CN106971051B - Flight guarantee service time estimation method and system - Google Patents
Flight guarantee service time estimation method and system Download PDFInfo
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Abstract
The invention discloses a flight guarantee service time estimation method and a flight guarantee service time estimation system, wherein the method comprises the following steps: establishing a flight guarantee service simulation model; calculating the correlation time of flight guarantee operation process transition according to the flight guarantee service simulation model; performing flight support service simulation according to the flight support service simulation model and the associated time; and calculating flight guarantee service time according to the flight guarantee service simulation. According to the flight support service time estimation method, a more comprehensive flight support service simulation model is established and used, so that the flight support service time is estimated more comprehensively and accurately.
Description
Technical Field
The invention relates to the field of flight support, in particular to a flight support service time estimation method and a flight support service time estimation system.
Background
With the rapid development of civil aviation transportation industry, 26 large airports with passenger throughput reaching the million level by 2015 are available. The rapid expansion of such airports has resulted in increasingly inefficient operation of the airports due to extensive administration of security services. The airport operation is developed by taking ground service guarantee flow as a core, wherein the guarantee service time reflects important indexes for guaranteeing the service flow.
In the prior art, a simulation model is generally used for estimating the guarantee Service time, for example, a Service model tool is used for realizing the business process simulation of a platform vehicle, or the simplified simulation is carried out on the dispatching process of an airport ferry vehicle, only the simulation is carried out on the exhibition of a single type of guarantee Service vehicle, and the model is difficult to accurately estimate the guarantee Service time. Later researchers have discussed the scheduling optimization mechanism of various special vehicles of the airport based on genetic algorithm, multi-objective genetic algorithm, particle swarm algorithm, linear integer programming method and simple time network and agent combination method, but the above scheme still lacks the overall description of the ground service guarantee and does not provide an accurate model for describing the overall guarantee service, so that the ground guarantee service time of the flight cannot be finely evaluated.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a flight guarantee service time estimation method and a flight guarantee service time estimation system, which can estimate the flight guarantee service time more comprehensively and accurately by establishing and using a more comprehensive flight guarantee service simulation model.
In a first aspect, the present invention provides a flight guarantee service time estimation method, including:
establishing a flight support service simulation model based on a coloring time Petri network;
calculating the correlation time of flight guarantee operation process transition according to the flight guarantee service simulation model;
performing flight support service simulation according to the flight support service simulation model and the associated time;
calculating flight guarantee service time according to the flight guarantee service simulation; the flight support service time is time consumed for completing all flight support operation processes.
Further, the establishing of the flight support service simulation model based on the colored time Petri network specifically includes:
describing the flight guarantee operation process by adopting basic elements of a coloring time Petri net;
describing the flight guarantee service state by adopting basic elements of a coloring time Petri network;
describing the logical relationship of the flight guarantee operation process by adopting basic elements of a coloring time Petri network;
describing the relationship between the flight guarantee service state and the flight guarantee operation process by adopting basic elements of a coloring time Petri network;
and establishing a flight support service simulation model based on the coloring time Petri network according to the description.
Further, the description of the flight assurance operation process specifically includes:
using T ═ T1,t2,t3,…,txDescribing each flight guarantee operation process; wherein, t isiGuaranteeing a work process for the ith flight;
using δ ═ δ1,δ2,δ3，…,δxDescribing the operation transition of each flight guarantee operation process; wherein, deltaiIs tiThe transition time of, said deltaiThe following formula is satisfied: deltai＝δiend-δistartWherein, deltaiendIs tiEnd time of δistartIs tiThe start time of (c);
where x is the total number of flight assurance processes, i is 1,2,3 …, x.
Further, the describing the flight guarantee service state specifically includes:
using the set P ═ { P ═ Ps,Puc,PcDescribing the flight guarantee service state; wherein the content of the first and second substances,
Ps＝{psh, aggregated in order to guarantee the job status library, psTo ensure the operation state library;
Puc＝{pucis aggregated by a non-consumable resource pool, pucIs a non-consumable repository;
Pc＝{pcis aggregated by a pool of consumable resources, pcIs a consumable repository.
