A high-speed railway train stopping method and system based on energy consumption control

Abstract

The present application relates to a kind of energy consumption control-based high-speed railway train stopping method and system, method includes: obtaining high-speed railway line diagram;According to high-speed railway line diagram and high-speed railway train train opening scheme and initial stop scheme, train operation network is constructed;According to passenger travel demand and train operation network, based on the user flat of elastic demand allocation problem constructs stop lower planning model;According to the principle of maximum operation income and minimum energy consumption, construct stop upper optimization model;Through genetic algorithm, stop lower planning model and stop upper optimization model are solved, and optimization stop scheme is obtained.On the basis of initial stop scheme, while considering economic benefit, the travel demand of passenger needs to be met, the double-layer decision mechanism of stop upper optimization model and stop lower planning model is constructed, and genetic algorithm is designed to solve the model.Make high-speed railway train meet passenger travel demand also reduce operating energy consumption.

Description

Technical Field

[0001] This invention belongs to the field of high-speed railway train stopping technology, and particularly relates to a high-speed railway train stopping method and system based on energy consumption control. Background Technology

[0002] The station-stopping plan is determined after the train frequency, route, and train formation are established. Based on station demand and passenger flow, the sequence of stops for each train is calculated. While station stops offer passengers more travel options, they also reduce the average train speed and increase energy consumption. Therefore, optimizing the station-stopping plan not only affects the service quality of high-speed railways but is also closely related to the efficiency of railway energy utilization and economic benefits.

[0003] Existing research on train stop schemes mostly combines the study with the operation scheme and related issues. For example, bi-level programming models and multi-objective optimization models for train stop schemes are designed, and simulated annealing and two-stage algorithms are used for solving them; based on the uncertainty of passenger flow demand at high-speed railway stations, an opportunity-constrained programming model for the design problem of high-speed railway train stop schemes under uncertain passenger flow conditions is constructed with the optimization objective of minimizing the total number of stops of trains operating within a section; and based on the minimum number of trains required to determine the maximum passenger flow density of a section, an optimization strategy for passenger train operation schemes based on stop schemes is proposed.

[0004] Currently, the main methods for calculating railway train traction energy consumption are work done in motion and current curves. This study uses computer-aided simulation to quantitatively analyze the relationship between unit traction energy consumption (unit consumption) of passenger trains and the train's maximum target speed, station spacing, and seat-kilometer utilization rate. The results show that, under a fixed maximum target speed, the train's unit consumption increases as the station spacing decreases. Considering resistance and the work done by gravitational potential energy, this study also takes into account various factors affecting train operation energy consumption, including average speed, maximum speed, station stopping speed, and number of stops.

[0005] Existing research has provided a good theoretical basis for optimizing the station stopping scheme of high-speed railway trains. However, most current station stopping schemes and train traction energy consumption are studied separately, which cannot take into account both the station stopping and operation energy consumption of high-speed railway trains. Summary of the Invention

[0006] To address the aforementioned technical problems, this invention provides a method and system for stopping high-speed railway trains based on energy consumption control.

[0007] The technical solution adopted in this invention is:

[0008] Firstly, a method for stopping high-speed trains at stations based on energy consumption control is provided, including:

[0009] Obtain high-speed railway route maps;

[0010] 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;

[0011] 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.

[0012] Based on the principles of maximizing operating revenue and minimizing energy consumption, an upper-level optimization model for the parking station is constructed.

[0013] 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.

[0014] Furthermore, obtaining high-speed railway route maps includes:

[0015] 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;

[0016] Determine any adjacent and The line segments between them are The set of line segments is obtained. ;

[0017] Obtained from known line parameters Line distance value ;

[0018] 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.

[0019] 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:

[0020] 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;

[0021] 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;

[0022] 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 ;

[0023] 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.

