CN108510106A - Airport security check flow optimization method based on queuing theory and generalized random petri net - Google Patents
Airport security check flow optimization method based on queuing theory and generalized random petri net Download PDFInfo
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Abstract
The invention discloses an airport security check flow optimization method based on a queuing theory and a generalized random petri net, which comprises the following steps of: acquiring airport security check layout information, and constructing a security check flow model based on a queuing theory, a generalized random petri net and the security check layout information; acquiring airport security inspection historical information; the security inspection historical information comprises a customer arrival time interval and the time for a service desk to detect a target; substituting the security inspection historical information into a security inspection process model to perform simulation calculation to obtain a simulation result; comparing the simulation results, judging whether the results meet the expected effect, if not, correspondingly increasing security inspection resources in the transition corresponding to the library with the largest average token number or reducing security inspection resources in the transition corresponding to the library with the smallest average token number; the simulation calculation is continued until the desired effect is met. The method and the device can accurately and effectively realize optimization of airport security check processes, and improve security check efficiency and quality.
Description
Technical field
The present invention relates to safety check workflow management correlative technology fields, particularly relate to a kind of random based on queueing theory and broad sense
The airport security flow optimization method of petri nets.
Background technology
Airport security inspection is a component part of national communication infrastructure and a weight of Global Airports operation
Want aspect.Due to the high value of Air transportation service, global airport is all different degrees of to be faced with by the attack of terrorism
It threatens.In order to ensure that the journey safety of all passengers, airdrome control side have to spend a large amount of manpower and materials for safety check, and
With being continuously increased for security process, the time needed for safety check is also increasing rapidly.After the outburst of 9-11 events, safety check flow, skill
The related improvement of art, measure and relevant law etc. causes rapidly the extensive concern of the U.S. and other countries.Normal conditions
Under, airdrome control department checks passenger and its luggage in security check point, to ensure all passengers during travelling
Safety.But existing safety check flow receives criticism since the stand-by period is long.For passenger, in safety and facility
Between to find a balance be very difficult.For client, carry out safety inspection is devoted a tremendous amount of time, will to navigate
No matter sky travelling on time, money all seems sufficiently expensive.Therefore, under the premise of ensuring safety, optimize safety check flow pair
Passenger and airline are all very favorable.
Current solution to this problem mainly has：First, analogue simulation is carried out with the method for discrete events simulation, this
The service time of each information desk in system can be described in kind method, with very in queuing system and process optimization
Extensively.Discrete events simulation, which can be good at the customer to regard an entity as, to be described, to record the reaching of customer, leave
The service efficiency etc. of time and each workbench, but such issues that for airport security in, the luggage number of each passenger institute band
It is uncertain, when inspection luggage of each passenger can be become multiple entities from an entity to be serviced, and next passenger examines
It the service that a upper passenger is received such as first has to before looking into complete, and it is opposite to service number of units in discrete events simulation
It is fixed, it is desirable to which that efficiency of service is relative complex from the point of view of changing Service Source number.
Another method：The method of petri network modellings analysis is suitable for use in computer scheduling of resource administrative analysis, this
Kind method can analyze the various resource utilizations in system, but i.e. without concept of time also without triggering probability.Therefore
Introduce the concept of time of the event occurrence rate and system of triggering transition, referred to as Stochastic Petri Nets.Country's external-open recently
Beginning someone carries out analysis optimization using Stochastic Petri Nets to the production procedure of workpiece, but these methods are mainly profit
With General Stochastic Petri Net come the Tokken number variation in research system, the handling capacity of Tokken in system can only be surveyed in this way
Amount, for the time that a specific Tokken uses in systems, the priority arrival relationship of Tokken residing for library and service regulation without
Method describes.Therefore not being suitable for this kind of customer of airport security had in the problem of quality of service requirement.
Therefore, current airport security has that the utilization of resources can not be unified with safety check efficiency.
Invention content
In view of this, a kind of based on the airport of queueing theory and General Stochastic Petri Net peace it is an object of the invention to propose
Flow optimization method is examined, can accurately and effectively realize the optimization of airport security flow, improves safety check efficiency and quality.
