Energy saving operation strategy optimization method, system, medium and device considering high-speed rail level characteristics

Abstract

The application discloses an energy-saving operation strategy optimization method, system, medium and equipment considering high-speed rail grade characteristics, which establishes a high-speed train operation basic model according to high-speed rail engineering characteristics and takes energy saving as a target; meanwhile, a train grade selection model is established according to the unique traction / braking grade characteristics of the high-speed train; subsequently, the model is subjected to segmented linearization processing, the nonlinear constraint in the linearization model, and the model is integrated into a comprehensive mixed integer linear programming model, namely, a high-speed rail comprehensive optimization model; in order to solve the phenomenon of no solution caused by the substantial narrowing of the solution space in the comprehensive model due to the high modeling dispersion, the model is relaxed based on the longitudinal space, the feasible region of the model is expanded under controllable precision, and the optimal speed trajectory and optimal grade strategy of the high-speed train are obtained. The high-speed train energy-saving operation method obtained by the application has an energy-saving effect, fully considers the high-speed train operation characteristics, and is beneficial to shortening the train tracking control link.

Description

Technical Field

[0001] This invention relates to the field of high-speed train operation optimization and control technology, specifically to an energy-saving operation strategy optimization method, system, medium, and equipment that takes into account the characteristics of high-speed trains. Background Technology

[0002] In recent years, the rapid growth of high-speed railway operating mileage and people's increasing travel demands have made the energy consumption problem of high-speed trains more prominent.

[0003] High-speed rail driver assistance systems (DAS) enable trains to achieve more efficient and energy-saving operations. Operation optimization methods that conform to actual operating characteristics provide strong support for DAS to guide drivers in controlling train operation, further promoting the low-carbon and intelligent development of high-speed rail. With the development of computer technology, mathematical programming methods relying on numerical iteration are often used to obtain the optimal speed trajectory that minimizes train operating energy consumption. However, when establishing train mathematical models, most studies neglect the class characteristics of high-speed railways, treating traction and braking forces as continuous variables. Consequently, the optimized operating curves correspond to continuous values ​​of traction and braking forces between zero and their maximum values.

[0004] For high-speed trains with stepped speed characteristics, the DAS (Direct Adaptive Speed ​​Controller) system, when applying stepped speed regulation to the optimal speed trajectory calculated based on continuously variable speed (CVT), suffers a significant reduction in its ability to track the optimal strategy. Specifically, in assisted driving scenarios with manual operation, the driver needs to adjust the speed by pushing the traction / brake lever on the control panel, and the actual force value corresponding to the lever may not match the theoretical value corresponding to the optimal speed trajectory. Furthermore, existing studies considering the stepped speed characteristics of high-speed trains simplify each step as a proportional representation of the maximum step, which does not reflect the actual stepped speed characteristics. Summary of the Invention

[0005] To overcome the defects and shortcomings of existing technologies, this invention provides an energy-saving operation strategy optimization method that considers the characteristics of high-speed trains. This method fully considers engineering constraints such as train class characteristics, designs a longitudinal relaxation mechanism, calculates the optimal speed curve and optimal class strategy of high-speed trains, and the resulting energy-saving operation method for high-speed trains has energy-saving effects. It fully considers the operating characteristics of high-speed trains, helps to shorten the train tracking control link, reduces the driver's reaction time to the optimal driving guidance, and improves the train's tracking effect on the optimal curve.

[0006] The second objective of this invention is to provide an energy-saving operation strategy optimization system that takes into account the characteristics of high-speed rail.

[0007] A third objective of this invention is to provide a computer-readable storage medium;

[0008] A fourth objective of this invention is to provide a computing device.

[0009] To achieve the above objectives, the present invention adopts the following technical solution:

[0010] This invention provides an energy-saving operation strategy optimization method considering the characteristics of high-speed rail, comprising the following steps:

[0011] Discretize the total operating intervals of high-speed rail, construct the operating constraints of the train based on the engineering characteristics of high-speed train operation, establish a basic model of high-speed train operation, and take the minimum sum of energy consumed in each discrete interval as the objective equation of the basic model of high-speed train operation.

[0012] Establish a train level selection model based on the traction or braking level characteristics of high-speed trains;

[0013] The basic high-speed train operation model and the level selection model are segmented and linearized to linearize the nonlinear constraints. The linearized basic high-speed train operation model and the level selection model are then integrated to obtain the high-speed rail comprehensive optimization model.

