Train cooperative operation prediction control method and system based on dynamic interval model

Abstract

The invention provides a train cooperative operation prediction control method and system based on a dynamic interval model, and belongs to the technical field of train operation control, and the method comprises the steps: calculating a train minimum tracking interval based on the real-time estimation state information of a train according to a train relative distance braking model; the communication delay time of the train-to-train communication system is considered, an additional safety margin is added on the basis of the minimum tracking interval of the train, and the influence of communication delay on cooperative operation control of the train is compensated; a system state space model is constructed based on the safety margin, and a train optimization control problem is formed based on the control constraint and the control target; and solving a train optimization control problem, obtaining an acceleration sequence for controlling train operation, and acting a first item of the sequence on the train to realize train operation control. According to the invention, while the tracking interval between the trains is effectively reduced, the influence of communication delay on the train operation is compensated by adding the additional safety margin, so that the train operation safety is improved, and the line transportation efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of train operation control, and in particular to a train cooperative operation prediction control method and system based on a dynamic interval model. Background Art

[0002] On the basis of existing railway lines, adopting mobile block system, shortening train tracking interval, increasing train running density on the line, and improving line transportation efficiency are effective solutions to solve the contradiction that the growth rate of railway lines cannot adapt to the rapid growth of railway transportation demand. Train coordinated operation control based on mobile block system is the key and core to shortening train tracking interval.

[0003] The train cooperative operation control system is based on mobile block, train-to-train communication and automatic driving, which can minimize the train tracking interval and improve the line transportation efficiency. Among them, train-to-train communication usually requires the help of wireless communication network. However, the wireless communication network has the inherent characteristics of time delay and packet loss, which will cause the train to receive the past operation status information of the preceding and following trains. Summary of the invention

[0004] The purpose of the present invention is to provide a train cooperative operation prediction control method and system based on a dynamic interval model, aiming at compensating the influence of communication delay on train cooperative operation control according to the wireless communication delay characteristics, so as to solve at least one technical problem existing in the above-mentioned background technology.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] In a first aspect, the present invention provides a train cooperative operation prediction control method based on a dynamic interval model, comprising:

[0007] Considering the communication delay time of the train-to-train communication system, the real-time status of the train is estimated;

[0008] Based on the real-time estimated state information of the train and the train relative distance braking model, the minimum tracking interval of the train is calculated;

[0009] Considering the communication delay time of the train-to-train communication system, an additional safety margin is added on the basis of the minimum train tracking interval to compensate for the impact of communication delay on the coordinated operation control of trains;

[0010] Based on the safety margin, the system state space model is constructed, and based on the control constraints and control objectives, the train optimization control problem is formed;

[0011] Solve the train optimization control problem, obtain the acceleration sequence for controlling the train operation, and apply the first item of the sequence to the train to realize train operation control.

[0012] Furthermore, the state estimation is performed based on the following train kinematic equations:

[0013]

[0014] Among them, s i (t), v i (t), a i (t) are the position, velocity and acceleration of train i at time t respectively.

[0015] Furthermore, the minimum tracking interval calculation model is as follows:

[0016]

[0017] Among them, D sm is the safety margin, is the estimated speed of the preceding train at the kth sampling moment, and represent the emergency braking deceleration of the following train and the preceding train respectively, and

[0018] Furthermore, the additional safety margin is divided into two parts. One part is the additional safety margin for the current information transmission delay time τ The other part is an additional safety margin for communication delays in the prediction time domain. The details are as follows:

[0019]

[0020]

[0021] Wherein, T is the sampling period, m represents the mth prediction period in the prediction time domain, and 1≤m≤p.

