Method and system for train scheduling adjustment, and electronic device and storage medium

Abstract

The present disclosure belongs to the technical field of rail transit. Provided are a train scheduling method and system, and an electronic device and a storage medium. The method comprises: acquiring a line status of a first train, and an operating condition of the first train and an operating condition of a second train adjacent to the first train; on the basis of the line status of the first train and the operating conditions of the first train and the second train, determining a scheduling adjustment scheme for a plurality of trains within a specific range; and transmitting the scheduling adjustment scheme to a train operation control system. Operating condition information of adjacent trains is used to adjust the scheduling of a plurality of trains within a specific range, thereby achieving the optimal overall operational efficiency of a rail transit system from a global perspective on the premise of ensuring operational safety.

Description

Methods, systems, electronic devices, and storage media for train scheduling and adjustment. Technical Field

[0001] This disclosure pertains to the field of rail transit technology, and particularly relates to methods, systems, electronic devices, and storage media for train scheduling and adjustment. Background Technology

[0002] Traditional train operation adjustments and control are independent and layered, with limited communication between them. After a disturbance occurs, the dispatcher formulates a train operation adjustment plan based on the disturbance situation, mainly modifying train arrival and departure times, arrival and departure sequences, and the use of arrival and departure tracks, aiming to reduce the impact of the disturbance on train operation. The generated dispatch instructions after the adjustment are transmitted to the trackside equipment through the dispatching command system. After the train receives the dispatch instructions at the trackside equipment, the driver formulates a corresponding driving control strategy based on the track conditions and the dispatch instructions, drives the train to the target stopping point according to the timetable, and, in some special cases, feeds back the train's operating status to the dispatcher. On busy lines, it is difficult for the dispatcher to fully grasp the real-time dynamic information of all trains. The delay in receiving train information and the lack of information on the operation of surrounding trains make it difficult for the dispatcher to formulate an effective operation adjustment plan.

[0003] Integrated train operation scheduling and control refers to the use of advanced sensing, transmission, and control methods and technologies to enhance the intelligence level of train operation control and scheduling, deeply integrate train operation control and scheduling, achieve optimal overall network operation efficiency, and comprehensively improve the ability to respond promptly to emergencies. After a disturbance occurs, the intelligent dispatching and command system can adjust and directly control train operations, enabling trains to directly execute operation adjustment plans, improving the accuracy of train operation control during operation, and, while ensuring operational safety, utilizing resources such as personnel, vehicles, roads, and networks from a global perspective to achieve optimal overall operational efficiency of the rail transit system.

[0004] Existing technologies for integrated operation of rail transit dispatching and control are mainly applied to problems such as train delay recovery, transportation organization adjustment and passenger flow matching. Their optimization effects are mainly to minimize the delay time and minimize passenger travel time. Generally, control elements are added to the dispatching decision-making process to improve the robustness and executability of the dispatching decision results. Summary of the Invention

[0005] In order to solve at least some of the above-mentioned technical problems, this disclosure provides methods, systems, electronic devices and storage media for train scheduling.

[0006] According to one aspect of this disclosure, a method for scheduling trains is proposed, the method may include: acquiring the line status of a first train, and the operating conditions of the first train and the operating conditions of a second train adjacent to the first train; determining a scheduling adjustment scheme for multiple trains within a specific range based on the line status of the first train and the operating conditions of the first train and the second train; and sending the scheduling adjustment scheme to a train operation control system.

[0007] According to another aspect of this disclosure, a system for scheduling trains is proposed, the system comprising: a condition acquisition module configured to acquire the line status of a first train, and the operating conditions of the first train and the operating conditions of a second train adjacent to the first train; a scheduling adjustment scheme determination module configured to determine a scheduling adjustment scheme for multiple trains within a specific range based on the line status of the first train and the operating conditions of the first train and the second train; and a scheduling adjustment scheme sending module configured to send the scheduling adjustment scheme to a train operation control system.

[0008] According to another aspect of this disclosure, an electronic device is provided, including a memory and a processor; the memory for storing a computer program; and the processor for executing the program stored in the memory to cause the electronic device to perform the above-described method for scheduling trains.

[0009] According to another aspect of this disclosure, a computer-readable storage medium is provided on which a computer program is stored, which, when executed by a processor, implements the above-described method for scheduling trains.

[0010] According to another aspect of this disclosure, a computer-readable storage medium storing program code thereon, which, when executed by a processor, implements the above-described method for scheduling trains.

[0011] According to another aspect of this disclosure, a computer program product storing program code is proposed, which, when executed by a processor, implements the above-described method for scheduling trains.

