Optimization Method for Group Operation of Heavy-Haul Trains with Constraints of Communication and Horizontal and Vertical Alignments

Abstract

The present invention provides an optimized method for the operation of a heavy-haul train group with communication and line vertical and horizontal profile constraints, which relates to the field of the operation of heavy-haul train groups and includes: establishing a longitudinal dynamics model of a heavy-haul train group considering vehicle-to-vehicle communication; decomposing the three-dimensional spatial alignment of the railway line corresponding to the heavy-haul train group into curves along the horizontal plane and ramps along the vertical direction to generate a railway line decomposition result; establishing an optimized operation model of a heavy-haul train group considering vehicle-to-vehicle communication according to the longitudinal dynamics model of a heavy-haul train group considering vehicle-to-vehicle communication and the railway line decomposition result; numerically solving the optimized operation model of a heavy-haul train group considering vehicle-to-vehicle communication to generate an operation curve; and solving the vehicle-to-vehicle communication system parameters that meet the communication transmission requirements according to the operation curve. This method provides direct guidance for the selection of communication systems for large-scale heavy-haul train groups in complex environments and has significant advantages in improving the operation smoothness of heavy-haul train groups.

Description

Technical Field

[0001] The present invention relates to the field of heavy-haul train group operation, and particularly to an optimization method for heavy-haul train group operation with communication and line vertical and horizontal profile constraints. Background Art

[0002] The development of vehicle-to-vehicle communication technology has promoted the engineering application process of heavier-haul trains with longer formations such as 30,000 tons. However, the increase in train formation makes the longitudinal impulse inside it more significant, and the resulting coupler fracture and derailment have become the primary challenges threatening the operation safety of heavy-haul trains. Achieving stable operation of longer-formation heavy-haul trains based on vehicle-to-vehicle communication is the main method to address this challenge. The optimal control of a single train and the trajectory optimization of multiple trains were the research hotspots in the early stage, which focused on finding the optimal speed curve of the train from the starting point to the ending point. In recent years, more attention has been paid to the comprehensive optimization of train operation diagrams and speed curves aiming to improve energy and operation efficiency. Operation optimization emphasizes the optimization of the operation behaviors of a single or multiple trains such as starting, traction, braking, cruising, and coasting, with the aim of ensuring the stable operation of the trains. In the field of heavy-haul trains, with the mixed operation of unit trains, combined trains, and group trains, the trajectory optimization or operation optimization for a single train can no longer meet the complex transportation organization mode. Therefore, on the basis of fully considering the basic attribute of vehicle-to-vehicle communication in this large-scale train cluster, it is extremely necessary to construct a large-scale train group operation optimization method with the goal of multi-machine synchronous control of combined trains or stable collaborative control of train groups. In terms of the research object, short-formation trains mainly including high-speed trains and subway trains have been mainly concerned. In terms of the model, trains are usually simplified into a single-particle train kinematic model with concentrated mass, and the line only considers the 2D line vertical profile mainly with slopes. In addition, due to the limitations of existing train configurations, vehicle-to-vehicle communication is rarely considered. Undoubtedly, years of application experience have confirmed the reliability of the optimization system mainly based on 2D lines and single-particle train kinematic models. However, with the development and evolution of train configurations, especially after the application of vehicle-to-vehicle communication technology in the field of heavy-haul railways, the train departure interval is shorter, the formation is longer, the longitudinal impulse is more significant, and the complex line formed by the combination of curves and slopes has become a typical section restricting operation safety, and the coupler force has become the primary factor directly quantifying the operation safety of heavy-haul trains. Therefore, introducing train longitudinal dynamics to quantify the train length and internal force, simulating the actual complex line with 3D spatial alignment, and constructing the internal interaction relationship of the train group based on vehicle-to-vehicle communication are the prerequisites for accurately operating heavy-haul trains.

[0003] The consideration of the above three factors also significantly increases the difficulty of solving the manipulation optimization problem. The existing technologies only consider the kinematic characteristics of trains on vertical profiles mainly composed of ramps, ignoring the influence of curves, the superposition of complex horizontal and vertical profiles, the train length, and its longitudinal impulse on the train handling performance. Moreover, they are generally oriented towards unit trains and fail to effectively consider the vehicle-to-vehicle communication relationship, making it difficult to achieve synchronized operation of combined trains and cooperative operation of train groups. Usually, heuristic or meta-heuristic algorithms and machine learning methods are used for solving, but they are highly dependent on parameters, prone to falling into local optima, and have poor stability. Most importantly, it is difficult to embed them into on-vehicle equipment.