[0024] Furthermore, 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, including:

[0025] 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, For along the running section Expenses for train tickets; The preset average time value; For along the running section Travel time consumption; For along the running section Congestion costs for travel; for exist to Passenger flow within the operating area;

[0026] The formula for calculating congestion charges is: ;

[0027] in, for The number of employees; for Maximum capacity; and These are preset adjustment parameters; G is greater than... Multiples of positive integers;

[0028] The expression for constructing the elastic demand function for passenger travel is as follows:

[0029] ;

[0030] in, express to Passenger travel demand; Indicates the initial stopping plan to Passenger travel demand; , Indicates the initial stopping plan under the following conditions to The number of train stops. Indicates the initial stopping plan under the following conditions to The number of train stops. b are preset fixed parameters; , express 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;

[0031] 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:

[0032] Min ;

[0033] st ;

[0034] ;

[0035] 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.

[0036] Furthermore, 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:

[0037] Based on the laws of kinematics, construct resistance With speed The function expression: ,in, , and Preset experimental coefficient values;

[0038] 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;

[0039] 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:

[0040] ;

[0041] in, For preset fixed parameters;

[0042] On the operating section Distance value The work done by the traction force during the entire journey of the train is The expression is:

[0043] ;

[0044] 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; Indicates the preset train coasting distance; This represents the distance the train travels during a known second acceleration process;

[0045] 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:

[0046] ;

[0047] in, Given the known mechanical efficiency of the train; This represents the elevation difference between the starting station and the ending station.

[0048] Furthermore, based on the principles of maximizing operating revenue and minimizing energy consumption, a higher-level optimization model for the parking lot is constructed, including:

[0049] Based on passenger travel demand And the total amount of expenses for all passengers Calculate the operating revenue ;

[0050] According to traction energy consumption The total energy consumption was calculated. The expression is:

[0051] ;

[0052] 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:

[0053] ;

[0054] ;

[0055] st ;

[0056] ;

[0057] ;

[0058] ;

[0059] 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.

[0060] Furthermore, the lower-level planning model and the upper-level optimization model of the stop are solved using a genetic algorithm to obtain the optimized stop scheme, including:

[0061] The chromosome matrix is ​​constructed by encoding chromosome codes.

[0062] The expression for the chromosome matrix is:

[0063] ;

[0064] The rows of the chromosome matrix represent the number of trains, the columns represent the number of stations, and the size of the chromosome matrix is ​​[value missing]. The elements in the chromosome matrix are genes.

[0065] The chromosome matrix is ​​used as a single individual, and multiple individuals form a population;

[0066] Perform a roulette wheel selection operation on the population to find the target chromosome matrix corresponding to the highest fitness in the population. Replace the first chromosome matrix and the chromosome matrix with the worst fitness in the population with the target chromosome matrix to obtain a new population.

[0067] Based on the crossover probability, perform pairwise crossover of the corresponding columns of the chromosome matrix in the new population;

[0068] The new population is subjected to mutation operations based on the set mutation probability until the mutation operation is completed. The optimal chromosome matrix in the new population is then output as the optimized stopping scheme.

[0069] Secondly, a high-speed railway train stopping system based on energy consumption control is provided, including:

[0070] The route map acquisition module is used to acquire high-speed railway route maps;

[0071] 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 high-speed railway trains;

[0072] The first model construction module is used to construct a lower-level planning model for stops based on passenger travel demand and the train operation network, and on the problem of user-balanced allocation of flexible demand.

[0073] 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.

[0074] The stop optimization module is used to solve the lower-level planning model and the upper-level optimization model of the stops using a genetic algorithm to obtain the optimized stop scheme.

[0075] The beneficial effects achieved by this invention are as follows:

[0076] The process involves: obtaining high-speed railway route maps; constructing a train operation network based on the route maps, train operation plans, and initial stop plans; building a lower-level stop planning model based on the user-balanced allocation problem of elastic demand, considering passenger travel demand and the train operation network; constructing an upper-level stop optimization model based on the principles of maximizing operating revenue and minimizing energy consumption; and solving both the lower-level stop planning model and the upper-level stop optimization model using a genetic algorithm to obtain the optimized stop plan. Based on the initial stop plan, and considering both economic benefits and passenger travel demand, a two-layer decision-making mechanism consisting of the upper-level stop optimization model and the lower-level stop planning model is constructed, and a genetic algorithm is designed to solve the model. This allows high-speed railway trains to both meet passenger travel demand and reduce operating energy consumption. Attached Figure Description

