CN116187706A - Configuration method, device, computer equipment and storage medium of baggage sorting AMR - Google Patents
Configuration method, device, computer equipment and storage medium of baggage sorting AMR Download PDFInfo
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Abstract
The invention provides a configuration method, a device, computer equipment and a storage medium of baggage sorting AMR, wherein the method comprises the following steps: acquiring flight data and passenger arrival data in a preset time period; acquiring the luggage flow in a preset time period according to the flight data and the passenger arrival data; acquiring average service time and average mean square error of AMR according to service capability of the baggage sorting AMR system; constructing an AMR resource allocation model based on the queuing theory M/G/s model; inputting the baggage flow, the AMR average service time and the mean square error into the AMR resource allocation model for calculation to obtain the allocation quantity of AMR in a preset time period; the method can realize the precise configuration of the quantity of the AMR systems for sorting the baggage at the airport, is convenient for an airport operator to adjust the operation strategy in time according to the flight condition, avoids the problems of baggage congestion or resource waste, and improves the baggage processing efficiency and economic benefit.
Description
Technical Field
The invention relates to the technical field of airport configuration planning, in particular to a configuration method, a device, computer equipment and a storage medium for baggage sorting AMR.
Background
Along with the continuous rapid development of Chinese economy, more and more passengers select air traffic for traveling, and a baggage handling system is used as the largest single system in an airport, and comprises 10 subsystems of check-in, security check, conveying, sorting, storage, transfer, loading, arrival, control and the like, so that the baggage handling system is one of the most important systems for airport construction. AMR (autonomous mobile robot) intelligent sorting technology is used as the most central technology of baggage handling systems, through which baggage on a conveyor can be transported to a designated baggage unloading port according to a planned path.
Therefore, how to reasonably configure the AMR quantity in an airport baggage handling system is a current challenge.
Disclosure of Invention
Aiming at the defects existing in the prior art, the configuration method, the device, the computer equipment and the storage medium for the baggage sorting AMR can realize the accurate configuration of the quantity of the baggage sorting AMR systems of the airport, simultaneously facilitate airport operators to adjust operation strategies in time according to flight conditions, avoid the problems of baggage congestion or resource waste, and improve the baggage processing efficiency and economic benefit.
In a first aspect, the present invention provides a configuration method of baggage sorting AMR, the method comprising: acquiring flight data and passenger arrival data in a preset time period; acquiring the luggage flow in a preset time period according to the flight data and the passenger arrival data; acquiring average service time and average mean square error of AMR according to service capability of the baggage sorting AMR system; based on a queuing theory M/G/s model, constructing an AMR resource allocation model by taking the allowable queuing quantity, the reliability coefficient and the charge-discharge coefficient in the system as constraint conditions; inputting the baggage flow, the AMR average service time and the mean square error into the AMR resource configuration model for calculation to obtain the configuration quantity of AMR in a preset time period.
Optionally, the flight data includes: flight model, number of flights and flight take-off and landing time; or/and, the passenger arrival data includes: passenger arrival distribution, domestic/international passenger proportion and domestic/international passenger luggage coefficient in different time periods before flight take-off; or/and, the service capability of the baggage sorting AMR system includes: AMR transport path, baggage handling time, transport speed, acceleration and AMR charge and discharge time.
Optionally, acquiring the baggage flow in a preset time period according to the flight data and the passenger arrival data, including: obtaining the data of the original passengers and the data of the transfer passengers in a preset time period according to the flight data and the arrival distribution of the passengers; obtaining the quantity of the original baggage according to the original passenger data and the domestic/international passenger baggage coefficient; obtaining the quantity of the transit passenger baggage according to the transit passenger data and the domestic/international passenger baggage coefficient; and obtaining the luggage flow in a preset time period according to the quantity of the initial luggage and the quantity of the transfer luggage.
Optionally, acquiring the average service time of the AMR and the mean square error of the average service time according to the service capability of the baggage sorting AMR system includes: obtaining the service time of each AMR path according to the AMR service path, the transportation speed, the acceleration and deceleration time and the loading and unloading time; obtaining average service time according to the service time of each path of the AMR; and obtaining the mean square error of the average service time according to the service time of each path of the AMR and the average service time.
