CN109204391A - A kind of target velocity curve based on multiobjective decision-making determines method - Google Patents
A kind of target velocity curve based on multiobjective decision-making determines method Download PDFInfo
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- CN109204391A CN109204391A CN201811152068.6A CN201811152068A CN109204391A CN 109204391 A CN109204391 A CN 109204391A CN 201811152068 A CN201811152068 A CN 201811152068A CN 109204391 A CN109204391 A CN 109204391A
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B61—RAILWAYS
- B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
- B61L27/00—Central railway traffic control systems; Trackside control; Communication systems specially adapted therefor
- B61L27/20—Trackside control of safe travel of vehicle or vehicle train, e.g. braking curve calculation
Abstract
The embodiment of the invention discloses a kind of target velocity curves based on multiobjective decision-making to determine method, comprising: determines multiobjective decision-making variable according to target requirement, and establishes Multi-objective Decision Model according to the multiobjective decision-making variable；The Multi-objective Decision Model is solved according to the objective function of the Multi-objective Decision Model and constraint condition, obtains the solution space of the Multi-objective Decision Model；According to train operation demand from the solution space selection target solution as target velocity curve.The embodiment of the present invention is by selecting different decision variables, establish different objective functions, different constraint condition is set, in conjunction with different train operation demands, met the target velocity curve of multiple targets simultaneously, to make train in ATO control, under conditions of guaranteeing safe train operation, the purpose of reaching reduction train delays, improving stopping accuracy, reduce operation energy consumption.
Description
Technical field
The present embodiments relate to technical field of rail traffic, and in particular to a kind of target velocity based on multiobjective decision-making
Curve determines method.
Background technique
With the fast development of rail traffic, having ATO, (Automatic Train Operation, train are controlled automatically
System) train operation control system of control function is increasingly becoming standard configuration.ATO control process is ATO according to train current state meter
Calculating control amount makes the process of train speed tracking target velocity, which needs while satisfaction is punctual, stops quasi-, comfortable, energy conservation, line
Multiple requirements such as road speed limit.Therefore, target velocity is to ensure the premise of ATO performance.
The calculating of target velocity is mainly according to MA (Movement Authority, mobile authorization) terminal in the prior art
Meet the target velocity curve being safely operated to calculate one, but the curve can not guarantee other demands simultaneously, as train is transported
Row punctuality, reduction operation energy consumption etc..
Summary of the invention
Since existing method is there are the above problem, the embodiment of the present invention proposes a kind of target velocity based on multiobjective decision-making
Curve determines method.
A kind of target velocity curve based on multiobjective decision-making that the embodiment of the present invention proposes determines method, comprising:
Multiobjective decision-making variable is determined according to target requirement, and multiobjective decision-making is established according to the multiobjective decision-making variable
Model；
The Multi-objective Decision Model is asked according to the objective function of the Multi-objective Decision Model and constraint condition
Solution, obtains the solution space of the Multi-objective Decision Model；
According to train operation demand from the solution space selection target solution as target velocity curve.
Optionally, described that Multi-objective Decision Model is established according to the multiobjective decision-making variable, it specifically includes:
The corresponding constraint condition of each objective decision variable is determined according to the multiobjective decision-making variable, and according to more mesh
Mark decision variable and the corresponding constraint condition of each objective decision variable establish Multi-objective Decision Model.
Optionally, the objective function and constraint condition according to the Multi-objective Decision Model is to the multiobjective decision-making
Model is solved, and is obtained the solution space of the Multi-objective Decision Model, is specifically included:
The Multi-objective Decision Model is asked according to the objective function of the Multi-objective Decision Model and constraint condition
Solution, obtains the multiobjective decision-making Pareto solution space of the Multi-objective Decision Model.
Optionally, the method for solving the multiobjective decision-making Pareto solution space include: Objective Programming, genetic algorithm or
Particle swarm algorithm.