Further, the logical relationship includes: serial, parallel; the description of the logical relationship of the flight support operation process specifically includes:
when t isiAnd tjWhen the elements of the front set and the back set of the same flight guarantee operation state base are respectively used, t is addediAnd tjDescribed as a serial relationship;
when t isiAnd tjRespectively being the back set elements of different flight support operation state libraries, and when the front set elements of the different flight support operation state libraries are the same support operation process, t is addediAnd tjDescribing a parallel relationship;
when t isiAnd tjRespectively the front elements of different flight support operation state libraries, and when the rear elements of the different flight support operation state libraries are the same support operation process, t isiAnd tjDescribed as a parallel relationship
Wherein, t isiFor the ith flight support operation process, tjFor the jth flight assurance process, i is 1,2,3 …, x, j is 1,2,3 …, x, x is the total number of flight assurance processes.
Further, the describing the relationship between the flight support service state and the flight support operation process specifically includes:
and embodying the relation among the flight guarantee operation state library, the non-consumable resource library and the consumable resource library by using an association arc.
Further, the calculating the correlation time of flight safeguard operation process transition according to the flight safeguard service simulation model specifically includes:
calculating the time distribution of each flight guarantee operation process according to the flight guarantee service simulation model;
and generating corresponding random numbers according to the time distribution, and taking the random numbers as the associated time of the flight guarantee operation process transition.
Further, the flight support service simulation according to the flight support service simulation model and the associated time specifically includes:
setting simulation initial time and simulation cycle times;
and performing flight guarantee service simulation for multiple times from the initial simulation moment according to the flight guarantee service simulation model and the associated time and the simulation cycle times.
Further, the calculating flight guarantee service time according to the flight guarantee service simulation specifically includes:
comparing the number of the current simulation cycle with the number of the simulation cycles;
when the number of the current simulation cycle is larger than or equal to the number of the simulation cycles plus 1, calculating first flight guarantee service time of each flight guarantee service simulation to obtain a plurality of first flight guarantee service times of multiple times of simulation;
and taking the values of the plurality of first flight guarantee service times as samples, calculating the average value of the samples, and taking the average value as the flight guarantee service time.
In a second aspect, the present invention further provides a flight support service time estimation system, including: the system comprises a model establishing module, a first time calculating module, a simulation module and a second time calculating module;
the model establishing module is connected with the first time calculating module, the first time calculating module is connected with the simulation module, and the simulation module is connected with the second time calculating module;
the model establishing module is used for establishing a flight support service simulation model based on a coloring time Petri network;
the first time calculation module is used for calculating the correlation time of flight guarantee operation process transition according to the flight guarantee service simulation model;
the simulation module is used for carrying out flight guarantee service simulation according to the flight guarantee service simulation model and the associated time;
the second time calculation module is used for calculating flight guarantee service time according to the flight guarantee service simulation; the flight support service time is time consumed for completing all flight support operation processes.
According to the technical scheme, the flight guarantee service time estimation method and the flight guarantee service time estimation system are provided, and the flight guarantee service time is estimated more comprehensively and accurately by establishing and using a more comprehensive flight guarantee service simulation model.
Drawings
Fig. 1 is a schematic flow chart of a flight guarantee service time estimation method provided by the invention.
FIG. 2 is an exemplary diagram of flight assurance work process logic relationships.
FIG. 3 is an exemplary diagram of a flight assurance service simulation model.
FIG. 4 is a graph of a comparison of estimated time and actual time for a portion of flight provisioning service and a graph of minimum transit time in accordance with an exemplary embodiment of the present invention.
Fig. 5 is a schematic structural diagram of a flight support service time estimation system provided by the present invention.
Detailed Description
Embodiments of the present invention will be described in detail below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and therefore are only examples, and the protection scope of the present invention is not limited thereby.
