Trolley number configuration method and system for rail guided vehicle logistics system

Abstract

The invention discloses a trolley number configuration method and system for a rail guided trolley logistics system. The method comprises the steps that S1, related data and parameters of the rail guided trolley logistics system are acquired and processed; s2, establishing a G / G / m single-queue queuing system model, and screening out a trolley number range; s3, a logistics system simulation model is established in discrete event system simulation software, models under different configurations are simulated, and the system utilization rate is determined through output data analysis; s4, establishing a corrected queuing system performance index calculation formula in combination with a simulation result and a variability theory, and determining a correction coefficient through regression methods such as nonlinear fitting; s5, establishing a regression model by taking simulation model input as a feature; s6, extracting features and variable coefficients from the data of the next shift, and respectively calculating system # imgabs0 # and # imgabs1 # by using a regression model and a correction formula; and S7, establishing scheme selection matrixes under different trolley numbers, designing different weight calculation total scores, and selecting the trolley number with the optimal total score.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent logistics, and in particular relates to a method system for configuring the number of trolleys in a rail trolley logistics system. Background Art

[0002] The railcar logistics system is a modern logistics system widely used in manufacturing enterprises. It is mainly composed of a trolley, a track, a switch, etc. The trolley transports pallet goods in one or two directions through a fixed track path, and has the advantages of fast speed, good flexibility and high degree of automation. Configuration optimization of the logistics system is the main way to improve the efficiency of the logistics system and thus improve the overall production efficiency. Due to the complexity of the logistics system, theoretical methods such as queuing theory and Petri nets are difficult to accurately describe the logistics system and provide a reliable solution for its configuration optimization. Logistics system simulation is currently a widely used method by establishing a discrete event system simulation model of the logistics system and analyzing the performance indicators of different configuration schemes. Compared with theoretical calculations, simulation methods often require a certain amount of simulation time and cannot quickly provide configuration solutions. Therefore, simulation methods are often used in the design stage of the logistics system. In the operation stage of the logistics system, manufacturing enterprises generally use the configuration scheme of the logistics system determined in the design stage. As production becomes dynamic and time-varying, there will be obvious differences between the production scene and the beginning of the design, and it will no longer be appropriate to continue to use the previous configuration.

[0003] According to different functional structures, railcar logistics systems can generally be divided into straight reciprocating and circular track types. The former is that one or more railcars reciprocate on a straight track to complete the handling task. The latter is that multiple railcars move in the same direction on a circular track. The number of trolleys has an important impact on the efficiency of the logistics system. When the number is too small, it cannot respond to the handling needs of the task in time. When the number is too large, it is easy to be blocked and reduce the efficiency of the logistics system. Therefore, determining a reasonable number of trolleys is of great significance to ensure the efficient operation of the logistics system. However, theoretical methods are difficult to describe complex logistics systems. The use of logistics system simulation methods is time-consuming and labor-intensive, and requires the deployment of expensive logistics system simulation software.

[0004] In view of the above problems, it is necessary to design a method and system for configuring the number of trolleys in a rail trolley logistics system that can respond in a timely manner. Summary of the invention

[0005] In order to solve the above-mentioned technical problems existing in the prior art, the present invention provides a method system for configuring the number of trolleys in a rail trolley logistics system, which combines the advantages of queuing theory and logistics system simulation methods, and has both fast solution and accurate calculation, while avoiding the need for on-site deployment of expensive logistics system simulation software, and provides a reliable decision-making solution for on-site staff.

[0006] The technical solution provided by the present invention is:

[0007] A method for configuring the number of trolleys in a rail trolley logistics system, characterized in that the configuration method comprises the following steps:

[0008] S1. Acquire and process data and parameters related to the rail trolley logistics system, including the time and quantity of the inbound and outbound tasks, and the parameters including the inbound and outbound time of each pallet, the time for the platform to hand over the pallet to the rail trolley, the handling time of the stacker, and the speed of the trolley;

[0009] S2. Establish a G / G / m single queue system model and select the range of vehicle quantity according to the system utilization requirements;

[0010] S3. Establish a logistics system simulation model in discrete event system simulation software, simulate the model under different configurations, and determine the system utilization rate through output data analysis;

[0011] S4. Combine the simulation results and the variability theory to establish a revised calculation formula for the queuing system performance index, and determine the correction coefficient through regression methods such as nonlinear fitting;

[0012] S5, characterized by the simulation model input described in step S3, the system utilization μ S For labels, a regression model is established using machine learning methods such as fully connected neural network, linear regression, and support correlation regression;

[0013] S6. Extract features and coefficient of variation from the data of the next shift, and use the regression model and correction formula to calculate the system utilization rate and the average queue length

[0014] S7. Establish a scheme selection matrix under different numbers of cars, consider factors such as car depreciation, usage cost, and average waiting time, design different weights to calculate the total score, and select the number of cars with the best total score.

