CN115817583A - Train formation cooperative collision avoidance control method, system and equipment - Google Patents
Train formation cooperative collision avoidance control method, system and equipment Download PDFInfo
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Abstract
The embodiment of the disclosure provides a train formation cooperative collision avoidance control method, a train formation cooperative collision avoidance control system and train formation cooperative collision avoidance control equipment, and relates to the field of train formation cooperative collision avoidance control; the method comprises the following steps: when the non-tail train in the train formation implements emergency braking, the non-tail train is controlled to brake and stop by adopting the maximum emergency braking rate; constructing a train formation cooperative collision avoidance control optimization model according to a dynamic characteristic model of trains in a formation; acquiring state information of a non-tail vehicle and each subsequent train of the non-tail vehicle; acquiring expected braking rates of the non-tail vehicle subsequent trains according to the state information of the non-tail vehicle and the non-tail vehicle subsequent trains and the train formation cooperative collision avoidance control optimization model; and controlling the operation of each non-tail train according to the expected braking rate of each non-tail train. In this way, the risk of collision and the danger of collision between trains is reduced.
Description
Technical Field
The disclosure relates to the technical field of rail transit, in particular to the field of train formation cooperative collision avoidance control.
Background
In order to further improve the operation efficiency and flexibility of rail transit, the researchers in the industry and the academic community propose the concept of train cooperative formation/virtual marshalling, which becomes one of the development trends of rail transit in the future. The virtual marshalling is that based on the vehicle-vehicle communication technology, the speed between the trains is converged through an advanced control means, and the effect of virtual reconnection is achieved by keeping short-interval operation.
When emergency braking of trains occurs in the formation due to emergency in the operation process of the formation trains, if the following trains continue to use the train control with the maximum emergency braking rate applied in the single train protection control strategy, some unnecessary collision accidents may be generated due to the random factors such as the limitation of the braking capacity of the following trains, the random disturbance of the braking force, the difference of the deceleration performance and the like. Therefore, the cooperative collision avoidance control algorithm for preventing the occurrence of the chain rear-end collision accident needs to be researched, wherein the cooperative safe braking parking of the trains in the formation is realized according to the self braking performance, the overall collision hazard of the formation is minimized, and the chain rear-end collision accident is avoided.
Disclosure of Invention
The disclosure provides a train formation cooperative collision avoidance control method, system and equipment.
According to a first aspect of the present disclosure, a method for controlling train formation cooperative collision avoidance is provided, where the method includes:
when the non-tail vehicle in the train formation implements emergency braking, controlling the non-tail vehicle to brake and stop by adopting the maximum emergency braking rate;
constructing a train formation cooperative collision avoidance control optimization model according to a dynamic characteristic model of trains in a formation;
acquiring the state information of the non-tail vehicle and each subsequent train of the non-tail vehicle;
acquiring expected braking rates of the non-tail vehicle and each subsequent train of the non-tail vehicle according to the state information of the non-tail vehicle and each subsequent train of the non-tail vehicle and the train formation cooperative collision avoidance control optimization model;
and controlling the operation of each non-tail train according to the expected braking rate of each non-tail train.
In some implementations of the first aspect, the constructing a train formation cooperative collision avoidance control optimization model according to a dynamic characteristic model of trains in a formation includes:
using the dynamic characteristic model, the line speed limit and the control rate of the trains in the formation as constraint conditions;
and constructing a train formation cooperative collision avoidance control optimization model according to the constraint conditions and the control optimization target.
In some implementation manners of the first aspect, the obtaining the expected braking rate of each train subsequent to the non-tail vehicle according to the state information of each train subsequent to the non-tail vehicle and the train formation cooperative collision avoidance control optimization model includes:
the state information of the non-tail vehicle and each subsequent train of the non-tail vehicle comprises the speed information and the position information of each train;
obtaining an optimal solution control sequence of each non-tail train according to the speed information and the position information of each non-tail train and each non-tail train follow-up train and the train formation cooperative collision avoidance control optimization model;
and respectively taking the first numerical value in the optimal solution control sequence of each non-tail train as the expected braking rate of each non-tail train.
