Communication and line flat and longitudinal section constrained heavy haul train group manipulation optimization method

Abstract

The invention provides a communication and line flat and vertical section constrained heavy-load train group manipulation optimization method, which relates to the field of heavy-load train group manipulation and comprises the following steps of: establishing a heavy-load train group longitudinal dynamic model considering train-train communication; decomposing the spatial three-dimensional line shape of the railway line corresponding to the heavy-load train group into a curve along the horizontal plane and a ramp along the vertical direction, and generating a railway line decomposition result; according to the heavy-load train group longitudinal dynamical model considering the train-train communication and the railway line decomposition result, a heavy-load train group manipulation optimization model considering the train-train communication is established; performing numerical solution on the heavy haul train group manipulation optimization model considering train-train communication to generate a manipulation curve; and according to the control curve, solving vehicle-vehicle communication system parameters meeting communication transmission requirements. The method provides direct guidance for large-scale heavy-load train group communication system selection in a complex environment, and has remarkable advantages in the aspect of improving heavy-load train group operation stability.

Description

Technical Field

[0001] The invention relates to the field of heavy-load train group operation, and in particular to a heavy-load train group operation optimization method with communication and line horizontal and vertical section constraints. Background Art

[0002] The development of train-to-train communication technology has promoted the engineering application of 30,000-ton and longer heavy-load trains. However, the increase in train formation has made its internal longitudinal impulse more significant, and the resulting coupler breakage and derailment have become the primary challenges threatening the safety of heavy-load train operation. The main method to cope with this challenge is to achieve stable operation of long and large heavy-load trains based on train-to-train communication. The optimal control of a single train and the trajectory optimization of multiple trains were hot topics in early research, which focused on finding the optimal speed curve from the starting point to the end point of the train. In recent years, the comprehensive optimization of operation diagrams and speed curves aimed at improving energy and operational efficiency has received more attention. Operation optimization emphasizes the optimization of single or multiple train operation behaviors such as starting, traction, braking, cruising, and coasting, with the aim of ensuring stable operation of the train. In the field of heavy-load trains, with the mixed operation of unit trains, combined trains, and group trains, the trajectory optimization or operation optimization of a single train can no longer meet the complex transportation organization mode. Therefore, it is extremely necessary to construct a large-scale train group operation optimization method with the goal of synchronous operation of multiple machines in combined trains or stable coordinated operation of train groups, based on the full consideration of the basic attribute of large-scale train clusters, namely, train-to-train communication. In terms of research objects, short-formation trains, mainly high-speed trains and subway trains, are the main focus. In terms of models, trains are usually simplified into single-particle train kinematic models with concentrated mass, and the line only considers the two-dimensional line longitudinal section dominated by ramps. In addition, due to the limitations of existing train configurations, car-to-car communication is rarely considered. Undoubtedly, years of application experience have confirmed the reliability of the optimization system based on the two-dimensional line and single-particle train kinematic model. However, with the development and evolution of train configurations, especially the application of car-to-car communication technology in the field of heavy-duty railways, train departure times are shorter, formations are longer, and longitudinal impulses are more significant. The complex lines formed by the combination of curves and ramps have become a typical section that restricts operational safety, and the coupler force has become the primary factor in directly quantifying the operational safety of heavy-duty trains. Therefore, introducing train longitudinal dynamics to quantify train length and internal force, simulating actual complex lines with three-dimensional spatial linear shapes, and constructing the internal interaction relationship of train groups based on car-to-car communication are the prerequisites for precise operation of heavy-duty trains.

[0003] The consideration of the above three factors also significantly increases the difficulty of solving the operation optimization problem. The existing technology only considers the kinematic characteristics of the train on the longitudinal section dominated by the ramp, ignoring the influence of the curve and the complex flat and longitudinal section overlap, the train length and its longitudinal impulse on the train operation performance. In addition, it is generally oriented to unit trains and fails to effectively consider the vehicle-to-vehicle communication relationship, making it difficult to achieve the synchronous operation of the combined train and the coordinated operation of the train group. Heuristic or meta-heuristic algorithms and machine learning methods are usually used for solution, but they have strong parameter dependence, are prone to fall into local optimality, have poor stability, and most importantly, are difficult to be embedded in on-board equipment.