[0022] Furthermore, based on the safety margin, the system state space model is constructed, and based on the control constraints and control objectives, the train optimization control problem is formed, including:

[0023] Define the system state as x i (k) = [Δs i (k),Δv i (k)] T , where Δs i (k) = s i-1 (k)-s i (k), Δv i (k) = v i-1 (k)-v i (k), then the system state space model is

[0024] xi (k+1)=Ax i (k)+Bu i (k)

[0025] Among them, u i (k) = a i-1 (k)-a i (k)

[0026]

[0027] The train running speed is between 0 and the maximum speed limit, that is

[0028]

[0029] in, is the maximum speed limit of the train;

[0030] The acceleration of the train is between the emergency braking deceleration and the maximum traction acceleration, that is,

[0031]

[0032] in The maximum deceleration is the maximum traction acceleration (

[0033]

[0034] The train interval cannot be less than the sum of the minimum tracking interval and the additional safety margin, that is,

[0035]

[0036] in, is the estimated position of the preceding train;

[0037] Considering efficiency and energy consumption, the cost function is expressed as:

[0038]

[0039] Among them, α and β are weight coefficients;

[0040] Minimize the tracking interval and energy consumption, then the objective function is expressed as:

[0041]

[0042] Furthermore, the train optimization control problem is solved to obtain the optimal control sequence of the following train.

[0043] In a second aspect, the present invention provides a train cooperative operation prediction control system based on a dynamic interval model, comprising:

[0044] The estimation module is used to estimate the real-time status of the train by taking into account the communication delay time of the train-to-train communication system;

[0045] A calculation module, used for calculating the minimum tracking interval of the train based on the real-time estimated state information of the train and the train relative distance braking model;

[0046] The compensation module is used to consider the communication delay time of the train-to-train communication system, add additional safety margin on the basis of the minimum tracking interval of the train, and compensate for the impact of communication delay on the coordinated operation control of the train;

[0047] A building module is used to build a system state space model based on the safety margin and to formulate a train optimization control problem based on the control constraints and control objectives;

[0048] The control module is used to solve the train optimization control problem, obtain the acceleration sequence for controlling the train operation, and apply the first item of the sequence to the train to realize train operation control.

[0049] In a third aspect, the present invention provides a non-transitory computer-readable storage medium, which is used to store computer instructions. When the computer instructions are executed by a processor, the train cooperative operation prediction control method based on the dynamic interval model as described in the first aspect is implemented.

[0050] In a fourth aspect, the present invention provides a computer device comprising a memory and a processor, wherein the processor and the memory communicate with each other, the memory stores program instructions executable by the processor, and the processor calls the program instructions to execute the train collaborative operation prediction control method based on the dynamic interval model as described in the first aspect.

[0051] In a fifth aspect, the present invention provides an electronic device, comprising: a processor, a memory and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory so that the electronic device executes instructions for implementing the train cooperative operation prediction control method based on the dynamic interval model as described in the first aspect.

[0052] The beneficial effects of the present invention are as follows: taking into account the characteristics of vehicle-to-vehicle wireless communication delay in the process of coordinated train operation control, by increasing the additional safety margin, the tracking interval between trains is effectively reduced, and at the same time, the influence of communication delay on train operation is compensated by increasing the additional safety margin, thereby providing the safety of train operation and improving the transportation efficiency of the line.

[0053] Additional advantages of the present invention will be more clearly given in the following description or learned through the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0055] Figure 1 This is a flow chart of the train cooperative operation prediction control method based on the dynamic interval model described in an embodiment of the present invention. DETAILED DESCRIPTION

[0056] The embodiments of the present invention are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements with the same or similar functions. The embodiments described below by the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be interpreted as limiting the present invention.

[0057] It should be understood by those skilled in the art that unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by those skilled in the art to which this invention belongs.

[0058] It should also be understood that terms, such as those defined in commonly used dictionaries, should be understood to have a meaning consistent with that in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless as defined herein.

[0059] Those skilled in the art will appreciate that, unless otherwise stated, the singular forms "a", "an", "said" and "the" used herein may also include plural forms. It should be further understood that the term "comprising" used in the specification of the present invention refers to the presence of the features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements and / or groups thereof.

[0060] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. Different embodiments or examples described in this specification and features of different embodiments or examples may be combined and combined by those skilled in the art without contradiction.

[0061] To facilitate understanding of the present invention, the present invention is further explained below with reference to specific embodiments in conjunction with the accompanying drawings, and the specific embodiments do not constitute a limitation on the embodiments of the present invention.

