CN101582203A - Realization system and method for airspace running simulation airflow engine - Google Patents

Abstract

The invention provides a realization system and a method for airspace running simulation airflow engine. The realization system comprises a flight flow distribution feature analysis module, a flight flow statistics module, a flight flow factor modeling module and a simulation solution flight flow module, wherein, the flight flow distribution feature analysis module is used for determining the factor information of the flight flow, analyzing the distribution feature of each factor and determining the corresponding data probability model; the flight flow statistics module is used for accounting value of each factor in the current flight schedule according to the flight flow factor data probability model provided by the flight flow distribution feature analysis module, and determining the distribution parameters; the flight flow factor modeling module is used for respectively building the probability distribution model of each factor in the current flight schedule according to the data provided by the flight flow statistics module, and comprehensively forming the flight flow modeling model; the simulation solution flight flow module is used for generating the flight flow by simulation according to the requirements like the simulation flow rate input by users on the basis of flight flow factor modeling. The invention can improve the reasonability and validity of the generation of the flight flow data, thus creating conditions to ensure the reliability of the simulation flight assessment result and improve the credibility of the simulation assessment result.

Description

The realization system and method for flight stream engine in a kind of airspace operation simulation

Technical field

The invention belongs to the aviation flight technical field, relate to a kind of traffic flow engine emulation mode, particularly a kind of engine analogue system and method that meets schedule flight distributions feature.

Background technology

The airspace management of a country comprises many contents such as the design, distribution in spatial domain.Owing to must coordinate the airspace capacity that use in ever-increasing spatial domain needs and almost remain unchanged, research efficiently utilizes existing spatial domain to become a focal issue how when guaranteeing security.

The Optimization Model of spatial domain design and AIRSPACE PLANNING must be used in the overall situation is bigger scope at least just can find out effect, and carrying out the airspace operation simulation assessment is an effective means of carrying out airspace structure optimization.The spatial domain assessment mainly is that the spatial domain of a country is estimated and estimated, by setting up the spatial domain model to airspace operation management carrying out emulation assessment, the airspace operation situation in the simulation whole nation or a certain zone, but the bottleneck that quantitative Analysis spatial domain resource is used, the result of test AIRSPACE PLANNING, or check solves situation to flight contradiction.

Be before the intelligent transportation system of target is set up to solve the control traffic problems, effectively to utilize the spatial domain resource, can be by the effectively extensive use of checking and simulator program, this system is carried out simulation analysis, all sidedly system performance, decision-making rationality are made correct assessment and effectively prediction, the traffic simulation technology is arisen at the historic moment thus.Air Traffic Emulation is the traffic analysis technology of a reproduction transport air flow change in time and space, can predict and assesses the various situations of traffic.

Whether spatial domain assessment will be estimated existing air traffic control system on the one hand can be safely, use efficiently; Again the spatial domain system in future is made prediction on the other hand, to guarantee the availability of this system in following certain hour.In order to assess the availability of air traffic control system in following certain hour, need the schedule flight stream in the following certain hour of analogue simulation, to meet the growing present situation of flight, realize at present ruuning situation of air traffic control system and the following comprehensive assessment that can bear the traffic ability.Rationality and validity that flight stream generates will have a direct impact simulation result, it must meet the primitive rule of flight layout based on existing flight stream, and does not introduce a large amount of conflicts, guarantee the reliability of simulated flight assessment result, improve the confidence level of emulation assessment result.

Outside the schedule flight stream that the following global traffic of simulated flight stream decapacitation analogue simulation increases, also should be able to simulate the schedule flight stream of the local flow increase that causes as incidents such as the Olympic Games, realization is to the assessment of the science of air traffic control system solution of emergent event ability, for the formulation of spatial domain emergency preplan provides the data support.Therefore, the realization of flight stream automotive engine system should have stronger dirigibility, to satisfy the demand under the different scenes of user.

China starts late in the research aspect airspace operation management emulation and the assessment, does not set up relevant evaluating system as yet and comes aid decision making, still adopts qualitative analysis and based on the evaluation measures of statistics.The U.S. has the aviation field emulation pogo plan of whole world maximum, Development and Production a large amount of emulation simulator and analysis tools, serve as that the typical case represents with the Future ATM Concepts Evaluation Tool (FACET) of the Total Airspace and AirportModeler (TAAM) of Boeing/Preston group and NASA exploitation.

For improving China's AIRSPACE PLANNING science decision ability, efficient profit has existing spatial domain, effectively increases airspace capacity, must adopt more scientific and effective evaluation measures.The stream simulation engine that flies is the basis of spatial domain assessment, also is the place of key, and just can makes more closing to reality of emulation to taking into full account with comparatively perfect air traffic control logic of enchancement factor in the flight stream simulation process, improves the confidence level of emulation assessment result.

Summary of the invention

The objective of the invention is the present situation that in AIRSPACE PLANNING decision-making, becomes more and more important at spatial domain emulation, the realization system and method for flight stream engine in a kind of airspace operation simulation is provided, this system and method can improve rationality and the validity that the flight flow data generates, and creates conditions for guaranteeing simulated flight assessment result reliability, raising emulation assessment result confidence level.