[0004] Therefore, it is necessary to provide an optimization method for the operation of heavy-haul train groups with communication and horizontal-vertical profile constraints of the line to improve the stability of the operation of heavy-haul train groups. Summary of the Invention

[0005] The present invention provides an optimization method for the operation of heavy-haul train groups with communication and horizontal-vertical profile constraints of the line, including: establishing a longitudinal dynamics model of a heavy-haul train group considering vehicle-to-vehicle communication; decomposing the spatial three-dimensional alignment of the railway line corresponding to the heavy-haul train group into curves along the horizontal plane and ramps along the vertical direction to generate a railway line decomposition result; establishing an operation optimization model of the heavy-haul train group considering vehicle-to-vehicle communication according to the longitudinal dynamics model of the heavy-haul train group considering vehicle-to-vehicle communication and the railway line decomposition result; numerically solving the operation optimization model of the heavy-haul train group considering vehicle-to-vehicle communication to generate an operation curve; and solving the vehicle-to-vehicle communication system parameters that meet the communication transmission requirements according to the operation curve.

[0006] Further, establishing a longitudinal dynamics model of a heavy-haul train group considering vehicle-to-vehicle communication includes: establishing the dynamic equation of the heavy-haul train group according to the connection positions of the couplers and buffers of two adjacent vehicles in the heavy-haul train group; establishing the vehicle-to-vehicle communication equation of the heavy-haul train group according to the communication relationships of multiple vehicles in the heavy-haul train group; and establishing the longitudinal dynamics model of the heavy-haul train group considering vehicle-to-vehicle communication according to the dynamic equation and the vehicle-to-vehicle communication equation of the heavy-haul train group.

[0007] Further, decomposing the spatial three-dimensional alignment of the railway line corresponding to the heavy-haul train group into curves along the horizontal plane and ramps along the vertical direction to generate a railway line decomposition result includes: decomposing the spatial three-dimensional alignment of the railway line corresponding to the heavy-haul train group into multiple line points; for each line point, determining the curve line inclination angle corresponding to the line point, calculating the line parameters at the curve corresponding to the line point according to the curve line inclination angle corresponding to the line point, determining the line slope angle corresponding to the line point, and calculating the line parameters at the ramp corresponding to the line point according to the line slope angle corresponding to the line point.

[0008] Further, calculate the track parameters at the curve corresponding to the track point and the track parameters at the ramp based on the following formulas: , , where is the track parameter at the curve corresponding to the track point, is the inclination angle of the curve track, is the superelevation corresponding to the track point, is the gauge, is the track parameter at the ramp corresponding to the track point, is the track slope angle corresponding to the track point, is the elevation difference corresponding to the track point, is the actual mileage length corresponding to the track slope angle and the elevation difference.

[0009] Further, based on the longitudinal dynamics model of the heavy-haul train group considering vehicle-to-vehicle communication and the railway line decomposition result, establish an optimization model for the operation of the heavy-haul train group considering vehicle-to-vehicle communication, including: determining the optimization objective of the optimization model for the operation of the heavy-haul train group considering vehicle-to-vehicle communication, and establishing an objective function, where the optimization objective at least includes the lowest energy consumption and / or the shortest line passing time; determining the constraint conditions of the optimization model for the operation of the heavy-haul train group considering vehicle-to-vehicle communication according to the longitudinal dynamics model of the heavy-haul train group considering vehicle-to-vehicle communication and the railway line decomposition result.

[0010] Further, the constraint conditions at least include the mileage coordinates and speed constraints at the beginning and end positions of the line, the constraints of the train traction and braking performance, the maximum speed curve constraint, the jerk constraint, and the longitudinal dynamics constraint of the train.

[0011] Further, perform numerical solution on the optimization model for the operation of the heavy-haul train group considering vehicle-to-vehicle communication to generate an operation curve, including: S11. Simplify the objective function of the optimization model for the operation of the heavy-haul train group considering vehicle-to-vehicle communication to determine the simplified objective function, where the simplified objective function includes the terms to be optimized related to speed and acceleration, the pre-solved terms related to track parameters, and the constant terms related to train and track parameters; S12. Linearize and perform quadratic approximation on the simplified objective function and the constraint conditions within a preset discrete time step; S13. Construct a quadratic programming sub-problem; S14. Solve the quadratic programming sub-problem to generate an operation curve; S15. Determine whether the convergence condition is satisfied. If so, stop the iteration and output the operation curve. If not, execute S13.

[0012] Further, according to the operation curve, determine the vehicle-to-vehicle communication system parameters that meet the communication transmission requirements, including: S21. Determine multiple preset communication system types; S22. Extract the current preset communication system type from the multiple preset communication system types; S23. Calculate the communication coverage range of the current preset communication system type in the current environment; S24. According to the operation curve and the communication coverage range of the current preset communication system type in the current environment, determine whether the current preset communication system type meets the communication transmission requirements. If so, output the communication system parameters corresponding to the current preset communication system type as the vehicle-to-vehicle communication system parameters that meet the communication transmission requirements. If not, execute S22.

