Train running speed control method, device, equipment, product, medium and system for virtual coupling marshalling

Abstract

The invention discloses a virtual coupling marshalling train operation speed control method, device, equipment, product, medium and system, and is applied to the technical field of traffic transportation control, and the method comprises the following steps: constructing a vehicle operation control model based on the traction characteristic and the braking characteristic of a virtual coupling marshalling; based on the vehicle operation control model, marshalling operation constraint conditions are determined according to train dispatching information, train safe operation requirements, train stress change requirements and train working condition switching requirements; and on the basis of the vehicle operation control model and the marshalling operation constraint condition, the vehicle motion parameters serve as state variables, the train impact rate serves as a control variable, and the cooperative operation speed and the control working condition of the leading vehicle and the following vehicle are determined by taking the requirement for the preset coupling marshalling operation index as the target. According to the method, the problem that the requirement for stable and efficient operation of the train set cannot be met in the prior art can be solved, operation of the virtual coupling marshalling train can be stably and efficiently controlled, and the comfort of a user is effectively improved.

Description

Technical Field

[0001] The present application relates to the field of transportation control technology, and in particular to a train speed control method, device, electronic equipment, computer program product, non-volatile storage medium and virtual coupling train control system for a virtual coupling train. Background Art

[0002] Virtual coupling and marshaling technology uses wireless communication instead of mechanical coupling. Through wireless communication between the lead car and the follower car, the follower car can obtain the operating status of the lead car, so that trains of the same or different models can be recoupled or decoupled in real time and quickly during operation, which can meet the needs of accurate matching of transport capacity and passenger flow.

[0003] Although the relevant technology can achieve the train operation performance and safety indicators of the virtually coupled train set, it cannot guarantee that the train can complete the transportation task smoothly and efficiently, and cannot meet the comfort needs of users.

[0004] In view of this, achieving smooth and efficient operation of virtual coupled train sets is a technical problem that needs to be solved by those skilled in the art.

[0005] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present application, and therefore may include information that does not constitute the prior art known to ordinary technicians in the field. Summary of the invention

[0006] The present application provides a method, device, electronic device, computer program product, non-volatile storage medium and virtual coupling train control system for controlling the train speed of a virtual coupling train, which can achieve smooth and efficient control of the operation of the virtual coupling train and effectively improve the comfort of users.

[0007] In order to solve the above technical problems, this application provides the following technical solutions:

[0008] On the one hand, the present application provides a method for controlling the running speed of a train of a virtual coupled train, comprising:

[0009] Based on the traction and braking characteristics of the lead vehicle and the following vehicle in the virtual coupled train, a vehicle operation control model is constructed;

[0010] Based on the vehicle operation control model, determining the constrained conditions of the train operation according to the train dispatching information, the train safe operation requirements, the train force change requirements and the train working condition switching requirements;

[0011] Based on the vehicle operation control model and the train operation constraints, with vehicle motion parameters as state variables and train impact rate as control variables, and with the goal of meeting preset coupled train operation indicators, the coordinated operation data and corresponding operating conditions of the lead vehicle and the follower vehicle during operation are determined.

[0012] Exemplarily, the vehicle operation control model includes a vehicle motion model and a vehicle longitudinal dynamic model. The vehicle operation control model is constructed based on the traction characteristics and braking characteristics of the lead vehicle and the follower vehicle of the virtual coupling formation, including:

[0013] According to the motion law of the pilot vehicle and the following vehicle in the virtual coupling marshaling operation mode, a vehicle motion model is constructed based on a single-mass model;

[0014] According to various external forces applied to the lead vehicle and the follower vehicle during the driving process in the virtual coupling operation mode, a vehicle longitudinal dynamic model is constructed based on the law of conservation of energy.

[0015] Exemplarily, the vehicle motion model is constructed based on a single-particle model according to the motion law of the lead vehicle and the follower vehicle in the virtual coupling operation mode, including:

[0016] The vehicle motion model relationship is called to construct the vehicle motion model; the vehicle motion model relationship is:

[0017]

[0018] In the formula, is the differential of the vehicle position s, t is the time, v(t) is the running speed of the vehicle, is the differential of v(t), is the differential of a(t), a(t) is the running acceleration of the vehicle, and j(t) is the impact rate of the vehicle.

[0019] Exemplarily, the vehicle longitudinal dynamic model is constructed based on the law of conservation of energy according to various external forces applied to the lead vehicle and the follower vehicle during the driving process in the virtual coupling operation mode, including:

[0020] Calculating respectively the motion consumption power, the power consumed by rolling resistance, the power consumed by air resistance and the power consumed by slope resistance of the pilot vehicle and the following vehicle during the running process;

[0021] respectively calculating the power corresponding to the traction force or the braking force exerted on the lead vehicle and the following vehicle under the traction condition or the braking condition;

[0022] Based on the power output by the traction and braking system being equal to the power consumption of the lead vehicle and the follower vehicle during operation, a vehicle longitudinal dynamic model is obtained.

[0023] Exemplarily, the vehicle longitudinal dynamic model is constructed based on the law of conservation of energy according to various external forces applied to the lead vehicle and the follower vehicle during the driving process in the virtual coupling operation mode, including:

[0024] The longitudinal dynamics equation of the train is called to construct a longitudinal dynamics model of the vehicle; the longitudinal dynamics equation of the train is:

[0025]

[0026] In the formula, F bra is the traction of the vehicle, F tra is the braking force of the vehicle, m is the vehicle mass, J wheel is the equivalent moment of inertia of the traction system relative to the wheel axle, r wheel is the wheel rolling radius, a is the vehicle acceleration, g is the acceleration due to gravity, α is the road slope, and f rol is the rolling resistance coefficient, C air is the air resistance coefficient, A air is the frontal area of ​​the vehicle, ρ air is the air density, and v is the running speed of the vehicle.

[0027] Exemplarily, the vehicle operation control model includes a vehicle motion model and a vehicle longitudinal dynamic model, and the determination of the marshaling operation constraint conditions based on the vehicle operation control model according to train scheduling information, train safety operation requirements, train force change requirements, and train operating condition switching requirements includes:

[0028] Determine the dispatching constraints according to the start / stop status, operation schedule and parking time difference of the virtual coupling train;

[0029] Based on the vehicle motion model, according to the traction characteristics and braking characteristics of the virtual coupling formation, a force threshold, an output power threshold and a train force change rate restriction condition are determined as train model constraint conditions;

[0030] Determining a working condition switching control condition based on the train working condition switching time and the vehicle longitudinal dynamic model;

[0031] Determining safety protection constraint conditions according to the maximum operating speeds of the pilot vehicle and the following vehicle and the operating mode switching control conditions;

[0032] Based on the scheduling constraints, the train model constraints and the safety protection constraints, the marshaling operation constraints are determined.

[0033] Exemplarily, determining the scheduling constraint condition according to the start / stop state, the operation schedule and the parking time difference of the virtual coupling train includes:

[0034] Determine the initial condition constraints of the coupling formation according to the initial positions, initial speeds, initial traction forces / initial braking forces of the lead vehicle and the follower vehicle respectively;

[0035] Determine the termination condition constraint of the coupling formation according to the respective termination positions, termination speeds, and termination traction forces / termination braking forces of the lead vehicle and the following vehicle;

[0036] Determine the time constraint of the coupling marshalling according to the start time and the end time of the virtual coupling marshalling;

[0037] The initial condition constraint of the coupling formation, the termination condition constraint of the coupling formation and the time constraint of the coupling formation are taken together as scheduling constraints.

[0038] Exemplarily, based on the vehicle motion model, according to the traction characteristics and braking characteristics of the virtual coupling formation, determining the force threshold, output power threshold and train force change rate restriction conditions as train model constraint conditions, including:

[0039] The train traction / braking characteristic constraint relation and the traction / braking force change rate constraint relation are called to generate train model constraint conditions;

[0040] The train traction / braking characteristic constraint relation is:

[0041]

[0042] Wherein, the traction / braking force change rate constraint relation is:

[0043]

[0044] In the formula, F bmin is the minimum braking force at the current train speed, i = 1, 2, ..., n, n is a time discrete fraction, F l(i) is the traction / braking force at the i-th discrete time fraction during the operation of the pilot vehicle, F lmax is the maximum braking force of the pilot vehicle during operation, P bmin is the minimum braking power of the train, v l(i) is the speed of the pilot vehicle at the i-th discrete time fraction during its operation, P tmax is the maximum traction power, F f(i) is the traction / braking force of the following vehicle during operation, F tmax is the maximum traction force of the train at the current speed, v f(i)is the speed of the following vehicle at the ith discrete time fraction during its operation, dF m is the maximum rate of change of traction / braking force, and t is time.

[0045] Exemplarily, the determining the operating mode switching control condition based on the train operating mode switching time and the vehicle longitudinal dynamic model includes:

[0046] The train operating condition switching constraint relation is called to represent the operating condition switching control condition; the train operating condition switching constraint relation is:

[0047]

[0048] Where, ΔF m is the switching time between traction and braking Δt m The maximum increment of force in the l(i) is the traction / braking force at the i-th discrete time fraction during the operation of the pilot vehicle, h l(i) is the first operating mode switching control coefficient at the i-th discrete time fraction during the operation of the pilot vehicle, F f(i) is the traction / braking force of the following vehicle during operation, h f ( i) is the first operating mode switching control coefficient at the i-th discrete time equal fraction during the operation of the following vehicle, k l ( i) is the second operating mode switching control coefficient at the i-th discrete time equal fraction during the operation of the pilot vehicle, k f(i) is the second operating mode switching control coefficient at the ith discrete time fraction during the following vehicle operation, m is the vehicle mass, J wheel is the equivalent moment of inertia of the traction system relative to the wheel axle, r wheel is the wheel rolling radius, a f(i) is the acceleration of the following vehicle at the ith discrete time fraction during its operation, a l(i) is the acceleration of the pilot vehicle at the ith discrete time fraction during its operation, v l(i) is the speed of the pilot vehicle at the ith discrete time fraction during its operation, v f(i) is the speed of the following vehicle at the ith discrete time fraction during its operation, g is the acceleration of gravity, α is the road slope, and f rol is the rolling resistance coefficient, C air is the air resistance coefficient, A air is the frontal area of ​​the vehicle, ρ air is the air density.