[0012] According to the solutions of the embodiments of this disclosure, for problems such as train delay recovery, transportation organization adjustment and passenger flow matching, the train operation plan is adjusted by considering the operating conditions of adjacent trains to minimize the delay time. Other features and advantages of this disclosure will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing this disclosure. The objects and other advantages of this disclosure can be realized and obtained by means of the structures pointed out in the description and drawings. Attached Figure Description

[0013] The above and other objects, features, and advantages of this disclosure will become clearer in the following detailed description of embodiments of this disclosure in conjunction with the accompanying drawings. In the drawings, the same reference numerals denote the same elements throughout. It should be understood that the embodiments described herein are merely illustrative and should not be construed as limiting the scope of this disclosure.

[0014] Figure 1 illustrates the basic architecture of an integrated dispatching and control system that applies a method for dispatching trains according to an embodiment of the present disclosure;

[0015] Figure 2 shows a flowchart of a method for scheduling trains according to an embodiment of the present disclosure;

[0016] Figure 3 illustrates the framework of the scheduling model used in the method for scheduling trains according to an embodiment of the present disclosure;

[0017] Figure 4 shows a schematic architecture diagram of a system for train dispatching according to an embodiment of the present disclosure; and

[0018] Figure 5 shows a schematic block diagram of an electronic device that can implement a method for scheduling trains according to embodiments of this application. Detailed Implementation

[0019] To aid in the details provided above, the following description is provided to enable those skilled in the art to understand, make, and use the invention as described herein and expressly claimed herein. Because specific combinations of the various features will result in numerous practical embodiments in which this disclosure can be practiced, and for the purpose of providing reasonably clear and concise description, only some exemplary embodiments will be presented herein. However, it should be recognized that those skilled in the art can similarly practice other embodiments not expressly described herein. Thus, any assessment of the scope of this disclosure should be made with respect to the claims expressly provided herein, and the scope of this disclosure should be interpreted in accordance with its broadest reasonable interpretation and taking into account ordinary knowledge and skill in the art. Nothing in this description is intended to limit the spirit and scope of this disclosure.

[0020] The method, system, electronic device, and storage medium for train scheduling according to embodiments of the present disclosure will now be described with reference to the accompanying drawings. It should be understood that the embodiments described herein are merely illustrative and should not be construed as limiting the scope of the present disclosure.

[0021] In the face of the increasingly prominent contradiction between transportation capacity and passenger demand, as well as the complex delay scenarios in actual operation, the method of automatically adjusting train operation by combining train operation and dynamic passenger flow information can further improve the efficiency and service quality of rail transit operation.

[0022] Furthermore, when scheduling and adjusting train operations, in addition to considering the parameters under the basic operating conditions of the train, we also fully consider the operating conditions under which parameters change abruptly.

[0023] Given the characteristics of modern rail transit operations, such as high train speeds and high departure frequencies, train delays caused by unpredictable events spread rapidly and have a wide impact. This necessitates more real-time adjustments to train operations after an emergency occurs. To address these issues (such as train delay recovery, transport organization adjustments, and passenger flow matching), this disclosure provides a method for train scheduling. By considering train operating conditions under abrupt changes, this method adjusts train operation plans to minimize delay times and passenger travel time.

[0024] Figure 1 illustrates the basic architecture of an integrated dispatching and control system 100 applying the train dispatching method according to an embodiment of the present disclosure. The integrated dispatching and control system 100, as an application scenario of the method of the present disclosure, will be described in detail below with reference to Figure 1.

[0025] As shown in Figure 1, the integrated dispatching and control system 100 may include a train operation control system 110, a comprehensive dispatching system 120, and a passenger service system 130. For example, the integrated dispatching and control system 100 and its subsystems may be integrated or distributed.

[0026] The train operation control system 110 can control equipment related to train operation to ensure the train operates according to real-time dispatch instructions from the integrated dispatch system 120. For example, this equipment may include central control equipment, station equipment, trackside equipment, and onboard equipment. The train autonomous operation control system 110 can also provide basic train information to the integrated dispatch system 120 and report train operation data in real time, such as location, speed, maximum forward operating capacity (hours / minutes), onboard equipment operating status, and trainset operating conditions.

[0027] The integrated dispatching system 120 can generate train schedules based on basic demand information such as passenger flow forecasts and provide these schedules to the train operation control system 110. Furthermore, the integrated dispatching system 120 can adjust train operation plans and determine dispatch adjustment schemes based on train operation data reported by the train operation control system 110. The integrated dispatching system 120 can send the dispatch adjustment schemes to the train autonomous operation control system 110 to control trains to operate according to the adjusted schemes, and also send them to the passenger service system 130 to update or adjust passenger service information.

[0028] The passenger service system 130 may include a ticketing service system 131 and a station service system 132. The ticketing service system 131 can be used for transportation demand forecasting and integrated ticketing services. The station service system 132 can be used to publish train schedule information, operation adjustment plans, and ticket checking services.

[0029] The integrated dispatch and control system 100 achieves real-time dispatch and adjustment of train operation, ticketing service and station service through data interaction and business interaction between the train operation control system 110, the integrated dispatch system 120 and the passenger service system 130.