Example one
Fig. 1 is a flowchart illustrating a flight guarantee service time estimation method according to an embodiment of the present invention. As shown in fig. 1, a flight guarantee service time estimation method includes:
step S1, establishing a flight guarantee service simulation model based on a coloring time Petri network;
step S2, calculating the correlation time of flight guarantee operation process transition according to the flight guarantee service simulation model;
step S3, flight guarantee service simulation is carried out according to the flight guarantee service simulation model and the associated time;
step S4, calculating flight guarantee service time according to the flight guarantee service simulation; the flight support service time is time consumed for completing all flight support operation processes.
The specific technical scheme of the embodiment is as follows:
and step S1, establishing a flight guarantee service simulation model based on the coloring time Petri network.
Establishing a flight support service simulation model, which specifically comprises the following steps: describing the flight guarantee operation process by adopting basic elements of a coloring time Petri net; describing the flight guarantee service state by adopting basic elements of a coloring time Petri network; describing the logical relationship of the flight guarantee operation process by adopting basic elements of a coloring time Petri network; describing the relationship between the flight guarantee service state and the flight guarantee operation process by adopting basic elements of a coloring time Petri network; and establishing a flight safeguard service simulation model based on the coloring time Petri network according to the description.
Wherein, the coloring time Petri net can be expressed as: CTPN ═ P, { n, { n, and M, and (M) } in its basic elements1,p2,p3,…，pnIs a finite set of libraries, T ═ T1,t2,t3,…，tnIs a finite set of transitions, and P ∩ T ≠ φ, P ∩ T ≠ φ, Pre is a forward correlation matrix, Post is a backward correlation matrix, C is a set of shading colors of the library, specifically C (P) isi)＝{c1,c2,c3,…,cnWhere i is 1,2,3, …, n, δ is { δ ═ δ }1,δ2,δ3，…,δnIs the set of time delays for all transitions, W: P × T ∪ T × P → l (c), M: P → l (c), where l (c) ═ a1c1+a2c2+…+ancnIs an integer linear function defined on the color set; graphically, the libraries are represented by circles, the transitions by rectangles, and the relationship between libraries and transitions by directed arcs.
The existing discrete modeling tool Petri network cannot meet modeling requirements due to the lack of various depicting elements for time and resources, and the basic elements of the colored time Petri network are adopted to describe flight guarantee service processes, so that a flight guarantee service simulation model is established.
The guarantee resources comprise non-consumable resources and consumable resources, the non-consumable resources specifically comprise resources such as guarantee service personnel and special vehicles, and the consumable resources specifically comprise resources such as aviation gasoline and aviation food.
The flight safeguard operation process is mainly a set of a series of flight safeguard operation processes in a time period from the gear-up to the gear-down, and specifically comprises 19 standard operation nodes such as the gear-up, a gallery bridge or passenger car butt joint, the oil filling, the power supply vehicle garbage truck operation and the like, wherein the flight safeguard operation processes have a certain sequence and logic sequence, and the flight safeguard service operation process is formed together.
Further, the description of the flight assurance operation process specifically includes: using T ═ T1,t2,t3,…,txDescribing each flight guarantee operation process; wherein, t isiGuaranteeing a work process for the ith flight; using δ ═ δ1,δ2,δ3，…,δxDescribing the operation transition of each flight guarantee operation process; wherein, deltaiIs tiTransition time of deltaiThe following formula is satisfied: deltai＝δiend-δistartWherein, deltaiendIs tiEnd time of δistartIs tiThe start time of (c); wherein x is the total number of flight assurance processes, 0<i≦x，i＝1,2,3…,x。
Further, the describing the flight guarantee service state specifically includes: using the set P ═ { P ═ Ps,Puc,PcDescribing the flight guarantee service state; wherein, Ps＝{psH, aggregated in order to guarantee the job status library, psTo ensure the operation state library; puc＝{pucIs aggregated by a non-consumable resource pool, pucIs a non-consumable repository; pc＝{pcIs aggregated by a pool of consumable resources, pcIs a consumable repository.
In particular, the use of said psThe said pucAnd said p iscThe marking function and the capacity function describe the flight guarantee service state.
Wherein, the p issIs the product of the number of the guaranteed job status libraries and the guaranteed job status flags, psThe capacity function of (2) is the guarantee job status flag. p is a radical ofsThe formula of the identification function and the capacity function of (2) is as follows:
wherein m issNumber of flight assurance job status libraries, csAnd marking the flight guarantee operation state.