[0004] Therefore, it is necessary to provide a heavy-load train group operation optimization method with communication and line horizontal and vertical section constraints to improve the stability of heavy-load train group operation. Summary of the invention

[0005] The present invention provides a method for optimizing the operation of a heavy-loaded train group with communication and line horizontal and vertical section constraints, comprising: establishing a longitudinal dynamic model of a heavy-loaded train group considering vehicle-to-vehicle communication; decomposing the spatial three-dimensional linear shape of a railway line corresponding to the heavy-loaded train group into a curve along a horizontal plane and a ramp along a vertical plane, and generating a railway line decomposition result; establishing a heavy-loaded train group operation optimization model considering vehicle-to-vehicle communication based on the longitudinal dynamic model of the heavy-loaded train group considering vehicle-to-vehicle communication and the railway line decomposition result; numerically solving the heavy-loaded train group operation optimization model considering vehicle-to-vehicle communication to generate a control curve; and solving vehicle-to-vehicle communication system parameters that meet communication transmission requirements based on the control curve.

[0006] Furthermore, a longitudinal dynamic model of a heavy-loaded train group considering vehicle-to-vehicle communication is established, including: establishing a dynamic equation of the heavy-loaded train group according to the connection positions of the couplers and buffers of two adjacent vehicles in the heavy-loaded train group; establishing a vehicle-to-vehicle communication equation of the heavy-loaded train group according to the communication relationship between multiple vehicles in the heavy-loaded train group; and establishing the longitudinal dynamic model of the heavy-loaded train group considering vehicle-to-vehicle communication according to the dynamic equation of the heavy-loaded train group and the vehicle-to-vehicle communication equation.

[0007] Furthermore, the spatial three-dimensional linear shape of the railway line corresponding to the heavy-load train group is decomposed into a curve along the horizontal plane and a ramp along the vertical direction to generate a railway line decomposition result, including: decomposing the spatial three-dimensional linear shape of the railway line corresponding to the heavy-load train group into multiple line points; for each of the line points, determining the line inclination of the curve corresponding to the line point, calculating the line parameters at the curve corresponding to the line point according to the line inclination of the curve 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] Furthermore, the line parameters at the curve and the line parameters at the ramp 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.

[0009] Furthermore, based on the longitudinal dynamics model of the heavy-loaded train group considering vehicle-to-vehicle communication and the railway line decomposition result, a heavy-loaded train group operation optimization model considering vehicle-to-vehicle communication is established, including: determining the optimization target of the heavy-loaded train group operation optimization model considering vehicle-to-vehicle communication, and establishing an objective function, wherein the optimization target at least includes the lowest energy consumption and / or the shortest line passing time; based on the longitudinal dynamics model of the heavy-loaded train group considering vehicle-to-vehicle communication and the railway line decomposition result, determining the constraint conditions of the heavy-loaded train group operation optimization model considering vehicle-to-vehicle communication.

[0010] Furthermore, 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.

[0011] Furthermore, the heavy-load train group manipulation optimization model considering vehicle-to-vehicle communication is numerically solved to generate a manipulation curve, including: S11, simplifying the objective function of the heavy-load train group manipulation 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 constraints within a preset discrete time step; S13, constructing a quadratic programming subproblem; S14, solving the quadratic programming subproblem to generate a manipulation curve; S15, judging whether the convergence condition is met, if so, stopping the iteration and outputting the manipulation curve, if not, executing S13.

[0012] Furthermore, according to the manipulation curve, vehicle-to-vehicle communication system parameters that meet the communication transmission requirements are determined, including: S21, determining multiple preset communication system types; S22, extracting the 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, judging whether the current preset communication system type meets the communication transmission requirements based on the manipulation curve and the communication coverage of the current preset communication system type in the current environment, and if so, outputting 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, and if not, executing S22.

[0013] Furthermore, according to the control curve and the communication coverage of the current preset communication system type in the current environment, it is judged whether the current preset communication system type meets the communication transmission requirements, including: S241, discretizing the control curve into multiple groups of data points; S242, for each group of data points, calculating the real-time distance between the vehicles that need to communicate in the heavy-load train group; S243, according to 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, it is judged whether the communication transmission requirements are met.

[0014] Furthermore, 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.