[0077] Figure 1 This is a flowchart of the high-speed railway train stopping method based on energy consumption control according to the present invention;

[0078] Figure 2 This is a schematic diagram of the high-speed railway line of the present invention;

[0079] Figure 3 This is a structural diagram of the high-speed railway train stopping system based on energy consumption control according to the present invention. Detailed Implementation

[0080] The present invention will be further described below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and should not be used to limit the scope of protection of the present invention.

[0081] like Figure 1 As shown, this embodiment of the invention provides a high-speed railway train stopping method based on energy consumption control, including:

[0082] 101. Obtain a high-speed railway route map;

[0083] In this embodiment, the problem of optimizing the station-stopping scheme is simplified to a 0-1 optimization problem of whether or not each train should stop at a station, given a fixed train operating section and number of train pairs, while meeting a certain service level. A schematic diagram of a high-speed railway line is shown below. Figure 2 As shown.

[0084] 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;

[0085] Determine any adjacent and The line segments between them are The set of line segments is obtained. ;

[0086] Obtained from known line parameters Line distance value ;

[0087] 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.

[0088] 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;

[0089] 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;

[0090] 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;

[0091] 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 ;

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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, For along the running section Expenses for train tickets; The preset average time value; For along the running section Travel time consumption; For along the running section Congestion costs for travel; for exist to Passenger flow within the operating area;

[0096] The formula for calculating congestion charges is: ;

[0097] in, for The number of employees; for Maximum capacity; and These are preset adjustment parameters; G is greater than... Multiples of positive integers;

[0098] Therefore, passengers choose routes The general cost of travel is:

[0099] ;

[0100] In the formula, Select routes for passengers The general costs of travel; For train At the station Stop time, The definition is as follows:

[0101] ;

[0102] The expression for constructing the elastic demand function for passenger travel is as follows:

[0103] ;

[0104] in, express to Passenger travel demand; Indicates the initial stopping plan to Passenger travel demand; , Indicates the initial stopping plan under the following conditions to The number of train stops. Indicates the initial stopping plan under the following conditions to The number of train stops. b are preset fixed parameters; , express 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;

[0105] 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:

[0106] Min ;

[0107] st ;

[0108] ;

[0109] 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.

[0110] 104. Based on the principles of maximizing operating revenue and minimizing energy consumption, construct an upper-level optimization model for the parking lot;

[0111] 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.

[0112] Before proceeding to step 104, it is necessary to first analyze the principle of train energy consumption calculation, as follows:

[0113] Based on the laws of kinematics, construct resistance With speed The function expression: ,in, , and Preset experimental coefficient values;

[0114] 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;

[0115] 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:

[0116] ;

[0117] in, For preset fixed parameters;

[0118] Assuming the maximum deceleration during the deceleration process and minimum deceleration The expression is as follows:

[0119] ;

[0120] ;

[0121] The coasting distance is at the minimum value and maximum value between, and The expression is as follows:

[0122] ;

[0123] ;

[0124] according to and The preset train coasting distance was calculated. ;

[0125] The formula for calculating the running distance during the secondary acceleration process The train travel distance during the known secondary acceleration process was calculated. ;

[0126] On the operating section Distance value The work done by the traction force during the entire journey of the train is The expression is:

[0127] ;

[0128] 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;

[0129] 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:

[0130] ;

[0131] in, Given the known mechanical efficiency of the train; The elevation difference between the starting station and the ending station;

[0132] Based on passenger travel demand And the total amount of expenses for all passengers Calculate the operating revenue ;

[0133] According to traction energy consumption The total energy consumption was calculated. The expression is:

[0134] ;

[0135] 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:

[0136] ;

[0137] ;

[0138] st ;

[0139] ;

[0140] ;

[0141] ;

[0142] 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.