[0062] Those skilled in the art should understand that the drawings are merely schematic diagrams of embodiments, and the components in the drawings are not necessarily necessary for implementing the present invention.

[0063] Example 1

[0064] In this embodiment 1, a train cooperative operation prediction control system based on a dynamic interval model is first provided, including: an estimation module, which is used to estimate the real-time state of the train taking into account the communication delay time of the vehicle-to-vehicle communication system. A calculation module, which is used to calculate the minimum tracking interval of the train based on the real-time estimated state information of the train and according to the train relative distance braking model. A compensation module, which is used to take into account the communication delay time of the vehicle-to-vehicle communication system, add an additional safety margin on the basis of the minimum tracking interval of the train, and compensate for the impact of the communication delay on the cooperative operation control of the train. A construction module, which is used to construct a system state space model based on the safety margin, and form a train optimization control problem based on the control constraints and control objectives. A control module, which is used to solve the train optimization control problem, obtain the acceleration sequence for controlling the train operation, and apply the first item of the sequence to the train to achieve train operation control.

[0065] In this embodiment 1, the above system is used to implement a train cooperative operation prediction control method based on a dynamic interval model, which is applicable to the cooperative operation control between two or more trains in rail transit, and the train position, speed, acceleration and other state information are used as the input of the control system, and the acceleration sequence of the train operation is used as the output of the control system. It includes:

[0066] In each sampling period, the train status information is obtained, and the status information is exchanged between trains through the vehicle-to-vehicle communication system. Considering the communication delay time of the vehicle-to-vehicle communication system, the real-time status of the train is estimated. Based on the real-time estimated state information of the train, the minimum tracking interval of the train is calculated according to the train relative distance braking model. Considering the communication delay time of the vehicle-to-vehicle communication system, an additional safety margin is added on the basis of the above minimum tracking interval of the train to compensate for the impact of communication delay on the coordinated operation control of the train. Design the model predictive control framework, set the predictive control time domain, construct the system state space model, form the train optimization control problem based on the control constraints and control objectives, and realize the coordinated operation control of the train. Solve the train optimization control problem, obtain the acceleration sequence for controlling the train operation, and apply the first item of the sequence to the train. Repeat the above steps until the control process ends. Train i-1 represents the leading train, and train i is the following train; in order to realize model predictive control, the coordinated operation control system is discretized.

[0067] Among them, the state estimation is performed based on the following train kinematic equation:

[0068]

[0069] Among them, s i (t), v i (t), a i (t) are the position, velocity and acceleration of train i at time t respectively.

[0070] The minimum tracking interval calculation model is as follows:

[0071]

[0072] Among them, D sm is the safety margin, is the estimated speed of the preceding train at the kth sampling moment, and represent the emergency braking deceleration of the following train and the preceding train respectively, and

[0073] The additional safety margin is divided into two parts. One part is the additional safety margin for the current information transmission delay time τ The other part is an additional safety margin for communication delays in the prediction time domain. The details are as follows:

[0074]

[0075]

[0076] Wherein, T is the sampling period, m represents the mth prediction period in the prediction time domain, and 1≤m≤p.

[0077] The system state space model, following train control constraints, and following train control objectives involved in model predictive control are as follows:

[0078] If the system state is defined as x i (k) = [Δs i (k),Δv i (k)] T , where Δs i (k) = s i-1 (k)-s i (k), Δv i (k) = v i-1 (k)-v i (k), then the system state space model is

[0079] x i (k+1)=Ax i(k)+Bu i (k) (5)

[0080] Among them, u i (k) = a i-1 (k)-a i (k)

[0081]

[0082]

[0083] The constraints include:

[0084] The train running speed is between 0 and the maximum speed limit, that is

[0085]

[0086] in, The maximum speed limit of the train.

