Heavy haul train cooperative control method and device based on virtual coupling technology

Abstract

The application relates to a heavy-load train cooperative control method and device based on a virtual coupling technology, wherein the method comprises the following steps: based on longitudinal dynamics, establishing a plurality of heavy-load train multi-particle models, and analyzing communication conditions of the plurality of heavy-load train multi-particle models to establish a train fleet model; according to a preset optimization index design target function, combining train control system characteristics to determine control variables, and according to train safety indexes to obtain constraint requirements, discretizing the train fleet model to obtain an optimization problem; introducing a logic variable, converting the optimization problem into a mixed integer linear programming problem, calculating optimal control gears based on a preset solver, and inputting the optimal control gears into a target control system. Thus, the problems of large optimization problem dimension and calculation complexity, low control frequency and poor control performance in the prior art are solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of train control technology, in particular to a heavy haul train cooperative control method and device based on virtual coupling technology. BACKGROUND

[0002] With the development of economic trade and logistics transportation, how to improve the carrying capacity of rail transportation has become a problem of worldwide concern. There are two solutions to this problem: increasing the traffic lines or increasing the carrying capacity of the existing rail. The former is too high in cost, so the transportation industry is mainly committed to the latter, hoping to break through the transportation bottleneck by studying new train control technology.

[0003] Currently, the train control technology used by various countries is mainly mobile block technology. Among them, the most representative are the control technology behind the 682-vehicle heavy haul train with a total length of 7353m and a total weight of 99700 tons tested by BHP Company of Australia in 2001 and the mobile block application research of Shuozhou-Huanghuagang heavy haul railway completed by China in June 2019 after 5 years of technical research and 10 months of real train test. The basic idea of mobile block technology is that the train sends the current running speed and position coordinates to the ground equipment near the line through sensors and positioning equipment, and the ground equipment then aggregates the train data to the control data center. The control data center analyzes and plans according to the timetable, relative distance of trains, track characteristics, etc., and sends control instructions to each train.

[0004] Although this mobile block technology improves the transportation capacity of the train, it also has the following shortcomings:

[0005] (1) The train cannot implement stoppage operation at the intermediate station;

[0006] (2) There is a problem of difficulty in loading and unloading goods at the starting station and the terminal station after physically connecting trains with different tasks;

[0007] (3) There is still room for further reducing the distance between the cars.

[0008] In view of the above problems, domestic and foreign researchers have proposed a virtual coupling technology based on car-car communication. Virtual coupling technology refers to that the train formation does not rely on physical connection, the lead car communicates through the ground, and the following car communicates through car-car communication, so that all trains in the coupling can run cooperatively at the same speed and with minimal spacing. This technology reduces the distance between cars under the premise of safety, thereby improving the capacity of the line. At the same time, the train formation can be dynamically decoupled according to the task requirements (intermediate station stoppage operation), thereby meeting the requirements of complex environments.

[0009] However, in the optimization problem of the current virtual coupling technology, the control variable is the value of the traction braking force instead of the real control gear of the train, and the control gear needs to be converted into the control gear through projection, which may make the control result lose optimality. In addition, since the terrain factor is fused, the optimization variable is increased, which makes the calculation complexity too large, thereby affecting the control frequency. Therefore, the track terrain factor is not considered in the train control decision. If the track terrain factor can be fused on the basis of ensuring the time consumption, the decision effect can be better. Since the virtual coupling technology mainly uses a single-particle model instead of a more detailed multi-particle model, there is a large model prediction error, which reduces the model predictive control effect, and needs to be solved urgently. SUMMARY

[0010] The application provides a heavy haul train cooperative control method and device based on virtual coupling technology, to solve the problems of large optimization problem dimension and calculation complexity, low control frequency, and poor control performance in the prior art.

[0011] The first aspect embodiment of the application provides a heavy haul train cooperative control method based on virtual coupling technology, including the following steps: based on longitudinal dynamics, establishing a plurality of heavy haul train multi-particle models, and analyzing the communication conditions of the plurality of heavy haul train multi-particle models to establish a train fleet model; according to a preset optimization index design target function, combining the characteristics of the train control system to determine the control variable, and obtaining the constraint requirement according to the train safety index, discretizing the train fleet model to obtain an optimization problem; and introducing a logic variable, converting the optimization problem into a mixed integer linear programming problem, calculating the optimal control gear based on a preset solver, and inputting the optimal control gear into the target control system.