Said p isucAn identification function of (a), which is an accumulated sum of the product of the number of the non-consumable resources and the color-marker of the non-consumable resources; said p isucIs the cumulative sum of the product of the maximum number of non-consumable resources and the color-label of the non-consumable resource. Said p isucThe formula of the identification function and the capacity function of (2) is as follows:
M(puc)＝muc1·cuc1+muc2·cuc2+muc3·cuc3+…+mucn·cucn
K(puc)＝kuc1·cuc1+kuc2·cuc2+kuc3·cuc3+…+kucn·cucn
wherein m isuciIs the number of i non-consumable resources, kuciMaximum number of resources allowed for the ith non-consumable resource, cuciFor i-th non-consumable resource, e.g. cuciA mark capable of indicating a security personnel, 0<i≦n，i＝1,2,3 …, n. n is the number of categories of the non-consumable resource.
Said p iscAn identification function of (a), which is an accumulated sum of the number of the consumable resources multiplied by the color-label of the consumable resource; said p iscIs the cumulative sum of the maximum number of consumable resources multiplied by the color-label of said consumable resource. Said p iscThe formula of the identification function and the capacity function of (2) is as follows:
M(pc)＝mc1·cc1+mc2·cc2+mc3·cc3+…+mcm·ccm
K(pc)＝kc1·cc1+kc2·cc2+kc3·cc3+…+kcm·ccm
wherein m isciFor the number of i-th consumable resources, kciMaximum number of resources allowed for the i-th consumable resource, cciFor the i-th consumable resource, 0<i ≦ m, i ≦ 1,2,3 …, m. m is the number of the types of the consumable resources
Further, the logical relationship includes: serial, parallel; the flight guarantee operation processes in the serial relation are sequentially carried out, and the state of the guarantee when the previous guarantee operation process is finished is a necessary condition for starting the next guarantee operation process.
The description of the logical relationship of the flight support operation process specifically includes: when t isiAnd tjBelonging to the same flight guarantee job status base, i.e. tiAnd tjWhen the elements of the front set and the back set of the same flight guarantee operation state base are respectively used, t is addediAnd tjDescribed as a serial relationship; when t isiAnd tjWhen the back set elements or the front set elements of different flight guarantee operation state libraries belong to the same guarantee operation process, t is addediAnd tjDescribing a parallel relationship; wherein, t isiFor the ith flight support operation process, tjFor the jth flight assurance work process, 0<i, j ≦ x, i, j ≦ 1,2,3 …, x. And x is the total number of flight guarantee operation processes.
The above description of the logical relationship can be expressed by the following formula:
if ti∈*psAnd t isj∈psThen tiAnd tjA serial relationship;
and p isi,pj∈psIf p isi∈*ti(ti*)、pj∈*tj(tjAnd p) and pi∈t*(*t)、pjE t ([ t ]), then tiAnd tjThe parallel relationship. Wherein, psFor the Procedent guarantee job State Bank, psAnd the operation state library of the back set guarantee operation does not have intersection with the operation state library of the front set guarantee operation. For example, t in FIG. 22Is added with aviation oil, t3Is a garbage truck operation, t4Is the luggage, goods and post operation, and the three belong to p1、p2、p3Of the last set element, and p1、p2、p3All of the preceding elements of (2) are t1And thus in a parallel relationship.
Further, the describing the relationship between the flight support service state and the flight support operation process specifically includes: reflecting the relationship among the flight guarantee operation state library, the non-consumable resource library and the consumable resource library by using an association arc; specifically, the relationship among the flight assurance job status base, the non-consumable resource base and the consumable resource base is embodied by a weight function of an association arc.