[0087] The acceleration of the train is between the emergency braking deceleration (maximum deceleration) and the maximum traction acceleration, that is,

[0088]

[0089] in The maximum deceleration The maximum traction acceleration

[0090] In order to ensure the safety of train operation, the train interval cannot be less than the sum of the minimum tracking interval and the additional safety margin, that is,

[0091]

[0092] in, is the estimated position of the preceding train.

[0093] The control objectives are:

[0094] Considering efficiency and energy consumption, the cost function can be expressed as:

[0095]

[0096] Among them, α and β are weight coefficients.

[0097] Minimize the tracking interval and energy consumption, then the objective function can be expressed as:

[0098]

[0099] By solving the train optimization control problem (Eq. (10)), the optimal control sequence of the following train is obtained

[0100] Example 2

[0101] In this embodiment 2, a train cooperative operation prediction and control method based on a dynamic interval model is provided to provide a methodological guarantee for the safe and efficient cooperative operation of trains.

[0102] S1: The train's position, speed, acceleration and other status information are used as the input of the control system, and the acceleration sequence that controls the train's operation is used as the output of the control system.

[0103] S2: In each sampling period, the train status information is obtained and the status information is exchanged between trains through the train-to-train communication system.

[0104] S3: Considering the communication delay time of the vehicle-to-vehicle communication system, the real-time status of the train is estimated.

[0105] S4: Based on the real-time estimated state information of the train and the train relative distance braking model, the minimum tracking interval of the train is calculated.

[0106] S5: Considering the communication delay time of the train-to-train communication system, an additional safety margin is added on the basis of the above-mentioned minimum train tracking interval to compensate for the impact of communication delay on the coordinated operation control of trains.

[0107] S6: Design the model predictive control framework, set the predictive control time domain, build the system state space model, formulate the train optimization control problem based on the control constraints and control objectives, and realize the coordinated operation control of the train.

[0108] S7: Solve the train optimization control problem, obtain the acceleration sequence for controlling the train operation, and apply the first item of the sequence to the train.

[0109] S8: Repeat steps S2-S7 until the control process ends.

[0110] Based on the above scheme, step S3 performs state estimation according to the following train kinematic equation:

[0111]

[0112] Among them, s i (t), v i (t), a i (t) are the position, velocity and acceleration of train i at time t respectively.

[0113] The minimum tracking interval calculation model of step S4 is as follows:

[0114]

[0115] Among them, Dsm is the safety margin, is the estimated speed of the preceding train at the kth sampling moment, and represent the emergency braking deceleration of the following train and the preceding train respectively, and

[0116] The additional safety margin in step S5 is divided into two parts. One part is the additional safety margin for the current information transmission delay time τ. The other part is an additional safety margin for communication delays in the prediction time domain. The details are as follows:

[0117]

[0118]

[0119] Wherein, T is the adoption period, m represents the mth prediction period in the prediction time domain, and 1≤m≤p.

[0120] The system state space model, following train control constraints, and following train control objectives involved in the model predictive control in step S6 are as follows:

[0121] S61: If the system state is defined as x i (k) = [Δs i (k),Δv i (k)] T , where Δs i (k) = s i-1 (k)-s i (k), Δv i (k) = v i-1 (k)-v i (k), then the system state space model is

[0122] x i (k+1)=Ax i (k)+Bu i (k) (5)

[0123] Among them, u i (k) = a i-1 (k)-a i (k)

[0124]

[0125]

[0126] S62: Constraints

[0127] The train running speed is between 0 and the maximum speed limit, that is

[0128]

[0129] in, The maximum speed limit of the train.

[0130] The acceleration of the train is between the emergency braking deceleration (maximum deceleration) and the maximum traction acceleration, that is,

[0131]

[0132] in The maximum deceleration is the maximum traction acceleration (

[0133]

[0134] In order to ensure the safety of train operation, the train interval cannot be less than the sum of the minimum tracking interval and the additional safety margin, that is,

[0135]

[0136] in, is the estimated position of the preceding train.

[0137] S63: Control Target

[0138] Considering efficiency and energy consumption, the cost function can be expressed as:

[0139]

[0140] Among them, α and β are weight coefficients.