[0015] Furthermore, in step S1, the specific process of obtaining and processing the relevant data and parameters of the railcar logistics system is as follows:

[0016] S101, for the data, arrange the inbound tasks and outbound tasks in chronological order in shifts to obtain an inbound task time sequence table and an inbound task time sequence table, and obtain an outbound task time interval sequence table and an inbound task time interval sequence table by single-step difference calculation;

[0017] S102, for the outbound task time interval sequence table and the inbound task time interval sequence table of different shifts in S101, determine their probability distribution and parameters through distribution hypothesis, parameter estimation and goodness of fit test, and use box plot to determine the fluctuation range of parameters;

[0018] S103, setting a sliding time window for the parameters, and taking the parameter mean within the sliding time window as the parameter of the current logistics system.

[0019] Furthermore, in step S2, a G / G / m single queue system model is established, and the specific process of selecting the range of the number of vehicles according to the system utilization requirement is as follows:

[0020] S201, multiple inbound and outbound platforms and warehouse-end inbound and outbound platforms are merged into a platform train, that is, the pallets are queued at a merged platform, waiting for the railcar to deliver the pallets to the destination platform. The railcar logistics system is described as a G / G / m single queue system, where the first G indicates that the time interval between pallet arrivals follows a general type distribution, the second G indicates that the service time of the trolley follows a general type distribution, and the third m indicates the number of trolleys;

[0021] S202, calculate the queuing system parameters, calculate the arrival rate of each inbound and outbound platform, and accumulate the arrival rate r of the merged platform a , using the box plot to determine r a range; for the inbound task, calculate the center distance of the inbound starting platform and the center distance of the inbound destination platform, and the distance between the two is the average time for the trolley to perform the inbound task; the same treatment is applied to the outbound task; the average inbound time and the average outbound time are weighted according to the number of tasks, and the reciprocal of the weighted average inbound time and the average outbound time is the service rate r e ;

[0022] S203, the utilization rate of the queuing system can be expressed as:

[0023]

[0024] Among them, m represents the number of cars, r a represents the arrival rate, r e represents the service rate, μ QT Represents the utilization of the queuing system.

[0025] If the system utilization rate is greater than 1, the system will be unstable. If the utilization rate is too low, there will be too many cars, resulting in expensive investment and car congestion. l <μ QT <1,μ l is the lower limit of utilization rate, r a By substituting the minimum value of into the formula, the range of m can be calculated.

[0026] Furthermore, in step S3, a logistics system simulation model is established in discrete event system simulation software, and the model under different configurations is simulated. The specific process of determining the system utilization rate through output data analysis is as follows:

[0027] S301, the logistics system parameter configuration includes the probability distribution parameters and the number of carts described in step S102, and the parameters are divided into equally spaced grids, where each grid represents a set of parameter configurations of the logistics system;

[0028] S302, using the parameter configuration described in step S301 and the parameters described in step S1 as input, establish a logistics system simulation model in discrete event system simulation software, and simulate the system utilization μ under different parameter configurations of step S301 S and the average queuing time CT q , the simulation model with the same configuration should be executed multiple times to ensure that the simulation results are statistically valid.

[0029] Furthermore, in step S4, the specific process of determining the correction coefficient by regression methods such as nonlinear fitting is as follows:

[0030] S401, the average queuing time of the traditional G / G / m single queue system is:

[0031]

[0032] There are two types of tasks in the railcar logistics system: inbound tasks and outbound tasks. Based on this, the formula is modified to

[0033]

[0034] In the above formula, c a,i represents the coefficient of variation of the time interval of the warehousing task, c a,o The coefficient of variation of the outbound task time interval, c e The coefficient of variation of the time required for the car to perform a single task, k a,i and k a,o Respectively represent c a,i and c a,o The correction coefficient of the formula is k, and the coefficient of variation is calculated as follows:

[0035]

[0036] Where σ is the standard deviation, is the mean;

[0037] The correction coefficients need to be obtained through fitting calculation, and k a,i and k a,o Should meet

[0038] ka,i +k a,o =1 (5);

[0039] S402, with the system utilization μ described in step S3 S is the independent variable, the average queuing time CT q The correction coefficient is determined by regression methods such as nonlinear fitting.