[0013] Further, according to the operation curve and the communication coverage range of the current preset communication system type in the current environment, determine whether the current preset communication system type meets the communication transmission requirements, including: S241. Discretize the operation curve into multiple groups of data points; S242. For each group of data points, calculate the real-time distance between the vehicles that need to communicate in the heavy-haul train group; S243. According to the real-time distance between the vehicles that need to communicate in the heavy-haul train group corresponding to each group of data points, the communication coverage range of the current preset communication system type in the current environment, and the ratio between the ATO system refresh rate and the vehicle-to-vehicle communication cycle, determine whether the communication transmission requirements are met.

[0014] Further, the vehicle-to-vehicle communication system parameters at least include carrier frequency, signal reception power, signal transmission power, transmitting antenna gain, and receiving antenna gain.

[0015] Compared with the prior art, the heavy-haul train group operation optimization method with communication and line vertical and horizontal section constraints provided by the present invention at least has the following beneficial effects:

[0016] It breaks through the limitation that the traditional train kinematic model cannot accurately evaluate various complex interaction relationships of the heavy-haul train group; the constructed numerical optimization method realizes the convexity preservation and global efficient and stable solution of the complex operation optimization problem of the large-scale heavy-haul train group. They promote the formation of a dynamic-oriented large-scale heavy-haul train group operation optimization theory system. Through the application in practical engineering problems, the superiority of this method in ensuring the stable operation of the heavy-haul train group is verified. At the same time, it also provides a quantitative means for the evaluation of the operation performance of the heavy-haul train group and provides direct guidance for the safe operation of the heavy-haul train group with a larger transportation volume. Description of the Drawings

[0017] This specification will be further described by way of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not restrictive, and in these embodiments, the same numbers represent the same structures, where:

[0018] Figure 1 is a schematic flow chart of an optimization method for the operation of a heavy-haul train group with communication and line profile constraints according to some embodiments of this specification;

[0019] Figure 2 is a schematic diagram of the topological features of a train configuration according to some embodiments of this specification;

[0020] Figure 3 is a schematic diagram of the principle of decomposing the three-dimensional spatial alignment of a railway line corresponding to a heavy-haul train group into a curve along the horizontal plane and a ramp along the vertical direction according to some embodiments of this specification;

[0021] Figure 4 is a schematic diagram of the conversion principle of line parameters at a curve according to some embodiments of this specification;

[0022] Figure 5 is a schematic diagram of the conversion principle of line parameters at a ramp according to some embodiments of this specification;

[0023] Figure 6 is a schematic diagram of the decomposition result of a railway line according to some embodiments of this specification;

[0024] Figure 7 is a schematic flow chart of the optimization of the operation of a heavy-haul train group according to some embodiments of this specification;

[0025] Figure 8 is a schematic diagram of the speed comparison between the operation curve generated by optimization and the curve collected by the existing ATO system according to some embodiments of this specification;

[0026] Figure 9 is a schematic diagram of the power comparison between the operation curve generated by optimization and the curve collected by the existing ATO system according to some embodiments of this specification;

[0027] Figure 10 is a schematic diagram of the operation of a 30,000-ton heavy-haul train group according to some embodiments of this specification;

[0028] Figure 11 is a schematic diagram of the signal transmission range according to some embodiments of this specification;

[0029] Figure 12 is a schematic diagram of the change in coupler force according to some embodiments of this specification. Detailed implementation manners

[0030] To more clearly illustrate the technical solutions of the embodiments of this specification, the following will briefly introduce the accompanying drawings required for the description of the embodiments. Obviously, the accompanying drawings in the following description are only some examples or embodiments of this specification. For those of ordinary skill in the art, without creative efforts, this specification can also be applied to other similar scenarios based on these drawings. Unless obvious from the language context or otherwise stated, the same reference numerals in the figures represent the same structure or operation.

[0031] Figure 1 is a schematic flow chart of an optimization method for the operation of a heavy-haul train group with communication and line profile constraints as shown in Figure 1 shown, the optimization method for the operation of a heavy-haul train group with communication and line profile constraints may include the following steps.

[0032] Step 110, establish a longitudinal dynamics model of a heavy-haul train group considering vehicle-to-vehicle communication.

[0033] In some embodiments, step 110 specifically includes:

[0034] Establish the dynamics equation of the heavy-haul train group according to the connection positions of the couplers and buffers of two adjacent vehicles in the heavy-haul train group;

[0035] Establish the vehicle-to-vehicle communication equation of the heavy-haul train group according to the communication relationships of multiple vehicles in the heavy-haul train group;

[0036] Establish a longitudinal dynamics model of the heavy-haul train group considering vehicle-to-vehicle communication according to the dynamics equation and vehicle-to-vehicle communication equation of the heavy-haul train group.