[0049] Exemplarily, based on the vehicle operation control model and the marshaling operation constraint conditions, with vehicle motion parameters as state variables and train impact rate as control variables, and with the goal of meeting preset coupled marshaling operation indicators, determining the coordinated operation data and corresponding operating conditions of the lead vehicle and the follower vehicle during operation, includes:

[0050] Generate an objective function by taking meeting the preset coupling marshaling operation index as the goal;

[0051] Based on the vehicle motion model of the vehicle operation control model, the state space information of the virtual coupling formation is generated by taking the vehicle position, vehicle speed and vehicle acceleration as state variables and the train impact rate as the control variable;

[0052] Calling a preset optimization algorithm, based on the marshaling operation constraint conditions, the objective function and the state space information, to obtain the operation data and force data corresponding to the following vehicle and the leading vehicle at each discrete moment;

[0053] Based on the force data of the following vehicle and the leading vehicle, a maneuvering condition is determined.

[0054] Exemplarily, the generating of the objective function by taking satisfying the preset coupling marshaling operation index as the goal includes:

[0055] The objective function relational expression is called to generate the objective function of the virtual coupling formation; the objective function relational expression is:

[0056]

[0057] In the formula, J is the objective function, j l(i) is the impact rate at the ith discrete time fraction during the operation of the pilot vehicle, j f(i) is the impact rate at the ith discrete time fraction during the running of the following vehicle, v febi(i) is the maximum running speed of the following vehicle during operation, ω v Optimize the weight of the difference between the following vehicle and the safe speed limit, ω l Optimize the weight of the pilot car's impact rate, ω f Optimize the weight for the impact rate of the following vehicle, v f(i) is the speed of the following vehicle at the i-th time discrete fraction during its operation, and n is the time discrete fraction.

[0058] Exemplarily, the vehicle motion model based on the vehicle operation control model uses vehicle position, vehicle speed, and vehicle acceleration as state variables and train impact rate as a control variable to generate state space information of a virtual coupling formation, including:

[0059] The state space equation is called to represent the state space information of the virtual coupling formation; the state space equation is:

[0060]

[0061] In the formula, is the vehicle position s of the pilot vehicle during operation l The differential of is the vehicle position v of the pilot vehicle during operation l The differential of is the acceleration a of the pilot vehicle at the i-th discrete time fraction during its operation l The differential of is the vehicle position s of the following vehicle during operation f The differential of is the vehicle position v of the following vehicle during operation f The differential of is the acceleration a of the following vehicle at the i-th discrete time fraction during its operation f The differential of l is the impact rate at the ith discrete time fraction during the operation of the pilot vehicle, j f is the impact rate at the i-th time discrete equal fraction during the operation of the following vehicle.

[0062] Exemplarily, determining the maneuvering condition based on the force data of the following vehicle and the leading vehicle includes:

[0063] Determine a threshold value for selecting a working condition based on a maximum increment of force during a switching time between traction and braking and a preset random number;

[0064] If the traction force / braking force of the following vehicle and the leading vehicle are both greater than the working condition selection threshold, the vehicle is in the traction working condition;

[0065] If the traction force / braking force of the following vehicle and the leading vehicle are both less than the negative value of the working condition selection threshold, the vehicle is in a braking working condition;

[0066] If the traction force / braking force of the following vehicle and the leading vehicle are not simultaneously greater than the operating condition selection threshold, or are not simultaneously less than the negative value of the operating condition selection threshold, they are in the coasting operating condition.

[0067] On the other hand, the present application provides a train speed control device for a virtual coupled train, comprising:

[0068] A model building module is used to build a vehicle operation control model based on the traction and braking characteristics of the lead vehicle and the following vehicle of the virtual coupling formation;

[0069] A constraint condition determination module is used to determine the marshaling operation constraint conditions based on the vehicle operation control model, according to train scheduling information, train safety operation requirements, train force change requirements and train operating condition switching requirements;

[0070] The marshaling control module is used to determine the collaborative operation data and corresponding operating conditions of the lead vehicle and the follower vehicle during operation based on the vehicle operation control model and the marshaling operation constraints, with the vehicle motion parameters as state variables and the train impact rate as control variables, with the goal of meeting the preset coupled marshaling operation indicators.

[0071] The present application also provides an electronic device, comprising a processor, wherein the processor is used to implement the steps of the train speed control method of the virtual coupled train formation as described in any of the preceding items when executing a computer program stored in a memory.

[0072] The present application also provides a non-volatile storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the train speed control method of the virtual coupled train formation as described in any of the preceding items are implemented.

[0073] The present application also provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the steps of the train speed control method of the virtual coupled train formation as described in any of the preceding items.

[0074] Finally, the present application also provides a virtual coupling marshaling control system, including a ground signal, a main train automatic operation subsystem, a main train automatic protection subsystem, and a main communication subsystem of a pilot car, and a slave train automatic operation subsystem, a slave train automatic protection subsystem, and a slave communication subsystem of a follower car;

[0075] Wherein, the master train automatic protection subsystem communicates with the ground signal through the master communication subsystem, and the slave train automatic protection subsystem communicates with the ground signal through the slave communication subsystem respectively;

[0076] The master train automatic operation subsystem exchanges vehicle operation status information through the master communication subsystem, and the slave train automatic operation subsystem exchanges vehicle operation status information through the slave communication subsystem;

[0077] The master train automatic operation subsystem and the slave train automatic operation subsystem implement the steps of the train speed control method of the virtual coupled formation as described in any of the preceding items.

[0078] The advantage of the technical solution provided by the present application is that a vehicle operation control model that reflects the vehicle operation conditions and stress conditions during the operation process is constructed by analyzing the traction characteristics and braking characteristics of the virtual coupling marshaling. When determining the operating speed of the following car and the pilot car during the operation process, the train scheduling conditions, safe operation, stress changes and working condition switching conditions are considered. It can not only meet the train operation performance and safety indicators, but also meet the requirements of punctuality, platform operation time and parking accuracy of the virtual coupling marshaling of the train, and ensure that the user's comfort is very good. It is possible to achieve smooth and efficient control of the operation of the virtual coupling marshaling train. Furthermore, the traction and braking forces of the pilot car and the following car are fully matched with the operating level, and the emergency braking will not be triggered during the whole process, and the operating level does not switch frequently, so as to achieve smooth switching and operation control of the train working conditions, and effectively improve the user's comfort.

[0079] In addition, the present application also provides corresponding implementation devices, electronic devices and non-volatile storage media for the train speed control method of virtual coupled formation, which further makes the method more practical, and the devices, electronic devices and non-volatile storage media have corresponding advantages.

[0080] The technical features mentioned above, the technical features to be mentioned below, and the technical features shown separately in the drawings can be combined with each other arbitrarily, as long as the combined technical features are not contradictory. All feasible feature combinations are technical contents clearly recorded in this application. Any of the multiple sub-features contained in the same sentence can be applied independently, and does not have to be applied together with other sub-features.

[0081] It should be understood that the foregoing general description and the following detailed description are exemplary only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0082] In order to more clearly illustrate the technical solutions of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments or related technical descriptions. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0083] Figure 1 A flow chart of a train speed control method for a virtual coupled train set provided in the present application;

[0084] Figure 2 A schematic diagram of a train traction characteristic curve for an exemplary application scenario provided in this application;

[0085] Figure 3A schematic diagram of an electrical braking characteristic curve for an exemplary application scenario provided in this application;

[0086] Figure 4 A schematic diagram of the operation process control of a virtual coupling train for an exemplary application scenario provided in this application;

[0087] Figure 5 A schematic diagram of an impact rate curve during the operation of a virtual coupling marshaling of an exemplary application scenario provided in this application;

[0088] Figure 6 A schematic diagram of an acceleration curve during the operation of a virtual coupling train in an exemplary application scenario provided in this application;

[0089] Figure 7 A schematic diagram of a speed curve during the operation of a virtual coupling train in an exemplary application scenario provided in this application;

[0090] Figure 8 A schematic diagram of a displacement curve during the operation of a virtual coupling train in an exemplary application scenario provided in this application;

[0091] Fig. 9 A schematic diagram of a traction process curve during the operation of a virtual coupling train in an exemplary application scenario provided in this application;

[0092] Fig.10 A schematic diagram of a braking process curve during the operation of a virtual coupling train in an exemplary application scenario provided in this application;

[0093] Fig.11 A structural diagram of a specific implementation of the train speed control device for virtual coupled train formation provided in this application;

[0094] Fig.12 A structural diagram of a specific implementation of the electronic device provided in this application;

[0095] Fig.13 A structural framework diagram of an exemplary implementation of a virtual coupling and marshaling control system provided in the present application;

[0096] Fig.14 A structural diagram of another exemplary implementation of the virtual coupling and train control system provided in the present application. DETAILED DESCRIPTION

[0097] In order to enable those skilled in the art to better understand the present application, the present application is further described in detail below in conjunction with the accompanying drawings and specific embodiments. Among them, the terms "first", "second", "third", "fourth", etc. in the specification and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations of the two are intended to cover non-exclusive inclusions. The term "exemplary" means "used as an example, embodiment or illustrative". Any embodiment described here as "exemplary" is not necessarily interpreted as being superior or better than other embodiments.

[0098] As urban rail trains are safe, convenient, comfortable and efficient, they are widely used as transportation vehicles in daily work and life. With the increase in passenger capacity and the expansion of the scale of the operating network, the phenomenon of uneven temporal and spatial distribution of passenger flow in urban rail transit in different operating stages, operating sections and operating time periods has become increasingly prominent. In order to solve the problem of dynamic matching of capacity and passenger flow, urban rail trains replace the traditional mechanical coupling fixed marshaling operation mode with a virtual coupling marshaling operation mode. This operation mode can not only effectively solve the problem of capacity shortage during peak passenger flow periods, thereby avoiding the phenomenon of crowded trains and queuing in stations, greatly shortening passenger travel time and improving travel service quality; it can also solve the problem of excess capacity during low passenger flow periods, avoid the phenomenon of low train load or even empty trains, and avoid idleness and waste of operating resources.