Optionally, the objective function and constraint condition according to the Multi-objective Decision Model is to the multiobjective decision-making
Model is solved, and is obtained the solution space of the Multi-objective Decision Model, is specifically included:
If the Multi-objective Decision Model includes several objective functions and constraint condition, need to meet all constraint items
In the case where part, several described objective functions are optimized simultaneously, obtain the solution space of the Multi-objective Decision Model.
Optionally, the objective function min f (X) includes: min f (X)=min (f1(x), f2(x), f3(x),f4
(x)), wherein f1(x)=t-t0, t0To plan runing time, x is the independent variable of objective function, and t indicates the time；f2(x)=Δ
At a distance from d, Δ d indicate train stopping position between target stop；f3(x)=Σ Δ a/t, f3(x) rate of acceleration change is indicated
The sum of, wherein rate of acceleration change Δ a/t can be used to indicate comfort level, and a indicates that acceleration, Δ a indicate the variation of acceleration, t
Indicate the time；f4(x)=Σ e, f4(x) accumulation energy consumption is indicated, e indicates energy consumption；
The constraint condition g (X) includes: g (X)=(g1(X),g2(X),g3(X)) >=0, wherein g1(X)=Vf-V-δ1, g2
(X)=Vtsr-V-δ2, g3(X)=Vmax-V-δ3, V expression train speed, VfIndicate the fixed speed limit of route, VtsrIndicate interim limit
Speed, VmaxIndicate train Maximum speed limit, δ1, δ2, δ3Indicate speed allowance, the purpose that the speed allowance is arranged is to prevent train fast
Degree is more than speed limit, and the speed allowance is positively correlated with train interval runing time；
Independent variable X in the objective function and constraint condition can by the speed of train different location in section,
Siding-to-siding block length and section runing time composition.
Optionally, the constraint condition further includes on time, stops quasi-, comfortable and energy-efficient any combination.
Optionally, the multiobjective decision-making variable includes that section runing time, stopping accuracy, comfort level, energy consumption, route are solid
Fixed limit speed, temporary speed limitation and train Maximum speed limit.
Optionally, the train operation demand includes: that the section runing time requirement of train or the stopping accuracy of train are wanted
It asks.
As shown from the above technical solution, the embodiment of the present invention establishes different targets by selecting different decision variables
Different constraint condition is arranged in function, in conjunction with different train operation demands, is met the target velocity of multiple targets simultaneously
Curve under conditions of guaranteeing safe train operation, reaches reduction train delays, raising stops to make train in ATO control
The purpose of vehicle precision, reduction operation energy consumption.
Detailed description of the invention
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below
There is attached drawing needed in technical description to be briefly described, it should be apparent that, the accompanying drawings in the following description is only this
Some embodiments of invention for those of ordinary skill in the art without creative efforts, can be with
Other attached drawings are obtained according to these figures.
Fig. 1 is a kind of stream that method is determined based on the target velocity curve of multiobjective decision-making that one embodiment of the invention provides
Journey schematic diagram.
Specific embodiment
With reference to the accompanying drawing, further description of the specific embodiments of the present invention.Following embodiment is only used for more
Technical solution of the present invention is clearly demonstrated, and not intended to limit the protection scope of the present invention.
Fig. 1 shows a kind of process that method is determined based on the target velocity curve of multiobjective decision-making provided in this embodiment
Schematic diagram, comprising:
S101, multiobjective decision-making variable is determined according to target requirement, and more mesh are established according to the multiobjective decision-making variable
Mark decision model.
Specifically, target requirement include it is punctual, stop quasi-, comfortable, energy conservation, route speed limit etc..
Determine that multiobjective decision-making variable refers to the demand that must satisfy when calculating target velocity curve and meets as far as possible
Demand, may include section runing time, stopping accuracy, comfort level, energy consumption, the fixed speed limit of route, temporary speed limitation, train most
High speed limit etc..