Based on a kind of above-mentioned purpose airport security flow based on queueing theory and General Stochastic Petri Net provided by the invention
Optimization method, including：
Airport security layout information is obtained, based on queueing theory, General Stochastic Petri Net and safety check layout information structure peace
Examine procedural model；Wherein, the safety check procedural model includes Tokken, transition and library institute three elements；Customer represented by library is lined up position
It sets；The transition indicate the information desk of customer；Tokken indicates customer and the luggage with fixed ID and timestamp, and luggage institute is right
Ability trigger transition after Tokken ID corresponding to the Tokken ID answered and customer merges；Library is so the queue of first in first out cares for stack
Visitor；
Obtain airport security historical information；Wherein, the safety check historical information includes customer arrival time interval and clothes
It is engaged in the time used in platform detection target；
The safety check historical information is substituted into safety check procedural model and carries out simulation calculation, obtains simulation result；
Simulation result is compared, whether judging result meets desired effect, if being unsatisfactory for, then accordingly average
Transition corresponding to the most library of Tokken quantity increase safety check resource or the transition corresponding to the minimum library of average Tokken number
Reduce safety check resource；Continue simulation calculation until meeting desired effect.
Optionally, the safety check procedural model further includes trigger；The trigger is set to library institute outlet end, is used for table
Show the triggering of transition；And the sum of probability is met for the triggering probability of the trigger of same library institute outlet end and is equal to 1.
Optionally, corresponding first library increases by one and changes at random before in the safety check procedural model, and give with
Machine transition assign a random triggering probability, and when for occur every time new customer, random transition will be triggered.
Optionally, the safety check procedural model further includes customer analysis module, for for customer group in different airports
Characteristic information, and then the corresponding relevant parameter adjusted in safety check procedural model；Wherein, the characteristic information includes nationality's letter
Breath, gender information.
From the above it can be seen that the airport security provided by the invention based on queueing theory and General Stochastic Petri Net
Flow optimization method is expanded using queuing theory Perti nets random to broad sense, one queue of increase residing for each library
Concept realizes the monitoring to each Tokken life cycle in Petri network and initiation quantity variation to describe its precedence relationship,
Finally obtain effective safety check procedural model.And it solves each to change corresponding service time in network and touch using queuing theory
Hair probability is calculated, and calculating of the Markov Chain to transition times and probability is different from, and being calculated using queuing theory can be with
Easily observe the influence to system migration come to the control strip of safety check resource.Therefore, the application being capable of accurate and effective
Realization airport security flow optimization, improve safety check efficiency and quality.
In addition, the application also at least has the following effects that：(1) by combining queuing theory and Stochastic Petri Nets,
The library of Petri network can be considered the queue of a Tokken, also by queuing theory to each changing corresponding touch in Petri network
Hair probability and time provide new calculation.Petri network is innovated with theory, solves general Petri
The problem of token precedence relationship can not describe in position in network.(2) method of workflow optimization is concentrated on greatly in the past
Yield, and the present processes can be to service quality that the customer waited in airport is enjoyed equally object as an optimization.
The stand-by period that may be needed to each customer in airport security flow is simulated, but unlike that general one
Customer's whole process receives the queuing problem of service, and customer can become multiple objects to be serviced and customer itself in service process
Receiving service concurrent simultaneously.(3) the triggering probability that transition are calculated with Markov Chain is compared, queuing system calculating is one white
BOX Model can be very good control variable parameter monitoring system variation, once and this kind of black-box model problem pair of Markov Chain
As transformation is with regard to needing to reconfigure.Accurate predictions and simulations effect can be obtained in the case where sufficient historical data expands
Fruit, can be with extensive utilization in actual prediction.