[0037] Specifically, decompose the longitudinal interaction relationships within the heavy-haul train group into physical interaction relationships connected by coupler-buffer devices and virtual interaction relationships connected by vehicle-to-vehicle communication. Assemble the corresponding dynamics equations according to the topological characteristics of the train configuration.

[0038] The physical interaction relationships connected by coupler-buffer devices can be divided into three categories according to the different positions of locomotives or vehicles in the train, namely: connected by coupler-buffer devices at both ends; connected by coupler-buffer devices at the front end; connected by coupler-buffer devices at the rear end.

[0039] The virtual interaction relationships connected by vehicle-to-vehicle communication are mainly divided into two categories according to their actual functions, namely: vehicle-to-vehicle communication mainly for train integrity inspection; vehicle-to-vehicle communication for transmitting status information such as train position and speed.

[0040] According to the connection positions of the coupler - buffer devices between each locomotive or vehicle in the train and its adjacent locomotives or vehicles, the dynamic equations of the heavy - haul train group are established; according to the communication relationships among multiple vehicles in the heavy - haul train group, the vehicle - to - vehicle communication equations of the heavy - haul train group are established; finally, the dynamic equations and vehicle - to - vehicle communication equations of the heavy - haul train group are spliced to form a longitudinal dynamic model of the heavy - haul train group considering vehicle - to - vehicle communication. Among them, the vehicle - to - vehicle communication equations mainly consider the coverage range of communication signals, and the COST231 - Hata model, Okumura / Hata model, IEEE 802.16d model, etc. can be used to determine it. The acting forces in the dynamic equations of the heavy - haul train group mainly include traction or braking forces, which can be directly obtained from the train traction or braking characteristic curves; the running resistance related to speed, and the additional line resistance related to the gradient and curve radius.

[0041] For example only, Figure 2 is a schematic diagram of the topological characteristics of the train configuration shown in some embodiments of this specification, Figure 2 The dynamic equations and vehicle - to - vehicle communication equations of the heavy - haul train group corresponding to the topological characteristics of the train configuration shown are:

[0042] ,

[0043] In the formula, M, A, X, V respectively represent the mass, acceleration, displacement, and velocity matrices; F cb 、F t / db 、F ab 、F r 、F g 、F ud respectively represent the coupler force, the traction or electric braking force of the locomotive, the air braking force, the running resistance, the component of gravity along the slope, and the uncertain disturbing force. The uncertain disturbing force mainly includes the random wind load and other random modeling loads suffered by the train during long - distance operation, and is simulated by a Gaussian function with mileage as the independent variable; P sr 、P st 、G tagain 、G ragain 、L tloss respectively represent the signal receiving power, signal transmitting power, transmitting antenna gain, receiving antenna gain, and transmission loss of the on - vehicle antenna.

[0044] Step 120: Decompose the three-dimensional spatial alignment of the railway line corresponding to the heavy-haul train group into a curve along the horizontal plane and a ramp along the vertical direction to generate a railway line decomposition result.

[0045] In some embodiments, step 120 specifically includes:

[0046] Decompose the three-dimensional spatial alignment of the railway line corresponding to the heavy-haul train group into multiple line points;

[0047] For each line point, determine the curve line inclination angle corresponding to the line point. According to the curve line inclination angle corresponding to the line point, calculate the line parameters at the curve corresponding to the line point, determine the line slope angle corresponding to the line point, and according to the line slope angle corresponding to the line point, calculate the line parameters at the ramp corresponding to the line point.

[0048] Decompose the three-dimensional spatial alignment of the actual railway line into a curve along the horizontal plane and a ramp along the vertical direction, and ensure the convexity of the operation optimization problem through the transformation processing of the horizontal and vertical profiles of the line.

[0049] The decomposition principle of the three-dimensional spatial alignment of the actual railway line is as Figure 3 shown. That is, project the actual alignment along the horizontal plane XOY in the space coordinate system O-XYZ to obtain the line horizontal profile parameters represented by the curve; project along the vertical plane XOZ to obtain the line vertical profile parameters represented by the ramp. Among them, the coordinate origin O is the starting mileage of the effective calculation interval of the line.

[0050] Transformation processing of the line parameters at the curve: The cosine value of the curve line inclination angle caused by the superelevation is essentially the ratio of the superelevation to the gauge, and the value is extremely small. Therefore, it is equivalently processed as :

[0051] ,

[0052] where is the line parameter at the curve corresponding to the line point, is the curve line inclination angle, is the superelevation corresponding to the line point, is the gauge, is the mileage of the line along the X-axis.

