Variable marshalling operation optimization method for intelligent rail tramcar

Abstract

The invention relates to the technical field of intelligent rail tramcar marshalling, and discloses an intelligent rail tramcar variable marshalling operation optimization method, which comprises the steps of obtaining intelligent rail tramcar variable marshalling operation data; calculating the average daily total cost and the operation efficiency of the intelligent rail tramcar according to the intelligent rail tramcar variable marshalling operation data; taking minimization of the daily average total cost and maximization of the operation efficiency as optimization targets, and constructing a variable marshalling operation optimization model of the intelligent rail tramcar; and solving the constructed intelligent rail tram variable marshalling operation optimization model to obtain an intelligent rail tram variable marshalling operation optimization result. According to the method, the excessive or insufficient transport capacity can be avoided while the transport requirement is met, the user cost, the fixed cost and the operation cost are reduced to the maximum extent, and the operation efficiency of the intelligent rail tramcar is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of variable marshaling of smart rail trams, and in particular to a method for optimizing the operation of variable marshaling of smart rail trams. Background Art

[0002] With the acceleration of global urbanization, urban transportation faces huge challenges, and problems such as traffic congestion, traffic energy consumption, and traffic noise remain severe. As a new type of urban public transportation system, the intelligent rail express system (Intelligent Rail) provides a new option for solving urban traffic problems. Intelligent Rail adopts advanced technologies, including virtual track following control technology, on-board sensor technology, multi-axis steering technology, variable marshaling technology, active safety prevention and control technology, etc., to enable it to operate relatively safely and efficiently. Compared with traditional public transportation systems (such as subways and buses), Intelligent Rail has advantages in many aspects. First, the construction cost of Intelligent Rail is lower than that of subways. Intelligent Rail has less infrastructure requirements, especially in the design of tracks and stations, and can adapt more flexibly to complex urban terrain and space constraints. Secondly, Intelligent Rail has advantages in energy efficiency, and its lightweight vehicle design greatly reduces energy consumption. Intelligent Rail has higher scheduling flexibility than subways and can adapt to complex road traffic environments.

[0003] There are two main problems with the existing fixed marshaling schemes: first, the low-number marshaling scheme leads to insufficient vehicle capacity, resulting in a shortage of transport capacity and increased travel costs for users; second, the high-number marshaling scheme leads to excess capacity, low operating efficiency, and increased fixed and operating costs. Summary of the invention

[0004] In view of the above-mentioned deficiencies in the prior art, the present invention provides a method for optimizing the operation of a smart rail tram with variable marshaling. On the basis of fully considering the variable marshaling characteristics of the smart rail system, the marshaling scheme is optimized, and the marshaling configuration is flexibly adjusted according to the actual passenger flow demand and the operation period, so as to achieve the best capacity matching. Through this optimization method, it is possible to avoid excess or insufficient capacity while ensuring transportation demand, minimize user costs, capital investment and operating expenses, and improve the operating efficiency of smart rail trams.

[0005] In order to achieve the above-mentioned object of the invention, the technical solution adopted by the present invention is:

[0006] A method for optimizing the operation of a smart rail tram with variable marshaling comprises the following steps:

[0007] Obtain the variable marshaling operation data of the smart rail tram; the variable marshaling operation data of the smart rail tram includes the headway, operation time, turnaround time and average daily passenger flow of the smart rail tram;

[0008] Calculate the daily average total cost and operating efficiency of the smart rail tram based on the variable marshaling operation data of the smart rail tram;

[0009] Taking minimizing the average daily total cost and maximizing the operating efficiency as the optimization goals, a variable marshaling operation optimization model for smart rail trams is constructed;

[0010] The constructed intelligent rail tram variable formation operation optimization model is solved to obtain the intelligent rail tram variable formation operation optimization results.

[0011] Furthermore, the daily average total cost of the smart rail tram is calculated based on the variable marshaling operation data of the smart rail tram, including:

[0012] C=C1+C2+C3

[0013] Among them, C represents the average daily total cost of the smart rail tram, C1 represents fixed cost, C2 represents operating cost, and C3 represents user cost.