Optionally, based on the queuing theory M/G/s model, taking the allowable queuing quantity, the reliability coefficient and the charge-discharge coefficient in the system as constraint conditions, constructing an AMR resource allocation model by the following formula:
wherein m represents the minimum AMR configuration quantity of the system under the current luggage flow; psi phi type 1 Representing the minimum configuration quantity of the AMR of the system under the condition that the reliability of the system is not lower than 99.99%; psi phi type 2 Representing the minimum AMR configuration quantity of the system under the conditions of the average AMR workbench number and the charge-discharge coefficient;
representation of psi 1 ，ψ 2 Is the maximum value of (2); ζ represents the calculated minimum configuration quantity of AMR when the reliability of the system is not lower than 99.99% under the current luggage flow; m represents the configuration number of the loading ports of the luggage system; />
Representing the number of queuing conveyor configurations for each loading port of the baggage system; />
Representing the average working quantity of AMR in the system; omega represents the charge-discharge coefficient of AMR; c (ζ, ρ)) Representing the probability of system congestion at the current baggage traffic.
Alternatively, the average working number of AMR is expressed as:
wherein pi k Indicating the probability that the number of AMR is k when the system reaches steady state (k=0, 1,2, …).
Optionally, inputting the baggage flow, the AMR average service time and the mean square error into the AMR resource configuration model for calculation, to obtain a configuration number of AMR within a preset time period, including: obtaining the average arriving luggage number in unit time according to the luggage flow; obtaining the average transported luggage number in unit time and the square variation coefficient of the average transport time of AMR according to the average AMR service time and the mean square error; acquiring probability density functions of the quantity of the plums in the queue according to the queuing theory M/G/s model; obtaining a probability distribution function of the quantity of the baggage in the queue and an average quantity of the baggage in the queue according to the probability density function of the quantity of the baggage in the queue; obtaining a first AMR lowest configuration quantity of the system meeting the reliability condition according to the probability distribution function; obtaining the second AMR lowest configuration quantity of the system under the condition of meeting the charge and discharge time according to the average luggage quantity in the queue and the charge and discharge coefficient; and taking the maximum value of the first AMR minimum configuration quantity and the second AMR minimum configuration quantity as the configuration quantity of AMR in the preset time period.
In a second aspect, the present invention provides a configuration apparatus for baggage sorting AMR, the apparatus comprising: the basic data acquisition module is used for acquiring flight data and passenger arrival data in a preset time period; the baggage flow obtaining module is used for obtaining the baggage flow in a preset time period according to the flight data and the passenger arrival data; the service time acquisition module is used for acquiring average service time and average mean square error of the AMR according to the service capability of the baggage sorting AMR system; the configuration model construction module is used for constructing an AMR resource configuration model by taking the allowable queuing quantity, the reliability coefficient and the charge-discharge coefficient in the system as constraint conditions based on the queuing theory M/G/s model; the AMR quantity configuration module inputs the baggage flow, the AMR average service time and the mean square error into the AMR resource configuration model for calculation to obtain the configuration quantity of AMR in a preset time period
In a third aspect, the present invention provides a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the following steps when executing the computer program: acquiring flight data and passenger arrival data in a preset time period; acquiring the luggage flow in a preset time period according to the flight data and the passenger arrival data; acquiring average service time and average mean square error of AMR according to service capability of the baggage sorting AMR system; based on a queuing theory M/G/s model, constructing an AMR resource allocation model by taking the allowable queuing quantity, the reliability coefficient and the charge-discharge coefficient in the system as constraint conditions; inputting the baggage flow, the AMR average service time and the mean square error into the AMR resource configuration model for calculation to obtain the configuration quantity of AMR in a preset time period.
In a fourth aspect, the present invention provides a readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of: acquiring flight data and passenger arrival data in a preset time period; acquiring the luggage flow in a preset time period according to the flight data and the passenger arrival data; acquiring average service time and average mean square error of AMR according to service capability of the baggage sorting AMR system; based on a queuing theory M/G/s model, constructing an AMR resource allocation model by taking the allowable queuing quantity, the reliability coefficient and the charge-discharge coefficient in the system as constraint conditions; inputting the baggage flow, the AMR average service time and the mean square error into the AMR resource configuration model for calculation to obtain the configuration quantity of AMR in a preset time period.