[0014] By relaxing the high-speed rail comprehensive optimization model based on the longitudinal space, and solving the relaxed model, the optimal speed trajectory and optimal level strategy of the high-speed train are obtained.

[0015] As a preferred technical solution, the step of constructing train operation constraints based on the engineering characteristics of high-speed train operation specifically includes:

[0016] The sum of the distances Δd of N discrete intervals equals the total distance D traveled by the train.

[0017] The velocity v at each discrete point i Less than or equal to the maximum speed limit value V lim,i ;

[0018] Calculation of frictional resistance f based on Davis equation i ;

[0019] Assume that the train speed changes monotonically within each section, that only one control level is used in each section, and that its average speed v ave,i From the interval boundary velocity v i and v i-1 Seek;

[0020] The time Δt elapsed for all intervals i The sum of these values ​​satisfies the preset total time T. total ;

[0021] The changes in various energy consumptions within each operating interval satisfy the following formula:

[0022]

[0023] Among them, F i This represents the traction or braking force corresponding to the dynamically changing level; M is the total mass of the high-speed train, and v... i and v i-1 ΔH is the velocity at the boundary of the interval, g is the gravitational constant; i It refers to the change in the train's elevation within the section;

[0024] Set the acceleration limit A for train traction or braking. max,d and S max,a ;

[0025] Calculation parameters for energy conversion efficiency under traction conditions and braking conditions are established.

[0026] As a preferred technical solution, based on the traction or braking level characteristics of high-speed trains, a binary variable model is used to select each level, thereby constructing a level selection model for the train. The traction or braking force is then adjusted according to the corresponding level characteristic curve, specifically as follows:

[0027]

[0028] Where k represents the index of all corresponding levels, K1 represents the number of corresponding traction levels, K2 represents the number of corresponding braking levels, and f k This represents the stage characteristic curve corresponding to the stage, v ave,i λ represents the average velocity. k,i A binary variable representing the choice of the corresponding level.

[0029] As a preferred technical solution, the basic model of high-speed train operation and the level selection model are processed by piecewise linearization. Each nonlinear term is piecewise linearized, the original nonlinear curve is interrupted by several nodes, and the original curve is approximately replaced by a straight line formed by connecting two adjacent nodes.

[0030] As a preferred technical solution, piecewise linearization is performed on each nonlinear term. The SOS2 variable set is used as a mathematical tool to achieve the piecewise linearization process, which specifically includes:

[0031] Assume the train's speed range is V min To V max Set the precision threshold δ for piecewise linearization, specifically as follows:

[0032] δ=(V max -V min / (J-1))

[0033] Where J is the number of variables in the SOS2 variable set, corresponding to the number of nodes taken in the piecewise linearization process;

[0034] Setting constraints for the SOS2 variable set includes:

[0035] All variables take values ​​in the range [0,1], and the sum of all SOS2 variables is 1;

[0036] As a preferred technical solution, piecewise linearization is performed on each nonlinear term. The nonlinear terms include: the nonlinear term 1 / v used to calculate the train running time. ave,i Nonlinear terms used to calculate train running resistance The stage characteristic curve f used to calculate traction or braking force k (v ave,i ) and the nonlinear term used to calculate the change in train kinetic energy Treating the nonlinear term as a whole variable, it is specifically expressed as follows:

[0037]

[0038]

[0039]

[0040]

[0041]

[0042]

[0043] Among them, F point,j α represents the discrete nodes on the stage characteristic curve. i,j To be with v ave,i The relevant SOS2 variable, β i,j To be with v i The relevant SOS2 variable.

[0044] As a preferred technical solution, the high-speed rail comprehensive optimization model is relaxed based on the longitudinal space to obtain the calculation formula for traction force or braking force, which is specifically expressed as follows:

[0045]

[0046]

[0047]

[0048] Among them, F max,It is the maximum traction or braking force obtained according to the corresponding level of the traction system, where k represents the index of all levels, K1 represents the number of corresponding traction levels, K2 represents the number of corresponding braking levels, and f k This represents the stage characteristic curve corresponding to the stage, v ave, λ represents the average velocity. k,i F represents a binary variable representing the choice of the corresponding level position. min, It is the minimum traction or braking force obtained by corresponding to the next smaller level, P con This indicates the permissible range of traction or braking force at the corresponding level. con When P = 0, it means no relaxation is performed; when P = 0, it means no relaxation is performed. con When the value is 1, it represents complete relaxation.