Description of the drawings
Fig. 1 is the airport security flow optimization method one provided by the invention based on queueing theory and General Stochastic Petri Net
The flow diagram of a embodiment；
Fig. 2 is common TSA safety checks flow diagram；
Fig. 3 is the basic petri pessimistic concurrency controls schematic diagram at safety check station provided by the invention；
Fig. 4 is the Generalized Stochastic Petri Net schematic diagram of multichannel Multiple server stations safe examination system provided by the invention；
Fig. 5 is the principle schematic provided by the invention that conversion time and toggle rate are obtained by queueing theory；
Fig. 6 is average traveler arrival time provided by the invention interval stochastic behaviour schematic diagram；
Fig. 7 is the arrival time spacing frequency schematic diagram of passenger provided by the invention；
Fig. 8 is that each passenger provided by the invention passes through the required time diagram of safety inspection；
The change schematic diagram that Fig. 9 occurs at any time and constantly for the queue length in the B of the corresponding regions Fig. 2；
Comparison schematic diagrams of the Figure 10 between close queue provided by the invention and loose queue；
Figure 11 is the difference schematic diagram of passenger's mean waiting time provided by the invention after different zones are jumped the queue.
Specific implementation mode
To make the objectives, technical solutions, and advantages of the present invention clearer, below in conjunction with specific embodiment, and reference
Attached drawing, the present invention is described in more detail.
It should be noted that all statements for using " first " and " second " are for differentiation two in the embodiment of the present invention
The non-equal entity of a same names or non-equal parameter, it is seen that " first " " second " only for the convenience of statement, does not answer
It is interpreted as the restriction to the embodiment of the present invention, subsequent embodiment no longer illustrates this one by one.
First choice is it should be noted that the application wants research safety to check the resource allocation problem in channel.First, using wide
Adopted stochastic Petri net (GSPN) establishes safety check procedural model.Then, we are described by applying to queuing theory in GSPN
Passenger traffic volume during airport security and its precedence relationship.On this basis, we can be very well in simulation safety check flow
The safety check station stream of people handle up situation.In the case where ensureing that safety inspection project is constant, proposed to airport according to simulation result
The suggestion that safety check flow optimizes.Generally speaking, present applicant proposes queueing theories and Petri network to be combined research work stream
Method, and successfully solve the optimization problem of airport security flow.
Specifically, being the airport security flow provided by the invention based on queueing theory and General Stochastic Petri Net referring to Fig.1
The flow diagram of optimization method one embodiment.The airport security flow based on queueing theory and General Stochastic Petri Net
Optimization method, including：
Step S1 obtains airport security layout information, based on queueing theory, General Stochastic Petri Net and safety check layout letter
Breath structure safety check procedural model；Wherein, the safety check procedural model includes Tokken, transition and library institute three elements；Library is represented to be cared for
Visitor is lined up position；The transition indicate the information desk of customer；Tokken indicates customer and the luggage with fixed ID and timestamp, and
Ability trigger transition after Tokken ID corresponding to Tokken ID and customer corresponding to luggage merges；Library thus first in first out queue
To stack customer；
Step S2 obtains airport security historical information；Wherein, the safety check historical information includes customer arrival time interval
And the time used in information desk detection target；
The safety check historical information is substituted into safety check procedural model and carries out simulation calculation, obtains simulation result by step S3；
Step S4, simulation result is compared, and whether judging result meets desired effect, if being unsatisfactory for, then accordingly
The libraries most in average Tokken quantity corresponding to transition increase safety check resource or average Tokken number it is minimum library institute it is right
The transition answered reduce safety check resource；Continue simulation calculation until meeting desired effect.
In the application some optional embodiments, the safety check procedural model further includes trigger；The trigger is set
It is placed in library institute outlet end, the triggering for indicating transition；And the triggering probability of the trigger of same library institute outlet end is met
The sum of probability is equal to 1.
In the application some optional embodiments, corresponding first library increases by one before in the safety check procedural model
A random transition, and a random triggering probability is assigned to random transition, it is random to become when for occur every time new customer
Moving will be triggered.
In the application some optional embodiments, the safety check procedural model further includes customer analysis module, is used for needle
To the characteristic information of customer group in different airports, and then the corresponding relevant parameter adjusted in safety check procedural model；Wherein, institute
It includes nationality's information, gender information to state characteristic information.