Compared with the prior art, the invention has the beneficial effects that:
according to the invention, the flight data and the passenger arrival data in the preset time period are predicted, so that the luggage flow in the preset time period can be predicted; then sorting the average service time and the mean square error of AMR according to the service capacity of the AMR system; then, the baggage flow, the AMR evaluation service time and the mean square error of the average service time in a preset time period are used as input parameters to be input into a constructed AMR resource allocation model, and the optimal AMR allocation quantity in the preset time period can be calculated; therefore, the method can realize the precise configuration of the quantity of the AMR system for sorting the baggage at the airport, is convenient for an airport operator to adjust the operation strategy in time according to the flight condition, avoids the problems of baggage congestion or resource waste, and improves the baggage processing efficiency and economic benefit.
Drawings
Fig. 1 is a schematic flow chart of a configuration method of baggage sorting AMR according to an embodiment of the present invention;
fig. 2 is a schematic flowchart showing the specific process of step S102 in fig. 1;
fig. 3 is a schematic flowchart showing a specific process of step S103 in fig. 1;
FIG. 4 is a schematic diagram of a model of AMR resource allocation according to an embodiment of the present invention;
fig. 5 is a schematic diagram showing a specific flow of step S105 in fig. 1.
Detailed Description
For the purposes of making the objects, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present application based on the embodiments herein.
Fig. 1 is a schematic flow chart of a configuration method of baggage sorting AMR according to an embodiment of the present invention; as shown in fig. 1, the configuration method of the baggage sorting AMR specifically includes the following steps:
step S101, acquiring flight data and passenger arrival data in a preset time period;
in this embodiment, the preset time period may be any time of 1 day, one week, one month, or the like in the future; the flight data includes: flight model, number of flights and flight take-off and landing time; the passenger arrival data includes: passenger arrival distribution, domestic/international passenger proportion and domestic/international passenger baggage coefficient in different time periods before flight take-off.
In the embodiment, the flight data in the preset time period can be obtained by performing numerical coupling analysis on the flight information on the airport flight schedule; in general, the flight schedule at an airport comes from two sources: (1) Airport design goal annual peak day flight schedule: the airport design target annual peak day flight schedule can be used as a basis for a breakfast planning design stage, the quantity of the baggage sorting AMR systems is calculated and configured according to the quantity of the peak baggage, the settlement result can meet the sorting requirement of the peak baggage, and meanwhile, the airport baggage sorting system can also run in a consumption reducing mode in the conventional flow, and the planning design requirement of the baggage system of the airport in the earlier stage can be met. Specifically, the specific process of acquiring the flight data in the peak period is as follows: inquiring a flight schedule of peak days in the airport design target year; and acquiring data such as departure, arrival flight times, machine types and the like in the peak time. (2) flight schedule for airport operations phase: flight data in a short period can be fed back, and the number of AMRs in the baggage sorting AMR system can be analyzed and calculated according to the flight data, so that AMR resources are scheduled in advance, and the running risk and cost are reduced.
In this embodiment, passenger arrival data may be obtained generally by: (1) For reconstructing or expanding an airport, the data can be obtained by researching the existing terminal building, and can also be obtained by statistical analysis of passenger data extracted from the existing baggage system; (2) For the existing airport in the same city, newly-built airport data can be obtained by referring to the investigation of the running airport, and can also be obtained by statistical analysis of passenger data extracted from the existing luggage system of the running airport; (3) If the city is not provided with an airport, and is completely newly built, the city can be obtained by carrying out airport investigation with reference to the economic development level, airport locations and regions with similar traffic conditions.
Step S102, acquiring the luggage flow in a preset time period according to the flight data and the passenger arrival data;
in this embodiment, as shown in fig. 2, according to the flight data and the arrival data of the passenger, the baggage flow in a preset time period is obtained, which specifically includes the following steps:
step S201, obtaining the data of the originating passenger and the data of the transit passenger within a preset time period according to the flight data and the arrival distribution of the passenger.
It should be noted that, the specific formula for calculating the data of the originating passenger is:
the concrete formula for calculating the transit passenger data is as follows:
wherein N is cs Representing the originating passenger data; n (N) ct Representing transit passenger data; η (eta) i Representing the estimated average passenger rate of the ith flight; alpha i Representing the nuclear passenger capacity of the ith flight; beta i Representing the proportion of transit passengers in the ith flight; y is i1 Representing the number of the ith flights and departure flights; y is i2 Representing the arrival duty cycle of the ith flight passenger; y is i3 Representing the number of the ith flights to the harbor; n represents the total data of the flight, i=1, 2, …, N.
