at random
Multiple different forward node are generated, forward-path is formed；For the mission bit stream to be distributed that need not be forwarded, section is forwarded
Point is set to -1；
Read the time window of each mission bit stream to be distributed；For each mission bit stream to be distributed, in the time window
One moment point of interior random generation, and at the time of using the moment point as mission bit stream arrival destination node to be distributed；It is right
In the mission bit stream to be distributed for needing to forward, mission bit stream to be distributed is extrapolated according to forward-path and reaches each forward node
Moment and from source node send at the time of；For the mission bit stream to be distributed that need not be forwarded, task letter to be distributed is extrapolated
Breath from source node send at the time of, and the forwarding moment of each forward node is set to -1；
By the first mark of each mission bit stream to be distributed, source node, forward node, destination node, send from source node
Moment, at the time of reach each forward node, at the time of reach the destination node, priority value, financial value and disturbance cost
It is worth the distribution as the mission bit stream to be distributed and transitive attribute, distribution and the transitive attribute of each mission bit stream to be distributed are formed
Initial solution.
Optionally, the use genetic algorithm is solved to the heavy plan model, including：
S1, setting iterations k initial value are 1；
S2, by the object function of the heavy plan model be fitness function, calculate initial population in chromosome adaptation
Spend functional value；
S3, selected using roulette wheel selection from parent colony in fitness function value highest predetermined number dyeing
Body is genetic in progeny population；
S4, single-point crossover operation two-by-two is carried out to the chromosome in population；
S5, the chromosome obtained to crossover operation carry out resetting variation processing；
S6, the chromosome obtained to resetting variation processing are updated operation, specially by fitness in progeny population most
The chromosomal of the second minimum predetermined number of fitness in the chromosome and progeny population of the first low predetermined number, is formed
New population；
S7, judge whether current iterations reaches default maximum iteration kmax；
If so, then regarding the corresponding solution of new population obtained in last time iterative process as the heavy programme；
Otherwise, using the new population as initial population, iterations adds 1, and returns to S2.
Optionally, before the chromosome that described pair of replacement variation processing is obtained is updated operation, methods described is also wrapped
Include：To resetting whether the corresponding distribution of gene on the chromosome after variation processing meets the default constraint bar with transitive attribute
Part；Operated if so, then performing described update；Otherwise, the fitness function value for resetting variation processing after stain colour solid is adjusted
After perform it is described update operation.
Optionally, the chromosome obtained to crossover operation carries out resetting variation processing, including：Generation one is between 0
And the random number between 1, if the random number is less than default mutation probability, generated according to the generation method of the initial solution
Item chromosome；Item chromosome is randomly choosed in progeny population, and is replaced with the chromosome of the generation according to the initial solution
For randomly selected chromosome, other chromosomes keep constant.
Optionally, the object function of the heavy plan model is：
In formula, Z is the three-level mission bit stream and the total revenue of second task information in task pool；T represents that any one is treated
Distribute information；TbRepresent three-level mission bit stream set；TcTable second task information aggregate；Decision variable1 or 0 is taken, 1 expression is taken
Information t to be distributed is sent to node j from node i, takes 0 expression information t to be distributed not to be sent to node j from node i；HtRepresent
Obtainable income after into information t to be distributed task.
Optionally, the default constraints includes：
ETt≤lt,t∈T
ETt≥et,t∈T
ETt-STt≤D,t∈T
In formula, ETtAt the time of representing that information t to be distributed is actually reached information sink；STtRepresent information t to be distributed from information source
Actually start delivery time；V={ 1,2 ..., m } represents communication network topology interior joint set, and m represents that communication network topology is total
Nodes；Represent that information t to be distributed is delivered to the propagation delay time of node j generations from node i；Represent information t to be distributed from
Node i is delivered to the propagation delay of node j generations；T is mission bit stream set to be distributed, does not include level Four task in the set and believes
Breath；ltRepresent that information t to be distributed reaches the time of information sink at the latest；etRepresent information t to be distributed earliest information sink arrival times；D
Represent acceptable maximum delay in communication network topology；TWtRepresent the bandwidth required for information t to be distributed；NWijRepresent communication
Directed edge in network topology<i,j>The maximum bandwidth that can bear；BvThe maximum amount of data that node v can be provided is represented, v is represented
Any node in communication network topology, v ∈ V；Decision variable1 or 0 is taken, wherein, 1 expression information t to be distributed is taken from node i
Node j is sent to, takes 0 expression information t to be distributed not to be sent to node j from node i；TaRepresent level Four mission bit stream.
Second aspect, the computer-readable recording medium that the present invention is provided, is stored thereon with computer program, the program quilt
Realized during computing device such as the step of above-mentioned method.
The third aspect, the computer equipment that provides of the present invention, including memory, processor and store on a memory simultaneously
The computer program that can be run on a processor, it is characterised in that realized such as during computer program described in the computing device
The step of above method.