[0049] To achieve the second objective mentioned above, the present invention adopts the following technical solution:

[0050] An energy-saving operation strategy optimization system considering the class characteristics of high-speed rail includes: a high-speed train operation basic model construction module, a class selection model construction module, a linearization processing module, a high-speed rail comprehensive optimization model construction module, a relaxation module, and a solution module;

[0051] The high-speed train operation basic model construction module is used to discretize the total interval of high-speed train operation, construct the train operation constraints according to the engineering characteristics of high-speed train operation, establish the high-speed train operation basic model, and take the minimum sum of energy consumed in each discrete interval as the objective equation of the high-speed train operation basic model.

[0052] The class selection model construction module is used to establish a class selection model for the train based on the traction or braking class characteristics of the high-speed train.

[0053] The linearization module is used to perform piecewise linearization on the basic model of high-speed train operation and the level selection model, and to linearize the nonlinear constraints.

[0054] The high-speed rail comprehensive optimization model construction module is used to integrate the linearized high-speed train operation basic model and the level selection model to obtain the high-speed rail comprehensive optimization model.

[0055] The relaxation module is used to relax the high-speed rail comprehensive optimization model based on the longitudinal space.

[0056] The solution module is used to solve the relaxed model to obtain the optimal speed trajectory and optimal level strategy of the high-speed train.

[0057] To achieve the third objective mentioned above, the present invention adopts the following technical solution:

[0058] A computer-readable storage medium includes a stored program that, when executed, implements the energy-saving operation strategy optimization method considering the characteristics of high-speed rail as described above.

[0059] To achieve the fourth objective mentioned above, the present invention adopts the following technical solution:

[0060] A computing device includes a processor and a memory for storing processor-executable programs. When the processor executes the program stored in the memory, it implements the energy-saving operation strategy optimization method that takes into account the characteristics of high-speed rail as described above.

[0061] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0062] (1) This invention takes into account the unique level characteristics of high-speed railways into the optimization model, which can not only calculate the optimal train speed trajectory, but also provide the optimal level control strategy to the train control system.

[0063] (2) The algorithm of this invention, which directly adds control signals to the optimization process, can solve the dilemma that the optimization results do not match the actual operation mechanism, so that the optimization results can better match the actual operation characteristics of the train and are easier to put into practical application.

[0064] (3) The present invention can effectively shorten the tracking control process. Compared with directly providing the optimal speed curve to the driver, directly providing the optimal level control strategy can reduce the driver's reaction time to the optimal driving guidance and improve the train's tracking effect on the optimal curve. Attached Figure Description

[0065] Figure 1 This is a flowchart illustrating the energy-saving operation strategy optimization method considering the characteristics of high-speed rail in this invention.

[0066] Figure 2 This is a schematic diagram illustrating the construction of the basic model for high-speed train operation according to the present invention;

[0067] Figure 3 This is a schematic diagram illustrating the construction of the level selection model according to the present invention;

[0068] Figure 4 This is a schematic diagram illustrating the piecewise linearization process of the present invention. Detailed Implementation

[0069] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0070] Example 1

[0071] like Figure 1 As shown in the figure, this embodiment provides an energy-saving operation strategy optimization method that considers the characteristics of high-speed rail. The method includes the following steps:

[0072] S1: Discretize the high-speed rail operation process and construct the train operation constraints based on the characteristics of high-speed train operation engineering to establish a basic model of high-speed train operation;

[0073] In this embodiment, the total operating range of the high-speed train is discretized into several equidistant segments. Each discrete segment contains information such as speed, time, and energy consumption changes. The objective equation of the model is to minimize the sum of energy consumed in each discrete segment. At the same time, the influence of gradient, speed limit, and other factors that conform to the actual operating characteristics of high-speed trains are considered. Based on the engineering characteristics of high-speed train operation, constraints such as gradient and speed limit are added to the model to construct a basic model of high-speed train operation.