Step S202, obtaining the quantity of the initial baggage according to the initial passenger data and the domestic/international passenger baggage coefficient.
It should be noted that, the specific formula of the baggage flow in the preset time period is as follows:
N b ＝N cs ×(α 1 ×γ 1 +α 2 ×γ 2 )
wherein N is bs Representing originations within a preset time periodBaggage flow; alpha 1 Representing the domestic passenger proportion; alpha 2 Representing international passenger proportion; gamma ray 1 Representing the domestic passenger baggage coefficient; gamma ray 2 Representing international passenger baggage claim coefficients.
Step S203, obtaining the quantity of the transfer passenger baggage according to the transfer passenger data and the domestic/international passenger baggage coefficient.
It should be noted that, the calculation formula of the quantity of the baggage of the transit passengers is:
N bt ＝N ct ×(α 1 ×γ 1 +α 2 ×γ 2 )
wherein N is bt Representing the flow of the transfer baggage in a preset time period; alpha 1 Representing the domestic passenger proportion; alpha 2 Representing international passenger proportion; gamma ray 1 Representing the domestic passenger baggage coefficient; gamma ray 2 Representing international passenger baggage claim coefficients.
And step S204, obtaining the luggage flow in a preset time period according to the quantity of the initial luggage and the quantity of the transfer luggage.
The calculation formula of the baggage flow in the preset time period is as follows: n (N) b ＝N bc +N bt 。
Step S103, acquiring average service time and average mean square error of the AMR according to the service capability of the baggage sorting AMR system;
in this embodiment, as shown in fig. 3, according to the service capability of the baggage sorting AMR system, the average service time of AMR and the mean square error of the average service time are obtained, which specifically includes the following steps:
step S301, obtaining the service time of each AMR path according to the AMR service path, the transportation speed, the acceleration and deceleration time and the loading and unloading time;
step S302, obtaining average service time according to the service time of each AMR path;
step S303, obtaining the mean square error of the average service time according to the service time of each path of the AMR and the average service time.
It should be noted that the service capability of the baggage sorting AMR system includes: AMR transport path, baggage handling time, transport speed, acceleration and AMR charge and discharge time. The total required time of the single material transportation process generally consists of the necessary steps of waiting time, running time of the empty vehicle, running time of the load and loading and unloading time of the material
T r ＝T w +T k +T m +T z
Wherein T is w For AMR latency; t (T) k AMR empty car running time; t (T) m AMR load driving time; t (T) z Is the luggage loading and unloading time; t (T) a Time for baggage demand (single response time interval); t (T) r Total round trip time for completing one baggage handling.
The calculation formula for obtaining the average service time based on the service time of each AMR path is as follows:
where r is the number of AMR service paths.
The calculation formula for obtaining the mean square error of the average service time based on the service time of each AMR path and the average service time is as follows:
step S104, based on a queuing theory M/G/S model, constructing an AMR resource allocation model by taking the allowable queuing quantity, the reliability coefficient and the charge-discharge coefficient in the system as constraint conditions;
it should be noted that, in the queuing theory M/G/s-based resource allocation model, the arrival time of the baggage is subject to negative exponential distribution, the service rule is first to first service, the service time is subject to general distribution, the AMR quantity is s, the constructed AMR resource allocation model is shown in FIG. 4, and the model formula comprises:
wherein m represents AMR (automatic dependent memory) maximum of the system under the current luggage flowA low number of configurations; psi phi type 1 Representing the minimum configuration quantity of the AMR of the system under the condition that the reliability of the system is not lower than 99.99%; psi phi type 2 Representing the minimum AMR configuration quantity of the system under the conditions of the average AMR workbench number and the charge-discharge coefficient;
representation of psi 1 ，ψ 2 Is the maximum value of (2); ζ represents the calculated minimum configuration quantity of AMR when the reliability of the system is not lower than 99.99% under the current luggage flow; m represents the configuration number of the loading ports of the luggage system; />
Representing the number of queuing conveyor configurations for each loading port of the baggage system; />
Representing the average working quantity of AMR in the system; omega represents the charge-discharge coefficient of AMR; c (ζ, ρ) represents the probability of system congestion at the current baggage traffic.