(3) beneficial effect
Unmanned plane formation information distribution weight planing method, computer-readable recording medium and the computer that the present invention is provided are set
It is standby, when the level Four mission bit stream in the task pool of unmanned plane fleet system is distributed completion, or, distributed based on initial information
When the income sum of three-level mission bit stream is more than disturbance cost sum in prediction scheme, the task pool not including level Four mission bit stream, adjust
With the heavy plan model pre-established, weight programme is determined using weight plan model, with ensure in task pool priority level compared with
The total revenue of high three-level mission bit stream and second task information is maximum, realize in task pool mission bit stream to be distributed it is reasonable
Arrange, form optimal information and distribute and translation sequence.
Brief description of the drawings
, below will be to embodiment or existing in order to illustrate more clearly of the embodiment of the present disclosure or technical scheme of the prior art
There is the accompanying drawing used required in technology description to be briefly described, it should be apparent that, drawings in the following description are only this
Some disclosed embodiments, for those of ordinary skill in the art, on the premise of not paying creative work, can be with
Other accompanying drawings are obtained according to these figures.
Fig. 1 shows the schematic diagram of the chromosome that one is made up of 5 genes in one embodiment of the invention；
Fig. 2 shows the part flow signal of unmanned plane formation information distribution weight planing method in one embodiment of the invention
Figure.
Embodiment
Below in conjunction with the accompanying drawing in the embodiment of the present disclosure, the technical scheme in the embodiment of the present disclosure is carried out clear, complete
Site preparation is described, it is clear that described embodiment is only a part of embodiment of the invention, rather than whole embodiments.It is based on
Embodiment in the disclosure, it is every other that those of ordinary skill in the art are obtained under the premise of creative work is not made
Embodiment, belongs to the scope of disclosure protection.
In a first aspect, the present invention provides a kind of unmanned plane formation information distribution weight planing method, this method includes：
A, it is distributed completion when the level Four mission bit stream in task pool, or, prediction scheme is distributed based on initial information, not wrapped
When the income sum for including three-level mission bit stream in the task pool of level Four mission bit stream is more than disturbance cost sum, calls and pre-establish
Heavy plan model；The optimization aim of the heavy plan model is in charge of a grade for three in the maximization task pool under default constraints
The total revenue of information of being engaged in and second task information；
Wherein, the significance level of the level Four mission bit stream, the three-level mission bit stream and the second task information according to
Secondary reduction, and the level Four mission bit stream is mandatory mission bit stream.
It will be appreciated that for not including the task pool of level Four mission bit stream, an initial distribution prediction scheme can be set, such as
Fruit task pool during being distributed according to the distribution prediction scheme to mission bit stream have received level Four mission bit stream, now can
Suspended task is distributed, but level Four mission bit stream is distributed immediately, it is necessary to right after the completion of level Four mission bit stream is distributed
Not distributed mission bit stream proceeds distribution in task pool, can now call weight plan model, and not distributed is appointed
Business information design distribution approach, that is, weigh programme.If according to initial distribution prediction scheme, determining the income of three-level mission bit stream
Sum is more than disturbance cost sum, now also calls weight plan model, and the distribution to the mission bit stream in task pool designs distribution
Scheme, that is, weigh programme.It is two trigger conditions for calling weight plan model above.
It will be appreciated that three-level mission bit stream is important business information in charge of a grade, second task information is general level mission bit stream.
One-level mission bit stream is also possible that in certain task pool, one-level mission bit stream is low priority task.Level Four mission bit stream, three
The significance level of level mission bit stream, second task information and one-level mission bit stream is reduced successively, and priority level is reduced successively.
B, the distribution using coding method to the mission bit stream to be distributed in task pool and transitive attribute are initialized, and are obtained just
Begin solution；
C, based on the initial solution, the heavy plan model is solved using genetic algorithm, obtains treating point to described
Send out the heavy programme of mission bit stream distribution and transmission；
D, according to the heavy programme mission bit stream to be distributed is distributed and transmitted.
The unmanned plane formation information distribution weight planing method that the present invention is provided, when the level Four mission bit stream in task pool is divided
Distribute into, or, three-level mission bit stream in prediction scheme, the task pool not including level Four mission bit stream is distributed based on initial information
When income sum is more than disturbance cost sum, the heavy plan model pre-established is called, determines that weight-normality is drawn using weight plan model
Scheme, to ensure that the total revenue of the higher three-level mission bit stream of priority level and second task information in task pool is maximum, is realized
To the reasonable arrangement of mission bit stream to be distributed in task pool, form optimal information and distribute and translation sequence.
To make it clear, being illustrated below to the parameters of formula being related in various：
Represented herein with digraph G (V, E, W) unmanned plane/have it is man-machine between all available communication network topologies, will
Unmanned plane/have the man-machine node being described as in communication network topology, concrete model parameter is as follows：
V={ 1,2 ..., m } represents communication network topology interior joint set, and m represents communication network topology total node number.
E=<i,j>| i, j ∈ V, i ≠ j } oriented line set is represented, wherein<i,j>Represent communication network topology interior joint i
To node j directed edge；
W={ wij| i, j ∈ V } represent figure in every directed edge weights set, wherein wiJ represent node i to node j it
Between Euclidean distance.