[0015] Compared with the prior art, the heavy-load train group operation optimization method with communication and line horizontal and vertical section constraints provided by the present invention has at least the following beneficial effects: It breaks through the limitation that the traditional train kinematic model cannot accurately evaluate the various complex interactions of heavy-loaded train groups; the constructed numerical optimization method achieves the convexity maintenance and global efficient and stable solution of the complex operation optimization problem of large-scale heavy-loaded train groups. They promote the formation of a dynamics-oriented large-scale heavy-loaded train group operation optimization theory system. Through the application in real engineering problems, the superiority of this method in ensuring the stable operation of heavy-loaded train groups has been confirmed. At the same time, it also provides a quantitative means for the evaluation of the operation performance of heavy-loaded train groups, and provides direct guidance for the safe operation of heavy-loaded train groups with larger transport capacity. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] This specification will be further described in the form of exemplary embodiments, which will be described in detail by the accompanying drawings. These embodiments are not restrictive, and in these embodiments, the same number represents the same structure, wherein: Figure 1 It is a flow chart of a heavy-load train group operation optimization method with communication and line horizontal and vertical section constraints according to some embodiments of this specification; Figure 2 is a schematic diagram of topological features of a train configuration according to some embodiments of this specification; Figure 3 It is a schematic diagram of the principle of decomposing the spatial three-dimensional linear shape of the railway line corresponding to the heavy-load train group into a curve along the horizontal plane and a ramp along the vertical direction according to some embodiments of this specification; Figure 4 is a schematic diagram of the conversion principle of line parameters at a curve according to some embodiments of this specification; Figure 5 is a schematic diagram of the conversion principle of line parameters at a ramp according to some embodiments of this specification; Figure 6 is a schematic diagram of a railway line decomposition result according to some embodiments of this specification; Figure 7 is a schematic diagram of a process flow of optimizing heavy-load train group operation according to some embodiments of this specification; Figure 8 It is a schematic diagram of the speed comparison between the optimized and generated control curve and the existing ATO system acquisition curve shown in some embodiments of this specification; Fig. 9 It is a schematic diagram of power comparison between the optimized control curve generated according to some embodiments of this specification and the existing ATO system acquisition curve; Fig.10 is a schematic diagram of the operation of a 30,000-ton heavy-load train group according to some embodiments of this specification; Fig.11 is a schematic diagram of a signal transmission range according to some embodiments of this specification; Fig.12 It is a schematic diagram of the change of the coupler force according to some embodiments of this specification. DETAILED DESCRIPTION

[0017] In order to more clearly illustrate the technical solutions of the embodiments of this specification, the following is a brief introduction to the drawings required for the description of the embodiments. Obviously, the drawings described below are only some examples or embodiments of this specification. For ordinary technicians in this field, this specification can also be applied to other similar scenarios based on these drawings without creative work. Unless it is obvious from the language environment or otherwise explained, the same reference numerals in the figures represent the same structure or operation.

[0018] Figure 1is a flow chart of a heavy-load train group operation optimization method with communication and line horizontal and vertical section constraints shown in some embodiments of this specification, such as Figure 1 As shown, the heavy-load train group operation optimization method with communication and line horizontal and vertical section constraints may include the following steps.

[0019] Step 110, establishing a longitudinal dynamic model of a heavy-load train group taking into account vehicle-to-vehicle communication.

[0020] In some embodiments, step 110 specifically includes: According to the connection positions of the couplers and buffers of two adjacent vehicles in the heavy-load train group, the dynamic equation of the heavy-load train group is established; 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 heavy-load train groups and the vehicle-to-vehicle communication equations, a longitudinal dynamic model of heavy-load train groups considering vehicle-to-vehicle communication is established.

[0021] Specifically, the longitudinal interaction relationship within the heavy-load train group is decomposed into a physical interaction relationship connected by a coupler-buffer device and a virtual interaction relationship connected by vehicle-to-vehicle communication. The corresponding dynamic equations are assembled according to the topological characteristics of the train configuration.

[0022] The physical interaction relationship of the coupler-buffer device connection can be divided into three categories according to the different positions of the locomotive or vehicle in the train, namely: the two ends are connected by a coupler-buffer device; the front end is connected by a coupler-buffer device; and the rear end is connected by a coupler-buffer device.