[0012] Optionally, in an embodiment of the application, the model expression of the plurality of heavy haul train multi-particle models is:

[0013] m1a1=F1-F cw1 ,

[0014] m i a i =F i -F cwi +F cwi-1 ,

[0015] m n a n =F n +F cwn-1 ,

[0016] wherein m i , a i , F i represent the mass, acceleration, and received power and resistance of the ith car, Fcwi represents the pulling force of the i-th buffer.

[0017] Optionally, in an embodiment of the present application, the expression of the target function is:

[0018]

[0019] wherein K f , K e , K v , K s is a man-made parameter for balancing different performance indexes; [T1, T2] is the optimization interval mentioned above; represents the pulling force of the i-th car of the j-th train; represents the pulling force of the i-th buffer of the j-th train; represents the speed and position of the i-th car of the j-th train; d des represents the relative distance of tracking.

[0020] Optionally, in an embodiment of the present application, the determining the control variable in combination with the features of the train control system comprises: introducing a control variable representing the gear used by the control law in the model prediction for a preset step.

[0021] Optionally, in an embodiment of the present application, the discretizing the train fleet model to obtain the optimization problem comprises: fusing the slope curvature information of the track in the target function.

[0022] The second aspect embodiment of the present application provides a heavy-haul train cooperative control device based on virtual coupling technology, comprising: a modeling module, configured to establish a plurality of heavy-haul train multi-particle models based on longitudinal dynamics, and analyze the communication conditions of the plurality of heavy-haul train multi-particle models to establish a train fleet model; an optimization module, configured to design a target function according to a preset optimization index, determine a control variable in combination with the features of a train control system, and obtain a constraint requirement according to a train safety index, and discretize the train fleet model to obtain an optimization problem; and a calculation module, configured to introduce a logic variable, convert the optimization problem into a mixed integer linear programming problem, calculate an optimal control gear based on a preset solver, and input the optimal control gear into a target control system.

[0023] Optionally, in an embodiment of the present application, the model expression of the plurality of heavy-haul train multi-particle models is:

[0024] m1a1=F1-F cw1 ,

[0025] m i a i =F i -F cwi+F cwi-1 ,

[0026] m n a n =F n +F cwn-1 ,

[0027] wherein m i , a i , F i represent the mass, acceleration, force received and resistance of the ith car, and F cwi represents the tension of the ith buffer.

[0028] Optionally, in an embodiment of the present application, the expression of the target function is:

[0029]

[0030] wherein K f , K e , K v , K s are artificially designed parameters for balancing different performance indexes; and [T1, T2] is the above-mentioned optimization interval. represents the traction of the ith car of the jth train; represents the tension of the ith buffer of the jth train; represents the speed and position of the ith car of the jth train.d des represents the tracked relative distance.

[0031] Optionally, in an embodiment of the present application, the optimization module comprises an introducing unit configured to introduce a control variable representing a gear used by the control law in model prediction.

[0032] Optionally, in an embodiment of the present application, the optimization module further comprises a fusion unit configured to fuse the slope curvature information of the track in the target function.

[0033] The third aspect embodiment of the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the heavy-haul train cooperative control method based on the virtual coupling technology as described in the above embodiments.

[0034] The fourth aspect embodiment of the present application provides a computer readable storage medium, which stores a computer program executable by a processor to implement the heavy-haul train cooperative control method based on the virtual coupling technology as described above.

[0035] Therefore, the embodiments of the present application have the following beneficial effects:

[0036] The embodiment of the present application can establish a plurality of heavy haul train multi-particle models based on longitudinal dynamics, analyze communication conditions of the plurality of heavy haul train multi-particle models, and establish a train fleet model; design a target function according to a preset optimization index, determine a control variable in combination with characteristics of a train control system, obtain a constraint requirement according to a train safety index, discretize the train fleet model to obtain an optimization problem; introduce a logic variable, convert the optimization problem into a mixed integer linear programming problem, calculate an optimal control gear based on a preset solver, and input the optimal control gear into a target control system, so that the embodiment of the present application can achieve a good control effect on the basis of ensuring track safety, and the calculation resource burden is small, which meets the engineering application conditions. Thus, the problems of large optimization problem dimension and calculation complexity, low control frequency, and poor control performance in the prior art are solved.