[0141] Minimize the tracking interval and energy consumption, then the objective function can be expressed as:

[0142]

[0143] Step S7 solves the train optimization control problem (Equation (10)) to obtain the optimal control sequence of the following train.

[0144] Example 3

[0145] This embodiment 3 provides a non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by a processor, the train cooperative operation prediction control method based on the dynamic interval model as described above is implemented. The method includes:

[0146] Considering the communication delay time of the train-to-train communication system, the real-time status of the train is estimated;

[0147] Based on the real-time estimated state information of the train and the train relative distance braking model, the minimum tracking interval of the train is calculated;

[0148] Considering the communication delay time of the train-to-train communication system, an additional safety margin is added on the basis of the minimum train tracking interval to compensate for the impact of communication delay on the coordinated operation control of trains;

[0149] Based on the safety margin, the system state space model is constructed, and based on the control constraints and control objectives, the train optimization control problem is formed;

[0150] Solve the train optimization control problem, obtain the acceleration sequence for controlling the train operation, and apply the first item of the sequence to the train to realize train operation control.

[0151] Example 4

[0152] This embodiment 4 provides a computer device, including a memory and a processor, the processor and the memory communicate with each other, the memory stores program instructions executable by the processor, the processor calls the program instructions to execute the above-mentioned train cooperative operation prediction control method based on the dynamic interval model, the method comprising:

[0153] Considering the communication delay time of the train-to-train communication system, the real-time status of the train is estimated;

[0154] Based on the real-time estimated state information of the train and the train relative distance braking model, the minimum tracking interval of the train is calculated;

[0155] Considering the communication delay time of the train-to-train communication system, an additional safety margin is added on the basis of the minimum train tracking interval to compensate for the impact of communication delay on the coordinated operation control of trains;

[0156] Based on the safety margin, the system state space model is constructed, and based on the control constraints and control objectives, the train optimization control problem is formed;

[0157] Solve the train optimization control problem, obtain the acceleration sequence for controlling the train operation, and apply the first item of the sequence to the train to realize train operation control.

[0158] Example 5

[0159] This embodiment 5 provides an electronic device, including: a processor, a memory, and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory, so that the electronic device executes instructions for implementing the train cooperative operation prediction control method based on the dynamic interval model as described above, the method comprising:

[0160] Considering the communication delay time of the train-to-train communication system, the real-time status of the train is estimated;

[0161] Based on the real-time estimated state information of the train and the train relative distance braking model, the minimum tracking interval of the train is calculated;

[0162] Considering the communication delay time of the train-to-train communication system, an additional safety margin is added on the basis of the minimum train tracking interval to compensate for the impact of communication delay on the coordinated operation control of trains;

[0163] Based on the safety margin, the system state space model is constructed, and based on the control constraints and control objectives, the train optimization control problem is formed;

[0164] Solve the train optimization control problem, obtain the acceleration sequence for controlling the train operation, and apply the first item of the sequence to the train to realize train operation control.

[0165] In summary, an embodiment of the present invention provides a train cooperative operation prediction control method based on a dynamic interval model, which is applicable to the cooperative operation control between two or more trains in rail transit. Considering the characteristics of vehicle-to-vehicle wireless communication delay during the cooperative operation control of trains, a train cooperative operation prediction control method based on a dynamic interval model is designed by adding an additional safety margin. While effectively reducing the tracking interval between trains, the present invention increases an additional safety margin to compensate for the impact of communication delay on train operation, thereby providing safety for train operation and improving the transportation efficiency of the line.

[0166] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0167] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1A device that provides the functions specified in a block or multiple blocks.

[0168] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0169] These computer program instructions can also be loaded onto a computer or other programmable data processing device, and a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0170] Although the above describes the specific implementation mode of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative work on the basis of the technical solution disclosed in the present invention should be included in the scope of protection of the present invention.