By above-described embodiment it is found that the herein described airport security flow based on queueing theory and General Stochastic Petri Net
Optimization method is expanded using queuing theory Perti nets random to broad sense, increases the concept of a queue residing for each library,
To describe its precedence relationship, realizes the monitoring to each Tokken life cycle in Petri network and cause quantity variation, final
To effective safety check procedural model.And it solves each to change corresponding service time and triggering probability in network using queuing theory
It is calculated, is different from calculating of the Markov Chain to transition times and probability, can be easy to using queuing theory calculating
Observe the influence to system migration come to the control strip of safety check resource.Therefore, the application can be realized accurately and effectively
The optimization of airport security flow improves safety check efficiency and quality.
In order to clearly explain the main inventive concept of the application, the application is also disclosed in some embodiments and is mainly set
Principle is counted, it is as follows：In general, the method for this kind of workflow optimization problem generally use Queuing network of safety check is ground
Study carefully.One of classical way as operational research field, queueing theory have good applicability in research Random Service platform system aspects.
In A.K.Erlang after 1909 are put forward for the first time the concept of queueing theory, Queuing network theory is also excellent initially as planning strategies in 1963
A part for change is proposed out.The scholars such as Shanthikumar use the flow-optimized problem of queuing network research work and acquirement
Good result.2011, Stephen Louis Dorton used Queuing network and discrete events simulation in his paper
Method has successfully completed the problem of about airport security process optimization.
During practical safety check, often exist each passenger can carry multiple luggage, also or multiple luggage simultaneously
The case where corresponding multiple passengers.Although queuing theory has fabulous agreeing with property in Random Service platform service process research.But it is right
" derivation " is presented between this kind of customer, and (it is to be subjected next that a customer becomes customer of multiple and different classifications etc. after service
The service of information desk) phenomenon the problem of modeled and analyzed and will seem sufficiently complex.Based on this problem, also have both at home and abroad
In the problem of many scholars start General Stochastic Petri Net being used for airport security process optimization.Not with petri pessimistic concurrency controls
Disconnected development, petri networks be extended for possessing the time from simple resource scheduling model and trigger the broad sense of probability with
Machine petri nets.It is very constructive that General Stochastic Petri Net, which is introduced this direction, but due to General Stochastic Petri Net
Can not describe same library in the sequencings of different Tokkens therefore have priority arrival relationship for this kind of customer of airport security
System describe there are certain defects.Between we want observation passenger when the abnormal behaviours such as existing " jumping the queue ", broad sense
Stochastic Petri Nets can not just show so good desired effect.Therefore, we combine queuing theory and the random petri of broad sense
Net, propose by library and its viewpoint that is combined with the concept of queue and information desk of corresponding trigger, to the row of constructing
Team's General Stochastic Petri Net, to analyze and optimize this problem.
In the application some optional embodiments, specific analysis example is also disclosed, with United States Transportation Security office
(TSA) it is studied for safety check flow.Its currently used safety check flow is as shown in Figure 2, can usually be divided
For four main regions：Region A：By the passenger of preliminary examination and average traveler after arriving at the airport, respectively in preliminary examination channel and general
It is waited in circulation passage.Then an optional identity veritifies platform and carries out identification check.Region B：It is general after confirming that identity is errorless
Logical passenger and the passenger Jing Guo preliminary examination respectively enter in the safety inspection channel of corresponding classification and carry out safety inspection.Region C：By
No abnormal passenger will take the luggage of oneself away in this region after safety inspection.Region D：It is found in safety inspection different
Normal passenger will be trapped in the areas D and carry out detailed inspection, can be just allowed to enter in airport lounge after by inspection.