[0030] Figure 2 shows a flowchart of a method 200 for scheduling trains according to an embodiment of the present disclosure. According to one aspect of the present disclosure, a method 200 for scheduling trains is provided. As shown in Figure 2, the train scheduling method 200 may include steps S210-S230. The specific flow of the method 200 for scheduling trains will be described in detail below with reference to Figures 1 to 5.

[0031] According to one embodiment of this disclosure, in step S210, the track status of the first train, as well as the operating conditions of the first train and the operating conditions of the adjacent second train, are obtained.

[0032] In another exemplary embodiment, in step S210, the line status and operating condition information of other trains may be further obtained.

[0033] As an example, a train's line may include the route of the first train. As another example, a train's line may also include other routes associated with the route, such as adjacent routes, branch lines, or alternative routes.

[0034] Furthermore, it should be noted that, in this application, the line status of the train may include, for example, at least one of the following: the level of traffic on the line, the total distance of the line, the length of the section and / or the number of tracks, the current condition of the line (dry, wet, slippery), etc. The train's operating conditions may include, for example, at least one of the following: the train's location, speed, operating capacity, and the operating status of onboard equipment, the operating conditions of the EMU (Electric Multiple Unit), etc.

[0035] According to an embodiment of this disclosure, in step S220, a scheduling adjustment scheme for multiple trains within a specific range is determined based on the line status of the first train and the operating conditions of the first and second trains obtained in step S210.

[0036] According to an exemplary embodiment of this disclosure, multiple trains within a specific range can be multiple trains within a certain spatial and / or temporal range. For example, the selection of this specific range needs to balance the factors of overall optimization and computational complexity. If the range is set too large, such as including the preceding train, adjacent trains, and multiple trains that will subsequently pass through switches to reach the current train's route within the specific range, it may lead to a surge in computational complexity, or even failure to find the optimal solution. Conversely, if the range is set too small, such as only considering the preceding and following trains, the margin for train operation adjustments is too small, which is not conducive to achieving overall optimization.

[0037] For example, a specific range can be set as three to five adjacent trains, trains running between two to three adjacent stations, or trains that will pass through the same route within 30-60 minutes. As an example, when the specific range is set as multiple adjacent trains, in step S220, the adjusted departure and arrival times for trains i to i+a can be determined based on the line status of train i and the operating conditions of adjacent trains i and i+1, where a is a positive integer, for example, a = 5. As another example, when the specific range is set as multiple adjacent stations, in step S220, the adjusted departure and arrival times for all trains located at stations j to j+b can be determined based on the line status of train i and the operating conditions of adjacent trains i and i+1, where b is a positive integer, for example, b = 3. As another example, when the specific range is set to 30-60 minutes, in step S220, the adjusted departure and arrival times for all trains traveling on the same route within 30-60 minutes can be determined based on the line status of train i and the operating conditions of adjacent trains i and i+1. It should be noted that the train numbers and station numbers and their quantities are listed here for illustrative purposes only and do not limit the range of trains and stations involved.

[0038] According to another embodiment of this disclosure, in step S220 of determining the scheduling adjustment scheme, in addition to using the obtained line status of the first train and the operating conditions of the first train and the second train, the line status and operating condition information of other trains can be further used to obtain a more accurate scheduling adjustment scheme.

[0039] As an example, when the specific range is set to multiple adjacent trains, in step S220, the adjusted departure and arrival times for trains i to i+a2 can be determined based on the line status and operating conditions of trains i to i+a1, where a1 and a2 are integers greater than zero, for example, a1 = 3 and a2 = 5. As another example, when the specific range is set to multiple adjacent stations, in step S220, the adjusted departure and arrival times for all trains expected to pass through station j can be determined based on the line status and operating conditions of trains i to i+c currently at station j, where c is an integer greater than zero, for example, c = 6. As yet another example, when the specific range is set to 30-60 minutes, in step S220, the adjusted departure and arrival times for trains i to i+d can be determined based on the line status and operating conditions of trains i to i+c expected to pass through the same route within 30-60 minutes, where d is an integer greater than zero, for example, d = 4. It should also be noted that the train numbers and station numbers and their quantities are listed here as examples for illustrative purposes only and do not limit the scope of the trains and stations involved.

[0040] In an exemplary embodiment, step S220 of determining a scheduling adjustment scheme may include, when the first train experiences an operational anomaly, determining a scheduling adjustment scheme for multiple trains within a specific range based on the line status of the first train and the operating conditions of the first and second trains. For example, depending on different line infrastructure characteristics, the adjustment scheme for train operation may include three adjustment strategies, such as rearranging routes, adjusting the order of operation, and adjusting the operating time.

[0041] Given the characteristics of modern rail transit operations, such as high train speeds and frequent departures, train delays caused by unpredictable events spread rapidly and have a wide impact. This necessitates even more real-time adjustments to train operation plans after an emergency occurs. In cases of train malfunctions, abnormal passenger flow, or track abnormalities leading to operational disruptions, timely adjustments to train operation plans are especially crucial to minimize the impact of the emergency.