[0040] Furthermore, in step S5, the simulation model input includes probability distribution parameters of the outbound task time interval sequence table and the inbound task time interval sequence table, as well as the number of carts.

[0041] Further, in step S6, the features and coefficient of variation are extracted from the data of the next shift, and the system utilization is calculated using the regression model and the correction formula. and The specific process is as follows:

[0042] S601, according to the next shift's inbound task time sequence table and outbound task time sequence table, obtain the outbound task time interval sequence table and the inbound task time interval sequence table through single-step difference calculation, and calculate their coefficient of variation c a,i and c a,o ; Calculate the coefficient of variation c of the handling time of the inbound and outbound tasks e and the average transport time t e ;

[0043] S602: Calculate the value of the regression model under different numbers of cars according to S5. Then calculate according to the revised formula

[0044] A system for configuring the number of trolleys in a rail trolley logistics system, characterized by comprising:

[0045] Data acquisition interface, to obtain data and parameters of the railcar logistics system;

[0046] The data processing module processes the data and parameters of the railcar logistics system to obtain the probability distribution of the time intervals of the inbound and outbound tasks and the fluctuation range of the probability distribution parameters;

[0047] The queuing system module establishes a G / G / m single queue queuing system model and selects the range of vehicle quantities based on system utilization requirements;

[0048] Logistics simulation module, builds a logistics system simulation model and determines the system performance indicators through output data analysis;

[0049] The statistical analysis module builds a regression model and fitting correction formula for system utilization, establishes a solution selection matrix, and outputs the number of cars with the highest total score.

[0050] In the present invention, the rail trolley logistics system is composed of multiple warehouse-end entry platforms, multiple warehouse-end exit platforms, multiple warehouse entrance platforms, multiple warehouse exit platforms, and multiple rail trolleys, and its plan view is shown in the figure. The operation process of the system is described as follows: 1) Warehouse entry operation, forklifts, AGVs and other equipment place the pallets loaded with goods on the warehouse entrance conveyor, convey them to the warehouse entry platform, and then the rail trolley transports them to the warehouse-end chain conveyor, and the chain conveyor is sent to the warehouse-end entry platform, and finally the lane stacker transports the pallets to the cargo space; 2) Out-of-warehouse operation, the stacker transports the goods in a certain cargo space to the warehouse-end exit platform, and then the rail trolley transports them to the exit platform.

[0051] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0052] The present invention combines the advantages of queuing theory and logistics system simulation methods, and provides a method and system for configuring the number of trolleys in a rail trolley logistics system that has both fast solution and accurate calculation, while avoiding the need to deploy expensive logistics system simulation software on site, and providing a reliable decision-making solution for on-site staff. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 This is a structural diagram of a trolley quantity configuration system for a rail trolley logistics system of the present invention.

[0054] Figure 2 The present invention is a flow chart of a method for configuring the number of trolleys in a rail trolley logistics system.

[0055] Figure 3 The present invention is a simplified plan view of a railcar logistics system involved in a specific implementation scheme of the present invention. DETAILED DESCRIPTION

[0056] The specific implementation of the embodiment of the present invention is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described here is only used to illustrate and explain the embodiment of the present invention, and is not used to limit the embodiment of the present invention.

[0057] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments may be combined with each other.

[0058] The present invention will be described in detail below in conjunction with exemplary embodiments with reference to the accompanying drawings.

[0059] Example 1

[0060] refer to Figure 2The present invention provides a method for configuring the number of trolleys in a rail trolley logistics system, the configuration method comprising the following steps:

[0061] S1. Acquire and process data and parameters related to the rail trolley logistics system, including the time and quantity of the inbound and outbound tasks, and the parameters including the inbound and outbound time of each pallet, the time for the platform to hand over the pallet to the rail trolley, the handling time of the stacker, and the speed of the trolley;

[0062] S101, for the data, arrange the inbound tasks and outbound tasks in chronological order in shifts to obtain an inbound task time sequence table and an inbound task time sequence table, and obtain an outbound task time interval sequence table and an inbound task time interval sequence table by single-step difference calculation;

[0063] S102, for the outbound task time interval sequence table and the inbound task time interval sequence table of different shifts in S101, determine their probability distribution and parameters through distribution hypothesis, parameter estimation and goodness of fit test, and use box plot to determine the fluctuation range of parameters;

[0064] S103, setting a sliding time window for the parameters, and taking the parameter mean within the sliding time window as the parameter of the current logistics system.