Based in single channel list information desk system, the passenger into safe examination system is considered by a source point production
Raw and one meeting point of eventually off arrival.According to the currently used safety check flows of TSA, a single channel list clothes have been initially set up
The petri pessimistic concurrency controls of business platform are as shown in Figure 3.It might as well assume that next customer starts when a upper customer receives the luggage of oneself
Carry out safety check.Therefore, the capacity of library institute p3 and p5 is 1.Meanwhile when, there are when Tokken, the Tokken in p1 will not in p3 or p5
It can be changed.
Next, considering the safety check station model in the case of multichannel Multiple server stations, and it is drawn in Fig. 4.It is more in multichannel
In information desk model, there are the identity veritification of multiple openings and a plurality of security check passage, thus passenger first can veritify platform from identity
In it is optional one progress identity veritification, then in multiple security check passages select one progress safety check.In this model, establish
Multiple libraries similar with p1 in Fig. 3 indicating to veritify the passengers to be checked such as platform j in identity.
Although traditional petri models can be good in safe examination system while property and concurrency feature is retouched
It states.But it can not indicate triggering probability and transition times corresponding to each transition.Therefore, the application expands the model in Fig. 4
Exhibition is a General Stochastic Petri Net.To make each safety check platform machine security check passage in safety check flow have corresponding to it
Transition times Δ tiAnd triggering probability λi。
Set of trigger events is：
Each the triggering probability corresponding to trigger is：
In this General Stochastic Petri Net, as library institute p4c1l1In there are when Tokken, trigger t4c1l1And t5c1l1Institute
Corresponding toggle rate will have following relationship：
Similarly, trigger t6c1l1And t7c1l1Also meet such condition.
Therefore, it is however generally that,
Further, it is contemplated that queueing theory reaches in description customer and has property on probation well in service process at random, and can
With indicate well customer reach precedence relationship and queuing in receive service principle.In general, when being changed in petri nets
Between and triggering probability obtained from timing statistics data and probability distribution.The probability that sets out of transition might as well be taken to be lined up equal to customer
Arrival rate in system.So just service time corresponding with customer is identical for transition times.Fig. 5 is described to be obtained by queueing theory
The principle of conversion time and toggle rate.
Process in order to which service is reached, received to customer is described, the library institute P of the application in Fig. 40Before plus
Enter a random transition t '0To randomly generate the customer to arrive at the airport.Then, in conjunction with queue theory model to transition times Δ
tkciliWith toggle rate λkcjljAssignment is carried out, and arrival time is assigned to distinguish to the Tokken for representing each customer and its luggage
Precedence relationship.For changing t ' at random0Assign constant its transition times Δ t '0A=0 and corresponding random big toggle rate λ '0。
Whenever thering is a passenger to reach, t ' is changed0It is just triggered primary.In this way, in General Stochastic Petri Net transition times Δ t and
Toggle rate λ corresponding to it can be calculated by the method for queueing theory.One is constructed on the basis of statistical data
Polynary array, then wherein one element of uniformly random selection again.It can be basic according to corresponding state in the system of elements of selection
It determines.Then Tokken in netting a customer and its corresponding multiple luggage with petri replaces.By to Tokken in system
Transition rule be adjusted can well by the correlation between customer and luggage depict come.
Since the arrival of customer in airport often shows in Various Seasonal different periods the characteristic of multiple peak periods.Cause
This, it is difficult to using general probability distribution description, it reaches characteristic.The application using SPSS to preliminary examination passenger in TSA statistical data and
The stochastic behaviour at average traveler arrival time interval is analyzed.Analysis result is as shown in Figure 6 and Figure 7.According to result it is found that
The value of all asymptotic conspicuousnesses is both less than 0.1.Therefore, average traveler arrival time interval is described simultaneously with common random probability distribution
Not very rationally.Equally, the application can also verify other data and be described with common random probability distribution nor very rationally
's.Therefore, the application indicates that it reaches rule using the carry out random value of statistical data.
Next, the application simulates this model by writing C++ programs.Due to the inspection of preliminary examination passenger and revenue passenger
It is the same in addition to putting down this part of luggage to look into process, and the application accounts for the passenger of both types respectively.It is false
If the safety check scan channel of ordinary customer, the safety check scan channel of preliminary examination passenger and the quantity of identity card inspection station are respectively 9,3
With 12.According to these data, stand-by period of each passenger within a hour is simulated and has recorded, and show in fig. 8.