Claims

1. A train cooperative operation prediction control method based on a dynamic interval model, characterized in that: include: Considering the communication delay time of the train-to-train communication system, the real-time status of the train is estimated; Based on the real-time estimated state information of the train and the train relative distance braking model, the minimum tracking interval of the train is calculated; Considering the communication delay time of the train-to-train communication system, an additional safety margin is added on the basis of the minimum train tracking interval to compensate for the impact of communication delay on the coordinated operation control of trains; Based on the safety margin, the system state space model is constructed, and based on the control constraints and control objectives, the train optimization control problem is formed; Solve the train optimization control problem, obtain the acceleration sequence for controlling the train operation, and apply the first item of the sequence to the train to realize train operation control.

2. The train cooperative operation prediction control method based on dynamic interval model according to claim 1 is characterized in that: The state estimation is performed based on the following train kinematic equations: Among them, s i (t), v i (t), a i (t) are the position, velocity and acceleration of train i at time t respectively.

3. The train cooperative operation prediction control method based on dynamic interval model according to claim 1 is characterized in that: The minimum tracking interval calculation model is as follows: Among them, D sm is the safety margin, is the estimated speed of the preceding train at the kth sampling moment, and deceleration of the following train and the preceding train, respectively, and 4. The train cooperative operation prediction control method based on dynamic interval model according to claim 1 is characterized in that: The additional safety margin is divided into two parts. One part is the additional safety margin for the current information transmission delay time τ The other part is an additional safety margin for communication delays in the prediction time domain. The details are as follows: Wherein, T is the sampling period, m represents the mth prediction period in the prediction time domain, and 1≤m≤p.

5. The train cooperative operation prediction control method based on dynamic interval model according to claim 1 is characterized in that: Based on the safety margin, the system state space model is constructed, and based on the control constraints and control objectives, the train optimization control problem is formed, including: Define the system state as x i (k) = [Δs i (k),Δv i (k)] T , where Δs i (k) = s i-1 (k)-s i (k), Δv i (k) = v i-1 (k)-v i (k), then the system state space model is x i (k+1)=Ax i (k)+Bu i (k) among them,in i (k)=a i-1 (to i (k), The train running speed is between 0 and the maximum speed limit, that is in, is the maximum speed limit of the train; The acceleration of the train is between the emergency braking deceleration and the maximum traction acceleration, that is, in The maximum deceleration The maximum traction acceleration The train interval cannot be less than the sum of the minimum tracking interval and the additional safety margin, that is, in, is the estimated position of the preceding train; Considering efficiency and energy consumption, the cost function is expressed as: Among them, α and β are weight coefficients; Minimize the tracking interval and energy consumption, then the objective function is expressed as:

6. The train cooperative operation prediction control method based on dynamic interval model according to claim 5 is characterized in that: Solve the train optimization control problem and obtain the optimal control sequence of the following train 7. A train cooperative operation prediction control system based on a dynamic interval model, characterized in that: include: The estimation module is used to estimate the real-time status of the train by taking into account the communication delay time of the train-to-train communication system; A calculation module, used for calculating the minimum tracking interval of the train based on the real-time estimated state information of the train and the train relative distance braking model; The compensation module is used to consider the communication delay time of the train-to-train communication system, add additional safety margin on the basis of the minimum tracking interval of the train, and compensate for the impact of communication delay on the coordinated operation control of the train; A building module is used to build a system state space model based on the safety margin and to formulate a train optimization control problem based on the control constraints and control objectives; The control module is used to solve the train optimization control problem, obtain the acceleration sequence for controlling the train operation, and apply the first item of the sequence to the train to realize train operation control.

8. A non-transitory computer-readable storage medium, characterized in that: The non-transitory computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by the processor, the train cooperative operation prediction control method based on the dynamic interval model as described in any one of claims 1-6 is implemented.

9. A computer device, characterized in that: It includes a memory and a processor, the processor and the memory communicate with each other, the memory stores program instructions that can be executed by the processor, and the processor calls the program instructions to execute the train cooperative operation prediction control method based on the dynamic interval model as described in any one of claims 1-6.

10. An electronic device, characterized in that: include: A processor, a memory and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory so that the electronic device executes instructions for implementing the train cooperative operation prediction control method based on the dynamic interval model as described in any one of claims 1-6.

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