[0099] Based on the multi-train virtual coupling and marshaling technology of train-to-train communication and relative moving block signal system, different trains on the same line can be dynamically recoupled or decoupled according to operational needs, either stopped at the station or not stopped in the section. This can greatly shorten the train running interval and improve the train passing rate per unit time on existing lines. It can also dynamically and flexibly adjust the train formation size according to transport capacity demand, increase vehicle utilization, achieve efficient utilization of operating resources, and meet the needs of precise matching of transport capacity and passenger flow.

[0100] At present, the relevant technologies have made improvements on the advantages of train virtual coupling in terms of flexibility, safety and collision avoidance, tracking distance reduction, stability tracking control, performance indicators, etc., but they cannot meet the requirements of punctuality, comfort, and sufficient effective platform operation time for virtual coupling of urban rail trains. For example, the operation control of virtual coupling of trains in the relevant technologies adopts a distributed speed planning and control mode, that is, the pilot car independently plans the speed curve according to the operation plan and performs tracking control, while the following car receives the operation status information of the pilot car in real time to track and control the platform with the goal of being close to the safe tracking distance, which can easily lead to a large difference in the parking time between the pilot car and the following car at the platform. Based on this, the present application analyzes the traction and braking characteristics of urban rail trains, constructs a vehicle operation control model, comprehensively considers the safety, punctuality, comfort and platform parking time difference of the coupling, calculates the data of the virtual coupling over time, and ensures that the train completes the transportation of large-volume passengers smoothly, efficiently and quickly. After introducing the technical solution of the present application, various non-limiting implementation methods of the present application are described in detail below. In order to better illustrate the present application, many specific details are given in the specific implementation methods below. Those skilled in the art will appreciate that the present application can be implemented without these specific details. In other examples, methods, means, components and circuits well known to those skilled in the art are not described in detail in order to highlight the subject matter of the present application.

[0101] First see Figure 1 , Figure 1 A flow chart of a train speed control method for a virtual coupled train set provided in this application, which may include the following contents:

[0102] S101: Construct a vehicle operation control model based on the traction characteristics and braking characteristics of the lead vehicle and the following vehicle of the virtual coupled formation.

[0103] The pilot car and the following car of this embodiment are coupled based on the virtual coupling technology. The pilot car and the following car can be, for example, urban rail trains. The pilot car and the following car are powered by direct current from the overhead line through the pantograph and driven by the traction motor. Under the traction condition, the traction characteristic curves of the pilot car and the following car can be, for example, Figure 2 As shown in the figure, the starting traction force under the train capacity load and the starting resistance and basic resistance is, for example, 106 kN. Correspondingly, the maximum traction acceleration and the minimum traction acceleration that can be achieved within the speed range of 0 to 80 km / h are 1.18 m / s 2 , 0.42m / s 2 The lead vehicle and the following vehicle give priority to electric braking, i.e. regenerative braking. When the electric braking is insufficient, part of the braking is supplemented by air braking. When in the electric braking condition, the electric braking characteristic curve can be as follows: Figure 3As shown, the minimum braking force of the pilot vehicle and the following vehicle under the specified load is, for example, -100 kN, and the average deceleration that can be achieved in the speed range of 0 to 80 km / h is -1.1 m / s 2 By analyzing the traction and braking characteristics of the virtual coupling formation, the characteristics are used as kinematic constraints to control the running speed of the virtual coupling encoding, thereby constructing a vehicle operation control model that reflects the running conditions of the lead vehicle and the following vehicle and the stress conditions during the running process.

[0104] S102: Based on the vehicle operation control model, the train scheduling information, the train safety operation requirements, the train force change requirements and the train operating condition switching requirements are determined.

[0105] In this step, the train dispatching information at least includes the timetable, start-stop status, and parking time difference of the virtual coupling marshaling. The train safety operation requirements at least include the maximum operating speed of the lead car and the following car under safe operation, the maximum traction force that can be exerted, the minimum braking force, the minimum braking power, and the maximum traction power. The train force change requirements are used to reflect the changes in the traction force / braking force received by the lead car and the following car at adjacent discrete moments during the driving process. During the driving process of the train, the smaller the change rate of the traction force / braking force, the higher the riding comfort. The train has a switching time when the traction-to-brake condition or the braking-to-traction condition is switched. The train condition switching requirements are used to reflect the switching frequency of the traction condition and the braking condition. The marshaling operation constraint conditions are the constraints when calculating the optimal coordinated operation data of the lead car and the following car during the operation process, so as to ensure that the coordinated operation data finally calculated can not only ensure the safe operation of the lead car and the following car, but also make the lead car and the following car punctual and synchronized in parking time, and ensure the comfort of passengers.

[0106] S103: Based on the vehicle operation control model and the train operation constraints, with the vehicle motion parameters as the state variables and the train impact rate as the control variable, the coordinated operation data and corresponding operating conditions of the lead vehicle and the follower vehicle during the operation are determined with the goal of meeting the preset coupled train operation indicators.

[0107] In this embodiment, the preset coupled marshaling operation index is the current virtual coupled marshaling driving demand, including but not limited to safe driving, punctuality, user comfort, synchronization of parking time, and sufficient platform operation time. The collaborative operation data includes but is not limited to the displacement, speed, acceleration, traction / braking force, impact rate of the virtual coupled marshaling over time, and the operating condition, that is, the operating level, including traction condition, idling condition, braking condition, and emergency braking condition. Since the operating condition also changes over time, the operating condition is a condition planning sequence accordingly. After the collaborative operation data and the corresponding operating condition are calculated, they are sent to the tracking control layer to realize the operation of the virtual coupled marshaling.

[0108] In the technical solution provided in the present application, a vehicle operation control model that reflects the vehicle operation conditions and stress conditions during the operation process is constructed by analyzing the traction characteristics and braking characteristics of the virtual coupling marshaling. When determining the operating speed of the following car and the pilot car during the operation process, the train scheduling conditions, safe operation, stress changes and working condition switching conditions are considered. It can not only meet the train operation performance and safety indicators, but also meet the requirements of punctuality, platform operation time and parking accuracy of the virtual coupling marshaling of the train, and ensure that the user comfort is very good. It is possible to achieve smooth and efficient control of the operation of the virtual coupling marshaling train. Furthermore, the traction and braking forces of the pilot car and the following car are fully matched with the operating level, and the emergency braking will not be triggered during the whole process, and the operating level does not switch frequently, so as to achieve smooth switching and operation control of the train working conditions, and effectively improve the user comfort.

[0109] It should be noted that there is no strict order of execution between the steps in this application. As long as they comply with the logical order, these steps can be executed simultaneously or in a certain preset order. Figure 1 This is just a schematic and does not mean that this is the only execution order.

[0110] In the above embodiment, there is no limitation on how to construct the vehicle operation control model. This embodiment also provides an exemplary simple construction method of the vehicle operation control model, which may include the following contents:

[0111] According to the motion laws of the lead car and the following car in the virtual coupled marshaling operation mode, the vehicle motion model is constructed based on the single-particle model; according to the various external forces exerted on the lead car and the following car in the virtual coupled marshaling operation mode during driving, the vehicle longitudinal dynamic model is constructed based on the law of conservation of energy. The vehicle motion model and the vehicle longitudinal dynamic model together constitute the vehicle operation control model.

[0112] To simplify the calculation, this embodiment uses the lead vehicle and the follower vehicle as a single-mass point model, and obtains the vehicle motion model of the lead vehicle and the follower vehicle based on the fact that the acceleration of the lead vehicle / follower vehicle is proportional to the force, inversely proportional to the mass of the train, and proportional to the inverse of the mass of the train. As an efficient model construction method, the vehicle motion model relationship can be stored locally in advance, and the vehicle motion model can be constructed by calling the vehicle motion model relationship. The vehicle motion model relationship can be expressed as:

[0113]

[0114] In the formula, is the differential of the vehicle position s, t is the time, v(t) is the running speed of the vehicle, is the differential of v(t), is the differential of a(t), a(t) is the running acceleration of the vehicle, and j(t) is the impact rate of the vehicle.

[0115] It is understandable that the train will be subject to various external forces during its travel. This embodiment analyzes the longitudinal dynamics model of each train in the virtual coupling formation from the perspective of energy conservation:

[0116] The motion power consumption, rolling resistance power, air resistance power and slope resistance power of the lead vehicle and the following vehicle are calculated respectively during operation. The power corresponding to the traction force or braking force of the lead vehicle and the following vehicle under traction or braking conditions is calculated respectively. The longitudinal dynamic model of the vehicle is obtained based on the power output of the traction and braking systems, which is equal to the power consumption of the lead vehicle and the following vehicle during operation.

[0117] In the embodiment, the kinetic energy E of the pilot vehicle and the following vehicle during operation is mov It can be expressed as:

[0118]

[0119] Among them, based on the wheel rolling radius r wheel , angular velocity ω wheel The relationship between it and the train running speed v can be expressed as:

[0120]

[0121] Substituting equation (3) into equation (2) and taking the derivative of kinetic energy with respect to time, we can obtain the power consumed by the train motion P mov :

[0122]

[0123] Among them, the power consumed by rolling resistance during the train running is P rol It can be expressed as:

[0124] P rol =mgcos(α)×f rol v; (5)

[0125] Among them, the power consumed by air resistance during the train running is P air It can be expressed as:

[0126] P air =0.5C air A air ρ air v 3 ; (6)

[0127] Among them, the power consumed by the slope resistance during the train running is Pgre It can be expressed as:

[0128] P gre =mgsin(α)×v; (7)

[0129] In traction or braking conditions, the traction force F tra or braking force P bra The corresponding power P tra , P bra It can be expressed as:

[0130] P tra =F tra v,P bra =F bra v; (8)

[0131] According to the law of conservation of energy, the power output of the traction and braking system is equal to all the power consumed:

[0132] P bra +P tra =P mov +P rol +P air +P gre ; (9)

[0133] Substituting the above equations (4) to (8) into equation (9), we can obtain the vehicle longitudinal dynamic model:

[0134]

[0135] As a more efficient model building method, this embodiment can store the train longitudinal dynamics relationship locally in advance, and build the vehicle longitudinal dynamic model by calling the train longitudinal dynamics relationship; the train longitudinal dynamics relationship can be expressed as:

[0136]

[0137] In the formula, F bra is the traction of the vehicle, F tra is the braking force of the vehicle, m is the vehicle mass, J wheel is the equivalent moment of inertia of the traction system relative to the wheel axle, r wheel is the wheel rolling radius, a is the vehicle acceleration, g is the acceleration due to gravity, α is the road slope, and f rol is the rolling resistance coefficient, C air is the air resistance coefficient, A air is the frontal area of ​​the vehicle, ρ air is the air density, and v is the running speed of the vehicle.