Flight stream engine is the starting point of analogue simulation, in fact the effect of engine modules is exactly the importation of stream of will flying, act on the air route starting point that simulated the spatial domain or advance the near region point, it relies on the random number technology to produce the status attribute and the flight stream parameter of the aircraft that meets given probability distribution, provide initial value with this to system, the ability that produces reasonable random occurrence is one of most important characteristic of flight flow field simulation.Flight stream engine model comprises two parts information: airplane information and aircraft stream information.Its essence has reflected two randomnesss of system initialization, i.e. the randomness of aircraft individuality and aircraft starting point randomness constantly.In simulation process, the stochastic variable of select type for use, when taking off dynamically flying and flowing in distance, speed and destination as sign.

In order to realize the object of the invention, the realization system of flight stream engine comprises in the airspace operation simulation of the present invention:

Flight distributions characteristics analysis module is used for the element information that definite flight flows, and analyzes the distribution characteristics of each key element, determines corresponding data probability model;

Flight stream statistical module, the flight stream factor data probability model that is used for providing according to flight distributions characteristics analysis module is added up the value condition of existing each key element of flight planning, determines distribution parameter;

Flight stream key element MBM is used for setting up the probability Distribution Model of existing each key element of flight planning respectively according to the data that flight stream statistical module provides, and comprehensively forms flight stream modeler model;

The flight flow module is found the solution in emulation, is used for flowing requirements such as flow according to the emulation of user's input on flight stream key element modeling basis, and emulation generates flight stream.

Described flight distributions characteristics analysis module comprises:

Flight stream key element determining unit is used for determining the included staple of flight stream information;

Flight flows the factor analysis unit, is used for flowing according to the data analysis flight that flight stream key element determining unit provides the distribution characteristics of each key element, determines corresponding data probability model.

Described flight stream statistical module comprises:

Distribution statistics unit, landing airport, the destination distribution situation that is used for adding up each field takeoff flight of existing flight planning;

The flight number distribution statistics unit, the departure time distribution situation that is used for adding up each field takeoff flight of existing flight planning;

Type proportioning distribution statistics unit is used for adding up the distribution situation of each type proportioning of existing flight planning;

The statistics comprehensive unit is used for the statistics of above-mentioned three unit is put, sets up in order association, forms flight distributions global feature statistics.

Described flight stream key element MBM comprises:

The flight number, modeling unit was used for based on Poisson distribution, determined the characteristic parameter value that distribute the flight number, set up the probability Distribution Model of flight takeoff time;

Landing airport modeling unit is used for based on Discrete Distribution, sets up the probability Distribution Model on landing airport, and the landing airport comprises domestic flight, immigration flight, the departure flight in the existing flight planning and leaps the inside and outside airport of the host country that occurs in the flight;

Type proportioning modeling unit is used for based on Discrete Distribution, analyzes the type distribution characteristics, sets up type proportioning probability Distribution Model;

Flight stream modeling unit is used for the model that comprehensive above-mentioned three unit are set up, and forms flight stream modeler model.

Described emulation is found the solution the flight flow module and is comprised:

Landing airport distributed simulation is found the solution the unit, is used to find the solution the landing airport that generates simulated flight stream;

The flight number, distributed simulation was found the solution the unit, was used to find the solution the departure time that generates simulated flight stream;

Type proportioning distributed simulation is found the solution the unit, is used to find the solution the type that generates simulated flight stream, simulates the performance datas such as flight envelope, climbing performance, radius of turn, speed and acceleration of each flight;

Emulation match inspection module, the simulation flight stream that information such as the landing airport that is used for above-mentioned three unit are generated, the departure time, type gather formation carries out the match verification.

The implementation method of flight stream engine comprises following content in the airspace operation simulation of the present invention:

One, determines the element information of flight stream, analyze the distribution characteristics of each key element, determine corresponding data probability model;

Two, add up the value condition of each key element in the existing flight planning according to flight stream factor data probability model, determine distribution parameter;

Three, the data that obtain according to statistics are set up the probability Distribution Model of each key element in the existing flight planning respectively; And comprehensively form flight stream modeler model.

Four, on flight stream key element modeler model basis, flow engine conditions such as flow, generate the flight stream that meets user's request at random according to the emulation of user's input.

The present invention is at theory of probability, on the basis of mathematical statistics and computer software technology, in conjunction with civil aviaton's flight flight layout rule, the historical flight of match flow data, take into full account the application scenarios of simulated flight stream, set up the probability Distribution Model of each key element in the existing flight planning, only need to change the parameter of expression formula just applicable to different traffic conditions, has good versatility, can be arbitrarily, the Air Traffic Emulation model provides single flight flow module, realize the flight planning of any flow of simulation layout, for emulation assessment in spatial domain provides the flight data source, be the reasonable formulation of blank pipe regulations, the planning of science activities in spatial domain provides supplementary means.

Below by drawings and Examples, technical scheme of the present invention is described in further detail.