Step S105, inputting the baggage flow, the AMR average service time and the mean square error into the AMR resource allocation model for calculation, thereby obtaining the AMR allocation quantity within a preset time period.
In this embodiment, as shown in fig. 5, the baggage flow, the AMR average service time and the mean square error are input into the AMR resource allocation model for calculation, so as to obtain the number of AMR allocation in a preset time period, which specifically includes the following steps:
step S401, obtaining the average arriving baggage number in unit time according to the baggage flow;
step S402, according to AMR average service time and the mean square error, obtaining the average transported luggage number in unit time and the square variation coefficient of AMR average transport time;
step S403, obtaining probability density functions of the quantity of the plums in the queue according to the queuing theory M/G/S model;
step S404, obtaining a probability distribution function of the quantity of the baggage in the queue and the average quantity of the baggage in the queue according to the probability density function of the quantity of the baggage in the queue;
step S405, obtaining a first AMR minimum configuration quantity of a system meeting reliability conditions according to a probability distribution function; obtaining the second AMR lowest configuration quantity of the system under the condition of meeting the charge and discharge time according to the average luggage quantity in the queue and the charge and discharge coefficient;
and step S406, taking the maximum value of the first AMR minimum configuration quantity and the second AMR minimum configuration quantity as the configuration quantity of AMR in the preset time period.
It should be noted that, a specific formula for obtaining the configuration number of AMR in the preset time period is as follows: m=max { ψ } 1 ，ψ 2 }
Wherein m represents the minimum AMR configuration quantity of the system under the current luggage flow; psi phi type 1 Representing the minimum configuration quantity of the AMR of the system under the condition that the reliability of the system is not lower than 99.99%; psi phi type 2 Representing the minimum AMR configuration quantity of the system under the conditions of the average AMR workbench number and the charge-discharge coefficient;
representation of psi 1 ，，ψ 2 Is a maximum value of (a).
ρ represents the service strength of the system:
lambda represents the number of baggage that arrive on average per unit time; mu represents the number of customers that can be serviced per unit time.
The calculation formula of the blockage probability c (ζ, ρ) under the current baggage system flow is:
/>
wherein pi k Representing the probability that the number of AMR is k (k=0, 1,2, …) when the system reaches steady state; pi k The calculation formula of (2) is
π k ＝π 0 θ m k＝1，...，m
Wherein θ k ＝(mρ) k /k！,k＝0,...,m；
k＝1,...,m；/>
k＝1,...,m；
b k ＝λ-kμ,k＝1,...,m.
Compared with the prior art, the invention has the beneficial effects that:
according to the invention, the flight data and the passenger arrival data in the preset time period are predicted, so that the luggage flow in the preset time period can be predicted; then sorting the average service time and the mean square error of AMR according to the service capacity of the AMR system; then, the baggage flow, the AMR evaluation service time and the mean square error of the average service time in a preset time period are used as input parameters to be input into a constructed AMR resource allocation model, and the optimal AMR allocation quantity in the preset time period can be calculated; therefore, the method can realize the precise configuration of the quantity of the AMR system for sorting the baggage at the airport, is convenient for an airport operator to adjust the operation strategy in time according to the flight condition, avoids the problems of baggage congestion or resource waste, and improves the baggage processing efficiency and economic benefit.
Furthermore, the invention uses the flight schedule as data input, combines the arrival distribution rule of the passengers, accurately obtains the arrival distribution curve of the passengers by a discrete method, determines the luggage flow of the luggage system in the preset period of the airport according to the type proportion of the passengers and the luggage coefficient, sets the luggage arrival as an equal probability random event, and is more in line with the actual flow and distribution condition of the luggage arrival of the airport.
In addition, the AMR in the baggage sorting AMR system is considered as a whole, any AMR possibly serves any loading port and any unloading port, and the method and the device more accord with the scheduling condition of the AMR system in actual operation.