[0014] Furthermore, the fixed cost is calculated as:

[0015]

[0016] Among them, T P represents the peak passenger flow period, H represents the service time, and x j represents the number of smart rail trams with a marshaling number of j, P j represents the purchase cost of a smart rail tram with a marshaling number of j, FS represents the fleet size, CRF represents the annualized cost, and N O Indicates the number of operating days per year, T F It represents the time period with off-peak passenger flow, and P2 represents the purchase cost of a smart rail tram with a marshaling number of 2.

[0017] Furthermore, the operating cost is calculated as follows:

[0018]

[0019] Among them, c VM represents the unit time cost of the smart rail, H represents the service time, c VD represents the unit distance cost of the smart rail, x j represents the number of smart rail trams with a marshaling number of j, T P Indicates the peak passenger flow period, T c represents the turnaround time, N CPj represents the average number of charging times of smart rail trams with a group size of j during peak hours, L D represents the distance from the terminal to the charging station, FS represents the fleet size, T F represents the time period of off-peak passenger flow, L represents the route length, N CO Indicates the average number of charging times of the smart rail tram during off-peak hours.

[0020] Furthermore, the unit time cost of the smart rail tram is calculated as follows:

[0021]

[0022] C VM =C O +(1-X)C F +(1-Y)C m

[0023] C F =C f ×N O

[0024]

[0025] Among them, C VM represents the annual cost associated with the vehicle operation time, VM represents the annual driving time of the vehicle, and C O represents the driver's salary and additional welfare costs, x represents the vehicle maintenance cost coefficient, C F represents the annual fuel cost, Y represents the fuel cost coefficient of the vehicle, C m represents the fuel cost of the vehicle, C f represents the daily fuel cost, N O represents the number of operating days per year, P E Represents the unit price of electric fuel, E Cj It represents the energy consumption coefficient of the smart rail tram with j number of trains, E C2 Represents the energy consumption coefficient of the smart rail tram with a marshaling number of 2.

[0026] Furthermore, the average charging times of the smart rail tram with a group size of j during the peak period is calculated as follows:

[0027]

[0028] Where VD represents the annual mileage of the vehicle, E Cj It represents the energy consumption coefficient of the smart rail tram with j number of trains, E bj represents the battery capacity of the smart rail tram with j number of trains, S min Indicates the minimum battery state of charge that needs to be maintained.

[0029] Furthermore, the average charging times of the smart rail tram during off-peak hours are calculated as follows:

[0030]

[0031] Among them, E C2 It represents the energy consumption coefficient of the smart rail tram with a marshaling number of 2, Eb2 Indicates the battery capacity of a smart rail tram with a train number of 2.

[0032] Furthermore, the user cost is calculated as:

[0033]

[0034] Among them, C P represents the unit time cost of passengers, P represents the average daily passenger demand along the route, T O It represents the time required to serve the route, and N1 represents the number of stations of the smart rail tram.

[0035] Furthermore, the operation efficiency of the smart rail tram is calculated based on the variable marshaling operation data of the smart rail tram, including:

[0036]

[0037]

[0038] Among them, Z P represents the operation efficiency of the intelligent rail line during peak hours, P P represents the average passenger flow per minute on the smart rail line during peak hours, T C represents the turnaround time, C Bj represents the capacity of the smart rail tram with j number of trains, x j represents the number of smart rail trams with a marshaling number of j, Z O represents the operation efficiency of the intelligent rail line during off-peak hours, P O represents the average passenger flow per minute on the smart rail line during off-peak hours, C B It represents the capacity of the smart rail tram, and FS represents the fleet size.

[0039] Furthermore, with minimizing the average daily total cost and maximizing the operating efficiency as the optimization goals, a variable marshaling operation optimization model for smart rail trams is constructed, including:

[0040] minC=C1+C2+C3

[0041]

[0042] st

[0043] 0≤x j ≤FSj=2,3,4

[0044]

[0045] 0≤Z P ≤1

[0046] Among them, C represents the average daily total cost of the smart rail tram, C1 represents the fixed cost, C2 represents the operating cost, C3 represents the user cost, and Z P represents the operation efficiency of the intelligent rail line during peak hours, P P represents the average passenger flow per minute on the smart rail line during peak hours, T C represents the turnaround time, C Bj represents the capacity of the smart rail tram with j number of trains, x j It represents the number of smart rail vehicles with a marshaling quantity of j, and FS represents the fleet size.

[0047] The present invention has the following beneficial effects:

[0048] (1) The variable formation optimization model proposed in the present invention further improves and perfects the cost estimation method of the intelligent rail system on the basis of the traditional cost estimation method, forming a more comprehensive and accurate estimation system. This model covers all key elements in the operation of the intelligent rail line, and can comprehensively and meticulously calculate and quantify different types of costs, thereby significantly improving the accuracy of cost accounting. Accurate cost accounting is the basis for the optimization of variable formation schemes, provides a reliable basis for the optimization of the intelligent rail system, and can effectively support the adjustment and optimization of the intelligent rail operation plan and its various parameters, thereby improving the overall operational efficiency and economy.

[0049] (2) The present invention fully considers the characteristics of the variable marshaling of the smart rail system, and accurately distinguishes and optimizes the operational needs during peak and off-peak periods. During peak hours, a mixed marshaling scheme is proposed to achieve higher operational efficiency while ensuring effective control and minimization of costs when capacity demand increases. By implementing differentiated marshaling strategies in different time periods, the present invention not only improves the overall operating efficiency of the system, but also dynamically adjusts the marshaling method according to the actual load, thereby optimizing the operating cost structure. This solution provides a new idea for the intelligent scheduling and resource allocation of the smart rail system, which helps to maximize economic benefits while ensuring service quality.

[0050] (3) The present invention can dynamically adjust the operation strategy of the intelligent rail system according to changes in factors such as daily passenger flow, has optimization and adjustment capabilities, and provides decision makers with an accurate basis for operation scheduling. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 A schematic diagram of a process flow of a variable marshaling operation optimization method for smart rail trams;

[0052] Figure 2 This is a schematic diagram of the relationship between the average daily total cost of a three-car smart tram and changes in headway and passenger flow demand. DETAILED DESCRIPTION

[0053] The specific implementation modes of the present invention are described below so that those skilled in the art can understand the present invention. However, it should be clear that the present invention is not limited to the scope of the specific implementation modes. For those of ordinary skill in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the attached claims, these changes are obvious, and all inventions and creations utilizing the concept of the present invention are protected.

[0054] like Figure 1 As shown, an embodiment of the present invention provides a method for optimizing the operation of a smart rail tram with variable marshaling, comprising the following steps S1 to S4:

[0055] S1. Obtaining variable marshaling operation data of smart rail trams; wherein the variable marshaling operation data of smart rail trams include headway, operation time, turnaround time and average daily passenger flow of smart rail trams;

[0056] In an optional embodiment of the present invention, the variable formation of the smart rail tram targeted by step S1 includes but is not limited to two formations, three formations and four formations.

[0057] Step S1 obtains the operating data of two-carriage, three-carriage and four-carriage smart rail trams respectively, including but not limited to headway, operating time, turnaround time and average daily passenger flow.

[0058] S2. Calculate the daily average total cost and operation efficiency of the smart rail tram based on the smart rail tram marshaling operation data;

[0059] In an optional embodiment of the present invention, step S2 includes headway, operating time, turnaround time and average daily passenger flow, etc. The proposed smart rail tram cost estimation model is used to calculate the average daily total cost of the current smart rail line from three aspects: fixed cost, operating cost and user cost. If a range restriction is set for the passenger flow data, the relationship between the average daily total cost of the smart rail and the average daily passenger flow under different passenger flow conditions can be analyzed, such as Figure 2 As shown. Then, according to the calculation formula of the intelligent rail operation efficiency, the operation efficiency of the intelligent rail tram in the case of three-section marshaling is analyzed, and how the efficiency changes with the daily average passenger flow is studied. Furthermore, the daily passenger flow is divided into peak period passenger flow and non-peak period passenger flow, and the changes in the intelligent rail operation efficiency under different passenger flow conditions are analyzed respectively. The results are presented in a graphical way to provide a reference for the subsequent optimization of marshaling according to the passenger flow demand in different time periods.

[0060] The smart rail cost estimation model proposed in step S1 estimates the average daily total cost C of the smart rail from three aspects: fixed cost C1, operating cost C2 and user cost C3. The specific calculation is as follows:

[0061] C=C1+C2+C3

[0062] Fixed costs C1 mainly include vehicle purchase costs and infrastructure investment costs. Since infrastructure construction costs are relatively fixed, the vehicle purchase costs are mainly discounted. The calculation method is discounted to each year and then to each day to accurately reflect its economic impact throughout the project life cycle. The calculation formula is as follows:

[0063]

[0064] Where P0 represents the capital cost of the vehicle in year 0 (yuan); CRF represents the annualized cost (capital recovery factor); N O Indicates the number of operating days per year (days).

[0065] The calculation formula for the annualized cost (capital recovery factor) CRF is as follows:

[0066]

[0067] Among them, i represents the discount rate; n represents the useful life of the smart rail vehicle (years).

[0068] The calculation of operating cost C2 mainly includes two parts. The first part is the annual cost C2 which is roughly proportional to the vehicle operation time. VM (Yuan). The calculation formula is as follows:

[0069] C VM =C O +(1-X)C F +(1-Y)C m

[0070] Among them, C O represents the driver's salary and additional benefits (day); C F represents the annual fuel cost (day); C m represents the maintenance cost of the vehicle (day); X, Y are used to represent the maintenance cost of the vehicle C m (Yuan) and fuel cost C F (Yuan) is related to the vehicle running time and vehicle driving distance, so coefficients X and Y are introduced to represent the relationship between them.

[0071] Annual fuel cost C F The calculation formula for (days) is as follows:

[0072] C F =C f ×N O

[0073] C f Represents daily fuel cost (day), and the calculation formula is as follows:

[0074]

[0075] Among them, P E represents the unit price of electric fuel (yuan / kWh); FS represents the required fleet size; H represents the service time (min); T c represents the turnaround time (min); L represents the route length (km); N C Indicates the average number of charging times per day; L D Indicates the distance from the terminal to the charging station (km); E C Indicates the energy consumption coefficient of the vehicle (kWh / km).

[0076] The required fleet size FS is calculated as follows:

[0077]

[0078] Wherein, h represents the headway time (min / veh).

[0079] Turnaround time c The calculation formula of (min) is as follows:

[0080] T c =2[T O +max(T L ,T Chg )]

[0081] Among them, T O Indicates the time required for the service route (min); T L Indicates the dwell time at the terminal (min); T Chg Indicates the charging time of the smart rail tram (min).

[0082] Average number of charging times per day N C The calculation formula is as follows:

[0083]

[0084] Where VD represents the annual mileage of the vehicle (km); E b Indicates the vehicle's battery capacity (kWh). min Is the minimum battery state of charge that needs to be maintained to prevent the battery from being completely drained. It is the ratio of the minimum allowed battery charge to the battery capacity and its value ranges from 0 to 1.

[0085] The second part is the annual cost C which is roughly proportional to the vehicle kilometers. VD (Yuan). The calculation formula is as follows:

[0086] C VD =XC F +YC m

[0087] The annual cost C is calculated to be roughly proportional to the vehicle operation time. VM (yuan) and an annual cost C which is roughly proportional to the vehicle kilometer VD (yuan). Then calculate the unit time cost c of the smart track VM (yuan / min) and unit distance cost c VD (Yuan / km), the calculation formula is as follows:

[0088]

[0089] Wherein, VM represents the annual driving time of the vehicle (min).

[0090] The daily operating cost calculation formula of the smart rail line is as follows:

[0091]

[0092] The calculation of user cost C3 mainly consists of two parts: user waiting time cost C WC , the user's time cost in the car is C IC Assuming that passengers are evenly distributed along the route, the calculation formula is as follows:

[0093] C3=C WC +C IC

[0094]

[0095] Among them, C P represents the unit time cost of passengers; P represents the average daily passenger demand along the line; N1 represents the number of smart rail tram platforms.

[0096] In order to further analyze the operation of the smart rail, an average operation efficiency index of the smart rail line is defined in step S1. With the help of this index, the operation efficiency of the smart rail line can be analyzed as the daily passenger flow changes. The calculation formula of this index is as follows:

[0097]

[0098] Among them, Z represents the operating efficiency or service level of the smart rail tram; C B Indicates the capacity of the smart rail tram.

[0099] However, under actual transportation conditions, the daily passenger flow is not evenly distributed in any time period. The daily passenger flow is divided into peak time period passenger flow and off-peak time period passenger flow, so it is necessary to analyze the operation efficiency separately. The operation efficiency formula is now divided into peak time period and off-peak time period. The calculation formula is as follows:

[0100]

[0101] Among them, Z P Indicates the operation efficiency of the smart rail line during peak hours; Z O P represents the operation efficiency of the smart rail line during off-peak hours; P represents the average passenger flow per minute on the smart rail line during peak hours; P O Indicates the average passenger flow per minute on the smart rail line during off-peak hours.

[0102] Through comparative analysis, it can be found that due to the uneven distribution of passenger flow in different time periods, a single marshaling method may face two major problems: on the one hand, although choosing a higher number of marshaling can improve user experience and reduce user costs, it may lead to a decrease in operating efficiency and push up fixed costs and operating costs; on the other hand, the use of fewer marshaling methods can maintain lower operating costs during off-peak hours, but it cannot meet the surging passenger flow demand during peak hours, resulting in a significant increase in user costs. Since user costs are extremely sensitive to the average daily total cost, it is difficult to balance costs and operating efficiency, especially during peak hours. Generally, the method of fewer marshaling can effectively meet passenger flow demand during off-peak hours and maintain low fixed costs and operating costs.

[0103] This embodiment proposes optimization suggestions for the existing intelligent rail tram marshaling scheme based on the analysis results of operational efficiency. On the basis of the original single marshaling cost calculation model, combined with the dual goals of minimizing the average daily total cost and maximizing operational efficiency, a variable marshaling operation optimization model for intelligent rail trams is constructed. The model can analyze the optimal mixed marshaling scheme and its corresponding minimum cost according to different passenger flow conditions, so as to achieve the goal of reducing costs and improving operational efficiency in different time periods.

[0104] Based on the comparison of operating efficiency in different time periods, in order to achieve the goal of reducing costs and improving operating efficiency during peak passenger flow periods, a variable marshaling operation optimization model for smart rail trams is proposed with the two goals of minimizing the average daily total cost and maximizing operating efficiency. First, the cost estimation formula of the smart rail tram needs to be modified accordingly. The calculation formula for the fixed cost C1 becomes:

[0105]

[0106] Among them, T P Indicates the peak passenger flow time period (min); T F Indicates the time period of off-peak passenger flow (min); x j P represents the number of smart rail trams with j-number of trains. Smart rail trams usually have two, three or four trains, so the value of j is 2, 3 or 4. j — represents the purchase cost of a smart rail tram with a marshaling number of j.

[0107] Secondly, the calculation formula of operating cost C2 is optimized as follows:

[0108] 1. The calculation formula for the average number of charging times of the train is:

[0109]

[0110] Among them, N CPj E represents the average number of charging times of a smart rail tram with a train number of j during peak hours; Cj represents the energy consumption coefficient of the smart rail tram with a marshaling number of j; E bj N represents the battery capacity of the smart rail tram with j number of trains; CO Indicates the average number of charging times of the smart rail tram during off-peak hours.

[0111] 2. The calculation formula for average daily fuel cost is:

[0112]

[0113] Among them, C f represents the daily fuel cost, N O represents the number of operating days per year, P E Indicates the unit price of electric fuel, E Cj It represents the energy consumption coefficient of the smart rail tram with j number of trains, E C2 Represents the energy consumption coefficient of the smart rail tram with a marshaling number of 2.

[0114] 3. Improved operating cost calculation formula:

[0115]

[0116] Among them, c VM represents the unit time cost of the smart rail tram, H represents the service time of the whole day, c VD represents the unit distance cost of the smart rail tram, x j represents the number of smart rail trams with a marshaling number of j, T P Indicates the peak passenger flow period, T c represents the turnaround time, N CPj represents the average number of charging times of smart rail trams with a group size of j during peak hours, L D represents the distance from the terminal to the charging station, FS represents the fleet size, T F represents the time period of off-peak passenger flow, L represents the route length, N CO Indicates the average number of charging times of the smart rail tram during off-peak hours.

[0117] 4. The calculation formula for operating efficiency during peak passenger flow period:

[0118]

[0119] Among them, Z P represents the operation efficiency of the intelligent rail line during peak hours, P P represents the average passenger flow per minute on the smart rail line during peak hours, T C represents the turnaround time, C Bj represents the capacity of the smart rail tram with j number of trains, x j Represents the number of smart rail trams with a marshaling quantity of j.

[0120] S3. Taking minimizing the daily average total cost and maximizing the operating efficiency as the optimization goals, a variable marshaling operation optimization model for smart rail trams is constructed;

[0121] In an optional embodiment of the present invention, step S3 takes minimizing the average daily total cost and maximizing the operating efficiency as the optimization objectives to construct a variable marshaling operation optimization model for smart rail trams, and its objective function is:

[0122] minC=C1+C2+C3

[0123]

[0124] The constraints are:

[0125] 0≤x j ≤FSj=2,3,4

[0126]

[0127] 0≤Z P ≤1

[0128] Among them, C represents the average daily total cost of the smart rail tram, C1 represents the fixed cost, C2 represents the operating cost, C3 represents the user cost, and Z P represents the operation efficiency of the intelligent rail line during peak hours, P P represents the average passenger flow per minute on the smart rail line during peak hours, T C represents the turnaround time, C Bj represents the capacity of the smart rail tram with j number of trains, x j It represents the number of smart rail vehicles with a marshaling quantity of j, and FS represents the fleet size.

[0129] In this embodiment, after determining the daily passenger flow data, the variable x can be solved j The optimal train formation plan is obtained by calculating the value of the train, and the new cost is calculated and compared with the previous cost. Through this process, the operational efficiency of the intelligent rail can be improved while reducing the average daily cost.

[0130] S4. Solve the constructed intelligent rail tram marshaling operation optimization model to obtain the intelligent rail tram marshaling operation optimization results.

[0131] In an optional embodiment of the present invention, step S4 uses CPLEX, Gurobi and other optimization model solver software to efficiently solve the constructed smart rail tram variable formation operation optimization model, and the key parameter relationship analysis diagram is drawn using Excel, MATLAB, Python and other software tools.

[0132] To sum up, the variable marshaling operation optimization method of the smart rail tram proposed in the present invention can effectively solve the optimization of the variable marshaling smart rail tram marshaling scheme in different time periods, achieve the improvement of operating efficiency and the reduction of costs, and has important application value.

[0133] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0134] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0135] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0136] The present invention uses specific embodiments to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

[0137] Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand the principles of the present invention, and should be understood that the protection scope of the present invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific variations and combinations that do not deviate from the essence of the present invention based on the technical revelations disclosed by the present invention, and these variations and combinations are still within the protection scope of the present invention.

Claims

1. A method for optimizing the operation of a variable formation of a smart rail tram, characterized in that: The following steps are involved: Obtain the variable marshaling operation data of the smart rail tram; the variable marshaling operation data of the smart rail tram includes the headway, operation time, turnaround time and average daily passenger flow of the smart rail tram; Calculate the daily average total cost and operating efficiency of the smart rail tram based on the variable marshaling operation data of the smart rail tram; Taking minimizing the average daily total cost and maximizing the operating efficiency as the optimization goals, a variable marshaling operation optimization model for smart rail trams is constructed; The constructed intelligent rail tram variable formation operation optimization model is solved to obtain the intelligent rail tram variable formation operation optimization results.

2. The method for optimizing the operation of a smart rail tram with variable formation according to claim 1 is characterized in that: The average daily total cost of the smart rail tram is calculated based on the variable marshaling operation data of the smart rail tram, including: C=C1+C2+C3 Among them, C represents the average daily total cost of the smart rail tram, C1 represents fixed cost, C2 represents operating cost, and C3 represents user cost.

3. The method for optimizing the operation of a smart rail tram with variable formation according to claim 2 is characterized in that: Fixed costs are calculated as: Among them, T P represents the peak passenger flow period, H represents the service time, and x j represents the number of smart rail trams with a marshaling number of j, P j represents the purchase cost of a smart rail tram with a marshaling number of j, FS represents the fleet size, CRF represents the annualized cost, and N O Indicates the number of operating days per year, T F It represents the time period with off-peak passenger flow, and P2 represents the purchase cost of a smart rail tram with a marshaling number of 2.

4. The method for optimizing the operation of a smart rail tram with variable formation according to claim 2 is characterized in that: The operating costs are calculated as follows: Among them, c VM represents the unit time cost of the smart rail, H represents the service time, c VD represents the unit distance cost of the smart rail, x j represents the number of smart rail trams with a marshaling number of j, T P Indicates the peak passenger flow period, T c represents the turnaround time, N CPj represents the average number of charging times of smart rail trams with a group size of j during peak hours, L D represents the distance from the terminal to the charging station, FS represents the fleet size, T F represents the time period of off-peak passenger flow, L represents the route length, N CO Indicates the average number of charging times of the smart rail tram during off-peak hours.

5. The method for optimizing the operation of a smart rail tram with variable formation according to claim 4 is characterized in that: The unit time cost of the smart rail tram is calculated as follows: C VM =C O +(1-X)C F +(1-Y)C m C F =C f ×N O Among them, C VM represents the annual cost associated with the vehicle operation time, VM represents the annual driving time of the vehicle, and C O represents the driver's salary and additional welfare costs, X represents the vehicle maintenance cost coefficient, C F represents the annual fuel cost, Y represents the fuel cost coefficient of the vehicle, C m represents the fuel cost of the vehicle, C f represents the daily fuel cost, N O represents the number of operating days per year, P E Indicates the unit price of electric fuel, E Cj It represents the energy consumption coefficient of the smart rail tram with j number of trains, E C2 Represents the energy consumption coefficient of the smart rail tram with a marshaling number of 2.

6. The method for optimizing the operation of a smart rail tram with variable formation according to claim 4 is characterized in that: The calculation method for the average charging times of the smart rail tram with a group size of j during peak hours is: Where VD represents the annual mileage of the vehicle, E Cj It represents the energy consumption coefficient of the smart rail tram with j number of trains, E bj represents the battery capacity of the smart rail tram with j number of trains, S min Indicates the minimum battery state of charge that needs to be maintained.

7. The method for optimizing the operation of a smart rail tram with variable formation according to claim 6 is characterized in that: The calculation method for the average charging times of the smart rail tram during off-peak hours is: Among them, E C2 It represents the energy consumption coefficient of the smart rail tram with a marshaling number of 2, E b2 Indicates the battery capacity of a smart rail tram with a train number of 2.

8. The method for optimizing the operation of a smart rail tram with variable formation according to claim 2 is characterized in that: The user cost is calculated as: Among them, C P represents the unit time cost of passengers, P represents the average daily passenger demand along the route, T O It represents the time required to serve the route, and N1 represents the number of stations of the smart rail tram.

9. The method for optimizing the operation of a smart rail tram with variable formation according to claim 1 is characterized in that: The operation efficiency of the smart rail tram is calculated based on the variable marshaling operation data of the smart rail tram, including: Among them, Z P represents the operation efficiency of the intelligent rail line during peak hours, P P represents the average passenger flow per minute on the smart rail line during peak hours, T C represents the turnaround time, C Bj represents the capacity of the smart rail tram with j number of trains, x j represents the number of smart rail trams with a marshaling number of j, Z O represents the operation efficiency of the intelligent rail line during off-peak hours, P O represents the average passenger flow per minute on the smart rail line during off-peak hours, C B It represents the capacity of the smart rail tram, and FS represents the fleet size.

10. The method for optimizing the operation of a smart rail tram with variable formation according to claim 1, characterized in that: Taking minimizing the daily average total cost and maximizing the operating efficiency as the optimization goals, a variable marshaling operation optimization model for smart rail trams is constructed, including: Among them, C represents the average daily total cost of the smart rail tram, C1 represents the fixed cost, C2 represents the operating cost, C3 represents the user cost, and Z P represents the operation efficiency of the smart rail line during peak hours, P P represents the average passenger flow per minute on the smart rail line during peak hours, T C represents the turnaround time, C Bj represents the capacity of the smart rail tram with j number of trains, x j It represents the number of smart rail vehicles with a marshaling quantity of j, and FS represents the fleet size.

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