[0053] When the heavy-haul train group passes through the curve, the centrifugal force causes the outer wheel flange to squeeze the inner side of the outer rail. At the same time, the difference in the length of the rails at the curve exacerbates the relative sliding of the two wheels, thus bringing additional curve resistance to the heavy-haul train group, as Figure 4 shown. Therefore, the curve resistance is calculated by multiplying the normal pressure on the inner side of the rail by the friction coefficient.

[0054] Conversion processing of line parameters at ramps: The conversion principle of line parameters at ramps is as Figure 5 shown. Since the cosine value of the line slope angle is essentially the ratio of the elevation difference to the mileage of the corresponding line along the X-axis, and the value is extremely small, so equivalent processing:

[0055] ,

[0056] wherein, is the line parameter at the ramp corresponding to the line point, is the line slope angle corresponding to the line point, is the elevation difference corresponding to the line point, is the actual mileage length corresponding to the line slope angle and the elevation difference.

[0057] Only as an example, the decomposition result of the railway line can be as Figure 6 shown.

[0058] Step 130, according to the longitudinal dynamics model of the heavy-haul train group considering vehicle-to-vehicle communication and the decomposition result of the railway line, establish an optimization model for the operation of the heavy-haul train group considering vehicle-to-vehicle communication.

[0059] In some embodiments, step 130 specifically includes:

[0060] Determine the optimization objective of the optimization model for the operation of the heavy-haul train group considering vehicle-to-vehicle communication, and establish an objective function, wherein the optimization objective includes at least the lowest energy consumption and / or the shortest line passing time;

[0061] According to the longitudinal dynamics model of the heavy-haul train group considering vehicle-to-vehicle communication and the decomposition result of the railway line, determine the constraint conditions of the optimization model for the operation of the heavy-haul train group considering vehicle-to-vehicle communication.

[0062] Among them, the constraint conditions at least include the mileage coordinates and speed constraints at the beginning and end positions of the line, the constraints of the train's traction and braking performance, the most restrictive speed curve constraint, the jerk constraint, and the longitudinal dynamics constraint of the train. It should be noted that the traction and braking characteristics of the train are already included in the established longitudinal dynamics model of the heavy-haul train group considering vehicle-to-vehicle communication, so they are not considered separately. Among the above six constraint conditions, the mileage coordinates and speed constraints at the beginning and end positions of the line determine the operating states at the beginning and end positions of the train, that is, the initial and final value boundaries of the maneuver optimization problem; the constraints of the train's traction and braking performance reflect the basic attributes of the train's traction and braking system, which are essentially the constraints on the train's acceleration in the maneuver optimization problem; the most restrictive speed curve is the set of the lowest values among all speed limit factors such as the static speed curve, the train's maximum speed curve, and temporary speed limits. This constraint reflects the maximum speed boundary of the train determined by the line conditions and operating environment within a given line section; the jerk is the first derivative of the train's longitudinal acceleration with respect to time. This constraint reflects the rate of change of the train's acceleration and is a direct quantification of the longitudinal impact caused by the current train's acceleration and deceleration maneuvers; the longitudinal dynamics constraint of the train means that the changes in the train's speed and acceleration must satisfy the dynamic equations of the heavy-haul train group.

[0063] Only as an example, taking the lowest energy consumption as the design objective function:

[0064] ,

[0065] In the formula, respectively represent power, speed, mass, acceleration, total resistance, control force, termination time, start time, and time variable.

[0066] Constraint conditions:

[0067] ,

[0068] and the dynamic equations of the heavy-haul train group.

[0069] In the formula, , , respectively represent the mileage coordinates of the line, the mileage at the starting point of the line, the mileage at the ending point of the line, the train speed at the starting point of the line, the train speed at the ending point of the line, the minimum train speed, the maximum train speed, and the maximum value of the jerk

[0070] Step 140, numerically solve the maneuver optimization model of the heavy-haul train group considering vehicle-to-vehicle communication to generate a maneuver curve.

[0071] Figure 7 is a schematic flow chart of the maneuver optimization of the heavy-haul train group shown in some embodiments of this specification. As Figure 7 shown, in some embodiments, step 140 specifically includes:

[0072] S11. Simplify the objective function of the optimization model for the platoon operation of heavy-haul trains considering vehicle-to-vehicle communication, and determine the simplified objective function. The simplified objective function includes terms to be optimized related to speed and acceleration, terms pre-solved related to line parameters, and constant terms related to train and line parameters.

[0073] S12. Linearize and perform quadratic approximation on the simplified objective function and constraint conditions within a preset discrete time step.

[0074] S13. Construct a quadratic programming sub-problem.