In some implementations of the first aspect, the controlling operation of the non-trail subsequent trains based on the desired braking rates of the non-trail subsequent trains comprises:
and the vehicle control system obtains the braking force which should be applied by each non-tail vehicle follow-up train according to the expected braking rate of each non-tail vehicle follow-up train, and controls each non-tail vehicle follow-up train to run.
In some implementations of the first aspect, the method further comprises:
comparing the state information of the non-tail vehicle and the subsequent trains of the non-tail vehicle, which is obtained by sampling by a sensor, with the state information obtained by predicting the dynamic characteristic model of the trains in the formation;
and correcting the dynamic characteristic model of the train in the formation according to the comparison result.
In some implementations of the first aspect, the method further comprises:
and performing rolling optimization control in each control period until the emergency collision avoidance control of train formation is completed.
In some implementations of the first aspect, the method further comprises:
and if the tail car in the train formation implements emergency braking, the tail car adopts the maximum emergency braking rate to brake and stop.
According to a second aspect of the present disclosure, there is provided a train formation cooperative collision avoidance control system, the system comprising:
the emergency braking unit is used for controlling the non-tail train to brake and stop at the maximum emergency braking rate when the non-tail train implements emergency braking in the train formation;
the control optimization model construction unit is used for constructing a train formation cooperative collision avoidance control optimization model according to the dynamic characteristic model of the trains in the formation;
the sampling unit is used for acquiring the state information of the non-tail vehicle and each subsequent train of the non-tail vehicle;
the prediction unit is used for acquiring the expected braking rate of each non-tail train and each train subsequent to the non-tail train according to the state information of each non-tail train and each train subsequent to the non-tail train and the train formation cooperative collision avoidance control optimization model;
and the control unit is used for controlling the operation of each non-tail train follow-up train according to the expected braking rate of each non-tail train follow-up train.
According to a third aspect of the present disclosure, an electronic device is provided. The electronic device includes: a memory having a computer program stored thereon and a processor implementing the method as described above when executing the program.
According to a fourth aspect of the present disclosure, there is provided a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a method as in accordance with the first aspect of the present disclosure.
The invention discloses a train formation cooperative collision avoidance control method in an emergency braking scene, which solves the problem of possible collision between trains in the emergency braking scene of train formation. When the non-tail train implements emergency braking in the train formation, the non-tail train is controlled to brake and stop by adopting the maximum braking rate, and all subsequent trains of the non-tail train are braked and stopped by the braking rate obtained by implementing the cooperative collision avoidance control algorithm optimization, so that the collision risk and collision hazard between the trains in an emergency braking scene are reduced; by adopting the idea of model predictive control rolling optimization control, each control period optimizes and solves a control sequence in real time, and the influence caused by random disturbance and noise interference is reduced; and a model feedback correction link is adopted, comparison correction is carried out according to the actually output measured value and the predicted value of the dynamic characteristic model of the train in the formation, closed-loop optimization control is formed, and the control precision and robustness of model prediction control are improved.
It should be understood that the statements herein reciting aspects are not intended to limit the critical or essential features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.
Drawings
The above and other features, advantages and aspects of various embodiments of the present disclosure will become more apparent by referring to the following detailed description when taken in conjunction with the accompanying drawings. The accompanying drawings are included to provide a further understanding of the present disclosure, and are not intended to limit the disclosure thereto, and the same or similar reference numerals will be used to indicate the same or similar elements, where:
fig. 1 shows a flow chart of a train formation cooperative collision avoidance control method according to an embodiment of the present disclosure;
fig. 2 shows a flow chart of another train formation cooperative collision avoidance control method according to an embodiment of the present disclosure;
fig. 3 illustrates a block diagram of a train formation cooperative collision avoidance control system, in accordance with an embodiment of the present disclosure;
FIG. 4 illustrates a block diagram of an exemplary electronic device capable of implementing embodiments of the present disclosure.