In another embodiment of the present invention, as shown in connection with fig. 4, the airport baggage sorting AMR system baggage sorting basic workflow is: (1) The baggage conveyor conveys the baggage to an inlet queuing conveyor of the sorting system for buffer queuing; (2) The baggage sorting upper system detects baggage to be sorted, and establishes sorting tasks and static transportation paths according to the data of the background baggage tracking system; (3) The baggage sorting upper system distributes sorting tasks to the formulated AMR according to task distribution algorithms (such as an auction algorithm and a genetic algorithm); (4) The target AMR receives the sorting task and loads baggage to a task target queuing sorter interface; (5) The target AMR automatically completes the transportation from the loading port to the target unloading port according to the static transportation path and the dynamic path planning algorithm; (6) the target AMR completes baggage sorting and unloading at the unloading port; (7) the target AMR returns to the stop point to wait for the task.
In another embodiment of the present invention, there is provided a configuration apparatus of baggage sorting AMR, the apparatus including:
the basic data acquisition module is used for acquiring flight data and passenger arrival data in a preset time period;
the baggage flow obtaining module is used for obtaining the baggage flow in a preset time period according to the flight data and the passenger arrival data;
the service time acquisition module is used for acquiring average service time and average mean square error of the AMR according to the service capability of the baggage sorting AMR system;
the configuration model construction module is used for constructing an AMR resource configuration model by taking the allowable queuing quantity, the reliability coefficient and the charge-discharge coefficient in the system as constraint conditions based on the queuing theory M/G/s model;
and the AMR quantity configuration module inputs the baggage flow, the AMR average service time and the mean square error into the AMR resource configuration model for calculation to obtain the configuration quantity of AMR in a preset time period.
In another embodiment of the invention, a computer device is provided comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of: acquiring flight data and passenger arrival data in a preset time period; acquiring the luggage flow in a preset time period according to the flight data and the passenger arrival data; acquiring average service time and average mean square error of AMR according to service capability of the baggage sorting AMR system; based on a queuing theory M/G/s model, constructing an AMR resource allocation model by taking the allowable queuing quantity, the reliability coefficient and the charge-discharge coefficient in the system as constraint conditions; inputting the baggage flow, the AMR average service time and the mean square error into the AMR resource configuration model for calculation to obtain the configuration quantity of AMR in a preset time period.
In yet another embodiment of the present invention, there is provided a readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of: acquiring flight data and passenger arrival data in a preset time period; acquiring the luggage flow in a preset time period according to the flight data and the passenger arrival data; acquiring average service time and average mean square error of AMR according to service capability of the baggage sorting AMR system; based on a queuing theory M/G/s model, constructing an AMR resource allocation model by taking the allowable queuing quantity, the reliability coefficient and the charge-discharge coefficient in the system as constraint conditions; inputting the baggage flow, the AMR average service time and the mean square error into the AMR resource configuration model for calculation to obtain the configuration quantity of AMR in a preset time period.
Those skilled in the art will appreciate that all or part of the processes in the methods of the above embodiments may be implemented by a computer program for instructing relevant hardware, where the program may be stored in a non-volatile computer readable storage medium, and where the program, when executed, may include processes in the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the various embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
It should be noted that in this document, relational terms such as "first" and "second" and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
Claims (10)
1. A method for configuring baggage sorting AMR, the method comprising:
acquiring flight data and passenger arrival data in a preset time period;
acquiring the luggage flow in a preset time period according to the flight data and the passenger arrival data;
acquiring average service time and average mean square error of AMR according to service capability of the baggage sorting AMR system;
based on a queuing theory M/G/s model, constructing an AMR resource allocation model by taking the allowable queuing quantity, the reliability coefficient and the charge-discharge coefficient in the system as constraint conditions;
inputting the baggage flow, the AMR average service time and the mean square error into the AMR resource configuration model for calculation to obtain the configuration quantity of AMR in a preset time period.
2. The baggage sorting AMR configuration method of claim 1, wherein the flight data comprises: flight model, number of flights and flight take-off and landing time;
or/and, the passenger arrival data includes: passenger arrival distribution, domestic/international passenger proportion and domestic/international passenger luggage coefficient in different time periods before flight take-off;
or/and, the service capability of the baggage sorting AMR system includes: AMR transport path, baggage handling time, transport speed, acceleration and AMR charge and discharge time.