[0042] According to an exemplary embodiment of this application, by considering the train track conditions and operating conditions under conditions of parameter mutation, the train operation plan is adjusted to minimize delay times and passenger travel time. For example, in abnormal situations, the track condition may be, for example, a slippery or worn condition, and the operating condition information may be, for example, the operating conditions when tunnels or arrival times mutate.

[0043] For example, train operation abnormalities can be caused by train scheduling issues, or by objective emergencies such as extreme weather, sudden changes in road conditions, or sudden changes in passenger flow.

[0044] As an example, an anomaly in the operation of the first train may include: the actual operation of the first train does not match its operating plan. This anomaly can be caused by objective emergencies such as extreme weather, sudden changes in road conditions, or sudden changes in passenger flow. Such anomalies may include: train operation anomalies (delays / inconsistencies with the plan due to malfunctions, trains failing to depart due to excessive passenger flow), track and environmental anomalies (such as clothing caught on the overhead contact line or mudslides causing operational interruptions, speed reduction due to rain, snow, or strong winds), and power system failures (such as reduced load capacity of the power supply arm). These anomalies usually affect train operation, therefore, timely adjustments to train scheduling are necessary to address these anomalies, such as changing train operating times or even rerouting.

[0045] Furthermore, train operation anomalies can also be caused by train scheduling issues. As another example, step S220, which determines a scheduling adjustment plan, may include adjusting the train distribution and operating status within a specific area when there is an anomaly or need in the scheduling process. For example, scheduling specialties may include train operation, passenger transport, rolling stock, maintenance, and power supply. As an example, multiple scheduling services such as train operation, passenger transport, rolling stock, maintenance, and power supply can be integrated to adjust the train distribution and operating status within a specific area.

[0046] As an exemplary embodiment, step S220 of determining the scheduling adjustment scheme may include: determining the scheduling adjustment scheme according to the block section settings. To ensure train operation safety and line throughput capacity, the line is divided into multiple block sections. When dividing block sections, train operation is required to balance safety, economy, and efficiency. Typically, block section division considers two objectives: minimizing train following intervals and minimizing section construction costs, and takes the safe operation of adjacent trains as a constraint. For example, the minimum safe distance between adjacent trains can be set using the relative braking distance principle in the block section settings, thereby effectively shortening the distance between adjacent trains and improving the train throughput efficiency while ensuring train operation safety.

[0047] Furthermore, as an example, the train running time requirements in the scheduling adjustment plan can also be adjusted in conjunction with the line change points and block section settings.

[0048] As an exemplary embodiment, step S220 of determining the scheduling adjustment scheme may include: determining the scheduling adjustment scheme based on the operating capacity information of the first train and the second train.

[0049] For example, based on information such as the maximum operating capacity (e.g., maximum operating time interval) and minimum operating capacity (e.g., minimum operating time interval) fed back by adjacent trains, the overall operation plans of current and future adjacent trains on the operating path can be optimized and adjusted. After obtaining the maximum operating capacity (uneconomical but short time) and the minimum operating capacity (meets the operating time requirement, train operation is smooth, long time but economical), the difference between these two capabilities can be used to adjust train scheduling. Typically, train operation will compromise between these two capabilities, such as using 80% of the maximum operating capacity to enhance driving smoothness, consider green energy conservation, and have some adjustment margin. In addition, when the maximum operating capacity of a train is obtained, invalid scheduling commands exceeding the train's operating capacity will not be identified, thereby improving the executability of scheduling commands and reducing the number of scheduling command interactions between trains and the ground.

[0050] According to one embodiment of this disclosure, in step S230, the scheduling adjustment scheme determined in step S220 can be sent to the train operation control system so that the train operation control system can control the train operation based on the scheduling adjustment scheme.

[0051] As an example, the train operation control system can issue a single time requirement for the operation of multiple trains, which is more conducive to the train acquiring more information, forming a better operation control strategy, and improving the energy efficiency and comfort of operation control.

[0052] According to another embodiment of this disclosure, optionally, in step S240, the scheduling adjustment scheme determined in step S220 can also be sent to the ticketing service system so that the ticketing service system can adjust the ticketing service according to the scheduling adjustment scheme.

[0053] According to another embodiment of this disclosure, optionally, in step S250, the scheduling adjustment plan determined in step S220 can also be sent to the station, so that the station can publish information about the scheduling adjustment plan. Furthermore, the station can provide station services such as ticket checking and waiting services according to the adjustment plan.

[0054] It should be noted that steps S240-S250 above, which involve sending the scheduling adjustment plan to the ticketing service system and the station, are all optional steps. These optional steps are shown as examples only to better illustrate the concept of this disclosure and are not intended to limit the scope of protection of this disclosure.