Description of drawings

Fig. 1 is an embodiment of the invention main functional modules structural representation;

Fig. 2 is an embodiment of the invention flight distributions characteristics analysis module structural representation;

Fig. 3 is embodiment of the invention flight stream statistical module structural representation;

Fig. 4 is embodiment of the invention flight stream key element MBM structural representation;

Fig. 5 finds the solution the modular structure synoptic diagram for embodiment of the invention flight stream engine;

Fig. 6 is an embodiment of the invention implementation method process flow diagram;

Fig. 7 is an embodiment of the invention flight distributions characteristics analysis module workflow diagram;

Fig. 8 is embodiment of the invention flight stream statistical module workflow diagram;

Fig. 9 is a Discrete Distribution stochastic variable solution procedure process flow diagram;

Figure 10 is the generative process process flow diagram of Poisson distribution;

Figure 11 finds the solution process flow diagram for the reasoning of aircraft spacing time;

Embodiment

Fig. 1 is a spatial domain of the present invention simulated flight automotive engine system main functional modules structural representation.As shown in Figure 1, native system comprises:

Flight distributions characteristics analysis module 1 is used for the element information that definite flight flows, and analyzes the distribution characteristics of each key element; Determine corresponding data probability model;

Flight stream statistical module 2, the flight stream factor data probability model that is used for providing according to flight distributions characteristics analysis module is added up the value condition of existing each key element of flight planning, determines distribution parameter;

Flight stream key element MBM 3 is used for setting up the probability Distribution Model of existing each key element of flight planning respectively according to the data that flight stream statistical module provides, and comprehensively forms flight stream modeler model;

Flight flow module 4 is found the solution in emulation, is used for flowing requirements such as flow according to the emulation of user's input on flight stream key element modeling basis, and emulation generates flight stream.

Above-mentioned spatial domain simulated flight automotive engine system is on the basis of analyzing flight distributions feature, determined flight stream key element, and add up the distribution character of each key element respectively at existing flight planning, set up flight with this and flow each feature model, according to the engine stream condition of user interface input, generate the flight stream that meets user's request at random again.

This system has taken into full account flight flight layout rule, follows traffic flow theory probability theory model, has realized the flight planning of any flow of simulation layout, for airspace management planning provides supplementary means.

Fig. 2 is that this routine flight distributions characteristics analysis module is tied 1 structure synoptic diagram, and this module comprises:

Flight stream key element determining unit 11 is used for determining the included staple of flight stream information;

Flight flows factor analysis unit 12, is used for flowing according to the data analysis flight that flight stream key element determining unit provides the distribution characteristics of each key element, determines corresponding data probability model.

Fig. 3 is spatial domain of the present invention simulated flight automotive engine system flight stream statistical module 2 structural representations, and this module comprises distribution statistics unit, landing airport 21, flight number distribution statistics unit 22, type proportioning distribution statistics unit 23 and statistics comprehensive unit 24.

Landing airport distribution statistics module 21 is by database access interface inquiry landing Airport information and tabulate statistics;

The flight number, distribution statistics module 22 was by the database access interface inquiry information flight number and tabulate statistics;

Type proportioning distribution statistics module 23 is by database access interface inquiry type proportioning information and tabulate statistics;

More than the statistics of 3 unit be responsible for arrangement, set up relatedly by statistics comprehensive unit 24, finally form flight distributions global feature statistics.

Fig. 4 is spatial domain of the present invention simulated flight automotive engine system flight stream key element MBM 3 structural representations.This module comprises modeling unit 31 flight number, landing airport modeling unit 32, type proportioning MBM 33 and flight stream modeling unit 34.

The flight number, MBM 31 was based on Poisson distribution, determine the characteristic parameter value that distribute the flight number, the probability Distribution Model of output flight takeoff time, landing airport comprise domestic flight, immigration flight, the departure flight in the existing flight planning and leap the inside and outside airport of the host country that occurs in the flight;

Landing airport modeling unit 32 is exported the probability Distribution Model on flight landing airport based on Discrete Distribution;

Type proportioning modeling unit 33 is exported type proportioning probability Distribution Model based on Discrete Distribution;

The model that the flight stream modeling unit 34 comprehensive flight number of modeling unit 31, landing airport modeling unit 32 type proportioning modeling unit 33 are set up forms flight and flows modeler model.

Fig. 5 finds the solution module 4 structural representations for spatial domain of the present invention simulated flight automotive engine system flight stream engine.Flight stream engine is found the solution module 4 and is comprised that the flight number, distributed simulation found the solution that unit 42 is found the solution in unit 41, landing airport distributed simulation, type proportioning distributed simulation is found the solution unit 43 and emulation match verification unit 44.

The flight number, distributed simulation was found the solution unit 41, was used to find the solution the landing airport of output simulated flight stream;

Landing airport distributed simulation is found the solution unit 42, is used to find the solution the departure time of output simulated flight stream;

Type proportioning distributed simulation is found the solution unit 43, is used to find the solution the type of output simulated flight stream, simulates the performance datas such as flight envelope, climbing performance, radius of turn, speed and acceleration of each flight;

Emulation match verification unit 44, the simulation flight stream that information such as the landing airport of flowing flying, the departure time, type gather formation carries out the match verification.