[0023] The virtual interaction relationship connected by vehicle-to-vehicle communication can be divided into two categories according to its actual functions, namely: vehicle-to-vehicle communication mainly for train integrity inspection; and vehicle-to-vehicle communication with the transmission of train position, speed and other status information as the destination.

[0024] According to the connection position of the coupler-buffer device between each locomotive or vehicle in the train and its adjacent locomotive or vehicle, the dynamic equation of the heavy-load train group is established; according to the communication relationship between multiple vehicles in the heavy-load train group, the vehicle-to-vehicle communication equation of the heavy-load train group is established; finally, the dynamic equation of the heavy-load train group and the vehicle-to-vehicle communication equation are spliced ​​to form a longitudinal dynamic model of the heavy-load train group considering vehicle-to-vehicle communication. Among them, the vehicle-to-vehicle communication equation mainly considers the coverage of the communication signal, which can be determined by the COST231-Hata model, Okumura / Hata model, IEEE 802.16d model, etc. The forces in the dynamic equation of the heavy-load train group mainly include traction or braking force, which can be directly obtained from the train traction or braking characteristic curve; running resistance related to speed, and line additional resistance related to slope and curve radius.

[0025] For example only, Figure 2 is a schematic diagram of topological features of a train configuration according to some embodiments of this specification, Figure 2 The topological characteristics of the train configuration shown in the figure correspond to the dynamic equations and vehicle-to-vehicle communication equations of the heavy-load train group: , In the formula, M, A, X, V Represent the mass, acceleration, displacement, and velocity matrices respectively; F cb 、F t / db 、F ab 、F r 、F g 、F ud They 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 disturbance force. The uncertain disturbance force mainly includes the random wind load and other random modeling loads that the train is subjected to 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 They represent the signal receiving power, signal transmitting power, transmitting antenna gain, receiving antenna gain, and transmission loss of the vehicle antenna respectively.

[0026] Step 120, 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.

[0027] In some embodiments, step 120 specifically includes: Decomposing the three-dimensional space of the railway line corresponding to the heavy-load train group into a plurality of line points; For each line point, 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.

[0028] The spatial three-dimensional linear shape of the actual railway line is decomposed into curves along the horizontal plane and ramps along the vertical plane. The convexity of the operation optimization problem is ensured by transforming the horizontal and vertical sections of the line.

[0029] The decomposition principle of the actual three-dimensional linear shape of railway lines is as follows: Figure 3 As shown. That is, in the spatial coordinate system O-XYZ, the actual line shape is projected along the horizontal plane XOY to obtain the line section parameters represented by the curve; along the vertical plane XOZ, the line longitudinal section parameters represented by the ramp are obtained. Among them, the coordinate origin O is the starting mileage of the effective calculation interval of the line.

[0030] Conversion of line parameters at curves: from superelevation The inclination of the curved line caused by The cosine value of is essentially super high and The ratio of the track gauge is very small, so it is equivalent to : , 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 mileage of the line along the X-axis.

[0031] When a heavy-loaded train group passes through a 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 rail length at the curve intensifies the relative sliding of the wheels on both sides, thus bringing additional resistance to the heavy-loaded train group. Figure 4 Therefore, the curve resistance is calculated according to the positive pressure on the inner side of the rail. Multiply by the coefficient of friction.

[0032] Conversion of line parameters at ramps: The principle of conversion of line parameters at ramps is as follows: Figure 5 As shown. The cosine of is essentially the elevation difference The ratio of the mileage of the corresponding line along the X axis is very small, so Equivalent treatment: , in, 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.

[0033] As an example only, the railway line decomposition result can be as follows Figure 6 shown.

[0034] Step 130, based on the longitudinal dynamics model of the heavy-loaded train group considering car-to-car communication and the railway line decomposition result, a heavy-loaded train group operation optimization model considering car-to-car communication is established.

[0035] In some embodiments, step 130 specifically includes: Determine the optimization target of the heavy-load train group operation optimization model considering vehicle-to-vehicle communication, and establish an objective function, wherein the optimization target at least includes the minimum energy consumption and / or the shortest line transit time; According to the longitudinal dynamics model of heavy-load train group considering car-to-car communication and the railway line decomposition results, the constraint conditions of the heavy-load train group operation optimization model with car-to-car communication are determined.