[0055] The train dispatching method 200 in this application can collaboratively formulate transportation plans for multiple rail transit lines under the jurisdiction of one or more operating entities within a certain range based on dynamic transportation demand and fixed transport capacity resources. It integrates multiple dispatching businesses such as train operation, passenger transport, rolling stock, maintenance, and power supply to generate a dispatching plan. It automatically controls, dynamically tracks, optimizes processes, and manages execution of dispatching commands, improves the accuracy of train operation control during operation, and optimizes the overall operating efficiency of the rail transit system by utilizing resources such as people, vehicles, roads, and networks from a global perspective while ensuring operational safety.

[0056] Figure 3 illustrates the framework of a train operation plan adjustment model 300 used in a train scheduling method according to an embodiment of the present disclosure.

[0057] According to one embodiment of this application, step S220 can apply the adjustment model 300 of FIG3 to determine the scheduling adjustment scheme.

[0058] Adjusting train departure and arrival times aims to minimize total train delays under both normal and abnormal conditions, while ensuring safe operation. Therefore, the objective of adjustment model 300 is to minimize the total departure and arrival delays of all determined trains. The constraints of train operation plan adjustment model 300 include departure time constraints, train operation constraints, interval operation time constraints, and departure interval time constraints.

[0059] As an example, the objective function can be expressed as follows:

[0060] As an example, the constraint can be expressed as follows:

[0061] In the above formula, the parameters have the following meanings: where m represents the number of trains, n represents the number of stations, i represents the train index, and j represents the index. i,j This indicates the adjusted departure time of train i at station j, y i,j This represents the adjusted arrival time of train i at station j. P i,j y represents the scheduled departure time of train i at station j. P i,j This indicates the scheduled arrival time of train i at station j. i,j,j+1 Let d represent the minimum travel time of train i in the interval (j, j+1). j Let a represent the minimum departure interval of station j. j ad represents the minimum arrival interval of station j. j S represents the minimum arrival / departure interval of station j. min i,jS represents the minimum dwell time of train i at station j. max i,j This represents the maximum dwell time of train i at station j.

[0062] The objective function Z represents minimizing the total delays in departure and arrival for all trains.

[0063] As shown in constraints (1)-(8) above, according to an example of this disclosure, the constraints may include: Referring to constraint (1), for any train i, its departure time at any station j cannot be earlier than the scheduled departure time (i.e., the timed departure time) to avoid impacting passenger boarding and alighting. Referring to constraint (2), for any train i, its departure time at any station j+1 is later than its departure time at the previous station, and its arrival time at any station j+1 is later than its arrival time at the previous station j. Referring to constraint (3), for any train i, the difference between its arrival time at station j+1 and its departure time at the previous station j is not less than the minimum running time of train i in the interval (j, j+1). Referring to constraints (4)-(5), for any train i, the difference between its departure time and arrival time at station j is within the range of the minimum and maximum dwell time of train i at station j. Referring to constraints (6)-(8), for any station j, the difference between the departure time of any train i+1 and the departure time of the previous train i is not less than the minimum departure interval of station j, the difference between the arrival time of any train i+1 and the arrival time of the previous train i is not less than the minimum arrival interval of station j, and the difference between the arrival time of any train i+1 and the departure time of the previous train i is not less than the minimum arrival-departure interval of station j. It should be understood that, depending on actual needs, the scheduling adjustment scheme can be determined by selecting some or all of the above constraints.

[0064] As an exemplary embodiment, the train operation plan adjustment model 300 described above can apply a dynamic optimization algorithm for operation plans based on deep Q-networks (DQN) when determining the scheduling adjustment scheme to obtain a scheduling adjustment scheme that satisfies the train operation constraints and minimizes the total delay. For example, DQN can be used to obtain the train departure and arrival times that satisfy the above formula 1-1.

[0065] DQN, originally proposed by DeepMind, is an algorithm based on deep learning and reinforcement learning, and is an improved version of the Q-learning algorithm. By using deep neural networks to approximate the Q-function, it can handle high-dimensional, large-scale state space problems, while leveraging techniques such as empirical replay and target Q-networks to accelerate training and improve convergence performance.

[0066] As an example, the basic elements involved in the DQN-based dynamic optimization algorithm for train operation planning may include: inputs, such as current train operation information, track status, and operating conditions of adjacent trains; outputs, such as scheduling adjustment schemes; and optimization objectives, such as minimizing the total delay time as shown in Equation 1-1 above. Overall, the current train operation information and the obtained scheduling adjustment scheme interact and iterate until a scheduling adjustment scheme that satisfies the train operation constraints and achieves the overall optimization objective is obtained, i.e., the learning algorithm converges.

[0067] For example, first, current operational information (such as the current departure and arrival timetables) is obtained as the initial state. Then, based on this initial state and using the train line status obtained in step 210 and the operating conditions of adjacent trains as adjustment factors, a scheduling adjustment plan is generated, including, for example, departure time, departure order, and whether to switch lines. Afterwards, based on the train operation according to this scheduling adjustment plan, the next stage of train operation information (such as the adjusted train timetable) and rewards (such as the inverse of the total delay time) are generated. The above steps are repeated until a scheduling adjustment plan with the maximum reward value (minimum total delay time) is obtained.