Detailed Description
To make the objects, technical solutions and advantages of the embodiments of the present disclosure more clear, the technical solutions of the embodiments of the present disclosure will be described clearly and completely with reference to the drawings in the embodiments of the present disclosure, and it is obvious that the described embodiments are some, but not all embodiments of the present disclosure. All other embodiments, which can be derived by a person skilled in the art from the embodiments disclosed herein without making any creative effort, shall fall within the protection scope of the present disclosure.
In addition, the term "and/or" herein is only one kind of association relationship describing an associated object, and means that there may be three kinds of relationships, for example, a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" herein generally indicates that the former and latter related objects are in an "or" relationship.
In the disclosure, a train formation cooperative collision avoidance control method in an emergency braking scene is used to solve the problem of possible collision between trains in the emergency braking scene of train formation. When the non-tail train implements emergency braking in the train formation, the non-tail train is controlled to brake and stop by adopting the maximum braking rate, and all subsequent trains of the non-tail train are braked and stopped by the braking rate obtained by implementing the cooperative collision avoidance control algorithm optimization, so that the collision risk and collision hazard between the trains in an emergency braking scene are reduced; the idea of model predictive control rolling optimization control is adopted, and a control sequence is optimized and solved in real time in each control period, so that the influence caused by random disturbance and noise interference is reduced; and a model feedback correction link is adopted, and comparison correction is carried out according to the actually output measured value and the predicted value of the dynamic characteristic model of the trains in the formation, so that closed-loop optimization control is formed, and the control precision and robustness of model prediction control are improved.
Fig. 1 shows a flowchart of a train formation cooperative collision avoidance control method according to an embodiment of the present disclosure, and as shown in fig. 1, the train formation cooperative collision avoidance control method 100 includes:
s101: when the non-tail vehicle in the train formation implements emergency braking, controlling the non-tail vehicle to brake and stop by adopting the maximum emergency braking rate;
s102: constructing a train formation cooperative collision avoidance control optimization model according to the dynamic characteristic model of the trains in the formation;
s103: acquiring the state information of the non-tail vehicle and each subsequent train of the non-tail vehicle;
s104: acquiring expected braking rates of the non-tail vehicle and each subsequent train of the non-tail vehicle according to the state information of the non-tail vehicle and each subsequent train of the non-tail vehicle and the train formation cooperative collision avoidance control optimization model;
s105: and controlling the operation of each non-tail train according to the expected braking rate of each non-tail train.
In S101, when the non-tail train in the train formation applies emergency braking, the non-tail train is controlled to brake and stop by adopting the maximum emergency braking rate.
In some embodiments, the formation of trains operates normally without adjustment when no train in the formation of trains applies emergency braking. When emergency braking is carried out on the train formation tail car, in order to prevent the tail car from impacting a front train to the maximum extent, the formation tail car is braked and stopped by adopting the maximum emergency braking rate. When emergency braking is carried out on non-tail trains in formation, in order to reduce the risk of multi-train collision in formation, a train carrying out emergency braking is used as a pilot train, the pilot train is braked and stopped by adopting the maximum emergency braking rate, and all subsequent trains of the pilot train are braked and stopped by the braking rate obtained by optimizing the cooperative collision avoidance control algorithm.
According to the embodiment of the disclosure, the train at different positions in the emergency braking scene is braked and stopped by adopting different braking rates: the tail car implementing emergency braking is braked and stopped by adopting the maximum braking rate, so that the collision of the front car is avoided, and the safety and the efficiency of train formation operation are improved; the method is characterized in that the maximum emergency braking rate braking parking is carried out on the non-tail vehicle which carries out emergency braking, meanwhile, the braking rate braking parking obtained by optimizing each subsequent train of the non-tail vehicle through a cooperative collision avoidance control algorithm is adopted, the risk of multi-vehicle collision of the formation trains is reduced by utilizing a model prediction control idea, and the occurrence of a chain rear-end collision accident is avoided.