[0065] S2. Establish a G / G / m single queue system model and select the range of vehicle quantity according to the system utilization requirements;

[0066] S201, multiple inbound and outbound platforms and warehouse-end inbound and outbound platforms are merged into a platform train, that is, the pallets are queued at a merged platform, waiting for the railcar to deliver the pallets to the destination platform. The railcar logistics system is described as a G / G / m single queue system, where the first G indicates that the time interval between pallet arrivals follows a general type distribution, the second G indicates that the service time of the trolley follows a general type distribution, and the third m indicates the number of trolleys;

[0067] S202, calculate the queuing system parameters, calculate the arrival rate of each inbound and outbound platform, and accumulate the arrival rate r of the merged platform a , using the box plot to determine r a range; for the inbound task, calculate the center distance of the inbound starting platform and the center distance of the inbound destination platform, and the distance between the two is the average time for the trolley to perform the inbound task; the same treatment is applied to the outbound task; the average inbound time and the average outbound time are weighted according to the number of tasks, and the reciprocal of the weighted average inbound time and the average outbound time is the service rate r e ;

[0068] S203, the utilization rate of the queuing system can be expressed as:

[0069]

[0070] Among them, m represents the number of cars, r a represents the arrival rate, r e represents the service rate, μ QT Represents the utilization of the queuing system.

[0071] If the system utilization rate is greater than 1, the system will be unstable. If the utilization rate is too low, there will be too many cars, resulting in expensive investment and car congestion. l <μ QT <1,μ l is the lower limit of utilization, r a By substituting the minimum value of into the formula, the range of m can be calculated.

[0072] S3. Establish a logistics system simulation model in discrete event system simulation software, simulate the model under different configurations, and determine the system utilization rate through output data analysis;

[0073] S301, the logistics system parameter configuration includes the probability distribution parameters and the number of carts described in step S102, and the parameters are divided into equally spaced grids, where each grid represents a set of parameter configurations of the logistics system;

[0074] S302, using the parameter configuration described in step S301 and the parameters described in step S1 as input, establish a logistics system simulation model in discrete event system simulation software, and simulate the system utilization μ under different parameter configurations of step S301 S and the average queuing time CT q , the simulation model with the same configuration should be executed multiple times to ensure that the simulation results are statistically valid.

[0075] S4. Combine the simulation results and the variability theory to establish a revised calculation formula for the queuing system performance index, and determine the correction coefficient through regression methods such as nonlinear fitting;

[0076] S401, the average queuing time of the traditional G / G / m single queue system is:

[0077]

[0078] There are two types of tasks in the railcar logistics system: inbound tasks and outbound tasks. Based on this, the formula is modified to

[0079]

[0080] In the above formula, c a，i represents the coefficient of variation of the time interval of the warehousing task, c a，o The coefficient of variation of the outbound task time interval, c eThe coefficient of variation of the time required for the car to perform a single task, k a，i and k a，o Respectively represent C a，i and c a，o The correction coefficient of the formula is k, and the coefficient of variation is calculated as follows:

[0081]

[0082] Where σ is the standard deviation, is the mean;

[0083] The correction coefficients need to be obtained through fitting calculation, and k a，i and k a，o Should satisfy k a，i +k a，o =1 (5);

[0084] S402, with the system utilization μ described in step S3 S is the independent variable, the average queuing time CT q The correction coefficient is determined by regression methods such as nonlinear fitting.

[0085] S5, characterized by the simulation model input described in step S3, the system utilization μ S For labels, a regression model is established using machine learning methods such as fully connected neural network, linear regression, and support correlation regression;

[0086] The simulation model input includes probability distribution parameters of the outbound task time interval sequence table and the inbound task time interval sequence table, as well as the number of carts.

[0087] S6. Extract features and coefficient of variation from the data of the next shift, and use the regression model and correction formula to calculate the system utilization rate and the average queue length

[0088] S601, according to the next shift's inbound task time sequence table and outbound task time sequence table, obtain the outbound task time interval sequence table and the inbound task time interval sequence table through single-step difference calculation, and calculate their coefficient of variation c a，i and c a，o ; Calculate the coefficient of variation c of the handling time of the inbound and outbound tasks e and the average transport time t e ;

[0089] S602: Calculate the value of the regression model under different numbers of cars according to S5. Then calculate according to the revised formula

[0090] S7. Establish a scheme selection matrix under different numbers of cars, consider factors such as car depreciation, usage cost, and average waiting time, design different weights to calculate the total score, and select the number of cars with the best total score.