BvThe maximum amount of data that node v can be provided is represented, wherein, v represents any node in communication network topology, v
∈V；
T represents information aggregate to be distributed, and n represents the number of element in set, and t represents any one information to be distributed, t ∈
T；Wherein TaRepresent mandatory mission bit stream, TbRepresent importance level information, TcRepresent general level information, TdRepresent low priority letter
Breath；
[et,lt] represent that information t to be distributed needs to reach information sink, e in this time windowtRepresent earliest arrival time, lt
Represent arrival time at the latest；
STtRepresent information t to be distributed delivery time, ET since information source is actualtRepresent that information t to be distributed is actually reached
At the time of information sink；
SNtRepresent information t to be distributed actual information source, ENtExpression needs to receive information t to be distributed information sink；
Represent that information t to be distributed is delivered to the propagation delay time of node j generations from node i；Represent information t to be distributed
The propagation delay of node j generations is delivered to from node i；
D represents acceptable maximum delay in communication network topology；
TWtRepresent the bandwidth required for information t to be distributed；
NWijRepresent directed edge in communication network topology<i,j>The maximum bandwidth that can bear；
PtRepresent information t to be distributed priority, Pt=1 represents low priority task, Pt=2 represent general level task, Pt
=3 represent importance level task, Pt=4 represent interrupt class task；
HtRepresent obtainable income after completion information t to be distributed task；
GtRepresent information t to be distributed weighted value；
CtRepresent information t to be distributed issuable disturbance cost；
Decision variable1 or 0 is taken, wherein, take 1 expression information t to be distributed to be sent to node j from node i, take 0 expression to treat
Distribution information t is not sent to node j from node i.
It is that the object function and constraints of weight plan model can be arranged as required in step A, example in specific implementation
Such as：
The object function of plan model is again：
In formula, Z is the three-level mission bit stream and the total revenue of second task information in task pool.
Default constraints includes time windows constraints, delay constraint, bandwidth constraint, information source constraint, access unique constraints
Deng wherein so-called time windows constraints need to complete distribution transmission, delay constraint for mandatory mission bit stream in preset time window
The maximum delay of communication network topology is no more than for the propagation delay time and propagation delay of the mandatory mission bit stream, bandwidth is about
Beam can hold for the mandatory mission bit stream data volume sum that can be transmitted in communication link simultaneously without departing from communication network topology
The maximum bandwidth received, information source is constrained to supply energy of the mandatory mission bit stream data volume without departing from information source that information source is sent
Power, it is that each mandatory mission bit stream only one of which information source, each mandatory mission bit stream only have one to access unique constraints
Individual information sink, any one node forward the number of times of same mandatory mission bit stream to be less than or equal to 1.Certainly, constraints is also
Including there is no mandatory mission bit stream in task pool.
Above-mentioned constraints can be represented with below equation：
ETt≤lt,t∈T
ETt≥et,t∈T
ETt-STt≤D,t∈T
The implication of each parameter in above formula is hereinbefore described in detail.
In the specific implementation, the design standard of initial distribution prediction scheme can be selected according to actual conditions, for example, initial
Information distribution prediction scheme is the weighted value of each mission bit stream to be distributed in the task pool for do not include level Four mission bit stream with maximization
The distribution approach that sum is set up for target.
In the specific implementation, distribution of the coding method to the mission bit stream to be distributed in task pool is used in above-mentioned steps B
The detailed process for obtaining initial solution with transitive attribute initialization can include：
B1, using coding method by the weight-normality draw solution to model be encoded on chromosome, the chromosome include with appoint
The one-to-one gene of mission bit stream to be distributed in business pond；
It will be appreciated that the quantity of mission bit stream to be distributed is identical with the number of gene on chromosome, a gene pairs should
One mission bit stream to be distributed.
For example, the quantity n of information to be distributed is made into the intragentic quantity of chromosome, gene is by the way of multi-component system
Encoded, m represents the node total number amount in communication network topology, basic coded system is as follows：
Gene=(Flag, Node1,Node2,...,Nodem,Time1,Time2,…,Timem, Priority, Profit,
Cost)
Wherein, Flag represents whether information to be distributed can be distributed, Node1,Node2,...NodemRepresent information to be distributed
The node passed through during forwarding, Node1Represent the information source of information to be distributed, NodemThe information sink of information to be distributed is represented,
Time1,Time2,…,TimemRepresent forwarding time of the information to be distributed in corresponding node, Time1Represent information to be distributed from letter
Breath source starts delivery time, TimemAt the time of representing that information border to be distributed reaches information sink；Priority represents task to be distributed
The priority level of information, Profit represents to complete the interests that can be obtained after the distribution and transmission of mission bit stream to be distributed；
Cost represent unfinished mission bit stream to be distributed distribution and transmission tasks produced by disturbance cost.
B2, by chromosome each gene first mark be set to 1, be set to 1 first mark characterize the gene it is corresponding
Mission bit stream to be distributed is that can be distributed and transmit；
It will be appreciated that the first mark here is above-mentioned Flag, the first mark is set to 1 mark is corresponding to be treated point
Hair mission bit stream can be allocated and transmit.