In S102, constructing a train formation cooperative collision avoidance control optimization model according to the dynamic characteristic model of the trains in the formation includes: using the dynamic characteristic model, the line speed limit and the control rate of the trains in the formation as constraint conditions; and constructing a train formation cooperative collision avoidance control optimization model according to the constraint conditions and the control optimization target.
In some embodiments, dynamic modeling of the formation train may be performed by selecting a modeling method including, but not limited to, parameter identification. Generally, an inertia link is adopted to model train braking delay
In the formula (1), a represents the actual acceleration of the train; a is des Representing a desired acceleration of the train; k is the system gain; τ is the time constant. The method comprises the steps of extracting control instructions in vehicle-mounted ATO communication control data and data information such as acceleration of an actual train, screening and removing invalid data through data, and identifying system gain and time constant in a model by adopting a data identification method. The modeling of the basic resistance and the ramp resistance can be carried out by using empirical values, and the operation of the train is controlled as interference compensation after the controller is designed. Taking the position, speed and acceleration of the train as system state variables, namely x = [ s va = [ [ s va ]] T ；u＝a des For a desired braking rate of the train, then the discrete system equation for train dynamics may be expressed as:
in the formula (2), T s The sampling interval is typically 200ms, which is determined by the control period of the onboard controller.
In some embodiments, a train formation cooperative collision avoidance control optimization model under an emergency braking scene is constructed based on a dynamic characteristic model of a train formation; firstly, the potential danger degree of collision between trains in formation needs to be quantified, the hazard degree of an accident in the aviation field can be measured by taking collision Energy as a quantitative measure, and the potential danger degree of collision between adjacent trains can be measured by using Relative Kinetic Energy, and the Total Relative Kinetic Energy (true) of formation can be expressed as follows:
v in formula (3) i (t) represents the current speed (m/s), v, of the train i at time t i-1 (t) the current speed (m/s) of the adjacent train in front of train i at time t; m is a unit of i Represents the load (kg) of the train; n represents the number of trains inside the formation. According to the dynamic characteristic model of the formation train, with the aim of minimizing the collision hazard, the optimization objective function can be expressed as:
in formula (4), j represents a model prediction step length; * (k + j | k) represents the predicted state at time k + j, based on the state information at time k; m represents the control step selected by the model predictive control (the general control step and the prediction step take the same value).
When a train is operating on a route, the state of the system must be constrained by the inherent attributes of the train and the conditions of the route. The constraints considered in the train formation cooperative collision avoidance control optimization model are as follows:
0≤v i (k+j|k)≤v lim (x i )
in the formula (5), the first and second groups,
represents the minimum braking acceleration (m/s) that the train can generate 2 )；
Represents the maximum braking acceleration (m/s) that the train can generate 2 )；v lim (s i ) The speed limit of the line of the train i is shown according to the position x of the train on the line i (m/s)；s m Representing minimum safety spacing redundancy (m) when the train is stationary;
indicating the safe separation that needs to be maintained by adjacent trains, in relation to the adjacent train speed (m).
Therefore, the optimization problem of the emergency stop of the train formation can be expressed as:
and the constraint conditions are met:
X i (k+j+1|k)＝A i X i (k+j|k)+B i u i，des (k+j|k)-W i (k)
in the formula (7)
X i (k+j|k)＝[x i (k+j|k)，v i (k+j|k)，u i (k+j|k)] T
Wherein X (k + j | k) represents the predicted state at time k + j, based on the state information at time k; w is a group of i (k) Represents the acceleration (m/s) generated by the basic resistance and the additional resistance at the moment k 2 )。
According to the embodiment of the disclosure, a dynamic characteristic model, a line speed limit, a control rate and the like of the trains in the formation are used as constraint conditions, and a train formation cooperative collision avoidance control optimization model is constructed by taking minimized collision hazards as a control target. In an emergency braking scene, the expected braking rates of all non-tail vehicle follow-up trains are obtained by using the model, so that the braking of each non-tail vehicle follow-up train in the formation can be controlled more accurately, and the risk of multi-train collision is reduced.