[0075] S14. Solve the quadratic programming sub-problem to generate a control curve, where the control curve may include the speeds at various positions where the heavy-haul train platoon passes through the corresponding railway line.

[0076] S15. Determine whether the convergence condition is satisfied. If so, stop the iteration and output the control curve. If not, execute S13. The convergence condition is that the difference in the numerical values of the objective function between two adjacent iteration steps is less than a preset tolerance. , for example, .

[0077] As an example only, the simplified objective function is:

[0078] ,

[0079] where E1 is the part to be optimized and solved related to train position, speed, and acceleration, E2 is the part that can be pre-solved related to parameters such as line gradient, curve superelevation, and curve radius, and E3 is the constant part.

[0080] ,

[0081] ,

[0082] ,

[0083] where is a non-negative coefficient related to the power loss of the heavy-haul train platoon; D A 、D B 、D C respectively represent the Davis coefficients of the heavy-haul train platoon; D Dr is the wheel-rail rolling friction coefficient; g is the gravitational acceleration constant; is the curve superelevation; is the rail gauge; R is the curve radius.

[0084] Step 150: Solve the vehicle-to-vehicle communication system parameters that meet the communication transmission requirements according to the maneuvering curve.

[0085] As Figure 7 shown, in some embodiments, step 150 specifically includes:

[0086] S21. Determine multiple preset communication system types. For example, the multiple preset communication system types may include the railway 400 MHz digital radio train dispatching system, the railway 800 MHz communication system, the LTE-R communication system, the GSM-R communication system, the 5G-R communication system, etc.;

[0087] S22. Extract the current preset communication system type from the multiple preset communication system types. For example, select the railway 400 MHz digital radio train dispatching system, the railway 800 MHz communication system, the LTE-R communication system, the GSM-R communication system, the 5G-R communication system in sequence as the current preset communication system type;

[0088] S23. Calculate the communication coverage range of the current preset communication system type in the current environment. Specifically, the communication coverage range in the current environment is mainly calculated based on the wireless channel propagation and fading theory, and can be calculated through models such as the COST231-Hata model, the Okumura / Hata model, the IEEE 802.16d model, etc.;

[0089] S24. According to the maneuvering curve and the communication coverage range of the current preset communication system type in the current environment, determine whether the current preset communication system type meets the communication transmission requirements. If so, output the communication system parameters corresponding to the current preset communication system type as the vehicle-to-vehicle communication system parameters that meet the communication transmission requirements. If not, execute S22.

[0090] Merely by way of example, taking the COST231-Hata model as an example, the communication coverage range of the current preset communication system type in the current environment is:

[0091] ,

[0092] where L tloss is the signal transmission loss; f c is the carrier frequency; h ta is the transmitting antenna height; h ra is the receiving antenna height; d st is the communication coverage range; C fmIs the terrain-related correction factor.

[0093] Such as Figure 7 As shown, in some embodiments, according to the maneuvering curve and the communication coverage of the current preset communication system type in the current environment, it is determined whether the current preset communication system type meets the communication transmission requirements, including:

[0094] S241. Discretize the maneuvering curve into multiple groups of data points. For example, algorithms such as time interval discretization, mileage interval discretization, linear interpolation discretization, spline interpolation discretization, and least squares fitting can be used to discretize the maneuvering curve into multiple groups of data points;

[0095] S242. For each group of data points, calculate the real-time distance between the vehicles that need to communicate in the heavy-haul train group;

[0096] S243. According to the real-time distance between the vehicles that need to communicate in the heavy-haul train group corresponding to each group of data points, the communication coverage of the current preset communication system type in the current environment, and the ratio between the ATO system refresh rate and the vehicle-to-vehicle communication cycle, it is determined whether the communication transmission requirements are met. Specifically, it is determined whether the real-time distance between the vehicles that need to communicate in the heavy-haul train group is within the communication coverage. If so, it indicates that a vehicle-to-vehicle communication relationship can be established; if not, it is necessary to determine whether communication cannot be established continuously within the period T corresponding to the ratio between the ATO system refresh rate and the vehicle-to-vehicle communication cycle. If so, it indicates that the current communication system cannot meet the vehicle-to-vehicle communication requirements. If not, it means that vehicle-to-vehicle communication can be established at the current position, and the position can be updated and the iteration verification can be continued.

[0097] Taking linear interpolation discretization as an example for illustration, that is, between the mileage points and , given the speed values and , the speed at any intermediate mileage can be calculated by linear interpolation method, where x is the interpolation point of the mileage.

[0098] The real-time distance between the vehicles that need to communicate can be calculated according to the following formula:

[0099] ,

[0100] Among them, respectively represent the longitudinal, lateral, and vertical position coordinates of the train emitting the communication signal in the three-dimensional coordinate system of the actual railway line space; respectively represent the longitudinal, lateral, and vertical position coordinates of the train receiving the communication signal in the three-dimensional coordinate system of the actual railway line space (that is, the coordinate system O-(X,Y,Z) in Figure 3 ).