Wherein, the weight function of the arc associated with the flight assurance operation state library is shown as the following formula:
the weight function of the arc associated with the non-consumable repository is shown in the following formula:
wherein, wuciIs a weight of a non-consumable resource, is a non-negative integer, and wuc1+wuc2+…+wucn≠0。
The weight function of the associated arc of the consumable repository is shown in the following formula:
wherein, wciIs a weight of a non-consumable resource, is a non-negative integer, and wc1+wc2+…+wcm≠0。
According to the above description of the multiple factors, the established flight support service model can be represented by the following formula: CTPN-SSM ═ (P, T, Pre, Post, F, K, W, C, δ, R, M). Wherein T ═ Ts∪TvAnd T iss∩Tv＝φ，TsFor flight support work process transitions, TvEnsuring the operation process transition for the virtual flight; the Pre and Post respectively serve a forward incidence matrix and a backward incidence matrix of flight guarantee service;
ensuring a logical relationship of the operation process for the flight; f ═ F1∪f2∪f3The remaining parameters are defined as described above for the weight function associated with the arc.
The flight guarantee service time is estimated according to the model and the Monte Carlo method. The Monte Carlo method is a method for randomly simulating system attributes by means of a random sampling technology, and is based on probability theory and takes a computer as a tool, and generates random numbers by utilizing probability distribution of each element to represent the duration of each element state, so that the duration of the whole system is simulated finally. Therefore, each operation transition in the service guaranteeing Petri network system is regarded as an independent element, the distribution generated by each operation transition is solved, corresponding random numbers are generated according to the distribution, and finally the whole service time is simulated according to the logical relation of the service guaranteeing Petri network system. According to the above, the implementation is based on the CTPN-SSM support service model and utilizes the monte carlo method to estimate the support service time for supporting the quantitative analysis and evaluation of the flight support service, and the specific scheme is as follows:
and step S2, calculating the associated time of flight guarantee operation process transition according to the flight guarantee service simulation model.
Step S2 specifically includes: calculating the time distribution of each flight guarantee operation process according to the flight guarantee service simulation model; and generating corresponding random numbers according to the time distribution, and taking the random numbers as the associated time of the flight guarantee operation process transition.
And step S3, performing flight guarantee service simulation according to the flight guarantee service simulation model and the associated time.
Step S3 specifically includes: setting simulation initial time and simulation cycle times, wherein the simulation cycle times are not less than 30; and performing flight guarantee service simulation for multiple times from the initial simulation time according to the flight guarantee service simulation model and the associated time and according to the simulation cycle times. Specifically, according to the flight support service simulation model, taking the associated time as a step length, starting from the simulation initial time, and performing multiple times of flight support service simulation according to the simulation cycle times.
Step S4, calculating flight guarantee service time according to the flight guarantee service simulation; the flight support service time is time consumed for completing all flight support operation processes.
Step S4 specifically includes: comparing the number of the current simulation cycle with the number of the simulation cycles; when the number of the current simulation cycle is not less than the number of the simulation cycles plus 1, calculating the number of the simulation cycles each timeObtaining a plurality of simulated first flight guarantee service times of a plurality of times by using the first flight guarantee service time of the flight guarantee service simulation; if tfiFor the flight guarantee operation transition associated time of i serial relations, the first flight guarantee service time is tf＝tf1+tf2+tf3+…+tfi(ii) a If tfiThe guarantee operation transition associated time of the parallel relation is defined, and the first flight guarantee service time is tf＝max(tf1,tf2,tf3,…,tfi). When the current simulation cycle number is smaller than the number of simulation cycles plus 1, the process needs to return to step S2 to regenerate the random number, and then perform the subsequent steps.
And taking the values of the plurality of first flight guarantee service times as samples, calculating the average value of the samples, and taking the average value as the flight guarantee service time.
The example of estimating the flight guarantee time by using the method of the embodiment is as follows: and establishing a CTPN-SSM model for the flight guarantee service flow of a certain large hub airport in China and estimating the flight guarantee service time. Aiming at aircrafts of different models, the minimum time of passing the station is different, but the domestic guarantee operation flows of airport agents are basically consistent, and the minimum time of passing the station is as follows: 1. gear shifting operation is carried out; 2. butt-jointing a gallery bridge or a passenger ladder vehicle; 3. opening a cabin door to enable passengers to get off the airplane; 4. cleaning a passenger cabin and performing catering operation; 5. aircraft aviation refueling operation; 6. passenger boarding operation; 7. operating a garbage truck; 8. opening a cargo compartment door, unloading luggage and cargo mails, loading the luggage and cargo mails, and closing the cargo compartment door; 9. closing a passenger cabin door, a gallery bridge or a passenger ladder vehicle for operation; 10. removing the wheel chock, and pushing out the tractor for operation; 11. and (4) performing virtual operation. The relevant job data is shown in table 1.