[0143] 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.

[0144] The chromosome matrix is ​​constructed by encoding chromosome codes.

[0145] The expression for the chromosome matrix is:

[0146] ;

[0147] 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.

[0148] 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;

[0149] 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:

[0150] ;

[0151] 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;

[0152] 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.

[0153] 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 follows: The columns for intersection are randomly selected, and the adaptive intersection probability is constructed as follows:

[0154] ;

[0155] 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;

[0156] 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:

[0157] ;

[0158] and This represents the parameter for adjusting the mutation probability;

[0159] 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:

[0160] 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;

[0161] Step 2: Generate the initial population pop according to the chromosome encoding method, with the current generation y=1;

[0162] Step 3: Conduct passenger flow distribution and determine the passenger flow distribution according to the passenger flow distribution situation;

[0163] 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;

[0164] Step 5: According to the crossover probability, cross the corresponding columns of the population chromosomes in pairs;

[0165] Step 6: Perform mutation operation on the population according to the set mutation probability, and judge whether the obtained new individual meets the constraint conditions. If it meets, retain it; if it does not meet, still retain the individuals in the initial solution;

[0166] Step 7: Algorithm termination determination. If y < Maxgen, go to Step 3 and y = y + 1; otherwise, output the optimal chromosome matrix in the new population, which is the optimized stop plan.

[0167] Beneficial effects achieved by the embodiments of the present invention:

[0168] Obtain the high - speed railway line map; construct the 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; construct the lower - level stop planning model for the user equilibrium distribution problem based on 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 a genetic algorithm to obtain the optimized stop plan. On the basis of the initial stop plan, while considering economic benefits and meeting the travel needs of passengers, a two - layer decision - making mechanism composed of the upper - level stop optimization model and the lower - level stop planning model is constructed, and a genetic algorithm is designed to solve the model. It enables the high - speed railway train to meet the travel needs of passengers and reduce the operation energy consumption.

[0169] 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.

[0170] As Figure 3 shown, the embodiments of the present invention provide a high - speed railway train stop system based on energy consumption control, including:

[0171] A line map acquisition module 301, configured to obtain the high - speed railway line map;

[0172] A train operation network construction module 302, configured to construct 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;

[0173] The first model construction module 303 is used to construct a lower-level planning model for stops based on the user balance allocation problem of flexible demand, according to passenger travel demand and train operation network.

[0174] The second model construction module 304 is used to construct the upper-level optimization model of the parking station based on the principles of maximizing operating revenue and minimizing energy consumption.

[0175] The stop optimization module 305 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.

[0176] The beneficial effects achieved by the embodiments of the present invention are as follows:

[0177] The route map acquisition module 301 acquires the high-speed railway route map; the train operation network construction module 302 constructs the train operation network based on the high-speed railway route map, train operation plans, and initial stop plans; the first model construction module 303 constructs a lower-level planning model for stops based on passenger travel demand and the train operation network, using a user-balanced allocation problem based on elastic demand; the second model construction module 304 constructs an upper-level optimization model for stops based on the principles of maximizing operating revenue and minimizing energy consumption; the stop optimization module 305 solves the lower-level planning model and the upper-level optimization model for stops using a genetic algorithm to obtain the optimized stop plan. Based on the initial stop plan, considering both economic benefits and passenger travel demand, a two-layer decision-making mechanism consisting of the upper-level optimization model and the lower-level planning model for stops is constructed, and a genetic algorithm is designed to solve the model. This allows high-speed railway trains to both meet passenger travel demand and reduce operating energy consumption.

[0178] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0179] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0180] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0181] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0182] The above are merely embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention are included within the scope of the claims of the present invention pending approval.

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. 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 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: ; 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 aforementioned 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 line distances of all line segments; 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; The elevation difference between the starting station and the ending station; 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.

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 1, 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.

5. 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 building module is used to perform the following steps: 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: ; 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 aforementioned 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; The second model building module is used to perform the following steps: 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; The elevation difference between the starting station and the ending station; 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; 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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