Fig. 6 is this routine spatial domain simulated flight automotive engine system implementation method process flow diagram:

Step a1: determine flight distributions key element,, determine that finally flight stream key element comprises: type, take off time, original base, arrive at the airport, advance the step down by analyzing the flight stream attribute information;

Step a2: analyze flight distributions feature, comprise analyzing whether spatial domain flight stream exists that cycle factor such as traffic signals is disturbed, whether traffic density is crowded and whether has very big randomness, advance the step down;

Step a3: determine each feature model according to flight stream feature, comprise that describing type based on Discrete Distribution distributes, describes departure time distribution, describes the destination airport distribution based on Discrete Distribution based on Poisson distribution, advances the step down;

Step a4: by user-machine interface, receive the emulation stream parameter of user's input, comprising: emulation flow, observation interval, sampling number, flight simulation result zero-time, flight simulation result concluding time etc., advance the step down;

Step a5:, advance the step down by all original base information in the database access interface inquiry flight planning;

Step a6: beginning is added up the flight distributions on each airport successively, gets the information on airport at first successively, advances the step down;

Step a7: get four word codes on this airport, advance the step down;

Step a8: according to airport four word codes, inquire about existing flight planning tables of data,, change executed in parallel step a9, step a10 and step a11 then down with the distribution situation of each key element in the existing flight planning of adding up this airport;

Step a9:,, set up the type distributed model on this airport based on Discrete Distribution according to the type statistics on this airport;

Step a10:,, set up the departure time distributed model on this airport based on Poisson distribution according to the departure time statistics on this airport;

Step a11:,, set up the distributed model that arrives at the airport on this airport based on Discrete Distribution according to the statistics that arrives at the airport on this airport;

After step a9, step a10 and step a11 all are finished, enter step a12:

Step a12: number=0 of initialization emulation flight;

Step a13: whether judge emulation flight number less than user's request, if emulation flight number then changes step 20 more than or equal to user's request; Otherwise following commentaries on classics executed in parallel step a14, a15, a16;

Step a14: this airport type distributed model that adopts step a9 to set up generates a type record, progressive then rapid a17 at random;

Step a15: this field takeoff time distributed model that adopts step a10 to set up generates a departure time record, progressive then rapid a17 at random;

Step a16: this airport of adopting step a11 to set up distributed model that arrives at the airport generates the record that arrives at the airport, progressive then rapid a17 at random;

The information that arrives at the airport that the departure time information that the model information that step a17: compilation steps a14 generates, step a15 generate, step a16 generate forms a flight planning record, advances the step down;

Step a18: deposit the flight planning of emulation in database, advance the step down;

Step a19: the number of emulation flight adds 1; And enter step a13;

Step a20: this airport emulation finishes; And enter step a21;

Step a21: judge that institute's organic field emulation finishes, if also there is untreated airport, then carries out the subsequent processes of step a7; As there not being then execution in step a22 of untreated airport;

Step a22: all emulation finishes.

Above-mentioned a1-a3 step is finished by flight distributions characteristics analysis module 1, the a4-a8 step is finished by flight stream statistical module 2, the a9-a11 step is finished by flight stream key element MBM 3, and the a12-a19 step is found the solution flight flow module 4 by emulation and finished.

Fig. 7 is the detailed operation flow process of flight distributions characteristics analysis module 1:

Step a11: analyze flight stream and characterize key element, advance the step down;

Step a12: determine flight stream key element, the aircraft key element of employing comprises type, takes off the time, original base, arrive at the airport, and advances the step down;

Step a21: analyze flight stream essential characteristic---flight flight stream does not exist that cycle factor such as traffic signals is disturbed, flight stream traffic density is very crowded and have bigger randomness, then and the a22 that advances, a23, a24 step;

Step a22: analyze the flight stream type regularity of distribution, determine its randomness feature; After complete, enter step a31;

Step a23: analytical engine field distribution rule, determine its randomness feature; After complete, enter step a32;

Step a24: analyze the characteristics that flight enters the spatial domain, determine its without aftereffect, stationarity, medium-sized feature; After complete, enter step a33;

Step a31: on the basis of step a22,, determine to adopt Discrete Distribution to describe type and distribute according to the discrete features at random that type distributes;

Step a32: on the basis of step a23,, determine to adopt Discrete Distribution to describe destination airport and distribute according to the discrete features at random that the airport distributes;

Step a33: on the basis of step a24,, determine to adopt Poisson distribution to describe departure time distribution according to the random character that the departure time distributes.

Fig. 8 is the detailed operation flow process of flight stream statistical module 2:

Step b1: the initialization data storehouse connects, and advances the step down;

Step b2: the airport table in the Query Database, obtain all airport tabulations, comprise domestic airport and external airport, advance the step down;

Step b3: get the Airport information record successively, and begin the processing of this airport related data;

Step b4: Query Database flight planning tables of data, obtain set out all destination airports tabulation of flight of this airport; After complete, enter step b7;

Step b5: Query Database flight planning tables of data, obtain the set out institute organic type tabulation of flight of this airport; After complete, enter step b8;

Step b6: Query Database flight planning tables of data, obtain set out all tabulation of flight of this airport departure times; After complete, enter step b9;

Step b4, step b5 and step b6 carry out simultaneously;

Step b7: on the basis of step b4, set out all destination airports of flight of this airport are added up the flight number of arrival one by one;

Step b8: on the basis of step b5, set out each type of flight of this airport is added up its flight number one by one;

Step b9: on the basis of step b6, each observes the flight number that takes off at interval statistics;

Step b10: on the basis of step b7, calculate the proportion of each landing station;

Step b11: on the basis of step b8, calculate the proportion of each type;

Step b12: on the basis of step b9, calculate the flight number in average per time interval;

After step b10, step b11 and step b12 are complete, enter step b13:

Step b13: the statistics on this airport is saved in the database, enters b14;

Step b14: judge whether also to exist the not airport of statistics,, then enter step b3 and continue to handle if also there is the not airport of statistics; Otherwise finish flight stream statistics flow process.