[0068] Taking the scheduling adjustment scheme for determining the current departure order at the station as an example, the main elements involved in the dynamic optimization algorithm for operation plans based on DQN include:

[0069] Initial state: the departure times of all trains at the current station i and the arrival times of all trains at the next station i+1, as shown in formula (9) below.

[0070] S i =[a 1,i+1 ,d 1,i ,...,a K,i ,d K,i+1 (9)

[0071] Dispatch adjustment scheme: The departure order of all trains at station i is as shown in the following formula (10).

[0072] A i =[o 1,i ,o 2,i ,...,o k,i ,...,o K,i (10)

[0073] Reward function: the negative of the average delay time per train, as shown in formula (11) below, where K represents the number of trains.

[0074] Referring to the above formulas (9)-(11), first input the initial state, such as the timetable S of K trains at the current station i as shown in formula (9). i Then, based on this initial state and according to the train line status and the operating conditions of adjacent trains, a scheduling adjustment scheme is generated, such as the departure order A of K trains at station i as shown in equation (10) above. i And / or a new train timetable generated based on this. Then, the reward R for operating according to this scheduling adjustment scheme is calculated. i (e.g., calculating the inverse of the total delay time according to equation (11) above). Input the adjusted train timetable to generate a new departure sequence A. i This generates the train timetable for the next stage and calculates the corresponding reward R. i The above process is repeated iteratively until the maximum reward value R is found. iMax and with the maximum reward value R iMax The corresponding adjustment scheme is used as the final output, thereby obtaining the scheduling adjustment scheme determined in step 220.

[0075] The method 200 of this disclosure has been described in detail above with reference to specific embodiments to enable any person skilled in the art to practice the method 200 of this disclosure. However, those skilled in the art should understand that the above specific embodiments and their specific number are merely provided as examples for better illustration and are not intended to limit the scope of protection of this disclosure.

[0076] According to another aspect of this application, a system for scheduling trains is provided. Figure 4 shows a schematic architecture diagram of a system 400 for scheduling trains according to an embodiment of this disclosure. As shown in Figure 4, a system 400 for scheduling trains may include: a working condition acquisition module 410; a scheduling adjustment scheme determination module 420; and a scheduling adjustment scheme transmission module 430.

[0077] According to an embodiment of this application, the operating condition acquisition module 410 can be configured to: acquire the line status of the first train, as well as the operating condition of the first train and the operating condition of the second train adjacent to the first train.

[0078] According to an embodiment of this application, the scheduling adjustment scheme determination module 420 can be configured to: determine a scheduling adjustment scheme for multiple trains within a specific range based on the line status of the first train and the operating conditions of the first and second trains.

[0079] According to an exemplary embodiment of this application, the scheduling adjustment scheme determination module 420 can be further configured to: when the first train experiences an operational anomaly, determine a scheduling adjustment scheme for multiple trains within a specific range based on the line status of the first train and the operating conditions of the first and second trains. As an example, the operational anomaly of the first train includes: the actual operation of the first train does not match its operational plan.

[0080] According to an exemplary embodiment of this application, the scheduling adjustment scheme determination module 420 can be further configured to: adjust the train distribution and operating status within a specific area when there is a scheduling anomaly or demand. As an example, a scheduling anomaly or demand includes a power outage or demand.

[0081] According to an exemplary embodiment of this application, the scheduling adjustment scheme determination module 420 may be further configured to: determine a scheduling adjustment scheme based on the block partition settings.

[0082] According to an exemplary embodiment of this application, the scheduling adjustment scheme determination module 420 may be further configured to: determine a scheduling adjustment scheme based on the operating capacity information of the first train and the second train.

[0083] According to an embodiment of this application, the scheduling adjustment scheme sending module 430 can be configured to send a scheduling adjustment scheme to the train operation control system.

[0084] According to an exemplary embodiment of this application, the scheduling adjustment scheme sending module 430 may be further configured to send a scheduling adjustment scheme to the ticketing service system so that the ticketing service system adjusts the ticketing service according to the scheduling adjustment scheme.

[0085] According to an exemplary embodiment of this application, the scheduling adjustment scheme sending module 430 may be further configured to send a scheduling adjustment scheme to the station so that the station publishes information about the scheduling adjustment scheme.

[0086] It should be noted that the specific models and details for determining the scheduling adjustment scheme have been described in detail in the above method embodiments, and they are also applicable to the train scheduling system 400. For the sake of brevity, they will not be repeated here.

[0087] Figure 5 shows a schematic block diagram of an electronic device that can implement a method for scheduling trains according to embodiments of this application.

[0088] According to another aspect of this disclosure, an electronic device 500 is provided, which includes a memory 510 and a processor 520 coupled to the memory 510. In one embodiment, the memory 510 is used to store program code; and the processor 520 executes the program code stored in the memory 510 to cause the electronic device 500 to perform the above-described method 200 for scheduling trains.