In S103, the state information of the non-trailing vehicle and each train following the non-trailing vehicle is acquired.
In some embodiments, speed and position state information of a non-tailgating vehicle implementing emergency braking and all trains subsequent to the non-tailgating vehicle is obtained through a sensor or a transponder; the non-tail vehicle and the non-tail vehicle follow-up trains obtain the speed and position state information of other trains except the non-tail vehicle through vehicle-to-vehicle communication.
In some embodiments, the train obtains train speed information through vehicle-mounted wheel diameter sensor, radar speed sensor or accelerometer measurement, and obtains train position status information through vehicle-mounted speed integral calculation in combination with the trackside transponder correction position information. The position and speed state information of other trains in the formation is acquired through a communication mode based on a Long Term Evolution (LTE) network or a 5G network and the like.
According to the embodiment of the disclosure, the real-time states of all the trains which are not the tail cars and follow-up trains of the tail cars and are not the tail cars and are subjected to emergency braking in the running or braking process can be monitored by obtaining the speed and position state information of all the trains which are not the tail cars and follow-up trains of the tail cars and are subjected to emergency braking; and according to the speed and position state information of the non-tail vehicle and all the trains following the non-tail vehicle, the expected braking rates of all the trains following the non-tail vehicle can be obtained more accurately.
In S104, obtaining the expected braking rate of each train following the non-tail vehicle according to the state information of each train following the non-tail vehicle and the train formation cooperative collision avoidance control optimization model includes: the state information of the non-tail vehicle and each subsequent train of the non-tail vehicle comprises the speed information and the position information of each train; obtaining an optimal solution control sequence of each non-tail train according to the speed information and the position information of each non-tail train and each non-tail train subsequent train and the train formation cooperative collision avoidance control optimization model; and respectively taking the first numerical value in the optimal solution control sequence of each non-tail train as the expected braking rate of each non-tail train.
In some embodiments, the obtained speed and position state information of each non-tail train and each train following the non-tail train are used as the initial state of a model predictive control optimization problem, and an optimal solution control sequence of each train following the non-tail train in a first control time domain is obtained by solving a formula (6):
u i，des ＝[u i，des (k|k)，…，u i，des (k+M-1|k)]
i＝1，2，...，N. (8)
and respectively taking the first numerical value in the optimal solution control sequence of each non-tail train subsequent to the current control time domain as the expected braking rate of each non-tail train subsequent to the current control period.
And when the next control period comes, re-sampling and acquiring the speed and position state information of the non-tail train and the non-tail train follow-up trains, and acquiring the expected braking rate of the non-tail train follow-up trains in the current control period. And repeating the optimization process in each control period to complete the rolling time domain optimization control until each subsequent train of the non-tail train is braked and stopped.
In some embodiments, the control time domain is a time duration of the model predictive control hypothesis control, for example, if the control time domain step size is set to 5, the control time domain is 5 × control period; the control period is a time interval for recalculating the control rate, and the control period for train operation is generally 200ms, namely how much control rate should be applied to the train currently every 200 ms.
In some embodiments, the model predictive control is to predict how to control the vehicle in the next time period (this time period is referred to as a control time period, for example, 5 control cycles, which is 1 s), so that 5 control rates are required in 1s, which is referred to as a control sequence, but only the first control rate is used in the current control cycle. And re-optimizing the control cycle to obtain 5 control rates, and still only adopting the first control rate, wherein the process is called rolling optimization control (model predictive control), namely the idea of the model predictive control. The chess playing is understood to be that what is played next 5 steps is thought in advance, after the first step is played, the opponent and the opponent are found to be different from what is thought before the opponent and the opponent want to play next 5 steps again, and although 5 steps are planned in advance, only 1 step can be played each time. The model predictive control takes the first value in the control sequence.