[0037] Additional aspects and advantages of the present application will be made apparent by the following description and the accompanying drawings. BRIEF DESCRIPTION OF DRAWINGS

[0038] The above and / or additional aspects and advantages of the present application will become apparent and be readily understood from the following description, taken in conjunction with the accompanying drawings, in which:

[0039] Figure 1 A flowchart of a heavy haul train cooperative control method based on a virtual coupling technology according to an embodiment of the present application;

[0040] Figure 2 A train fleet model schematic diagram according to an embodiment of the present application;

[0041] Figure 3 A logic architecture schematic diagram of a heavy haul train cooperative control method based on a virtual coupling technology according to an embodiment of the present application;

[0042] Figure 4 A control system structure block diagram according to an embodiment of the present application;

[0043] Figure 5 A complex terrain schematic diagram of a simulation experiment according to an embodiment of the present application;

[0044] Figure 6 A position-time curve schematic diagram of each train according to an embodiment of the present application;

[0045] Figure 7 A speed-time curve schematic diagram of each train according to an embodiment of the present application;

[0046] Figure 8 A schematic diagram of relative distance between trains is provided according to an embodiment of the present application;

[0047] Figure 9 An example diagram of a heavy haul train cooperative control device based on virtual coupling technology according to an embodiment of the present application;

[0048] Figure 10 A structural schematic diagram of an electronic device provided according to an embodiment of the present application.

[0049] Legend of reference signs:

[0050] Heavy haul train cooperative control device based on virtual coupling technology -10; modeling module -100, optimization module -200, calculation module -300; memory -1001, processor -1002, communication interface -1003. DETAILED DESCRIPTION

[0051] The embodiments of the present application are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference signs represent the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the accompanying drawings are exemplary and are intended to explain the present application, and cannot be understood as a limitation of the present application.

[0052] The heavy haul train cooperative control method and device based on virtual coupling technology of the embodiments of the present application are described below with reference to the accompanying drawings. In view of the problems mentioned in the above background art, the present application provides a heavy haul train cooperative control method based on virtual coupling technology, in which a heavy haul train multi-particle model composed of a cargo compartment, a power unit, a buffer, etc. is first established, and the communication conditions of multiple heavy haul train multi-particle models are analyzed to establish a train fleet model. Then, a target function is designed according to an optimization index, control variables are determined in combination with the characteristics of a train control system, constraint requirements are obtained according to train safety indexes, and the train fleet model is discretized to obtain an optimization problem. Finally, by introducing a logic variable, the optimization problem is converted into a mixed integer linear programming problem, so that it can be quickly calculated using a commercial solver, thereby the present application can achieve good control effect on the basis of ensuring track safety, and the calculation resource burden is small, which meets the engineering application conditions. Thus, the problems of large optimization problem dimension and calculation complexity, low control frequency, and poor control performance in the prior art are solved.

[0053] Specifically, Figure 1 A flowchart of a heavy haul train cooperative control method based on virtual coupling technology provided according to an embodiment of the present application.

[0054] As Figure 1 shown, the heavy haul train cooperative control method based on virtual coupling technology includes the following steps:

[0055] In step S101, based on longitudinal dynamics, multiple multi-mass models of heavy-load trains are established, and the communication conditions of the multiple multi-mass models of heavy-load trains are analyzed to establish a train fleet model.

[0056] It should be noted that the embodiments of this application can first establish a multi-mass mathematical model of the heavy-haul train based on longitudinal dynamics, such as... Figure 2 As shown, a fleet model is built based on this.

[0057] Optionally, in one embodiment of this application, the model expression for multiple heavy-haul train multi-mass models is:

[0058] m1a1=F1-F cw1 ,

[0059] m i a i =F i -F cwi +F cwi-1 ,

[0060] m n a n =F n +F cwn-1 ,

[0061] Where, m i a i F i Let F represent the mass, acceleration, driving force, and resistance of the i-th carriage. cwi This represents the tension of the i-th buffer.