[0101] The magnification between the ATO system refresh rate and the vehicle-to-vehicle communication cycle can be calculated according to the following formula:

[0102] ,

[0103] where T is the magnification between the ATO system refresh rate and the vehicle-to-vehicle communication cycle, T ATO 、T T2T respectively represent the ATO system refresh rate and the vehicle-to-vehicle communication cycle, is the floor function, represents natural numbers.

[0104] The vehicle-to-vehicle communication system parameters at least include carrier frequency, signal reception power, signal transmission power, transmitting antenna gain, and receiving antenna gain.

[0105] Figure 8 is a schematic diagram of the speed comparison between the operation curve generated by optimization according to some embodiments of this specification and the acquisition curve of the existing ATO system, Figure 9 is a schematic diagram of the power comparison between the operation curve generated by optimization according to some embodiments of this specification and the acquisition curve of the existing ATO system, Figure 8 and Figure 9 show that the optimization method for the operation of heavy-haul train groups with communication and line vertical and horizontal section constraints proposed in this specification reduces energy consumption while effectively improving the train operation speed.

[0106] Figure 10 is a schematic diagram of the operation of a 30,000-ton heavy-haul train group according to some embodiments of this specification. The 30,000-ton heavy-haul train group consists of 6 basic train units of 5,000 tons each. The departure time interval between units in the group is 105 seconds. According to Figure 10 it can be seen that the optimization method for the operation of heavy-haul train groups with communication and line vertical and horizontal section constraints proposed in this specification can effectively ensure the formation stability of the heavy-haul train group.

[0107] Figure 11 is a schematic diagram of the signal transmission range according to some embodiments of this specification. From Figure 11 it can be seen that the physical point-to-point transmission range of the existing vehicle-to-vehicle communication system generally does not exceed 1,800 meters, which does not meet the requirements of the 30,000-ton heavy-haul train group for the vehicle-to-vehicle communication range. Therefore Figure 11The results shown are the simulation results of vehicle-to-vehicle logical point-to-point in consideration of the actual communication relay mode. In the figure, RS-400, LTE-R, RS-800, GSM-R, and 5G-R respectively represent the current five typical railway vehicle-to-vehicle / ground communication systems: 400 MHz radio, LTE-R, 800 MHz radio, GSM-R, and 5G-R. PMaxS represents the maximum acceptable signal power of the vehicle-mounted antenna.

[0108] Figure 12 is a schematic diagram of the coupler force change shown according to some embodiments of this specification. From Figure 12 it can be seen that the coupler force change is much lower than the recommended safety limit under normal operating conditions, indicating the reliability of the optimized method for the operation of heavy-haul train groups with communication and line profile constraints proposed in this specification in ensuring the stability of heavy-haul train group operation.

[0109] Finally, it should be understood that the embodiments described in this specification are only used to illustrate the principles of the embodiments of this specification. Other variations may also fall within the scope of this specification. Therefore, by way of example and not limitation, alternative configurations of the embodiments of this specification may be considered consistent with the teachings of this specification. Accordingly, the embodiments of this specification are not limited to the embodiments specifically introduced and described in this specification.

Claims

1. A heavy-load train group operation optimization method with communication and line horizontal and vertical section constraints, characterized in that: include: Establish a longitudinal dynamic model of heavy-load train groups considering vehicle-to-vehicle communication; Decomposing the three-dimensional linear shape of the railway line corresponding to the heavy-load train group into curves along the horizontal plane and ramps along the vertical direction, and generating a railway line decomposition result; Establishing a heavy-load train group operation optimization model considering car-to-car communication according to the longitudinal dynamics model of the heavy-load train group considering car-to-car communication and the railway line decomposition result; Numerically solving the heavy-load train group maneuvering optimization model considering vehicle-to-vehicle communication to generate a maneuvering curve; According to the control curve, solving the vehicle-to-vehicle communication system parameters that meet the communication transmission requirements; A longitudinal dynamics model of heavy-load train groups considering vehicle-to-vehicle communication is established, including: Establishing a dynamic equation of the heavy-load train group according to the connection positions of the couplers and buffers of two adjacent vehicles in the heavy-load train group; According to the communication relationship between multiple vehicles in the heavy-load train group, a vehicle-to-vehicle communication equation of the heavy-load train group is established; According to the dynamic equations of the heavy-load train group and the vehicle-to-vehicle communication equations, a longitudinal dynamic model of the heavy-load train group considering vehicle-to-vehicle communication is established; The spatial three-dimensional linear shape of the railway line corresponding to the heavy-load train group is decomposed into curves along the horizontal plane and ramps along the vertical direction, and the railway line decomposition results are generated, including: Decomposing the railway line corresponding to the heavy-load train group into a plurality of line points in a three-dimensional space; For each of the line points, determine the curve line inclination angle corresponding to the line point, calculate the line parameters at the curve corresponding to the line point based on the curve line inclination angle corresponding to the line point, determine the line slope angle corresponding to the line point, and calculate the line parameters at the ramp corresponding to the line point based on the line slope angle corresponding to the line point.