B3, the destination node for obtaining each mission bit stream to be distributed, priority value, financial value and disturbance value at cost, and be directed to
Each mission bit stream to be distributed generates a source node different from its destination node at random；
It will be appreciated that because destination node is different from source node, therefore Node1≠Nodem。
B4, judge each mission bit stream to be distributed whether need forwarding；Mission bit stream to be distributed for needing forwarding, with
Machine generates multiple different forward node, forms forward-path；For the mission bit stream to be distributed that need not be forwarded, it is forwarded
Node is set to -1；
It will be appreciated that for the mission bit stream to be distributed that need not be forwarded, making Node2=Node3=...=Nodem-1
=-1.
It will be appreciated that for the mission bit stream to be distributed for needing to forward, random forwarding number of times c<=m-2, will give birth at random
Into c forward node number record to Node2…,Nodem-1, and ensure Node1 ≠ Node2≠…≠Nodem。
B5, the time window for reading each mission bit stream to be distributed；For each mission bit stream to be distributed, in the time
One moment point of generation, and at the time of using the moment point as mission bit stream arrival destination node to be distributed at random in window；
For the mission bit stream to be distributed for needing to forward, mission bit stream to be distributed is extrapolated according to forward-path and reaches each forward node
At the time of and from source node send at the time of；For the mission bit stream to be distributed that need not be forwarded, task to be distributed is extrapolated
Information from source node send at the time of, and the forwarding moment of each forward node is set to -1；
B6, by the first mark of each mission bit stream to be distributed, source node, forward node, destination node, send from source node
At the time of, at the time of reach each forward node, at the time of reach the destination node, priority value, financial value and disturb into
Distribution and transitive attribute of this value as the mission bit stream to be distributed, distribution and the transitive attribute shape of each mission bit stream to be distributed
Into initial solution.
For example, as shown in figure 1, forming a chromosome by 5 genes, by taking first gene as an example, (1,1, -1,
2,9.5, -1,12.5,3,10,5) represent first information priorities to be distributed not be 3, from number be 1 information source be sent to volume
Number be 2 information sink, centre without forwarding.The transmission time be the 9.5th second arrival time be the 12.5th second.Distribution is completed with passing
The income obtained after passing is 10, if task is not completed, it is necessary to which the disturbance cost paid is 5.
In the specific implementation, as shown in Fig. 2 being asked in above-mentioned steps C using genetic algorithm the heavy plan model
The detailed process of solution can include：
S1, setting iterations k initial value are 1；
S2, by the object function of the heavy plan model be fitness function, calculate initial population in chromosome adaptation
Spend functional value；
S3, selected using roulette wheel selection from parent colony in fitness function value highest predetermined number dyeing
Body is genetic in progeny population；
It will be appreciated that the basic thought of so-called roulette wheel selection is：The selected probability of each chromosome is fitted with it
Response functional value size is directly proportional.The fitness function value fitness of chromosome is calculated according to fitness function, dyeing is calculated
Ratio relativefitness=fitness./sum shared by individual fitness summation of the body individual in population
(fitness), as be selected heredity to follow-on probability, ratio is bigger, then be chosen heredity to follow-on probability just
It is bigger.
S4, single-point crossover operation two-by-two is carried out to the chromosome in population；
It will be appreciated that using single-point interleaved mode, that is, a crosspoint is randomly generated, successively will be two neighboring in population
The part that chromosome is located at after the point is exchanged with each other, and generates two new chromosomes.
S5, the chromosome obtained to crossover operation carry out resetting variation processing；
S6, the chromosome obtained to resetting variation processing are updated operation, specially by fitness in progeny population most
The chromosomal of the second minimum predetermined number of fitness in the chromosome and progeny population of the first low predetermined number, is formed
New population；
For example, the progeny population after variation is arranged by the ascending order of fitness value, SonNum dye before taking out
Colour solid, is arranged by the descending of fitness value parent colony, FatherNum chromosome after taking-up, constitutes new population.
S7, judge whether current iterations reaches default maximum iteration kmax；
If so, then regarding the corresponding solution of new population obtained in last time iterative process as the heavy programme；
Otherwise, using the new population as initial population, iterations adds 1, and returns to S2.
Here, by the operation such as being selected chromosome, being intersected, made a variation, it is used as weight-normality to draw side obtained chromosome
Case.Said process is iterative process, and only iterations reaches default iterations, can just exited, be that last
The corresponding solution of new population is used as weight programme in secondary iterative process.
In the specific implementation, the chromosome obtained in S6 to resetting variation processing is updated before operation, the side
Method also includes：
To resetting whether the corresponding distribution of gene on the chromosome after variation processing meets described preset about with transitive attribute
Beam condition；
Operated if so, then performing described update；
Otherwise, after being adjusted the fitness function value for resetting variation processing after stain colour solid described update is performed to operate.
Need to meet the constraint such as the bandwidth of communication network topology, time delay, time window and information source in view of information to be distributed,
Therefore constraint checking also is carried out to chromosome here.For failing the chromosome by constraint checking, in its fitness function value
On increase on demand or subtract penalty factor, degree of adapting it to functional value diminishes or become greatly, is unsatisfactory in selection operation with removing
The chromosome of given constraint.