[0138] As can be seen from the above, this embodiment constructs a kinematic model and a dynamic model of the train operation process in a simple way, providing data support that is closer to the actual operating environment for calculating the optimal marshaling operation parameters, which is conducive to improving the accuracy of determining the operation parameters.

[0139] The above embodiment defines the contents included in the marshaling operation constraint conditions. This embodiment also provides an exemplary method for determining the marshaling operation constraint conditions, which may include the following contents:

[0140] Determine the dispatching constraints based on the start / stop status, operating schedule and parking time difference of the virtual coupled formation; determine the force threshold, output power threshold and train force change rate limit conditions as train model constraints based on the vehicle motion model and the traction and braking characteristics of the virtual coupled formation; determine the operating condition switching control conditions based on the train operating condition switching time and the vehicle longitudinal dynamic model; determine the safety protection constraints based on the maximum operating speeds of the lead car and the following car and the operating condition switching control conditions; determine the formation operation constraints based on the dispatching constraints, train model constraints and safety protection constraints.

[0141] In this embodiment, the train scheduling information included in the marshaling operation constraint condition includes at least the start and stop status, operation schedule and parking time difference of the virtual coupling marshaling, and the scheduling constraint condition is determined based on these three data. As a simpler way to determine the scheduling constraint condition, the initial position s of the pilot car can be used to determine the train scheduling information. l0 , the initial position of the following vehicle s f0 , the initial speed v of the pilot car l0 , the initial velocity v of the following vehicle f0 , initial traction force / initial braking force of the pilot vehicle F l0 、The initial traction force / initial braking force of the following vehicle F f0 , determine the initial condition constraints of the coupling formation; according to the terminal position s of the pilot vehicle ln , the ending position s of the following vehicle fn , the terminal speed v of the pilot vehicle ln , the terminal speed v of the following vehicle fn , the pilot vehicle's final traction force / final braking force F ln , the following vehicle's final traction force / final braking force F fn , determine the termination condition constraints of the coupling group; according to the start time t0 and the termination time t n , determine the time constraint of the coupling marshalling; take the initial condition constraint of the coupling marshalling, the termination condition constraint of the coupling marshalling and the time constraint of the coupling marshalling as the scheduling constraint. Among them, the initial condition constraint of the coupling marshalling can be expressed as:

[0142]

[0143] Among them, the termination condition constraint of the coupling marshaling can be expressed as:

[0144]

[0145] Among them, the coupling marshaling time constraint can be expressed as: △t=(t n -t0) / n; (13)

[0146] In the formula, the displacement, speed, traction / braking force of the pilot vehicle at the initial and final moments are s respectively. l(0) 、s l(n) 、v l(0) 、v ln 、F l(0) 、F ln , the displacement, speed, traction / braking force of the following vehicle at the initial and final moments are s f(0) 、s fn 、v f(0) 、v fn 、F f(0) 、F fn , △t represents the time difference, and n is a discrete fraction of time.

[0147] According to the analysis of the traction characteristics and braking characteristics of the train in the above embodiment S101, it can be known that the traction characteristics and braking characteristics determine the maximum traction, minimum braking force and power that the vehicle can exert. The force threshold includes at least the maximum traction and the minimum braking force, and the output power threshold includes at least the minimum braking power and the maximum traction power. In order to ensure the safe operation of the train, the pilot car and the following car will be restricted by the force threshold and the output power threshold during the driving process. This embodiment will use it as a constraint when calculating the optimal marshaling operation parameters. In addition, the smaller the change rate of the traction / braking force during the driving of the pilot car and the following car, the higher the riding comfort. In order to ensure the riding comfort of the user, the traction / braking force of the pilot car and the following car at adjacent moments during the driving process is as close as possible. As a simple and efficient way to generate train model constraint conditions, this embodiment can store the train traction / braking characteristic constraint relationship and the traction / braking force change rate constraint relationship in advance locally, and generate the train model constraint conditions by calling the train traction / braking characteristic constraint relationship and the traction / braking force change rate constraint relationship; wherein, the train traction / braking characteristic constraint relationship can be expressed as:

[0148]

[0149] Among them, the traction / braking force change rate constraint relationship can be expressed as:

[0150]

[0151] In the formula, F bmin is the minimum braking force at the current train speed, i = 1, 2, ..., n, n is a time discrete fraction, F l(i) is the traction / braking force at the i-th discrete time fraction during the operation of the pilot vehicle, F lmax is the maximum braking force of the pilot vehicle during operation, P bmin is the minimum braking power of the train, v l(i) is the speed of the pilot vehicle at the i-th discrete time fraction during its operation, P tmax is the maximum traction power, F f(i) is the traction / braking force of the following vehicle during operation, F tmax is the maximum traction force of the train at the current speed, v f(i) is the speed of the following vehicle at the ith discrete time fraction during its operation, dF m is the maximum rate of change of traction / braking force, and t is time.

[0152] Furthermore, in order to ensure the driving safety of the coupled train, to avoid the occurrence of emergency braking conditions, and to improve the comfort of users, the speed of the pilot car cannot exceed the speed limit v calculated in real time by the train automatic protection subsystem based on the single-car movement authorization. lebi The speed of the following vehicle cannot exceed the speed limit v calculated in real time by the automatic train protection subsystem based on the relative braking distance. febi Based on this, this embodiment also needs to be subject to this restriction in the process of calculating the optimal running parameters of the formation, that is, the safety protection constraint condition at least includes the maximum running speed of the leading vehicle and the following vehicle. For the convenience of operation, the maximum running speed constraint condition in the safety protection constraint condition can be expressed as:

[0153]

[0154] In addition, there is a switching time when the train switches from traction to braking or from braking to traction, which is about 2s to 3s. To avoid frequent switching between traction and braking conditions, the train condition switching constraints can be further determined in combination with the vehicle longitudinal dynamic model. As a simple and efficient way to generate condition switching control conditions, the train condition switching constraint relationship can be stored locally in advance, and the condition switching control conditions can be expressed by calling the train condition switching constraint relationship; the train condition switching constraint relationship can be expressed as:

[0155]

[0156] Where, ΔF m is the switching time between traction and braking Δt m The maximum increment of force in thel(i) is the traction / braking force at the i-th discrete time fraction during the operation of the pilot vehicle, h l(i) is the first operating mode switching control coefficient at the i-th discrete time fraction during the operation of the pilot vehicle, F f(i) is the traction / braking force of the following vehicle during operation, h f(i) is the first operating mode switching control coefficient at the i-th discrete time equal fraction during the operation of the following vehicle, k l(i) is the second operating mode switching control coefficient at the i-th discrete time equal fraction during the operation of the pilot vehicle, k f(i) is the second operating mode switching control coefficient at the ith discrete time fraction during the following vehicle operation, m is the vehicle mass, J wheel is the equivalent moment of inertia of the traction system relative to the wheel axle, r wheel is the wheel rolling radius, a f(i) is the acceleration of the following vehicle at the ith discrete time fraction during its operation, a l(i) is the acceleration of the pilot vehicle at the ith discrete time fraction during its operation, v l(i) is the speed of the pilot vehicle at the ith discrete time fraction during its operation, v f(i) is the speed of the following vehicle at the ith discrete time fraction during its operation, g is the acceleration of gravity, α is the road slope, and f rol is the rolling resistance coefficient, C air is the air resistance coefficient, A air is the frontal area of ​​the vehicle, ρ air is the air density.

[0157] From the above, it can be seen that this embodiment describes the collaborative constraints of the virtual coupled formation from the perspective of timetable, safety protection, traction and braking performance, parking time difference, force conditions during vehicle driving, and start-stop state constraints, which is conducive to obtaining the optimal formation operation parameters on the basis of safe operation and ensuring user comfort.

[0158] The above embodiment does not limit how to determine the optimal operating parameters of the virtual coupling formation. In order to solve the related technology based on the above embodiment, this embodiment combines the characteristics of urban rail train operation control, without changing the tracking control layer, and provides an information centralized decision-making collaborative planning method to determine the optimal operating parameters of the virtual coupling formation, which may include the following contents:

[0159] The objective function is generated by taking the preset coupled marshaling operation index as the goal; based on the vehicle motion model of the vehicle operation control model, the state space information of the virtual coupled marshaling is generated with the vehicle position, vehicle speed, and vehicle acceleration as state variables and the train impact rate as the control variable; the preset optimization algorithm is called to obtain the operation data and force data corresponding to the following vehicle and the leading vehicle at each discrete moment based on the marshaling operation constraints, the objective function and the state space information; the operating condition is determined based on the force data of the following vehicle and the leading vehicle. The operating condition and operation data are sent to the tracking control layer, and the tracking control layer uses the traction control unit (TCU) and brake control unit (BCU) of the leading vehicle and the following vehicle to track and control the driving process of the leading vehicle and the following vehicle.