TABLE 1 Job correspondence transition
According to the relevant rules of civil aviation, the aircrafts are divided into five types, five types of aircrafts and corresponding minimum station passing time, and as shown in table 2, the modeling process of the example mainly considers the guarantee service processes of models such as B737 series, A320 series and A321 series.
TABLE 2 minimum time of passing station for different models
The model that can be built according to the above is shown in fig. 3. The experimental data of this example is derived from the airport ground service support production system, and the data includes the relation with the resources, and for the classification of the aircraft model given in table 2, according to the support service time estimation method of this embodiment, the simulation cycle number is set to 10000, and the support service time of the representative model of the classes B and C is respectively found, as shown in table 3, wherein the representative model mainly includes B737 series, a320 series, a321 series, and the like. FIG. 4 shows a comparison graph of a part of flight guarantee service time estimated values and actual values and a minimum station-crossing time graph. As can be seen from table 3 and fig. 4, under the constraint of minimum station-crossing time, the estimation method provides possibility for evaluating the efficiency of the guaranteed service and proposing the related opinion of resource improvement.
TABLE 3 partial flight guarantee service estimated time versus actual time, and minimum transit time
And (3) carrying out analysis and verification on the model by using an error analysis method according to the experimental result, wherein the analysis and verification are shown in the following formula:
in the formula YiFor the purpose of the estimation of the value,
for true values, n is the total number of flights.
The MAE is absolute value-converted due to the dispersion, so that the situation that the positive and negative are cancelled out does not occur. Thus, the MAE better reflects the reality of the estimated value error. TIC (hill coefficient) is always between 0 and 1, and the smaller the value, the smaller the difference between the estimated value and the true value, and the higher the estimation accuracy. From table 2 and the two formulas above, it can be calculated: category B aircraft MAE 2.59min, TIC 0.0223; category C aircraft MAE 3.88min and TIC 0.0347. As can be seen from table 2, MAE and TIC, the maximum value of the relative error of the aircraft of category B is 4.40min, the minimum value is 1.26min, the maximum value of the absolute error is 0.074, the minimum value is 0.022, 1.26< MAE <4.40, and 0.022< TIC < 0.074. The maximum value of the relative error of the aircraft in the C category is 8.35min, the minimum value is 0.29min, the maximum value of the absolute error is 0.14, the minimum value is 0.004, 0.29< MAE <8.35, and 0.004< TIC < 0.14. According to the analysis, the following results are obtained: under the condition of considering the influence of the model, a better experimental result is still obtained, so that the effectiveness and better accuracy of estimating the flight guarantee time by using the model can be verified.
Based on the above, the first embodiment of the present invention can achieve the following technical effects: the flight guarantee service flow is described by adopting the basic elements of the coloring time Petri network, so that a flight guarantee service simulation model based on the coloring time Petri network is established, various factors including time and resources can be considered in the simulation of the model, the flight guarantee service time can be estimated more comprehensively and accurately, and the quantitative analysis and evaluation of the flight guarantee service can be supported more favorably.
Example two
Correspondingly to the embodiment of the present invention, fig. 5 is a schematic structural diagram illustrating a flight guarantee service time estimation system according to the embodiment of the present invention. As shown in fig. 5, a flight support service time estimation system includes: the simulation system comprises a model building module 101, a first time calculation module 102, a simulation module 103 and a second time calculation module 104. The model building module 101 is connected to the first time calculation module 102, the first time calculation module 102 is connected to the simulation module 103, and the simulation module 103 is connected to the second time calculation module 104.