In the specific implementation, the chromosome obtained in above-mentioned S5 to crossover operation reset the detailed process of variation processing
It can include：
A random number between 0 and 1 is generated, if the random number is less than default mutation probability, according to institute
State the generation method generation item chromosome Newchrom of initial solution；
Wherein, default mutation probability is between zero and one.
A random number between 0 and 1 is generated, if the random number is less than default mutation probability, according to institute
State the generation method generation item chromosome Newchrom of initial solution；
Second aspect, the present invention also provides a kind of computer-readable recording medium, is stored thereon with computer program, and it is special
Levy and be, the step of program realizes the above method when being executed by processor.
The third aspect, the present invention also provides a kind of computer equipment, including memory, processor and is stored in memory
Computer program that is upper and can running on a processor, it is characterised in that real during computer program described in the computing device
Now such as the step of above-mentioned method.
It will be appreciated that computer-readable recording medium, the beneficial effect of computer equipment and this hair that the present invention is provided
The beneficial effect of the unmanned plane formation information distribution weight planing method of bright offer is identical, repeats no more here.
In summary, the unmanned plane formation information that the present invention is provided distributes weight planing method, computer-readable recording medium
And computer equipment, when the level Four mission bit stream in task pool is distributed completion, or, prediction scheme is distributed based on initial information,
When the income sum for not including three-level mission bit stream in the task pool of level Four mission bit stream is more than disturbance cost sum, call in advance
The heavy plan model of foundation, weight programme is determined using weight plan model, with ensure that priority level is higher in task pool three
The total revenue of level mission bit stream and second task information is maximum, realizes the reasonable arrangement to mission bit stream to be distributed in task pool,
Optimal information is formed to distribute and translation sequence.
Finally it should be noted that：Various embodiments above is rather than right only to the technical scheme for illustrating embodiments of the invention
It is limited；Although embodiments of the invention are described in detail with reference to foregoing embodiments, the ordinary skill of this area
Personnel should be understood：It can still modify to the technical scheme described in foregoing embodiments, or to which part
Or all technical characteristic carries out equivalent substitution；And these modifications or replacement, do not make the essence disengaging of appropriate technical solution
The scope of each embodiment technical scheme of embodiments of the invention.
Claims (10)
1. a kind of unmanned plane formation information distribution weight planing method, it is characterised in that including：
When the level Four mission bit stream in task pool is distributed completion, or, prediction scheme is distributed based on initial information, not including level Four
When the income sum of three-level mission bit stream is more than disturbance cost sum in the task pool of mission bit stream, the weight-normality pre-established is called
Draw model；The optimization aim of the heavy plan model is the three-level mission bit stream in maximizing task pool under default constraints
With the total revenue of second task information；
The distribution of the mission bit stream to be distributed in task pool is initialized with transitive attribute using coding method, initial solution is obtained；
Based on the initial solution, the heavy plan model is solved using genetic algorithm, obtained to the task to be distributed
Information distributes the heavy programme with transmission；
The mission bit stream to be distributed is distributed and transmitted according to the heavy programme；
Wherein, the significance level of the level Four mission bit stream, the three-level mission bit stream and the second task information drops successively
It is low, and the level Four mission bit stream is mandatory mission bit stream.
2. according to the method described in claim 1, it is characterised in that the initial information distribution prediction scheme is not wrapped with maximizing
Include the distribution approach that the weighted value sum of the mission bit stream to be distributed of each in the task pool of level Four mission bit stream is set up for target.
3. according to the method described in claim 1, it is characterised in that the use coding method is to be distributed in task pool
The distribution for information of being engaged in is initialized with transitive attribute, obtains initial solution, including：
The weight-normality, which is drawn solution to model and is encoded on chromosome, the chromosome, using coding method is included with being treated in task pool
The one-to-one gene of distributed tasks information；
By on chromosome each gene first mark be set to 1, be set to 1 first mark characterize the gene it is corresponding to be distributed
Business information is that can be distributed and transmit；
Destination node, priority value, financial value and the disturbance value at cost of each mission bit stream to be distributed are obtained, and is treated for each
Distributed tasks information generates a source node different from its destination node at random；
Judge whether each mission bit stream to be distributed needs forwarding；For the mission bit stream to be distributed for needing to forward, random generation
Multiple different forward node, form forward-path；For the mission bit stream to be distributed that need not be forwarded, it is forwarded node and puts
For -1；
Read the time window of each mission bit stream to be distributed；For each mission bit stream to be distributed, in the time window with
Machine generates a moment point, and at the time of using the moment point as mission bit stream arrival destination node to be distributed；For needing
The mission bit stream to be distributed to be forwarded, at the time of extrapolating mission bit stream to be distributed according to forward-path and reach each forward node
And from source node send at the time of；For the mission bit stream to be distributed that need not be forwarded, extrapolate mission bit stream to be distributed from
At the time of source node is sent, and the forwarding moment of each forward node is set to -1；
By the first mark of each mission bit stream to be distributed, source node, forward node, destination node, from source node send at the time of,
At the time of reaching each forward node, at the time of reach the destination node, priority value, financial value and disturbance value at cost conduct
The distribution of the mission bit stream to be distributed and transitive attribute, distribution and the transitive attribute of each mission bit stream to be distributed are formed initially
Solution.