In the type proportioning MBM 33, be Random assignment, adopt the discrete probability distribution modeling owing to arrive the aircraft model on certain airport.For simplifying narration, aircraft model simply is divided into large scale computer, medium-sized machine, minicomputer three classes.

Now the number percent that accounts for all flight plannings with large scale computer, medium-sized machine, minicomputer is represented the probability that the type aircraft arrives, establish aircraft and flow medium-and-large-sized machine, medium-sized machine, the shared number percent of minicomputer and be respectively p1, and p2, p3, and hypothesis:

p1＜p2＜p3，p1+p2+p3＝1。

Below with stochastic variable X represent the to fly appearance situation of large, medium and small type aircraft in the stream.X=0 represents that the type that occurs is a large scale computer, and X=1 represents that the type that occurs is medium-sized machine, and X=2 represents that the type that occurs is a minicomputer, so the probability density function of stochastic variable X is:

$f\left(X\right)=\left\{\begin{array}{c}P\{X=0\}=\mathrm{<figure-callout\; id="1"\; label="p"\; filenames="A2009100858290028H1.png"\; state="\{\{state\}\}">p</figure-callout>}1,\\ P\{X=1\}=\mathrm{<figure-callout\; id="2"\; label="p"\; filenames="A2009100858290022H1.png,A2009100858290028H1.png"\; state="\{\{state\}\}">p</figure-callout>}2,\\ P\{X=2\}=\mathrm{<figure-callout\; id="3"\; label="p"\; filenames="A2009100858290022H1.png"\; state="\{\{state\}\}">p</figure-callout>}3.\end{array}\right.$

The probability distribution function F (x) of stochastic variable X is respectively:

$F\left(x\right)=\left\{\begin{array}{cc}0& x<0,\\ \mathrm{<figure-callout\; id="1"\; label="p"\; filenames="A2009100858290028H1.png"\; state="\{\{state\}\}">p</figure-callout>}1& 0\&le;x<1,\\ \mathrm{<figure-callout\; id="1"\; label="p"\; filenames="A2009100858290028H1.png"\; state="\{\{state\}\}">p</figure-callout>}1+\mathrm{<figure-callout\; id="2"\; label="p"\; filenames="A2009100858290022H1.png,A2009100858290028H1.png"\; state="\{\{state\}\}">p</figure-callout>}2& 1\&le;x<2,\\ \mathrm{<figure-callout\; id="1"\; label="p"\; filenames="A2009100858290028H1.png"\; state="\{\{state\}\}">p</figure-callout>}1+\mathrm{<figure-callout\; id="2"\; label="p"\; filenames="A2009100858290022H1.png,A2009100858290028H1.png"\; state="\{\{state\}\}">p</figure-callout>}2+\mathrm{<figure-callout\; id="3"\; label="p"\; filenames="A2009100858290022H1.png"\; state="\{\{state\}\}">p</figure-callout>}3=1& x\&GreaterEqual;2.\end{array}\right.$

Adopt the inversion method to calculate this discrete random variable X, its inversion formula is as follows:

$X=\left\{\begin{array}{cc}0& 0<u\&le;\mathrm{<figure-callout\; id="1"\; label="p"\; filenames="A2009100858290028H1.png"\; state="\{\{state\}\}">p</figure-callout>}1,\\ 1& \mathrm{<figure-callout\; id="1"\; label="p"\; filenames="A2009100858290028H1.png"\; state="\{\{state\}\}">p</figure-callout>}1<u\&le;\mathrm{<figure-callout\; id="1"\; label="p"\; filenames="A2009100858290028H1.png"\; state="\{\{state\}\}">p</figure-callout>}1+\mathrm{<figure-callout\; id="2"\; label="p"\; filenames="A2009100858290022H1.png,A2009100858290028H1.png"\; state="\{\{state\}\}">p</figure-callout>}2,\\ 2& \mathrm{<figure-callout\; id="1"\; label="p"\; filenames="A2009100858290028H1.png"\; state="\{\{state\}\}">p</figure-callout>}1+\mathrm{<figure-callout\; id="2"\; label="p"\; filenames="A2009100858290022H1.png,A2009100858290028H1.png"\; state="\{\{state\}\}">p</figure-callout>}2<u\&le;\mathrm{<figure-callout\; id="1"\; label="p"\; filenames="A2009100858290028H1.png"\; state="\{\{state\}\}">p</figure-callout>}1+\mathrm{<figure-callout\; id="2"\; label="p"\; filenames="A2009100858290022H1.png,A2009100858290028H1.png"\; state="\{\{state\}\}">p</figure-callout>}2+\mathrm{<figure-callout\; id="3"\; label="p"\; filenames="A2009100858290022H1.png"\; state="\{\{state\}\}">p</figure-callout>}3=1\end{array}\right.$

The solution procedure of discrete random variable X is as shown in Figure 9:

Step c1: generate at random one (0,1] random number u in the scope;

Step c2: judge that whether u is greater than 0 and smaller or equal to p1; If 0＜u＜=p1 then directly enters step c6 and handles; Otherwise continue next step processing of execution in step c3;

Step c3: judge that whether u is greater than p1 and smaller or equal to (p1+p2); If p1＜u＜=p1+p2 then directly enters step c4 and handles; Otherwise enter step c5;

Step c4: try to achieve X=1;

Step c5: try to achieve X=2;

Step c6: try to achieve X=0.