The model establishing module 101 is configured to establish a flight support service simulation model based on a colored time Petri network. The method is specifically used for: describing the flight guarantee operation process by adopting basic elements of a coloring time Petri net; describing the flight guarantee service state by adopting basic elements of a coloring time Petri network; describing the logical relationship of the flight guarantee operation process by adopting basic elements of a coloring time Petri network; describing the relationship between the flight guarantee service state and the flight guarantee operation process by adopting basic elements of a coloring time Petri network; and establishing a flight safeguard service simulation model based on the coloring time Petri network according to the description.
The first time calculation module 102 is configured to calculate the correlation time of flight support operation process transition according to the flight support service simulation model. The method is specifically used for: calculating the time distribution of each flight guarantee operation process according to the flight guarantee service simulation model; generating corresponding random numbers according to the time distribution, and taking the random numbers as the associated time of the flight guarantee operation process transition
The simulation module 103 is configured to perform flight support service simulation according to the flight support service simulation model and the associated time. The method is specifically used for: setting simulation initial time and simulation cycle times, wherein the simulation cycle times are not less than 30; and performing flight guarantee service simulation for multiple times from the initial simulation time according to the flight guarantee service simulation model and the associated time and according to the simulation cycle times. Specifically, according to the flight support service simulation model, taking the associated time as a step length, starting from the simulation initial time, and performing multiple times of flight support service simulation according to the simulation cycle times.
The second time calculation module 104 is configured to calculate flight guarantee service time according to the flight guarantee service simulation; the flight support service time is time consumed for completing all flight support operation processes. The method is specifically used for: comparing the number of the current simulation cycle with the number of the simulation cycles; when the number of the current simulation cycle is not less than the number of the simulation cycles plus 1, calculating first flight guarantee service time of each flight guarantee service simulation to obtain a plurality of first flight guarantee service times of multiple times of simulation; and taking the values of the plurality of first flight guarantee service times as samples, calculating the average value of the samples, and taking the average value as the flight guarantee service time.
Based on the above, the second embodiment of the present invention can achieve the following technical effects: the system describes the flight guarantee service flow by adopting the basic elements of the colored time Petri network, so that a flight guarantee service simulation model based on the colored time Petri network is established, and various factors including time and resources can be considered in the simulation of the model, so that the flight guarantee service time can be estimated more comprehensively and accurately, and the quantitative analysis and evaluation of the flight guarantee service can be supported more favorably.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the present invention, and they should be construed as being included in the following claims and description.
Claims (9)
1. A flight guarantee service time estimation method, comprising:
establishing a flight support service simulation model based on a coloring time Petri network;
calculating the correlation time of flight guarantee operation process transition according to the flight guarantee service simulation model;
performing flight support service simulation according to the flight support service simulation model and the associated time;
calculating flight guarantee service time according to the flight guarantee service simulation; the flight support service time is the time consumed for completing all flight support operation processes;
the establishing of the flight support service simulation model based on the coloring time Petri network specifically comprises the following steps:
describing the flight guarantee operation process by adopting basic elements of a coloring time Petri net;
describing the flight guarantee service state by adopting basic elements of a coloring time Petri network;
describing the logical relationship of the flight guarantee operation process by adopting basic elements of a coloring time Petri network;
describing the relationship between the flight guarantee service state and the flight guarantee operation process by adopting basic elements of a coloring time Petri network;
and establishing a flight support service simulation model based on the coloring time Petri network according to the description.
2. The flight guarantee service time estimation method according to claim 1, wherein the description of the flight guarantee operation process specifically includes:
using T ═ T1,t2,t3,…,txDescribing each flight guarantee operation process; wherein, tiGuaranteeing a work process for the ith flight;
using δ ═ δ1,δ2,δ3，…,δxDescribing the operation transition of each flight guarantee operation process; wherein, deltaiIs tiThe transition time of (a) is,delta. theiThe following formula is satisfied: deltai＝δiend-δistartWherein, deltaiendIs tiEnd time of δistartIs tiThe start time of (c);
where x is the total number of flight assurance processes, i is 1,2,3 …, x.