[0089] It should be noted that the memory may include random access memory (RAM) or non-volatile memory, such as at least one disk storage device.

[0090] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0091] According to another aspect of this disclosure, a computer-readable storage medium storing program code is provided, which, when executed by a processor, can perform the above-described method 200 for scheduling trains.

[0092] According to another aspect of this disclosure, a computer program product storing program code is provided, which, when executed by a processor, can perform the above-described method 200 for scheduling trains.

[0093] The preceding description is provided to enable any person skilled in the art to practice the various aspects described herein. Various modifications to these aspects will be apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects. Therefore, the claims are not intended to be limited to the aspects shown herein, but should be given the full scope consistent with the language of the claims, wherein reference to an element in the singular is not intended to mean “one and only one” (unless expressly stated otherwise), but rather “one or more.” Unless expressly stated otherwise, the term “some” means one or more. All structural and functional equivalents of elements known or to be known hereafter by those skilled in the art throughout the various aspects described in this disclosure are expressly incorporated herein by reference and are intended to be covered by the claims.

[0094] The various operations described above can be performed by any suitable component capable of performing the corresponding functions. This component may include one or more various hardware and / or software components and / or modules, including but not limited to circuits, application-specific integrated circuits (ASICs), or processors. Typically, in cases where operations are illustrated in the accompanying drawings, those operations may have corresponding equivalent devices plus functional components with similar labeling.

[0095] The various illustrative logic blocks, modules, and circuits described in this disclosure may be implemented or executed using a general-purpose processor, digital signal processor (DSP), application-specific integrated circuit (ASIC), field-programmable gate array (FPGA) or other programmable logic device (PLD), discrete gate or transistor logic, discrete hardware components, or any combination thereof, designed to perform the functions described herein. The general-purpose processor may be a microprocessor, but optionally, the processor may be any commercially available processor, controller, microcontroller, or state machine. The processor may also be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors combined with a DSP core, or any other such configuration.

[0096] If implemented in hardware, the example hardware configuration may include a processing system. This processing system can be implemented using a bus architecture. The bus may include any number of interconnect buses and bridges, depending on the specific application and overall design constraints of the processing system. The bus can link together various circuits, including processors, computer-readable storage media, and bus interfaces. The bus can also link various other circuits, such as timing sources, peripherals, voltage regulators, power management circuits, etc., which are known in the art and will not be described further. The processor may be implemented using one or more general-purpose and / or special-purpose processors. Examples include microprocessors, microcontrollers, DSP processors, and other circuits capable of executing software. Those skilled in the art will recognize how best to implement the described functionality for the processing system, depending on the specific application and the overall design constraints imposed on the system as a whole.

[0097] If implemented in software, functionality can be stored or sent to a computer-readable storage medium as one or more instructions or code. Software should be interpreted broadly as instructions, data, or any combination thereof, whether referred to as software, firmware, middleware, microcode, hardware description language, or otherwise. The processor may be responsible for managing the bus and routine processing, including executing software modules stored on the machine-readable storage medium. The computer-readable storage medium may be coupled to the processor so that the processor can read information from and write information to the storage medium. Alternatively, the storage medium may be integrated with the processor. For example, a computer-readable storage medium or any part thereof may be integrated into the processor, such as in cases where it may have a cache and / or a general-purpose register file. Examples of machine-readable storage media may include, for example, RAM (Random Access Memory), flash memory, ROM (Read-Only Memory), PROM (Programmable Read-Only Memory), EPROM (Erasable Programmable Read-Only Memory), EEPROM (Electrically Erasable Programmable Read-Only Memory), registers, disks, optical disks, hard disks, or any other suitable storage medium, or any combination thereof. The computer-readable storage medium may be embodied in a computer program product.

[0098] Software modules can include single instructions or multiple instructions and can be distributed across several different code segments, between different programs, and across multiple storage media. Computer-readable storage media can include multiple software modules. Software modules include instructions that, when executed by a device such as a processor, cause the processing system to perform various functions. Software modules can include sending modules and receiving modules. Each software module can reside in a single storage device or be distributed across multiple storage devices. For example, when a triggering event occurs, a software module can be loaded from a hard disk drive into RAM. During the execution of a software module, the processor can load some instructions into a cache to improve access speed. One or more cache lines can then be loaded into a general-purpose register file for processor execution. When referring to the functionality of the software module below, it will be understood that such functionality is implemented by the processor when instructions are executed from that software module.

[0099] Furthermore, any connection is appropriately referred to as a computer-readable storage medium. For example, if software is transmitted from a website, server, or other remote source using coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technologies such as infrared (IR), radio, and microwave, then coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included in the definition of medium. The disks and optical discs used herein include compact optical discs (CDs), laser discs, optical discs, digital versatile optical discs (DVDs), floppy disks, and... Optical discs, where magnetic disks typically copy data magnetically, and optical discs copy data using lasers. Therefore, in some aspects, computer-readable storage media can include non-transitory computer-readable storage media (e.g., tangible media). Additionally, in other aspects, computer-readable storage media can include transient computer-readable storage media (e.g., signals). Combinations of the above should also be included within the scope of computer-readable storage media.