When establishing Multi-objective Decision Model, according to identified decision variable, Multi-objective Decision Model can establish, it is described
Multi-objective Decision Model includes objective function and constraint condition, and the constraint condition is made of the decision variable met as far as possible,
And the objective function is made of section runing time, stopping accuracy, comfort level, energy consumption etc., the corresponding optimization of each decision variable
Target, which can be converted to, minimizes its objective function, then objective function can use min f (X)=min (f1(x),f2(x),……
fr(x)) it indicates, wherein fiIt (x) is the corresponding objective function of different decision variables, X is the independent variable about objective function, is such as arranged
Vehicle speed, siding-to-siding block length etc..The constraint condition is made of the decision variable met as far as possible, such as the fixed speed limit of route, temporarily
Speed limit, train Maximum speed limit etc., can use gi(X) >=0 it indicates, wherein gi(X) different speed limits is indicated, X is in constraint condition
Independent variable.
S102, according to the objective function of the Multi-objective Decision Model and constraint condition to the Multi-objective Decision Model into
Row solves, and obtains the solution space of the Multi-objective Decision Model.
Wherein, the solution space solved, it is necessary to meet constraint condition.
S103, according to train operation demand from the solution space selection target solution as target velocity curve.
Wherein, the train operation demand includes: the requirement of section runing time or the stopping accuracy requirement of train of train.
Specifically, one group of solution is chosen in the solution space acquired as target velocity curve.It, can be according to side when choosing solution
Emphasis difference is chosen, and such as requires highest to the section runing time of train, then can choose in solution space and meet section fortune
The best solution of row time demand；Highest such as is required to the stopping accuracy of train, then can choose and meet Train Stopping in solution space
The best solution of accuracy requirement.
The present embodiment decision variable different by selection, establishes different objective functions, different constraint condition is arranged,
In conjunction with different train operation demands, met the target velocity curve of multiple targets simultaneously, so that train be made to control in ATO
When, under conditions of guaranteeing safe train operation, reach reduction train delays, improve stopping accuracy, reduces the mesh such as operation energy consumption
's.
Further, it on the basis of above method embodiment, is built described in S101 according to the multiobjective decision-making variable
Vertical Multi-objective Decision Model, specifically includes:
The corresponding constraint condition of each objective decision variable is determined according to the multiobjective decision-making variable, and according to more mesh
Mark decision variable and the corresponding constraint condition of each objective decision variable establish Multi-objective Decision Model.
By the corresponding constraint condition of each objective decision variable of determination, facilitate subsequent foundation and solution multiobjective decision-making mould
Type.
Further, on the basis of above method embodiment, S102 is specifically included:
The Multi-objective Decision Model is asked according to the objective function of the Multi-objective Decision Model and constraint condition
Solution, obtains the multiobjective decision-making Pareto solution space of the Multi-objective Decision Model.
Wherein, the method for solving the multiobjective decision-making Pareto solution space includes: Objective Programming, genetic algorithm or grain
Swarm optimization.
The embodiment of the present invention is suitable for calculating ATO target velocity curve, by selecting different decision variables, establishes different
Objective function, different constraint condition is set, different method for solving, mesh that is available while meeting multiple targets are chosen
Mark rate curve.To make train in ATO control, under conditions of guaranteeing safe train operation, reach reduction train delays,
The purpose of improving stopping accuracy, reducing operation energy consumption.
Further, on the basis of above method embodiment, S102 is specifically included:
If the Multi-objective Decision Model includes several objective functions and constraint condition, need to meet all constraint items
In the case where part, several described objective functions are optimized simultaneously, obtain the solution space of the Multi-objective Decision Model.
Specifically, it when solving plurality of target function and a variety of constraint condition problems, needs to multiple targets mutually restricted
It optimizes simultaneously, therefore is not necessarily present an absolute optimal solution and multiple targets are all optimal.If X* makes about
Beam condition gi(X) >=0 it sets up, while f (x*) reaches that Pareto is optimal, then the disaggregation being made of X* is decision-making problem of multi-objective
Solution space.Under the solution space, the decision variable that must satisfy is met, at the decision variable for needing to meet as far as possible
In one " equalization point ", that the target velocity curve solved can satisfy is punctual, stops the items such as quasi-, comfortable, energy conservation, route speed limit
The target velocity curve of part demand.