3. The method for estimating flight guarantee service time according to claim 1, wherein the describing the flight guarantee service state specifically includes:
using the set P ═ { P ═ Ps,Puc,PcDescribing the flight guarantee service state; wherein the content of the first and second substances,
Ps＝{psh, aggregated in order to guarantee the job status library, psTo ensure the operation state library;
Puc＝{pucis aggregated by a non-consumable resource pool, pucIs a non-consumable repository;
Pc＝{pcis aggregated by a pool of consumable resources, pcIs a consumable repository.
4. The flight assurance service time estimation method of claim 3, wherein the logical relationship comprises: serial, parallel; the description of the logical relationship of the flight support operation process specifically includes:
when t isiAnd tjWhen the elements of the front set and the back set of the same flight guarantee operation state base are respectively used, t is addediAnd tjDescribed as a serial relationship;
when t isiAnd tjRespectively being the back set elements of different flight support operation state libraries, and when the front set elements of the different flight support operation state libraries are the same support operation process, t is addediAnd tjDescribing a parallel relationship;
when t isiAnd tjRespectively in different flight support operation formsWhen the front set element of the state base, the different flight guarantee operation state bases and the back set element of the different flight guarantee operation state bases are the same guarantee operation process, t is addediAnd tjDescribing a parallel relationship;
wherein, t isiFor the ith flight support operation process, tjFor the jth flight assurance process, i is 1,2,3 …, x, j is 1,2,3 …, x, x is the total number of flight assurance processes.
5. The flight guarantee service time estimation method according to claim 3, wherein the describing the relationship between the flight guarantee service state and the flight guarantee operation process specifically comprises:
and embodying the relation among the flight guarantee operation state library, the non-consumable resource library and the consumable resource library by using an association arc.
6. The flight assurance service time estimation method according to claim 1, wherein the calculating the correlation time of flight assurance operation process transition according to the flight assurance service simulation model specifically comprises:
calculating the time distribution of each flight guarantee operation process according to the flight guarantee service simulation model;
and generating corresponding random numbers according to the time distribution, and taking the random numbers as the associated time of the flight guarantee operation process transition.
7. The flight support service time estimation method according to claim 1, wherein the flight support service simulation according to the flight support service simulation model and the associated time specifically includes:
setting simulation initial time and simulation cycle times;
and performing flight guarantee service simulation for multiple times from the initial simulation moment according to the flight guarantee service simulation model and the associated time and the simulation cycle times.
8. The method for estimating flight guarantee service time according to claim 7, wherein the calculating flight guarantee service time according to the flight guarantee service simulation specifically comprises:
comparing the number of the current simulation cycle with the number of the simulation cycles;
when the number of the current simulation cycle is larger than or equal to the number of the simulation cycles plus 1, calculating first flight guarantee service time of each flight guarantee service simulation to obtain a plurality of first flight guarantee service times of multiple times of simulation;
and taking the values of the plurality of first flight guarantee service times as samples, calculating the average value of the samples, and taking the average value as the flight guarantee service time.
9. A flight support service time estimation system, comprising: the system comprises a model establishing module, a first time calculating module, a simulation module and a second time calculating module;
the model establishing module is connected with the first time calculating module, the first time calculating module is connected with the simulation module, and the simulation module is connected with the second time calculating module;
the model establishing module is used for establishing a flight support service simulation model based on a coloring time Petri network;
the first time calculation module is used for calculating the correlation time of flight guarantee operation process transition according to the flight guarantee service simulation model;
the simulation module is used for carrying out flight guarantee service simulation according to the flight guarantee service simulation model and the associated time;
the second time calculation module is used for calculating flight guarantee service time according to the flight guarantee service simulation; the flight support service time is the time consumed for completing all flight support operation processes;
the establishing of the flight support service simulation model based on the coloring time Petri network specifically comprises the following steps:
describing the flight guarantee operation process by adopting basic elements of a coloring time Petri net;
describing the flight guarantee service state by adopting basic elements of a coloring time Petri network;
describing the logical relationship of the flight guarantee operation process by adopting basic elements of a coloring time Petri network;
describing the relationship between the flight guarantee service state and the flight guarantee operation process by adopting basic elements of a coloring time Petri network;
and establishing a flight support service simulation model based on the coloring time Petri network according to the description.
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