[0100] Therefore, certain aspects may include a computer program product for performing the operations presented herein. For example, such a computer program product may include a computer-readable storage medium on which instructions are stored (and / or encoded) that can be executed by one or more processors to perform the operations described herein.

[0101] Furthermore, it should be understood that, where applicable, modules and / or other suitable components for performing the transmission sequence control methods and techniques described herein can be downloaded to a computer and / or otherwise obtained. For example, such a device can be coupled to a server to facilitate the transfer of components for performing the methods described herein. Optionally, the various methods described herein can be provided via storage components (e.g., RAM, ROM, physical storage media such as optical discs (CDs) or floppy disks). Additionally, any other suitable techniques for providing the methods and techniques described herein to the device can be utilized.

[0102] It should be understood that the claims are not limited to the precise configurations and components shown above. Various modifications, alterations, and variations may be made to the arrangement, operation, and details of the methods and apparatus described above without departing from the scope of the claims.

Claims

1. A method for dispatching trains, the method comprising: obtaining a line state of a first train, and a working condition of the first train and a working condition of a second train adjacent to the first train; determining a dispatch adjustment scheme for a plurality of trains within a certain range according to the line state of the first train and the working conditions of the first train and the second train; and sending the dispatch adjustment scheme to a train operation control system. 2.The method of claim 1, further comprising: sending the dispatch adjustment scheme to a ticket service system to cause the ticket service system to adjust a ticket service according to the dispatch adjustment scheme. 3.The method of claim 1, further comprising: sending the dispatch adjustment scheme to a station to cause the station to publish information about the dispatch adjustment scheme. 4.The method of claim 1, wherein determining the dispatch adjustment scheme for the plurality of trains within the certain range according to the line state of the first train and the working conditions of the first train and the second train comprises: determining the dispatch adjustment scheme for the plurality of trains within the certain range according to the line state of the first train and the working conditions of the first train and the second train when the first train is operating abnormally. 5.The method of claim 4, wherein an actual operation of the first train does not match a planned operation of the first train. The first train operation abnormality includes: 6.The method of claim 1, further comprising: adjusting a train distribution and an operation state within a certain area when there is a dispatch professional abnormality or a demand. 7.The method of claim 6, wherein the dispatch professional abnormality or the demand comprises a power abnormality or a demand. 8.The method of claim 1, further comprising: determining the dispatch adjustment scheme according to a block section setting. 9.The method of claim 1, further comprising: determining the dispatch adjustment scheme according to operation capability information of the first train and the second train. 10.A system for dispatching trains, the system comprising: a working condition obtaining module configured to obtain a line state of a first train, and a working condition of the first train and a working condition of a second train adjacent to the first train; a dispatch adjustment scheme determining module configured to determine a dispatch adjustment scheme for a plurality of trains within a certain range according to the line state of a first train and the working conditions of the first train and the second train; and a dispatch adjustment scheme sending module configured to send the dispatch adjustment scheme to a train operation control system. 11.The system of claim 10, further comprising: sending the dispatch adjustment scheme to a ticket service system to cause the ticket service to adjust a ticket service according to the dispatch adjustment scheme. 12.The system of claim 10, further comprising: sending the dispatch adjustment scheme to a station to cause the station to publish information regarding the dispatch adjustment scheme. 13.The system of claim 10, wherein determining the dispatch adjustment scheme for the plurality of trains within the certain range according to the first train and the working conditions of the first train and the second train comprises: ​ ​ When the first train operation is abnormal, a dispatch adjustment scheme for a plurality of trains in a specific range is determined according to a line state of the first train and working conditions of the first train and the second train.

14. The system of claim 13, wherein The first train operation abnormality includes: The actual operation of the first train does not match the operation plan of the first train.

15. The system of claim 10, further comprising: When there is a dispatch professional abnormality or demand, adjusting train distribution and operation state in a specific area.

16. The system of claim 15, wherein The dispatch professional abnormality or demand includes power abnormality or demand.

17. The system of claim 10, further comprising: According to a block section setting, determining the dispatch adjustment scheme.

18. The system of claim 10, further comprising: According to operation capacity information of the first train and the second train, determining the dispatch adjustment scheme.

19. An electronic device, comprising a memory and a processor; The memory is used to store program code; And the processor is coupled with the memory, and the processor is used to execute the program code stored on the memory, so that the electronic device executes the method for dispatching trains according to any one of claims 1-9.

20. A computer readable storage medium storing program code, which is executed by a processor to execute the method for dispatching trains according to any one of claims 1-9.

21. A computer program product storing program code, which is executed by a processor to execute the method for dispatching trains according to any one of claims 1-9. ​

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