[0074] In this embodiment, the objective equation specifically includes:

[0075] like Figure 2 As shown, based on the engineering characteristics of high-speed rail, the entire operation process of the high-speed rail is first discretized into N equally spaced intervals. Each discrete interval contains the speed change within that interval (from v). i-1 to v i ), duration (Δt) i ) and information on energy consumed (E) i To minimize energy consumption during the entire operation of the high-speed train, the following objective equation is established:

[0076]

[0077] Where: J represents the total energy consumed by the high-speed train across all sections, E i This represents the energy consumed in the i-th interval. Its meaning is to minimize the sum of energy consumed in each discrete interval. A value greater than 0 indicates that the train is performing traction control, consuming energy from the power grid; a value less than 0 indicates that the train is performing regenerative braking, converting some kinetic energy into electrical energy.

[0078] Meanwhile, in order to ensure that the optimization results conform to the actual characteristics of high-speed train operation, the following constraints are added to the train operation model based on its associated physical information:

[0079] 1) The sum of the distances Δd between N discrete intervals equals the total distance D traveled by the train;

[0080] 2) The velocity v at each discrete point i Less than or equal to the maximum speed limit value V lim,i ;

[0081] 3) The train encounters resistance from various sources, including air, wheels and rails, and the transmission system, as it traverses each section. i Here, the Davis equation is used to calculate the frictional resistance. The Davis coefficients A, B, and C are empirical coefficients, and υ ave,i It is the average speed of the train within each section, and the resistance is obtained by the following formula:

[0082]

[0083] 4) Assume that the train speed changes monotonically within each section, that only one control level is used in each section, and that its average speed v ave,i It can be determined by the interval boundary velocity υ i and v i-1 To obtain.

[0084] 5) The time Δt elapsed for all intervals i The sum needs to conform to the timetable arrangement planned by the railway operating company, corresponding to the total time T. total .

[0085] 6) High-speed trains experience corresponding energy changes within each operating section, and the relationships between various energy consumptions should satisfy the law of conservation of energy:

[0086]

[0087] Wherein: F i This represents the traction / braking force corresponding to the dynamically changing stage; M is the total mass of the high-speed train; v i and v i-1 It is the velocity at the boundary of the interval; g is the gravitational constant; ΔH i It refers to the change in the train's elevation within the section;

[0088] 7) To ensure passenger comfort, train speed changes cannot be too large; therefore, acceleration needs to be kept within a certain range. An acceleration limit A is given. max,d and A max,a .

[0089] 8) Due to the inherent efficiency limitations of the train's power system, the train does not utilize 100% of its energy during traction and regenerative braking; therefore, energy efficiency must be considered. η t It is the energy conversion efficiency during train traction, corresponding to the traction operating condition; η b It is the efficiency of regenerative braking energy conversion during train braking, corresponding to the braking conditions.

[0090] S2: Establish a train class selection model based on the traction / braking class characteristics of high-speed trains;

[0091] In this embodiment, based on the control mechanism of high-speed rail traction / braking levels, a series of binary variables are used to correspond to the selection of each level. After the level is determined, the corresponding binary variable has a definite 0 and 1 value. When the corresponding binary variable is 1, the corresponding level is activated, and when it is 0, it is not activated. The train level selection model established in this way can dynamically adjust the traction / braking force according to the corresponding level characteristic curve.

[0092] In this embodiment, the high-speed train driver's cab in manual driving mode has two handles on the control panel corresponding to traction and braking operations: a traction handle and a braking handle. When the corresponding handle is pushed to its designated position, the corresponding speed level is selected, and a specific signal is output to the power system to control the train's speed. Corresponding to the actual speed level characteristics of the high-speed train, this invention uses a binary variable to achieve this speed level selection. Each handle's position corresponds to a binary variable; when a speed level is selected, the corresponding binary variable is 1, and the rest are 0. Thus, as the train's speed changes, the traction / braking force dynamically changes along the characteristic curves of each speed level as different speed levels are selected. It should be noted that when the braking handle is selected, the traction handle is inactive. When neither handle is selected, the train enters a coasting state.

[0093] like Figure 3 As shown, the train class selection model is obtained, where λ k,i These are a series of binary variables corresponding to the selection of each level in this model, and the level characteristic curve for each level is also shown in the attached figure. To ensure that only one level is selected at any given time, the following condition is set:

[0094]

[0095] Where: k is the index of all levels, including the case of train coasting (k=0); K1 corresponds to the number of traction levels, and K2 corresponds to the number of braking levels. The traction / braking force of the train in each section can be calculated using equation (5) through the level characteristic curve:

[0096]

[0097] Where: f k It is the characteristic curve of the level corresponding to the level.