[0062] It should be noted that the embodiments of this application can establish a multi-mass mathematical model of the heavy-haul train based on longitudinal dynamics, and its expression is as follows:

[0063] m1a1=F1-F cwl

[0064] m i a i =F i -F cwi +F cwi-1

[0065] m n a n =F n +F cwn-1

[0066] Where, m i a i F i Let F represent the mass, acceleration, driving force, and resistance of the i-th carriage.cwi F

[0067] Further, the embodiment of the present application can define a power coupling coefficient set S T , if i∈S T , the i th car is a power coupling. Thus, the expression of F i

[0068]

[0069] F ti , F ri , F cri , F gi respectively represent the traction force / braking force, sliding resistance, bending resistance, gravity component along the track direction of the i th car. The expression of F cwi is obtained by piecewise linearization approximation of the buffer weight test curve.

[0070] Thus, the embodiment of the present application establishes a heavy haul train multi-particle model composed of freight cars, power couplings, buffers, etc., analyzes the communication conditions of multiple heavy haul train multi-particle models, establishes a train fleet model, and guarantees the effect of coordinated control of heavy haul trains.

[0071] In step S102, a target function is designed according to a preset optimization index, control variables are determined in combination with the characteristics of the train control system, constraint requirements are obtained according to train safety indexes, and an optimization problem is obtained by discretizing the train fleet model.

[0072] After the train fleet model is established, further, the embodiment of the present application can design a target function according to an optimization index, determine control variables in combination with the characteristics of the train control system, obtain constraint requirements according to train safety indexes, and then discretize the train fleet model to obtain an optimization problem.

[0073] Optionally, in an embodiment of the present application, the expression of the target function is as follows:

[0074]

[0075] K f , K e , K v , K s are artificially designed parameters for balancing different performance indexes; [T1, T2] is the above-mentioned optimization interval; represents the traction force of the i th car of the j th train; represents the tension of the i th buffer of the j th train; represents the speed and position of the i th car of the j th train; d des ​denotes the relative distance of the tracking.

[0076] Firstly, the embodiment of the present application can design a target function according to an optimization index, and the expression of the continuous form is as follows:

[0077]

[0078] wherein, K f , K e , K v , K s is a parameter designed artificially, used for balancing different performance indexes. [T1, T2] is the optimization interval mentioned above. denotes the traction of the ith car of the jth train. denotes the tension of the ith buffer of the jth train. denotes the speed and position of the ith car of the jth train. des denotes the relative distance of the tracking.

[0079] Optionally, in an embodiment of the present application, the control variable is determined in combination with the characteristics of the train control system, including: introducing a control variable representing the gear used by the control law in the model prediction of the preset step.

[0080] Further, the embodiment of the present application introduces a control variable representing the gear z used by the control law in the model prediction of the hth step; the constraint needs to be met:

[0081]

[0082] Considering that the speed of the car changes very little within the prediction time domain, the embodiment of the present application uses the speed of the current train to approximate the speed of the car within the prediction time domain. Thus, the traction and braking force of the gear z can be estimated:

[0083]

[0084] The gear and the corresponding estimated traction and braking force are combined and summed to obtain the predicted traction and braking force, as shown in the following formula:

[0085]

[0086] It should be understood by those skilled in the art that the safety requirements of the car buffer, the speed limit constraint, and the gear control constraint need to be considered for each heavy haul train. The relative distance between trains needs to meet the safety distance constraint, and therefore, the embodiment of the present application needs to design appropriate constraint conditions.

[0087] Considering that the maximum range of the car buffer is [-f in_max , fin_max , so that:

[0088]

[0089] Considering the maximum value v of the train moving speed max , so that each car satisfies:

[0090]

[0091] Considering the gear control of the train:

[0092] N j (t) ∈ {-8, -7,..., 8}

[0093] At the same time, a safety distance needs to be maintained between trains:

[0094]

[0095] Therefore, the embodiment of the present application designs an optimization function according to the performance index, and designs a constraint condition in combination with the safety requirement of the train, so as to further guarantee a better control effect.