[0091] The advantage of this method is that it integrates machine learning and queuing theory, avoids the tedious and complicated process of traditional logistics simulation analysis, can quickly respond to on-site data, and decide the number of vehicles suitable for on-site conditions.

[0092] Example 2

[0093] refer to Figure 1 The present invention provides a system for configuring the number of trolleys in a rail trolley logistics system, comprising:

[0094] Data acquisition interface, to obtain data and parameters of the railcar logistics system;

[0095] The data processing module processes the data and parameters of the railcar logistics system to obtain the probability distribution of the time intervals of the inbound and outbound tasks and the fluctuation range of the probability distribution parameters;

[0096] The queuing system module establishes a G / G / m single queue queuing system model and selects the range of vehicle quantities based on system utilization requirements;

[0097] Logistics simulation module, builds a logistics system simulation model and determines the system performance indicators through output data analysis;

[0098] The statistical analysis module builds a regression model and fitting correction formula for system utilization, establishes a solution selection matrix, and outputs the number of cars with the highest total score.

[0099] Figure 3 It describes a circular railcar logistics system, which consists of 2 inbound platforms and 4 outbound platforms (located below the track) and 4 lanes (located above the track). Each lane has a stacker, and on both sides there is a row of high-rise warehouses and a warehouse section conveyor (inbound conveyor and outbound conveyor). The track length, the location of the inbound and outbound platforms, and the location of the high-rise warehouses are as follows: Figure 3 Marked.

[0100] This embodiment can quickly calculate the trolley utilization rate and average queuing time of the logistics system under different numbers of trolleys through regression models and correction formulas. The traditional logistics simulation method needs to simulate the logistics system under different numbers of trolleys, and each configuration of the logistics system needs to be simulated dozens of times to ensure the quality of the simulation output results, which takes about 1 hour.

[0101] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A method for configuring the number of trolleys in a rail trolley logistics system, characterized in that: The configuration method comprises the following steps: S1. Acquire and process data and parameters related to the rail trolley logistics system, including the time and quantity of the inbound and outbound tasks, and the parameters including the inbound and outbound time of each pallet, the time for the platform to hand over the pallet to the rail trolley, the handling time of the stacker, and the speed of the trolley; S2. Establish a G / G / m single queue system model and select the range of vehicle quantity according to the system utilization requirements; S3. Establish a logistics system simulation model in discrete event system simulation software, simulate the model under different configurations, and determine the system utilization rate through output data analysis; S4. Combine the simulation results and the variability theory to establish a revised calculation formula for the queuing system performance index, and determine the correction coefficient through regression methods such as nonlinear fitting; S5, characterized by the simulation model input described in step S3, the system utilization μ s For labels, a regression model is established using machine learning methods such as fully connected neural network, linear regression, and support correlation regression; S6. Extract features and coefficient of variation from the data of the next shift, and use the regression model and correction formula to calculate the system utilization rate and the average queue length S7. Establish a scheme selection matrix under different numbers of cars, consider factors such as car depreciation, usage cost, and average waiting time, design different weights to calculate the total score, and select the number of cars with the best total score.

2. A method for configuring the number of trolleys in a rail trolley logistics system as claimed in claim 1, characterized in that: In step S1, the specific process of obtaining and processing the relevant data and parameters of the railcar logistics system is as follows: S101, for the data, arrange the inbound tasks and outbound tasks in chronological order in shifts to obtain an inbound task time sequence table and an inbound task time sequence table, and obtain an outbound task time interval sequence table and an inbound task time interval sequence table by single-step difference calculation; S102, for the outbound task time interval sequence table and the inbound task time interval sequence table of different shifts in S101, determine their probability distribution and parameters through distribution hypothesis, parameter estimation and goodness of fit test, and use box plot to determine the fluctuation range of parameters; S103, setting a sliding time window for the parameters, and taking the parameter mean within the sliding time window as the parameter of the current logistics system.