[0036] Among them, the constraints include at least the mileage coordinates and speed constraints of the line start and end positions, the constraints of the train traction and braking performance, the most limiting speed curve constraints, the constraints of jerkiness, and the longitudinal dynamics constraints of the train. It should be pointed out that the traction and braking characteristics of the train are already included in the established longitudinal dynamics model of the heavy-duty train group considering the vehicle-to-vehicle communication, so they are no longer considered separately. Among the above six constraints, the mileage coordinates and speed constraints of the starting and ending positions of the line determine the operating status of the train at the starting and ending positions, that is, they give the initial and final value boundaries of the operation optimization problem; the constraints on the train traction and braking performance reflect the basic properties of the train traction and braking system, which is essentially a constraint on the train acceleration in the operation optimization problem; the most limiting speed curve is the set of the lowest values ​​of all speed limiting factors such as the static speed curve, the maximum train speed curve, and the temporary speed limit. 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-order derivative of the train's longitudinal acceleration with respect to time. This constraint reflects the speed of change of the train's acceleration and is a direct quantification of the longitudinal impact caused by the current train acceleration and deceleration operation; the train longitudinal dynamics constraint refers to the fact that the changes in train speed and acceleration must satisfy the dynamic equations of the heavy-loaded train group.

[0037] As an example only, the objective function is designed with the lowest energy consumption: , In the formula, They represent power, speed, mass, acceleration, total resistance, control force, end time, start time, and time variable respectively.

[0038] Constraints: , and the dynamic equations of a heavily loaded train group.

[0039] In the formula, , , Respectively represent the mileage coordinates of the line, the mileage of the line starting point, the mileage of the line end point, the train speed at the line starting point, the train speed at the line end point, the minimum train speed, the maximum train speed, and the maximum jerk Step 140 , numerically solve the heavy-load train group operation optimization model considering vehicle-to-vehicle communication to generate a control curve.

[0040] Figure 7 is a schematic diagram of a process flow of optimizing heavy-load train group operation according to some embodiments of this specification, such as Figure 7 As shown, in some embodiments, step 140 specifically includes: 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, wherein the control curve may include the speed of the heavy-load train group at each position of the corresponding railway line; S15, determine whether the convergence condition is met, if so, stop iteration and output the control curve, if not, execute S13, where the convergence condition is that the difference between the objective function values ​​of two adjacent iteration steps is less than the preset tolerance ,For example, .

[0041] As an example only, the simplified objective function is: , Among them, E1 is the part to be optimized and solved related to the train position, speed, and acceleration, E2 is the part that can be solved in advance related to parameters such as line slope, curve superelevation, curve radius, etc., and E3 is the constant part.

[0042] , , , in, is a non-negative coefficient related to the power loss of the heavy-load train group; D A 、D B 、D C They represent the Davis coefficient of the heavy-load train group respectively; D Dr is the wheel-rail rolling friction coefficient; g is the gravitational acceleration constant; Superelevation for curves; is the track gauge; R is the curve radius.

[0043] Step 150, according to the manipulation curve, solve the vehicle-to-vehicle communication system parameters that meet the communication transmission requirements.

[0044] like Figure 7 As shown, in some embodiments, step 150 specifically includes: S21. Determine multiple preset communication system types. For example, the multiple preset communication system types may include a railway 400 MHz digital wireless train modulation system, a railway 800 MHz communication system, an LTE-R communication system, a GSM-R communication system, a 5G-R communication system, etc.; S22, extracting a current preset communication system type from a plurality of preset communication system types, for example, selecting a railway 400MHz digital wireless train modulation system, a railway 800MHz communication system, an LTE-R communication system, a GSM-R communication system, and a 5G-R communication system in sequence as the current preset communication system type; S23, calculating the communication coverage of the current preset communication system type in the current environment. Specifically, the communication coverage in the current environment is mainly calculated based on wireless channel propagation and fading theory, and can be calculated by COST231-Hata model, Okumura / Hata model, IEEE 802.16d model, etc.; 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.