3. The baggage sorting AMR configuration method according to claim 2, wherein obtaining a baggage traffic in a predetermined period of time based on the flight data and the passenger arrival data, comprises:
obtaining the data of the original passengers and the data of the transfer passengers in a preset time period according to the flight data and the arrival distribution of the passengers;
obtaining the quantity of the original baggage according to the original passenger data and the domestic/international passenger baggage coefficient;
obtaining the quantity of the transit passenger baggage according to the transit passenger data and the domestic/international passenger baggage coefficient;
and obtaining the luggage flow in a preset time period according to the quantity of the initial luggage and the quantity of the transfer luggage.
4. The baggage sorting AMR configuration method of claim 2, wherein obtaining an average AMR service time and a mean square error of the average AMR service time based on a service capability of the baggage sorting AMR system comprises:
obtaining the service time of each AMR path according to the AMR service path, the transportation speed, the acceleration and deceleration time and the loading and unloading time;
obtaining average service time according to the service time of each path of the AMR;
and obtaining the mean square error of the average service time according to the service time of each path of the AMR and the average service time.
5. The baggage sorting AMR configuration method according to claim 1, wherein the formula for constructing the AMR resource configuration model is based on a queuing theory M/G/s model, taking a number of allowable queues, a reliability coefficient, and a charge-discharge coefficient in a system as constraint conditions, wherein the formula is as follows:
wherein m represents the minimum AMR configuration quantity of the system under the current luggage flow; psi phi type 1 Representing the minimum configuration quantity of the AMR of the system under the condition that the reliability of the system is not lower than 99.99%; psi phi type 2 Representing the minimum AMR configuration quantity of the system under the conditions of the average AMR workbench number and the charge-discharge coefficient; max { ψ } 1 ，ψ 2 The } represents taking psi 1 ，ψ 2 Is the maximum value of (2); ζ represents the calculated minimum configuration quantity of AMR when the reliability of the system is not lower than 99.99% under the current luggage flow; m represents the configuration number of the loading ports of the luggage system;
representing the number of queuing conveyor configurations for each loading port of the baggage system; />
Representing the average working quantity of AMR in the system; omega represents the charge-discharge coefficient of AMR; c (ζ, ρ) represents the probability of system congestion at the current baggage traffic.
6. The baggage sorting AMR configuration method according to claim 5, wherein the average number of works of AMR is expressed as:
wherein pi k Indicating the probability that the number of AMR is k when the system reaches steady state (k=0, 1,2, …).
7. The baggage sorting AMR configuration method of claim 5, wherein inputting the baggage traffic, the AMR average service time, and the mean square error into the AMR resource configuration model for calculation, obtaining a configuration number of AMR within a predetermined period of time, comprises:
obtaining the average arriving luggage number in unit time according to the luggage flow;
obtaining the average transported luggage number in unit time and the square variation coefficient of the average transport time of AMR according to the average AMR service time and the mean square error;
acquiring probability density functions of the quantity of the plums in the queue according to the queuing theory M/G/s model;
obtaining a probability distribution function of the quantity of the baggage in the queue and an average quantity of the baggage in the queue according to the probability density function of the quantity of the baggage in the queue;
obtaining a first AMR lowest configuration quantity of the system meeting the reliability condition according to the probability distribution function; obtaining the second AMR lowest configuration quantity of the system under the condition of meeting the charge and discharge time according to the average luggage quantity in the queue and the charge and discharge coefficient;
and taking the maximum value of the first AMR minimum configuration quantity and the second AMR minimum configuration quantity as the configuration quantity of AMR in the preset time period.
8. A configuration device for baggage sorting AMR, the device comprising:
the basic data acquisition module is used for acquiring flight data and passenger arrival data in a preset time period;
the baggage flow obtaining module is used for obtaining the baggage flow in a preset time period according to the flight data and the passenger arrival data;
the service time acquisition module is used for acquiring average service time and average mean square error of the AMR according to the service capability of the baggage sorting AMR system;
the configuration model construction module is used for constructing an AMR resource configuration model by taking the allowable queuing quantity, the reliability coefficient and the charge-discharge coefficient in the system as constraint conditions based on the queuing theory M/G/s model;
and the AMR quantity configuration module inputs the baggage flow, the AMR average service time and the mean square error into the AMR resource configuration model for calculation to obtain the configuration quantity of AMR in a preset time period.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the method of any one of claims 1 to 7 when the computer program is executed.
10. A readable storage medium having stored thereon a computer program, characterized in that the computer program, when executed by a processor, realizes the steps of the method of any of claims 1 to 7.
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