According to the embodiment of the disclosure, the speed and position state information of the non-tail vehicle and the non-tail vehicle subsequent trains and the train formation cooperative collision avoidance control optimization model are used for more accurately calculating the braking rate to be applied to the non-tail vehicle subsequent trains in each control period. Meanwhile, rolling optimization control is carried out in each control period, the train state information of each control period is obtained in real time, and the expected braking rate of the train in each control period is obtained in real time. Therefore, the brake is stopped in real time according to the expected braking rate of each control period in the process of implementing the brake of each follow-up train of the non-tail train to a certain extent, and the serious damage of rear-end collision to the train is effectively avoided.
In S105, controlling the operation of each train following the non-tail train according to the expected braking rate of each train following the non-tail train includes: and the vehicle control system obtains the braking force which should be applied by each non-tail train according to the expected braking rate of each non-tail train and controls each non-tail train to run.
In some embodiments, the train control system outputs a braking rate and the vehicle control system outputs a corresponding braking force to control train operation based on the braking rate and the load condition.
According to the embodiment of the disclosure, the vehicle control system outputs the corresponding braking force to control the train to run according to the braking rate and the loading condition, so that each non-tail train subsequent train stops according to the safe braking cooperated with the self braking performance under the emergency braking condition, the overall collision hazard of the formation train is minimized, and unnecessary damage to the train and the driver and passengers is avoided.
In some embodiments, the method further comprises: comparing the state information of the non-tail vehicle and the non-tail vehicle subsequent trains obtained by sampling of the sensor with the state information obtained by predicting the dynamic characteristic model of the trains in the formation; and correcting the dynamic characteristic model of the trains in the formation according to the comparison result.
According to the embodiment of the disclosure, the speed and position state information of the non-tail train and the non-tail train follow-up trains obtained by sampling of the sensor is compared with the state information obtained by predicting the dynamic characteristic model of the trains in the formation, and the dynamic characteristic model of the trains in the formation is corrected according to the comparison result. Therefore, a model feedback correction link is adopted, comparison correction is carried out according to the actually output measured value and the predicted value of the model, closed-loop optimization control is formed, and the control precision and robustness of model prediction control are improved.
In some embodiments, the method further comprises: and performing rolling optimization control in each control period until the emergency collision avoidance control of train formation is completed.
In some embodiments, the method further comprises: and if the tail car in the train formation implements emergency braking, the tail car adopts the maximum emergency braking rate to brake and stop.
Fig. 2 shows a flowchart of another train formation cooperative collision avoidance control method according to an embodiment of the present disclosure, and as shown in fig. 2, the train formation cooperative collision avoidance control method 200 includes:
s201: judging whether a train in the train formation implements emergency braking; when no train performs emergency braking in the train formation, the train formation normally runs without adjustment. When the train formation tail car implements emergency braking, the formation tail car is controlled to adopt the maximum emergency braking rate to brake and stop. When the non-tail vehicle in the formation implements emergency braking, the non-tail vehicle is controlled to brake and stop by adopting the maximum emergency braking rate, and all subsequent trains of the non-tail vehicle implement braking rate braking and stopping obtained by optimizing the cooperative collision avoidance control algorithm;
s202: modeling the dynamic characteristics of the trains in the formation;
s203: constructing a train formation cooperative collision avoidance control optimization model under an emergency braking scene based on a dynamic characteristic model of trains in the formation;
s204: obtaining the speed and position state information of the non-tail vehicle and the subsequent trains of the non-tail vehicle through a sensor or a transponder, and obtaining the speed and position information of other trains in formation through vehicle-to-vehicle communication;
s205: solving an optimization control problem according to the speed and position state information of the non-tail vehicle and the non-tail vehicle subsequent trains to obtain the expected braking rate of the non-tail vehicle subsequent trains in the current control period;
s206: the expected braking rate of the current control period of each non-tail train is issued to each non-tail train, the braking force to be applied by each non-tail train is obtained through a train control system, and the train operation is controlled;
s207: the non-tail vehicle and each subsequent train of the non-tail vehicle are sampled by the sensor and are continuously compared, fed back and corrected with the prediction state value of the dynamic characteristic model of the trains in the formation, so that the accuracy of the prediction model in model prediction control is improved.