[0160] like Figure 4 As shown, this embodiment determines the optimal operating parameters of the following vehicle and the leading vehicle, such as position, speed, traction / braking force, and operating conditions, through the collaborative constraint layer, collaborative target layer, and model solution layer from top to bottom. Among them, the collaborative constraint layer is the constrained conditions for the operation of the grouping recorded in the above embodiment, and the collaborative target layer is the state space information of the virtual coupling grouping. It is based on the state variables and control variables determined by the vehicle kinematic model, and the objective function is designed with comfort, efficiency, and minimum parking time difference as optimization indicators. The model solution layer calculates the operating data of the leading vehicle and the following vehicle and the planning sequence of the operating conditions through state decision and state transfer, and outputs them to the tracking control layer to realize the operation of the urban rail train coupling grouping. In other words, the optimal operating parameters for calculating the virtual coupling grouping are nonlinear mathematical relations jointly determined by the collaborative constraint layer and the collaborative target layer, which can also be called the optimal control model of collaborative speed planning. The method for solving the model can adopt a preset optimization algorithm. The preset optimization algorithm can be, for example, an optimization problem solving method or various genetic algorithms. Solving the optimization problem of the nonlinear quadratic programming can obtain the s corresponding to each discrete moment of the virtual coupling grouping. l(i) 、v l(i) 、a l(i) 、F l(i) 、j l ( i) 、s f(i) 、v f(i) 、a f(i) 、F f(i) 、j f(i) , thus obtaining the coordinated speed planning curve.

[0161] Furthermore, in order to make the tracking of the lead vehicle and the following vehicle as close as possible, while ensuring that the driving process is as smooth as possible, and taking into account both the efficiency and comfort of the ride, an objective function can be constructed based on the impact rate of the lead vehicle and the following vehicle during operation and the maximum operating speed of the following vehicle. For example, the objective function relationship can be called to generate the objective function of the virtual coupling formation. The objective function relationship can be expressed as:

[0162]

[0163] In the formula, J is the objective function, j l(i) is the impact rate at the ith discrete time fraction during the operation of the pilot vehicle, j f(i) is the impact rate at the ith discrete time fraction during the running of the following vehicle, v febi(i) is the maximum running speed of the following vehicle during operation, ω v Optimize the weight of the difference between the following vehicle and the safe speed limit, ω l Optimize the weight of the pilot car's impact rate, ω f Optimize the weight for the impact rate of the following vehicle, v f(i) is the speed of the following vehicle at the i-th time discrete fraction during its operation, and n is the time discrete fraction.

[0164] Furthermore, the coordinated speed planning of the lead vehicle and the follower vehicle needs to meet the safety, punctuality, and comfort indicators, which is essentially a comprehensive optimal control problem. According to the vehicle kinematic model, the train position, speed, and acceleration are selected as state variables, and the train impact rate is selected as the control variable to obtain the state space information of the virtual coupling formation. For example, the state space equation can be used to represent the state space information of the virtual coupling formation; the state space equation can be expressed as:

[0165]

[0166] In the formula, is the vehicle position s of the pilot vehicle during operation l The differential of is the vehicle position v of the pilot vehicle during operation l The differential of is the acceleration a of the pilot vehicle at the i-th discrete time fraction during its operation l The differential of is the vehicle position s of the following vehicle during operation f The differential of is the vehicle position v of the following vehicle during operation f The differential of is the acceleration a of the following vehicle at the i-th discrete time fraction during its operation f The differential of lis the impact rate at the ith discrete time fraction during the operation of the pilot vehicle, j f is the impact rate at the i-th time discrete equal fraction during the operation of the following vehicle.

[0167] After the above embodiment determines the traction / braking force at different discrete moments during the driving process of the following car and the leading car, since the emergency braking condition is constrained by limiting the maximum running speed of the train during the formation constraint condition generation process, the following car and the leading car can avoid being in the emergency braking condition. The determination method of other operating conditions can be:

[0168] The operating condition selection threshold is determined based on the maximum force increment and the preset random number within the traction and braking switching time; if the traction / braking force of the following vehicle and the leading vehicle are both greater than the operating condition selection threshold, the vehicle is in the traction condition; if the traction / braking force of the following vehicle and the leading vehicle are both less than the negative value of the operating condition selection threshold, the vehicle is in the braking condition; if the traction / braking force of the following vehicle and the leading vehicle are not greater than the operating condition selection threshold at the same time, or are not less than the negative value of the operating condition selection threshold at the same time, the vehicle is in the coasting condition.

[0169] In this embodiment, the preset random number can be a value selected by a person skilled in the art according to actual conditions, which is used as an influencing factor for distinguishing which working condition it is, for example, it can be 2. For example, if the preset random number is 2, the working condition selection threshold can be ΔF m / 2, when F l(i) 、F f(i) >ΔF m / 2 is the traction condition, when F l(i) 、F f(i) <ΔF m / 2 is the braking condition, otherwise it is the coasting condition.

[0170] It can be seen from the above that the operating parameters of the lead vehicle and the following vehicle in this embodiment, such as speed, displacement, acceleration, traction / braking force, are calculated in real time. The lead vehicle no longer independently plans the speed curve and performs tracking control according to the operation plan, and the following vehicle does not receive the operating status information of the lead vehicle in real time to perform tracking control with the goal of approaching the safe tracking distance. This can solve the problem of excessive difference in parking time between the lead vehicle and the following vehicle at the platform, and ensure that the lead vehicle and the following vehicle are on time and the parking time is synchronized.

[0171] Finally, in order to verify that the technical solution of this application can smoothly, efficiently and quickly complete the transportation of large-volume passengers, this application also provides a verification example, where the pilot car and the following car are subways, which may include the following contents: The test scenario parameters of this embodiment are: the total length from the starting station to the terminal station is 570m, the parking distance between the pilot car and the following car is 5.92m, and the running time between stations is 70s. The vehicle operation characteristic parameters of the test line subway are shown in Table 1:

[0172] Table 1 Metro operation characteristic parameters

[0173]

[0174]

[0175] After the virtual coupled marshaling consisting of two subway trains completed the full process test of departure, cruising and entry, the test results of the marshaling operation impact rate, acceleration, speed and displacement curves were obtained as follows: Figure 5-Figure 8 As shown in the figure, the traction acceleration of the leading vehicle and the following vehicle is 0.5m / s 2 Within, braking deceleration is -0.75m / s 2 The speed of the whole process is below the speed limit, which meets the train operation performance and safety indicators. The absolute value of the impact rate of the two cars is less than 0.1m / s3, and the comfort of subway operation is very good. Further, the displacement data of the virtual coupling marshaling process are compared, as shown in Table 2:

[0176] Table 2 Comparison of displacement-time data of virtual coupling formation

[0177] parameter Pilot car front Following the car head Starting displacement / (m) 252.76 128.79 Departure time / (s) 0.0 0.63 Parking displacement / (m) 822.63 698.78 Parking time / (s) 70.21 72.15

[0178] As shown in Table 2, the departure time difference between the front and rear cars of the virtual coupled marshaling is 0.63s, the stopping time difference is 1.94s, the difference between the complete stopping time of the marshaling at the station and the planned time is 2.15s, the initial stopping distance is 5.97m, and the stopping distance at the station is 5.85m, which can well meet the requirements of punctuality, platform operation time and parking accuracy of virtual coupled train marshaling.

[0179] In addition, this embodiment divides the control level into 4 types: traction, coasting, braking, and emergency braking, and represents them with the values ​​of 1, 0, -1, and -5 respectively. This embodiment analyzes the traction process data and braking process data during the operation of the virtual coupling marshaling, and obtains the change curves of traction force, braking force and control working condition level during the operation of the virtual coupling marshaling. Fig. 9 and Fig.10 As shown in the two figures, the traction and braking forces of the lead car and the following car are fully matched with the operating level. No emergency braking is triggered during the whole process, and the operating level does not switch frequently, thus achieving smooth switching of train working conditions and operation control.

[0180] As can be seen from the above, this embodiment proposes an information-centralized decision-making method for determining virtual coupling coordinated operation parameters that are compatible with the traction / braking characteristics, vehicle kinematic model, and vehicle dynamic model of urban rail trains, and ultimately obtains the optimal operating speed and the corresponding operating condition sequence, ensuring that the virtual coupling marshaling can smoothly, efficiently, and quickly complete the transportation of large passenger flows.

[0181] The present application also provides a corresponding device for the train speed control method of the virtual coupling marshaling, which further makes the method more practical. Among them, the device can be described from the perspective of functional modules and hardware. The following is an introduction to the train speed control device of the virtual coupling marshaling provided by the present application. The device is used to implement the train speed control method of the virtual coupling marshaling provided by the present application. In this embodiment, the train speed control device of the virtual coupling marshaling may include or be divided into one or more program modules, which are stored in a storage medium and executed by one or more processors to complete the train speed control method of the virtual coupling marshaling disclosed in the first embodiment. The program module referred to in this application refers to a series of computer program instruction segments that can complete specific functions, which is more suitable for describing the execution process of the train speed control device of the virtual coupling marshaling in the storage medium than the program itself. The following description will specifically introduce the functions of each program module of this embodiment. The train speed control device of the virtual coupling marshaling described below and the train speed control method of the virtual coupling marshaling described above can be referred to each other.

[0182] From the perspective of functional modules, see Fig.11 , Fig.11 This is a structural diagram of a train speed control device for a virtual coupled train set provided by the present application in a specific implementation manner. The device may include:

[0183] A model building module 111 is used to build a vehicle operation control model based on the traction characteristics and braking characteristics of the leading vehicle and the following vehicle of the virtual coupling formation;

[0184] The constraint condition determination module 112 is used to determine the marshaling operation constraint conditions based on the vehicle operation control model, according to the train scheduling information, the train safety operation requirements, the train force change requirements and the train working condition switching requirements;

[0185] The formation control module 113 is used to determine the coordinated operation data and corresponding operating conditions of the lead vehicle and the follower vehicle during operation based on the vehicle operation control model and the formation operation constraints, with the vehicle motion parameters as state variables and the train impact rate as control variables, with the goal of meeting the preset coupled formation operation indicators.

[0186] Exemplarily, in some implementations of this embodiment, the model building module 111 may also be used to:

[0187] According to the motion law of the lead vehicle and the following vehicle in the virtual coupled marshaling operation mode, the vehicle motion model is constructed based on the single-particle model.

[0188] According to the various external forces on the leading vehicle and the following vehicle during the driving process in the virtual coupling operation mode, the vehicle longitudinal dynamic model is constructed based on the law of conservation of energy.