[0096] Optionally, in an embodiment of the present application, the discretized train fleet model obtains an optimization problem, including: fusing the slope curvature information of the track in the objective function.

[0097] It should be noted that the embodiment of the present application can also fuse the slope curvature information of the track in the optimization equation.

[0098] Specifically, the embodiment of the present application considers the i th car of the train j, and the car movement always has upper and lower bounds within L-step prediction:

[0099]

[0100] Wherein, k represents the current time, k+h represents the predicted time, v max is the maximum speed of the heavy-haul train, and T is the sampling time of time domain discretization. The upper and lower bounds are defined as:

[0101]

[0102]

[0103] In the upper and lower bound interval Uniformly sample Num positions: Wherein Thus, a plurality of subintervals are divided:

[0104]

[0105] Further, in the embodiments of the present application, the logical variable respectively used to determine the sampling interval in which the train j car i is located, has the following meaning, if indicates that the car i has moved to the interval To this end, the following needs to be met:

[0106]

[0107]

[0108] approximately the same curvature at , and is Similarly, the horizontal included angle is Then the curvature and the horizontal included angle of the train j car i at the predicted h th step are:

[0109]

[0110]

[0111] In combination with the above formula, the prediction of the curvature radius and the horizontal included angle, and the calculation formula of the bending resistance and the gravity component, the calculation expression of the bending resistance and the gravity component of the train j car i at the predicted h th step can be obtained:

[0112]

[0113]

[0114] It can be understood that the embodiments of the present application fuse the track slope and curvature data in the train control decision process, so that the model prediction accuracy is high, and good control effect is still maintained under complex terrain. In addition, the embodiments of the present application regard the gear as an optimization variable, so that the solving result can be directly applied to the control system of the train, realizing end-to-end optimization, not only improving the usability of the control system, but also improving the control performance.

[0115] In step S103, a logical variable is introduced, the optimization problem is converted into a mixed integer linear programming problem, the optimal control gear is calculated based on a preset solver, and the optimal control gear is input into the target control system.

[0116] The embodiment of the present application obtains a mixed integer linear programming problem by combining the above optimization objectives, constraint conditions, and control variables. A commercial solver is called to solve the optimal control gear, and the gear is input into the control system. At the next time, the above optimization objectives, constraint conditions, and control variables are written again, and the optimal control gear is solved again. The above process is repeated in the control process, as shown in Figure 3

[0117] Therefore, the embodiment of the present application realizes virtual coupling through the model predictive control method, and converts the original nonlinear optimization problem into a mixed integer linear programming problem through linearization and introduction of a logic variable, thereby reducing the calculation burden, improving the stability of the calculation result, and making it suitable for engineering application.

[0118] The following simulation of the embodiment of the present application will be described through specific embodiments of the coordinated control method of the heavy-haul train based on the virtual coupling technology proposed in the present application.

[0119] Figure 4 The control system structure diagram of the embodiment of the present application is shown in the figure. The simulation experiment can be performed according to the framework, and the specific simulation experiment is as follows.

[0120] (1) Simulation setting

[0121] The sampling period T is set to 0.1 ms, the model prediction period T is set to 100 ms, the prediction step L is set to 3, the weight coefficient K is set to 250, K is set to 2.5x10, K is set to 10 s, K is set to 10, the safety distance d is set to 80 m, and the ideal relative distance d is set to 200 m. s v s -5 f -21 e -23 min des

[0122] The simulated train fleet consists of three trains, which are denoted as train A, train B, and train C. The starting positions of the trains A, B, and C are 5.4 km, 4.2 km, and 3 km, respectively, and the initial speeds of the carriages of the three trains are all 10 m / s. When the first train reaches 20 km, the simulation ends.

[0123] (2) Simulation results

[0124] Figure 5 The terrain map used in the simulation experiment of the embodiment of the present application is shown in the figure. In the position interval 6.0 km-12.0 km, the terrain first descends and then ascends. In the position interval 16.0 km-17.0 km, a curve appears. This terrain simulates a relatively complex situation in reality. ​​​​​​​​​​​

[0125] As shown in Figure 6 and Figure 8 shown, the embodiment of the present application proposes a heavy train cooperative control method based on virtual coupling technology, so that the train spacing not only meets the safety index, but also tends to the ideal tracking distance, shortens the train spacing under the current moving block technology, and improves the carrying capacity of the track. As shown in Figure 7 and Figure 8 shown, the embodiment of the present application makes the speed and relative distance of multiple groups of trains gradually consistent, achieving the effect of virtual coupling.