For example, under the premise of considering a variety of decision objectives, when calculating the target velocity curve of ATO, including it is following
Detailed step:
Step S1: multiobjective decision-making variable in need of consideration when calculating target velocity curve is determined.To guarantee train operation
Safety, the decision variable that need to meet may include the fixed speed limit V of routef, temporary speed limitation Vtsr, train Maximum speed limit Vmax.It is protecting
It demonstrate,proves under safety condition, the decision variable for needing to meet as far as possible may include section runing time t, stopping accuracy Δ d, comfort level
Δ a/t, energy consumption e.
Step S2: the objective function and constraint condition when calculating target velocity curve are established.Objective function can be min f
(X)=min (f1(x),f2(x),f3(x),f4(x)), wherein f1(x)=t-t0, t0To plan runing time, x is objective function
Independent variable, t indicate the time；f2(x) at a distance from=Δ d, Δ d indicate train stopping position between target stop；f3(x)=
Σ Δ a/t, f3(x) the sum of rate of acceleration change is indicated, wherein rate of acceleration change Δ a/t can be used to indicate comfort level, and a is indicated
Acceleration, Δ a indicate the variation of acceleration, and t indicates the time；f4(x)=Σ e, f4(x) accumulation energy consumption is indicated, e is energy consumption.Constraint
Condition can be g (X)=(g1(X),g2(X),g3(X)) >=0, wherein g1(X)=Vf-V-δ1, g2(X)=Vtsr-V-δ2, g3(X)
=Vmax-V-δ3, V expression train speed, VfIndicate the fixed speed limit of route, VtsrIndicate temporary speed limitation, VmaxIndicate train highest limit
Speed, δ1, δ2, δ3Indicate speed allowance, the purpose that the speed allowance is arranged is to prevent train speed more than speed limit, the speed
Allowance is positively correlated with train interval runing time；Independent variable X in the objective function and constraint condition can be existed by train
Speed in section at different location, siding-to-siding block length and section runing time composition.
Step S3: according to identified objective function and constraint condition, the target velocity collection of curves for the condition that meets is solved.
It can be using Objective Programming, genetic algorithm, particle swarm algorithm etc. when solution.
Step S4: different target velocity curves is generated according to different emphasis.It is empty in the Pareto solution being calculated
Between carried out choosing different disaggregation X* according to emphasis difference, utilize speed of the train for including in X* in section at different location
Degree is used as target velocity curve.Highest such as is required to the section runing time of train, then can choose and meet section in solution space
The best solution of runing time demand；Highest such as is required to the stopping accuracy of train, then can be chosen in solution space and be met train and stop
The best solution of vehicle accuracy requirement；Such as need comfort level during train best, then can choose makes Comfortability of Train in solution space
Best solution；It such as needs to keep energy consumption in train journey minimum, then can choose the solution for keeping energy consumption in train journey minimum in solution space；Such as
It needs to meet multiple conditions simultaneously, then can choose some solution makes multiple conditions reach one " equalization point ".
Compared with prior art, method provided in this embodiment has the advantage that first, is calculating target velocity curve
When consider plurality of target, can make generate target velocity curve and meanwhile meet multiple targets；Second, can in different routes,
It is bent to generate target velocity not of the same race by the way that different objective function and constraint condition is arranged under varying environment for different weather situation
Line has stronger portability.Third can choose the target velocity curve of different emphasis, in solution space to adapt to
Different demands, the present invention have stronger adaptability.
Through the above description of the embodiments, those skilled in the art can be understood that each embodiment can
It realizes by means of software and necessary general hardware platform, naturally it is also possible to pass through hardware.Based on this understanding, on
Stating technical solution, substantially the part that contributes to existing technology can be embodied in the form of software products in other words, should
Computer software product may be stored in a computer readable storage medium, such as ROM/RAM, magnetic disk, CD, including several fingers
It enables and using so that a computer equipment (can be personal computer, server or the network equipment etc.) executes each implementation
Method described in certain parts of example or embodiment.