Compare for convenience, when the result (integer) according to emulation experiment calculates handling capacity, average latency and waits for
Between variance.It obtains preliminary examination passenger and revenue passenger emulation comparison result is as shown in the table
The comparison of table 1. preliminary examination passenger and revenue passenger
As the results in table 1 show, preliminary examination passenger is in most cases less by the security check passage required time.
In addition, the handling capacity of preliminary examination passenger is also larger than revenue passenger.But from the point of view of the average latency of passenger, in advance
The stand-by period that inspection passenger faces fluctuates also bigger.
Before having a large amount of passengers to pour in system, each flow of safety check system can run well.In order to
The bottleneck of safety check problem can be found from the links of safety check workflow.To in experiment customer reach generation data into
Modification gone to improve the arrival rate of passenger.By experimental analysis, the difference that the length that B sectors arrange is stabbed at any time is changed
Situation is showed with Fig. 9.
When throughput of system is less than 310, wait for the length of passenger's queue of safety check that can be fluctuated in specific level.One
Denier handling capacity is more than this value, and the length of queue will fluctuate upwards.It means that the continuous increasing of the passengers quantity with arrival
Add, the waiting list in the areas B will become more and more longer.Therefore, cause throughput of system that can not increase, cause the passenger's queue of safety check station
The reason for the more arranging the more long is to change t2cili, t3cili, i.e., to the scanning of passenger and to the sweep test of luggage.
It, can be by changing the capacity of these bottlenecks where luggage scanning and scanning passenger are the bottleneck of safety check problem
To improve the handling capacity of system.By shortening the priority of the corresponding transition times or adjustment process in these programs, to
It is allowed to show outstanding than work at present method.
On the other hand, unique difference due to preliminary examination passenger and revenue passenger during safety check is just whether to need to go
Lee is totally placed in this process on baggage scan frame, but the stand-by period variance bigger of preliminary examination passenger.Therefore, luggage is taken
The process for going out scanning is also one of the main reason for fluctuation customer's average latency.In fact, all random processes are all
The source that variance generates in system.It can be by further to operation sequence and checking process to the major limitation method of variance
It standardizes to realize.
For a specific airport, luggage scanning checks that the data of most of flow such as file do not have obviously
Variation.But the arrival situation of passenger is very likely different, such as dull season or 1 year busy season in different times, one day
Peak.People from country variant culturally might have different behaviors and difference in character.These are likely to one
Determine to influence the result that we analyze in degree.Therefore, customer's personality and behavior situation will be further discussed in we.
Different Culture background means different education experience and growth environment, it is however generally that from the most of same culture
Number people's often shows certain similitude in some aspects of personality.The difference in character of one passenger is not enough to influence whole
A airport security checks the state that system is shown, but the personality of many passengers may generate difference in systems.For example,
Indian seems very crowded and urgent when waiting in line mostly, but American may get used to loose queue.Due to this
The presence of species diversity, may be different to the review time of passenger.We are by slightly increasing or decreasing the transition of passenger
Time compares different caused by these situations.Comparison closely between queue and loose queue is as shown in Figure 10.
The waiting time of passenger is more shorter than the passenger in more loose queue in more crowded queue.Therefore,
Our model is for certain specific general character personality, for example those are happy to queue arranging to obtain close people, are sensitive.
Another common difference between culture be it is most of can be when the behaviors such as being lined up from people from different cultures
It shows specifically to be accustomed to difference.American is famous with other people personal space of respect, has a kind of opposition " to insert in face of other people
The social consensus of team ".But Chinese are well-known to pay the utmost attention to personal efficiency.Therefore, it is lined up more on the more airport of Chinese
It is likely encountered " jumping the queue " situation.Although " jumping the queue " will not impact the entire throughput at safety check station, for other
For the passenger waited in queue, after encountering " jumping the queue ", still to take more time.In order to simulate this case,
The application changes customer with respect to Queue sequence in array obtained by several libraries that customer is lined up using the method for being inserted into element.Figure
11 show the difference of passenger's mean waiting time after different zones " jumping the queue ".