[0189] A vehicle operation control model is generated according to the vehicle motion model and the vehicle longitudinal dynamic model.

[0190] As an exemplary implementation of the above embodiment, the above model building module 111 may also be used for:

[0191] The vehicle motion model relationship is called to construct the vehicle motion model; the vehicle motion model relationship is:

[0192]

[0193] In the formula, is the differential of the vehicle position s, t is the time, v(t) is the running speed of the vehicle, is the differential of v(t), is the differential of a(t), a(t) is the running acceleration of the vehicle, and j(t) is the impact rate of the vehicle.

[0194] As another exemplary implementation of the above embodiment, the above model building module 111 may also be used for:

[0195] Calculate the power consumed by the pilot car and the following car during the operation, the power consumed by rolling resistance, the power consumed by air resistance, and the power consumed by slope resistance;

[0196] Calculate the power corresponding to the traction force or braking force on the leading vehicle and the following vehicle under traction or braking conditions respectively;

[0197] Based on the power output of the traction and braking systems, which is equal to the power consumption of the lead vehicle and the following vehicle during operation, the vehicle longitudinal dynamic model is obtained.

[0198] As another exemplary implementation of the above embodiment, the above model building module 111 may also be used for:

[0199] The longitudinal dynamics relationship of the train is called to construct the longitudinal dynamic model of the vehicle; the longitudinal dynamics relationship of the train is:

[0200]

[0201] In the formula, F bra is the traction of the vehicle, F tra is the braking force of the vehicle, m is the vehicle mass, J wheel is the equivalent moment of inertia of the traction system relative to the wheel axle, r wheel is the wheel rolling radius, a is the vehicle acceleration, g is the acceleration due to gravity, α is the road slope, and f rol is the rolling resistance coefficient, C air is the air resistance coefficient, A air is the frontal area of ​​the vehicle, ρ air is the air density, and v is the running speed of the vehicle.

[0202] Illustratively, in some other implementations of this embodiment, the constraint condition determination module 112 may also be used to:

[0203] Determine the dispatching constraints according to the start / stop status, operation schedule and parking time difference of the virtual coupling train;

[0204] Based on the vehicle motion model, according to the traction characteristics and braking characteristics of the virtual coupling formation, the force threshold, output power threshold and train force change rate limit conditions are determined as train model constraints;

[0205] Determine the operating mode switching control conditions based on the train operating mode switching time and the vehicle longitudinal dynamic model;

[0206] Determine the safety protection constraint conditions according to the maximum operating speed of the pilot vehicle and the following vehicle and the operating mode switching control conditions;

[0207] Based on scheduling constraints, train model constraints and safety protection constraints, the marshaling operation constraints are determined.

[0208] As an exemplary implementation of the above embodiment, the constraint condition determination module 112 may also be used for:

[0209] Determine the initial condition constraints of the coupling formation according to the initial positions, initial speeds, initial traction forces / initial braking forces of the lead vehicle and the following vehicle;

[0210] Determine the termination condition constraints of the coupling formation according to the respective termination positions, termination speeds, termination traction forces / termination braking forces of the lead vehicle and the following vehicle;

[0211] Determine the time constraint of the coupling marshalling according to the start time and the end time of the virtual coupling marshalling;

[0212] The initial condition constraint of the coupling marshalling, the termination condition constraint of the coupling marshalling and the time constraint of the coupling marshalling are taken as the scheduling constraints.

[0213] As another exemplary implementation of the above embodiment, the constraint condition determination module 112 may also be used to:

[0214] The train traction / braking characteristic constraint relation and the traction / braking force change rate constraint relation are called to generate train model constraint conditions;

[0215] Among them, the train traction / braking characteristic constraint relationship is:

[0216]

[0217] Among them, the constraint relationship of the traction / braking force change rate is:

[0218]

[0219] In the formula, F bmin is the minimum braking force at the current train speed, i = 1, 2, ..., n, n is a time discrete fraction, F l(i) is the traction / braking force at the i-th discrete time fraction during the operation of the pilot vehicle, F lmax is the maximum braking force of the pilot vehicle during operation, P bmin is the minimum braking power of the train, v l(i) is the speed of the pilot vehicle at the i-th discrete time fraction during its operation, P tmax is the maximum traction power, F f(i) is the traction / braking force of the following vehicle during operation, F tmax is the maximum traction force of the train at the current speed, v f(i) is the speed of the following vehicle at the ith discrete time fraction during its operation, dF m is the maximum rate of change of traction / braking force, and t is time.

[0220] As another exemplary implementation of the above embodiment, the constraint condition determination module 112 may also be used to:

[0221] The train operating condition switching constraint relation is called to express the operating condition switching control condition; the train operating condition switching constraint relation is:

[0222]

[0223] Where, ΔF m is the switching time between traction and braking Δt m The maximum increment of force in the l(i) is the traction / braking force at the i-th discrete time fraction during the operation of the pilot vehicle, h l(i) is the first operating mode switching control coefficient at the i-th discrete time fraction during the operation of the pilot vehicle, Ff(i) is the traction / braking force of the following vehicle during operation, h f(i) is the first operating mode switching control coefficient at the i-th discrete time equal fraction during the operation of the following vehicle, k l(i) is the second operating mode switching control coefficient at the i-th discrete time equal fraction during the operation of the pilot vehicle, k f(i) is the second operating mode switching control coefficient at the ith discrete time fraction during the following vehicle operation, m is the vehicle mass, J wheel is the equivalent moment of inertia of the traction system relative to the wheel axle, r wheel is the wheel rolling radius, a f(i) is the acceleration of the following vehicle at the ith discrete time fraction during its operation, a l(i) is the acceleration of the pilot vehicle at the ith discrete time fraction during its operation, v l(i) is the speed of the pilot vehicle at the ith discrete time fraction during its operation, v f(i) is the speed of the following vehicle at the ith discrete time fraction during its operation, g is the acceleration of gravity, α is the road slope, and f rol is the rolling resistance coefficient, C air is the air resistance coefficient, A air is the frontal area of ​​the vehicle, ρ air is the air density.

[0224] For example, in some other implementations of this embodiment, the above-mentioned grouping control module 113 may also be used for:

[0225] Generate an objective function by taking meeting the preset coupling marshaling operation index as the goal;

[0226] The vehicle motion model based on the vehicle operation control model uses vehicle position, vehicle speed, and vehicle acceleration as state variables and train impact rate as control variable to generate state space information of virtual coupling formation;

[0227] Call the preset optimization algorithm to obtain the operation data and force data corresponding to the follower vehicle and the lead vehicle at each discrete moment based on the marshaling operation constraints, objective function and state space information;

[0228] Based on the force data of the following vehicle and the leading vehicle, the maneuvering condition is determined.

[0229] As an exemplary implementation of the above embodiment, the above grouping control module 113 may also be used for:

[0230] The objective function relational expression is called to generate the objective function of the virtual coupling formation; the objective function relational expression is:

[0231]

[0232] In the formula, J is the objective function, j l(i) is the impact rate at the ith discrete time fraction during the operation of the pilot vehicle, j f(i) is the impact rate at the ith discrete time fraction during the running of the following vehicle, v febi(i) is the maximum running speed of the following vehicle during operation, ω v Optimize the weight of the difference between the following vehicle and the safe speed limit, ω l Optimize the weight of the pilot car's impact rate, ω f Optimize the weight for the impact rate of the following vehicle, v f(i) is the speed of the following vehicle at the i-th time discrete fraction during its operation, and n is the time discrete fraction.

[0233] As another exemplary implementation of the above embodiment, the above grouping control module 113 may also be used for:

[0234] The state space equation is used to represent the state space information of the virtual coupling formation; the state space equation is:

[0235]

[0236] In the formula, is the vehicle position s of the pilot vehicle during operation l The differential of is the vehicle position v of the pilot vehicle during operation l The differential of is the acceleration a of the pilot vehicle at the i-th discrete time fraction during its operation l The differential of is the vehicle position s of the following vehicle during operation f The differential of is the vehicle position v of the following vehicle during operation f The differential of is the acceleration a of the following vehicle at the i-th discrete time fraction during its operation f The differential of l is the impact rate at the ith discrete time fraction during the operation of the pilot vehicle, j f is the impact rate at the i-th time discrete equal fraction during the operation of the following vehicle.

[0237] As another exemplary implementation of the above embodiment, the above grouping control module 113 may also be used for:

[0238] Determine a threshold value for selecting a working condition based on a maximum increment of force during a switching time between traction and braking and a preset random number;

[0239] If the traction / braking forces of the following vehicle and the leading vehicle are both greater than the working condition selection threshold, the vehicle is in the traction working condition;

[0240] If the traction / braking force of the following vehicle and the leading vehicle are both less than the negative value of the working condition selection threshold, the vehicle is in the braking working condition;

[0241] If the traction / braking force of the following vehicle and the leading vehicle are not greater than the working condition selection threshold at the same time, or are not less than the negative value of the working condition selection threshold at the same time, they are in the coasting working condition.

[0242] The functions of various functional modules of the train speed control device of the virtual coupled formation of the present application can be specifically implemented according to the method in the above-mentioned method embodiment. The specific implementation process can refer to the relevant description of the above-mentioned method embodiment, which will not be repeated here.

[0243] It can be seen from the above that this embodiment can realize smooth and efficient control of the operation of virtual coupled trains, effectively improving user comfort.

[0244] The train speed control device of the virtual coupled train formation mentioned above is described from the perspective of functional modules. Furthermore, the present application also provides an electronic device, which is described from the perspective of hardware. Fig.12 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application in one implementation manner. Fig.12 As shown, the electronic device includes a memory 120 for storing computer programs; a processor 121 for implementing the steps of the train speed control method of virtual coupled formation mentioned in any of the above embodiments when executing the computer program.

[0245] Among them, the processor 121 may include one or more processing cores, such as a 4-core processor, an 8-core processor, and the processor 121 may also be a controller, a microcontroller, a microprocessor or other data processing chip. The processor 121 may be implemented in at least one hardware form of DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array). The processor 121 may also include a main processor and a coprocessor. The main processor is a processor for processing data in the awake state, also known as a CPU (Central Processing Unit); the coprocessor is a low-power processor for processing data in the standby state. In some embodiments, the processor 121 may be integrated with a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content to be displayed on the display screen. In some embodiments, the processor 121 may also include an AI (Artificial Intelligence) processor, which is used to process computing operations related to machine learning.