[0098] S3: Perform piecewise linearization on the basic model of high-speed train operation and the level selection model, linearize the nonlinear constraints, and integrate them to obtain the high-speed rail comprehensive optimization model.

[0099] In this embodiment, piecewise linearization (PWL) is used to linearize the nonlinear constraints in the model. Each nonlinear term is piecewise linearized, with several nodes breaking up the original nonlinear curve, and straight lines connecting adjacent nodes approximating the original curve. A special variable set (Special Ordered Set type 2, SOS2) is used as a mathematical tool to implement the piecewise linearization process. The two linearized models are then integrated to obtain a mixed-integer linear programming model. The two models share common variables and can be connected through these common variables, ultimately forming a mixed-integer linear programming model, namely the high-speed rail integrated optimization model.

[0100] In this embodiment, the variables in the SOS2 variable set have at most two non-zero values, and these two non-zero values ​​must be adjacent.

[0101] like Figure 4 As shown, the specific process of piecewise linearization includes:

[0102] Assume the train's speed range is from V min To V max A constant value δ is set to control the precision of piecewise linearization:

[0103] δ=(V max -V min / (J-1)) (6)

[0104] Where J is the number of variables in the SOS2 variable set, and also the number of nodes taken in the piecewise linearization process. To achieve the above approximation process, the following constraints are additionally imposed on the SOS2 variable set:

[0105] 1) All variables take values ​​in the range [0,1];

[0106] 2) The sum of all SOS2 variables is 1.

[0107] The nonlinear terms involved in the high-speed rail integrated optimization model in this invention include: 1 / v used to calculate train travel time. ave, Used for calculating train running resistance f is used to calculate the stage characteristic curve of traction / braking force. k (v ave, ) and the method used to calculate changes in train kinetic energy Based on the properties of the SOS2 variable set, all the above nonlinear terms can be expressed as a single variable using the following formula:

[0108]

[0109]

[0110]

[0111]

[0112]

[0113]

[0114] Wherein: F point, Represents discrete nodes on the stage characteristic curve, α i,j To be with v ave, The relevant SOS2 variable, β i,j To be with v i The relevant SOS2 variable.

[0115] Since the original nonlinear terms can all be expressed as global variables using the above formulas, the original nonlinear constraints are transformed into linear constraints, thus achieving the linearization of the high-speed rail integrated operation model.

[0116] S4: Based on the longitudinal space, the discrete model of the high-speed rail integrated optimization model is relaxed to ensure that the model has a mathematical solution under controllable accuracy, thus obtaining the optimal speed trajectory and optimal class strategy of the high-speed train. This is achieved through a controllable factor P. con This allows the traction / braking force to be at its maximum value F. max, and minimum value F min, Previously obtained, this is a relaxation. Before relaxation, the traction / braking force can only be obtained on the characteristic curve shown in formula (9).

[0117] In this embodiment, to address the unsolvable phenomenon caused by the significant narrowing of the solution space due to excessively high modeling discreteness in the high-speed rail integrated optimization model, longitudinal relaxation is applied to the high-speed rail integrated optimization model based on the longitudinal space. This relaxes the constraints on traction / braking forces to maintain the feasibility of the solution. After longitudinal relaxation, the traction / braking forces are allowed to fall within a controllable small range between the characteristic curves of two adjacent levels, meaning that the finite discrete points corresponding to the constraints are transformed into a controllable and feasible range. The relaxed model is then solved using mathematical programming solver software. The solution includes the correspondence between distance, speed, and level, such as the speed and level at 1km. The solution yields the optimal speed trajectory and optimal level strategy for the high-speed train.

[0118] In this embodiment, after longitudinal relaxation, the calculation formula for traction / braking force can be obtained again:

[0119]

[0120]

[0121]

[0122] Wherein: F max, It is the maximum traction / braking force obtained based on the corresponding level of the traction system; F min, It is the minimum traction / braking force obtained by corresponding to the next smaller level, P con This indicates the permissible range of traction / braking force at the corresponding level, from 0 to 100%. When P con When P = 0, it means no relaxation is performed; when P = 0, it means no relaxation is performed. con When the value is 1, it represents complete relaxation, which is equivalent to stepless control.