According to simulation result it is found that model is also sensitive to the simulation result of behaviors such as " jumping the queue ".
Therefore, generally speaking, the present processes are feasible.Petri network is suitable for description Asynchronous parallel computation machine system
System model, and queueing theory is then the classical way for studying the stochastic service system course of work.What Petri network and queueing theory were combined
Method is also a kind of feasible and effective method to workflow issues in research system.Especially when system asynchronous concurrent complicated or
When the resource quantities such as the service station in system are changeable, and there are when precedence relationship for Tokken.
It is as follows according to the discussion of front and explanation, then the suggestion listed here to each airport：
Standardization：In order to reduce the variance of Customer waiting time, major airport should take the operation of strict standard
Regulation.
Custom constraint：In order to reduce the variance of Customer waiting time and shorten the stand-by period, airport side should pass through
Queue area situation is rationally designed to be lined up behavior that is even closer, and avoiding " jumping the queue " between customer.
The operation of profession：When changing corresponding transformation by training employee and employee to reduce each in petri nets
Between.
High power capacity：Throughput of system is improved by increasing office worker's quantity and scanning device, especially body scanner.
Flexibly use resource：In order to limit the cost for increasing airport personnel and equipment, a feasible method is preparation one
A little scan channels only opened in peak period.
More particularly passenger：In order to expand the handling capacity of security checkpoints, it is logical that more preliminary examination customers can be arranged in airport
Road, and further increase the privilege of preliminary examination passenger.
By above-described embodiment it is found that the application at least has the following effects that：(1) by combining queuing theory and broad sense random
Petri network, the library of Petri network can be considered the queue of a Tokken, also by queuing theory to each being changed in Petri network
Corresponding triggering probability and time provide new calculation.Petri network is innovated with theory, solves one
As the problem of token precedence relationship can not describe in position in petri net.(2) in the past big for the method for workflow optimization
Concentrate on yield, and the present processes can be to service quality that the customer waited in airport is enjoyed equally as an optimization
Object.The stand-by period that may be needed to each customer in airport security flow is simulated, but unlike that generally
Customer's whole process receive the queuing problem of service, customer can become multiple objects and Gu to be serviced in service process
Visitor itself receiving service concurrent simultaneously.(3) the triggering probability that transition are calculated with Markov Chain is compared, queuing system calculating is
One whitepack model can be very good control variable parameter monitoring system variation, and this kind of black-box model of Markov Chain is once
Problem objects transformation is with regard to needing to reconfigure.Accurately prediction and mould can be obtained in the case where sufficient historical data expands
Quasi- effect, can be with extensive utilization in actual prediction.
Those of ordinary skills in the art should understand that：The discussion of any of the above embodiment is exemplary only, not
It is intended to imply that the scope of the present disclosure (including claim) is limited to these examples；Under the thinking of the present invention, above example
Or can also be combined between the technical characteristic in different embodiments, step can be realized with random order, and be existed such as
Many other variations of the different aspect of the upper present invention, for simplicity, they are not provided in details.
The embodiment of the present invention be intended to cover fall within the broad range of appended claims it is all it is such replace,
Modifications and variations.Therefore, all within the spirits and principles of the present invention, any omission, modification, equivalent replacement, the improvement made
Deng should all be included in the protection scope of the present invention.