[0246] The memory 120 may include one or more computer-readable storage media, which may be non-transitory. The memory 120 may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices and flash storage devices. In some embodiments, the memory 120 may be an internal storage unit of an electronic device, such as a hard disk of a server. In other embodiments, the memory 120 may also be an external storage device of an electronic device, such as a plug-in hard disk equipped on a server, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. Further, the memory 120 may also include both an internal storage unit of an electronic device and an external storage device. The memory 120 can not only be used to store application software and various types of data installed in the electronic device, such as: the code of the program in the process of executing the train speed control method of the virtual coupling formation, but also can be used to temporarily store data that has been output or is to be output. In this embodiment, the memory 120 is used to store at least the following computer program 1201, wherein after the computer program is loaded and executed by the processor 121, the relevant steps of the train speed control method of the virtual coupled formation disclosed in any of the aforementioned embodiments can be implemented. In addition, the resources stored in the memory 120 may also include an operating system 1202 and data 1203, etc., and the storage method may be temporary storage or permanent storage. Among them, the operating system 1202 may include Windows, Unix, Linux, etc. The data 1203 may include but is not limited to data corresponding to the train speed control result of the virtual coupled formation, etc.

[0247] In some embodiments, the electronic device may further include a display screen 122, an input / output interface 123, a communication interface 124 or a network interface, a power supply 125 and a communication bus 126. Among them, the display screen 122 and the input / output interface 123, such as a keyboard, belong to the user interface, and the optional user interface may also include a standard wired interface, a wireless interface, etc. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, and an OLED (Organic Light-Emitting Diode) touch device, etc. The display may also be appropriately referred to as a display screen or a display unit, which is used to display information processed in the electronic device and to display a visual user interface. The communication interface 124 may optionally include a wired interface and / or a wireless interface, such as a WI-FI interface, a Bluetooth interface, etc., which are generally used to establish a communication connection between the electronic device and other electronic devices. The communication bus 126 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Fig.12 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0248] Those skilled in the art will understand that Fig.12 The structure shown in the figure does not constitute a limitation on the electronic device, and may include more or fewer components than shown in the figure, for example, it may also include a sensor 127 to implement various functions.

[0249] It is understandable that if the train speed control of the virtual coupling marshaling in the above embodiment is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a non-volatile storage medium. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the relevant technology or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium to execute all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes but is not limited to: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), electrically erasable programmable ROM, register, hard disk, multimedia card, card-type memory (such as SD or DX memory, etc.), magnetic memory, removable disk, CD-ROM, magnetic disk or optical disk, etc. Various media that can store program codes. Based on this, the present invention also provides a non-volatile storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the train speed control of the virtual coupling marshaling recorded in any of the above embodiments are performed.

[0250] It is understandable that if the train speed control of the virtual coupled formation in the above embodiment is implemented in the form of a software functional unit and sold or used as an independent product, the computer software product does not need to be stored in a physical storage medium. For example, it can be directly transmitted to a computer or other device with information processing capabilities through a wired network or a wireless network to execute all or part of the steps of the methods of each embodiment of the present invention. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the relevant technology or all or part of the technical solution can be embodied in the form of a software product. Based on this, the present invention also provides a computer program product, which stores a computer program. When the computer program is executed by a processor, the steps of the train speed control of the virtual coupled formation recorded in any of the above embodiments are performed.

[0251] This application also provides a virtual coupling and marshaling control system, see Fig.13 , which may include:

[0252] The virtual coupling control system may include a ground signal machine 130, a pilot vehicle 131 and a follower vehicle 132. The pilot vehicle 131 and the follower vehicle 132 constitute a coupling and exchange vehicle operation status information and ground signal information through their respective communication subsystems.

[0253] like Fig.14As shown, the pilot car 131 includes a main train automatic operation subsystem, a main train automatic protection subsystem, and a main communication subsystem. The main train automatic operation subsystem also includes a main traction control unit (TCU) and a main brake control unit (ATP), and a main communication system. The main traction control unit and the main brake control unit are responsible for the control and execution of the traction and braking instructions of the pilot car. The following car 132 includes a slave train automatic operation subsystem, a slave train automatic protection subsystem, and a slave communication subsystem, wherein the slave train automatic operation subsystem includes a slave traction control unit and a slave brake control unit, which are responsible for the control and execution of the traction and braking instructions of the following car. The main train automatic operation subsystem and the slave train automatic operation subsystem are both train automatic operation subsystems ATO, which are used to coordinately plan the optimal speed curve of the formation based on information such as operation scheduling, operation lines, the operating status of the pilot car and the operating status of the following car, taking into account indicators such as safety, punctuality, comfort, energy saving, and platform parking time difference; and make the pilot car and the following car track and control with their respective speed curves as targets. For the convenience of description, they are defined as the master train automatic operation subsystem and the slave train automatic operation subsystem respectively. Similarly, the master train automatic protection subsystem and the slave train automatic protection subsystem are the train automatic protection subsystem ATP, and the master communication subsystem and the slave communication subsystem are also communication subsystems. The master train automatic protection subsystem communicates with the ground signal through the master communication subsystem, and the slave train automatic protection subsystem communicates with the ground signal through the slave communication subsystem; the master train automatic operation subsystem communicates with the vehicle operation status information through the master communication subsystem, and the slave train automatic operation subsystem communicates with the slave communication subsystem; the master train automatic operation subsystem and the slave train automatic operation subsystem implement the steps of the train speed control method of the virtual coupled formation as recorded in the previous embodiment, obtain the operation data and corresponding operating conditions at each discrete moment in the train operation process, and output them to their respective tracking control layers to realize the operation of the virtual coupled formation.

[0254] The functions of the functional modules of the virtual coupling and marshaling control system in the embodiment of the present invention can be specifically implemented according to the method in the above method embodiment. The specific implementation process can refer to the relevant description of the above method embodiment, which will not be repeated here.

[0255] It can be seen from the above that this embodiment can realize smooth and efficient control of the operation of virtual coupled trains, effectively improving user comfort.

[0256] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the hardware disclosed in the embodiments, including devices and electronic devices, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method part.

[0257] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0258] The above is a detailed introduction to a train speed control method, device, electronic device, computer program product, non-volatile storage medium and virtual coupled train control system provided by the present application. Specific examples are used herein to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea. It should be pointed out that based on the embodiments in the present application, for ordinary technicians in this technical field, all other embodiments obtained without creative work are within the scope of protection of the present application. Without departing from the principles of the present application, several improvements and modifications may be made to the present application, and these improvements and modifications also fall within the scope of protection of the present application.

Claims

1. A method for controlling the running speed of a train with a virtual coupling formation, characterized in that: include: Based on the traction and braking characteristics of the lead vehicle and the following vehicle in the virtual coupled train, a vehicle operation control model is constructed; Based on the vehicle operation control model, determining the constrained conditions of the train operation according to the train dispatching information, the train safe operation requirements, the train force change requirements and the train working condition switching requirements; Based on the vehicle operation control model and the train operation constraints, with vehicle motion parameters as state variables and train impact rate as control variables, and with the goal of meeting preset coupled train operation indicators, the coordinated operation data and corresponding operating conditions of the lead vehicle and the follower vehicle during operation are determined.

2. The train speed control method of virtual coupled train formation according to claim 1, characterized in that: The vehicle operation control model includes a vehicle motion model and a vehicle longitudinal dynamic model. The vehicle operation control model is constructed based on the traction characteristics and braking characteristics of the pilot vehicle and the following vehicle of the virtual coupling formation, including: According to the motion law of the pilot vehicle and the following vehicle in the virtual coupling marshaling operation mode, a vehicle motion model is constructed based on a single-mass model; According to various external forces applied to the lead vehicle and the follower vehicle during the driving process in the virtual coupling operation mode, a vehicle longitudinal dynamic model is constructed based on the law of conservation of energy.

3. The train speed control method of virtual coupled train formation according to claim 2, characterized in that: The vehicle motion model is constructed based on a single-particle model according to the motion law of the pilot vehicle and the following vehicle in the virtual coupling marshaling operation mode, including: The vehicle motion model relationship is called to construct the vehicle motion model; the vehicle motion model relationship is: In the formula, is the differential of the vehicle position s, t is the time, v(t) is the running speed of the vehicle, is the differential of v(t), is the differential of a(t), a(t) is the running acceleration of the vehicle, and j(t) is the impact rate of the vehicle.

4. The train speed control method of virtual coupled train formation according to claim 2, characterized in that: The vehicle longitudinal dynamic model is constructed based on the law of conservation of energy according to various external forces received by the lead vehicle and the follower vehicle during the driving process in the virtual coupling operation mode, including: Calculating respectively the motion consumption power, the power consumed by rolling resistance, the power consumed by air resistance and the power consumed by slope resistance of the pilot vehicle and the following vehicle during the running process; respectively calculating the power corresponding to the traction force or the braking force exerted on the lead vehicle and the following vehicle under the traction condition or the braking condition; Based on the power output by the traction and braking system being equal to the power consumption of the lead vehicle and the follower vehicle during operation, a vehicle longitudinal dynamic model is obtained.

5. The train speed control method of virtual coupled train formation according to claim 2, characterized in that: The vehicle longitudinal dynamic model is constructed based on the law of conservation of energy according to various external forces received by the lead vehicle and the follower vehicle during the driving process in the virtual coupling operation mode, including: The train longitudinal dynamics relationship is called to construct a vehicle longitudinal dynamic model; the train longitudinal dynamics relationship is: In the formula, F bra is the traction of the vehicle, F tra is the braking force of the vehicle, m is the vehicle mass, J wheel is the equivalent moment of inertia of the traction system relative to the wheel axle, r wheel is the wheel rolling radius, a is the vehicle acceleration, g is the acceleration due to gravity, α is the road slope, and f rol is the rolling resistance coefficient, C air is the air resistance coefficient, A air is the frontal area of ​​the vehicle, ρ air is the air density, and v is the running speed of the vehicle.