4. according to the method described in claim 1, it is characterised in that the use genetic algorithm is carried out to the heavy plan model
Solve, including：
S1, setting iterations k initial value are 1；
S2, by the object function of the heavy plan model be fitness function, calculate initial population in chromosome fitness letter
Numerical value；
S3, selected using roulette wheel selection from parent colony in fitness function value highest predetermined number chromosome lose
Pass in progeny population；
S4, single-point crossover operation two-by-two is carried out to the chromosome in population；
S5, the chromosome obtained to crossover operation carry out resetting variation processing；
S6, the chromosome obtained to resetting variation processing are updated operation, are specially that fitness in progeny population is minimum
The chromosomal of the second minimum predetermined number of fitness in the chromosome and progeny population of first predetermined number, is formed newly
Population；
S7, judge whether current iterations reaches default maximum iteration kmax；
If so, then regarding the corresponding solution of new population obtained in last time iterative process as the heavy programme；
Otherwise, using the new population as initial population, iterations adds 1, and returns to S2.
5. method according to claim 4, it is characterised in that the chromosome progress that variation processing is obtained is reset at described pair
Update before operation, methods described also includes：Belong to resetting the corresponding distribution of gene on the chromosome after variation processing with transmission
Whether property meets the default constraints；Operated if so, then performing described update；Otherwise, to resetting variation processing poststaining
The fitness function value of body performs described update and operated after being adjusted.
6. method according to claim 4, it is characterised in that the chromosome obtained to crossover operation carries out replacement change
Different processing, including：A random number between 0 and 1 is generated, if the random number is less than default mutation probability, root
Item chromosome is generated according to the generation method of the initial solution；Item chromosome is randomly choosed in progeny population, and uses basis
The chromosome of the generation of the initial solution substitutes randomly selected chromosome, and other chromosomes keep constant.
7. according to any described method of claim 1~6, it is characterised in that the object function of the heavy plan model is：
<mrow>
<mi>M</mi>
<mi>a</mi>
<mi>x</mi>
<mi>Z</mi>
<mo>=</mo>
<munder>
<mo>&Sigma;</mo>
<mrow>
<mi>t</mi>
<mo>&Element;</mo>
<msup>
<mi>T</mi>
<mi>b</mi>
</msup>
<mo>&cup;</mo>
<msup>
<mi>T</mi>
<mi>c</mi>
</msup>
</mrow>
</munder>
<msubsup>
<mi>x</mi>
<mrow>
<mi>i</mi>
<mi>j</mi>
</mrow>
<mi>t</mi>
</msubsup>
<msub>
<mi>H</mi>
<mi>t</mi>
</msub>
</mrow>
In formula, Z is the three-level mission bit stream and the total revenue of second task information in task pool；T represents that any one is to be distributed
Information；TbRepresent three-level mission bit stream set；TcTable second task information aggregate；Decision variable1 or 0 is taken, takes 1 to represent to treat point
Photos and sending messages t is sent to node j from node i, takes 0 expression information t to be distributed not to be sent to node j from node i；HtRepresent that completion is treated
Distribute obtainable income after information t task.
8. according to any described method of claim 1~6, it is characterised in that the default constraints includes：
<mrow>
<msub>
<mi>ET</mi>
<mi>t</mi>
</msub>
<mo>=</mo>
<msub>
<mi>ST</mi>
<mi>t</mi>
</msub>
<mo>+</mo>
<munder>
<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
<mo>,</mo>
<mi>j</mi>
<mo>&Element;</mo>
<mi>V</mi>
</mrow>
</munder>
<mrow>
<mo>(</mo>
<msubsup>
<mi>ct</mi>
<mrow>
<mi>i</mi>
<mi>j</mi>
</mrow>
<mi>t</mi>
</msubsup>
<mo>+</mo>
<msubsup>
<mi>ft</mi>
<mrow>
<mi>i</mi>
<mi>j</mi>
</mrow>
<mi>t</mi>
</msubsup>
<mo>)</mo>
</mrow>
<mo>,</mo>
<mi>t</mi>
<mo>&Element;</mo>
<mi>T</mi>
</mrow>
ETt≤lt,t∈T
ETt≥et,t∈T
ETt-STt≤D,t∈T
<mrow>
<msubsup>
<mi>&Sigma;x</mi>
<mrow>
<mi>i</mi>
<mi>j</mi>
</mrow>
<mi>t</mi>
</msubsup>
<msub>
<mi>TW</mi>
<mi>t</mi>
</msub>
<mo>&le;</mo>
<msub>
<mi>NW</mi>
<mrow>
<mi>i</mi>
<mi>j</mi>
</mrow>
</msub>
<mo>,</mo>
<mi>i</mi>
<mo>,</mo>
<mi>j</mi>
<mo>&Element;</mo>
<mi>V</mi>
<mo>,</mo>
<mi>t</mi>
<mo>&Element;</mo>
<mi>T</mi>
</mrow>
<mrow>
<mo>&Sigma;</mo>
<mo>&Sigma;</mo>
<mrow>
<mo>(</mo>
<msubsup>
<mi>x</mi>
<mrow>
<mi>v</mi>
<mi>j</mi>
</mrow>
<mi>t</mi>
</msubsup>
<mo>&CenterDot;</mo>
<msub>
<mi>TW</mi>
<mi>t</mi>
</msub>
<mo>)</mo>
</mrow>
<mo>&le;</mo>
<msub>
<mi>B</mi>
<mi>v</mi>
</msub>
<mo>,</mo>
<mi>v</mi>
<mo>&Element;</mo>
<mi>V</mi>
</mrow>
<mrow>
<msub>
<msubsup>
<mi>&Sigma;x</mi>
<mi>v</mi>
<mi>t</mi>
</msubsup>
<mi>j</mi>
</msub>
<mo>&le;</mo>