3. A method for configuring the number of trolleys in a rail trolley logistics system as claimed in claim 2, characterized in that: In step S2, a G / G / m single queue system model is established. The specific process of selecting the range of vehicle numbers according to the system utilization requirement is as follows: S201, multiple inbound and outbound platforms and warehouse-end inbound and outbound platforms are merged into a platform train, that is, the pallets are queued at a merged platform, waiting for the railcar to deliver the pallets to the destination platform. The railcar logistics system is described as a G / G / m single queue system, where the first G indicates that the time interval between pallet arrivals follows a general type distribution, the second G indicates that the service time of the trolley follows a general type distribution, and the third m indicates the number of trolleys; S202, calculate the queuing system parameters, calculate the arrival rate of each inbound and outbound platform, and accumulate the arrival rate r of the merged platform a , using the box plot to determine r a range; for the inbound task, calculate the center distance of the inbound starting platform and the center distance of the inbound destination platform, and the distance between the two is the average time for the trolley to perform the inbound task; the same treatment is applied to the outbound task; the average inbound time and the average outbound time are weighted according to the number of tasks, and the reciprocal of the weighted average inbound time and the average outbound time is the service rate r e ; S203, the utilization rate of the queuing system can be expressed as: Among them, m represents the number of cars, r a represents the arrival rate, r e represents the service rate, μ QT Represents the utilization of the queuing system. If the system utilization rate is greater than 1, the system will be unstable. If the utilization rate is too low, there will be too many cars, resulting in expensive investment and car congestion. l <μ QT <1,μ l is the lower limit of utilization rate, r a By substituting the minimum value of into the formula, the range of m can be calculated.

4. A method for configuring the number of trolleys in a rail trolley logistics system as claimed in claim 3, characterized in that: In step S3, a logistics system simulation model is established in the discrete event system simulation software, and the model under different configurations is simulated. The specific process of determining the system utilization rate through output data analysis is as follows: S301, the logistics system parameter configuration includes the probability distribution parameters and the number of carts described in step S102, and the parameters are divided into equally spaced grids, where each grid represents a set of parameter configurations of the logistics system; S302, using the parameter configuration described in step S301 and the parameters described in step S1 as input, establish a logistics system simulation model in discrete event system simulation software, and simulate the system utilization μ under different parameter configurations of step S301 S and the average queuing time CT q , the simulation model with the same configuration should be executed multiple times to ensure that the simulation results are statistically valid.

5. A method for configuring the number of trolleys in a rail trolley logistics system as claimed in claim 4, characterized in that: In step S4, the specific process of determining the correction coefficient by regression methods such as nonlinear fitting is as follows: S401, the average queuing time of the traditional G / G / m single queue system is: There are two types of tasks in the railcar logistics system: inbound tasks and outbound tasks. Based on this, the formula is modified to In the above formula, c a,i represents the coefficient of variation of the time interval of the warehousing task, c a,o The coefficient of variation of the outbound task time interval, c e The coefficient of variation of the time required for the car to perform a single task, k a,i and k a,o Respectively represent c a,i and c a,o The correction coefficient of the formula is k, and the coefficient of variation is calculated as follows: Where σ is the standard deviation, is the mean; The correction coefficients need to be obtained through fitting calculation, and k a,i and k a,o Should meet k a,i +k a,o =1 (5); S402, with the system utilization μ described in step S3 s is the independent variable, the average queuing time CT q The correction coefficient is determined by regression methods such as nonlinear fitting.

6. A method for configuring the number of trolleys in a rail trolley logistics system as claimed in claim 5, characterized in that: In step S5, the simulation model input includes probability distribution parameters of the outbound task time interval sequence table and the inbound task time interval sequence table, and the number of carts.

7. A method for configuring the number of trolleys in a rail trolley logistics system as claimed in claim 6, characterized in that: In step S6, the features and coefficient of variation are extracted from the data of the next shift, and the system utilization is calculated using the regression model and the correction formula respectively. and The specific process is as follows: S601, according to the next shift's inbound task time sequence table and outbound task time sequence table, obtain the outbound task time interval sequence table and the inbound task time interval sequence table through single-step difference calculation, and calculate their coefficient of variation c a,i and c a,o ; Calculate the coefficient of variation c of the handling time of the inbound and outbound tasks e and the average transport time t e ; S602: Calculate the value of the regression model under different numbers of cars according to S5. Then calculate according to the revised formula 8. A system for configuring the number of trolleys in a rail trolley logistics system, characterized in that: include: Data acquisition interface, to obtain data and parameters of the railcar logistics system; The data processing module processes the data and parameters of the railcar logistics system to obtain the probability distribution of the time intervals of the inbound and outbound tasks and the fluctuation range of the probability distribution parameters; The queuing system module establishes a G / G / m single queue queuing system model and selects the range of vehicle quantities based on system utilization requirements; Logistics simulation module, builds a logistics system simulation model and determines the system performance indicators through output data analysis; The statistical analysis module builds a regression model and fitting correction formula for system utilization, establishes a solution selection matrix, and outputs the number of cars with the highest total score.

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