6. The train speed control method of virtual coupled train formation according to claim 1, characterized in that: The vehicle operation control model includes a vehicle motion model and a vehicle longitudinal dynamic model. The determination of the marshaling operation constraint conditions based on the vehicle operation control model according to train scheduling information, train safety operation requirements, train force change requirements and train operating condition switching requirements includes: Determine the dispatching constraints according to the start / stop status, operation schedule and parking time difference of the virtual coupling train; Based on the vehicle motion model, according to the traction characteristics and braking characteristics of the virtual coupling formation, a force threshold, an output power threshold and a train force change rate restriction condition are determined as train model constraint conditions; Determining a working condition switching control condition based on the train working condition switching time and the vehicle longitudinal dynamic model; Determining safety protection constraint conditions according to the maximum operating speeds of the pilot vehicle and the following vehicle and the operating mode switching control conditions; Based on the scheduling constraints, the train model constraints and the safety protection constraints, the marshaling operation constraints are determined.

7. The train speed control method of virtual coupled train formation according to claim 6, characterized in that: The scheduling constraint conditions are determined according to the start / stop status, operation schedule and parking time difference of the virtual coupling train, including: Determine the initial condition constraints of the coupling formation according to the initial positions, initial speeds, initial traction forces / initial braking forces of the lead vehicle and the follower vehicle respectively; Determine the termination condition constraint of the coupling formation according to the respective termination positions, termination speeds, and termination traction forces / termination braking forces of the lead vehicle and the following vehicle; Determine the time constraint of the coupling marshalling according to the start time and the end time of the virtual coupling marshalling; The initial condition constraint of the coupling formation, the termination condition constraint of the coupling formation and the time constraint of the coupling formation are taken together as scheduling constraints.

8. The train speed control method of virtual coupled train formation according to claim 6, characterized in that: The determining of the force threshold, the output power threshold and the train force change rate limiting condition based on the vehicle motion model and the traction characteristics and braking characteristics of the virtual coupling formation as train model constraint conditions includes: The train traction / braking characteristic constraint relation and the traction / braking force change rate constraint relation are called to generate train model constraint conditions; The train traction / braking characteristic constraint relation is: Wherein, the traction / braking force change rate constraint relation is: In the formula, F bmin is the minimum braking force at the current train speed, i = 1, 2, ..., n, n is a discrete time fraction, F l(i) is the traction / braking force at the i-th discrete time fraction during the operation of the pilot vehicle, F lmax is the maximum braking force of the pilot vehicle during operation, P bmin is the minimum braking power of the train, V l(i) is the speed of the pilot vehicle at the ith discrete time fraction during its operation, P tmax is the maximum traction power, F f(i) is the traction / braking force of the following vehicle during operation, F tmax is the maximum traction force of the train at the current speed, v f(i) is the speed of the following vehicle at the i-th discrete time fraction during its operation, dF m is the maximum rate of change of traction / braking force, and t is time.

9. The train speed control method of virtual coupled train formation according to claim 6, characterized in that: The determining of the operating mode switching control condition based on the train operating mode switching time and the vehicle longitudinal dynamic model includes: The train operating condition switching constraint relation is called to represent the operating condition switching control condition; the train operating condition switching constraint relation is: Where, ΔF m is the switching time between traction and braking Δt m The maximum increment of force in the l(i) is the traction / braking force at the i-th discrete time fraction during the operation of the pilot vehicle, h l(i) is the first operating mode switching control coefficient at the i-th discrete time fraction during the operation of the pilot vehicle, F f(i) is the traction / braking force of the following vehicle during operation, h f(i) is the first operating mode switching control coefficient at the i-th discrete time equal fraction during the operation of the following vehicle, k l(i) is the second operating mode switching control coefficient at the i-th discrete time equal fraction during the operation of the pilot vehicle, k f(i) is the second operating mode switching control coefficient at the ith discrete time fraction during the following vehicle operation, m is the vehicle mass, J wheel is the equivalent moment of inertia of the traction system relative to the wheel axle, r wheel is the wheel rolling radius, a f(i) is the acceleration of the following vehicle at the ith discrete time fraction during its operation, a l(i) is the acceleration of the pilot vehicle at the ith discrete time fraction during its operation, v l(i) is the speed of the pilot vehicle at the ith discrete time fraction during its operation, v f(i) is the speed of the following vehicle at the ith discrete time fraction during its operation, g is the acceleration of gravity, α is the road slope, and f rol is the rolling resistance coefficient, C air is the air resistance coefficient, A air is the frontal area of ​​the vehicle, ρ air is the air density.

10. The train speed control method of virtual coupled train formation according to any one of claims 1 to 9, characterized in that: The method of determining the coordinated operation data and corresponding operating conditions of the lead vehicle and the follower vehicle during operation based on the vehicle operation control model and the marshaling operation constraint conditions, taking the vehicle motion parameters as state variables and the train impact rate as control variables, and aiming at meeting the preset coupled marshaling operation index, includes: Generate an objective function by taking meeting the preset coupling marshaling operation index as the goal; Based on the vehicle motion model of the vehicle operation control model, the state space information of the virtual coupling formation is generated by taking the vehicle position, vehicle speed and vehicle acceleration as state variables and the train impact rate as the control variable; Calling a preset optimization algorithm, based on the marshaling operation constraint conditions, the objective function and the state space information, to obtain the operation data and force data corresponding to the following vehicle and the leading vehicle at each discrete moment; Based on the force data of the following vehicle and the leading vehicle, a maneuvering condition is determined.

11. The train speed control method of virtual coupled train formation according to claim 10, characterized in that: The objective function is generated by taking meeting the preset coupling marshaling operation index as the goal, including: The objective function relational expression is called to generate the objective function of the virtual coupling formation; the objective function relational expression is: In the formula, J is the objective function, j l(i) is the impact rate at the ith discrete time fraction during the operation of the pilot vehicle, j f(i) is the impact rate at the ith discrete time fraction during the running of the following vehicle, v febi(i) is the maximum running speed of the following vehicle during operation, ω v Optimize the weight of the difference between the following vehicle and the safe speed limit, ω l Optimize the weight of the pilot car's impact rate, ω f Optimize the weight for the impact rate of the following vehicle, v f(i) is the speed of the following vehicle at the i-th time discrete fraction during its operation, and n is the time discrete fraction.

12. The train speed control method of virtual coupled train formation according to claim 10, characterized in that: The vehicle motion model based on the vehicle operation control model uses vehicle position, vehicle speed, and vehicle acceleration as state variables and train impact rate as a control variable to generate state space information of a virtual coupling formation, including: The state space equation is called to represent the state space information of the virtual coupling formation; the state space equation is: In the formula, is the vehicle position s of the pilot vehicle during operation l The differential of is the vehicle position v of the pilot vehicle during operation l The differential of is the acceleration a of the pilot vehicle at the i-th discrete time fraction during its operation l The differential of is the vehicle position s of the following vehicle during operation f The differential of is the vehicle position v of the following vehicle during operation f The differential of is the acceleration a of the following vehicle at the i-th discrete time fraction during its operation f The differential of l is the impact rate at the ith discrete time fraction during the operation of the pilot vehicle, j f is the impact rate at the i-th time discrete equal fraction during the operation of the following vehicle.

13. The train speed control method of virtual coupled train formation according to claim 10, characterized in that: The determining of the operating condition based on the force data of the following vehicle and the leading vehicle includes: Determine a threshold value for selecting a working condition based on a maximum increment of force during a switching time between traction and braking and a preset random number; If the traction force / braking force of the following vehicle and the leading vehicle are both greater than the working condition selection threshold, the vehicle is in the traction working condition; If the traction force / braking force of the following vehicle and the leading vehicle are both less than the negative value of the working condition selection threshold, the vehicle is in a braking working condition; If the traction force / braking force of the following vehicle and the leading vehicle are not simultaneously greater than the operating condition selection threshold, or are not simultaneously less than the negative value of the operating condition selection threshold, they are in the coasting operating condition.

14. A train speed control device for virtual coupled train formation, characterized in that: include: A model building module is used to build a vehicle operation control model based on the traction and braking characteristics of the lead vehicle and the following vehicle of the virtual coupling formation; A constraint condition determination module is used to determine the marshaling operation constraint conditions based on the vehicle operation control model, according to train scheduling information, train safety operation requirements, train force change requirements and train operating condition switching requirements; The marshaling control module is used to determine the collaborative operation data and corresponding operating conditions of the lead vehicle and the follower vehicle during operation based on the vehicle operation control model and the marshaling operation constraints, with the vehicle motion parameters as state variables and the train impact rate as control variables, with the goal of meeting the preset coupled marshaling operation indicators.

15. An electronic device, characterized in that: It comprises a processor and a memory, wherein the processor is used to implement the steps of the train speed control method of the virtual coupled formation as claimed in any one of claims 1 to 13 when executing the computer program stored in the memory.

16. A non-volatile storage medium, characterized in that: The non-volatile storage medium stores a computer program, which, when executed by a processor, implements the steps of the method for controlling the train speed of a virtual coupled train formation as claimed in any one of claims 1 to 13.

17. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instruction is executed by a processor, the steps of the train speed control method of the virtual coupled formation described in any one of claims 1 to 13 are implemented.

18. A virtual coupling marshaling control system, characterized in that: It includes the main train automatic operation subsystem, main train automatic protection subsystem, main communication subsystem of the ground signal machine and the pilot car, and the slave train automatic operation subsystem, slave train automatic protection subsystem, and slave communication subsystem of the following car; Wherein, the master train automatic protection subsystem communicates with the ground signal through the master communication subsystem, and the slave train automatic protection subsystem communicates with the ground signal through the slave communication subsystem respectively; The master train automatic operation subsystem exchanges vehicle operation status information through the master communication subsystem, and the slave train automatic operation subsystem exchanges vehicle operation status information through the slave communication subsystem; The master train automatic operation subsystem and the slave train automatic operation subsystem implement the steps of the train speed control method of the virtual coupled formation as described in any one of claims 1 to 13.

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