<mn>1</mn>
<mo>,</mo>
<mi>t</mi>
<mo>&Element;</mo>
<mi>T</mi>
<mo>,</mo>
<mi>v</mi>
<mo>&Element;</mo>
<mi>V</mi>
</mrow>
<mrow>
<munder>
<mo>&Sigma;</mo>
<mrow>
<mi>t</mi>
<mo>&Element;</mo>
<msup>
<mi>T</mi>
<mi>a</mi>
</msup>
</mrow>
</munder>
<msubsup>
<mi>x</mi>
<mrow>
<mi>i</mi>
<mi>j</mi>
</mrow>
<mi>t</mi>
</msubsup>
<mo>=</mo>
<mn>0</mn>
</mrow>
In formula, ETtAt the time of representing that information t to be distributed is actually reached information sink；STtRepresent that information t to be distributed is actual from information source
Start delivery time；V={ 1,2 ..., m } represents communication network topology interior joint set, and m represents the total node of communication network topology
Number；Represent that information t to be distributed is delivered to the propagation delay time of node j generations from node i；Represent information t to be distributed from node
I is delivered to the propagation delay of node j generations；T is mission bit stream set to be distributed, and level Four mission bit stream is not included in the set；
ltRepresent that information t to be distributed reaches the time of information sink at the latest；etRepresent information t to be distributed earliest information sink arrival times；D tables
Show acceptable maximum delay in communication network topology；TWtRepresent the bandwidth required for information t to be distributed；NWijRepresent communication network
Directed edge in network topology<i,j>The maximum bandwidth that can bear；BvThe maximum amount of data that node v can be provided is represented, v represents logical
Any node in communication network topology, v ∈ V；Decision variable1 or 0 is taken, wherein, take 1 expression information t to be distributed to be sent out from node i
Node j is sent to, takes 0 expression information t to be distributed not to be sent to node j from node i；TaRepresent level Four mission bit stream.
9. a kind of computer-readable recording medium, is stored thereon with computer program, it is characterised in that the program is held by processor
The step of methods described as any such as claim 1~8 is realized during row.
10. a kind of computer equipment, including memory, processor and storage can be run on a memory and on a processor
Computer program, it is characterised in that realized described in the computing device during computer program such as any institute of claim 1~8
The step of stating method.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710415126.9A CN107229285B (en) | 2017-06-05 | 2017-06-05 | Unmanned aerial vehicle formation information distribution re-planning method and computer readable storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710415126.9A CN107229285B (en) | 2017-06-05 | 2017-06-05 | Unmanned aerial vehicle formation information distribution re-planning method and computer readable storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN107229285A true CN107229285A (en) | 2017-10-03 |
| CN107229285B CN107229285B (en) | 2020-03-31 |
Family
ID=59934714
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710415126.9A Active CN107229285B (en) | 2017-06-05 | 2017-06-05 | Unmanned aerial vehicle formation information distribution re-planning method and computer readable storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107229285B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108398958A (en) * | 2018-03-14 | 2018-08-14 | 广州亿航智能技术有限公司 | Unmanned plane formation route matching method, apparatus and storage medium |
| CN111104561A (en) * | 2019-11-05 | 2020-05-05 | 合肥工业大学 | Heuristic unmanned platform information perception network topology generation method and device |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101286071A (en) * | 2008-04-24 | 2008-10-15 | 北京航空航天大学 | Multiple no-manned plane three-dimensional formation reconfiguration method based on particle swarm optimization and genetic algorithm |
| CN102298329A (en) * | 2011-08-19 | 2011-12-28 | 北京航空航天大学 | Small-size unmanned rotary wing aircraft dynamic model identification method based on adaptive genetic algorithm |
| CN104359473A (en) * | 2014-10-24 | 2015-02-18 | 南京航空航天大学 | Collaborative flight path intelligent planning method for formation flying of unmanned planes under dynamic environment |
| US9056679B1 (en) * | 2013-08-13 | 2015-06-16 | The United States Of America As Represented By The Secretary Of The Navy | System and method for airborne deployment of object designed for waterborne task |
| CN105223964A (en) * | 2015-09-28 | 2016-01-06 | 沈阳航空航天大学 | A kind of control system for UAV Formation Flight |
| KR20160082627A (en) * | 2014-12-26 | 2016-07-08 | 서울대학교산학협력단 | Market-based decentralized task assignment method for UAV and recording medium recorded program to perform the method |
-
2017
- 2017-06-05 CN CN201710415126.9A patent/CN107229285B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101286071A (en) * | 2008-04-24 | 2008-10-15 | 北京航空航天大学 | Multiple no-manned plane three-dimensional formation reconfiguration method based on particle swarm optimization and genetic algorithm |