[0123] After longitudinal relaxation of the model, the feasible region of the model is expanded in a controllable manner, making the model mathematically solvable, and obtaining the optimal speed trajectory and optimal level strategy of high-speed train under controllable accuracy.

[0124] Example 2

[0125] This invention provides an energy-saving operation strategy optimization system that considers the characteristics of high-speed rail class, including: a high-speed train operation basic model construction module, a class selection model construction module, a linearization processing module, a high-speed rail comprehensive optimization model construction module, a relaxation module, and a solution module;

[0126] In this embodiment, the high-speed train operation basic model construction module is used to discretize the total interval of high-speed train operation, construct the train operation constraints according to the engineering characteristics of high-speed train operation, establish the high-speed train operation basic model, and take the minimum sum of energy consumed in each discrete interval as the objective equation of the high-speed train operation basic model.

[0127] In this embodiment, the level selection model construction module is used to establish a level selection model for the train based on the traction or braking level characteristics of the high-speed train.

[0128] In this embodiment, the linearization processing module is used to perform piecewise linearization processing on the basic model of high-speed train operation and the level selection model, thereby linearizing the nonlinear constraints;

[0129] In this embodiment, the high-speed rail integrated optimization model construction module is used to integrate the linearized high-speed train operation basic model and the level selection model to obtain the high-speed rail integrated optimization model;

[0130] In this embodiment, the relaxation module is used to relax the high-speed rail comprehensive optimization model based on the longitudinal space;

[0131] In this embodiment, the solver module is used to solve the relaxed model to obtain the optimal speed trajectory and optimal level strategy of the high-speed train.

[0132] Example 3

[0133] This embodiment provides a computer-readable storage medium, which may be a ROM, RAM, disk, optical disk, or other storage medium. The storage medium stores one or more programs. When the programs are executed by a processor, they implement the energy-saving operation strategy optimization method considering the characteristics of high-speed rail as described in Embodiment 1.

[0134] Example 4

[0135] This embodiment provides a computing device, which may be a desktop computer, laptop computer, smartphone, PDA handheld terminal, tablet computer or other terminal device. The computing device includes a processor and a memory. The memory stores one or more programs. When the processor executes the program stored in the memory, it implements the energy-saving operation strategy optimization method considering the characteristics of high-speed rail in Embodiment 1.

[0136] The above embodiments are preferred embodiments of the present invention, but the embodiments of the present invention are not limited to the above embodiments. Any changes, modifications, substitutions, combinations, or simplifications made without departing from the spirit and principle of the present invention shall be considered equivalent substitutions and shall be included within the protection scope of the present invention.

Claims

1. A method for optimizing energy-saving operation strategies considering the characteristics of high-speed rail, characterized in that, Includes the following steps: Discretize the total operating intervals of high-speed rail, construct the operating constraints of the train based on the engineering characteristics of high-speed train operation, establish a basic model of high-speed train operation, and take the minimum sum of energy consumed in each discrete interval as the objective equation of the basic model of high-speed train operation. A train class selection model is established based on the traction or braking class characteristics of high-speed trains, specifically including: Based on the traction or braking level characteristics of high-speed trains, a binary variable model is constructed to correspond to the selection of each level. The traction or braking force is then adjusted according to the corresponding level characteristic curve, specifically as follows: ； in, This represents the index corresponding to all levels of bits. This indicates the number of corresponding traction level positions. This indicates the number of corresponding braking stage positions. This represents the characteristic curve of the corresponding level. Indicates average speed. A binary variable representing the position selection at the corresponding level; The basic high-speed train operation model and the level selection model are segmented and linearized to linearize the nonlinear constraints. The linearized basic high-speed train operation model and the level selection model are then integrated to obtain the high-speed rail comprehensive optimization model. Based on the relaxation of the high-speed rail comprehensive optimization model in the longitudinal space, the calculation formula for traction or braking force is obtained, which is specifically expressed as follows: ； ； ； in, It is the maximum traction or braking force obtained based on the corresponding level of the traction system. This represents the index corresponding to all levels of bits. This indicates the number of corresponding traction level positions. This indicates the number of corresponding braking stage positions. This represents the characteristic curve of the corresponding level. Indicates average speed. A binary variable representing the position selection at the corresponding level. It is the minimum traction or braking force obtained at the level one level lower. This indicates the permissible range of traction or braking force at the corresponding level. When, it means no relaxation is performed, when At this time, it represents complete relaxation; Solving the relaxed model yields the optimal speed trajectory and optimal level strategy for the high-speed train.