It is noted that the above embodiments are merely illustrative of the technical solutions of the present invention, rather than its limitations；Although reference
Invention is explained in detail for previous embodiment, those skilled in the art should understand that: it still can be right
Technical solution documented by foregoing embodiments is modified or equivalent replacement of some of the technical features；And this
It modifies or replaces, the spirit and model of technical solution of various embodiments of the present invention that it does not separate the essence of the corresponding technical solution
It encloses.
Claims (9)
1. a kind of target velocity curve based on multiobjective decision-making determines method characterized by comprising
Multiobjective decision-making variable is determined according to target requirement, and multiobjective decision-making mould is established according to the multiobjective decision-making variable
Type；
The Multi-objective Decision Model is solved according to the objective function of the Multi-objective Decision Model and constraint condition, is obtained
To the solution space of the Multi-objective Decision Model；
According to train operation demand from the solution space selection target solution as target velocity curve.
2. the method according to claim 1, wherein described establish multiple target according to the multiobjective decision-making variable
Decision model specifically includes:
The corresponding constraint condition of each objective decision variable is determined according to the multiobjective decision-making variable, and is determined according to the multiple target
Plan variable and the corresponding constraint condition of each objective decision variable establish Multi-objective Decision Model.
3. the method according to claim 1, wherein the objective function according to the Multi-objective Decision Model
The Multi-objective Decision Model is solved with constraint condition, obtains the solution space of the Multi-objective Decision Model, it is specific to wrap
It includes:
The Multi-objective Decision Model is solved according to the objective function of the Multi-objective Decision Model and constraint condition, is obtained
To the multiobjective decision-making Pareto solution space of the Multi-objective Decision Model.
4. according to the method described in claim 3, it is characterized in that, the method for solving the multiobjective decision-making Pareto solution space
It include: Objective Programming, genetic algorithm or particle swarm algorithm.
5. the method according to claim 1, wherein the objective function according to the Multi-objective Decision Model
The Multi-objective Decision Model is solved with constraint condition, obtains the solution space of the Multi-objective Decision Model, it is specific to wrap
It includes:
If the Multi-objective Decision Model includes several objective functions and constraint condition, need to meet institute's Prescribed Properties
In the case of, several described objective functions are optimized simultaneously, obtain the solution space of the Multi-objective Decision Model.
6. the method according to claim 1, wherein the objective function min f (X) include: min f (X)=
min(f1(x),f2(x),f3(x),f4(x)), wherein f1(x)=t-t0, t0To plan runing time, x is becoming certainly for objective function
Amount, t indicate the time；f2(x) at a distance from=Δ d, Δ d indicate train stopping position between target stop；f3(x)=Σ Δ a/
T, f3(x) the sum of rate of acceleration change is indicated, wherein rate of acceleration change Δ a/t can be used to indicate comfort level, and a indicates to accelerate
Degree, Δ a indicate the variation of acceleration, and t indicates the time；f4(x)=Σ e, f4(x) accumulation energy consumption is indicated, e indicates energy consumption；
The constraint condition g (X) includes: g (X)=(g1(X),g2(X),g3(X)) >=0, wherein g1(X)=Vf-V-δ1, g2(X)=
Vtsr-V-δ2, g3(X)=Vmax-V-δ3, V expression train speed, VfIndicate the fixed speed limit of route, VtsrIndicate temporary speed limitation, Vmax
Indicate train Maximum speed limit, δ1, δ2, δ3Indicate speed allowance, the purpose that the speed allowance is arranged is to prevent the train speed to be more than
Speed limit, the speed allowance are positively correlated with train interval runing time；
Independent variable X in the objective function and constraint condition can be by the speed of train different location in section, section
Length and section runing time composition.
7. according to the method described in claim 6, it is characterized in that, the constraint condition further includes on time, stops quasi-, comfortable and section
Any combination of energy.
8. the method according to claim 1, wherein the multiobjective decision-making variable include section runing time,
The fixed speed limit of stopping accuracy, comfort level, energy consumption, route, temporary speed limitation and train Maximum speed limit.
9. the method according to claim 1, wherein the train operation demand includes: the section operation of train
The requirement of the stopping accuracy of time requirement or train.
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