Claims (4)
1. a kind of airport security flow optimization method based on queueing theory and General Stochastic Petri Net, which is characterized in that including：
Airport security layout information is obtained, safety check stream is built based on queueing theory, General Stochastic Petri Net and safety check layout information
Journey model；Wherein, the safety check procedural model includes Tokken, transition and library institute three elements；Customer represented by library is lined up position；Institute
State the information desk that transition indicate customer；Tokken indicates customer and the luggage with fixed ID and timestamp, and corresponding to luggage
Ability trigger transition after Tokken ID corresponding to Tokken ID and customer merges；Library is so customer is stacked in the queue of first in first out；
Obtain airport security historical information；Wherein, the safety check historical information includes customer arrival time interval and information desk
Detect the time used in target；
The safety check historical information is substituted into safety check procedural model and carries out simulation calculation, obtains simulation result；
Simulation result is compared, whether judging result meets desired effect, if being unsatisfactory for, then accordingly in average Tokken
Transition corresponding to the most library of quantity increase safety check resource or the transition corresponding to the minimum library of average Tokken number are reduced
Safety check resource；Continue simulation calculation until meeting desired effect.
2. optimization method according to claim 1, which is characterized in that the safety check procedural model further includes trigger；Institute
It states trigger and is set to library institute outlet end, the triggering for indicating transition；And touching for the trigger of same library institute outlet end
Hair probability meets the sum of probability and is equal to 1.
3. optimization method according to claim 1, which is characterized in that corresponding first library institute in the safety check procedural model
Increase a random transition before, and a random triggering probability is assigned to random transition, for so that occurring new care for every time
When objective, random transition will be triggered.
4. optimization method according to claim 1, which is characterized in that the safety check procedural model further includes customer analysis mould
Block for the characteristic information for customer group in different airports, and then adjusts the corresponding ginseng in safety check procedural model accordingly
Number；Wherein, the characteristic information includes nationality's information, gender information.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110765664A (en) * | 2019-11-26 | 2020-02-07 | 北京首都国际机场股份有限公司 | Security check efficiency optimization method based on simulation |
| CN112036053A (en) * | 2020-09-04 | 2020-12-04 | 中国航空无线电电子研究所 | Petri net-based airport surface sliding simulation and optimization method |
| CN113378416A (en) * | 2021-08-16 | 2021-09-10 | 傲林科技有限公司 | Event network simulation-based optimization method and device and computer equipment |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103632005A (en) * | 2013-12-04 | 2014-03-12 | 中国航空综合技术研究所 | Task reliability modeling method for failure reconfiguration process of dynamic reconfiguration system |
| CN106650985A (en) * | 2016-09-12 | 2017-05-10 | 南京航空航天大学 | Method for predicting the chain effect of abnormal flight derived event based on stochastic Petri nets |
| CN107730053A (en) * | 2017-11-07 | 2018-02-23 | 民航成都信息技术有限公司 | System model method for building up and device |
-
2018
- 2018-03-06 CN CN201810184141.1A patent/CN108510106A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103632005A (en) * | 2013-12-04 | 2014-03-12 | 中国航空综合技术研究所 | Task reliability modeling method for failure reconfiguration process of dynamic reconfiguration system |
| CN106650985A (en) * | 2016-09-12 | 2017-05-10 | 南京航空航天大学 | Method for predicting the chain effect of abnormal flight derived event based on stochastic Petri nets |
| CN107730053A (en) * | 2017-11-07 | 2018-02-23 | 民航成都信息技术有限公司 | System model method for building up and device |
Non-Patent Citations (3)
| Title |
|---|
| 冯小博等: "机场安检站优化乘客吞吐量研究", 《无线互联科技》 * |
| 樊荣: "机场安检口客流量的优化", 《环球市场信息导报》 * |
| 马宏飞等: "基于Petri网络的安检流程优化的研究", 《信息科学》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110765664A (en) * | 2019-11-26 | 2020-02-07 | 北京首都国际机场股份有限公司 | Security check efficiency optimization method based on simulation |
| CN112036053A (en) * | 2020-09-04 | 2020-12-04 | 中国航空无线电电子研究所 | Petri net-based airport surface sliding simulation and optimization method |
| CN112036053B (en) * | 2020-09-04 | 2024-05-03 | 中国航空无线电电子研究所 | Airport scene sliding simulation and optimization method based on Petri network |
| CN113378416A (en) * | 2021-08-16 | 2021-09-10 | 傲林科技有限公司 | Event network simulation-based optimization method and device and computer equipment |
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