2. The energy-saving operation strategy optimization method considering the characteristics of high-speed rail as described in claim 1, characterized in that, The aforementioned construction of train operation constraints based on the engineering characteristics of high-speed train operation specifically includes: Distance of discrete intervals The sum equals the total distance the train traveled. ; velocity at each discrete point Less than or equal to the maximum speed limit ; Calculation of frictional resistance based on Davis equation ; Assume that the train speed changes monotonically within each section, that only one control level is used for each section, and that its average speed... From the interval boundary velocity and Seek; Time elapsed for all intervals The sum satisfies the preset total time. ; The changes in various energy consumptions within each operating interval satisfy the following formula: ； in, This indicates the traction or braking force corresponding to the dynamically changing level. It is the total mass of the high-speed train. and It is the velocity at the interval boundary. It is the gravitational constant; It refers to the change in the train's elevation within the section; Set acceleration limits for train traction or braking. and ; Calculation parameters for energy conversion efficiency under traction conditions and braking conditions are established.

3. The energy-saving operation strategy optimization method considering the characteristics of high-speed rail as described in claim 1, characterized in that, The basic model and level selection model of high-speed train operation are processed by piecewise linearization. Each nonlinear term is piecewise linearized, the original nonlinear curve is interrupted by several nodes, and the original curve is approximately replaced by a straight line formed by connecting two adjacent nodes.

4. The energy-saving operation strategy optimization method considering the characteristics of high-speed rail as described in claim 3, characterized in that, Piecewise linearization of each nonlinear term is performed using the SOS2 variable set as a mathematical tool. The process includes: Assuming the train's speed range is arrive Set the precision threshold for piecewise linearization. Specifically, it is expressed as: ； in, It represents the number of variables in the SOS2 variable set, corresponding to the number of nodes taken during the piecewise linearization process; Setting constraints for the SOS2 variable set includes: All variables take values ​​in the range [0, 1], and the sum of all SOS2 variables is 1.

5. The energy-saving operation strategy optimization method considering the characteristics of high-speed rail as described in claim 4, characterized in that, Piecewise linearization is performed on each nonlinear term. The nonlinear terms include: the nonlinear term used to calculate train running time. Nonlinear terms used to calculate train running resistance Stage characteristic curves used to calculate traction or braking force and the nonlinear term used to calculate the change in train kinetic energy Treating the nonlinear term as a whole variable, it is specifically expressed as follows: ； ； ； ； ； ； in, This represents discrete nodes on the level characteristic curve. To and The relevant SOS2 variables, To and The relevant SOS2 variable.

6. An energy-saving operation strategy optimization system considering the characteristics of high-speed rail, characterized in that, The method for optimizing energy-saving operation strategies considering the characteristics of high-speed rail class as described in any one of claims 1-5 includes: a high-speed train operation basic model construction module, a class selection model construction module, a linearization processing module, a high-speed rail comprehensive optimization model construction module, a relaxation module, and a solution module; The high-speed train operation basic model construction module is used to discretize the total interval of high-speed train operation, construct the train operation constraints according to the engineering characteristics of high-speed train operation, establish the high-speed train operation basic model, and take the minimum sum of energy consumed in each discrete interval as the objective equation of the high-speed train operation basic model. The class selection model construction module is used to establish a class selection model for the train based on the traction or braking class characteristics of the high-speed train. The linearization module is used to perform piecewise linearization on the basic model of high-speed train operation and the level selection model, and to linearize the nonlinear constraints. The high-speed rail comprehensive optimization model construction module is used to integrate the linearized high-speed train operation basic model and the level selection model to obtain the high-speed rail comprehensive optimization model. The relaxation module is used to relax the high-speed rail comprehensive optimization model based on the longitudinal space. The solution module is used to solve the relaxed model to obtain the optimal speed trajectory and optimal level strategy of the high-speed train.

7. A computer-readable storage medium comprising a stored program, characterized in that, When the program is executed, it implements the energy-saving operation strategy optimization method considering the characteristics of high-speed rail as described in any one of claims 1-5.

8. A computing device, comprising a processor and a memory for storing a processor-executable program, characterized in that, When the processor executes the program stored in the memory, it implements the energy-saving operation strategy optimization method considering the characteristics of high-speed rail as described in any one of claims 1-5.

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