2. The method for optimizing heavy-load train group operation with communication and line horizontal and vertical section constraints according to claim 1 is characterized in that: The curve line parameters and ramp line parameters corresponding to the line point are calculated based on the following formula: ， in, is the line parameter at the curve corresponding to the line point, is the inclination angle of the curved line, is the superelevation corresponding to the route point, is the track gauge, is the line parameter at the ramp corresponding to the line point, is the line slope angle corresponding to the line point, is the elevation difference corresponding to the route point, It is the actual mileage length corresponding to the line slope angle and elevation difference.

3. The method for optimizing heavy-load train group operation with communication and line horizontal and vertical section constraints according to claim 1 is characterized in that: According to the longitudinal dynamics model of the heavy-load train group considering vehicle-to-vehicle communication and the railway line decomposition result, a heavy-load train group operation optimization model considering vehicle-to-vehicle communication is established, including: Determining the optimization goal of the heavy-load train group operation optimization model considering vehicle-to-vehicle communication, and establishing an objective function, wherein the optimization goal at least includes minimum energy consumption and / or shortest line transit time; According to the longitudinal dynamics model of the heavy-load train group considering vehicle-to-vehicle communication and the railway line decomposition result, the constraint conditions of the heavy-load train group operation optimization model with vehicle-to-vehicle communication are determined.

4. The method for optimizing heavy-load train group operation with communication and line horizontal and vertical section constraints according to claim 3 is characterized in that: The constraint conditions at least include the mileage coordinates and speed constraints of the line start and end positions, the constraints of the train traction and braking performance, the constraints of the most limiting speed curve, the constraints of jerkiness and the longitudinal dynamics constraints of the train.

5. The method for optimizing heavy-load train group operation with communication and line horizontal and vertical section constraints according to claim 3 is characterized in that: The heavy-load train group operation optimization model considering vehicle-to-vehicle communication is numerically solved to generate a control curve, including: S11, simplifying the objective function of the heavy-load train group operation optimization model considering vehicle-to-vehicle communication, and determining the simplified objective function, wherein the simplified objective function includes items to be optimized related to speed and acceleration, pre-solved items related to line parameters, and constant items related to train and line parameters; S12, linearizing and quadratically approximating the simplified objective function and constraint conditions within a preset discrete time step; S13, construct quadratic programming sub-problems; S14, solving the quadratic programming sub-problem to generate a control curve; S15, determine whether the convergence condition is met, if so, stop iteration and output the control curve, if not, execute S13.

6. The method for optimizing heavy-load train group operation with communication and line horizontal and vertical section constraints according to any one of claims 1 to 5, characterized in that: According to the manipulation curve, vehicle-to-vehicle communication system parameters that meet communication transmission requirements are determined, including: S21, determining a plurality of preset communication system types; S22, extracting a current preset communication system type from the multiple preset communication system types; S23, calculating the communication coverage of the current preset communication system type in the current environment; S24. Determine whether the current preset communication system type meets the communication transmission requirements based on the control curve and the communication coverage of the current preset communication system type in the current environment. If so, output the communication system parameters corresponding to the current preset communication system type as vehicle-to-vehicle communication system parameters that meet the communication transmission requirements. If not, execute S22.

7. The method for optimizing heavy-load train group operation with communication and line horizontal and vertical section constraints according to claim 6 is characterized in that: Judging whether the current preset communication system type meets the communication transmission requirement according to the manipulation curve and the communication coverage of the current preset communication system type in the current environment includes: S241, discretizing the manipulation curve into multiple groups of data points; S242, for each group of data points, calculating the real-time distance between the vehicles in the heavy-load train group that need to communicate; S243. Determine whether the communication transmission requirements are met based on the real-time distance between the vehicles that need to communicate in the heavy-load train group corresponding to each group of data points, the communication coverage of the current preset communication system type in the current environment, and the ratio between the ATO system refresh rate and the vehicle-to-vehicle communication cycle.

8. The method for optimizing heavy-load train group operation with communication and line horizontal and vertical section constraints according to any one of claims 1 to 5, characterized in that: The vehicle-to-vehicle communication system parameters include at least carrier frequency, signal receiving power, signal transmitting power, transmitting antenna gain, and receiving antenna gain.

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