And repeating the methods of S204-S207 in each control period until the non-tail train and all trains subsequent to the non-tail train finish parking.
The above is a description of embodiments of the method, and the embodiments of the apparatus are further described below.
Fig. 3 shows a block diagram of a train formation cooperative collision avoidance control system according to an embodiment of the present disclosure, and as shown in fig. 3, the train formation cooperative collision avoidance control system 300 includes:
emergency brake unit 301: the system is used for controlling the non-tail vehicle to brake and stop at the maximum emergency braking rate when the non-tail vehicle implements emergency braking in the train formation;
control optimization model construction unit 302: the system is used for constructing a train formation cooperative collision avoidance control optimization model according to a dynamic characteristic model of trains in a formation;
the sampling unit 303: the system is used for acquiring the state information of the non-tail vehicle and each subsequent train of the non-tail vehicle;
the prediction unit 304: the system comprises a control optimization model, a train formation cooperative collision avoidance control optimization model and a control optimization model, wherein the control optimization model is used for acquiring the expected braking rate of each non-tail train and each non-tail train follow-up train;
the control unit 305: and the control device is used for controlling the operation of each non-tail train following train according to the expected braking rate of each non-tail train following train.
It is noted that while for simplicity of explanation, the foregoing method embodiments have been described as a series of acts or combination of acts, it will be appreciated by those skilled in the art that the present disclosure is not limited by the order of acts, as some steps may, in accordance with the present disclosure, occur in other orders and concurrently. Further, those skilled in the art should also appreciate that the embodiments described in the specification are exemplary embodiments and that acts and modules referred to are not necessarily required by the disclosure.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working process of the described module may refer to the corresponding process in the foregoing method embodiment, and is not described herein again.
The present disclosure also provides an electronic device, a readable storage medium, and a computer program product according to embodiments of the present disclosure.
FIG. 4 illustrates a block diagram of an exemplary electronic device capable of implementing embodiments of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the disclosure described and/or claimed herein.
The device 400 comprises a computing unit 401 which may perform various suitable actions and processes in accordance with a computer program stored in a Read Only Memory (ROM) 402 or a computer program loaded from a storage unit 408 into a Random Access Memory (RAM) 403. In the RAM 403, various programs and data required for the operation of the device 400 can also be stored. The computing unit 401, ROM402, and RAM 403 are connected to each other via a bus 404. An input/output (I/O) interface 405 is also connected to bus 404.
A number of components in device 400 are connected to I/O interface 405, including: an input unit 406 such as a keyboard, a mouse, or the like; an output unit 407 such as various types of displays, speakers, and the like; a storage unit 408 such as a magnetic disk, optical disk, or the like; and a communication unit 409 such as a network card, modem, wireless communication transceiver, etc. The communication unit 409 allows the device 400 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program code may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program code, when executed by the processor or controller, causes the functions/acts specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), and the Internet.
The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server may be a cloud server, a server of a distributed system, or a server with a combined blockchain.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present disclosure may be executed in parallel or sequentially or in different orders, and are not limited herein as long as the desired results of the technical solutions of the present disclosure can be achieved.
The above detailed description should not be construed as limiting the scope of the disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present disclosure should be included in the scope of protection of the present disclosure.