[0045] For example, taking the COST231-Hata model as an example, the communication coverage of the current preset communication system type in the current environment is: , in, L tloss is the signal transmission loss; f c is the carrier frequency; h ta is the height of the transmitting antenna; h ra is the receiving antenna height; d st For communication coverage; C fm is the terrain related correction factor.

[0046] like Figure 7 As shown, in some embodiments, 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 control curve into multiple groups of data points, for example, using time interval discretization, mileage interval discretization, linear interpolation discretization, spline interpolation discretization, least squares fitting and other algorithms to discretize the control curve into multiple groups of data points; S242, for each set of data points, calculating the real-time distance between 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. Specifically, determine whether the real-time distance between the vehicles that need to communicate in the heavy-load 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 within a 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 indicates that vehicle-to-vehicle communication can be established at the current position, and the position can be updated to continue iterative verification.

[0047] Take linear interpolation discretization as an example to illustrate that at the mileage point and Between, the known speed value and , the speed of any intermediate mileage can be calculated by linear interpolation , where x is the interpolation point of the mileage.

[0048] The real-time distance between vehicles that need to communicate can be calculated according to the following formula: , in, They respectively represent the longitudinal, transverse and vertical position coordinates of the train that sends the communication signal in the three-dimensional coordinate system of the actual railway line space; They represent the three-dimensional coordinate system of the communication signal receiving train in the actual railway line space (i.e. Figure 3 The longitudinal, lateral and vertical position coordinates in the coordinate system O-(X,Y,Z)).

[0049] The ratio between the ATO system refresh rate and the vehicle-to-vehicle communication cycle can be calculated according to the following formula: , Where T is the ratio between the ATO system refresh rate and the vehicle-to-vehicle communication cycle, T ATO 、T T2T They represent the ATO system refresh rate and vehicle-to-vehicle communication cycle respectively. To round down, Represents a natural number.

[0050] 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.

[0051] Figure 8 is a schematic diagram of the speed comparison between the optimized control curve generated according to some embodiments of this specification and the existing ATO system acquisition curve, Fig. 9 is a schematic diagram of power comparison between the optimized control curve generated according to some embodiments of this specification and the existing ATO system acquisition curve, Figure 8 and Fig. 9 It is shown that the heavy-load train group operation optimization method with communication and line horizontal and vertical section constraints proposed in this specification reduces energy consumption while effectively improving the train running speed.

[0052] Fig.10 is a schematic diagram of the operation of a 30,000-ton heavy-load train group according to some embodiments of this specification. The 30,000-ton heavy-load train group includes a total of 6 5,000-ton basic train units. The departure time between each unit in the group is 105 seconds. Fig.10 It can be seen that the heavy-load train group operation optimization method with communication and line horizontal and vertical section constraints proposed in this specification can effectively ensure the stability of the formation of the heavy-load train group.

[0053] Fig.11 is a schematic diagram of a signal transmission range according to some embodiments of this specification, Fig.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 vehicle-to-vehicle communication range requirements of a 30,000-ton heavy-load train group. Fig.11 The results shown are simulation results of vehicle-to-vehicle logical point-to-point in 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, namely 400MHz radio station, LTE-R, 800MHz radio station, GSM-R, and 5G-R, and PMaxS represents the maximum acceptable signal power of the vehicle antenna.

[0054] Fig.12 is a schematic diagram of the change of the coupler force according to some embodiments of this specification, Fig.12 It can be seen that the change in coupler force is far lower than the recommended safety limit under normal operating conditions, which shows that the heavy-load train group operation optimization method with communication and line horizontal and longitudinal section constraints proposed in this specification is reliable in ensuring the stability of heavy-load train group operation.

[0055] 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, as an example and not a 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 explicitly 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 manipulation curve, vehicle-to-vehicle communication system parameters that meet communication transmission requirements are solved.

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: 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.

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: 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.

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 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.

5. The method for optimizing heavy-load train group operation with communication and line horizontal and vertical section constraints according to claim 2 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.

6. The method for optimizing heavy-load train group operation with communication and line horizontal and vertical section constraints according to claim 5 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.

7. The method for optimizing heavy-load train group operation with communication and line horizontal and vertical section constraints according to claim 5 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.

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 7, 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.

9. The method for optimizing heavy-load train group operation with communication and line horizontal and vertical section constraints according to claim 8 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.

10. 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 7, 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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