The type proportioning MBM 33 of spatial domain of the present invention simulated flight stream automotive engine system and type proportioning distributed simulation are found the solution module 43 and are realized that principle is with above-mentioned process.

Analyze the distribution characteristics of flight stream landing airport key element, determine that the destination is a stochastic distribution, satisfies the Discrete Distribution characteristic.The number percent that named place of destination air station flight number accounts for total flight planning number is the probability of Discrete Distribution.The implementation procedure that landing airport MBM 32 and landing airport distributed simulation are found the solution module 42 and spatial domain simulated flight flow the type proportioning MBM 33 of automotive engine system and type proportioning distributed simulation, and to find the solution module 43 similar.

Enter the characteristics of simulating area itself according to aircraft, should satisfy following condition theoretically:

(1) generation of aircraft is mutually independently in the time interval that does not overlap mutually, i.e. without aftereffect;

(2) to abundant little Δ t, the probability and the t that have an airplane to produce in time interval [t, t+ Δ t] are irrelevant, and are directly proportional with burst length Δ t, i.e. the generation of aircraft has stationarity;

(3) for abundant little Δ t, on the interior navigation channel of time interval [t, t+ Δ t], there are 2 or 2 probability that produce to board a plane minimum, promptly have universality.

According to above characteristics, select for use Poisson distribution to represent the distribution situation of flying and flowing.Because the coefficient of variation of Poisson distribution is D (x)/E (x)=1, then according to coefficient of variation definition, the probability curve concentration degree of this distribution is more even, can embody evenly to distribute.Formula is then arranged:

$P_{t^{*}}\left(n\right)=\frac{{\mathrm{\&lambda;}}^n}{n!}{e}^{-\mathrm{\&lambda;}},n>0---\left(1\right)$

N is the aircraft number; λ is a parameter.

Get according to experiment image data mode: the parameter in the formula (1) has corresponding physical significance, and λ is illustrated in the aircraft number in the sampling time.

As shown in figure 10, the generative process of Poisson distribution is:

Steps d 1: generate the random number R between [0,1];

Steps d 2: initialization I=0;

Steps d 3: calculate $P(\&le;x)=\underset{i=0}{\overset{x}{\mathrm{\&Sigma;}}}\frac{m^i{e}^{-m}}{i!}$

Steps d 4: judge that whether P (X) is more than or equal to R; If P (X)＜R then enters steps d 6 and continues to handle; Otherwise continue to enter steps d 5;

Steps d 5: corresponding X value got make stochastic variable, promptly take off number, finish the Poisson distribution modeling;

Steps d 6:I adds 1.

After parameter lambda is determined, can produce the random number of obeying Poisson distribution according to formula (1).But realistic model is required is interval time between two airplanes arrive, but not this random number representative at the aircraft number of fixed sample at interval.Through reasoning, if flight stream is represented in the employing Poisson distribution, then the time interval t of aircraft generation obeys the negative exponent distribution.Suppose that the Poisson distribution of flight stream is satisfied

$P_{t^{*}}\left(n\right)=\frac{{\mathrm{\&lambda;}}^n}{n!}{e}^{-\mathrm{\&lambda;}},n>0$

As shown in figure 11, the reasoning of aircraft spacing time is solved to:

Step e1: make λ=α t*, α then represents aircraft mean arrival rate (veh/s), then

$P_n\left(t^{*}\right)=\frac{{\left({\mathrm{at}}^{*}\right)}^n}{n!}{e}^{-{\mathrm{at}}^{*}},t^{*}>0$

This formula physical significance is: the possibility that has the n airplane to enter simulating area in time section t* is Pn (t*);

Step e2: make n=1, obtain P ₁(λ)=λ e ^-λλ＞0;

Step e3:, obtain the distribution function of λ to following formula both sides integration

F(λ)＝(-λ-1)e ^-λ+1 λ＞0

Verified, F (λ) is the monodrome increasing function.And 0≤F (λ)≤1;

Step e4: generate at random (0,1] between a random number u;

Step e5: make F (λ)=u, try to achieve λ;

Step e6: try to achieve t=λ/α interval time.

When this routine emulation match verification unit 44 carries out the match verification at the simulation flight stream that information such as the landing airport of flight stream, the departure time, type is gathered formation, use X ²Method of inspection is checked under certain level of signifiance a and (is generally got 0.05), the situation of the Poisson distribution match reality population distribution of flight stream flow.The arrival number of supposing aircraft satisfies Poisson distribution, cruise speed satisfies normal distribution, and the situation of real airspace operation is owing to be subjected to the influence of feature, air traffic control rules and accident that the spatial domain is different from common land communications, the distribution situation of practical flight stream and desirable distribution may produce bigger discrepancy, therefore need to distribute and the ideal distribution situation according to true, match situation to true distribution is assessed, make truly to distribute deviation with ideal distribution in the range of tolerable variance that the user allows, otherwise the ideal distribution situation of refusal hypothesis.

This example adopts X ²Whether the method for inspection checking flight stream time interval satisfies Poisson distribution, and whether cruise speed satisfies normal distribution.The method can be overall when being distributed as the unknown, according to sample x1, and x2 ..., xn checks the match situation about population distribution, judges whether the distribution function of overall x is the distribution function F (x) of appointment.Suppose:

H0: the distribution function of overall x is F (x),

H1: the distribution function of overall x is not F (x).