[0126] The embodiment of the present application is simulated under the Linux system of a conventional PC, which is fast in optimization and solving, overcomes the long calculation time of traditional model predictive control, and can be used in a train control system.

[0127] According to the heavy train cooperative control method based on virtual coupling technology proposed in the embodiment of the present application, a heavy train multi-particle model composed of cargo carriages, power units, buffers, etc. is first established, and the communication conditions of multiple heavy train multi-particle models are analyzed to establish a train fleet model. Then, a target function is designed according to optimization indexes, control variables are determined in combination with the characteristics of a train control system, constraint requirements are obtained according to train safety indexes, and the train fleet model is discretized to obtain an optimization problem. Finally, by introducing a logic variable, the optimization problem is converted into a mixed integer linear programming problem, so that it can be quickly calculated using a commercial solver, thereby effectively improving the train control effect and performance, and since the optimization problem is a mixed integer linear programming problem with moderate dimensions, the calculation burden is small and the control frequency is high.

[0128] Secondly, the heavy train cooperative control device based on virtual coupling technology proposed in the embodiment of the present application is described with reference to the accompanying drawings.

[0129] Figure 9 is a block schematic diagram of the heavy train cooperative control method and device based on virtual coupling technology of the embodiment of the present application.

[0130] As shown in Figure 9 , the heavy train cooperative control device 10 based on virtual coupling technology includes a modeling module 100, an optimization module 200, and a calculation module 300.

[0131] Among them, the modeling module 100 is used to establish multiple heavy train multi-particle models based on longitudinal dynamics, analyze the communication conditions of multiple heavy train multi-particle models, and establish a train fleet model.

[0132] An optimization module 200 is configured to design a target function according to a preset optimization index, determine a control variable in combination with a feature of a train control system, obtain a constraint requirement according to a train safety index, discretize a train fleet model to obtain an optimization problem.

[0133] A calculation module 300 is configured to introduce a logic variable, convert the optimization problem into a mixed integer linear programming problem, calculate an optimal control gear based on a preset solver, and input the optimal control gear into a target control system.

[0134] Optionally, in an embodiment of the present application, a model expression of the multiple heavy-load train multi-particle models is as follows:

[0135] m1a1=F1-F cw1 ,

[0136] m i a i =F i -F cwi +F cwi-1 ,

[0137] m n a n =F n +F cwn-1 ,

[0138] wherein m i , a i , F i represent a mass, an acceleration, a force received and a resistance of an i-th car, and F cwi represents a tension of an i-th buffer.

[0139] Optionally, in an embodiment of the present application, an expression of the target function is as follows:

[0140]

[0141] wherein K f , K e , K v , K s are artificially designed parameters for balancing different performance indexes; [T1, T2] is the above-mentioned optimization interval; represents a traction force of an i-th car of a j-th train; represents a tension of an i-th buffer of a j-th train; represents a speed and a position of an i-th car of a j-th train; and d des represents a tracked relative distance.

[0142] Optionally, in an embodiment of the present application, the optimization module 200 comprises: an introducing unit, configured to introduce a control variable representing a gear used by the control law in model prediction of a first preset step.

[0143] Optionally, in an embodiment of the present application, the optimization module 300 further comprises: a fusing unit, configured to fuse the slope curvature information of the track in the objective function.

[0144] It should be noted that the foregoing description of the embodiment of the method for cooperative control of heavy-haul trains based on virtual coupling technology is also applicable to the method for cooperative control of heavy-haul trains based on virtual coupling technology and the device of the embodiment, which will not be described here again.