| CN102298329A (en) * | 2011-08-19 | 2011-12-28 | 北京航空航天大学 | Small-size unmanned rotary wing aircraft dynamic model identification method based on adaptive genetic algorithm |
| US9056679B1 (en) * | 2013-08-13 | 2015-06-16 | The United States Of America As Represented By The Secretary Of The Navy | System and method for airborne deployment of object designed for waterborne task |
| CN104359473A (en) * | 2014-10-24 | 2015-02-18 | 南京航空航天大学 | Collaborative flight path intelligent planning method for formation flying of unmanned planes under dynamic environment |
| KR20160082627A (en) * | 2014-12-26 | 2016-07-08 | 서울대학교산학협력단 | Market-based decentralized task assignment method for UAV and recording medium recorded program to perform the method |
| CN105223964A (en) * | 2015-09-28 | 2016-01-06 | 沈阳航空航天大学 | A kind of control system for UAV Formation Flight |
Non-Patent Citations (4)
| Title |
|---|
| HU, XIAOXUAN .ET: "Task Assignment for Multi-UAV under Severe Uncertainty by Using Stochastic Multicriteria Acceptability Analysis", 《MATHEMATICAL PROBLEMS IN ENGINEERING》 * |
| 张延松: "基于遗传算法的无人机航迹规划研究", 《中国优秀硕士学位论文全文数据库工程科技Ⅱ辑》 * |
| 罗贺等: "基于Agent的多无人机任务分配模型", 《火力与指挥控制》 * |
| 苏菲: "动态环境下多UCAV分布式在线协同任务规划技术研究", 《中国博士学位论文全文数据库工程科技Ⅱ辑》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108398958A (en) * | 2018-03-14 | 2018-08-14 | 广州亿航智能技术有限公司 | Unmanned plane formation route matching method, apparatus and storage medium |
| CN108398958B (en) * | 2018-03-14 | 2021-04-23 | 广州亿航智能技术有限公司 | Unmanned aerial vehicle formation path matching method and device and storage medium |
| CN111104561A (en) * | 2019-11-05 | 2020-05-05 | 合肥工业大学 | Heuristic unmanned platform information perception network topology generation method and device |
| CN111104561B (en) * | 2019-11-05 | 2022-09-27 | 合肥工业大学 | Heuristic unmanned platform information-aware network topology generation method and device |
Also Published As
| Publication number | Publication date |
|---|---|
| CN107229285B (en) | 2020-03-31 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107168366A (en) | Unmanned plane formation adaptive information distribution processing method | |
| CN106487005B (en) | A kind of Electric power network planning method considering T-D tariff | |
| Pardalos et al. | Pareto optimality, game theory and equilibria | |
| CN108632831A (en) | A kind of unmanned aerial vehicle group frequency spectrum resource allocation method based on dynamic flight path | |
| CN104842564B (en) | A kind of 3 D-printing multitask Optimization Scheduling based on NSGA II | |
| CN107706921A (en) | Micro-capacitance sensor voltage adjusting method and device based on Nash Game | |
| CN104077634B (en) | active-reactive type dynamic project scheduling method based on multi-objective optimization | |
| Geng et al. | Research on Self‐Organization in Resilient Recovery of Cluster Supply Chains | |
| CN107229285A (en) | Unmanned plane formation information distribution weight planing method, computer-readable recording medium | |
| Vijayaraj et al. | Multi-area economic dispatch using flower pollination algorithm | |
| CN107890675A (en) | AI behaviors implementation method and device | |
| CN103995749B (en) | The calculation task allocating method and system of cell cloud system | |
| CN104392317B (en) | A kind of project scheduling method based on hereditary cultural gene algorithm | |
| CN106686681A (en) | Cluster head satellite selection method and device | |
| CN110048411B (en) | Micro-grid networking method, device and equipment based on multiport electric energy router | |
| Zhang et al. | Combining extended imperialist competitive algorithm with a genetic algorithm to solve the distributed integration of process planning and scheduling problem | |
| CN107247461A (en) | Consider bursty interference nobody have man-machine formation information distribution processing method | |
| CN110533279A (en) | For dispatching the method, system and storage medium of cloud logistics platform transport power | |
| CN107229286A (en) | Consider manual intervention nobody have man-machine formation information distribution processing method | |
| CN104268639A (en) | Ship piloting and scheduling method based on forest model and improved genetic algorithm | |
| CN115390946A (en) | Mine accident rescue task unloading method based on mobile edge calculation | |
| CN108539799A (en) | The dispatching method and device of wind-powered electricity generation in a kind of power grid | |
| CN114021914A (en) | Unmanned aerial vehicle cluster flight control method and device | |
| CN109934411B (en) | Emergency disaster relief intelligent path planning method and device considering emergency degree | |
| CN104660525B (en) | Bandwidth allocation method, controller and communication system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |