High-speed railway train station stopping method and system based on energy consumption control

Abstract

The invention relates to a high-speed railway train station stopping method and system based on energy consumption control. The method comprises the steps that a high-speed railway line diagram is obtained; constructing a train operation network according to the high-speed railway line diagram and the train operation scheme and the initial stop scheme of the high-speed railway train; according to passenger travel demands and a train operation network, building a station-stopping lower-layer planning model based on a user and distribution problem of elastic demands; according to an operation income maximization and energy consumption minimization principle, constructing a stop station upper layer optimization model; and solving the station-stopping lower-layer planning model and the station-stopping upper-layer optimization model through a genetic algorithm to obtain an optimized station-stopping scheme. On the basis of an initial station stopping scheme, economic benefits are considered, travel requirements of passengers need to be met, a double-layer decision-making mechanism composed of a station stopping upper-layer optimization model and a station stopping lower-layer planning model is constructed, and a genetic algorithm is designed to solve the model. Therefore, the high-speed railway train can meet the travel requirements of passengers, and the operation energy consumption is reduced.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of high-speed railway train stopping, and particularly relates to a high-speed railway train stopping method and system based on energy consumption control. BACKGROUND

[0002] The stopping scheme is to determine the stopping sequence of each train according to the requirements of the station and passenger flow after determining the train operation frequency, operation route and marshalling quantity. The train stopping will bring the passengers a choice for travel, but at the same time, it will reduce the average speed of the train and increase the energy consumption of the train operation. Therefore, the optimization of the stopping scheme not only affects the service quality of the high-speed railway, but also is closely related to the utilization efficiency and economic benefits of railway energy.

[0003] Most of the existing train stopping scheme researches are combined with the train operation scheme and related problems. For example, a double-layer planning model and a multi-objective optimization model of the train stopping scheme are designed, and a simulated annealing algorithm and a two-stage algorithm are used for solving; on the basis of using uncertain variables to describe the uncertainty of high-speed railway station passenger flow demand, an opportunity constraint programming model of high-speed railway train stopping scheme design problem under uncertain passenger flow condition is constructed, with the minimum total stopping number of trains in the section as the optimization objective; based on the maximum passenger flow density of the section to determine the minimum number of trains required, a passenger train operation scheme optimization strategy based on the stopping scheme is proposed.

[0004] At present, the calculation methods of railway train traction energy consumption mainly include two methods of motion work and current curve. By using the computer-aided simulation method, the relationship between the unit passenger transport train traction energy consumption (unit consumption) and the train maximum target speed, stopping interval, seat kilometer utilization rate is quantitatively analyzed, and the research results show that under the condition of a certain maximum target speed, the unit consumption of the train increases with the shortening of the train stopping interval. On the basis of considering the resistance and gravitational potential energy work, various factors of train operation energy consumption are considered, including average speed, maximum speed, stopping speed, stopping number, etc.

[0005] The existing research provides a good theoretical basis for the optimization of high-speed railway train stopping scheme, but the current stopping scheme and train traction energy consumption are mostly studied separately, and the high-speed railway train stopping and operation energy consumption cannot be considered. SUMMARY

[0006] In order to solve the above technical problems, the present application provides a high-speed railway train stopping method and system based on energy consumption control.

[0007] The technical scheme adopted by the present application is: In a first aspect, a high-speed railway train stopping method based on energy consumption control is provided, comprising: obtaining a high-speed railway line map; Based on the high-speed railway route map and the train operation plan and initial stop plan of high-speed railway trains, construct the train operation network; Based on passenger travel demand and train operation network, a lower-level planning model for stops is constructed based on the problem of user-balanced allocation of flexible demand. Based on the principles of maximizing operating revenue and minimizing energy consumption, an upper-level optimization model for the parking station is constructed. The optimal stopping scheme is obtained by solving the lower-level planning model and the upper-level optimization model of the stopping using a genetic algorithm.

[0008] Furthermore, obtaining high-speed railway route maps includes: Get the set of stations for the preset high-speed railway line. , where n represents the total number of stations on the preset high-speed railway line. Indicates the i-th station; Determine any adjacent and The line segments between them are The set of line segments is obtained. ; Obtained from known line parameters Line distance value ; Comprehensive station collection The high-speed railway route map is obtained by combining the set of route segments E and the route distance values ​​of each route segment in the corresponding set of route segments E.

[0009] Furthermore, based on the high-speed railway route map and the train operation and initial stop plans of high-speed railway trains, a train operation network is constructed, including: Obtain the train operation plan for high-speed rail, and then obtain the set of trains to be operated based on the train operation plan. K represents the total number of high-speed trains. Indicates the k-th train; Obtain the initial stopping plan for high-speed trains, and then obtain the set of train stops based on the initial stopping plan. ,like ,express exist If you do not stop, ,express exist parking; The set of train operation segments is obtained based on the high-speed railway route map and the initial stopping plan. ;in, express and for Adjacent parking stations; operating sections The distance value of the train route section ; According to the open train set T, the train stop set and the train running section set L, the train operation network (S, L) is constructed.

[0010] Further, according to the passenger travel demand and the train operation network, a stop lower planning model is constructed based on the user equilibrium assignment problem of elastic demand, including: The passenger travel generalized cost of the train route section is calculated by the passenger travel generalized cost formula ; The expression of the passenger travel generalized cost formula is: ; Wherein, is the train ticket price spent along the train route section ; is the preset average time value; is the time consumption along the train route section ; is the congestion cost along the train route section ; is the passenger flow in the running interval from to ; The congestion cost calculation formula of ; Wherein, is the seating capacity of ; is the maximum capacity of ; and are preset adjustment parameters; G is a positive integer greater than times; The expression of the passenger travel elastic demand function is: ; Wherein, represents the passenger travel demand from to ; represents the passenger travel demand from to under the initial stop scheme; , represents the number of train stops from to under the initial stop scheme, represents the number of train stops from to under the initial stop scheme, and b are preset fixed parameters; , represents to the minimum generalized travel cost, represents when to the generalized travel cost, is and the change relationship of the difference, and are preset fixed parameters; represents the inverse matrix of the matrix ; Based on the user equilibrium assignment problem of elastic demand, a stop lower-level planning model is constructed, and the expression of the stop lower-level planning model is: Min ; s.t ; ; Among them, represents the passenger travel satisfaction, f represents the passenger travel elastic demand function, represents to the line distance value of the first line section, W represents to the sum of the line distance values of all line sections.

[0011] Further, before constructing the stop upper-level optimization model according to the principles of maximizing operating income and minimizing energy consumption, it also includes: According to the kinematic law, the function expression of the resistance and the speed is constructed: Among them, , and are preset experimental coefficient values; According to the weight M of the high-speed railway train, the work done by the traction force in the process of accelerating from 0 to at a constant acceleration is calculated , represents the acceleration of gravity; The work done by the traction force in the process of increasing the speed from to the target speed is calculated , and the expression is: ; wherein, is a preset fixed parameter; in the running section of the distance value The work of the train running full course of the tractive force is , the expression is: ; wherein, The number of full course stop intervals during the train running process of the train mass M is represented by ; The deceleration during the train braking is represented by The minimum distance value of ; The preset train coasting distance is represented by The known train running distance during the secondary acceleration process is represented by The tractive energy consumption of the train running full course is obtained by considering the change of the tractive energy consumption of the train potential energy based on the mechanical efficiency condition, and is , the expression is: ; wherein, The known train mechanical efficiency is represented by The elevation difference between the starting station and the terminal station is represented by

[0012] Further, according to the operation income maximization and energy consumption minimization principle, a stop upper optimization model is constructed, including: According to the passenger travel demand and the amount of money spent by all passengers , the operation income is calculated; The total energy consumption is calculated according to the tractive energy consumption , the expression is: ; Based on the operation income maximization and energy consumption minimization principle, the expression of the stop upper optimization model is: ; ; s.t ; ; ; ; The preset maximum number of train stops of is represented by is represented by preset train minimum stop number; denotes maximum stop number; denotes that there is a direct high-speed train between every two stations, if denotes not stopping, if stopping. denotes stopping. stopping.

[0013] Further, the genetic algorithm is used to solve the stop lower planning model and the stop upper optimization model to obtain an optimized stop scheme, including: The chromosome matrix is constructed by encoding through the chromosome coding. The expression of the chromosome matrix is: ; The row of the chromosome matrix represents the number of trains, the column of the chromosome matrix represents the number of stations, and the size of the chromosome matrix is ; the element in the chromosome matrix is a gene. The chromosome matrix is used as a single individual, and multiple individuals form a population. The roulette selection operation is performed on the population to find a target matrix chromosome corresponding to the maximum fitness in the population, and the target matrix chromosome replaces the first chromosome matrix and the chromosome matrix with the worst fitness in the population to obtain a new population. According to the crossover probability, the chromosome matrices in the new population are crossed with each other corresponding to the columns. According to the set mutation probability, the mutation operation is performed on the new population until the mutation operation ends, and the optimal chromosome matrix in the new population is output as the optimized stop scheme.

[0014] In the second aspect, a high-speed railway train stop system based on energy consumption control is provided, including: A line map acquisition module is configured to acquire a high-speed railway line map. A train operation network construction module is configured to construct a train operation network according to the high-speed railway line map and a train operation scheme and an initial stop scheme of a high-speed railway train. A first model construction module is configured to construct a stop lower planning model based on a user equilibrium assignment problem according to passenger travel demand and the train operation network. A second model construction module is configured to construct a stop upper optimization model according to the principles of maximizing operation income and minimizing energy consumption. A stop optimization module is configured to solve the stop lower planning model and the stop upper optimization model through a genetic algorithm to obtain an optimized stop scheme.

[0015] The beneficial effects achieved by the present application are: The high-speed railway line map is acquired; the train operation network is constructed according to the high-speed railway line map and the train running scheme and the initial stop station scheme of the high-speed railway train; the stop station lower planning model is constructed based on the user equilibrium problem of elastic demand according to the passenger travel demand and the train operation network; the stop station upper optimization model is constructed according to the principle of maximizing operation income and minimizing energy consumption; the optimized stop station scheme is obtained by solving the stop station lower planning model and the stop station upper optimization model through the genetic algorithm. On the basis of the initial stop station scheme, the economic benefits are considered at the same time, the passenger travel demand is met, the double-layer decision mechanism composed of the stop station upper optimization model and the stop station lower planning model is constructed, and the genetic algorithm is designed to solve the model. The high-speed railway train meets the passenger travel demand and reduces the operation energy consumption. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 The flowchart of the high-speed railway train stop station method based on energy consumption control of the present application; Figure 2 The schematic diagram of the high-speed railway line of the present application; Figure 3 The structure diagram of the high-speed railway train stop station system based on energy consumption control of the present application. DETAILED DESCRIPTION

[0017] The present application will be further described below in combination with the drawings. The following examples are only used to more clearly illustrate the technical solutions of the present application, and cannot be used to limit the protection scope of the present application.

[0018] As shown in Figure 1 , the embodiment of the present application provides a high-speed railway train stop station method based on energy consumption control, which comprises: 101, acquiring a high-speed railway line map; In the embodiment, the stop station scheme optimization problem is simplified as: the 0-1 optimization problem of whether each train stops or not under the condition that the train running section and the number of running pairs are certain and a certain service level is met, and the schematic diagram of the high-speed railway line is shown in Figure 2 .

[0019] Acquiring the station set of the preset high-speed railway line , n represents the total number of stations on the preset high-speed railway line, represents the i-th station; Determining the line section between any adjacent and as , obtaining the line section set ; obtaining Line distance value ; Comprehensive station collection The high-speed railway route map is obtained by combining the set of route segments E and the route distance values ​​of each route segment in the corresponding set of route segments E.

[0020] 102. Based on the high-speed railway route map and the train operation plan and initial stop plan of high-speed railway trains, construct the train operation network; Obtain the train operation plan for high-speed rail, and then obtain the set of trains to be operated based on the train operation plan. K represents the total number of high-speed trains. Indicates the k-th train; Obtain the initial stopping plan for high-speed trains, and then obtain the set of train stops based on the initial stopping plan. ,like ,express exist If you do not stop, ,express exist parking; The set of train operation segments is obtained based on the high-speed railway route map and the initial stopping plan. ;in, express and for Adjacent parking stations; operating sections The distance value is ; Based on the train departure assembly T and the train stopping assembly The train operation network (S, L) is constructed by combining the set of train operation sections L.

[0021] 103. Based on passenger travel demand and the train operation network, a lower-level planning model for stops is constructed based on the problem of user-balanced allocation of flexible demand. Train operation plans and their stop plans constitute the railway passenger service network. Passengers choose their travel plans according to their travel preferences. In the selection process, passengers tend to choose the travel plan with the lowest total impedance. Due to the limitation of train transport capacity, too many passengers will cause difficulties in purchasing tickets and train congestion, and the corresponding path impedance will increase. At the same time, changes in the stop plans will change the travel costs of passengers, which will in turn affect the travel demand of passengers. Therefore, the travel of passengers will eventually reach a user equilibrium state under the condition of elastic demand. The operating route is calculated using the generalized cost formula for passenger travel. passenger travel expenses The expression for the generalized cost formula for passenger travel is: ;in, the running section the train fare expenditure of the trip; the preset average time value; the time consumption of the trip along the running section ; the congestion fee of the trip along the running section ; the passenger flow of the running section ; to ; The congestion fee calculation formula of the running section is: ; wherein, the passenger capacity of the running section ; the maximum passenger capacity of the running section ; and are preset adjustment parameters; and G is a positive integer greater than times; Therefore, the generalized fee of the passenger trip along the path is: ; wherein, the generalized fee of the passenger trip along the path ; the stop time of the train at the station , is defined as follows: ; The expression of the passenger trip elastic demand function is constructed as: ; wherein, the passenger trip demand from to ; the passenger trip demand from to under the initial stop scheme; , the number of train stops from to under the initial stop scheme, the number of train stops from to under the initial stop scheme, and b are preset fixed parameters; , denote to The minimum generalized travel cost, express hour to Broad travel costs for and The relationship between the changes in the difference and For preset fixed parameters; express matrix The inverse matrix; Based on the problem of user allocation with elastic demand, a lower-level planning model for stops is constructed. The expression of the lower-level planning model for stops is: Min ; st ; ; in, Let f represent passenger travel satisfaction, and let f represent the passenger travel elasticity demand function. express to The line distance value of the first line segment, W represents to The sum of the distance values ​​of all route segments.

[0022] 104. Based on the principles of maximizing operating revenue and minimizing energy consumption, construct an upper-level optimization model for the parking lot; Under the constraints of station preparation capacity, railway enterprises should provide maximum convenience for passengers, make reasonable use of section capacity, and formulate passenger train stopping plans based on the principle of maximizing railway operating efficiency; the objectives consist of two parts: operating revenue and energy consumption. Before proceeding to step 104, it is necessary to first analyze the principle of train energy consumption calculation, as follows: Based on the laws of kinematics, construct resistance With speed The function expression: ,in, , and Preset experimental coefficient values; Calculate the constant acceleration based on the weight M of the high-speed train. Accelerating from 0 to Work done by traction during the process , Represents gravitational acceleration; Calculate when the speed drops to Then pull to increase the speed to the target speed. Work done by traction during the process The expression is: ; in, For preset fixed parameters; Assuming the maximum deceleration during the deceleration process and minimum deceleration The expression is as follows: ; ; The coasting distance is at the minimum value and maximum value between, and The expression is as follows: ; ; according to and The preset train coasting distance was calculated. ; The formula for calculating the running distance during the secondary acceleration process The train travel distance during the known secondary acceleration process was calculated. ; On the operating section Distance value The work done by the traction force during the entire journey of the train is The expression is: ; in, This represents the number of stops along the entire route of a train of mass M. ; This indicates the deceleration during train braking. Indicates reaching The minimum distance value; Considering the change in train potential energy due to traction energy consumption, the traction energy consumption for the entire train operation is obtained under the condition of mechanical efficiency as follows: The expression is: ; in, Given the known mechanical efficiency of the train; The elevation difference between the starting station and the ending station; Based on passenger travel demand And the total amount of expenses for all passengers Calculate the operating revenue ; According to traction energy consumption The total energy consumption was calculated. The expression is: ; Based on the principles of maximizing operating revenue and minimizing energy consumption, the expression for the upper-level optimization model of the parking lot is as follows: ; ; st ; ; ; ; express The maximum number of stops for a pre-set train; express The minimum number of stops for a pre-set train; express Maximum number of stops; This indicates that there are direct high-speed trains between every two stations. ,express exist If you do not stop, ,express exist parking.

[0023] 105. The genetic algorithm is used to solve the lower-level planning model and the upper-level optimization model of the parking station to obtain the optimized parking station scheme.

[0024] The chromosome matrix is ​​constructed by encoding chromosome codes. The expression for the chromosome matrix is: ; The rows of the chromosome matrix represent the number of trains, and the columns represent the number of stations where the trains stop. The size of the chromosome matrix is ​​n. The elements in the chromosome matrix are genes. Using the chromosome matrix as a single individual, multiple individuals form a population; the matrix as a whole is regarded as a genetically inherited individual, without the need to expand the matrix into a string of elements, thus ensuring the integrity of the genes of offspring individuals; To evaluate the quality of individuals in a population, a corresponding fitness function needs to be designed. For the model's objective function, the fitness function is defined as follows: ; in, Represents a weighting coefficient, function value The larger the fitness value, the better. The larger the value, the stronger the individual's adaptability, and the greater the probability that it will be passed on to the next generation; A roulette wheel selection operation is performed on the population to find the target chromosome matrix corresponding to the highest fitness in the population. The target chromosome matrix replaces the first chromosome matrix and the chromosome matrix with the worst fitness in the population to obtain a new population. In order to prevent crossover, mutation and other operations from changing all the chromosomes of the offspring and causing fitness degradation, crossover and mutation operations are not performed on the first chromosome. This method can avoid the algorithm from converging too early to a certain extent. Based on the crossover probability, perform pairwise crossovers on the corresponding columns of the chromosome matrix in the new population; the crossover probability is defined as... The columns for intersection are randomly selected, and the adaptive intersection probability is constructed as follows: ; in, This represents the maximum fitness in the new population. This represents the average fitness in the new population. This indicates the higher fitness of the two chromosomes involved in the crossing over. and This represents the crossover probability adjustment parameter; Except for the first chromosome, each chromosome in the population is randomly selected to perform a 0-1 mutation on a gene. The mutation probability is defined as follows: The mutation probability is as follows: ; and This represents the parameter for adjusting the mutation probability; The new population is subjected to mutation operations based on the set mutation probability until the mutation operation is completed; the specific genetic algorithm solution process is as follows: Step 1: Set the parameters, including the population size (popsize) and the crossover probability adjustment parameters. , The mutation probability adjustment parameters are respectively and Maximum number of iterations, Maxgen; Step 2: Generate the initial population pop according to the chromosome encoding method, with the current generation y=1; Step 3: Distribute passenger flow and determine passenger flow distribution based on the passenger flow distribution. Step 4: Perform roulette wheel selection operation on the population, find the matrix chromosome corresponding to the maximum fitness in the population, and replace the first chromosome and the chromosome with the worst fitness in the current population with it; Step 5: According to the crossover probability, cross the corresponding columns of the population chromosomes in pairs; Step 6: Perform mutation operation on the population according to the set mutation probability, and judge whether the obtained new individuals meet the constraint conditions. If they meet, retain them; if not, still retain the individuals in the initial solution; Step 7: Algorithm termination determination. If y < Maxgen, go to Step 3, y = y + 1; otherwise, output the optimal chromosome matrix in the new population, which is the optimized stop plan.

[0025] Advantages achieved by the embodiments of the present invention: Obtain the high - speed railway line map; construct the train operation network according to the high - speed railway line map, the train operation plan and the initial stop plan of the high - speed railway train; construct the lower - level stop planning model based on the user equilibrium distribution problem with elastic demand according to the passenger travel demand and the train operation network; construct the upper - level stop optimization model according to the principles of maximizing operating income and minimizing energy consumption; solve the lower - level stop planning model and the upper - level stop optimization model through the genetic algorithm to obtain the optimized stop plan. On the basis of the initial stop plan, while considering economic benefits, it is necessary to meet the travel needs of passengers, construct a two - layer decision - making mechanism composed of the upper - level stop optimization model and the lower - level stop planning model, and design a genetic algorithm to solve the model. This enables the high - speed railway train to not only meet the travel needs of passengers but also reduce the operating energy consumption.

[0026] Combined with the high - speed railway train stop method based on energy consumption control described in the above embodiments, the high - speed railway train stop system based on energy consumption control will be described below through embodiments.

[0027] As Figure 3 shown, the embodiments of the present invention provide a high - speed railway train stop system based on energy consumption control, including: A line map acquisition module 301 for acquiring the high - speed railway line map; A train operation network construction module 302 for constructing a train operation network according to the high - speed railway line map, the train operation plan of the high - speed railway train and the initial stop plan; A first model construction module 303 for constructing a lower - level stop planning model based on the user equilibrium distribution problem with elastic demand according to the passenger travel demand and the train operation network; A second model construction module 304 for constructing an upper - level stop optimization model according to the principles of maximizing operating income and minimizing energy consumption; The stop station optimization module 305 is used for solving the stop station lower planning model and the stop station upper optimization model by a genetic algorithm to obtain an optimized stop station scheme.

[0028] The embodiment of the present application has the following beneficial effects: The line map acquisition module 301 acquires a high-speed railway line map; the train operation network construction module 302 constructs a train operation network according to the high-speed railway line map and a train operation scheme and an initial stop station scheme of a high-speed railway train; the first model construction module 303 constructs a stop station lower planning model based on a user equilibrium assignment problem according to passenger travel demand and the train operation network; the second model construction module 304 constructs a stop station upper optimization model according to the principle of maximizing operation income and minimizing energy consumption; and the stop station optimization module 305 solves the stop station lower planning model and the stop station upper optimization model by a genetic algorithm to obtain an optimized stop station scheme. On the basis of the initial stop station scheme, the economic benefit is considered while meeting the passenger travel demand, the double-layer decision mechanism composed of the stop station upper optimization model and the stop station lower planning model is constructed, and the genetic algorithm is designed to solve the model. The high-speed railway train meets the passenger travel demand and reduces the operation energy consumption.

[0029] Those skilled in the art will understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can adopt a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can adopt a computer program product in the form of being implemented on one or more computer usable storage media containing computer usable program codes (including but not limited to disk storage, CD-ROM, optical storage, etc.).

[0030] The present application is described with reference to flowcharts and / or block diagrams of the method, device (system), and computer program product according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of the flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to produce a machine, so that the instructions executed by the computer or other programmable data processing devices produce a device for implementing the functions specified in the flowcharts and / or block diagrams. Figure 1 The device for implementing the functions specified in one flow or multiple flows and / or blocks Figure 1 The device for implementing the functions specified in one flow or multiple flows and / or blocks

[0031] These computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture including instructions which implement the flow Figure 1 The flow or flows and / or blocks Figure 1 The flow or flows and / or blocks

[0032] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions that execute on the computer or other programmable apparatus provide steps for implementing the flow Figure 1 The flow or flows and / or blocks Figure 1 The flow or flows and / or blocks

[0033] The above merely provides an embodiment of the present application, but is not intended to limit the present application, and any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall fall within the scope of the claims of the present application.

Claims

1. A method for stopping high-speed railway trains based on energy consumption control, characterized in that, include: Obtain high-speed railway route maps; Based on the high-speed railway route map and the train operation plan and initial stop plan of the high-speed railway trains, a train operation network is constructed; Based on passenger travel demand and the train operation network, a lower-level planning model for stops is constructed based on the user balanced allocation problem of flexible demand. Based on the principles of maximizing operating revenue and minimizing energy consumption, an upper-level optimization model for the parking station is constructed. The optimized stopping scheme is obtained by solving the lower-level planning model and the upper-level optimization model of the stopping using a genetic algorithm.

2. The high-speed railway train stopping method based on energy consumption control according to claim 1, characterized in that, The acquisition of the high-speed railway route map includes: Get the set of stations for the preset high-speed railway line. The n represents the total number of stations on the preset high-speed railway line. Indicates the i-th station; Determine any adjacent and The line segments between them are The set of line segments is obtained. ; The information is obtained from known line parameters. Line distance value ; The above station collection The high-speed railway route map is obtained by using the set of route segments E and the corresponding route distance values ​​of each route segment in the set of route segments E.

3. The high-speed railway train stopping method based on energy consumption control according to claim 2, characterized in that, The process of constructing a train operation network based on the high-speed railway route map and the train operation plan and initial stop plan of the high-speed railway includes: Obtain the train operation plan for high-speed rail, and then obtain the set of trains to be operated based on the train operation plan. K represents the total number of high-speed railway trains. Indicates the k-th train; Obtain the initial stopping plan for high-speed trains, and then obtain the train stopping set based on the initial stopping plan. ,like , indicating the In the If you do not stop, , indicating the In the parking; Based on the high-speed railway route map and the initial stopping plan, a set of train operation segments is obtained. ; wherein, the Indicates the and stated For the Adjacent parking stations; the operating section The distance value is ; Based on the train set T and the train stop set The train operation network (S, L) is constructed by combining the set of train operation sections L.

4. The high-speed railway train stopping method based on energy consumption control according to claim 3, characterized in that, The step involves constructing a lower-level planning model for stops based on passenger travel demand and the train operation network, using a flexible demand-based user allocation problem. This model includes: The operating segment was calculated using the generalized cost formula for passenger travel. passenger travel expenses The expression for the generalized cost formula for passenger travel is: ; wherein, the For along the said operating section Expenses for train tickets during travel; The preset average time value; For along the said operating section Travel time consumption; the aforementioned For along the said operating section Congestion costs for travel; the aforementioned For the In the To the above Passenger flow within the operating area; The The formula for calculating congestion charges is: ; Among them, the For the The number of employees; For the The maximum capacity; and stated For preset adjustment parameters; G is greater than the Multiples of positive integers; The expression for constructing the elastic demand function for passenger travel is as follows: ； Among them, the Indicates the To the above The passenger travel demand; This indicates the initial stopping scheme under the following conditions. To the above The passenger travel demand; The This indicates that under the initial stopping plan, the following... To the above The number of train stops, the This indicates that under the initial stopping plan, the following... To the above The number of train stops, the And b is a preset fixed parameter; The Indicates the To the above The minimum generalized travel cost, the Indicates the The time mentioned To the above The generalized travel costs, the For the and stated The relationship between the changes in the difference, the above and stated For preset fixed parameters; the Indicates the matrix The inverse matrix; Based on the problem of user allocation with flexible demand, a lower-level planning model for stops is constructed. The expression of the lower-level planning model for stops is: Min ; s.t ; ； Among them, the The value represents passenger travel satisfaction, where f represents the passenger travel elasticity demand function. Indicates the To the above The line distance value of the first line segment, where W represents the line distance. To the above The sum of the distance values ​​of all route segments.

5. The high-speed railway train stopping method based on energy consumption control according to claim 4, characterized in that, Before constructing the upper-level optimization model for the parking lot based on the principles of maximizing operating revenue and minimizing energy consumption, the following steps are also included: Based on the laws of kinematics, construct resistance With speed The function expression: , wherein The above and the aforementioned Preset experimental coefficient values; Calculate the constant acceleration based on the weight M of the high-speed train. Accelerating from 0 to the aforementioned Work done by traction during the process The Represents gravitational acceleration; Calculate when the speed drops to Then pull to increase the speed to the target speed. Work done by traction during the process The expression is: ； Among them, the For preset fixed parameters; In the operating section Distance value The work done by the traction force during the entire journey of the train is The expression is: ； Among them, the This represents the number of stops along the entire route of a train with a mass of M. The The deceleration during train braking is described in the following text. Indicates that the above has been achieved The minimum distance value; Indicates the preset train coasting distance; the aforementioned This represents the distance the train travels during a known second acceleration process; Considering the change in train potential energy due to traction energy consumption, the traction energy consumption for the entire train operation is obtained under the condition of mechanical efficiency as follows: The expression is: ； Among them, the The known mechanical efficiency of the train; This represents the elevation difference between the starting station and the ending station.

6. The high-speed railway train stopping method based on energy consumption control according to claim 5, characterized in that, The above-mentioned upper-level optimization model for parking stations, constructed based on the principles of maximizing operating revenue and minimizing energy consumption, includes: Based on the aforementioned passenger travel demand And the total amount of expenses for all passengers Calculate the operating revenue ; According to the traction energy consumption The total energy consumption was calculated. The expression is: ； Based on the principles of maximizing operating revenue and minimizing energy consumption, the expression for the upper-level optimization model of the parking lot is as follows: ； ； s.t ; ； ； ； The Indicates the The preset maximum number of train stops; Indicates the The preset minimum number of train stops; Indicates the The maximum number of stops; This indicates that there are direct high-speed trains between every two stations. , indicating the In the If you do not stop, , indicating the In the parking.

7. The high-speed railway train stopping method based on energy consumption control according to claim 6, characterized in that, The process of solving the lower-level planning model and the upper-level optimization model of the stop using a genetic algorithm to obtain an optimized stop scheme includes: The chromosome matrix is ​​constructed by encoding chromosome codes. The expression for the chromosome matrix is: ； The rows of the chromosome matrix represent the number of trains, the columns represent the number of stations where the trains stop, and the size of the chromosome matrix is ​​[missing information]. The elements in the chromosome matrix are genes. The chromosome matrix is ​​considered as a single individual, and multiple individuals form a population; A roulette wheel selection operation is performed on the population to find the target matrix chromosome corresponding to the highest fitness in the population. The target matrix chromosome replaces the first chromosome matrix and the chromosome matrix with the worst fitness in the population to obtain a new population. Based on the crossover probability, perform pairwise crossover of the corresponding columns in the chromosome matrix of the new population; The new population is subjected to mutation operations according to the set mutation probability until the mutation operation ends. The optimal chromosome matrix in the new population is then output as the optimized stopping scheme.

8. A high-speed railway train stopping system based on energy consumption control, characterized in that, include: The route map acquisition module is used to acquire high-speed railway route maps; The train operation network construction module is used to construct the train operation network based on the high-speed railway route map and the train operation plan and initial stop plan of the high-speed railway trains; The first model construction module is used to construct a lower-level planning model for stops based on the passenger travel demand and the train operation network, and on the user peaceful allocation problem of elastic demand. The second model building module is used to build an upper-level optimization model for the parking lot based on the principles of maximizing operating revenue and minimizing energy consumption. The stop optimization module is used to solve the lower-level planning model and the upper-level optimization model of the stop using a genetic algorithm to obtain an optimized stop scheme.

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