[0145] According to the method and device for cooperative control of heavy-haul trains based on virtual coupling technology provided in the embodiments of the present application, a heavy-haul train multi-particle model composed of a cargo compartment, a power unit, a buffer, etc. is first established, and communication conditions of multiple heavy-haul train multi-particle models are analyzed to establish a train fleet model. Then, an objective function is designed according to an optimization index, control variables are determined in combination with characteristics of a train control system, constraint requirements are obtained according to train safety indexes, and the train fleet model is discretized to obtain an optimization problem. Finally, by introducing logical variables, the optimization problem is converted into a mixed integer linear programming problem, so that it can be quickly calculated by using a commercial solver, thereby effectively improving train control effect and performance, and since the optimization problem is a mixed integer linear programming problem with moderate dimensions, the calculation burden is small and the control frequency is high.

[0146] Figure 10 A structural schematic diagram of an electronic device provided in an embodiment of the present application is shown in FIG. 1. The electronic device can include:

[0147] The memory 1001, the processor 1002, and a computer program stored in the memory 1001 and executable on the processor 1002.

[0148] The processor 1002 implements the method for cooperative control of heavy-haul trains based on virtual coupling technology provided in the above embodiments when executing the program.

[0149] Further, the electronic device further includes:

[0150] The communication interface 1003 is configured to communicate between the memory 1001 and the processor 1002.

[0151] The memory 1001 is configured to store a computer program executable on the processor 1002.

[0152] The memory 1001 can include a high-speed RAM memory, and can also include a non-volatile memory, such as at least one disk memory.

[0153] If the memory 1001, the processor 1002 and the communication interface 1003 are implemented independently, the communication interface 1003, the memory 1001 and the processor 1002 can be connected with each other through a bus and complete communication between each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For convenience of representation, Figure 10 In the figure, only one thick line is used to represent the bus, but it does not mean that there is only one bus or only one type of bus.

[0154] Optionally, in a specific implementation, if the memory 1001, the processor 1002 and the communication interface 1003 are integrated on a chip, the memory 1001, the processor 1002 and the communication interface 1003 can complete communication between each other through an internal interface.

[0155] The processor 1002 can be a Central Processing Unit (CPU), or an Application Specific Integrated Circuit (ASIC), or one or more integrated circuits configured to implement one or more embodiments of the present application.

[0156] The embodiment of the present application further provides a computer readable storage medium, which stores a computer program, and the program is executed by a processor to implement the above-mentioned method for coordinated control of heavy-haul trains based on virtual coupling technology.

[0157] In the description of the present specification, the description of the terms "one embodiment", "some embodiments", "an example", "a specific example" or "some examples" means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In the present specification, the illustrative description of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or N embodiments or examples in a suitable manner. In addition, the person skilled in the art can combine and combine the different embodiments or examples described in the present specification and the features of the different embodiments or examples without contradiction.

[0158] Moreover, the terms "first", "second", "third", etc. are used herein only to describe different steps or categories of steps. Thus, the use of the term "first" does not imply that different steps must be in a time sequence. Nor is it implied that a "first" step must precede a "second" step, that a "second" step, etc. must follow a "first" step, etc. Furthermore, when a process or method is described herein with several steps or several categories of steps, it should be understood that these are merely illustrative of the steps that can be employed in the process or method. Not all of the steps can be required, and in some cases, additional steps can be employed. The order of the steps can be varied, and some of the steps can be performed simultaneously. The steps can be performed in an order different than that described herein. The steps can be performed in any order, unless otherwise specified.

[0159] Any process or method described in flowcharts or otherwise described herein can be understood as representing code modules, segments, or portions of code that include one or more executable instructions for performing specific logic functions (or steps) or portions thereof, and the various embodiments of the application can include additional or fewer steps performing the same or equivalent functions. In some embodiments, the order of steps can be varied, and / or some steps can be performed simultaneously, unless otherwise specifically noted. The various embodiments of the application can be embodied in a number of different forms, all of which have been contemplated to be within the scope of the applicable patent princi¬ ples described herein.

[0160] Logic and / or steps represented in flowcharts or otherwise described herein, for example, can be embodied in computer-readable media, which can be any available media that can be accessed by a general purpose or special purpose computing system, device, or apparatus to execute instructions stored in the media. By way of example, such computer-readable media can comprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a general purpose or special purpose computing system, device, or apparatus. When information is transferred or provided over a network or another communications connection (either hardwired, wireless, or combination thereof) to a general purpose or special purpose computing system, device, or apparatus, the computer-readable media used for this purpose can be any available media that can be accessed by this system, device, or apparatus. Combinations of the above should also be included within the scope of the computer-readable media.