X ²The basic thought of method of inspection is: all Ω as a result are divided into k objectionable intermingling set A 1 with random test, A2 ..., Ak ( ${\mathrm{\&Sigma;}}_{i=1}^k{A}_i=\mathrm{\&Omega;},$

i≠j，i，j＝1，2，...，k)。So under hypothesis H0, we can calculate pi=P (Ai), i=1,2 ..., k.In n test, frequency f i/n and pi that incident Ai occurs are often variant, but in general, if H0 be very, and the number of times of testing is when a lot of again, and then this species diversity should be very not big.Based on this idea, Pearson came is used:

${\mathrm{\&chi;}}^2=\underset{i=1}{\overset{k}{\mathrm{\&Sigma;}}}\frac{{(f_i-{\mathrm{np}}_i)}^2}{{\mathrm{np}}_i}---\left(1\right)$

As the statistic of test-hypothesis H0, and proved following theorem.

Theorem is as if n fully big (n 〉=50), and then working as H0 is true time (distributing no matter what distribution among the H0 belongs to), statistic x ²Always obey the x that degree of freedom is k-r-1 approx ²Distribute, wherein, r is the number of estimative parameter.

So, if under hypothesis H0, calculate to such an extent that have:

${\mathrm{\&chi;}}^2\&GreaterEqual;{\mathrm{\&chi;}}_{\mathrm{\&alpha;}}^2,(k-r-1)$

Then under level of significance a, refuse H0, otherwise just accept H0.

Before test of hypothesis, need to use the square estimation technique or the maximum likelihood estimation technique to estimate the parameter of distribution function F (x); Must be noted that during use that n wants enough big, and npi is not too little.According to practice, require sample size n sample range to be not less than 50, and each npi is not less than 5, and npi is preferably more than 5.Otherwise should suitably merge Ai, to meet this requirement.

It should be noted last that, above embodiment is only unrestricted in order to technical scheme of the present invention to be described, although the present invention is had been described in detail with reference to preferred embodiment, those of ordinary skill in the art is to be understood that, can make amendment or be equal to replacement technical scheme of the present invention, and not break away from the spirit and scope of technical solution of the present invention.

Claims (10)

1, the realization system of flight stream engine in a kind of airspace operation simulation, it is characterized in that: this system comprises:

Flight distributions characteristics analysis module is used for the element information that definite flight flows, and analyzes the distribution characteristics of each key element, determines corresponding data probability model;

Flight stream statistical module, the flight stream factor data probability model that is used for providing according to flight distributions characteristics analysis module is added up the value condition of existing each key element of flight planning, determines distribution parameter;

Flight stream key element MBM is used for setting up the probability Distribution Model of existing each key element of flight planning respectively according to the data that flight stream statistical module provides, and comprehensively forms flight stream modeler model;

The flight flow module is found the solution in emulation, is used for flowing requirements such as flow according to the emulation of user's input on flight stream key element modeler model basis, and emulation generates flight stream.

2, the realization system of flight stream engine in the airspace operation simulation according to claim 1, it is characterized in that: described flight distributions characteristics analysis module comprises:

Flight stream key element determining unit is used for determining the included staple of flight stream information;

Flight flows the factor analysis unit, is used for flowing according to the data analysis flight that flight stream key element determining unit provides the distribution characteristics of each key element, determines corresponding data probability model.

3, the realization system of flight stream engine in the airspace operation simulation according to claim 1 is characterized in that: described flight stream statistical module comprises:

Distribution statistics unit, landing airport, the destination distribution situation that is used for adding up each field takeoff flight of existing flight planning;

The flight number distribution statistics unit, the departure time distribution situation that is used for adding up each field takeoff flight of existing flight planning;

Type proportioning distribution statistics unit is used for adding up the distribution situation of each type proportioning of existing flight planning;

The statistics comprehensive unit is used for the statistics of above-mentioned three unit is put, sets up in order association, forms flight distributions global feature statistics.

4, the realization system of flight stream engine in the airspace operation simulation according to claim 3 is characterized in that: described flight stream key element MBM comprises:

The flight number, modeling unit was used for based on Poisson distribution, determined the characteristic parameter value that distribute the flight number, set up the probability Distribution Model of flight takeoff time;

Landing airport modeling unit is used for based on Discrete Distribution, sets up the probability Distribution Model on landing airport, and the landing airport comprises domestic flight, immigration flight, the departure flight in the existing flight planning and leaps the inside and outside airport of the host country that occurs in the flight;

Type proportioning modeling unit is used for based on Discrete Distribution, analyzes the type distribution characteristics, sets up type proportioning probability Distribution Model;

Flight stream modeling unit is used for the model that comprehensive above-mentioned three unit are set up, and forms flight stream modeler model.

5, the realization system of flight stream engine in the airspace operation simulation according to claim 4, it is characterized in that: described emulation is found the solution the flight flow module and is comprised:

Landing airport distributed simulation is found the solution the unit, is used to find the solution the landing airport that generates simulated flight stream;

The flight number, distributed simulation was found the solution the unit, was used to find the solution the departure time that generates simulated flight stream;

Type proportioning distributed simulation is found the solution the unit, is used to find the solution the type that generates simulated flight stream, simulates the performance datas such as flight envelope, climbing performance, radius of turn, speed and acceleration of each flight;

Emulation match inspection module, the simulation flight stream that information such as the landing airport that is used for above-mentioned three unit are generated, the departure time, type gather formation carries out the match verification.