Claims (10)
1. A train formation cooperative collision avoidance control method is characterized by comprising the following steps:
when the non-tail vehicle in the train formation implements emergency braking, controlling the non-tail vehicle to brake and stop by adopting the maximum emergency braking rate;
constructing a train formation cooperative collision avoidance control optimization model according to a dynamic characteristic model of trains in a formation;
acquiring the state information of the non-tail vehicle and each subsequent train of the non-tail vehicle;
acquiring expected braking rates of the non-tail vehicle and each subsequent train of the non-tail vehicle according to the state information of the non-tail vehicle and each subsequent train of the non-tail vehicle and the train formation cooperative collision avoidance control optimization model;
and controlling the operation of each non-tail train according to the expected braking rate of each non-tail train.
2. The method of claim 1, wherein the constructing a train formation cooperative collision avoidance control optimization model according to the dynamic characteristic model of the trains in the formation comprises:
using the dynamic characteristic model, the line speed limit and the control rate of the trains in the formation as constraint conditions;
and constructing a train formation cooperative collision avoidance control optimization model according to the constraint conditions and the control optimization target.
3. The method of claim 1, wherein the obtaining the expected braking rate of each train subsequent to the non-tail vehicle according to the state information of each train subsequent to the non-tail vehicle and the train formation cooperative collision avoidance control optimization model comprises:
the state information of the non-tail vehicle and each subsequent train of the non-tail vehicle comprises the speed information and the position information of each train;
obtaining an optimal solution control sequence of each non-tail train according to the speed information and the position information of each non-tail train and each non-tail train subsequent train and the train formation cooperative collision avoidance control optimization model;
and respectively taking the first numerical value in the optimal solution control sequence of each non-tail train as the expected braking rate of each non-tail train.
4. The method of claim 1, wherein said controlling operation of the non-trail subsequent trains based on the desired braking rates of the non-trail subsequent trains comprises:
and the vehicle control system obtains the braking force which should be applied by each non-tail train according to the expected braking rate of each non-tail train and controls each non-tail train to run.
5. The method of claim 1, further comprising:
comparing the state information of the non-tail vehicle and the non-tail vehicle subsequent trains obtained by sampling of the sensor with the state information obtained by predicting the dynamic characteristic model of the trains in the formation;
and correcting the dynamic characteristic model of the trains in the formation according to the comparison result.
6. The method of claim 1, further comprising:
and performing rolling optimization control in each control period until the emergency collision avoidance control of train formation is completed.
7. The method of claim 1, further comprising:
and if the tail car in the train formation implements emergency braking, the tail car adopts the maximum emergency braking rate to brake and stop.
8. A train formation cooperative collision avoidance control system, the system comprising:
the emergency braking unit is used for controlling the non-tail train to brake and stop by adopting the maximum emergency braking rate when the non-tail train implements emergency braking in the train formation;
the control optimization model construction unit is used for constructing a train formation cooperative collision avoidance control optimization model according to the dynamic characteristic model of the trains in the formation;
the sampling unit is used for acquiring the state information of the non-tail vehicle and each subsequent train of the non-tail vehicle;
the prediction unit is used for acquiring the expected braking rate of each non-tail train and each train subsequent to the non-tail train according to the state information of each non-tail train and each train subsequent to the non-tail train and the train formation cooperative collision avoidance control optimization model;
and the control unit is used for controlling the operation of each non-tail train follow-up train according to the expected braking rate of each non-tail train follow-up train.
9. An electronic device, comprising:
at least one processor;
and a memory communicatively coupled to the at least one processor;
wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-7.
10. A non-transitory computer readable storage medium storing computer instructions, wherein,
the computer instructions are for causing the computer to perform the method of any one of claims 1-7.
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| CN202211640989.3A CN115817583A (en) | 2022-12-19 | 2022-12-19 | Train formation cooperative collision avoidance control method, system and equipment |
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| CN202211640989.3A CN115817583A (en) | 2022-12-19 | 2022-12-19 | Train formation cooperative collision avoidance control method, system and equipment |
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