[0161] It should be understood that parts of the present application can be realized in hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be realized by software or firmware stored in a memory and executed by a suitable instruction execution system. If realized in hardware and in another embodiment, any one or a combination of the following technologies known in the art can be used: discrete logic circuit with logic gate circuit for implementing logic functions on data signals, application specific integrated circuit with suitable combination logic gate circuit, programmable gate array (PGA), field programmable gate array (FPGA), etc.

[0162] Those skilled in the art of the present technology can understand that all or part of the steps carried out by the above-mentioned embodiment methods can be completed by a program instructing the relevant hardware, and the program can be stored in a computer readable storage medium. When the program is executed, it includes one of the steps of the method embodiment or a combination thereof.

[0163] In addition, each functional unit in each embodiment of the present application can be integrated into one processing module, or each unit can exist physically alone, or two or more units can be integrated into one module. The above-mentioned integrated module can be realized in the form of hardware or in the form of a software functional module. The integrated module, if realized in the form of a software functional module and sold or used as an independent product, can also be stored in a computer readable storage medium.

[0164] The above-mentioned storage medium can be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it should be understood that the above-mentioned embodiments are exemplary and cannot be understood as limiting the present application, and those skilled in the art can make changes, modifications, replacements and variations to the above-mentioned embodiments within the scope of the present application.

Claims

1. A cooperative control method for heavy-haul trains based on virtual coupling technology, characterized in that, Includes the following steps: Based on longitudinal dynamics, multiple multi-mass models of heavy-haul trains are established, and the communication conditions of these multiple multi-mass models are analyzed to establish a train fleet model. The objective function is designed based on the preset optimization index, the control variables are determined in combination with the characteristics of the train control system, and the constraints are obtained based on the train safety index. The train fleet model is discretized to obtain the optimization problem. as well as By introducing logical variables, the optimization problem is transformed into a mixed-integer linear programming problem, and the optimal control gear is calculated based on a preset solver and then input into the target control system. The model expressions for the multiple heavy-haul train multi-mass models are as follows: ， ， ， in, , , This represents the mass, acceleration, driving force, and resistance of the i-th carriage. This represents the tension of the i-th buffer; The expression for the objective function is: in, These are parameters designed by humans to balance different performance indicators; It is the optimization interval; This represents the traction force of the i-th carriage of the j-th train; This represents the tension in the i-th buffer section of the j-th train; This represents the speed and position of the i-th carriage of the j-th train; S represents the relative distance being tracked. T Represents the set of dynamic formation coefficients; The optimization problem obtained by discretizing the train fleet model includes: incorporating track gradient and curvature information into the objective function.

2. The method according to claim 1, characterized in that, The determination of control variables based on the characteristics of the train control system includes: Introduce control variables representing the gears used in the model prediction of the preset step.

3. A heavy-haul train cooperative control device based on virtual coupling technology, characterized in that, The cooperative control device is used to implement the heavy-haul train cooperative control method based on virtual coupling technology as described in any one of claims 1-2, wherein the cooperative control device comprises: The modeling module is used to establish multiple multi-mass models of heavy-haul trains based on longitudinal dynamics, analyze the communication conditions of the multiple multi-mass models of heavy-haul trains, and establish a train fleet model. The optimization module is used to design an objective function based on preset optimization indicators, determine control variables based on the characteristics of the train control system, obtain constraints based on train safety indicators, and discretize the train fleet model to obtain the optimization problem; and The calculation module is used to introduce logical variables, transform the optimization problem into a mixed integer linear programming problem, calculate the optimal control gear based on a preset solver, and input the optimal control gear into the target control system. A fusion unit is used to fuse the slope and curvature information of the track in the objective function.

4. The apparatus according to claim 3, characterized in that, The optimization module includes: The unit is used to introduce control variables representing the gears used by the control law in the preset step of model prediction.

5. An electronic device, characterized in that, include: The system includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the heavy-haul train cooperative control method based on virtual coupling technology as described in any one of claims 1-2.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the heavy-haul train cooperative control method based on virtual coupling technology as described in any one of claims 1-2.

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