6, the implementation method of flight stream engine in a kind of airspace operation simulation, it is characterized in that: this method comprises following content:

One, determines the element information of flight stream, analyze the distribution characteristics of each key element, determine corresponding data probability model;

Two, add up the value condition of each key element in the existing flight planning according to flight stream factor data probability model, determine distribution parameter;

Three, the data that obtain according to statistics are set up the probability Distribution Model of each key element in the existing flight planning respectively; And comprehensively form flight stream modeler model.

Four, on flight stream key element modeler model basis, find the solution, generate the flight stream that meets user's request at random according to the engine conditions such as emulation stream flow of user's input.

7, the implementation method of flight stream engine in the airspace operation simulation according to claim 6 is characterized in that: when setting up the probability Distribution Model of each key element in the existing flight planning in the 3rd step, comprise following content

Based on Poisson distribution, determine the characteristic parameter value that distribute the flight number, set up the probability Distribution Model of flight takeoff time;

Based on Discrete Distribution, set up the probability Distribution Model on landing airport, the landing airport comprises domestic flight, immigration flight, the departure flight in the existing flight planning and leaps the inside and outside airport of the host country that occurs in the flight;

Based on Discrete Distribution, analyze the type distribution characteristics, set up type proportioning probability Distribution Model;

And the model of comprehensive above-mentioned three unit foundation, form flight stream modeler model.

8, the implementation method of flight stream engine in the airspace operation simulation according to claim 7, it is characterized in that: find the solution according to the engine conditions such as emulation stream flow of user's input in the 4th step, generate at random in the process of the flight stream that meets user's request, comprise following content:

Based on Poisson distribution, find the solution the departure time that generates simulated flight stream;

Based on Discrete Distribution, find the solution the landing airport that generates simulated flight stream;

Based on Discrete Distribution, find the solution the type that generates simulated flight stream, simulate the performance datas such as flight envelope, climbing performance, radius of turn, speed and acceleration of each flight;

Find the solution the simulation flight stream that information such as departure time of obtaining, landing airport, type gather formation and carry out the match verification above-mentioned.

9, the implementation method of flight stream engine in the airspace operation simulation according to claim 8, it is characterized in that: described match method of calibration adopts X ²Method of inspection.

10, the implementation method of flight stream engine in the airspace operation simulation according to claim 6 is characterized in that: comprise following step:

Step a1: determine flight distributions key element,, determine that finally flight stream key element comprises: type, take off time, original base, arrive at the airport, go forward side by side down the step by analyzing the flight stream attribute information;

Step a2: analyze flight distributions feature, comprise analyzing whether spatial domain flight stream exists that cycle factor such as traffic signals is disturbed, whether traffic density is crowded and whether has very big randomness, the step of going forward side by side down;

Step a3: determine each feature model according to flight stream feature, comprise that describing type based on Discrete Distribution distributes, describes departure time distribution, describes the destination airport distribution based on Discrete Distribution based on Poisson distribution, goes forward side by side down the step;

Step a4: by user-machine interface, receive the emulation stream parameter of user's input, comprising: emulation flow, observation interval, sampling number, flight simulation result zero-time, flight simulation result concluding time etc., the step of going forward side by side down;

Step a5:, advance the step down by all original base information in the database access interface inquiry flight planning;

Step a6: beginning is added up the flight distributions on each airport successively, gets the information on airport at first successively, goes forward side by side down the step;

Step a7: get four word codes on this airport, go forward side by side down the step;

Step a8: according to airport four word codes, inquire about existing flight planning tables of data,, change executed in parallel step a9, step a10 and step a11 then down with the distribution situation of each key element in the existing flight planning of adding up this airport;

Step a9:,, set up the type distributed model on this airport based on Discrete Distribution according to the type statistics on this airport;

Step a10:,, set up the departure time distributed model on this airport based on Poisson distribution according to the departure time statistics on this airport;

Step a11:,, set up the distributed model that arrives at the airport on this airport based on Discrete Distribution according to the statistics that arrives at the airport on this airport;

After step a9, step a10 and step a11 all are finished, enter step a12:

Step a12: number=0 of initialization emulation flight;

Step a13: whether judge emulation flight number less than user's request, if emulation flight number then changes step 20 more than or equal to user's request; Otherwise following commentaries on classics executed in parallel step a14, a15, a16;

Step a14: this airport type distributed model that adopts step a9 to set up generates a type record, progressive then rapid a17 at random;

Step a15: this field takeoff time distributed model that adopts step a10 to set up generates a departure time record, progressive then rapid a17 at random;

Step a16: this airport of adopting step a11 to set up distributed model that arrives at the airport generates the record that arrives at the airport, progressive then rapid a17 at random;

The information that arrives at the airport that the departure time information that the model information that step a17: compilation steps a14 generates, step a15 generate, step a16 generate forms a flight planning record, goes forward side by side down the step;

Step a18: deposit the flight planning of emulation in database, go forward side by side down the step;

Step a19: the number of emulation flight adds 1; And enter step a13;

Step a20: this airport emulation finishes; And enter step a21;

Step a21: judge that institute's organic field emulation finishes, if also there is untreated airport, then carries out the subsequent processes of step a7; As there not being then execution in step a22 of untreated airport;

Step a22: all emulation finishes.

Images